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  • Solving Solar’s Biggest Problem

    AI transcript
    0:00:02 (upbeat music)
    0:00:07 Pushkin.
    0:00:14 Today’s show is a good show,
    0:00:17 and I can give you several reasons why.
    0:00:20 One, it’s a show about the energy transition.
    0:00:23 So we got the high stakes of climate change.
    0:00:26 Two, it’s more specifically about
    0:00:28 long duration energy storage,
    0:00:32 which may be the key problem to solve in climate change.
    0:00:34 Now that solar power is so cheap,
    0:00:36 but also so intermittent.
    0:00:39 Sun’s not always out, you gotta store the energy.
    0:00:42 Three, the story has some very clever,
    0:00:44 fun, technical insights.
    0:00:48 Four, we get some real moments of harrowing drama.
    0:00:52 And finally, five, perhaps most surprising,
    0:00:56 today’s show includes a defense of fighting in hockey.
    0:00:59 (upbeat music)
    0:01:04 I’m Jacob Goldstein, and this is What’s Your Problem.
    0:01:07 My guest today is Curtis Van Wallingham.
    0:01:09 He is the co-founder and CEO of a company
    0:01:11 called HygroStore.
    0:01:13 Curtis’s problem is this.
    0:01:16 How can you store energy by compressing air
    0:01:19 in giant underground caverns?
    0:01:21 And how can you do it efficiently
    0:01:23 almost anywhere in the world?
    0:01:25 The idea of compressed air storage
    0:01:26 has been around for a long time,
    0:01:28 but it’s had some fundamental problems
    0:01:30 that have limited its use.
    0:01:32 As you’ll hear, Curtis thinks he and his colleagues
    0:01:34 have solved those problems.
    0:01:37 Since co-founding the company 15 years ago,
    0:01:40 HygroStore has built a fully functioning plant
    0:01:43 and has recently signed multiple billion dollar contracts
    0:01:46 to build facilities around the world.
    0:01:48 Curtis told me he got interested in compressed air
    0:01:52 back in 2008, when he had kind of a weird problem.
    0:01:53 He was working at a power plant
    0:01:58 that was generating more energy than anyone could use.
    0:02:01 So I was the head of planning at a nuclear plant.
    0:02:03 I think it’s the world’s second largest
    0:02:07 single site nuclear plant in Ontario called Bruce Power.
    0:02:11 And because we have so much hydro and nuclear in Ontario,
    0:02:13 when we started adding wind and solar,
    0:02:15 we started to see we had too much power.
    0:02:17 We couldn’t export it.
    0:02:18 And so we would have to shed power,
    0:02:20 which for a nuclear plant,
    0:02:23 it means essentially dumping steam into the Great Lakes.
    0:02:26 But it required a lot of manual labor, moving valves,
    0:02:28 and doing stuff that it wasn’t designed to do.
    0:02:30 So it was driving up maintenance costs.
    0:02:32 – And also it’s a bummer, right?
    0:02:35 Like you’re generating, you want to make energy.
    0:02:37 You don’t want to dump steam into a lake.
    0:02:38 – Exactly.
    0:02:41 And so then I tried to build a pumped hydro plant
    0:02:44 ’cause back there, this is 2008,
    0:02:46 the only real common place to store power
    0:02:48 was through pumped hydro.
    0:02:51 So I tried to find a site and it was just impossible
    0:02:54 to see how you could find a site, get permits,
    0:02:56 and build something anywhere near a time frame.
    0:02:58 – And just to be clear, sorry,
    0:03:02 but pumped hydro is sort of the classic
    0:03:04 kind of long duration energy storage, right?
    0:03:06 Where you pump water essentially up a hill
    0:03:08 from some lower level to some higher level
    0:03:09 when you have the power.
    0:03:12 And then when you want to discharge the battery,
    0:03:13 you just let it flow downhill
    0:03:15 and spin a turbine more or less, right?
    0:03:16 – Exactly.
    0:03:19 Any hydroelectric dam, just stop the dam from producing
    0:03:22 and start pumping water back up to the top reservoir.
    0:03:23 – Got it.
    0:03:25 – And so I tried to do that and realized
    0:03:27 there was no possible way of doing it.
    0:03:29 So I had a team of five or six people at the time
    0:03:30 and I asked them to research,
    0:03:32 is there other ways of storing power?
    0:03:35 And that’s when I came across my co-founder, Cam,
    0:03:38 who had this idea, and it’s essentially the opposite
    0:03:42 of pumped hydro, put air underwater and get the buoyancy.
    0:03:44 So instead of lifting water in air,
    0:03:47 why don’t you sink air underwater and get the buoyancy?
    0:03:51 – And as an engineer, it was kind of intriguing to me.
    0:03:53 So I thought about it, ran some numbers
    0:03:54 and lo and behold, I said,
    0:03:57 “Hey, this is compact, it can be easier to site.
    0:04:00 “It has all the advantages of pumped hydro,
    0:04:02 “much easier to permit.”
    0:04:05 So took the plunge, quit my job and threw some money in
    0:04:08 and 15 years later, here we are.
    0:04:10 – So your co-founder’s idea,
    0:04:15 it was a variation on this old idea of compressed air, right?
    0:04:17 When you have more energy than you need,
    0:04:20 use it to compress air underground.
    0:04:21 And then when you need the energy,
    0:04:23 sort of blow the air back up
    0:04:25 and use that to generate energy, right?
    0:04:27 That’s the basic idea, it’s an old idea.
    0:04:30 And in its basic form, it’s pretty limited
    0:04:31 in what it can do, right?
    0:04:35 Like what are the classic problems with compressed air?
    0:04:37 – The first one is around how you manage heat.
    0:04:40 In traditional compressed air,
    0:04:43 they would compress the air, it gets hot.
    0:04:44 They would lose that heat,
    0:04:46 but put the compressed air underground.
    0:04:48 And when you need it to discharge.
    0:04:49 – When you say they would lose that heat,
    0:04:51 what does that mean?
    0:04:53 – They would essentially just compress
    0:04:55 and then hot air would get sent underground
    0:04:57 and it would dissipate into the earth.
    0:05:01 And then when it comes up and you want to expand that air,
    0:05:04 then the opposite happens, it gets very cold.
    0:05:06 And because there’s moisture in the air,
    0:05:08 it’ll cause icicles and snow
    0:05:10 and it’ll freeze up your turbine,
    0:05:12 going cryogenic, we call it.
    0:05:14 – So that’s the problem.
    0:05:16 It’s fine, you put it down in the earth
    0:05:17 and the heat dissipates, that’s actually okay.
    0:05:20 The problem is when you want to blow it back up
    0:05:23 to spin a turbine, it basically freezes the turbine
    0:05:25 and the turbine snaps or whatever.
    0:05:27 That’s why it’s bad.
    0:05:29 – Exactly, it’ll ice it up and it won’t spin.
    0:05:32 And so to prevent that, they have to preheat it.
    0:05:34 So they would burn some natural gas.
    0:05:36 So when the air comes up from the cavern,
    0:05:38 they would heat it up so that once it expands,
    0:05:40 it doesn’t drop below zero.
    0:05:43 – So A, that’s inefficient, it costs money.
    0:05:46 And then B now, when we’re talking about doing this
    0:05:50 to fight climate change, it’s totally not what you want.
    0:05:52 – Yeah, you have less emissions than a gas plant,
    0:05:54 but you still have half the emissions, call it.
    0:05:56 – Okay, so that’s problem number one,
    0:05:59 is you gotta burn natural gas to heat up the air
    0:06:02 when you’re blowing it because it gets really cold.
    0:06:03 What’s problem number two
    0:06:06 with sort of classic compressed air?
    0:06:09 – The problem number two is just where can you build them?
    0:06:10 And traditionally, you would only build them
    0:06:13 where there’s salt caverns and salt caverns
    0:06:15 are as rare to find as good pumped hydro sites.
    0:06:19 So once you overlay, where does the grid need storage?
    0:06:20 Where is their transmission?
    0:06:23 The odds that there’s a salt cavern there
    0:06:24 are very, very de minimis.
    0:06:26 So it just didn’t have sites.
    0:06:28 – Why traditionally did it have to be a salt cavern?
    0:06:30 That seems so random.
    0:06:32 – Because you need so much air
    0:06:36 and for the air to be at quite high pressures
    0:06:38 because it gets kind of complicated.
    0:06:40 But as you think of a scuba tank,
    0:06:43 you pumping air in,
    0:06:45 the pressure starts rising very dramatically.
    0:06:46 Well, to get a sufficient amount of air,
    0:06:49 you gotta go to really high pressures.
    0:06:51 That’s too high a pressure in most rocks
    0:06:54 that it would just, you would lose all of the air
    0:06:55 and force its way out.
    0:06:56 – So it just, it just dissipates.
    0:06:57 You pump all this air in there
    0:06:59 and it just goes through whatever cracks
    0:07:01 or whatever little tiny holes there are.
    0:07:03 The air just kind of blows out
    0:07:04 and you don’t have compressed air anymore.
    0:07:06 – Exactly, where salt is airtight.
    0:07:08 And so that’s why you would typically do it in salt.
    0:07:10 That’s why they store a lot of the natural gas
    0:07:14 in salt, but the salt formations aren’t that common
    0:07:16 and they’re not where the grid needs them.
    0:07:19 – Okay, so these are our two problems.
    0:07:23 And you encounter this guy who seems to have solved them.
    0:07:25 Who is the guy and what has he figured out?
    0:07:28 – So the guy’s name is Cameron Lewis
    0:07:33 and he’s a serial entrepreneur, engineering technician.
    0:07:35 He worked in the oil patch in Canada,
    0:07:37 repurposing compressors and turbines
    0:07:39 for use in the oil patch.
    0:07:43 Then he moved to Ontario to start developing wind farms.
    0:07:44 And that’s when he realized,
    0:07:48 look, this wind is so intermittent, I need storage.
    0:07:49 And then he was like, how do I make storage?
    0:07:51 And that’s when his mind started going
    0:07:53 ’cause he is a true entrepreneur.
    0:07:55 – It reminds me, I should have had the name at hand,
    0:07:59 but I talked to that guy who worked as an engineer
    0:08:03 in the, in Texas, Texas in Oklahoma
    0:08:05 and sort of brought the technologies
    0:08:07 of the fracking boom to geothermal energy.
    0:08:08 It’s kind of– – Tim Latimer.
    0:08:10 – Yeah, Tim Latimer, Tim Latimer.
    0:08:13 Some parallels, right, to the story.
    0:08:14 – Very similar.
    0:08:14 – So there are these two problems.
    0:08:18 One, you have the problem of when you decompress,
    0:08:20 when you use your compressed air, it gets really cold.
    0:08:22 And two, you have the problem that
    0:08:24 you can only put the compressed air in a few places,
    0:08:26 otherwise it just sort of dissipates.
    0:08:28 How does using water solve each of those problems?
    0:08:31 First of all, how does it solve the cold problem?
    0:08:35 – So what we do is when the air comes out hot,
    0:08:40 it comes out about 230 degrees Celsius from the compressor.
    0:08:43 We run it through a heat exchanger to pull that heat out
    0:08:44 and we store it in hot water.
    0:08:46 So on the surface, we have a water tank,
    0:08:48 think of an LNG sphere,
    0:08:51 filled with water, now at 200 degrees C.
    0:08:54 So we’ve captured that energy and that heat
    0:08:56 that otherwise would have dissipated
    0:08:58 and we store it and wrap it in insulation.
    0:08:59 – Okay.
    0:09:01 – Then when the air comes back up,
    0:09:03 we go in reverse through those heat exchangers
    0:09:05 and use that same heat to preheat the air
    0:09:08 so that it doesn’t go cryogenic
    0:09:10 and it eliminates the need for natural gas.
    0:09:11 – Yeah, it’s elegant, right?
    0:09:14 It’s so elegant ’cause that heat is actually energy
    0:09:19 that in conventional compressor was just getting lost, right?
    0:09:21 And then you had to provide more energy
    0:09:22 to heat the air back up.
    0:09:25 So in your model, you capture that energy
    0:09:27 by heating up water and then you use the hot water
    0:09:29 to heat up the air when you’re blowing it through the turbine
    0:09:31 so you don’t have to burn gas.
    0:09:33 – Exactly, moving the round trip efficiency
    0:09:36 from 35% to 70%.
    0:09:37 – Satisfying, very satisfying.
    0:09:40 Okay, so the other problem is in the conventional version,
    0:09:43 you need a salt cavern so that your air
    0:09:45 doesn’t just dissipate through whatever
    0:09:47 the little tiny holes in the rock.
    0:09:48 – Right.
    0:09:51 – And why does using water mean you don’t need that?
    0:09:53 You can do it in a lot more places.
    0:09:56 – So the way our cavern works is if you think of a cavern
    0:09:59 where 2000 feet underground, we hollow out,
    0:10:01 think of a cubic football field,
    0:10:04 we backfill it with water, a one-time fill.
    0:10:08 When you finish construction, you fill it up with water.
    0:10:10 Now when you’re pushing air in, it’s moving the water down
    0:10:13 and lifting it all the way to the surface.
    0:10:15 – So you just did a hand motion
    0:10:17 that I want to explain for people listening.
    0:10:20 So basically, it goes down like, whatever,
    0:10:22 in a sink, in your kitchen sink, it goes down,
    0:10:24 but then it turns back up, it’s like a J.
    0:10:27 There’s basically a pipe that goes down
    0:10:28 out of the bottom of the cavern
    0:10:30 and then makes a U turn and goes up to the surface.
    0:10:31 – That’s right.
    0:10:33 – And then at the surface, we have a little pond
    0:10:38 with enough volume that once the air is full of the cavern,
    0:10:40 i.e. the cavern’s filled with air,
    0:10:42 all that water that used to be in the cavern
    0:10:45 has now been lifted up to the surface.
    0:10:48 – And why does this solve that problem?
    0:10:50 Why does that mean you don’t need a salt cavern?
    0:10:54 – Because the air pressure will move the water down
    0:10:56 that J hook and lift to the surface
    0:10:58 before it would protrude through the rocks.
    0:11:01 Okay, so there’s a very elegant idea.
    0:11:07 You’re still, you’re working for a nuclear power plant.
    0:11:09 You come across this idea,
    0:11:13 but you don’t decide to try it for the nuclear power plant.
    0:11:15 Like, what happens next?
    0:11:17 – Well, that was my desire.
    0:11:19 And so I asked him, I said, well, what if we wanted
    0:11:21 to pilot one or build one with you?
    0:11:23 And he basically said, I have a company name
    0:11:25 and a patent and that’s it.
    0:11:28 And I don’t really know what to do next.
    0:11:30 And then I started looking for other options.
    0:11:31 There was no other options.
    0:11:34 And I said, look, we’re a kind of a canary in the coal mine.
    0:11:36 Every utility is gonna ultimately see
    0:11:38 these challenges eventually.
    0:11:40 So I saw the opportunity, so I quit my job,
    0:11:44 drained some savings and joined as the co-founder.
    0:11:46 And we started HydraStore.
    0:11:51 – And, you know, now in the past few years,
    0:11:54 basically since solar took off,
    0:11:57 everybody has been talking about long duration storage,
    0:11:59 right, long duration energy storage.
    0:12:01 Everybody was not talking about it then.
    0:12:04 Was solar power cost, what did it cost then?
    0:12:05 10 times what it cost now?
    0:12:06 I don’t know.
    0:12:09 It was not at all like it is now, right?
    0:12:11 It was not at all obvious that solar power
    0:12:12 was gonna be everywhere and the problem
    0:12:13 was gonna be storage.
    0:12:16 Like, what were you thinking about at that time
    0:12:17 in that context?
    0:12:21 – It kind of comes back to the nuclear power plant
    0:12:23 ’cause we had half of Ontario’s grid
    0:12:25 was nuclear that can’t turn down
    0:12:29 and 40% of the rest was hydro that can’t turn down.
    0:12:30 So once we added wind and solar,
    0:12:33 you really started to see the swings.
    0:12:36 And I realized that Ontario’s grid is very unique.
    0:12:40 But once you take away your base load,
    0:12:44 once intermittency is a decent portion of what’s left,
    0:12:46 everyone needs storage.
    0:12:48 And so I realized that Ontario’s grid
    0:12:49 was just seeing it first,
    0:12:52 but that every grid will eventually see it
    0:12:54 when wind and solar penetration rose.
    0:12:57 – Interesting, basically, you saw it first
    0:13:01 because nuclear and hydro are uniquely difficult
    0:13:02 to turn on and off really fast.
    0:13:04 Whereas most other places,
    0:13:06 they’re using natural gas, they’re using coal,
    0:13:08 which you can basically turn on and off.
    0:13:10 So the intermittency was not such
    0:13:12 an acute problem so early.
    0:13:13 – Exactly. – Interesting.
    0:13:16 You were right about that, good job.
    0:13:18 – And then once wind and solar started coming on,
    0:13:20 they started saying, well, we need storage,
    0:13:22 but we only need 15 minutes.
    0:13:24 And then that turned to half hour, then an hour,
    0:13:26 then two, then four.
    0:13:28 Now it’s eight in a lot of markets.
    0:13:29 Now they’re moving to 12.
    0:13:32 They’re even talking 40, 50 hours of storage.
    0:13:35 And it’s true that as the wind and solar penetration rise,
    0:13:38 your duration of storage keeps growing.
    0:13:42 And right now, I’d say roughly a third of the world
    0:13:43 needs eight hour storage,
    0:13:45 and the other two thirds still aren’t there yet,
    0:13:47 but it’s coming.
    0:13:51 – And it’s basically the more wind and solar you have,
    0:13:53 the larger a share of your power they represent,
    0:13:56 the longer duration you need for storage.
    0:13:57 – Exactly.
    0:14:01 And you confirm it up with natural gas to a point,
    0:14:03 but then you start curtailing so much solar,
    0:14:04 burning so much natural gas,
    0:14:06 you stop putting solar on,
    0:14:08 whereas with long duration storage,
    0:14:11 we allow you to keep adding more solar, more wind,
    0:14:14 and that’s how you get to 100% renewable.
    0:14:17 – I mean, long duration storage is clearly the problem
    0:14:19 at this point, right?
    0:14:21 At least in places where there’s a decent amount of solar
    0:14:25 or wind or wires going to places
    0:14:26 where there’s a decent amount of solar wind,
    0:14:29 it does seem like storage in general
    0:14:33 and long duration storage in particular is the bottleneck.
    0:14:37 – Yes, and I think that technologies are there now,
    0:14:39 but we don’t have the market structures
    0:14:42 to properly pay for it and compensate it,
    0:14:44 which is holding it up from really taking off.
    0:14:47 So California, Australia,
    0:14:48 they’ve been pretty innovative.
    0:14:53 The UK is doing some stuff now, Ontario and Canada,
    0:14:56 but it requires you to change market reform.
    0:14:57 – Yeah, I wanna get to that.
    0:14:59 I feel like there’s a fair bit to talk about
    0:15:01 in terms of policy.
    0:15:06 But first, I wanna get you from 2010 to 2025.
    0:15:09 So you have essentially an idea, right?
    0:15:13 You have intellectual property and a name
    0:15:15 and two guys and a dream.
    0:15:18 Like what, and I don’t wanna do every day
    0:15:19 of the last 15 years obviously,
    0:15:22 but what are a few of the key points,
    0:15:24 like the few key marks you had to hit?
    0:15:29 – The first one was we worked with a university,
    0:15:32 University of Windsor, to validate the engineering
    0:15:34 that it would work, the heat mass balance
    0:15:37 and all the basic physics were sound.
    0:15:41 So we did that, then we ran pool tests.
    0:15:44 So using underwater structures,
    0:15:47 we were able to show it at a very small scale
    0:15:50 to validate the models in real world.
    0:15:54 Then we moved to a bigger pilot out in a lake
    0:15:56 and we built that to further validate.
    0:15:59 We had a truck with a compressor in it, heat exchangers,
    0:16:02 big balloons sunk by concrete out in the lake
    0:16:03 with a hose coming back.
    0:16:04 – Sounds cool, yeah.
    0:16:09 – That was enough to get enough data
    0:16:10 to get enough grant money.
    0:16:13 We secured about eight, nine million of grant money.
    0:16:16 Then we were able to attract the venture capital investor,
    0:16:19 our turn ventures and Toronto Hydro
    0:16:21 was willing to host a pilot plant.
    0:16:23 So a grid connected one megawatt,
    0:16:26 one megawatt hour pilot plant.
    0:16:28 So we took that grant money and the venture money
    0:16:31 and we’re now six, seven people
    0:16:33 and we built this pilot plant.
    0:16:36 Grid connected and it was pretty neat.
    0:16:39 We sunk giant structures offshore in the lake.
    0:16:41 So instead of digging a cavern and filling it with water,
    0:16:44 we sunk essentially a structure within a lake
    0:16:46 and connected it back with drill pipe.
    0:16:50 – Like a bubble sitting there on the bottom of the lake?
    0:16:51 Like a case on?
    0:16:53 – It was massive.
    0:16:56 And so that data, it proved that everything worked.
    0:16:59 We optimized the heat exchangers, the control system
    0:17:02 and filed a lot more intellectual property.
    0:17:06 And then so that was a five year temporary pilot.
    0:17:10 Once we had that data, then an IESO was running a procurement
    0:17:13 for piloting long duration storage.
    0:17:14 – What’s an IESO?
    0:17:19 – Electricity system operator here in Ontario.
    0:17:21 So they ran a pilot, we submitted a bid,
    0:17:23 we won the contract to build a plant
    0:17:26 that had a 10 year revenue contract.
    0:17:30 – Okay, a real thing, that’s a real thing, yeah.
    0:17:32 – And so that was called our Goderich facility
    0:17:35 and we turned that on in 2019
    0:17:36 and have been running it ever since.
    0:17:39 So then we had, we call that our commercial reference
    0:17:42 facility, so it references our technology,
    0:17:43 albeit at a fairly small scale,
    0:17:46 it’s two megawatts, eight megawatt hours.
    0:17:49 But we ran that and then we brought insurance companies
    0:17:53 through and engineering construction companies
    0:17:56 and said, go through this and tell us why this wouldn’t work
    0:17:58 at a larger scale and they were able to then give us
    0:18:01 insurance products and different things
    0:18:03 that would make a larger project bankable.
    0:18:05 – It was like, you’re in the infrastructure business
    0:18:06 basically, right?
    0:18:09 And so you need like tons of capital,
    0:18:13 tons of insurance, you gotta spend a lot of money now
    0:18:16 and you’ll get paid back every year for whatever,
    0:18:19 20 years or something, whatever is the life of it.
    0:18:22 It’s like a big, hard, complicated,
    0:18:23 ultimately you want it to be boring,
    0:18:26 although I’m sure it’s not boring yet business, right?
    0:18:29 – Right, so this was setting us up for that.
    0:18:32 So then this plant kind of got us all the financial
    0:18:35 instruments that we would need to do a big one.
    0:18:38 So then we went and started developing big plants.
    0:18:41 We now are about to start construction in Australia
    0:18:44 for a $1 billion plant and in California
    0:18:47 for about a $1.5 billion plant.
    0:18:49 And because we had the pilot plant,
    0:18:52 we have the insurance, the confidence of the constructors
    0:18:55 and we’re able to get debt and project level equity
    0:18:58 and project finance those first two big plants.
    0:19:00 So now we’re in the process of constructing those
    0:19:03 and once they’re operational,
    0:19:06 then the technology should be boring and completely
    0:19:09 de-risk, which would then allow utilities
    0:19:11 to start building it on their own balance sheet
    0:19:14 without us having to mobilize all the capital.
    0:19:18 We would say utility X or a Google or Microsoft
    0:19:20 or a data center, if you want one,
    0:19:21 you can pay for it and build it
    0:19:23 and we’ll just take a technology license fee.
    0:19:26 – So you don’t want to be in the infrastructure business,
    0:19:28 you want to be in the intellectual property business
    0:19:31 and you’re just in the infrastructure business
    0:19:34 to prove that it’s a good idea.
    0:19:36 – Yes, I think we’ll always stay
    0:19:37 in the infrastructure business.
    0:19:39 It’s just the opportunity is so huge
    0:19:40 and these plants are so big.
    0:19:42 Like we have 18 we’re developing,
    0:19:44 but each one is a billion and a half.
    0:19:47 Well, that’s $25, $30 billion
    0:19:49 and we’re only in a handful of markets.
    0:19:51 – The idea is you’re gonna finance all of those?
    0:19:55 Like in the current model, they’re yours?
    0:19:58 – There are, we’re backed by some pension funds,
    0:20:01 Goldman Sachs and so we’ve got a decent amount of capital,
    0:20:03 we’ll keep bringing partners in.
    0:20:04 But like we’re not doing anything in India,
    0:20:08 Japan, China, I can’t do that globally.
    0:20:11 And so the model is allow other people to build it
    0:20:15 on their balance sheet and become that IP licensing company
    0:20:17 while we still do the core infrastructure
    0:20:19 in the markets that we choose to offer.
    0:20:21 (upbeat music)
    0:20:24 – Still to come on the show,
    0:20:27 competing against lithium ion batteries,
    0:20:30 making 300 failed pitches.
    0:20:33 And also what political change in Washington
    0:20:34 might mean for HydroStore.
    0:20:46 I’d heard Curtis talking in other interviews
    0:20:49 about how hard it was to build his company.
    0:20:51 And of course, founders always talk about
    0:20:54 how hard it is to build a company.
    0:20:56 But somehow when Curtis talked about it,
    0:20:59 I really felt it, really believed him.
    0:21:02 And so I asked him to tell me about some of the moments
    0:21:04 when it got really hard.
    0:21:05 – There were quite a few to be honest.
    0:21:09 I mean, one certainly was one that structure sunk
    0:21:12 when we were building that first pilot plant.
    0:21:13 – Yeah, so tell me about that.
    0:21:15 So this is the first time you’re actually doing it.
    0:21:18 What’s happening there?
    0:21:20 Like you’re just, what’s it look like?
    0:21:24 – It’s about the size of a basketball court,
    0:21:28 maybe 20 feet high and it’s all concrete.
    0:21:31 Think of a culvert pipe when you, under the highways,
    0:21:36 a bunch of those strapped together, essentially,
    0:21:40 filled with air so it would float itself almost as a barge.
    0:21:43 There was then to be lowered down onto the ground
    0:21:46 and then that would serve as our air cavity
    0:21:48 with water moving in and out.
    0:21:52 And so we get it out to the deep depths of Lake Ontario
    0:21:55 and we’re towing it and kind of a rogue wave hits
    0:21:58 and something pops and it starts filling with water
    0:22:00 where it’s not supposed to.
    0:22:03 And it drops down and smashes on the sea floor.
    0:22:05 – Are you watching it?
    0:22:07 Where are you when this is happening?
    0:22:10 – I’m at home, our team is on a barge trailing it
    0:22:12 and they’ve got drones down there and cameras.
    0:22:16 But as you can imagine when it hits all the plume of sand
    0:22:17 and stuff comes up.
    0:22:18 So it’s not till the next morning
    0:22:20 where we see how catastrophic it is,
    0:22:22 but we knew when we lost it,
    0:22:24 you know, I can be able to lift this thing up.
    0:22:27 It’s so deep divers can’t even go to access it.
    0:22:31 I was on the phone like speaker phone two in the morning
    0:22:34 at sitting in my son’s bedroom
    0:22:35 while he was sleeping with my wife
    0:22:38 ’cause I was pulling it all night or working and yeah.
    0:22:42 And it was, so then we all just kind of looked at each other
    0:22:45 and I told people to safely wrap up the work, go home.
    0:22:48 We had a meeting the next day called the board in
    0:22:50 and it was essentially we’re toast.
    0:22:52 It was good run, but I think we’re done.
    0:22:56 And that’s when we said, well, we got all risk insurance.
    0:23:00 That’s what all risk really means.
    0:23:03 And to the insurance company’s credit,
    0:23:05 they said, yeah, you had all risk.
    0:23:06 Here’s $4 million.
    0:23:08 Try again however you want,
    0:23:09 but we’re not ensuring the second time.
    0:23:11 Oh, huh.
    0:23:12 And we pulled it off the second time.
    0:23:14 We changed design and we got it built.
    0:23:17 And so we had our plant operational,
    0:23:20 which was a great milestone for the team.
    0:23:23 There was another time when we realized
    0:23:24 we had it to be a developer.
    0:23:27 We couldn’t just jump to that licensing model
    0:23:29 that we would have to build the first big plant.
    0:23:30 So was that your initial,
    0:23:34 so your initial idea was like, let’s just do a pilot
    0:23:36 and show that it works and then license the technology
    0:23:40 because who are we, we’re not gonna get billions of dollars
    0:23:43 to build a gigantic hole in the ground
    0:23:44 that’s gotta last for 20 years, that’s not us.
    0:23:45 Exactly.
    0:23:49 And so then I, we realized that after talking to dozens
    0:23:52 of utilities and developers and everyone said,
    0:23:55 there’s no way I’m building the first one on my balance sheet.
    0:23:57 Well, that’s the nature, right?
    0:24:01 I mean, it’s a famously conservative industry, right?
    0:24:02 It’s highly regulated.
    0:24:04 They’re not gonna take a billion dollar risk
    0:24:07 on a thing that plausibly might not work, right?
    0:24:09 It wouldn’t be crazy if it didn’t work.
    0:24:10 Yeah, exactly.
    0:24:13 And so then I had to call my board at the time in
    0:24:14 and say, we have to be a developer.
    0:24:17 And they’re like, well, that means
    0:24:19 we gotta start letters of credit.
    0:24:21 We need tens of millions of dollars
    0:24:23 and we gotta staff up a team that looks completely different
    0:24:25 than the team of engineers we have.
    0:24:30 So then I got on my bike and tried to find an investor
    0:24:32 that would back that new model
    0:24:33 and it took me six months.
    0:24:35 When you say got on your bike, what do you,
    0:24:38 are you speaking metaphorically or literally?
    0:24:40 Yeah, speaking metaphorically.
    0:24:42 I was hoping you went on some crazy ride.
    0:24:45 Well, it was 300 investors.
    0:24:48 And I think it was 316th said yes.
    0:24:50 So it was a long road.
    0:24:51 We ran out of capital.
    0:24:53 I had to mortgage my house.
    0:24:55 My wife was saying, you’re not drawing a salary.
    0:24:56 You’ve put in our savings.
    0:24:59 Now you’ve mortgaged our house.
    0:25:00 How do you know the investor’s gonna come?
    0:25:03 You’ve already been rejected a couple hundred times.
    0:25:05 And then we eventually found an investor
    0:25:08 that saw the vision and was willing
    0:25:11 to put that risk capital into become a developer.
    0:25:13 I then got a team that was working
    0:25:15 at Brookfield Renewable.
    0:25:18 Wait, before you keep going, what did you say
    0:25:19 when your wife said, why are you doing this?
    0:25:23 You spent all of our money and more.
    0:25:27 And hundreds of people have said, no, like she,
    0:25:29 I mean, in retrospect, I guess you were right,
    0:25:32 but like in any kind of expected value,
    0:25:34 rational universe, she’s right at that point.
    0:25:39 Yeah, what I said was, I believe in it.
    0:25:42 When I look at first principles,
    0:25:44 the technology makes perfect sense
    0:25:45 and the world’s gonna need it.
    0:25:48 Like, and so I said, there’s no fundamental reason,
    0:25:50 the whole, the reason the investors were saying,
    0:25:52 no, was I’m not the right one.
    0:25:54 It’s too much risk for me.
    0:25:58 It’s too much capital, the timeline’s too long.
    0:26:00 No one said, I don’t think it’ll work
    0:26:03 or I don’t think long duration storage is needed.
    0:26:07 So to me, I just had to find the right fit more so
    0:26:09 than there was a fundamental problem with what I was doing.
    0:26:10 And I wasn’t willing to give up
    0:26:14 because I felt so passionate about climate change,
    0:26:17 but also it was, so many people had invested money
    0:26:18 already at that point.
    0:26:21 And the team members that had joined me,
    0:26:23 I didn’t want to let everyone down.
    0:26:25 So I was wanting to turn over every stone
    0:26:29 until there was absolutely no possible other option.
    0:26:32 And luckily an investor that had said, no, before,
    0:26:35 I picked up the phone and called them back and said,
    0:26:37 you sure you don’t want to take a harder look?
    0:26:41 And he had just received a big payday from another investment.
    0:26:43 I caught him while he was in a good mood.
    0:26:44 Said, you know what?
    0:26:46 Sure, let’s rekindle these conversations.
    0:26:48 And that resulted in an investment,
    0:26:51 really our Series B from Warren Partners.
    0:26:54 And that allowed us to staff up a development team,
    0:26:56 gave us the capital to start investing
    0:26:59 in our Australian project in California.
    0:27:02 And that’s really when the ball started rolling.
    0:27:03 We had that Goddard facility done.
    0:27:05 And then we started winning contracts
    0:27:07 and getting interconnect.
    0:27:10 And now, you know, the team’s close to 140 people
    0:27:12 and in multiple countries
    0:27:14 and going really well at the moment.
    0:27:19 – I’ve heard you mentioned this moment in, was it 2020?
    0:27:23 Like in the intense COVID era of COVID
    0:27:26 when you got the contract in Australia,
    0:27:28 which was that your first like big contract?
    0:27:29 – That’s right.
    0:27:31 – Tell me about that moment.
    0:27:33 – Yeah, so we started investing in Australia.
    0:27:35 It was a really creative solution.
    0:27:37 My commercial team came up.
    0:27:38 So we offered the utility something
    0:27:39 they weren’t really asking for,
    0:27:43 but they saw the benefit of it using storage
    0:27:44 instead of transmission
    0:27:47 to essentially connect this remote community.
    0:27:51 And they won that by the regulatory rules wasn’t allowed.
    0:27:55 So then we had to change the regulatory environment.
    0:27:57 So it was a long road, but their light came on.
    0:28:01 I said, wow, this is dramatically cheaper, no emissions.
    0:28:04 So then they became an ally for us trans grid
    0:28:06 to help change the rules in Australia
    0:28:09 to allow projects like this to go ahead.
    0:28:10 And so we’re going through the rules
    0:28:14 and then they say, here’s the contract.
    0:28:17 And so it’s a 40 year contract.
    0:28:19 Like I say, a billion dollar plant
    0:28:21 and we’re starting the permitting work
    0:28:22 and engaging the stakeholders.
    0:28:25 And then COVID hits and Australia shuts their borders.
    0:28:30 So then we had to strike a deal with a local team
    0:28:32 so that they would be our boots on the ground
    0:28:35 until all the border restrictions lifted.
    0:28:37 And then we ultimately bought that partner back out.
    0:28:40 So it was a bit of an expensive foray
    0:28:42 but allowed the project to keep moving.
    0:28:45 – Did you think it was, was there a minute
    0:28:49 where you thought COVID was gonna mean it wouldn’t work?
    0:28:54 – COVID was another time that I had to mortgage the house
    0:28:56 ’cause we needed some bridge financing.
    0:28:58 No investors were investing.
    0:29:00 – Had you paid back the other mortgage
    0:29:01 or are you just the value going up?
    0:29:04 How did it keep mortgaging your house in this story?
    0:29:06 – No, I got paid back that one
    0:29:09 and then we were running out of capital again.
    0:29:11 We had a bunch of interested investors
    0:29:12 but then when COVID hit, they said,
    0:29:14 “Look, until I figure out where the world goes,
    0:29:16 “I’m not putting new money to work.”
    0:29:20 So we had to find a way of bridging ourselves for a year
    0:29:23 which essentially me and some board members
    0:29:26 loaned the company money from our personal balance sheets
    0:29:27 to get us through.
    0:29:30 And then we ultimately struck a new investor came in
    0:29:32 and then ultimately Goldman Sachs
    0:29:35 and the Canadian pension plan gave us a very large check
    0:29:36 at the end of ’21.
    0:29:38 – Just so I’m not missing any,
    0:29:40 how many times have you mortgaged your house
    0:29:41 to keep the company going?
    0:29:44 – That would be three or four.
    0:29:46 – Oh, three or four, okay.
    0:29:49 I mean, the bigger the company gets,
    0:29:50 the bigger the company gets,
    0:29:51 the less helpful it’s gonna be
    0:29:54 unless you keep buying bigger houses, right?
    0:29:56 – Exactly, I’m still in the same house
    0:29:57 so it ain’t getting any bigger.
    0:29:59 – It’s not gonna do much
    0:30:01 if you got 140 people working for you
    0:30:03 and you’re building billion dollar plants
    0:30:06 unless it’s an amazing house
    0:30:08 in which case congratulations.
    0:30:12 So I found out about your company
    0:30:17 when I read that you had been awarded a provisional loan
    0:30:21 of I think it was $1.7 billion
    0:30:23 from the U.S. Department of Energy.
    0:30:27 That was, I don’t know, a month ago or something
    0:30:31 which congratulations,
    0:30:34 but also the federal government’s a lot different now
    0:30:35 than it was a month ago.
    0:30:38 And I’m curious what that means for you?
    0:30:41 I mean, what political change in the U.S. in particular
    0:30:42 means for you?
    0:30:46 – Yeah, I think there’s still a bit of uncertainty out there
    0:30:50 but we started working with the loan program office
    0:30:52 three plus years ago.
    0:30:55 So they’ve done a tremendous amount of diligence
    0:30:58 and this is exactly what this program is set up for
    0:31:02 is newer technologies that have a ton of domestic content
    0:31:04 and are gonna show a new technology
    0:31:07 adding to grid resiliency in the U.S.
    0:31:09 So that’s what the loan is for.
    0:31:13 It’s to provide the debt for our project in California.
    0:31:17 And we’re putting in a lot of equity into the project
    0:31:20 and the debt covers obviously the debt to construct
    0:31:23 and the interest during the construction period.
    0:31:25 So we’re really excited about the loan.
    0:31:27 It’s a legally binding loan
    0:31:30 but with the new administration
    0:31:31 there’s a lot of things moving around
    0:31:33 but I think it’s aligned with their agenda.
    0:31:35 It’s actually in a Republican County
    0:31:38 but it drives grid resiliency
    0:31:43 and lowers energy costs for the energy dominance
    0:31:47 that is required for all the data centers
    0:31:48 and low growth out there.
    0:31:51 So I think it, and like I said
    0:31:55 it’s virtually 100% domestic labor and content
    0:31:56 that goes into the project.
    0:31:59 So I think it’s consistent with the newest
    0:32:02 administration’s goals and it’s like I say
    0:32:03 the binding loan commitment.
    0:32:05 So we’ve got a couple of condition precedents
    0:32:07 we got to get through before it.
    0:32:10 We can start drawing on the loan but really excited
    0:32:12 and it’s been great working with the Department of Energy
    0:32:13 and their tremendous staff.
    0:32:15 – I wanna note that you said energy dominance
    0:32:16 that’s good, right?
    0:32:17 You got that one.
    0:32:21 And Republican district is interesting, right?
    0:32:25 My sense is with the Inflation Reduction Act
    0:32:27 or you know, a ton of money
    0:32:29 for the energy transition basically.
    0:32:33 A lot of it has been going to projects
    0:32:34 in Republican districts.
    0:32:36 So I mean, it’ll be interesting to see
    0:32:39 how that plays out politically, right?
    0:32:42 You mentioned there’s a couple conditions.
    0:32:44 I mean, is it basically if you do certain things
    0:32:45 you get the money?
    0:32:46 Is that what that means?
    0:32:47 – That’s right.
    0:32:48 – What are the things?
    0:32:49 What are the conditions?
    0:32:50 – If I had to bubble it down to two
    0:32:53 as we’ve got one more revenue contract
    0:32:56 that we’ve got to sign and then our permits.
    0:32:57 Our permits are working through
    0:32:59 the California Energy Commission.
    0:33:02 We expect roughly Q3, Q4, we would have that permit.
    0:33:04 So we need those.
    0:33:05 We need the permit to construct
    0:33:08 and then the last revenue contract to be signed.
    0:33:10 – So let’s talk a little bit about
    0:33:14 long duration storage kind of more broadly, right?
    0:33:16 Like it’s a huge problem.
    0:33:18 As you said, it’s a bigger problem
    0:33:23 the more there is wind and solar power.
    0:33:27 Lots of people are trying lots of different ways to solve it.
    0:33:29 Like give me a sense of the landscape
    0:33:31 more broadly and where you fit.
    0:33:32 Like what are other people doing?
    0:33:34 And then what are you particularly good at?
    0:33:35 What’s the one thing that you can do
    0:33:37 more reliably, more cheaply, whatever?
    0:33:40 – Yeah, you almost have to look at it
    0:33:42 in a kind of a two by two quadrant.
    0:33:44 On the one axis is like scale.
    0:33:48 Is it really big like city size or is it for the home?
    0:33:54 And then on the Y axis if you would is duration.
    0:33:56 So do you need an hour or two of storage?
    0:34:00 Or do you need many days or a season of storage?
    0:34:03 We kind of fit in the really large scale.
    0:34:05 So we are hundreds of megawatts.
    0:34:09 So to give you a sense, it’s like a quarter of a city’s load.
    0:34:11 That’s the sort of scale much bigger
    0:34:13 than any individual wind farm or solar farm.
    0:34:15 So we are pretty big scale.
    0:34:19 – Quarter of a city meaning like 100,000 homes?
    0:34:20 Like something like that?
    0:34:25 – Hundreds of thousands if not a million homes, yeah.
    0:34:27 So quite large.
    0:34:31 And then we tend to do eight hours to 24 hours.
    0:34:33 So we’ll cover you for through the night
    0:34:35 if you’re a solar dominant region,
    0:34:37 if you’ve got a day or two with low winds,
    0:34:39 that’s where we fit in.
    0:34:40 We’re not seasonal.
    0:34:44 Like we won’t do your spring shoulder season.
    0:34:46 And we’re definitely not an hour or two.
    0:34:51 So lithium ion at both large scale and home
    0:34:55 for like six hours and less is dominated by lithium ion.
    0:34:59 So they own shorter durations, six hours and less.
    0:35:04 And then in large scale, really long duration,
    0:35:06 that’s really been the pumped hydro,
    0:35:07 but they’re quite limited
    0:35:09 in what other solutions are out there.
    0:35:11 And then people are talking hydrogen for seasonal.
    0:35:15 I’m not sure if that’s gonna make sense or not,
    0:35:17 but we kind of have a clear where we play.
    0:35:20 Now there’s four or five different technologies
    0:35:23 going at the other pockets of that grid
    0:35:25 that I haven’t mentioned.
    0:35:30 – So lithium ion batteries are getting cheaper fast,
    0:35:33 which is good news for the world.
    0:35:34 What does it mean for you?
    0:35:36 I mean, is it the case that the cheaper
    0:35:38 lithium ion batteries get,
    0:35:42 the longer the duration they can economically provide?
    0:35:45 – Yes.
    0:35:48 They used to, we used to think it was two to four hours.
    0:35:49 Now they’ve pushed the six.
    0:35:51 If the cost keep coming down
    0:35:54 and tariffs don’t kind of reverse the declines,
    0:35:57 that could move to eight.
    0:36:01 I can’t really see them moving too much farther beyond that.
    0:36:03 To give you a sense, when today’s cost,
    0:36:06 if you install the lithium ion battery,
    0:36:11 it’s about $300 per kilowatt hour of storage capacity
    0:36:12 and it lasts maybe 10 years
    0:36:15 with degradation and everything in there.
    0:36:20 To add one hour of our system is $50 a kilowatt hour
    0:36:23 and it lasts 50 years with no degradation.
    0:36:27 So it’s a pretty, on the marginal basis,
    0:36:28 it’s a pretty high bar.
    0:36:31 They would have to drop by an order of magnitude
    0:36:35 and then they still would have to extend their life by 5X
    0:36:37 to kind of get into the same realm.
    0:36:39 – That’s compelling.
    0:36:42 So what’s next?
    0:36:44 Like what are you working on now?
    0:36:45 – Construct those two plants.
    0:36:47 So in Australia and California,
    0:36:49 construct them on time, on budget,
    0:36:52 show the world what they can do at scale.
    0:36:54 While we take those other 18 that we’re developing
    0:36:57 and stack them up ready for construction,
    0:37:02 that’ll then allow us to start licensing to utilities.
    0:37:04 And we’d like to partner with groups in Japan
    0:37:06 and China and India and Europe
    0:37:09 to start offering the solution in those markets,
    0:37:12 as opposed to us standing up development teams
    0:37:13 all around the world.
    0:37:15 We’re predominantly focused in North America
    0:37:19 and Australia and the UK as a development platform.
    0:37:21 – What are you worried about at this point?
    0:37:22 Like what might go wrong?
    0:37:26 – No, I think it’s just general team building,
    0:37:29 culture, project management.
    0:37:33 There’s nothing fundamental with our technology,
    0:37:36 supply chain, policy environment
    0:37:38 that I’m really worried about.
    0:37:41 It’s really just execution from our team.
    0:37:44 I guess there is a bit of the pace
    0:37:47 for long duration storage will be set by policy makers.
    0:37:50 Do they fix the rules of the road for the grids?
    0:37:52 Do they allow queue reform
    0:37:54 so you can get interconnection spots?
    0:37:56 Will they properly pay for long duration storage?
    0:38:00 How long do permits take?
    0:38:03 That sort of thing will dictate the pace of the build out,
    0:38:05 but I’m confident it’s coming.
    0:38:08 It’s just a matter of the pace that it accelerates at.
    0:38:14 – We’ll be back in a minute with the lightning round.
    0:38:18 (upbeat music)
    0:38:27 – Let’s finish with the lightning round.
    0:38:30 It’ll just be sort of random questions
    0:38:31 is basically what it’s gonna be.
    0:38:37 So I read that you spent many years
    0:38:39 as an energy consultant living in different parts
    0:38:40 of the world.
    0:38:44 And I’m curious of all of the places you lived.
    0:38:46 What was the most underrated?
    0:38:48 It’s the place that’s like great
    0:38:49 that nobody knows is great.
    0:38:52 – Seoul, South Korea.
    0:38:53 – Oh, interesting.
    0:38:54 Tell me.
    0:38:59 – Yeah, I got to spend five months in South Korea.
    0:39:03 It was for a guy that grew up in Northern Ontario
    0:39:07 in a rural background being in a city that dense
    0:39:12 and that intense from just a stimulation perspective.
    0:39:17 I found very compelling and just a very unique culture,
    0:39:20 unique food and just a great experience.
    0:39:22 – What’s one thing I should do if I go to Seoul?
    0:39:23 – Karaoke.
    0:39:25 – Of course.
    0:39:26 What’s your go-to karaoke song?
    0:39:29 – Oh, it was The Beatles.
    0:39:30 I can’t remember which one.
    0:39:31 Hey Jude, I think.
    0:39:32 – Ah, it’s a winner.
    0:39:35 I bet you killed with Hey Jude in Seoul.
    0:39:38 What’s the, what’s one thing I should do
    0:39:41 if I go to Kenora, Ontario, your hometown?
    0:39:45 – MS Kenora, a little cruise ship around Lake of the Woods.
    0:39:46 Beautiful.
    0:39:50 I cook there growing up, helping to pay for my fun
    0:39:52 on the weekends.
    0:39:55 – You played hockey at the University of Toronto
    0:39:58 and I’m curious, I have a friend who’s into hockey
    0:40:01 and I said, I’m talking to a guy who played hockey.
    0:40:02 What should I ask him?
    0:40:06 And he said, ask him what his view is on fighting in hockey.
    0:40:07 What’s your view on fighting in hockey?
    0:40:13 – It’s a bit complicated, but I, it keeps everyone honest.
    0:40:16 So I’m a supporter and you know, there’s a code
    0:40:19 if someone doesn’t want to fight, you don’t fight,
    0:40:22 but it is a way of keeping things honest.
    0:40:23 – What does that mean?
    0:40:25 That is in fact really interesting to me.
    0:40:27 What does it mean that it keeps everyone honest?
    0:40:30 – You know, it can be a very dangerous sport.
    0:40:32 You know, you think of the stick and you can wind up
    0:40:35 and smash someone in the ankle and smash their ankle
    0:40:36 and blow out their career.
    0:40:38 Someone does that, you know, the ref just throwing them
    0:40:41 in the penalty box isn’t fair retribution
    0:40:42 for something like that.
    0:40:45 So no one runs around swinging their sticks
    0:40:48 ’cause they know what would happen if they did such a thing.
    0:40:49 – That’s really interesting.
    0:40:52 What’s one thing you tell somebody
    0:40:55 who’s becoming a CEO for the first time?
    0:41:00 – You know, I don’t know that I’m at the stage
    0:41:02 where I’m giving advice yet.
    0:41:05 I’m still figuring it out. – 15 years is a long time man.
    0:41:07 – Yeah, I guess so.
    0:41:09 I still feel like I’m learning as I’m going,
    0:41:12 but I guess I would say it’s about the team
    0:41:14 more than anything is you can only do so much
    0:41:15 in the early days you’re doing a lot,
    0:41:18 but then it’s about the team, team, team,
    0:41:20 surround yourself with the best possible people.
    0:41:22 And it’s amazing.
    0:41:24 I wake up every day just amazed
    0:41:27 with what the team does and gets accomplished.
    0:41:30 And you just start realizing the power of other people
    0:41:33 and how much strength there is in the numbers
    0:41:34 and in the team.
    0:41:37 I would just be focused on getting the right team.
    0:41:39 – I heard you say that if you had known
    0:41:41 when you started the company,
    0:41:43 how hard it was gonna be, what you know now,
    0:41:45 you wouldn’t have done it.
    0:41:49 And so I’m curious, like, do you think 15 years from now,
    0:41:51 if you look back at 2025, you would say,
    0:41:54 man, if I’d have known how hard that 15 years would be,
    0:41:56 I would have got out in 2025.
    0:41:59 – No, I think I’ve made it through the, you know,
    0:42:01 the J-curve, if you will.
    0:42:03 – Like your pipe.
    0:42:05 You’re on your way up to the surface.
    0:42:06 – That’s right.
    0:42:07 Yeah, no, it’s now,
    0:42:09 because I think if it would have failed,
    0:42:12 call it four or five years ago.
    0:42:15 Every, you know, I wouldn’t have had much pride in it.
    0:42:18 Like there’s some pride, but we didn’t accomplish much.
    0:42:20 We didn’t, it wasn’t that big of a team.
    0:42:22 We didn’t accomplish many milestones.
    0:42:24 We didn’t raise that much money.
    0:42:26 So I basically just burned my personal capital
    0:42:28 and years of my life.
    0:42:30 And I think people would have scoffed
    0:42:31 ’cause a lot of people were scoffing at us saying,
    0:42:32 “What are you guys doing?
    0:42:34 This doesn’t make any sense.”
    0:42:36 And it just would have fulfilled those.
    0:42:37 And I think I would have been, you know,
    0:42:39 pretty embarrassed to be honest.
    0:42:42 Whereas now I think we’ve accomplished a lot.
    0:42:43 I would be proud.
    0:42:44 And I’d look back and say, you know,
    0:42:46 we made a really good go at it.
    0:42:50 And, but I think moving forward,
    0:42:52 it’s just, it’s just up from here.
    0:42:55 So I’m excited for the next 15 years.
    0:42:56 – Great.
    0:42:57 Thank you for your time.
    0:43:00 It was great to talk with you.
    0:43:01 – Thanks, Jacob.
    0:43:03 – Thanks for your time and pleasure.
    0:43:05 (upbeat music)
    0:43:07 – Curtis Van Wallingham is the co-founder
    0:43:10 and CEO of HygroStore.
    0:43:13 Today’s show was produced by Gabriel Hunter-Chang.
    0:43:15 It was edited by Lydia Jean-Cott
    0:43:17 and engineered by Sarah Brugger.
    0:43:21 You can email us at problem@pushkin.fm.
    0:43:23 I’m Jacob Goldstein and we’ll be back next week
    0:43:26 with another episode of What’s Your Problem.
    0:43:28 (upbeat music)
    0:43:29 – Bye.
    0:43:32 (upbeat music)
    0:43:40 [BLANK_AUDIO]

    We need better, cheaper ways to store solar and wind energy when it’s dark out and the wind isn’t blowing.

    One option: Compressing air in underground caverns when energy is abundant, then blowing it back out to create energy when you need it. It’s an old idea, but it has some fundamental problems.

    Curtis van Wallingham, the co-founder and CEO of Hydrostor, thinks his company has solved those problems with a new approach. If he’s right, his firm will help fix the biggest bottleneck slowing down the adoption of solar and wind power.

    See omnystudio.com/listener for privacy information.

  • The Lasting Impact of Citizens United, How to Ask for a Raise at Work, and When Is It the Right Time to Have Kids?

    AI transcript
    0:00:03 Support for Prop 3 comes from Viori.
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    0:01:43 That’s BetterHelp, H-E-L-P.com.
    0:01:46 Welcome to Office Hours with PropG.
    0:01:48 This is the part of the show where we answer your questions
    0:01:51 about business, big tech, entrepreneurship and whatever else is on your mind.
    0:01:55 If you’d like to submit a question, please email a voice recording
    0:01:57 to officehours@propgmedia.com.
    0:01:59 Again, that’s officehours@propgmedia.com.
    0:02:01 So with that, first question.
    0:02:07 Scott, hey, long time listener, my name is Doug.
    0:02:10 I’m an environmental consultant and I work on biodiversity
    0:02:12 and climate change issues across the U.S.,
    0:02:15 often involving public-private partnerships.
    0:02:17 I really appreciate your recent comments with regard to the interaction
    0:02:24 between businesses and politics and how unequal society has become.
    0:02:27 I’m wondering, is there a way for a business leader such as yourself
    0:02:31 to get together and work to reverse Citizens United
    0:02:35 to provide some rational guardrails on campaign finance?
    0:02:38 Maybe you can call yourselves the SuperFriends.
    0:02:39 Thanks so much.
    0:02:41 Ah, the SuperFriends, I like that.
    0:02:45 If I were a superhero, I think my power would be, what is my power?
    0:02:49 I don’t know, the ability to pee three or four times in any given evening.
    0:02:52 It’s like I wake up and I don’t even think, oh, why am I waking up?
    0:02:53 I know I’m waking up.
    0:02:57 It’s my bladder going, hey, your prostate’s the size of a fucking grapefruit.
    0:03:00 And it says it’s time to pee, even if it’s not time to pee.
    0:03:01 Anyway, isn’t that exciting?
    0:03:03 Isn’t that why you come here?
    0:03:07 So Citizens United, you could argue if you were to reverse engineer
    0:03:11 a lot of the problems we have, it’s, well, OK, it’s that we send crazies.
    0:03:14 The majority of people in America now identify as independent
    0:03:18 or somewhere in the middle on most issues and kind of think, OK, we can,
    0:03:21 we can accommodate both sides and sort of come to some sort of agreement.
    0:03:24 Instead, we send far left crazies and far right crazies.
    0:03:27 In addition, because the incumbents can raise a lot more money
    0:03:31 and because there’s no caps on how much money they can raise,
    0:03:32 it creates more and more incumbency.
    0:03:35 And not only that, corporations are now considered people
    0:03:41 or money is considered voice and free speech, such that if you’re the pharmaceutical lobby
    0:03:44 and you want to give a bunch of money to a candidate or a better yet,
    0:03:48 the private equity lobby and you give $800,000 to Senator Kristen Sinema
    0:03:51 such that she is a holdout and says, I won’t pass.
    0:03:53 I’ll be the swing vote against the infrastructure bill
    0:03:56 unless you pull out this loophole such that some of the wealthiest people in the world,
    0:04:00 private equity billionaires, maintain carried interest loophole
    0:04:03 where they get long term capital gains or they get a lower tax rate
    0:04:04 on what is essentially a commission.
    0:04:08 Whereas if you sell a car and get a commission, you pay a much higher tax rate.
    0:04:11 This is nothing but pure grift for the rich that has been weaponized.
    0:04:14 So unless we put some sort of campaign finance limits on this
    0:04:18 and de-jarring manner these districts, it’s just not going to get better.
    0:04:22 There’s other things you could do, rank choice voting, final five,
    0:04:26 where it’s not just the crazies, it’s people who across the spectrum.
    0:04:29 Lisa Murkowski is a fantastic moderate center from Alaska.
    0:04:32 Why? Because they have final five voting in Alaska
    0:04:35 where everybody votes for the first, second, third and fourth candidate.
    0:04:39 And they get the lower ones get kicked out and the other ones get votes.
    0:04:42 So you end up with the moderates have a shot, if you will.
    0:04:45 And it was back to Citizens United following the 2010 Citizens United ruling,
    0:04:50 which allowed corporations and unions to spend unlimited money on political campaigns.
    0:04:52 Independent political spending surged.
    0:04:55 Over the past decade, election related spending by non-party independent groups
    0:04:59 skyrocketed to get this four and a half billion dollars
    0:05:02 compared to just 750 million 20 years prior.
    0:05:03 So it’s up about six fold.
    0:05:06 Additionally, political campaigns are now spending more than ever.
    0:05:08 Between the presidential and congressional races,
    0:05:12 American political candidates spent a total of 16 billion dollars
    0:05:14 this past election cycle.
    0:05:18 The overwhelming impact of Citizens United could be addressed in a few ways.
    0:05:20 The Supreme Court could revisit the decision.
    0:05:22 Good luck with that.
    0:05:25 Pongas could propose a new amendment to limit corporate political spending,
    0:05:27 but the majority of them are such whores.
    0:05:30 Why would they want to shut off the spicket, right?
    0:05:32 Congress could pass laws to increase funding transparency
    0:05:35 and cut off communication between campaigns and super PACs.
    0:05:37 We need to work around here.
    0:05:40 If the Supreme Court isn’t going to overturn Citizens United,
    0:05:46 we’re going to have to come up with a bunch of hacks such that money gets out of DC.
    0:05:49 Because if you look at the fact that we pay twice as much
    0:05:54 as any other G7 nation for healthcare, despite the fact we have lower life expectancy,
    0:05:57 higher infant mortality, higher rates of obesity,
    0:06:02 you can directly go to the weaponization of our elected representatives by money
    0:06:07 from pharmaceutical, the health industrial complex, hospital systems, et cetera.
    0:06:10 So money in politics has been a real cancer.
    0:06:13 And I think your question is the correct one.
    0:06:19 Last night, I watched Senator Michael Bennett give what I thought was just an outstanding grilling
    0:06:22 of RFK Jr., who’s up for health and human services.
    0:06:22 Oh, that’s a good idea.
    0:06:27 Let’s have an anti-vax conspiracy theorist decide the healthcare of our children.
    0:06:28 That makes sense.
    0:06:29 That makes sense.
    0:06:32 Anyways, the way I express affection or support for somebody is I send them money.
    0:06:35 So today I’m going to send money to Senator Michael Bennett.
    0:06:39 And that is, I realize I’m part of the problem, but at a minimum,
    0:06:44 if they’re going to fire bazookas at us, I’m going to get a javelin missile or whatever they call it.
    0:06:46 Anyways, thanks for the question.
    0:06:48 Question number two.
    0:06:50 Hi, Professor Galloway.
    0:06:52 My name is Pete from DC.
    0:06:57 My question is about how to ask for a raise and if it’s always appropriate to do so.
    0:07:01 I’m an account executive for a medium-sized tech company and had a decent 2024,
    0:07:04 exceeding my quota by about 30%.
    0:07:07 I’d like to ask for a raise because who doesn’t like or need more money.
    0:07:11 But I’d be interested in hearing about times when people ask you for raises,
    0:07:14 both when they’ve done it effectively and when they’ve done it ineffectively.
    0:07:16 Thank you.
    0:07:17 I think this is a tough one.
    0:07:25 So one, I think that in a pre-interview, typically a good firm will ask you to review yourself
    0:07:28 and you will have access to management throughout the year.
    0:07:32 I think it’s okay to constantly check in and say, or not constantly,
    0:07:34 we’re regularly checking and say, how am I doing?
    0:07:36 These are my goals for the year.
    0:07:38 I feel as if I’m hitting them.
    0:07:43 And then when you typically come in for compensation once at the end of the year,
    0:07:44 they’ll give you the number.
    0:07:49 And I think it’s okay to ask questions about the number and also to express disappointment
    0:07:53 and say, I don’t feel as if I’m getting the type of compensation I’d hope for
    0:07:56 or nowhere’s warranted by my performance.
    0:07:59 Now, typically expressing that sort of disappointment won’t result.
    0:08:03 I never change bonuses or decisions around raises.
    0:08:07 And I tell my employees, these decisions only happen once a year.
    0:08:10 Otherwise, there’s a line in my office of people every two months thinking,
    0:08:11 oh, I just did a good job.
    0:08:13 I’m going to go in and ask for a raise or a promotion.
    0:08:16 So these discussions need to happen once a year.
    0:08:20 I think what’s helpful is if you have senior level sponsorship in the organization,
    0:08:23 it’s just to be very transparent saying, I’m looking to make more money here.
    0:08:26 What do you think I can do?
    0:08:26 How am I doing?
    0:08:30 And also just to be honest with your direct report, your boss saying,
    0:08:31 you know, I’m ambitious.
    0:08:32 I want to make more money.
    0:08:33 I want to be promoted.
    0:08:35 What do you think I need to do to get there?
    0:08:39 Instead of saying, I want more money, saying, what do you think I need to do
    0:08:46 to increase the likelihood that I’ll be promoted or register an increase in compensation?
    0:08:51 And if you don’t get the compensation you want, I think it’s okay to say, I’m disappointed.
    0:08:52 I was expecting more.
    0:08:53 I was hoping for more.
    0:08:57 Also at the end of the day, and there’s evidence that shows this,
    0:09:01 the people who typically make more money on average are people who switch jobs every three
    0:09:03 to five years because this is the issue with employers.
    0:09:08 You have a tendency to see employees through the lens through which they were hired.
    0:09:11 And that is we romanticize strangers.
    0:09:16 We had an editor in chief who I have been working with since he was 22, 25 years ago.
    0:09:25 And I see him as Jason, the recent Yale grad who I was paying $60,000 a year to in 1995 or ’98.
    0:09:30 And I realize now, no, he’s a 40-something-year-old man who is very talented and should be making,
    0:09:33 you know, two, $300,000 a year.
    0:09:35 But I still see him as Jason.
    0:09:39 And the folks who leave typically take advantage of this,
    0:09:42 how attracted we are to strangers, if you will.
    0:09:47 So if you really don’t feel like you’re getting good compensation or being fairly treated,
    0:09:52 I would talk to your mentor or your boss there saying, “Yeah, I was unhappy with my compensation.”
    0:09:56 But at the end of the day, if you really are unhappy with your compensation
    0:10:00 and don’t feel as if they’re likely to change it,
    0:10:03 quite frankly, the easiest way to increase your compensation if you are in fact being underpaid
    0:10:07 is to let the market decide and go out and try and find another job.
    0:10:15 And what I did every three to five years at NYU is I would get an offer from a competitor institution
    0:10:19 and then I would go back and say, “Full transparency, I don’t want to leave NYU,
    0:10:24 but according to whoever, Cornell or Columbia or Wharton, I’m worth this.
    0:10:27 I need you to match it. That appears to be my market rate.”
    0:10:31 And quite frankly, had I not gone in and said, “You have to develop your own currency.
    0:10:32 My currency was putting butts in seats.
    0:10:37 My course quickly became one of the most popular courses in the marketing department and in the school.
    0:10:40 And I would go in and say, I need more money or I would do a market check.
    0:10:41 So what are we going to do?
    0:10:45 We’re going to check in with our boss and see how you’re doing.
    0:10:51 You’re going to lay out your expectations and say or your desires that you want to get promoted
    0:10:53 and you want an increase in salary and ask for advice.
    0:10:55 How can I make sure I’m tracking for that?
    0:11:00 And if you don’t get the compensation of the promotion in a very thoughtful, civilized way,
    0:11:02 so I got to be honest, I’m disappointed.
    0:11:05 And at the end of the day, you have to show a willingness to leave
    0:11:09 and that is start doing a market check if you feel you’re being unfairly compensated.
    0:11:11 Appreciate the question.
    0:11:14 We have one quick break before our final question.
    0:11:15 Stay with us.
    0:11:23 This episode is brought to you by Cresed Family Office.
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    0:12:16 There are no material conflicts other than this paid endorsement.
    0:12:19 All investing involves risk, including loss of principal.
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    0:13:34 [Music]
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    0:13:41 very frustrating problem that many men deal with,
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    0:14:48 Welcome back, question number three.
    0:14:51 Hello, Scott. I’m David from Mexico.
    0:14:54 I’m a longtime listener and have learned a lot through the path
    0:14:55 and your books.
    0:14:56 Thank you for that.
    0:14:59 I am 30 years old, currently living in Monterrey,
    0:15:01 expanding the family construction business.
    0:15:04 My wife and I did marriage in 2023,
    0:15:07 and I’m currently enjoying our time as a couple.
    0:15:11 We honestly have a great relationship and have been together for 10 years.
    0:15:16 Lately, we have started to discuss the best timing of when to have kids.
    0:15:20 We are both working in good and stable jobs,
    0:15:23 living a good life and have been saving and investing,
    0:15:25 but I know means have our future secured.
    0:15:28 She prefers sooner rather than later,
    0:15:30 while I prefer to wait a little bit longer
    0:15:33 and prioritize our economic security and time as a couple.
    0:15:37 What are your thoughts about when to have children?
    0:15:39 What are some factors we should consider in our decision?
    0:15:42 And is there any advice you could give us?
    0:15:44 I would really appreciate your opinion.
    0:15:45 Thank you.
    0:15:49 David from Mexico, this is such a personal decision,
    0:15:52 so you should take everything I say with a grain of salt,
    0:15:53 because I’m going to tell you kind of my way,
    0:15:55 but that doesn’t necessarily mean it’s the right way.
    0:15:59 I think these, at the end of the day, are decisions that you, your wife,
    0:16:02 and quite frankly, your sperm and her eggs,
    0:16:04 because sometimes it’s just not easy to get pregnant,
    0:16:05 and sometimes it’s super easy.
    0:16:08 By the way, probably under the auspices of TMI,
    0:16:12 my girlfriend and I decided I didn’t want to have kids,
    0:16:16 and she said, “Well, I have to have kids,
    0:16:17 otherwise we can’t be together.”
    0:16:18 And I said, “Well, I don’t want to get married.”
    0:16:20 And she called my bluff and said, “I don’t need to get married to have kids.”
    0:16:23 So we pulled the goalie and started having unprotected sex.
    0:16:29 Oh my god, and literally, I’ve had two kind of, I don’t know what you’ll call it,
    0:16:32 surreal, mystical things happen to me.
    0:16:34 One, four months after my mom passed away, she came to me,
    0:16:36 and it was so real.
    0:16:37 It just felt real, and she said,
    0:16:40 “I just want you to know I’m doing fine, and I love you.”
    0:16:43 But it was so real, it felt, I don’t even have to explain it,
    0:16:45 that’s the first one of two.
    0:16:52 And the other one was, after my girlfriend and I had fornicated in Vegas,
    0:16:57 at CES of all places, I went into the bathroom and I came out,
    0:16:59 and I knew we had just conceived a son.
    0:17:02 And I said to her, “We just conceived a son.”
    0:17:07 And what do you know, you know, pregnancy tests, bright blue,
    0:17:14 and now the purpose of my life and my biggest joy is my son who’s now 17,
    0:17:18 and tied for that position as the son we had three years later.
    0:17:22 It’s so funny, you spend your whole life trying not to get pregnant,
    0:17:24 and then sometimes it’s not easy to get pregnant.
    0:17:27 Anyways, not what you asked.
    0:17:30 Look, I’m not sure there’s ever a perfect time to have kids.
    0:17:36 I would argue that it’s very kind of base pillars you need in place.
    0:17:40 One is, you have to have a partner that you think is competent,
    0:17:45 and that you can see being with for the next 18 years at least.
    0:17:49 Because once you have kids, you’re in each other’s lives for 18 years.
    0:17:50 Even if you get divorced, you’re in each other’s lives.
    0:17:53 Some semblance of economic security.
    0:17:56 You don’t have to be rich, but not strained.
    0:18:01 If you’re strained now, and you throw a kid into the mix, wow, that’s a lot of stress.
    0:18:06 So having a little bit of economic security and some professional trajectory.
    0:18:10 If you have those things, I would err on the side of doing it,
    0:18:15 because there is never a perfect time to bring this little thing into your life
    0:18:21 that’s going to demand constant attention, additional cost, and a lot of unknowns.
    0:18:23 So there’s never a time when it’s like, okay, this is definitely the time.
    0:18:28 And there really is an advantage, I think, to being a young parent.
    0:18:30 Having said that, I had kids later.
    0:18:31 It was nice to have some economic security.
    0:18:33 I was a little bit more thoughtful.
    0:18:35 Again, really personal decisions.
    0:18:36 Do you have a support group around you?
    0:18:40 Do you have family or young parents that could be involved in the kids’ lives?
    0:18:45 One of my biggest blessings is that our in-laws are fairly young,
    0:18:49 and they’ve played a hugely positive and supportive role in raising our children.
    0:18:51 So that’s been a real factor.
    0:18:53 So I think there are a variety of things.
    0:18:56 But if you get to what I call 70% or 80%,
    0:18:57 don’t let perfect be the enemy of good.
    0:19:01 And what I mean by that is if you’re mostly kind of there,
    0:19:04 then I would just go there and start procreating.
    0:19:06 I just don’t think there’s ever a perfect time.
    0:19:09 And I have found I was sort of wandering.
    0:19:11 I don’t think you have to have kids to be happy.
    0:19:14 I don’t think kids are the right decision for everybody.
    0:19:21 But I know that for me, having kids has been the first time I’ve ever felt a sense of purpose.
    0:19:23 So anyways, what am I saying?
    0:19:24 Get on it.
    0:19:26 Get on it.
    0:19:27 Make sweet sweet love.
    0:19:29 Procreate.
    0:19:30 Have progeny.
    0:19:30 That’s right.
    0:19:31 Progeny.
    0:19:32 That’s right.
    0:19:34 Anyways, congratulations to you a nice time in your life.
    0:19:38 That’s all for this episode.
    0:19:39 If you’d like to submit a question,
    0:19:42 please email a voice recording to officehours@propertymedia.com.
    0:19:46 Again, that’s officehours@propertymedia.com.
    0:19:57 This episode was produced by Jennifer Sanchez.
    0:19:59 Our intern is Dan Chalon.
    0:20:01 Drew Burroughs is our technical director.
    0:20:05 Thank you for listening to the Proprety Pod from the Vox Media Podcast Network.
    0:20:09 We will catch you on Saturday for No Mercino Mouse, as read by George Hahn.
    0:20:13 And please follow our Prodigy Markets Pod wherever you get your pods
    0:20:17 for new episodes every Monday and Thursday.
    0:20:27 [BLANK_AUDIO]

    Scott discusses the Citizens United decision and its repercussions fifteen years later, specifically how it’s pure grift for the rich. He then offers advice to a listener asking for a raise at work and wraps up with his thoughts on the right time to have children.

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  • Sandra Matz: The Personal Data Privacy Crisis

    AI transcript
    0:00:02 (upbeat music)
    0:00:10 – Hi, I’m Guy Kawasaki.
    0:00:12 This is the Remarkable People Podcast.
    0:00:14 And as you well know,
    0:00:16 we’re on a mission to make you remarkable.
    0:00:20 And the way we do that is we bring you remarkable guests
    0:00:23 who explain why they’re remarkable
    0:00:25 and how they’re remarkable and their remarkable work.
    0:00:28 And today’s special guest is Sandra Mott.
    0:00:31 – She’s a professor at the Columbia Business School
    0:00:35 and she’s gonna talk to us about psychological targeting
    0:00:37 and changing mindset.
    0:00:39 Congratulations.
    0:00:43 Shipping a book is a big, big accomplishment.
    0:00:45 Trust me, I know this firsthand, so.
    0:00:48 – I know, it feels good when it’s out.
    0:00:50 Even though I had a great time writing it.
    0:00:52 So I think I probably enjoyed it a lot more
    0:00:55 than what I was told by other authors.
    0:00:57 So I already enjoyed the process.
    0:00:59 – I have written 17 books
    0:01:02 and I have told people 16 times
    0:01:04 that I am not writing another book.
    0:01:06 – Good luck with that one.
    0:01:07 – Yeah, exactly.
    0:01:08 – I’m just waiting for,
    0:01:11 what would he be placing the order for number 18,
    0:01:12 if that’s the case?
    0:01:19 – Alrighty, so first of all, if you don’t mind,
    0:01:21 let me just tell you something kind of off the wall
    0:01:25 that your story about how you met your husband
    0:01:27 at that speaking event,
    0:01:29 that was the closest thing to porn
    0:01:32 in a business book that I have ever read.
    0:01:35 – And I spared you the details.
    0:01:37 It’s actually a lot more to the story.
    0:01:39 It’s a good one.
    0:01:40 – I was reading that.
    0:01:44 I said like, man, where is this going?
    0:01:47 Like, is she gonna have this great lesson about how to,
    0:01:50 you know, tell men to stick it and get out of my face?
    0:01:52 And then I keep reading and it says,
    0:01:56 oh, and the night went very, very well.
    0:01:57 – What?
    0:02:02 – It’s such a fun anecdote in my life,
    0:02:02 how I met him.
    0:02:05 So it was just like conference.
    0:02:07 He was late for the people who have read the book
    0:02:08 and I was like, what a jerk.
    0:02:10 And I kind of had written him off.
    0:02:12 And then as the night progressed
    0:02:15 and I learned more about him by spying on him
    0:02:18 as part of, in his place.
    0:02:20 I was like, interesting guy.
    0:02:22 I think I’m gonna give him a second chance.
    0:02:23 And we’re married.
    0:02:25 We have a kid now who’s one year old.
    0:02:27 So it all worked out.
    0:02:30 – And is he still meticulously neat?
    0:02:32 Or, you know, was that just a demo?
    0:02:34 And this is the real thing now.
    0:02:35 – No, no, no.
    0:02:36 So yeah, as part of the story,
    0:02:39 it’s like, one of the first things I learned about him
    0:02:41 is that I think he’s borderline OCD
    0:02:43 ’cause he just sorts everything.
    0:02:47 It’s like the person who sorts his socks by color.
    0:02:48 We just moved apartments,
    0:02:50 which is with a one year old,
    0:02:52 not the most fun thing to do in the world.
    0:02:54 And they were like boxes everywhere.
    0:02:56 You could barely walk around the apartment
    0:02:58 and I just opened one of the drawers
    0:03:01 and he had put the cutlery, like perfection.
    0:03:05 I’m like, there’s a hundred thousand boxes in this place.
    0:03:08 I can barely find anything for the baby,
    0:03:11 but I’m really glad that you spent at least an hour
    0:03:14 perfecting the organization of the cutlery.
    0:03:16 So it’s, he’s still.
    0:03:18 – I hope your new place has a dishwasher
    0:03:22 so he can load the utensils in the tray.
    0:03:23 – Tell me about it.
    0:03:24 – That’s exactly what happens.
    0:03:27 Yeah, I’m not allowed to touch the dishwasher anymore
    0:03:28 ’cause I don’t do it perfectly.
    0:03:30 So you’re spot on, yeah.
    0:03:33 (laughing)
    0:03:35 – So you listeners out there, basically,
    0:03:39 we have an expert in psychological targeting
    0:03:43 and now she’s explaining how she had absolutely no targeting
    0:03:46 in meeting her future husband, right?
    0:03:48 – I think I nailed it from the beginning
    0:03:51 and that his place, I looked at his,
    0:03:54 him being put together and it gave me a pretty good
    0:03:56 understanding I think of who he was.
    0:03:58 I feel like I know what I signed up for.
    0:04:02 – Okay, so this is proof that her theories work.
    0:04:06 So I’ve already, you know, said this word,
    0:04:08 psychological targeting twice.
    0:04:10 So I would really like,
    0:04:13 this is an easy question to start you off,
    0:04:16 not that we got past the porn part of this podcast,
    0:04:21 which is from a psychological targeting perspective.
    0:04:25 What’s your analysis of the 2024 election?
    0:04:26 – It’s a, I mean, interesting one.
    0:04:30 So psychological targeting typically looks at individuals.
    0:04:32 So it’s trying to see what can we learn
    0:04:34 about the psychological makeup of people,
    0:04:36 not by asking them questions,
    0:04:38 but really observing what they do, right?
    0:04:40 You can imagine in the analog world,
    0:04:43 I might look at how someone treats other people,
    0:04:46 whether they’re organized as my husband is.
    0:04:48 And I think you can learn a lot by making these observations.
    0:04:49 That’s true in the offline world.
    0:04:51 That’s also true in the online world.
    0:04:54 And I think if you just look at them,
    0:04:57 presidential candidates, the way that they talk,
    0:05:00 if Trump writes in all caps all the time
    0:05:04 and doesn’t necessarily give it a second thought
    0:05:06 before something comes out on Twitter,
    0:05:08 I think that is an interesting glimpse
    0:05:11 into what might be going on behind the scenes.
    0:05:14 – And do you think that his campaign
    0:05:18 did like really great psychological targeting
    0:05:20 of the undecided in the middle?
    0:05:24 Or, you know, like from an academic perspective,
    0:05:29 as a case study, how would you say his campaign was run?
    0:05:31 – I talk a lot about psychological targeting
    0:05:33 ’cause for me, it’s interesting to understand
    0:05:35 how the data translates into something
    0:05:37 that we can make sense of as humans, right?
    0:05:41 So if I get access to all of your social media data,
    0:05:43 an algorithm might be very good at understanding
    0:05:45 your preferences and motivations
    0:05:46 and then play into these preferences.
    0:05:48 But I, as a user, as a human,
    0:05:51 can’t really make sense of a million data points
    0:05:52 at the same time.
    0:05:54 If I translate it into something
    0:05:55 that tells me whether you’re more impulsive
    0:05:58 or more neurotic or more open-minded,
    0:06:00 that just kind of goes a long way in saying,
    0:06:03 okay, now I know which topics you might be interested in
    0:06:04 in the context of an election
    0:06:08 or how I might talk to you in a way that most resonates.
    0:06:11 Now, politics is an interesting case, right?
    0:06:15 ‘Cause ideally a politician would go knock on every door,
    0:06:16 have a conversation with you
    0:06:18 about the stuff that you care about,
    0:06:19 and obviously they don’t have the time.
    0:06:23 So there’s, I think, a lot of potential
    0:06:26 of using some of these tools to make politics better,
    0:06:29 but obviously, I think the way that some of these tools
    0:06:32 were introduced in the context of the 2016 election,
    0:06:35 which really shows that the more dark side,
    0:06:37 and I don’t know if they’re using
    0:06:39 any of these tools on the campaign trail.
    0:06:41 I think there are many ways in which you can use data
    0:06:46 to drive engagement that’s not necessarily based
    0:06:49 on predictions of your psychology at the individual level,
    0:06:52 but certainly this idea that the more we know about people
    0:06:54 and their motivations, their preferences,
    0:06:57 dreams, fears, hopes, aspirations, you name it,
    0:07:00 the easier it is for us to manipulate them.
    0:07:03 – Well, in politics, as well as marketing,
    0:07:06 which you bring up in your book,
    0:07:09 I kind of got the feeling that what you’re saying is that,
    0:07:13 you know, you psychologically target people
    0:07:18 with different messages, but you could have the same product.
    0:07:21 So in a sense, you’re saying that, you know,
    0:07:22 yes, with the same product,
    0:07:26 whether it’s Donald Trump or an iPhone or, you know,
    0:07:30 whatever, a Prius, you can change your messaging
    0:07:34 to make diverse people buy the product.
    0:07:36 So did I get that right?
    0:07:39 Or am I like imagining something
    0:07:42 that’s kind of nefarious, actually?
    0:07:44 – I think it depends on how you think about this, right?
    0:07:48 ‘Cause the fact that we talk to people
    0:07:49 in different ways all the time.
    0:07:52 So imagine a kid who wants the same thing.
    0:07:54 The kid wants candy.
    0:07:58 The kid knows exactly that they should talk to their mom
    0:08:01 in one way and that they should talk to their dad
    0:08:02 in a different way, right?
    0:08:03 So the goal is exactly the same.
    0:08:05 The goal is to get the candy,
    0:08:08 but we’re so good as humans,
    0:08:10 making sense of who’s on the other side,
    0:08:12 understanding what makes them tick,
    0:08:14 how do I best persuade them to buy something?
    0:08:17 And the same is true, I think, in politics and marketing.
    0:08:19 The more that we understand where someone is coming from
    0:08:21 and where they want to be in the end,
    0:08:23 the easier it is for us to sell a product, right?
    0:08:28 So products have the benefit that it’s not just what you buy,
    0:08:28 right?
    0:08:30 A lot of the times we buy products
    0:08:32 because they have like this meaning to us.
    0:08:33 They help us express ourselves.
    0:08:35 They serve a certain purpose.
    0:08:38 And if we can figure out what’s the purpose of a camera
    0:08:40 for a certain person, what’s the purpose of the iPhone
    0:08:43 for a camera, why do people care about immigration?
    0:08:46 A certain take, why do people care about climate change?
    0:08:48 Is it because they’re concerned about their kids?
    0:08:51 Is it because they’re concerned about their property?
    0:08:54 Then I think we just have a much easier way
    0:08:55 of tapping into some of these needs.
    0:08:57 And whether that’s offline,
    0:09:00 when we, again, talk to our three-year-old,
    0:09:02 not in the same way that we talk to our boss and our spouse,
    0:09:05 or whether that’s market is doing that at scale,
    0:09:07 it’s really the more you understand about someone,
    0:09:11 the more power you have over their behavior.
    0:09:14 – So are you saying that at an extreme,
    0:09:16 you could say to like a Republican person,
    0:09:20 you know, the reason why we have to control the border
    0:09:22 is because of physical security,
    0:09:25 where to a liberal, you might say, you know,
    0:09:27 there’s a different message,
    0:09:32 but in both cases, you want to secure the border,
    0:09:37 one for maybe job displacement, another for security.
    0:09:38 I mean, it would be different,
    0:09:41 but the same product in a sense.
    0:09:42 – Yeah, or the same, yeah.
    0:09:44 So a hundred percent, there’s all of this research,
    0:09:46 and this is actually is not my own.
    0:09:48 It’s very similar to psychological targeting.
    0:09:52 And in that space, it’s usually called moral reframing
    0:09:53 or moral framing.
    0:09:56 So the idea that once I understand
    0:09:58 your set of moral values, right?
    0:10:00 So there’s a framework that kind of describes
    0:10:02 these five moral values.
    0:10:04 The way that we think about what’s right or wrong
    0:10:06 in the world, that’s how I think about it myself.
    0:10:10 And some of it is loyalty, fairness, care, purity,
    0:10:11 and authority.
    0:10:14 And what we know is that across the political spectrum,
    0:10:15 so from liberal to conservative,
    0:10:18 people place different emphasis on some of these values.
    0:10:20 So if you take a liberal,
    0:10:22 typically they care about care and fairness.
    0:10:24 So if you make an argument about immigration again,
    0:10:27 that’s, or climate change does matter,
    0:10:29 that’s tapping into these values,
    0:10:32 you’re more likely to convince someone who’s liberal.
    0:10:34 Now, if you take something like loyalty,
    0:10:35 authority, or purity, you’re more likely
    0:10:37 to convince someone who’s more conservative.
    0:10:39 And for me, the interesting part is that,
    0:10:42 as humans, we’re so stuck with our own perspective, right?
    0:10:46 If I as a liberal try to convince a conservative
    0:10:48 that immigration might be a good thing,
    0:10:51 I typically make that argument from my own perspective.
    0:10:55 So I might be very much focused on fairness and care,
    0:10:56 and it’s just not resonating with the other side,
    0:10:58 ’cause it’s not what they’re coming from.
    0:11:00 And algorithms, because they don’t have an incentive,
    0:11:03 they don’t necessarily have their own perspective
    0:11:05 on the world that’s driven by ideology.
    0:11:07 It’s oftentimes much easier for them to say,
    0:11:11 I try and figure out what makes you care about the world,
    0:11:13 what makes you think about what’s right or wrong in the world.
    0:11:16 And now I’m gonna craft that argument along those lines.
    0:11:19 And what’s interesting for me is that,
    0:11:20 depending on how you construe it,
    0:11:22 it can either be seen as manipulation.
    0:11:24 So I’m trying to convince you of something
    0:11:25 that you might not otherwise believe,
    0:11:27 but it could also be construed as,
    0:11:29 I’m really trying to understand
    0:11:30 how you think about the world.
    0:11:32 But I’m really trying to understand and engage with you
    0:11:35 in a way that doesn’t necessarily come from my point of view,
    0:11:37 but is trying to take your point of view.
    0:11:41 So it really has, for me, these two sides.
    0:11:46 – So I could say to a Republican is the reason why
    0:11:51 you wanna support the H1B visa program
    0:11:53 is because those immigrants have a history
    0:11:55 of creating large companies
    0:11:58 which will create more jobs for all of us,
    0:12:01 which is a very different pitch.
    0:12:03 – Yeah, and so in addition to the fact
    0:12:05 that we can just tap into people’s psychology,
    0:12:07 there’s also this research that I love.
    0:12:08 I think it’s mostly done,
    0:12:10 I think in the context of climate change,
    0:12:12 but it’s looking at what do people think
    0:12:14 the solutions to problems are,
    0:12:17 and how does that relate to what they believe in anyway?
    0:12:20 If I tell you, well, solving climate change
    0:12:23 means reducing government influence,
    0:12:25 it means reducing taxes.
    0:12:26 Then suddenly Republicans are like,
    0:12:28 “Oh my God, climate change is a big problem
    0:12:30 “because the solutions are very much aligned
    0:12:32 “with what I believe in anyway.”
    0:12:33 If you tell that to Democrats,
    0:12:35 they’re like, “Actually, it’s not such a big deal
    0:12:37 “’cause I don’t really believe in the solution.”
    0:12:41 So the way I think that we play with people’s psychology
    0:12:43 and how they think about the world
    0:12:45 and show up in the world just means
    0:12:47 that oftentimes it gives us a lot of power
    0:12:50 over how they think, feel, and behave.
    0:12:54 – Another point that I hope I interpreted correctly
    0:12:57 is like, you know, I’ve been trained so long
    0:12:58 to understand the difference
    0:13:01 between correlation and causation, right?
    0:13:05 So like, if you wear a black mock turtleneck,
    0:13:06 so did Steve Jobs.
    0:13:08 So you should wear a black mock turtleneck
    0:13:10 because you’ll be the next Steve Jobs.
    0:13:13 Well, didn’t quite work out that way for Elizabeth Holmes,
    0:13:16 but I think you take a different direction.
    0:13:17 I just want to verify this.
    0:13:21 So you don’t really discuss correlation versus causation.
    0:13:24 In a sense, what you’re saying is that
    0:13:29 there doesn’t need to be a causative relationship
    0:13:34 if there is a predictive relationship that you can harness.
    0:13:37 So I don’t know, if for some reason people,
    0:13:40 we noticed a lot of people with iPhones by German cars,
    0:13:41 well, that’s predictive.
    0:13:44 I don’t have to understand why that’s true.
    0:13:45 – Yeah, no, totally.
    0:13:48 And I’ll give you an example that I think is interesting.
    0:13:52 So one of the relationships that I still find fascinating
    0:13:54 that we observe in the data that I don’t think
    0:13:57 I would have intuited even as a psychologist
    0:13:59 is the use of first person pronouns,
    0:14:01 like people post on social media
    0:14:02 about what’s going on in their life.
    0:14:05 And I remember being at this conference,
    0:14:07 it’s like a room full of psychologists
    0:14:09 and this guy who was really like a leading figure,
    0:14:12 Jamie Panner Baker in the space of natural language processing,
    0:14:14 he comes up and he just asks the audience,
    0:14:17 what do you think the use of first person pronouns?
    0:14:21 So just using I, me, myself more often than other people,
    0:14:23 what do you think this is related to?
    0:14:25 And I remember all of us sitting at the table
    0:14:27 and we’re like, oh, it’s gotta be narcissism.
    0:14:30 If someone talks about themself constantly,
    0:14:33 that’s probably a sign that someone is a bit more narcissistic
    0:14:35 and self-centered than other people.
    0:14:38 Turns out that it’s actually a sign of emotional distress.
    0:14:40 So if you talk a lot about yourself,
    0:14:43 that makes it more likely that you suffer
    0:14:45 from something like depression, for example.
    0:14:49 And now taking a step back, it actually makes sense, right?
    0:14:50 If you think back to the last time
    0:14:53 that you felt blue or sad or down,
    0:14:54 you probably were not thinking about
    0:14:56 how to fix the Southern border
    0:14:58 or how to solve climate change.
    0:14:59 What you were thinking about is like,
    0:15:00 why am I feeling so bad?
    0:15:02 Am I ever gonna get better?
    0:15:03 What can I do to get better?
    0:15:05 And this inner monologue that we have with ourselves
    0:15:07 just creeps into the language that we use
    0:15:10 as we express ourselves on these social platforms.
    0:15:14 Now, the causal link is not entirely clear, right?
    0:15:16 It could be that I’m just using
    0:15:18 a lot more first person pronouns
    0:15:19 because I have this inner monologue.
    0:15:21 What you see in the language of people
    0:15:23 who are suffering from emotional distress
    0:15:25 is all of these physical symptoms.
    0:15:28 So just being sick, having body aches.
    0:15:30 And again, it’s not entirely clear
    0:15:32 if maybe you’re having a hard time mentally
    0:15:33 because you’re physically sick,
    0:15:35 but also maybe you’re physically sick
    0:15:37 ’cause you’re having like a hard time
    0:15:39 with the problems that you’re dealing with.
    0:15:43 So on some level, I don’t even care that much, right?
    0:15:45 If I’m just trying to understand and say,
    0:15:47 is there someone who might be suffering
    0:15:49 from something like depression
    0:15:50 who’s currently having a hard time
    0:15:52 regulating their emotions?
    0:15:54 I don’t necessarily care if it’s going from
    0:15:57 physical symptoms to mental health problems
    0:15:58 or the other way.
    0:16:01 What I care about is if I see these words popping up
    0:16:03 or if I see some of these topics popping up,
    0:16:06 that’s an increase in the likelihood
    0:16:08 that someone is actually having a hard time right now.
    0:16:11 Now, I think what is interesting is that
    0:16:13 the more causal these explanations get
    0:16:14 and these relationships get,
    0:16:17 oftentimes they’re a lot more stable.
    0:16:20 So it could be that if it’s like a causal mechanism,
    0:16:22 and first of all, it allows us to understand
    0:16:23 something about interventions,
    0:16:26 like how do we actually then help people get better?
    0:16:30 And they’re also oftentimes the ones that last for longer
    0:16:32 because it’s not something just the fluke and the data
    0:16:34 that maybe goes this direction or the other,
    0:16:36 but it’s something that is really driving it
    0:16:37 on a more fundamental level.
    0:16:39 So you’re absolutely right in that,
    0:16:41 oftentimes when we think of prediction,
    0:16:44 we don’t need to understand which direction it goes in.
    0:16:49 It’s still helpful to know if you think of interventions.
    0:16:51 – So at a very simplistic level,
    0:16:54 could you make the case to a pharmaceutical company?
    0:16:57 You know, look at a person’s social media
    0:16:59 and if the person is saying aye a lot,
    0:17:04 sell them some lorazeprame or some anti-anxiety drugs,
    0:17:08 is it that simple?
    0:17:10 – I personally probably not go to the pharma companies
    0:17:13 and make that proposition, but it is that simple.
    0:17:15 And again, one of the points that I make in the book
    0:17:17 that is super important to me
    0:17:20 is that those are all predictions with a lot of error, right?
    0:17:24 So it means that on average, if you use these words more,
    0:17:28 you’re more likely to suffer from emotional distress.
    0:17:30 That doesn’t mean that it’s terministic.
    0:17:32 There’s a lot of error at the individual level.
    0:17:36 So if I’m a pharma company and I wanna sell these products,
    0:17:38 yeah, on average, I might do better
    0:17:39 by targeting these people,
    0:17:42 but it still means that we’re not always going to get it right.
    0:17:44 And then on the other side, what is interesting for me
    0:17:46 is if you think about it,
    0:17:48 not from the perspective of a pharma company,
    0:17:50 but from the perspective of an individual,
    0:17:52 I think there’s ways in which we can acknowledge
    0:17:54 the fact that it’s not always perfect, right?
    0:17:56 You could have this early warning system
    0:17:58 for people who know, for example,
    0:18:00 that they have a history of mental health conditions
    0:18:02 and they know that it’s really difficult
    0:18:05 once they’re at this valley of depression to get out.
    0:18:07 So they could have something on their phone
    0:18:08 that just tracks their GPS record,
    0:18:11 sees that they’re not leaving the house as much anymore,
    0:18:15 less physical activity, more user first person pronouns.
    0:18:17 And it almost has this early warning system.
    0:18:20 It just puts a flag out and says, “It might be nothing.
    0:18:22 It’s not a diagnostic tool. There’s a lot of error,
    0:18:25 but we see that there’s some deviations from your baseline.
    0:18:26 Why don’t you look into this?”
    0:18:29 And for me, those are the interesting use cases
    0:18:31 where we involve the individual,
    0:18:33 acknowledging that there’s mistakes that we make
    0:18:35 and the predictions,
    0:18:36 but we’re using it to just help them
    0:18:39 accomplish some of the goals that they have for themselves.
    0:18:48 So speaking of interesting use cases,
    0:18:50 would you do the audience a favor
    0:18:53 and explain how you help the hotel chain
    0:18:57 optimize their offering? ‘Cause I love that example.
    0:19:00 – It was one of the first projects
    0:19:01 and industry collaborations that we did
    0:19:04 when I was still doing my PhD.
    0:19:06 And there’s many reasons for why I actually liked the example.
    0:19:09 But the idea was that we were approached by Hilton
    0:19:11 and we worked with a PR company.
    0:19:13 And the idea of Hilton was,
    0:19:16 can we use something like psychological targeting?
    0:19:19 So really tapping into people’s psychological motivations,
    0:19:21 what makes them tick,
    0:19:23 what makes them care about vacations and so on
    0:19:25 to make their campaigns more engaging
    0:19:28 and then also sell vacations
    0:19:30 that really resonated with people.
    0:19:33 And what I like about the example is that Hilton didn’t say,
    0:19:36 well, we’re just gonna run a campaign on Facebook and Google
    0:19:39 where we just passively predict people’s psychology
    0:19:42 and then we try to sell them more stuff.
    0:19:46 They turned it into this mutual two-way conversation
    0:19:47 where they said, hey,
    0:19:49 we wanna understand your traveler profile.
    0:19:51 And for us to be able to do that,
    0:19:53 if you connect with your social media profile,
    0:19:55 we can run it through this algorithm
    0:19:57 that actually we don’t control.
    0:19:59 It’s the University of Cambridge is doing it.
    0:20:01 We don’t even get to see the data.
    0:20:05 But what we can do is we can spit out this traveler profile
    0:20:07 and then make recommendations
    0:20:09 that really tap into that profile.
    0:20:11 So it was this campaign.
    0:20:15 And you can imagine that doing that increased like engagement.
    0:20:18 People were excited about sharing it with friends.
    0:20:21 It was essentially good for the business bottom line.
    0:20:24 But it also gave, I think, users the feeling
    0:20:27 that it’s a genuine value proposition.
    0:20:30 So there was a company that operated first of all
    0:20:32 with consent ’cause it was all,
    0:20:35 it’s up to you whether you wanna share the data or not.
    0:20:37 Here’s like, how does this works behind the scenes?
    0:20:39 Here’s what we give you in return.
    0:20:42 So it was very transparent with the entire process.
    0:20:44 And it was also transparent in terms of
    0:20:46 here’s what we have to offer, right?
    0:20:48 It’s by understanding your traveler profile.
    0:20:50 We can just make your vacation a lot better.
    0:20:53 So that’s one of the reasons why I like this example a lot.
    0:20:59 – Now, just as a point of clarification,
    0:21:01 you said the University of Cambridge, right?
    0:21:02 – Yeah.
    0:21:05 – Which has nothing to do with Facebook
    0:21:08 and Cambridge associates, right?
    0:21:10 – With Cambridge Analytica has nothing to do at all.
    0:21:13 It was funny ’cause I get mixed up with them all the time.
    0:21:16 Not surprising ’cause I got my PhD there on the same topic.
    0:21:20 And there was like, I mean, the idea originated there, right?
    0:21:23 The idea that we could take someone’s social media profile,
    0:21:25 predict things about their psychology,
    0:21:29 originated at Cambridge and that’s where it was taken from.
    0:21:31 But we were involved and for me,
    0:21:33 it’s almost like a point of pride
    0:21:36 and like a point that made me think about the ethics a lot
    0:21:39 is we helped the journalists break the story.
    0:21:41 So when the journalists in, first in Switzerland,
    0:21:43 were working on trying to see what happened
    0:21:45 behind the scenes of Cambridge Analytica,
    0:21:47 we just helped them understand the science.
    0:21:49 How can you get all of the data?
    0:21:51 How do you translate it into profile?
    0:21:55 So yeah, not related to Cambridge Analytica in any way,
    0:21:57 other than trying to take them down.
    0:21:59 – Okay, so I misspoke.
    0:22:02 I said Cambridge associates, not Analytica.
    0:22:04 So if you were for Cambridge associates,
    0:22:07 if there’s such a thing out there, I’d correct myself.
    0:22:08 (laughing)
    0:22:09 – I’m not sure.
    0:22:13 – So listen, in the United States,
    0:22:15 this is a very broad question,
    0:22:19 but in the United States, who owns my data?
    0:22:21 Me or the companies?
    0:22:23 – Well, as you might have imagined,
    0:22:24 it’s typically not you.
    0:22:27 So the US is an interesting case
    0:22:29 ’cause it very much depends on the state that you live in.
    0:22:32 So Europe, I would say has the strictest
    0:22:34 data protection regulations.
    0:22:37 So they very much try to operate on these principles
    0:22:39 of transparency and control
    0:22:42 and giving you at least the ability to request your own data
    0:22:44 to delete it and so on and so forth.
    0:22:46 In the US, California is the closest.
    0:22:48 So California’s CCPA,
    0:22:51 which is the Consumer Protection Act,
    0:22:53 I can’t remember the exact name,
    0:22:57 but this is like very close to the European Union principles
    0:23:00 where you as a producer of data
    0:23:03 and even though companies also can hold a copy,
    0:23:04 you at least get to request your own data.
    0:23:05 In most parts of the US,
    0:23:07 the data that you generate,
    0:23:09 you don’t even have a shot at getting it
    0:23:10 because it sits with the companies
    0:23:12 and you don’t even have the right to request it.
    0:23:16 So I think we’re a very long way from this idea
    0:23:18 that you’re not just the owner of the data,
    0:23:23 but it’s also you can limit who else has control to add.
    0:23:25 – So I live in California.
    0:23:28 So you’re telling me there’s a way that I could go to Meta
    0:23:31 or Apple or Google and say, I want my data
    0:23:33 and I don’t want you selling it.
    0:23:34 – That’s a great question.
    0:23:36 So what you can do is you can request a copy of your data.
    0:23:37 That’s one thing.
    0:23:39 In many states, you can’t even do that.
    0:23:42 You might generate a lot of medical data,
    0:23:43 social media data and you,
    0:23:45 even though you generated it,
    0:23:47 you can’t even request a copy.
    0:23:50 Now what you can do is you can go to Meta request a copy
    0:23:53 and you can also request it to be deleted
    0:23:55 or to be transferred for it somewhere else.
    0:23:58 Now it’s still really hard to say,
    0:24:00 I want to use a service and product.
    0:24:02 And this is one of the things that I think makes it really
    0:24:05 challenges for people to manage the data properly.
    0:24:07 Because it’s a binary choice,
    0:24:09 you can say, yeah, I want you to delete my data
    0:24:11 and I’m not going to use the service anymore.
    0:24:12 But then you also can’t be part of Facebook.
    0:24:14 And yes, there are certain permissions
    0:24:15 that you can play with.
    0:24:16 What is public?
    0:24:17 What is not public?
    0:24:19 You can even play around with here’s some of the traces
    0:24:22 that I don’t want you to use in marketing.
    0:24:24 But typically, and this is true for,
    0:24:26 I think still Meta and other companies,
    0:24:28 it’s usually a binary choice.
    0:24:30 Either you use our product with most of your data
    0:24:33 being tracked and most of your data being commercialized
    0:24:36 in a way that you might not always benefit from.
    0:24:38 But you get to use the product for free
    0:24:39 or you don’t use it at all.
    0:24:41 And I think that’s the dichotomy
    0:24:43 that’s really hard for the brain to deal with.
    0:24:45 ‘Cause if the trade-off that we have to make as humans
    0:24:48 is service, convenience,
    0:24:51 the ability to connect with other people in an easy way,
    0:24:53 that’s what we’re going to choose over privacy
    0:24:55 and maybe a risk of data breaches in the future
    0:24:58 and maybe a risk of us not being able
    0:24:59 to make our own choices.
    0:25:01 So I think there’s now ways
    0:25:03 in which you can somehow eliminate that trade-off.
    0:25:06 ‘Cause I think if that’s what we’re dealing with,
    0:25:08 it’s an uphill battle.
    0:25:11 – I need to go dark for a little bit here.
    0:25:14 I read in your book about the example of Nazis.
    0:25:18 And I just want to know like today,
    0:25:22 could the Nazis go to Facebook, Apple and Google
    0:25:25 and get enough information from the breadcrumbs
    0:25:29 that we leave to track down where all the Jewish people are?
    0:25:31 Would that be easy today?
    0:25:33 – I think it would be incredibly easy.
    0:25:35 And it’s one of these examples in the book
    0:25:37 that I think is hard to process
    0:25:39 and that’s why it’s so powerful.
    0:25:41 I teach this class on the ethics of data
    0:25:43 and there’s always a couple of people who say,
    0:25:44 “Well, I don’t care about my privacy
    0:25:47 ’cause I have nothing to hide and the perks that I get,
    0:25:50 they’re so great that I’m willing to give up my privacy.”
    0:25:53 And what I’m trying to say is that it’s a risky gamble.
    0:25:55 But first of all, it’s a very privileged position
    0:25:57 ’cause just because you don’t have to worry
    0:25:58 about your data being out there,
    0:26:01 doesn’t mean that that doesn’t apply to other people.
    0:26:02 So I think in the US,
    0:26:05 even the role versus way it’s a Supreme Court decision
    0:26:08 to meddle with abortion rights,
    0:26:12 I think overnight essentially made women across the US
    0:26:14 realize, “Hey, my data being out there
    0:26:15 in terms of the Google searches that I make,
    0:26:18 my GPS records showing where I go,
    0:26:20 me using some period tracking apps,
    0:26:21 it’s incredibly intimate.”
    0:26:24 And it could overnight be used totally against me.
    0:26:28 So the example that you mentioned about Nazi Germany
    0:26:30 is such a powerful one
    0:26:33 ’cause it shows that leadership can change overnight.
    0:26:34 And I care so much about it
    0:26:36 ’cause I obviously grew up in Germany.
    0:26:38 So it was a democracy in 1938.
    0:26:40 And then the next year it wasn’t.
    0:26:42 And what we know is that atrocities
    0:26:44 within the Jewish community across Europe
    0:26:47 totally dependent on whether religious affiliation
    0:26:48 was part of the census.
    0:26:50 So you can imagine if you have a country
    0:26:52 where whether you’re Jewish or not
    0:26:54 is written in the census,
    0:26:57 all that Nazi Germany had to do is go to city hall,
    0:26:58 get hold of that census data
    0:27:00 and find the members of the Jewish community
    0:27:03 made it incredibly easy to track them down.
    0:27:06 But of course you don’t even need that census data anymore
    0:27:09 ’cause you can now have all of this data that’s out there
    0:27:10 that allows us to make these predictions
    0:27:13 about anything from political ideology,
    0:27:15 sexual orientation, religious affiliation,
    0:27:17 just based on what you talk about on Facebook.
    0:27:20 And even you could make the argument
    0:27:22 that maybe it’s the leaders of those companies
    0:27:25 handing over the data voluntarily.
    0:27:28 And I think we’ve even seen in the last couple of days
    0:27:31 how there’s like this political shifts in leadership
    0:27:33 when it comes to the big tech companies.
    0:27:35 But even if they weren’t playing the game,
    0:27:38 it would have been easy for a government to just replace
    0:27:41 that C-suite executives with new ones
    0:27:43 that are probably much more tolerant to
    0:27:44 some of the requests that they have.
    0:27:46 And I think it’s terrifying.
    0:27:47 And I think it’s a good example
    0:27:50 for why we should care about personal data.
    0:27:55 – Okay, so what you’re saying is,
    0:27:57 if I look at pictures of the inauguration
    0:28:02 and I see Apple, Google, Meta, Amazon up on stage.
    0:28:07 And so now the government can say,
    0:28:10 you know, according to Apple,
    0:28:13 you were in Austin and then you landed in an SFO.
    0:28:16 And then according to your visa statement,
    0:28:17 you know, you purchased this.
    0:28:19 And according to your phone’s GPS,
    0:28:21 you went to a Planned Parenthood
    0:28:23 in San Francisco, California.
    0:28:26 So we suspect you of going out of state
    0:28:27 to getting an abortion.
    0:28:30 So we’re opening up an investigation of you.
    0:28:33 That’s all easy today.
    0:28:34 – I think it’s very easy.
    0:28:37 And again, I’m not saying that the leaders
    0:28:40 of those big tech companies are sharing the data right now,
    0:28:41 but it’s certainly possible.
    0:28:44 And for me, there’s like this thing that I have in the book
    0:28:46 is data is permanent and leadership is.
    0:28:48 And right, so once your data is out there,
    0:28:50 it’s almost impossible to get it back.
    0:28:52 And you don’t know what’s gonna happen tomorrow.
    0:28:56 Even if Zuckerberg is not willing to share the data,
    0:28:59 there could be a completely new CEO tomorrow
    0:29:01 who might be a lot more willing to do that.
    0:29:05 So I think that the notion that we don’t have to worry
    0:29:07 in the here and now about our data being out there
    0:29:10 is just a very short-sighted notion.
    0:29:12 And ideally we can find a system.
    0:29:15 And I think there are ways now in which we can get
    0:29:17 some of these perks and some of the benefits
    0:29:20 and they come from using data without us necessarily having
    0:29:23 to collect the data in a central server.
    0:29:24 – Okay, so if I’m listening to this
    0:29:27 and I’m scared stiff because, you know,
    0:29:29 yes, you could look at what I do.
    0:29:31 You could look at, I went to the synagogue
    0:29:34 or I went to the, you know, temple or whatever.
    0:29:36 So yeah, and you’re right.
    0:29:39 Any of those people could replace and who knows.
    0:29:41 So then what do I do?
    0:29:46 – I do think that people should be to some extent scared.
    0:29:48 So I’m really trying to not say that technology,
    0:29:52 it’s all, like we’re all doomed because the data is out
    0:29:53 there and technology can be used too many.
    0:29:55 But I think there’s like many good use cases,
    0:29:58 but I do think we should be changing the system
    0:30:00 in a way that protects us from these abuses.
    0:30:03 And the one thing that I describe in the book,
    0:30:06 which I think we’re actually seeing a lot more of,
    0:30:08 but just not that many people know of,
    0:30:11 are these technologies that allow us to benefit from data
    0:30:13 without necessarily running the risk
    0:30:15 of a company collecting it centrally.
    0:30:17 So what I mean is, and there’s a technology
    0:30:19 that’s called federated learning.
    0:30:22 And you can imagine the example that I give
    0:30:24 is take medical data.
    0:30:27 So if we wanna better understand disease
    0:30:30 and we wanna find treatment that work for all of us,
    0:30:32 not just the majority of people who usually
    0:30:34 the pharma companies collect data of,
    0:30:37 but like we wanna know, given my medical history,
    0:30:39 given my genetic data, here’s what I should be doing
    0:30:42 to make sure that I don’t get sick in the first place
    0:30:44 or I can treat a disease that’s either rare
    0:30:46 or not as easily understood,
    0:30:49 we would all benefit from pooling data
    0:30:51 and better understanding disease.
    0:30:52 Now there’s a way in which you can say,
    0:30:56 instead of me sending all of this data to a central server
    0:30:59 and now this entity that collects all of the data,
    0:31:00 they have to safeguard it.
    0:31:03 Same way that Facebook is supposed to safeguard your data
    0:31:05 against intrusion from the government.
    0:31:08 Instead of having to sit in the central server,
    0:31:10 what we can do is we can make use of the fact
    0:31:12 that we all have supercomputers,
    0:31:14 but that might be your smartphone.
    0:31:16 Your smartphone is so much more powerful
    0:31:18 than the computers that we used to launch rockets to space
    0:31:19 a few decades ago.
    0:31:22 So what this entity that’s trying to understand disease
    0:31:25 could do is they could essentially send the intelligence
    0:31:28 to my phone or ask questions from my data
    0:31:30 and say, okay, here’s like how we’re tracking your symptoms.
    0:31:32 Here’s what we know about your medical history,
    0:31:35 but that data lives on my phone.
    0:31:37 And all I’m doing is I’m sending intelligence
    0:31:40 to the central entity to better understand the disease.
    0:31:42 Apple Siri, for example, is trained that way.
    0:31:44 So instead of Apple going in
    0:31:46 and capturing all of your speech data
    0:31:49 and centrally collecting it right now,
    0:31:51 Apple would be one of these companies
    0:31:54 who has to protect it now and tomorrow.
    0:31:56 And they just send the model to your phone.
    0:31:58 So they send Siri’s intelligence to your phone.
    0:32:00 It listens to what you say.
    0:32:03 It gets better at understanding, gets better at responding.
    0:32:05 And instead of you sending the data,
    0:32:07 it essentially just sends back a better model.
    0:32:10 It learns, it updates, sends back the model to Siri.
    0:32:13 And now everybody benefits ’cause we have a better speech.
    0:32:14 And that’s a totally different system
    0:32:16 ’cause we don’t have to collect the data
    0:32:18 in a central spot and then protected.
    0:32:22 – But Sandra, I mean, the point that you just made
    0:32:27 is that, yeah, Tim Cook may be saying that to us now.
    0:32:29 We’re only sending you the model
    0:32:31 and all your data is staying on your phone,
    0:32:34 but tomorrow’s Apple CEO
    0:32:36 could have a very different attitude, right?
    0:32:38 So how do we know if they’re only still
    0:32:41 sending the model right now?
    0:32:42 – So I think it’s a great question.
    0:32:44 And it’s funny that you mentioned Apple in that space
    0:32:46 ’cause I think they’re thinking about it this way.
    0:32:50 So again, I would much rather have Tim say,
    0:32:53 we’re only gonna locally process on your phone
    0:32:55 and that even if they change it tomorrow,
    0:32:57 what I’m mostly worried about
    0:33:00 is that they collect my data today under Tim Cook
    0:33:02 with the intention of making my experience better.
    0:33:05 They collect it today and then tomorrow there’s a new CEO
    0:33:08 ’cause now that CEO can just go back into the existing data
    0:33:11 and make all of these inferences that we talked about
    0:33:13 that are very intrusive and we don’t want to be out there.
    0:33:15 At least even if Apple decides tomorrow
    0:33:18 to shift from that model to a new one,
    0:33:20 that’s gonna be publicly out there.
    0:33:23 So if that happens, at least people can start from scratch
    0:33:25 and decide whether they still want to use Apple products
    0:33:26 or not.
    0:33:29 My main concern is that all the data that gets collected
    0:33:31 and now leadership changes.
    0:33:32 – Wow.
    0:33:35 Okay, speaking of collected data,
    0:33:39 you mentioned an example of a guy who applied to a store
    0:33:41 and he took a personality test
    0:33:46 and the personality test yielded let’s say undesirable traits.
    0:33:50 And so he didn’t get that job
    0:33:52 and that personality test stuck with him
    0:33:56 and kind of hurt his employment in the future too.
    0:33:58 So what’s the advice?
    0:34:02 Don’t take the personality test or lie on the personality test.
    0:34:04 What’s the guy supposed to do
    0:34:07 if he’s required to take a personality test
    0:34:08 to apply for a job?
    0:34:11 – Yeah, and you’re really going to other dark places
    0:34:13 but I think which I think is important
    0:34:15 ’cause for me, this example
    0:34:18 and this one is not even using predictive technology, right?
    0:34:20 So this one is a guy sitting down
    0:34:22 and admitting that I think in his case,
    0:34:24 he was like suffering from bipolar disorder.
    0:34:27 So kind of sends the score on neuroticism
    0:34:29 which is one of the personality traits
    0:34:32 that kind of says how you regulate emotions through the roof.
    0:34:34 And because he admitted to that,
    0:34:38 he was essentially almost discarded from all of the jobs
    0:34:40 that had like a customer facing interface
    0:34:42 ’cause companies were worried that he wouldn’t be dealing
    0:34:45 and well with people who come and complain.
    0:34:48 Now, the reason for why I think this example is important
    0:34:52 is it just means who other people think we are
    0:34:55 kind of closes some doors in our lives, right?
    0:34:56 So sometimes it opens doors.
    0:34:59 If someone thinks that you’re the most amazing person
    0:35:01 and you absolutely deserve a loan,
    0:35:04 maybe you have opportunities that other people don’t have
    0:35:07 but oftentimes that the danger comes in
    0:35:10 when someone thinks that we have certain traits
    0:35:13 that then would lead to behavior that we don’t wanna see.
    0:35:16 And now in the context of self-reported personality tests,
    0:35:19 at least you have like some say over what that image is.
    0:35:23 If you take it to an automated prediction of an algorithm
    0:35:24 and coming back to this notion
    0:35:26 that those algorithms are pretty good
    0:35:28 at understanding of psychology,
    0:35:29 but they’re certainly not perfect.
    0:35:31 So now you suddenly live in a world
    0:35:33 where someone make a prediction about you
    0:35:35 based on the data that you generate,
    0:35:37 you never even touched that prediction
    0:35:39 ’cause you don’t even get to see it.
    0:35:40 They predict that you’re neurotic
    0:35:42 and maybe they even get it wrong.
    0:35:44 Maybe you’re one of the people where the algorithm
    0:35:46 makes a mistake and gets it wrong.
    0:35:47 And now you suddenly you’re excluded
    0:35:50 from all of these opportunities for jobs, loans and so on.
    0:35:53 And so I think for me, this notion that there’s someone
    0:35:55 who passively tries to understand who you are
    0:35:59 and then takes action that again, sometimes open doors,
    0:36:01 sometimes it’s incredibly helpful
    0:36:03 because maybe we connect you with mental health support
    0:36:07 but at other times it might also close doors
    0:36:09 in a way that you don’t even have insights to.
    0:36:10 And for me, that’s the scary part
    0:36:12 where I feel like we’re losing control
    0:36:15 over essentially our lives.
    0:36:18 – Wait, but are you saying that you should refuse
    0:36:22 to take the personality test or you should lie?
    0:36:26 – So in the case of the personality test,
    0:36:27 first of all, it’s not a good practice.
    0:36:29 So as a personality psychologist,
    0:36:31 the way that we think of these personality tests
    0:36:34 is that it shouldn’t be an exclusion criteria.
    0:36:37 So I think that what they’re meant to do
    0:36:40 is to say, here’s certain professions
    0:36:43 that you might just be more suited for.
    0:36:44 ‘Cause if you’re an introvert
    0:36:46 who kind of hates dealing with other people
    0:36:49 and you’re constantly at the forefront of like a sales pitch,
    0:36:51 you’re probably not gonna enjoy it as much.
    0:36:53 They were never really meant to say,
    0:36:55 you got a low score on conscientiousness
    0:36:56 and we’re gonna exclude you.
    0:36:58 It’s also very short-sighted
    0:37:01 because technically what makes a company successful
    0:37:04 and what makes team successful is to have many people
    0:37:05 who think about the world differently.
    0:37:08 So I have this recent research that’s still very preliminary,
    0:37:11 but it’s looking at startups
    0:37:13 and it just looks at how quickly do they manage
    0:37:15 to hire people with all of these different traits.
    0:37:17 So you can come together and you can say, well,
    0:37:19 but I think this way and then you think this way
    0:37:21 and we all bring a different perspective to the table.
    0:37:23 And they’re usually more successful.
    0:37:25 So this notion that companies just say,
    0:37:27 here’s a trait that we don’t wanna see.
    0:37:29 It is very short-sighted.
    0:37:30 What we do know, and this is,
    0:37:33 I promise coming back to your question,
    0:37:35 is that saying that you don’t wanna respond
    0:37:38 to a questionnaire is typically seen as the worst sign.
    0:37:41 So there was this study where they looked at things
    0:37:42 that people don’t like to admit to, right?
    0:37:44 I think it was like stuff about health,
    0:37:46 stuff about people’s sexual preferences
    0:37:50 and saying, I don’t wanna answer the question is worst
    0:37:53 and hitting the worst option on the menu.
    0:37:55 So I absolutely agree that in that case,
    0:37:58 the guy essentially didn’t have a shot,
    0:38:00 but the problem is once it’s recorded,
    0:38:02 he didn’t even get to take the test again
    0:38:04 because the results were just shared
    0:38:06 from company to company.
    0:38:09 – So what I hear you say is lie.
    0:38:14 – In this case, frankly, if it had been me,
    0:38:15 I probably would have lied.
    0:38:17 If I had known that this is,
    0:38:19 if the company is making the mistake
    0:38:22 of using the test in that way,
    0:38:26 what I would recommend to people taking the test is,
    0:38:29 yeah, like think about what the company wants to hear.
    0:38:30 – Okay.
    0:38:33 – Which is harder to do with data, by the way.
    0:38:36 It’s funny ’cause oftentimes when we think of predictions
    0:38:39 of our psychology based on our digital lives,
    0:38:40 we think of social media and it’s always,
    0:38:43 but I can to some extent manipulate
    0:38:45 how I portray myself on social media.
    0:38:47 That’s true for some of these explicit identity claims
    0:38:50 that we think about and have control over.
    0:38:51 There’s so many other traces.
    0:38:53 Take your phone again.
    0:38:58 The fact like my thing is that I’m not the most organized
    0:38:59 person even though I’m German.
    0:39:02 So I think I was expelled for a reason.
    0:39:06 And I don’t organize my cutlery the way that my husband does.
    0:39:09 And would I admit to this happily on a personality test
    0:39:11 that like in the context of an assessment center,
    0:39:12 probably not, right?
    0:39:14 If someone gives me the question there
    0:39:16 that says I make a mess of things,
    0:39:18 would I be inclined to say I strongly agree?
    0:39:20 Maybe not ’cause I understand
    0:39:21 that’s probably not what they want to hear.
    0:39:23 Now, if they tap into my data,
    0:39:26 they see that my phone is constantly running out of battery,
    0:39:28 which is like one of these strong predictors
    0:39:30 of you not being super conscientious.
    0:39:34 I constantly, I go to the deli on the corner five times a day
    0:39:36 ’cause I can’t even plan ahead for the next meal.
    0:39:37 And I constantly run to the bus.
    0:39:40 So if someone was tapping into my data,
    0:39:42 they would understand 100%
    0:39:44 that I’m not the most organized person.
    0:39:46 So there’s something about this data world
    0:39:48 and all of these traces that we generate,
    0:39:52 which are in a way much harder to manipulate
    0:39:54 than a question on a questionnaire.
    0:39:57 – Well, and now people listening to this podcast
    0:40:02 are thinking, how many times did I use the pronoun I?
    0:40:06 Oh my God, I’m telling people that I have, you know,
    0:40:07 depression and stuff.
    0:40:10 – And again, it’s not deterministic.
    0:40:13 So you might be using a lot of I
    0:40:15 because something happened that you want to share.
    0:40:18 It’s just like on average, it increases your likelihood.
    0:40:21 – Up next on Remarkable People.
    0:40:24 – If I wanted to get a portfolio, a data portfolio,
    0:40:25 on most of the people,
    0:40:27 I would be able to get it really cheaply.
    0:40:29 And that’s something that, again,
    0:40:32 I think most of us or all of us should be worried about.
    0:40:35 And you do see use cases where policymakers
    0:40:37 are actually waking up to this reality.
    0:40:40 There was this case of a judge actually across the bridge
    0:40:41 from here in New Jersey,
    0:40:44 whose son was murdered by someone
    0:40:46 that she litigated against in the past.
    0:40:48 They found her data online from data brokers,
    0:40:51 tracked her down, and in this case, killed her son.
    0:40:55 (gentle music)
    0:40:59 – Thank you to all our regular podcast listeners.
    0:41:02 It’s our pleasure and honor to make the show for you.
    0:41:04 If you find our show valuable,
    0:41:08 please do us a favor and subscribe, rate, and review it.
    0:41:11 Even better, forward it to a friend,
    0:41:13 a big mahalo to you for doing this.
    0:41:18 – Welcome back to Remarkable People with Guy Kawasaki.
    0:41:22 So you had a great section about how,
    0:41:25 by looking at what people have searched Google for,
    0:41:27 you can tell a lot about a person
    0:41:30 or at least draw conclusions.
    0:41:35 So do you think prompts will have the same effect?
    0:41:37 Like, you know, what I asked chat,
    0:41:41 GPT is a very good window into what I am.
    0:41:42 – I think so, right?
    0:41:44 And I don’t necessarily, I think it’s prompts.
    0:41:46 I think it’s questions that we have.
    0:41:47 And if you think about Google,
    0:41:51 there’s questions that I type into the Google search bar
    0:41:54 that I wouldn’t feel comfortable asking my friends
    0:41:55 or even sharing with my spouse.
    0:41:57 So it’s like this very intimate window
    0:41:59 into what is top of mind for us
    0:42:02 that we might not feel comfortable sharing with others.
    0:42:03 Yeah, so I was actually,
    0:42:04 which I thought was so interesting
    0:42:06 ’cause I was part of this.
    0:42:09 It was like a documentary about artistic
    0:42:11 and what they did is they invited a person.
    0:42:13 So they found a person online.
    0:42:15 They looked at all of her Google searches
    0:42:19 and then they recreated her life all the way from,
    0:42:20 here’s the job that she took,
    0:42:22 kind of suffered from anxiety
    0:42:24 and the feeling that she wasn’t good enough
    0:42:26 in the space that she was working in,
    0:42:29 all the way to her becoming pregnant
    0:42:30 and then having a miscarriage.
    0:42:33 And they kind of recreated her life with an actress.
    0:42:35 And then at some point bring in the real person
    0:42:37 and the person watches the movie
    0:42:39 and you can see how just over time,
    0:42:43 she realizes just how intimate those Google searches are
    0:42:46 ’cause what the documentary team had created,
    0:42:49 the life that they had recreated was so close
    0:42:50 to her actual experience.
    0:42:52 And again, just by looking at their data.
    0:42:54 So for me, it was a nice way of showcasing
    0:42:57 that it’s really not just this one data point
    0:42:58 or a collection of data points,
    0:43:02 but it’s a window into our lives and our psychology.
    0:43:05 – And not to get too dark,
    0:43:08 but the CEO of Google was on the stage, right?
    0:43:12 So what happens when generative AI takes over
    0:43:17 and the AI is drafting my email,
    0:43:21 drafting my responses and to take an even further step,
    0:43:25 what happens when it’s my agent answering for me?
    0:43:28 Then is it still as predictive
    0:43:31 or will the agent reflect who I really am
    0:43:32 or it throws everything off
    0:43:36 because it’s not guy answering anymore?
    0:43:37 – So to me, that’s a super interesting question.
    0:43:40 First of all, in a way like generative AI
    0:43:42 democratized the entire process.
    0:43:44 So when I started this research,
    0:43:48 we had to get a data set that takes your digital traces.
    0:43:50 Let’s say what you post on social media
    0:43:52 and maybe a self-report of your personality.
    0:43:55 And then we train a model that gets from social media
    0:43:56 to your personality.
    0:43:59 Now I can just ask chat GPT and say,
    0:44:01 hey, here are guys Google searches.
    0:44:02 Here’s what he bought on Amazon.
    0:44:05 Here’s what we talked about on Facebook.
    0:44:07 What do you think is his big five personality traits?
    0:44:10 What do you think are his moral values?
    0:44:11 What do you think is again,
    0:44:12 like some of these very intimate traits
    0:44:14 that we don’t want to share?
    0:44:15 And it does a remarkable job.
    0:44:17 It’s never been trained to do that,
    0:44:18 but because it’s read the entire internet,
    0:44:21 it has to understand so much about psychology.
    0:44:23 And then obviously taking it to the next level,
    0:44:25 it’s not just understanding,
    0:44:28 but also replicating your behavior.
    0:44:32 And the one thing that I’m most concerned about,
    0:44:33 aside from like manipulative,
    0:44:35 it’s just that it’s going to make us so boring.
    0:44:37 If these language models,
    0:44:39 they’re very good at coming up with an answer
    0:44:43 that works reasonably well, like 80%.
    0:44:45 But it’s very unlikely that it comes up with something
    0:44:47 like super unique that we’ve never thought about,
    0:44:49 that makes us different from other people.
    0:44:51 So I think what happens is that we’re just going to see
    0:44:54 more and more of who the AI believes we are.
    0:44:57 ‘Cause it’s essentially almost like the solidified system
    0:44:59 of here’s who I think guy is,
    0:45:01 and now I’m just optimizing.
    0:45:02 And in the way that humans learn,
    0:45:05 there’s this trade off between exploitation.
    0:45:08 So that is doing the stuff that you know is good for you.
    0:45:11 So if you think about restaurant choices,
    0:45:14 you can either go to the same restaurant time and again,
    0:45:15 because you know that you like it.
    0:45:17 So there’s not going to be any surprise.
    0:45:19 It’s going to be a good experience.
    0:45:21 But the second part of human learning
    0:45:23 and experience is the exploration part.
    0:45:25 And it exposes you to risk,
    0:45:26 because maybe you go to a restaurant
    0:45:28 and it turns out to be not great
    0:45:29 and you would have been better off
    0:45:31 going to your typical choice.
    0:45:33 But maybe you actually also stumble on a restaurant
    0:45:35 that you love.
    0:45:36 And for that, you had to take the risk
    0:45:38 and explore something new.
    0:45:40 And my worry with these AI systems
    0:45:42 and most types of personalization
    0:45:45 is that they very much focus on exploitation.
    0:45:47 They take what you’ve done in the past,
    0:45:47 who they think you are,
    0:45:50 and they try to give you more of that.
    0:45:52 But you don’t get like the fun parts of exploring.
    0:45:54 It’s like Google Maps is amazing
    0:45:57 at getting you from A to B most efficiently,
    0:46:00 but you also never stumble upon these cute little coffee shops
    0:46:01 that you didn’t know were there before
    0:46:03 because you got lost.
    0:46:05 And for me, that’s in a way the danger
    0:46:07 of having these systems replaces.
    0:46:10 Is that just gonna make us basic and boring?
    0:46:15 – What if I ask the opposite question,
    0:46:19 which is I want to help companies be more accurate
    0:46:23 in predicting my choices, right?
    0:46:25 So I wanna tell Google,
    0:46:29 stop sending me world wrestling news and Google news
    0:46:32 and stop telling me about the Pittsburgh Steelers
    0:46:36 and stop sending me ads for trucks
    0:46:38 ’cause I don’t want a truck and I don’t want a Tesla.
    0:46:42 And I wanna make a case that what if you want companies
    0:46:44 to understand you better, then what do you do?
    0:46:47 – First of all, I think it should be an option, right?
    0:46:49 So there should be two different modes for you guys
    0:46:51 that says right now I’m trying to explore.
    0:46:53 Right now I just wanna see something
    0:46:55 that’s different to what I typically want.
    0:46:57 But also now I’m in this mode
    0:46:59 where I just want you to know exactly what I’m looking for.
    0:47:01 And I don’t want you to send me the camera
    0:47:03 even though I was not interested in the camera
    0:47:05 for the last three weeks.
    0:47:08 So in this case, I think what companies can do,
    0:47:11 which is what they I think oftentimes don’t do enough of.
    0:47:14 So it’s like having a conversation with you
    0:47:17 that kind of allows you to interact with the profile.
    0:47:19 Most of the time they just passively say,
    0:47:20 here’s who I think guy is
    0:47:23 and now we’re optimizing for their profile.
    0:47:24 But if they get it wrong,
    0:47:26 there’s no way for you to say no, no, no,
    0:47:28 why don’t you just take out this prediction
    0:47:30 that you’ve made ’cause it’s not accurate,
    0:47:32 which is annoying for me ’cause now as you said,
    0:47:34 you get like ads for wrestling
    0:47:36 that you might not be interested in at all.
    0:47:37 And it’s also bad for business
    0:47:39 ’cause now they’re optimizing for something
    0:47:41 that is not who you are.
    0:47:43 So I think first of all, give people the choice
    0:47:45 whether they wanna be in an explorer mode
    0:47:47 or an exploitation mode.
    0:47:50 And then second part is even within the exploitation mode
    0:47:51 where we’re just trying to optimize
    0:47:53 for who we think you are,
    0:47:56 give people the choice and say, no, you’re wrong.
    0:47:57 I wanna correct that.
    0:47:59 It’s good for the user and it’s good for the company.
    0:48:03 – Well, if anybody out there is listening
    0:48:05 and embraces this idea,
    0:48:09 I suggest you not call it exploitation mode,
    0:48:12 maybe optimization mode might be a more pleasant marketing.
    0:48:14 – Personalization mode, yeah, that’s true, that’s true.
    0:48:16 – Personalization mode, yeah.
    0:48:19 Okay, so some three short,
    0:48:21 tactical and practical questions.
    0:48:23 So knowing all that you know,
    0:48:26 and I think we went dark a few times
    0:48:28 and show people the risk here.
    0:48:33 So do you use email, messages, WhatsApp or signal?
    0:48:36 What do you use personally?
    0:48:37 – I mostly use WhatsApp.
    0:48:38 First of all, it’s encrypted,
    0:48:41 but then it also just what everybody in Europe uses.
    0:48:44 So I wouldn’t even give myself any credit for that.
    0:48:46 And it’s funny ’cause I think the fact
    0:48:48 that I’ve become a lot more pessimistic over the years
    0:48:50 has to do with my own behavior.
    0:48:53 So I know that we can be tracked all the time
    0:48:56 and I still mindlessly say yes to all of the permissions
    0:48:57 and so on and so forth.
    0:48:59 So I think we just don’t have the time
    0:49:02 and the mental capacity to do it all by ourselves.
    0:49:04 There’s only 24/7 in a day.
    0:49:06 And I’d much rather spend a meal with my family
    0:49:08 than going through all the terms and conditions and permission.
    0:49:11 So I think if it’s just up to us,
    0:49:13 it’s an unfair battle that we don’t stand a chance.
    0:49:16 – And why, of all people in the world,
    0:49:18 would you not default to signal
    0:49:21 because it’s encrypted both the message
    0:49:24 and the meta information?
    0:49:26 – It’s mostly because not that many of my friends
    0:49:27 are using it.
    0:49:29 So again, in this case, it would be a trade-off
    0:49:31 between I get protected more,
    0:49:33 but there’s also like a downside
    0:49:35 because I can’t reach out to the people
    0:49:36 that I want to reach out.
    0:49:38 And I feel like if that’s the trade-off,
    0:49:40 the brains of most people will gravitate to,
    0:49:43 I’m just gonna get all of the convenience that I want.
    0:49:46 – Okay, second short question is,
    0:49:49 when you use social media,
    0:49:52 do you use it like read only and you don’t post,
    0:49:54 you don’t comment and don’t like
    0:49:57 or like are you all in on social media
    0:50:00 and dropping breadcrumbs all over the place?
    0:50:02 – I think even if you don’t use social media,
    0:50:05 even if I was completely absent from social media,
    0:50:07 I would still be generating breadcrumbs all the time
    0:50:09 ’cause I have a credit card and I have a smartphone
    0:50:11 and there’s facial recognition.
    0:50:12 I just don’t want people to think
    0:50:15 that social media is the only way to produce traces.
    0:50:17 Now I don’t actively use it as much,
    0:50:21 but not because I know that I shouldn’t be doing it.
    0:50:22 It’s just because it’s so much work.
    0:50:26 I feel like I much rather have interesting offline conversations
    0:50:29 that thinking about what I should post on X
    0:50:31 and some of the other ones.
    0:50:35 So it’s a different reason than worries about privacy.
    0:50:39 – Okay, now is the logic that, yes,
    0:50:41 Google knows something, Apple knows something,
    0:50:44 Meta knows something, X knows something,
    0:50:45 everybody knows something,
    0:50:47 but nobody knows everything.
    0:50:51 So the fact that it’s all sort of siloed
    0:50:55 keeps me safe or is that a delusion?
    0:50:56 – I think it’s probably a delusion.
    0:51:00 So my argument would be that they have most of these traces.
    0:51:03 So if you think of applications, again,
    0:51:05 like when you download Facebook here,
    0:51:07 it asks you to tap into your GPS records,
    0:51:10 into your microphone, into your photo gallery.
    0:51:12 You use Facebook to log into most of the services
    0:51:14 that you’re using elsewhere.
    0:51:17 So they have a really holistic picture
    0:51:20 of what your life looks like across all of these dimensions.
    0:51:21 And by the way, they also have it for users
    0:51:24 who don’t use Facebook because it’s so cheap now
    0:51:27 to buy these data points from data brokers
    0:51:31 that if I wanted to get a portfolio, a data portfolio,
    0:51:32 on most of the people,
    0:51:34 I would be able to get it really cheaply.
    0:51:36 And that’s something that, again,
    0:51:39 I think most of us or all of us should be worried about.
    0:51:42 And you do see use cases where policymakers
    0:51:43 are actually waking up to this reality.
    0:51:47 There was this case of a judge actually across the bridge
    0:51:49 from here in New Jersey, whose son was murdered
    0:51:52 by someone that she litigated against in the past.
    0:51:55 They found her data online from data brokers,
    0:51:58 tracked her down, and in this case, killed her son,
    0:52:00 Biden signed something into a fact
    0:52:03 that now protects judges from having their data out there
    0:52:05 with data brokers, which makes me think
    0:52:07 if we do this for judges and we’re concerned
    0:52:09 that we can easily buy data about judges,
    0:52:12 why not protect everybody else?
    0:52:16 I think there’s a good point to be made that data on us
    0:52:18 is so cheap and available from different sources
    0:52:21 that even if you don’t use social media,
    0:52:23 it’s easy to get your hands on.
    0:52:27 – You introduced the concept in the last part of your book,
    0:52:29 which I don’t quite understand.
    0:52:33 So please explain what a data co-op does.
    0:52:36 – Yeah, it’s one of my favorite parts of the book, actually,
    0:52:38 ’cause it thinks of how do you help people
    0:52:39 make the most of their data, right?
    0:52:43 So we’ve talked a lot about the dark sides,
    0:52:44 and I think regulation is needed
    0:52:48 if we wanna try to prevent the most egregious abuses,
    0:52:50 but it doesn’t really give you a way of,
    0:52:53 first of all, managing your data in the absence of regulation,
    0:52:54 and it also doesn’t give you a way
    0:52:56 to make the most of it in a positive way.
    0:53:01 So data co-ops are essentially these member-owned entities
    0:53:04 that help people who have a shared interest in using data
    0:53:06 to both protect it and make the most of it.
    0:53:09 So my favorite example is one in Switzerland
    0:53:11 that’s called MyData, and they’re focused on the medical space.
    0:53:14 So one of the applications that they have
    0:53:17 is working with MS patients.
    0:53:19 So patients who suffer from multiple sclerosis,
    0:53:20 which is one of these diseases
    0:53:22 that, again, is so poorly understood
    0:53:24 ’cause it’s determined by genetics,
    0:53:25 and it’s determined by your medical history,
    0:53:27 by your environment, and what they do
    0:53:30 is they have a co-op of people.
    0:53:34 So patients who suffer from MS and healthy controls
    0:53:36 that own the data together.
    0:53:39 So it’s a little bit similar to the financial space
    0:53:41 where you oftentimes have entities
    0:53:43 that have fiduciary responsibilities.
    0:53:47 So they’re legally obligated to act in your best interest.
    0:53:50 So data co-ops are entities that are owned by the members.
    0:53:53 They are legally obligated to act in their best interest,
    0:53:56 and now you can imagine, in the case of the MS patients,
    0:53:57 they can pool the data,
    0:53:59 they can learn something about the disease,
    0:54:01 and they can also then, in this case,
    0:54:03 work with doctors of the patients
    0:54:05 and say, here’s something that we’ve learned from the data.
    0:54:08 This treatment might be particularly promising
    0:54:10 for a patient at this stage with these symptoms.
    0:54:12 Why don’t you try this?
    0:54:15 So the people benefit immediately,
    0:54:16 and also because they’re now together,
    0:54:20 they can hire experts that help them manage their data,
    0:54:23 think about, well, here’s maybe some of the companies
    0:54:24 that we wanna share the data with,
    0:54:26 but maybe we do it in a secure place
    0:54:28 that doesn’t require us to send all of the data.
    0:54:30 So these data co-ops, for me,
    0:54:33 is just like a new form of data governance
    0:54:35 that gives us, I think of it as allies.
    0:54:38 So if we have a way that we wanna use data,
    0:54:40 we need other people with a similar goal
    0:54:42 so that we make data, first of all, more valuable,
    0:54:45 ’cause if I have my data, my medical history
    0:54:47 and my genetic data as an MS patients,
    0:54:49 doesn’t help me at all, I need these other people,
    0:54:53 but it’s not coming together as a pharma company
    0:54:56 that’s grabbing all of this data and then making profits,
    0:54:58 but it’s coming together as a community
    0:55:00 and benefiting directly.
    0:55:02 So that’s what data co-ops are.
    0:55:06 But a data co-op doesn’t exactly solve the problem
    0:55:09 of all my breadcrumbs on social media and Apple
    0:55:11 and all the other stuff, right?
    0:55:14 This is for a very specific set of data.
    0:55:17 – Agreed, so it’s not necessarily a specific set of data.
    0:55:19 You could imagine in the European Union
    0:55:21 where you’re allowed to pull your data,
    0:55:24 you could have a data co-op of people
    0:55:26 who just pull together their Facebook data
    0:55:27 and now they go to Facebook and say,
    0:55:31 “Hey, look, we’re all gonna leave if there’s no way,
    0:55:33 “if you’re not putting in, let’s say,
    0:55:34 “technology like federated learning
    0:55:36 “to protect our privacy a bit more.”
    0:55:38 So I do think that there is also ways
    0:55:40 in which people can come together
    0:55:42 and get just a lot more negotiation power at the table.
    0:55:44 Then if you go to Facebook and say,
    0:55:48 “Hey, I’m Guy, I wanna force you to do something different,”
    0:55:49 not sure if they’re gonna listen.
    0:55:52 If you suddenly have 10 million people doing that,
    0:55:54 you are in a better spot.
    0:55:57 – Okay, I like this idea.
    0:55:59 Okay, now I understand it better.
    0:56:01 Thank you very much.
    0:56:04 Listen, I like to end my podcast with one question
    0:56:06 that I ask all the remarkable people
    0:56:08 and clearly you’ve proven you’re remarkable
    0:56:10 with this interview.
    0:56:13 And that would be stepping aside, stepping back,
    0:56:17 stepping up whatever direction you wanna use.
    0:56:20 Like, what’s the most important piece of advice
    0:56:24 you can give to people who wanna be remarkable?
    0:56:26 – I think it’s don’t take yourself too seriously.
    0:56:28 I think some humility and the way
    0:56:32 that you approach yourself and others goes a long, long way.
    0:56:33 – Alrighty.
    0:56:35 This is a great episode.
    0:56:36 Thank you so much.
    0:56:38 And I hope I didn’t go too dark for you,
    0:56:40 but this is a dark subject, actually.
    0:56:41 – I do think it is.
    0:56:44 And I think there’s a lot of room for improvement.
    0:56:46 That’s why I care about the topic so much.
    0:56:48 – Alrighty, so Sandra Matz.
    0:56:50 Thank you very much for being a guest.
    0:56:52 This has been Remarkable People.
    0:56:55 I’m Guy Kawasaki, and I hope we helped you
    0:56:57 be a little bit more remarkable today.
    0:57:00 So my thanks to Matt as a Nizmer, the producer,
    0:57:04 Tessa Nizmer, our researcher, Jeff C. and Shannon Hernandez,
    0:57:05 who make it sound so great.
    0:57:08 So this is the Remarkable People podcast.
    0:57:12 Until next time, mahalo and aloha.
    0:57:18 – This is Remarkable People.

    What can your Google searches reveal about your personality? In this episode of Remarkable People, Guy Kawasaki explores the fascinating world of psychological targeting with Sandra Matz, Professor at Columbia Business School.

    Matz shares eye-opening insights about how our digital footprints expose our deepest traits and behaviors. She reveals how companies predict our personalities through social media posts, explains the surprising link between language use and emotional states, and discusses why data privacy isn’t just about personal convenience—it’s about protecting ourselves in an uncertain future. Whether you’re concerned about data security or curious about what your online behavior reveals about you, this episode provides essential insights for navigating our increasingly digital world.

    Guy Kawasaki is on a mission to make you remarkable. His Remarkable People podcast features interviews with remarkable people such as Jane Goodall, Marc Benioff, Woz, Kristi Yamaguchi, and Bob Cialdini. Every episode will make you more remarkable.

    With his decades of experience in Silicon Valley as a Venture Capitalist and advisor to the top entrepreneurs in the world, Guy’s questions come from a place of curiosity and passion for technology, start-ups, entrepreneurship, and marketing. If you love society and culture, documentaries, and business podcasts, take a second to follow Remarkable People.

    Listeners of the Remarkable People podcast will learn from some of the most successful people in the world with practical tips and inspiring stories that will help you be more remarkable.

    Episodes of Remarkable People organized by topic: https://bit.ly/rptopology

    Listen to Remarkable People here: **https://podcasts.apple.com/us/podcast/guy-kawasakis-remarkable-people/id1483081827**

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  • The insane story of Blake Schroll: the high school dropout who’s building supersonic jets

    AI transcript
    0:00:06 Okay, I have an amazing story. If you’re if you’re entrepreneurial Sam if you have a pulse in your body if there is a
    0:00:09 Single blood vessel in your body
    0:00:13 You are gonna be fired the fuck up after this. I don’t know what you got. What do you have scheduled after this?
    0:00:15 Cancel it
    0:00:27 All right, let’s hear it. What is it? All right, so you’ve probably heard of this company boom supersonic. Yes
    0:00:32 Yeah, uh, I had the opportunity to do invest in this company years ago
    0:00:39 And I didn’t have any money then but also I even if I did I would have passed on this because it’s it’s a it’s a ridiculous proposition
    0:00:42 I didn’t have any courage then either. That was the real problem
    0:00:48 Well, like a guy said that uh guide silken valley said he’s gonna build a commercial airline
    0:00:54 He’s gonna build a jet that can be a supersonic commercial airplane, right? That sounds ridiculous
    0:00:58 Exactly. So the the thing that’s going viral right now is
    0:01:05 Former like group on product manager because he worked at group on is linked in literally goes group on product manager
    0:01:09 Then created the first supersonic jet and like, you know, whatever
    0:01:11 56 years
    0:01:15 So it’s this insane jump on linked in and that’s kind of going viral right now
    0:01:18 It got me curious because I remember back of the day. This was 2016
    0:01:21 Did you see the deal?
    0:01:24 I uh, I wasn’t it didn’t it’s not like somebody sent it to me to invest
    0:01:29 I could have if I chased it down and then actually several times since then I could have invested but I didn’t
    0:01:30 um
    0:01:32 Maybe not too late. Maybe now’s the time
    0:01:38 But I remember watching demo day. Do you remember this story? Do you remember his demo day pitch at yc demo day?
    0:01:43 I didn’t know he went through yc. I didn’t pay that close attention to it. So he went through y-combinator
    0:01:45 Exactly went through y-combinator now
    0:01:50 I remember at the time it stood up now yc does a lot more like moonshot type of companies back then
    0:01:58 It was all apps. It was all software. You’re making an iphone app. You’re making like a b2b sas tool and this is 16
    0:02:02 16 and there was this one guy there who was like
    0:02:04 We’re creating supersonic air travel
    0:02:10 Like you’re gonna be able to fly from new york to london in three and a half hours. That’s what we’re doing
    0:02:12 remember the concord
    0:02:14 we’re gonna do that again and
    0:02:19 He gets on stage and I remember thinking while he’s pitching. I’m like, how is he gonna have
    0:02:26 Attraction because every yc pitch ends the same where they show their user growth. We’re growing 30 percent week over week
    0:02:28 But it’s like going from like, you know
    0:02:32 Three to six to nine right like it’s like they have some crazy growth rate
    0:02:34 But it’s on a very small customer base, but that’s always the pitch
    0:02:38 So I remember wondering what’s this guy gonna do and at the very end of his pitch
    0:02:42 He whips out a piece of paper and he goes and as of last night
    0:02:45 We have five billion in pre-orders
    0:02:49 Thanks to virgin. Thanks to richard branson and virgin
    0:02:54 And we were like what five billion and it was like he had an lly. It wasn’t actually a purchase order
    0:03:01 Which basically just says uh one day if you can actually build a safe supersonic jet. We will definitely buy it
    0:03:05 We will maybe buy it. That’s what it means
    0:03:11 Right like when somebody invites me to like a party. I don’t want to go to I’m gonna start sending llys
    0:03:13 Because at lly just means
    0:03:19 I’m generally interested, but I’m probably not gonna do it. All right. Yeah, but still it was impressive five billion
    0:03:25 Still to this day. Nobody’s walked into yc demo day with five billions dollars worth of letters letter of intent
    0:03:29 So, okay, I remember seeing that then he disappeared for a while then he actually had to go do the work
    0:03:40 Do you guys remember when marketing was fun when you had time to be creative and connect with your customers
    0:03:46 With hub spot marketing can be fun again turn one piece of content into everything you need
    0:03:50 Know which prospects are ready to buy and see all your campaign results in one place
    0:03:56 Plus it’s easy to use helping hub spot customers double their leads in just 12 months
    0:04:03 Which means you have more time to you know, enjoy marketing again. Visit hubspot.com to get started for free
    0:04:10 So let me tell you this guy’s story because
    0:04:13 right now in silicon valley
    0:04:14 there is a
    0:04:16 buzzword
    0:04:22 Actually, I would say it’s kind of like an early buzzword like I would be buying stock in this buzzword because it’s about to go mainstream
    0:04:26 And that word is high agency. You’ve seen high agency floating around
    0:04:29 No, but that is is high agency the new contrarian
    0:04:33 Oh, it is I mean it’s shits on contrarian, dude
    0:04:41 Like it runs where contrarian walked. We have to sell democratize democratize is done. That was the grandfather
    0:04:44 We have the short contrarian. We’re going all in on high agency
    0:04:47 We’re long high agency the man in the arena
    0:04:50 Is that just plummeted? Had a moment had a moment
    0:04:53 Uh, butt him off just butchered that one. So that one’s done
    0:04:59 So all right, so check this out. So high agency is this word and there’s uh, our buddy, george mack
    0:05:04 By the way is like all in on the high agency like he I think he’s writing a book about high agency right now
    0:05:08 I think he by the way, he bought the domain high agency.com
    0:05:15 And I was like, how’d you get that? He goes the guy let it expire after 10 years and I sniped it right away
    0:05:17 Very low agency move
    0:05:19 High agency by him, low agencies by the other, right?
    0:05:24 It’s like in football the low man wins. No, no in business the high agency man wins and so
    0:05:28 um, the classic meme which we should put on the screen on youtube is
    0:05:32 Guy is the cartoon guy is trapped on an island
    0:05:37 And he has these like pieces of bark or wood or whatever and he he spells out help
    0:05:39 He’s waiting for someone to come save him
    0:05:43 That’s the low agency guy and the high agency guy takes the wood and makes a boat and rows away
    0:05:47 Um, right. So that that’s like the the gist of this. Okay, but this guy
    0:05:50 This dude from boom supersonic. What’s his name? Blake?
    0:05:56 He’s just absolutely dripping with agency. All right, so let’s take this out
    0:06:04 He’s guy is soaked. This guy is a yeah, he’s a buffalo wing and he is tossed hand tossed in high agency. All right, so here we go
    0:06:10 Story starts he drops out of high school. His parents sent him to a good high school like a nice private school
    0:06:16 And he doesn’t feel like he fits in he doesn’t really look like uh get that interested in class
    0:06:22 he drops out and um, so now he’s high school dropout black parents are kind of concerned
    0:06:28 He ends up finding something that he’s really into like science and math like he in this like after school program
    0:06:32 Or there’s some shit like that, but forget that first high agency move
    0:06:38 Once he started getting interested in science math engineering. He’s like actually I think I do like school
    0:06:42 I just didn’t know what I just didn’t like my high school the way I was doing it
    0:06:45 But now I found I actually do like to learn I want to go to college
    0:06:49 Well, how do you go to college? You don’t have a high school degree. You’re a high school dropout
    0:06:55 He finds that at Carnegie Mellon, they have a special program for high school dropouts who want to go to Carnegie Mellon
    0:07:01 So he applies and he applies as a junior and he writes an essay about why you have to write an essay why you didn’t finish high school
    0:07:07 And he writes high school had nothing left to teach me he gets in not only is he getting he gets the merit scholarship
    0:07:10 And he graduates with a you know bachelors in computer science. Okay, so
    0:07:16 What does he do next his next move is that he’s like I want to be an entrepreneur
    0:07:18 But I don’t know what idea I want to start yet
    0:07:22 So why don’t I go and work for who I think is the greatest entrepreneur right now?
    0:07:25 And that was jet bezos in 2001
    0:07:32 So amazon is out the dot-com crash just happened amazon stock is in the tank
    0:07:37 I that’s what I’m betting you’re looking that up right now 2001. It was I mean, it was shit. It was uh,
    0:07:40 A dollar now it’s two hundred and seventy three dollars great
    0:07:46 So he joins amazon in 2001 and his goal is to learn as much as he can from bezos
    0:07:49 So he’s like all right, I got to build something that’s of interest to bezos and at the time
    0:07:57 Bezos had an idea which was like, hey, we need to we need amazon products to show up number one in google search results
    0:07:59 so
    0:08:02 Blake builds this automated system where he is
    0:08:04 Um
    0:08:09 Buying google ads for every single product on amazon like the entire catalog. He builds an auto bidder
    0:08:14 So that amazon will bid not too much but bid enough to be the top result on amazon
    0:08:18 Which is one of the reasons by the way that like the you know, there’s like stories of like
    0:08:23 There’s a story that a company wants you to know and then there’s a story that actually happened
    0:08:25 and I’ve read a lot about amazon and one of the
    0:08:30 One of the make or break moments was the fact that they were one of google’s biggest spenders
    0:08:33 And they crushed it and knocked it out the park because google was underpriced and they were able to
    0:08:39 Get big fast because people think all these companies are competitors. They’re also co-conspirators, right?
    0:08:45 google pays apple built tens of billions of dollars to be the default search engine apple in uh,
    0:08:52 Microsoft invested in apple when they were big competitors bill gates basically kind of saved apple at a time when steve jobs needed investment
    0:08:58 And in this case amazon is the biggest spender on google like you know that oh what I thought these as were competitors
    0:09:02 And bezos was one of the angel investors of google or something like that exactly
    0:09:04 So he builds this system by 24
    0:09:09 Now he’s 24 years old three years in and he was basically working under direct view of bezos
    0:09:13 So he goes every three months. I had to give bezos a report on how we were doing
    0:09:20 And by the age of 24, he’s running a 300 million dollar p&l inside of amazon. He’s the he’s the gm of that business
    0:09:22 but
    0:09:27 Even though he’s learned a bunch and he uh, kind of you know cut his cut his teeth here
    0:09:33 He wanted to be an entrepreneur. So he quits even though. He’s like a rising star at amazon and he knows amazon is like a
    0:09:37 amazon’s a winner right amazon was a winner if he had done nothing else
    0:09:40 But just stay at amazon get promoted as an exact keep getting stock options every year
    0:09:45 He would have made hundreds of millions of dollars essentially risk-free at that point
    0:09:47 Yeah, but he quits and him and some guy
    0:09:51 Decided to create a start so the greatest startup it kind of fails to create another startup
    0:09:58 Eventually they sell that startup to group on and this is where the linkedin part of group on comes in is like this hilarious quote on on his
    0:10:01 Group on linkedin description
    0:10:05 So he says what he does like, you know senior whatever manager of this thing
    0:10:12 And then his description goes nothing like working on internet coupons to make you yearn for doing something that you truly love
    0:10:15 So he after two years at group on he quits
    0:10:18 And he decides, you know, what I want to do a company
    0:10:20 I don’t know what I want to do it in and his whole life
    0:10:23 He had been interested in flight. He talks about like a music kid
    0:10:26 He was always interested in model airplanes. His parents took up to a museum
    0:10:29 He was always obsessed with flight one of his things on his bucket list
    0:10:34 Was that you know in his 20s? He wanted to go mock too like he got his pilot’s license
    0:10:36 He always just was interested in flight, but it was a hobby
    0:10:41 And he’s trying to think of what startup ideas to do and his method for figuring this out is
    0:10:44 I’m going to write a list of all the ideas starting with
    0:10:47 What would be the most awesome if I did it
    0:10:49 Down from there
    0:10:54 And then I’ll just probably cross out the first five because they’re unrealistic or impractical
    0:10:56 And I’ll probably do number five on the list was his plan
    0:11:00 But number one was create a supersonic jet because like elan
    0:11:07 The way elan started SpaceX you’ve heard the story where he went into it like it thinking this is not going to work
    0:11:13 Well, but even before that he said, you know, he sell uh paypal sells elan now has 180 million dollars
    0:11:15 He leaves and he’s
    0:11:19 Just curious like what okay, what else and he’s curious
    0:11:23 Um, what’s the latest with nasa’s mars mission like I haven’t heard about our mars mission
    0:11:28 Like are we is it in progress and I just missed it. Is it launching soon? I can’t wait to go see it
    0:11:31 Maybe I’ll fly there and go see it. I want to see the launch. I love rockets
    0:11:35 And he goes on the nasa website and there’s no mention of mars
    0:11:42 And he’s like what we went to the moon like in whatever 73 like how are we not? Where’s the next mission?
    0:11:44 And it wasn’t even on the website
    0:11:51 He’s like what and he got so upset by that that the start of SpaceX was actually that he was going to privately fund a mission
    0:11:54 Just sound like a plant to mars
    0:11:57 And then it became like a turtle or jelly or something
    0:11:59 It was like, you know some living some technically living thing
    0:12:04 And it was like I’m gonna spend 25 million dollars doing this this there’s just like a stunt
    0:12:09 And uh just to kind of like reinvigorate the interest in this same way this guy blake
    0:12:14 Puts on a google alert for supersonic jet. He’s like, I just can’t wait till there’s a supersonic flight
    0:12:16 I’ll be the first guy to buy it
    0:12:19 And he’s waiting and he’s waiting and he’s like is nobody working on this
    0:12:25 And he’s like, oh it must be that it just doesn’t the numbers don’t the math ain’t math it like that’s got to be the problem
    0:12:27 so he creates a spreadsheet and
    0:12:30 In it he writes down the assumptions
    0:12:33 He’s like basically like the engineering assumptions and he gives himself like a few months
    0:12:36 To work on this where he’s like, all right, let me just dig into this
    0:12:40 So I think it was two weeks where he does like it goes and buys a book because he’s like
    0:12:43 I don’t know anything about how airplanes fly but he goes he buys a book
    0:12:49 And he creates an excel spreadsheet on one tab was the cost model. He’s like, is it an economic problem?
    0:12:50 because
    0:12:54 The brief history is we had this thing called the concord back in the day
    0:12:58 Have you ever seen the concord like uh, have you ever looked at photos of it? It looked great photos
    0:13:04 Yes, like and it was like inspiring and everybody loved it and it was the same idea you could get from you know here to london
    0:13:09 In new york to london and three and a half hours ish. Yeah, it was like three and a half hours or three hours in 45 minutes
    0:13:12 Was the fastest time ever which what’s it like double the speed?
    0:13:16 So it’s eight hours normal flight. So it was twice you could fly twice as fast
    0:13:21 And the idea was the concord was like this luxury item in a way. It was priced really expensive
    0:13:23 So this is you know
    0:13:25 Almost 50 years ago that it was invented
    0:13:31 But it was priced at like 15 000 dollars a ticket and if you look at the inside of it the photos
    0:13:36 I think it was only like three seats by three so it was like a it was like two or three seats something passengers
    0:13:39 I think yeah, so there wasn’t a lot of people at all
    0:13:42 So it was hard to make a lot of money high priced low volume
    0:13:45 Not a lot of routes only limited number of routes
    0:13:49 But it had all these problems like if you go look up like the problems with the concord, right? Like it was like
    0:13:56 Yes, it was expensive it also like got really hot like there’s this famous thing where when they first launched it to do
    0:14:00 Like a pr blitz or like to sponsor it. They had Pepsi sponsored the concord
    0:14:06 And so they painted it light blue and the concord before that was this white reflective material
    0:14:08 To reflect heat to not store heat
    0:14:14 But the light blue thing made it where it got so hot inside the cabin that you know the hour flight
    0:14:17 They can only do for 20 minutes because it was like cooking everything inside
    0:14:21 So like heat was a problem noise was a problem price was the problem
    0:14:27 Um, you know duration was a problem. I also think there was a really famous crash too where it was like
    0:14:31 That was the best now and I don’t remember if I don’t you know, this was years
    0:14:33 I was a kid or I don’t even think I was alive actually
    0:14:37 But I remember reading about it in the what I read was the reality
    0:14:39 Is that it could have worked or it could have been great?
    0:14:44 But the perception was that this thing is dangerous and it’s too radical and it crashed. I’m never getting on that. Is that right?
    0:14:50 Exactly. So the the last the thing that kind of killed the concord was they had this flight where
    0:14:54 During takeoff the tire hit something on the runway
    0:15:00 And it ruptured the tire and then the plane basically exploded to kill everybody on board and that was like all right
    0:15:05 We’re done with this thing too dangerous. So it had safety problems price problems heat problems
    0:15:10 Um, you know like noise problems because it was like, you know shatter windows when it would do a sonic boom
    0:15:12 So it was like
    0:15:15 Oh, whatever. So I think in like 2003 was the last flight
    0:15:19 So in about 20 years has been no no no flight since nobody was even working on it
    0:15:22 So he he has this spreadsheet
    0:15:26 And he’s like look am I just nuts because the way I’m penciling this out
    0:15:32 You know, we only need like a 30 improvement in like fuel efficiency
    0:15:37 A 30 improvement in this one other thing and he’s like dude. It’s been 50 years like
    0:15:41 Our tv’s got better our phones got better our computers got like dramatically better not 30 better
    0:15:43 They got like 3 000 percent better
    0:15:47 He’s like I’m pretty sure we could make the plane 30 percent more efficient
    0:15:53 And then this would work. So he goes to this like you can buy a t you can I can get a tcl 75 inch tv for literally $600
    0:15:55 And it’s delivered to my door the next day
    0:15:59 Like if I can do that we can make a we can make a chat
    0:16:02 So he goes to this stanford professor
    0:16:05 And he’s like he’s like, hey like, you know this
    0:16:10 We need a 30 i my calculations show when you’re 30 fuel efficiency
    0:16:15 And by the way that conquered was designed at a time where like they used slide rulers and like, you know
    0:16:19 Wind tunnels to test things like I think we could do this and the professor was like
    0:16:24 Yeah, I think you can uh the math is correct. He’s like, so, you know, you might have engineering problems
    0:16:26 But like the physics are fine
    0:16:30 And so he gets encouraged he’s like, I just thought I must not know something
    0:16:32 And that’s why this hasn’t been done
    0:16:38 But it’s one of these classic things where like the beginner’s mind goes in and they don’t see the problems and therefore they do it
    0:16:42 Whereas all the experts just assume that’s we tried that it doesn’t work
    0:16:47 And so he goes and he he decides to create an airplane company. Okay, so what’s the next move?
    0:16:50 How’s he gonna fund this thing and how’s he gonna build it?
    0:16:54 Because again, this dude just bought a textbook about flight. He doesn’t know anything about this stuff
    0:16:58 So he’s like, all right, I need to recruit a great team and we got a we need some money
    0:17:01 So he decides he takes half of all of his life savings
    0:17:05 And he decides to fund the company bank account with it and he hires up
    0:17:09 Something like six to ten people one of which he poaches the
    0:17:16 Former chief engineer of Gulfstream and he’s like, I thought it would be hard to get talent because here I am
    0:17:21 I’m this fucking group on kid saying I’m gonna build a you know a supersonic jet
    0:17:25 This never been hasn’t been done in, you know, 50 years like the last American
    0:17:30 New American Airlines company that worked was like, you know, eight years ago or something
    0:17:32 So the business line is a little crazy
    0:17:36 Probably gonna, you know, it I think it cost the conquered like a billion dollars to make the conquered
    0:17:40 Like it was like a billion dollar project. They thought it was 20 million and it ended up spending a billion
    0:17:43 He’s like, I think well, I think we’ll need 200 million
    0:17:47 But I don’t know where I’m gonna get all that so let me just start by fun to get myself
    0:17:51 Did he did he say how much he hasn’t said the the amount but he at one point he’s like
    0:17:55 I realized like I’m playing chicken with my own bank account here. He’s like, I want to go raise money
    0:17:58 He ends up raising like a million bucks still almost nothing
    0:18:00 But he gets like a few early believers in
    0:18:04 I have one of my really close friends chris was in that round
    0:18:06 Oh wow
    0:18:10 Did he say what he saw or why he’s been telling me about it for years
    0:18:16 And that’s originally how I heard about this and he was like this guy’s just magical and I I I just couldn’t get
    0:18:24 I’m like a silicon valley software person starting a jet company. That just that’s really dumb which is evidenced by
    0:18:28 dumb ideas being potentially amazing and how that’s actually
    0:18:29 quite common
    0:18:32 And by the way, the airline mark airplane market
    0:18:39 Is super fragmented. Oh wait. No, it’s not. There’s two companies one owns 51 percent. The other one owns 49 percent
    0:18:42 It’s just air bus and Boeing and that’s the whole market, right? So
    0:18:48 You know, he’s trying to go to basically break up this duopoly in in theory. Okay, so let’s go back to the to it
    0:18:49 So he hires this guy
    0:18:56 So he hires deem and he’s like actually one of the surprising things was it was way easier to hire great talent
    0:18:58 because
    0:19:00 The bigger the mission the better the pitch
    0:19:04 So he’s like I had a super exciting mission. I didn’t have a lot of other things
    0:19:07 I didn’t have a lot of traction. I didn’t have a lot of funding, but I had a great mission
    0:19:11 I had an exciting project that was basically like nerd porn
    0:19:14 So he’s like any nerdy, you know engineer who worked on airplanes
    0:19:17 You could either go work at Boeing or airbus
    0:19:19 And your job is to like, you know
    0:19:23 Make sure that the cabin temperature goes down by two degrees so that we could sell more, you know
    0:19:27 Vibe electronics like that was they weren’t trying to innovate at all
    0:19:30 So if you were somebody who wanted to innovate, this was like the only option
    0:19:32 So you’ve recruited a team then he goes into yc
    0:19:35 And he was skeptical he goes into y-combinator
    0:19:40 He’s like, isn’t it just for software companies, but sam altman’s like no, no, no, we like hard hard stuff
    0:19:42 um
    0:19:47 Sam’s like, I don’t know if you’re gonna get in but you should go talk to these four other guys who had hardware companies that kind of failed in yc
    0:19:52 But but they’ll tell you that like they would have failed much worse had they not had yc
    0:19:54 So you go stop them. They’re like, yeah, it was great. Let’s do it
    0:19:57 So he joins and he’s like, I realized something pretty quickly
    0:20:01 He goes yc is architected in this three month program around demo day
    0:20:03 Three months you’re going to stand on stage in front of all the investors
    0:20:06 You’re going to make your pitch and you got to raise money. That’s the whole point
    0:20:08 And so he’s like, what the hell am I going to do in three months?
    0:20:11 so he goes
    0:20:13 I realized that during yc
    0:20:19 If I went to them and I was like, hey, we reduced the drag coefficient by 30 and this engine design is really awesome
    0:20:21 And like that’s our progress update
    0:20:22 We’re cooked
    0:20:25 And so he’s like, I got to figure out something that I’m going to do so he comes up with two plans
    0:20:27 He’s like the number one
    0:20:28 I got to sell the dream
    0:20:31 So he spends a good chunk of his time and money
    0:20:37 Just building like a model airplane that looks sick. He’s like, all right. This is not going to actually help us
    0:20:38 Make the plane
    0:20:42 But uh, it’s going to help people see that we’re making a plane and it’s going to live awesome
    0:20:44 How much does yc give you like 125 grand?
    0:20:46 Yeah, something like at the time
    0:20:51 He probably just spent all like $100,000 hiring some firm making like a really great model
    0:20:54 So he had like a model like oh, you’re like a model, but then he also had
    0:20:57 A like a gnarly looking engine
    0:21:02 And he’s like, we just want to stand there with a big ass engine and then people will walk by like, whoa
    0:21:05 Is that a jet engine? He’s like, yeah, that’s the jet engine
    0:21:08 We’re going to use on our supersonic jet. Would you like to hear more?
    0:21:11 So he’s like, I need that eye candy. You needed a booth babe
    0:21:14 And for him, the booth babe was the model and the engine so smart
    0:21:21 Right because like one of the common out is elan and these other guys is it’s you can’t just be an engineer in your in your little engineering hole
    0:21:26 You got to know just enough about the rest about the marketing the capital raising all the other things to survive
    0:21:29 So he did the second thing was he was like, I needed these like purchase orders
    0:21:31 So he makes a list and he’s like
    0:21:38 All right, how can they go get LOI’s and he’s like he makes this list and so he’s like, uh, here’s his quote. He goes
    0:21:42 I realized I couldn’t show up to demo day with no sales
    0:21:43 Otherwise my goose was pretty cooked
    0:21:48 So I looked at my sales pipeline and it was united delta lufthansa air china
    0:21:51 And we only had eight or nine weeks before demo day and I thought to myself
    0:21:55 There’s no way i’m closing with fan lufthansa airlines by demo day is not going to happen
    0:21:58 I’ll be lucky if I close them in nine years let alone nine weeks
    0:22:02 So I realized I actually only had two options a startup airline or virgin
    0:22:09 He goes so we I went all in and focused all of my air my sales efforts on startups and virgin
    0:22:10 he goes
    0:22:14 I went to the startups who were currently operating and I got one of them to do an LOI
    0:22:20 But it was a company you’ve never heard of but it was the best I could do and here I was 24 hours before demo day
    0:22:24 And the lucky break we got was that demo day was split into two days if it was demo day one
    0:22:28 He would have stood by the stage and said we have a small LOI from this company. You’ve never heard of
    0:22:31 Luckily we got randomly assigned to demo day two
    0:22:35 So he had bought an extra 24 hours in that extra 24 hours
    0:22:39 He ends up getting an email from virgin the night before demo day that says you’re allowed to announce
    0:22:41 We’ll take the first 10 planes
    0:22:46 We’ve got options on them and through virgin galactic will even help you build it. It goes. I fell off my chair
    0:22:50 I almost screamed. I had to read the things three times. I couldn’t believe it
    0:22:53 We went from we went from being the biggest laughing stock on demo day
    0:22:56 So the company that showed up with five billion dollars worth of an LOI
    0:23:02 Worth of LOIs a record that won’t be broken sin. Dude, that’s so sick. What year is this?
    0:23:05 What year is this 2016? So I yeah, I was reading
    0:23:09 I was looking up my email to try to figure out did I ever talk to this person?
    0:23:12 How do I know him and the earliest thing I could only find was I think in 2020
    0:23:18 It says the hustle actually wrote about him said united airline wants to buy 15 supersonic airliners from boom sonic
    0:23:22 So it took him an additional two years to get the united airlines. The next for the next LOI
    0:23:24 So, okay, so then how did he get branson?
    0:23:30 High agency, baby. I told you he’s dripping an agency. All right. So what does he do? So he’s like, all right
    0:23:33 If I want to get to richard branson, I got to go through somebody I trust
    0:23:36 He goes i’m also not just going to try one path to branson
    0:23:40 We got to try multiple paths for branson. So he’s like if I get blocked over a year. I got this other
    0:23:42 Long shot going
    0:23:46 So he’s like I had this guy on our board who was an astronaut this guy mark kelly probably heard of him
    0:23:51 So that guy was on his advisory board. He’s like, he knows branson. He’s like, so i’m thinking. All right, we got
    0:23:55 We got to find a way to uh to get to richard and so he goes
    0:23:56 um
    0:24:00 We asked we asked mark. What’s the best way to get to richard branson? How do we make that happen?
    0:24:07 He got and he told me he goes hey, you you can’t get him interested in you but go where he’s interested in his own thing
    0:24:12 So what do you mean? He goes virgin galactic has this big roll out for their spaceship richard’s got to be there
    0:24:16 So you just got to get there go where he is and they’re like, okay
    0:24:20 And so they get he’s like could you make could you email him and say hey?
    0:24:26 These guys i’m advising from boom supersonic are going to be in mojave when you’re doing your rollout
    0:24:28 You should meet with them when you’re there
    0:24:31 And then he’s like um, and then he reaches out to the virgin guys
    0:24:34 He’s like hey, we’re going to be in town uh to see richard. Can we come to the rollout?
    0:24:37 Like, you know, so basically they get invited to the rollout
    0:24:39 They get there
    0:24:41 He’s super busy
    0:24:46 They end up getting 15 minutes with him when he’s at brunch with his mom and they go up to him and they go he goes
    0:24:49 um
    0:24:54 Richard they show him what they’re doing to like we’re building the first supersonic jet since the concord we fix these problems
    0:24:58 We’re making that happen and he goes look we’re not asking you for your money
    0:25:03 All we want to know is this when this thing flies. Do you want a virgin logo on it?
    0:25:08 And he goes i think that was the key you got to ask for the right thing
    0:25:13 When you do a deal you it’s probably going to be hard to close you got to ask for what’s really going to help you
    0:25:16 So we told richard look if you’re our customer, we will get money elsewhere
    0:25:19 We don’t need money from you and that was crucial
    0:25:22 Uh, basically like the first few babies of these that are in the air
    0:25:24 Do you want them with a virgin logo or not?
    0:25:30 And so that was the thing that got richard branson across the line was making the intelligent ask and hustling their way
    0:25:32 Dude, this is a movie. This is a movie
    0:25:36 If it works if it ends well, it’s a movie. Well, they just did their flight
    0:25:40 So they just did their first demo flight where but it wasn’t a um
    0:25:42 I think what the test was if I remember correctly
    0:25:47 Was it the engine that they were testing they got the engine because the plane is like a it’s a small plane
    0:25:50 right this was a smaller plane than the real plane that they’re gonna have
    0:25:54 And I don’t think it was the full speed that they’re gonna have but they did achieve
    0:25:57 I believe they achieved supersonic flight. You could check me on that. But yeah, yeah
    0:26:01 They they achieved the right that they crossed the threshold. They did it safely
    0:26:05 They landed it and is there like whatever xb1 or whatever they’re calling that thing
    0:26:10 Okay, he’s got some great startup advice. So here’s here’s the way he described his startup. I like the way he phrased this
    0:26:12 so he goes
    0:26:14 Here’s my advice for startups
    0:26:17 Pick something that’s going to be worth it to you personally
    0:26:20 That is what that is how you stack the deck in your favor
    0:26:26 Stack to the deck so that you will be motivated and when you look at what you’re creating versus if you went to google or facebook or amazon
    0:26:28 It should be a no contest
    0:26:32 If you haven’t found that just don’t even start a startup. I did that my first time
    0:26:36 I just started a startup just for just because and it was a horrible idea
    0:26:40 You you should start a startup when you’re like I must make this happen
    0:26:41 and
    0:26:47 Something I’ve come to believe is that the bigger the idea the easier it becomes because it motivates you and the people around you
    0:26:52 And you attract better people to come work on it. When we were brainstorming this most of the ideas we came up with were like
    0:26:56 No one’s going to really appreciate this blah blah blah blah, but for us
    0:27:01 It was like the most exciting thing we could ever do and so that’s all that mattered was like you know stacking the deck in your favor
    0:27:07 Doing it as a must rather than a we could do this. Um, and so that that’s I thought one
    0:27:09 Really great takeaway
    0:27:18 My friends if you like mfm then you’re gonna like the following podcast
    0:27:23 It’s called a billion dollar moves and of course it’s brought to you by the hub spot podcast network
    0:27:28 The number one audio destination for business professionals billion dollar moves
    0:27:34 It’s hosted by sarah chen spelling sarah is a venture capitalist and strategist and with billion dollar moves
    0:27:40 She wants to look at unicorn founders and funders and she looks for what she calls the unexpected leader
    0:27:45 Many of them were underestimated long before they became huge and successful and iconic
    0:27:50 She does it with unfiltered conversations about success failure fear courage and all that great stuff
    0:27:56 So again, if you like my first million check out billion dollar moves, it’s brought to you by the hub spot podcast network again
    0:27:59 billion dollar moves. All right back to the episode
    0:28:05 I was reading this quote by the guy I don’t know how to say his last name
    0:28:08 But the guy who wrote crime and punishment, you know, like this famous novel and he said um
    0:28:13 Your worst sin is that you have destroyed and betrayed yourself
    0:28:21 For nothing and I was reading I I just read that like an hour ago and it’s sat with me and what you’ve described is exactly that
    0:28:25 Yeah, I love this guy. Uh, so I don’t know him, but I’m rooting for them
    0:28:29 I want to invest now because you know, why not? Let’s let’s get on the team
    0:28:35 You know they um and there’s been a lot of I think there’s been well there definitely has been but there’s been
    0:28:40 I think he’s wrote publicly about a lot of the downsides of all this which is that it’s this has not been clear
    0:28:44 I think he had to take a huge down round and he tweeted out about it
    0:28:48 I believe and I think his most recent valuation was two billion dollars
    0:28:50 Um, which was down from the other amount
    0:28:55 But didn’t he tweet about the down round and how like they basically almost ran out of money and how it’s almost failed
    0:28:57 a bunch of different times
    0:28:59 I don’t know. I didn’t see that I also
    0:29:06 I’m different than you. I feel like you love to know the downsides of things like even when a guest comes on
    0:29:10 That’s one of the questions you tend to ask which is interesting to me because I never really think about it
    0:29:12 But you’re always like what are the honest
    0:29:19 Like it’s not all peaches and cream like what what are the downsides of your approach, which is obviously an intelligent question
    0:29:22 But I think there’s something
    0:29:27 Good about being delusional about it too and just being like I have a convenient
    0:29:30 Hole in my memory and in my brain
    0:29:34 To not even really pay attention or remember those things
    0:29:40 The reason I ask that is because I have been a victim of this many times where I go and say well
    0:29:45 This guy did it. Why can’t I do it and then I start doing it and I think to myself
    0:29:48 This is way harder than it than it than it looked. Why is this so hard?
    0:29:54 And and so I always look for those I always ask those questions not to say that someone shouldn’t do something
    0:30:01 But just to say that it is much more challenging than it appears and that is okay and normal because I remember thinking on my journey
    0:30:08 This sucks, you know, this person he never felt that way. Why do I feel that way? Like I’m I’m full of shit
    0:30:13 What the hell I’m not I’m not good enough and then I don’t want to like be reminded that like this is a normal feeling
    0:30:19 This is a normal problem. I’m having you got to keep going. That’s why I ask those questions
    0:30:23 Yeah, I think it’s totally helpful to have that the one that helps me is um
    0:30:26 Tony Robbins does this thing where he talks about dabblers
    0:30:30 Versus stressor achievers versus masters and I’ve talked about this up. I won’t do the whole thing
    0:30:35 But like one of the things he says at the end is that anybody who’s pursuing anything
    0:30:40 No matter what if you are in it, you’re going to hit plateaus
    0:30:45 So frigid even downs but like just plateaus where it feels like it’s not working the progress
    0:30:49 It’s not happening. It’s not happening as fast as you want. It’s not growing. It’s not working
    0:30:52 Everyone’s saying no, we don’t have that investor whatever it is, right?
    0:30:56 And the one thing that Tony said that just always stuck in my mind almost like as a
    0:31:02 It’s like a it’s like a jingle in a song or like a commercial where he goes
    0:31:05 When that plateau comes the dabbler quits
    0:31:12 The stressor stresses and the master says, ah, I thought I’d be seeing you soon. Hello, my old friend
    0:31:18 You’re here. I was expecting you which is like we have this one company. We started uh
    0:31:23 Just a little over a year ago. That’s just been like up and to the right like this company
    0:31:26 is I haven’t announced it yet, but it’s like
    0:31:34 Almost a 10 million dollar run rate already profitable bootstrapped in one here. It’s like insanely insanely successful
    0:31:38 But we just hit like our you know our first plateau and I feel like
    0:31:39 When we called it out
    0:31:43 it was almost like this kind of like embarrassing down feeling of like, oh man like
    0:31:46 We didn’t have ridiculous growth this month
    0:31:52 And we’re out whereas I was like, dude, I’ve been expecting you. Where is the plateau? I know it’s coming
    0:31:57 I almost don’t even trust that it’s happening until you’re that this is real until we start to face some of these and so
    0:32:03 Yeah, that’s why I ask those questions is I don’t want us as a media or whatever we are to be the ones who
    0:32:09 Who I want to be realistic the biggest business influencers on itunes. Well, yeah
    0:32:12 As Spotify’s 13th biggest business influencer
    0:32:18 I just want to say that it is a fucking pain in the ass and it’s still worth it. Do you know what I mean?
    0:32:20 Yeah, yeah, you’re right
    0:32:21 Speaking of
    0:32:26 Insane agency speaking of insane downs on a way to a huge up
    0:32:29 Have you listened to this episode I did with nick mowbray?
    0:32:31 It came out this morning and I hadn’t listened to it yet
    0:32:36 But I went and like saw I saw it pop up this morning and I went and googled his company
    0:32:41 And I own a ton of the toys that he makes like like not my kid me
    0:32:43 So like he owns uh
    0:32:50 He owns mini brands you bathe with the robo fish. Well, I’ve we have a robo fish actually for my daughter does
    0:32:55 But you know, I told you about mini toys, right? Uh and how like I oh, that’s his
    0:33:00 Mini brands he owns mini brands and so like my wife is fucking weird and when she was pregnant and like not feeling well
    0:33:05 I would go and surprise her by buying these mini toys, which is the stupidest thing ever
    0:33:11 It’s just like a miniature. It’s a ball full of miniature things like a miniature can of coke a miniature kick cap
    0:33:16 A miniature notebook the weirdest shit if people love it and then the other thing
    0:33:20 Dude, I don’t know why my wife like loves your 32 year old loves those
    0:33:25 Dude, you know what I did was I I went and bought like literally $300 worth and I hid them in my drawer
    0:33:29 And whenever like she had a bad pregnancy day when she was like uh sick or something
    0:33:31 I would go and get one and be like I got something for you
    0:33:33 It was like I was like, you know, I had like a secret stash
    0:33:37 And then also he makes these electric water guns that I went and bought
    0:33:40 Last summer that I would shoot at my daughter like messing around
    0:33:44 Have you seen the electric water guns? I think it’s hilarious how into the toys you are right now
    0:33:47 Uh, could I tell you the biggest?
    0:33:51 Oh, holy shit moment from the whole thing. Well, you got to tell the background
    0:33:52 It’s from what I know
    0:33:57 It’s two brothers in New Zealand that have a toy company that does like three or four or five more than that more than that
    0:34:04 So let’s just let me start with this. We have interviewed. I don’t know how many 200 guests on this podcast 250 maybe
    0:34:06 hundreds
    0:34:13 Millionaires millionaires young old man woman, whatever criminals not criminals criminals non criminals
    0:34:16 Guys, you don’t want to hang out with guys that you don’t want to hang out with right?
    0:34:18 There’s a lot of people we’ve had on this podcast
    0:34:20 he is
    0:34:25 At the top of the mountain. He’s number one number one most impressive entrepreneur
    0:34:32 Of all of them. I was blown away. He is these he has these scrappiest hustle story
    0:34:36 And like the like his bottom was like the bottom
    0:34:40 Literally living on a dollar a day for years
    0:34:44 Literally, this is all funny. This guy is so he is okay
    0:34:47 Now I’ll zoom out now. I’ll give you the context that you wanted
    0:34:51 But like I just needed to say that that out of every yes, I’ve ever had on this podcast ever
    0:34:56 He is the number one most impressive founder that I’ve ever had on the podcast. Okay, and we’ve had some cool people
    0:35:01 No offense everybody. Yes. All right, so um, but sort of story. Yeah
    0:35:04 But no your place
    0:35:08 With all due respect you fucking suck
    0:35:12 Okay robins I found Batman
    0:35:15 This guy I say he’s the wealthiest man in new zealand
    0:35:21 He’s got a company that him and his brother started family owned bootstraps self-made billionaire
    0:35:26 They do a few billion dollars a year in revenue. They do a billion dollars a year and just profit
    0:35:29 I like that and they’ve been doing that
    0:35:34 Um, they never had outside investors. He has dominated the toy industry
    0:35:36 So he built the most profitable toy company in the world
    0:35:40 He then built the fastest growing diaper company in the world
    0:35:43 You ever heard of rascals diapers or million moon at target?
    0:35:47 Yeah, he’s got the fastest growing diaper company in the world doing hundreds of millions
    0:35:49 And by the way, he made that while he was on sick leave
    0:35:55 He got his intestine removed because he got croon’s disease and while he was on his sick bed laying there
    0:36:00 He’s like, oh, I guess I’ll just dominate diapers now. I even say I need a diaper. That’s what happened
    0:36:06 So he creates the fast growing diaper company creates the fastest growing haircare company on tiktok now. Wait, what?
    0:36:09 He’s Monday hair hair caro. Whenever he owns Monday
    0:36:15 Um, not only all that he now is building the largest factory in the world
    0:36:21 bigger than the tesla factory bigger and all that where robots are going to build houses
    0:36:25 Autonomously and that’s what he’s doing. He’s building houses with robots now
    0:36:28 um self funding the whole thing by the way
    0:36:34 Problem I asked all those likes that just got to be hundreds of millions of dollars of self funding and he just laughed
    0:36:36 um, so
    0:36:40 So take that for what it’s worth. Let me now tell you okay, so that’s that’s what the guy does
    0:36:44 Can I tell you some of the just holy shit stories from this thing? Yeah
    0:36:47 all right, so the dude’s 18 years old
    0:36:50 and he
    0:36:53 Like after selling door-to-door in new zealand where they lived
    0:36:58 Uh trying to sell his brother like invented this hot air balloon toy at a science fair
    0:37:02 And his brother his older brother and put him to work and was like, yo, you need to go sell this door-to-door
    0:37:08 Um, the dude the they were they’re like, you know what we need to factory and they’re like, oh all the manufacturing is in china
    0:37:12 So they moved to china, but the hilarious thing is at 19 at 18
    0:37:15 I think 18 19 something like that
    0:37:16 He they go to china
    0:37:19 That’s the story where they sleep in the bush outside the airport because the airport lights were too bright
    0:37:25 So they leave the airport sleeping a bush get bit by mosquitoes. They have no money. So they’re like there’s on their own
    0:37:27 so he
    0:37:31 I was like, oh, so you found a manufacturer in china that you scaled up because what do you found a manufacturer?
    0:37:35 We went to china and then built a factory on the side of a river
    0:37:39 Like we just went with like wood and like a hammer and we built a factory and I was like
    0:37:43 What was the point of going to china? You know, you go to china to their factories are already there
    0:37:48 He’s like, yeah, we didn’t understand that like we just went there and we just
    0:37:51 Built our own factory on the side of a river and we found this like
    0:37:55 Mom to make us food for two rmb a day 30 cents a day
    0:38:00 And then she had some people that could come work in the factory and then we just had the we had our own production line
    0:38:02 And it sounds like a it sounds like a shed
    0:38:06 Yeah, they literally built a shed and then they just kept scaling up the shed
    0:38:09 And he’s like my brother didn’t come back home for like eight or nine years
    0:38:14 He just slept on the floor of the factory and I I slept on the floor too then except for when I had to go sell
    0:38:18 All right, so how did they win? He’s like and our product sucked by delay. This is one of my favorite things
    0:38:23 So it’s like everybody who makes it they’re like the key is just to have a great product
    0:38:28 And then if you’re a founder, you’re like, dude, what like, okay, I’ve made a great product now. What nothing’s happening
    0:38:32 um, and you know, they don’t like people tend to leave out the like
    0:38:39 The manual work it took the lucky breaks it took to like get get the thing to start working
    0:38:43 What was the product it was a hot it was like a plastic hot air balloon toy or what?
    0:38:47 Um, even worse so starts with a hot air balloon quickly rear is like we did again
    0:38:49 We didn’t know anything about anything
    0:38:53 So we go we build a shed by the river in china or making our hot air balloons
    0:38:56 Then we’ve learned you can’t even export a hot air balloon
    0:39:02 He’s like literally it’s a fire in a tiny can like we can’t sell that so he’s like we realized that was a waste
    0:39:06 So we got to come up with a new toy and again, everybody everybody tries to say we’re so innovative
    0:39:11 So we innovated innovation is great and then we made a great product. He is the opposite
    0:39:14 He’s like so we found that this other toy was selling so we copied it
    0:39:20 Uh, we ruthlessly copied that toy and we made a shitty version of it because we didn’t know anything about manufacturer
    0:39:27 Dude, so this like white guy moved to china and out chinese the chinese. Yeah, exactly exactly
    0:39:29 It was like dude, we lived on a dollar a day
    0:39:34 I was like so you I was like I heard the story you ate mcdonald’s every day and he’s like every day that I wish
    0:39:38 mcdonald’s was our christmas treat we would go to mcdonald’s and
    0:39:42 He’s like I for years I just remember saying like Merry Christmas, bro. He’s like Merry Christmas, bro
    0:39:44 We would cheers our fries
    0:39:46 He’s like I used to eat half the fries and take it back and be like hey
    0:39:52 There’s only half the fries in here you guys jit me and get like the second serving of fries and he’s like that was the treat
    0:39:55 Dude, I used to do that all the time at bars. I would order an iced tea and be like
    0:40:00 You made the long island iced tea wrong. You forgot the liquor and by the way, he was not saying never works pride
    0:40:02 He was like I was almost like
    0:40:06 A therapist because this guy’s never done a podcast by the way. This was the first podcast
    0:40:10 He’s I think he’s ever done and there’s no stories you could find at this guy like I went to do the research
    0:40:16 There’s no like oh him you know normally we do research you you go walks the three most watch podcast that they’ve done and you
    0:40:18 Pick up a bunch of stuff. He had zero
    0:40:23 He had to download google chrome to do the interview. He’s like it’s not letting me do this
    0:40:26 I were like what and we’re like just open chrome. He’s like what’s chrome?
    0:40:32 And I was like you’re a billionaire. You don’t know google. What are you using? He’s like. Oh, I think I have chrome hold on
    0:40:37 He’s like oh, I got chrome. Okay. Got it. Got it. Let me switch to my laptop. This is gonna work
    0:40:41 Okay, he had like just what’s he talking with you? No, he was dead serious
    0:40:47 And so uh, I was like, dude, why don’t you do any podcasts to tell your stories? Like, I don’t know. I just never did
    0:40:56 So I’m obsessed with being transparent about money particularly with ultra high net worth people
    0:41:00 The reason being is that there’s not a lot of information on this demographic
    0:41:03 And so because I own Hampton, which is a community for founders
    0:41:07 I have access to thousands of young and incredibly high net worth people
    0:41:11 We have people worth hundreds of millions and sometimes billions of dollars inside of Hampton
    0:41:16 And so every year we do this thing called the Hampton wealth report where we survey over a thousand entrepreneurs
    0:41:20 And we ask them all types of information about their personal finances
    0:41:26 We ask them about how they’re investing their money what their portfolio looks like we ask them about their monthly spend habits
    0:41:31 We ask them how they’ve set up their estate how much money they’re gonna lead to charity how much money they keep in cash
    0:41:37 How much money they’re paying themselves from their businesses basically every question that you want to ask a rich person
    0:41:41 We went and we do it for you and we do it with hundreds and hundreds of people
    0:41:44 So if you want to check out the report, it’s called the Hampton wealth report
    0:41:49 Just go to joinhampton.com click our menu and you’re gonna see a section called reports and you’re gonna see it all right there
    0:41:52 It’s very easy. So again, it’s called the Hampton wealth report
    0:41:57 Go to joinhampton.com click the menu and then click the report button and let me know what you think
    0:42:07 What motivates this guy it’s just a maniac dude. He uh, he’s like was he was he calm very calm
    0:42:11 I felt like I was almost like a therapy session because I’m asking him about these times back in the day
    0:42:16 And he’s almost like, yeah, I don’t know what we were doing. Why we were thinking like he just doesn’t think about
    0:42:19 And he was like not he’s like, yeah, we you know, we we were super scrappy
    0:42:25 But he’s like we were also a little mentally ill like we just were he goes we were so naive that naive is not even the right word
    0:42:29 So he’s like for example, we got sued for millions of dollars because again, we like copied someone’s toy
    0:42:32 And so it’s like a two million dollar lawsuit
    0:42:36 It’s either going to bankrupt the company because we don’t have two million dollars or we have to fight it
    0:42:39 But the lawyers that we talked to were like it’s going to cost you a million dollars to fight this
    0:42:41 He’s like, dude, we had like a few thousand dollars
    0:42:44 He’s like, so I go to colorado and I find this lawyer
    0:42:50 He’s like this guy eventually got disbarred, but I find this guy chad and I tell him
    0:42:53 Hey, I’m going to do all the legal work. You put your name on this
    0:42:56 And we’re going to fight this this way and I paid him 50 dollars
    0:43:02 To put his name on it and I did the all the lawsuit stuff myself just like googling
    0:43:07 And we fought the lawsuit that way and then chad got disbarred like later for other reasons
    0:43:12 He told the story of he’s like, dude, we got this
    0:43:18 Expect we got this he goes I used to I was like, so how did you sell the thing like how did you sell these toys?
    0:43:21 You’re saying the products up and you were copying somebody else. So why would anybody buy this?
    0:43:26 He goes I emailed because I made a list of every retail buyer
    0:43:32 Of every store in every region of the world and I emailed her called them every single day
    0:43:34 He remembered the name of every buyer
    0:43:37 This is from like 15 years ago because he would call or email them every day
    0:43:39 He goes the walmart buyer
    0:43:41 He’s like I would call her or email her every day
    0:43:44 He’s like I never forget her and he’s like one day she emailed me being like nick
    0:43:49 I do not need your daily email about your product and he goes
    0:43:51 Well listen jen. I’m sorry. It’s just a great product
    0:43:54 And I just had to tell you about it because we’ve really got some exciting things to the party
    0:43:56 It’s like I just didn’t let up
    0:44:00 Finally, he goes she just sent me to the two most beautiful words in the in the english language
    0:44:05 Send sample and he’s like we’re in and he gets this like walmart order
    0:44:10 And by the way, actually before that somebody’s like, hey, do you have your showroom in hong kong?
    0:44:15 I’ll send one of my buyers. He goes, of course, of course showroom in hong kong. Yeah. Yeah, we got one of those
    0:44:17 It’s like, what’s a showroom in hong kong? So he goes to hong kong
    0:44:22 He rents this little cubicle where they put like escorts and you know, it’s like the red light district
    0:44:27 And like it’s enough for one human body and he just sleeps in there because he doesn’t have enough money for our hotel
    0:44:31 Sleeps in there and then when a buyer shows up he just pops out and he’s like, hey, it’s me
    0:44:34 Whatever you would like to see our showroom and they’re like, yeah, we’re what is what is this?
    0:44:39 There’s a dressing room for like one person to change clothes and he’s like, yeah, yeah toys are inside. Here you go
    0:44:42 And he ends up getting this order from walmart
    0:44:44 for
    0:44:49 Two million units and he’s like, oh my god, that’s 30 million dollars. He’s like, this is amazing
    0:44:51 At that time the revenue was like 150k a year
    0:44:54 And district tells this story about how he’s like
    0:44:58 First he’s like I go he goes I learned two words of chinese
    0:45:05 Too slow and go faster because our factory our little old factory by the river had never produced two million units of anything
    0:45:08 And he’s like so we had to produce two million units of this thing
    0:45:09 I come home
    0:45:12 I take over the factory because I’m just like my brother’s like not working fast enough
    0:45:17 And then he’s like and then walmart basically cancels the order after he’s like super deep in it
    0:45:22 And he like took a loan from the like from a contract manufacturer to like help him build the two million units
    0:45:27 And then he like goes and harasses the walmart buyer to like hold up to the end of the bargain
    0:45:29 And he’s like by the way
    0:45:35 Product doesn’t sell not a single one. He’s like it retails at 30 dollars discount to 25 discount to 20
    0:45:38 He just got to 15 because it ends up selling for 50 cents
    0:45:41 He’s like he’s like it was like concrete on the shelf
    0:45:48 How’d you find this guy? I don’t know. I I read something. I I must have heard something or read something
    0:45:54 Because about a few years ago we talked about him on the podcast and then yeah, I remember that like three years ago
    0:45:59 Ben has emailed him. No joke. He showed me 17 times over the next 18 months
    0:46:02 Um to get him to come on the podcast
    0:46:06 Dude, it’s a finally said. Yeah, it sounds awesome. I need to go watch this. What are you listening to this episode?
    0:46:08 It’s not like
    0:46:14 You know, he’s not like mr. Like super storyteller mr. Swab or he’s a great guy
    0:46:19 But like he just tells it like it is but if you listen to this thing, I don’t know how long it is like 90 minutes or something like that
    0:46:28 I mean, it is one of the most incredible entrepreneurial stories. I’ve never heard by the way. All right, so I let me tell you a story now because we
    0:46:32 This is chinese related, but we’re just telling stories about interesting people
    0:46:34 I got to tell you about someone I just read about have you ever
    0:46:39 Have you ever even been to new york? Have you ever been to new york like Manhattan? Yes, I am. Yeah, okay
    0:46:46 When you were here, did you see these crazy guys on electric? They’re like they’re called electric bikes
    0:46:50 But they’re basically motorcycles. They’re like the uber eats and door-dash drivers
    0:46:54 No, I don’t think those happen even dude. So in new york like four years ago
    0:47:03 It just popped up that all of these ubers there’s 70 000 uber eats and door-dash people in new york city and like the Manhattan and brooklyn area
    0:47:07 It’s like and they’re all riding these electric bikes that fly
    0:47:12 They look like they’re normal bicycles, but these things literally go 60 miles an hour
    0:47:19 And I ride my bike everywhere when i’m in new york city and like I these fuckers have almost killed me tons of times and they’re and they’re dangerous
    0:47:24 They’re really dangerous and what you’ll notice is that they all have like these gloves on their bikes
    0:47:27 Have you ever seen these like door-dash drivers in the winter? Yeah, I’ve seen that
    0:47:30 Well, they keep them there in the winter in the summertime too
    0:47:37 And the reason they do that is because underneath those gloves they have like a motorcycle style acceleration
    0:47:41 Like a twisty thing on the grip, which is illegal. You can’t have that because that like
    0:47:45 You know at what point are you do go from being a bicycle to being a motorcycle where you there has to be rules
    0:47:47 and so there’s
    0:47:51 There’s this weird thing where I’d be walking around and I’m like I even would ask these guys
    0:47:54 What is this bike and they don’t speak english like like they couldn’t tell me
    0:47:59 But I was like I want to know what this bike is that goes 60 miles an hour and has like a twisty throttle thing because
    0:48:00 This is sick
    0:48:05 I want one and so I started doing research around it and I think I talked about it in the podcast
    0:48:07 I couldn’t find out what it was
    0:48:14 Well two or three days ago an article came out and it told the story about what this thing was and it’s pretty ridiculous and so
    0:48:17 Basically, there’s a guy who comes from china
    0:48:25 He’s got a two heart of a name to pronounce but his american name is andy. He comes in 2011 and when he comes
    0:48:31 He basically admits that he goes I paid a guy 45 grand and he smuggled me in from mexico
    0:48:34 I came illegally and then in this article
    0:48:38 They even linked to a rap video that he did when he was broke and poor and he was like
    0:48:42 I decided to come to america to make it rich that was like the chinese lyrics of this video
    0:48:48 Well, it turns out the first thing that he did was he noticed that there was all these uber each
    0:48:53 Like bike riders and they get punished if they don’t bring or if they don’t show up in time
    0:48:56 Like if you don’t deliver quickly you get like docked like you don’t get the full rate
    0:49:02 And so he starts importing he I think he borrowed 12 grand from someone and he starts importing these electric bikes from china
    0:49:08 And he opens up a shop and one of these shops was right by my house where I used to stay in brooklyn years ago
    0:49:11 And I would always see these guys like like they would kind of hang out outside the shop
    0:49:17 Well, what I didn’t realize was that he scaled this thing so fast to where he started having um
    0:49:23 Like literally 30 different stores all around Manhattan to where he’s selling these things. It’s called fly e-bikes
    0:49:29 He’s selling these e-bikes that go like 40 50 miles an hour and they’re only a thousand dollars
    0:49:32 It’s incredible and they’re incredibly illegal like
    0:49:37 Like they literally just don’t file any of the laws
    0:49:41 But if you look at the under there’s like a when you order these bikes
    0:49:45 There’s like says something in the plate. It says like there’s literally like a metal plate and it says like, you know, this
    0:49:52 Uh meets this usa standard this and that and like in the article someone was like nobody checks that
    0:49:55 It’s just basically an honor system and that we hope that you do it
    0:49:57 But there’s no one actually checking these things
    0:50:01 But the guy ends up andy selling 70 000 bikes
    0:50:04 In four years and a few months ago
    0:50:09 I think it was in september he had the audacity to take the company public
    0:50:11 and so
    0:50:19 It goes public on the nasdaq and it’s uh, I think the ticker is f l e y and you can see the ticker
    0:50:21 I think that uh
    0:50:23 The and you can see I put all their financials in this document
    0:50:29 But the year before they went public they were doing like 30 million in revenue and like two or three million dollars in profit
    0:50:35 But they’re getting sued like crazy because one of their bikes catch on fire because they follow no rules
    0:50:41 He basically admits that he’s like, yeah, we’re cowboys. We uh bill first and we ask questions later
    0:50:45 And so like homes that built down people have actually died from the batteries being
    0:50:52 Catching on fire in someone’s apartment and burning the fucking apartment down and they’re getting sued and one of the lawyers suing them
    0:50:55 He’s got this quote where he’s like dude
    0:51:01 Six years ago these guys were busboys serving dishes or cleaning dishes in restaurants now
    0:51:04 They’re running a publicly traded company. They know nothing about building bikes
    0:51:07 They’re just like getting the cheapest shit they can over here
    0:51:09 And it’s all true by the way, they’re insane
    0:51:16 But I cannot believe they went public and in like the the one of the I went and watched listen to this
    0:51:18 I I listened to one of their um
    0:51:23 Their reports or like when they were in like investors were interviewing the CEO andy
    0:51:27 Before he took it public and they’re like, why’d you go decide to take a public?
    0:51:30 And he says he was inspired by the movie wolf of wall street
    0:51:33 And that’s why he decided to take the company public
    0:51:37 He says that in a youtube video in a youtube video
    0:51:40 He says I decided to take it public because I saw a wolf of wall street
    0:51:45 And so now because of all these lawsuits and because the company is just like shit
    0:51:48 Like it’s just like they’re breaking so many laws. They just do whatever the hell they want
    0:51:51 The the stock has crashed
    0:51:55 We need to spin off pod of just absolutely
    0:52:01 Renegade chinese founders just doing doing things that that we would never dream of
    0:52:09 I would subscribe to that instantaneously. Well, he’s like the real-life zhinyang from uh silicon valley
    0:52:11 Where like he just sit there smoking cigars
    0:52:14 Sigs and like someone’s like, you know, you really need safety stuff and he’s like
    0:52:17 Who says like, you know what I mean? Like he did
    0:52:23 It’s like the deep-seek founder right right now the deep-seek founder is gonna get a lot of pub for this same reason
    0:52:29 It’s just this guy’s hilarious and but it is actually inspiring that like all these dorks are like we got to do this
    0:52:32 We got to do we got to like have this process. We got to do this and meanwhile
    0:52:35 We brought this guy nick moving to china and not knowing shit and then we got this chinese guy
    0:52:38 moving to new york and not knowing shit and like
    0:52:43 It’s and it still works and his company by the way. He started a
    0:52:47 Electric bike company. It does 30 million in revenue and three million in income. So like it’s
    0:52:55 Not nothing. Yeah, so and he started with 12 000. Uh, it’s pretty badass though. It besides the fact that it’s um
    0:52:59 You know like incredibly illegal and dangerous and people are dying
    0:53:05 But besides that it’s like kind of an interesting story because I’ve always wondered where these freaking bikes have come from
    0:53:10 I got to give a shout-out to kerb. They have um an article and it’s called the moped king
    0:53:14 How an x delivery worker upended the streets of new york city for better and for worse
    0:53:21 Um, and it’s called fly e-bike e-bike. Yeah. Yeah. Yeah, I just leave the spin off somebody needs to make the uh
    0:53:23 Not not the right way to do it, but still impressive
    0:53:26 version of much
    0:53:28 Uh, yeah, we went from like
    0:53:36 We went from like um inspiring who cares about money. We’re gonna like make travel better to um
    0:53:42 Hardcore shark does whatever it takes, but still ethical, but like I’m gonna get rich and just conquer the world
    0:53:45 to uh
    0:53:47 Fuck everyone get paid
    0:53:52 The full journey exactly. Yeah. All right. That’s it. That’s the pot. Is that it? That’s it. That’s the pot. All right
    0:53:56 I feel like I can rule the world. I know I could be what I want to
    0:54:02 I put my all in it like no days on for the road. Let’s travel never looking back
    0:54:10 Hey, shon here a quick break to tell you an ev william stories
    0:54:14 He started twitter and before that he sold a company to google for a hundred million dollars and somebody asked him
    0:54:19 They said ev what’s the secret man? How do you create these huge businesses billion-dollar businesses?
    0:54:22 And he says well, I think the answer is that you take a human desire
    0:54:24 Preferably one that’s been around for thousands of years
    0:54:28 And then you just use modern technology to take out steps
    0:54:32 Just remove the friction that exists between people getting what they want
    0:54:37 And that is what my partner mercury does they took one of the most basic needs any entrepreneur has managing your money
    0:54:39 And being able to do your financial operations
    0:54:43 So they’ve removed all the friction that has existed for decades no more clunky interfaces
    0:54:46 No more 10 tabs to get something done
    0:54:49 No more having to drive to a bank get out of your car just to send a wire transfer
    0:54:53 They made it fast. They made it easy. You can actually just get back to running your business
    0:54:56 You don’t have to worry about the rest of it. I use it for not one not two
    0:55:01 But six of my companies right now and it’s used by also 200 000 other ambitious founders
    0:55:07 So if you want to be like me head to mercury.com open them account in minutes and remember mercury is a financial technology company
    0:55:14 Not a bank banking services provided by choice financial group and involve bank and trust members fdic. All right back to the episode
    0:55:16 (upbeat music)

    Episode 675: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) tell the three stories of founders with insanely high agency. 

    Show Notes: 

    (0:00) Boom Supersonic

    (30:50) Nick Mowbray, #1 most impressive founder

    (43:43) The Moped King

    Links:

    • Boom Supersonic – https://boomsupersonic.com/ 

    • ZURU – http://zurutoys.com/ 

    • Fly E-Bike – https://www.flyebike.com/ 

    Check Out Shaan’s Stuff:

    Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it’s called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd

    Check Out Sam’s Stuff:

    • Hampton – https://www.joinhampton.com/

    • Ideation Bootcamp – https://www.ideationbootcamp.co/

    • Copy That – https://copythat.com

    • Hampton Wealth Survey – https://joinhampton.com/wealth

    • Sam’s List – http://samslist.co/

    My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

  • The ‘Crypto Wizard’ vs. Nigeria

    The trip that changed Tigran Gambaryan’s life forever was supposed to be short — just a few days. When he flew to Nigeria in February of 2024, he didn’t even check a bag. Tigran is a former IRS Special Agent. He made his name investigating high-profile dark web and cryptocurrency cases. Some colleagues called him the ‘Crypto Wizard’ because of his pioneering work tracing crypto transactions for law enforcement. Since 2021, he’s worked at the world’s largest crypto exchange, Binance.

    Tigran was in Nigeria as a sort of envoy. He was supposed to meet with government officials and show them that Binance – and crypto itself – was safe, reliable, and law-abiding.

    One of the most important meetings was at the headquarters of the Office of the National Security Advisor. He says officials there made him wait hours. And when officials finally came into the room, they accused Binance of a host of crimes and of tanking the Nigerian economy. They then told Tigran that they weren’t going to let him leave Nigeria until they were satisfied that Binance was going to remedy the situation.

    On today’s show, in a collaboration with Click Here from Recorded Future News, we hear about Tigran’s eight month detention in Nigeria. In his first recorded interview after his release, he shares details about his captivity, how he survived one of Nigeria’s most infamous prisons, and how he got out.

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  • The Critical Technology in Finding Critical Materials

    AI transcript
    0:00:03 There is a phrase that’s common in the mining world,
    0:00:05 which goes something like,
    0:00:08 if you can’t grow it, you must mine it.
    0:00:10 That’s so central to understand
    0:00:12 that so much of our physical world around us,
    0:00:16 our homes, our cars, everything on our table tops,
    0:00:19 these things require materials that must be mined
    0:00:21 from the earth’s surface and below.
    0:00:25 That is Connie Chan, a 16Z general partner.
    0:00:28 Connie has led investments into all kinds of companies,
    0:00:30 from live shopping to religious super apps,
    0:00:32 to AI leasing agents.
    0:00:35 But one of Connie’s most important bets
    0:00:37 isn’t your standard technology story.
    0:00:39 It’s in a mineral exploration company,
    0:00:41 one that uses artificial intelligence,
    0:00:43 but also human intelligence
    0:00:48 to find critical materials across five continents.
    0:00:49 Now, for many of you,
    0:00:51 this is a topic that is just starting to bubble up,
    0:00:54 because if you want to build the future,
    0:00:55 so many of the new technologies
    0:00:57 that we’re talking about and dreaming about
    0:00:59 are going to require more metals.
    0:01:03 One very clear example is for electric vehicles.
    0:01:05 EVs require massive batteries,
    0:01:07 and those batteries need more copper,
    0:01:09 more lithium, more nickel.
    0:01:12 And there’s very clearly a big supply gap
    0:01:14 that’s coming in a couple of decades.
    0:01:16 EV cars right now globally account
    0:01:18 for already 14% of car sales.
    0:01:22 In China, it was the majority of car sales in 2024.
    0:01:25 And these EVs require four X amount of copper
    0:01:26 as a normal gas vehicle.
    0:01:29 So if we want to power the spring revolution,
    0:01:32 we definitely need more mining.
    0:01:34 And it’s not just electric vehicles.
    0:01:36 If you think about data centers,
    0:01:38 BHP estimates by 2050,
    0:01:40 we’re going to have 6% to 7% of the world’s copper
    0:01:43 going directly to data centers.
    0:01:45 Now, you might say, well, 2050 sounds so far away,
    0:01:48 but the reality is it can take years to find the mine,
    0:01:50 years to find the deposit,
    0:01:52 years to then estimate the size of the deposit,
    0:01:54 figure out how to extract it best,
    0:01:56 then years to build out the mine,
    0:02:00 and then decades to actually extract all of that metal.
    0:02:04 Which means if we need more metal in one, two, three decades,
    0:02:06 we have to find that metal today.
    0:02:09 So as this topic moves to the forefront
    0:02:12 of not only the technology conversation,
    0:02:14 but the national conversation,
    0:02:17 we brought in three guests from all over the COBOL team.
    0:02:20 – Tom Hunt, VP of Technology at COBOL.
    0:02:22 My whole career has been at the junction
    0:02:25 of technology and climate change.
    0:02:27 – My name is MfK Makai.
    0:02:30 I am Zambian and I’m a trained mining and civil engineer.
    0:02:34 I’ve been in the industry for a little over 16 years.
    0:02:38 – Hi, George Gilchrist, VP of Geosciences for COBOLT,
    0:02:40 geologist here in Johannesburg, South Africa.
    0:02:44 – Tom, MfK and George have worked in a combination of projects
    0:02:48 from solar printing to working in existing mines.
    0:02:49 And in today’s episode,
    0:02:53 we explore what makes these metals truly irreplaceable,
    0:02:55 the current process for discovery,
    0:02:58 how technology and data are changing the game,
    0:03:00 plus how to decide when to drill, when?
    0:03:04 – The single drill hole can cost up to a million dollars.
    0:03:07 – So without further ado, let’s get started.
    0:03:11 As a reminder, the content here
    0:03:13 is for informational purposes only.
    0:03:15 Should not be taken as legal, business, tax,
    0:03:16 or investment advice,
    0:03:19 or be used to evaluate any investment or security
    0:03:20 and is not directed at any investors
    0:03:23 or potential investors in any A16Z fund.
    0:03:26 Please note that A16Z and its affiliates
    0:03:27 may also maintain investments
    0:03:29 in the companies discussed in this podcast.
    0:03:32 For more details, including a link to our investments,
    0:03:35 please see a16z.com/disclosures.
    0:03:42 – To kick off, I’d like to talk about
    0:03:44 why are these metals so critical?
    0:03:46 – For the energy transition,
    0:03:49 we will need to build about two billion electric vehicles,
    0:03:51 which means that we actually have to discover
    0:03:53 about a thousand new mines
    0:03:56 in order to provide the lithium, nickel, copper, and cobalt
    0:03:58 that’s going to go into those vehicles.
    0:04:00 After we’ve built those vehicles,
    0:04:02 we can recycle the batteries,
    0:04:04 but we need to put the batteries into those cars
    0:04:06 to begin with.
    0:04:10 So there is no substitute for copper or lithium.
    0:04:14 Copper is the second most conductive metal after silver,
    0:04:17 and unless we find an enormous pile of silver,
    0:04:20 we’re gonna be using copper long into the future.
    0:04:23 Lithium is both the lightest element
    0:04:25 and the most electronegative element,
    0:04:28 and having worked on next-generation batteries,
    0:04:30 there’s really no substitute for the energy density
    0:04:32 you can get at lithium.
    0:04:35 So these metals are irreplaceable
    0:04:37 in the global supply chain for the energy transition,
    0:04:41 and they’re also critical for the buildout of solar,
    0:04:42 of the utility-scale batteries
    0:04:45 of all kinds of new power generation,
    0:04:47 as well as the next-generation data centers.
    0:04:49 So there’s no shortage of demand
    0:04:52 when it comes to these critical metals.
    0:04:55 – What is the main problem that we’re trying to solve though?
    0:04:56 Is there a shortage of supply?
    0:04:58 Are these metals rare?
    0:05:00 Are they very difficult to find?
    0:05:03 – There’s plenty of metal in the Earth’s crust,
    0:05:07 and the question is how do we find places
    0:05:10 where the history of the Earth has concentrated
    0:05:11 these metal deposits to the point
    0:05:15 where we can extract them both cost-effectively
    0:05:17 and with minimal environmental impact?
    0:05:20 So the more concentrated a metal deposit is,
    0:05:22 the less rock you have to process
    0:05:25 in order to extract a certain amount of metal.
    0:05:29 – Can you talk about how these metals are not that rare?
    0:05:31 Does that mean we can find them in our backyard?
    0:05:35 Should we be searching for it locally, domestically?
    0:05:39 Like, where do these metal deposits usually live?
    0:05:41 – Yeah, I think my kids and my neighbors dog-serve me,
    0:05:43 try to look them over at the yard for metals,
    0:05:46 but they don’t concentrate in very many places.
    0:05:49 There’s a very unique combination of factors
    0:05:53 that are required to concentrate any of the metals
    0:05:56 into a small, mineable target,
    0:05:59 and these will vary depending on what you’re looking for.
    0:06:01 So one of the tricks in exploration
    0:06:04 is to really become super familiar
    0:06:06 with the deposit style that you’re targeting,
    0:06:09 to understand where these controls are coming together.
    0:06:11 So if you’re looking for copper,
    0:06:13 it might be in a very different place
    0:06:15 to where you might be looking for lithium.
    0:06:18 The techniques that you use are different.
    0:06:19 Even within copper,
    0:06:21 there will be different types of deposits
    0:06:22 that will have very different characteristics
    0:06:25 and require different tools, different approaches.
    0:06:27 You have to be really flexible.
    0:06:31 There’s no formulated approach to making a discovery.
    0:06:32 And the question now is,
    0:06:36 can we do it faster or more effectively than we have been?
    0:06:39 Can we use more of the data that we have at our disposal
    0:06:41 to guide those decisions
    0:06:43 and to really help identify
    0:06:45 where the most prospective areas are?
    0:06:49 And Cobble goes around the world to find the best rocks.
    0:06:51 We go to the high arctic,
    0:06:53 if that’s where the geological conditions
    0:06:55 for our formation were best,
    0:06:57 and the Central African Copper Belt
    0:07:00 is one of the best copper locations in the entire world,
    0:07:02 which is why we’re there.
    0:07:03 – So give me a sense.
    0:07:06 What does it mean to be high grade or low grade?
    0:07:09 – Copper is quite a stark example.
    0:07:12 Many of the big deposits that are mined
    0:07:14 for copper or called porphyry copper deposits,
    0:07:18 particularly in South America, Indonesia, and other places,
    0:07:23 they will be by mass, half a percent, 0.6% copper.
    0:07:25 In the copper belts,
    0:07:28 the average deposits will be about two to 3% copper,
    0:07:30 and the really high grade deposits,
    0:07:33 such as the one that we’ve discovered at Mngomba,
    0:07:36 will be a 5% to 6% copper.
    0:07:38 So an order of magnitude higher in grade,
    0:07:42 and that has significant advantages, obviously, economically,
    0:07:43 but also environmentally,
    0:07:46 you can mine a much smaller footprint
    0:07:48 and produce a large volume of copper,
    0:07:51 so it’s a very attractive target.
    0:07:55 – So you’re saying if someone was extracting a ton of rock
    0:07:58 that potentially less than 1% of that ton
    0:07:59 would be usable copper?
    0:08:01 – Yes, you’re always going to lose a little bit
    0:08:04 in the mining and a little bit in the processing,
    0:08:07 and so you end up getting less than 1% of that mass
    0:08:10 that you’ve mined is your actual product.
    0:08:11 – Right, George, a lot of mines today
    0:08:15 are being expanded as opposed to new mines being discovered.
    0:08:16 Why is that?
    0:08:19 – It’s really hard to find new mines.
    0:08:22 So 40, 50, 60 years ago,
    0:08:25 a lot of the earth hadn’t been tested.
    0:08:27 A lot of the deposits were at surface.
    0:08:30 They might have had an expression at surface.
    0:08:32 – Meaning I can see it with my eyes on the ground
    0:08:34 or just digging with the shovel?
    0:08:36 – Yes, so copper, if it gets to surface,
    0:08:39 it will oxidize and form minerals that look green
    0:08:40 or look blue.
    0:08:42 And so in the copper belt, for instance,
    0:08:45 there would be hills that had green staining on them.
    0:08:49 So it wasn’t difficult to discover those deposits,
    0:08:51 but those have largely been found.
    0:08:54 I haven’t stumbled into a green hill
    0:08:56 that’s just waiting to be mined.
    0:08:59 So now we’ve got to start looking underneath the surface
    0:09:01 and we need a lot more tools, a lot more data.
    0:09:05 And so it’s easier to expand your existing mine
    0:09:08 than spend the money to try and make a discovery elsewhere.
    0:09:12 – So who usually discovers these deposits to date?
    0:09:16 – A lot of the discoveries are made by smaller companies
    0:09:19 that are really focused on just making discoveries.
    0:09:21 They’re willing to take more risks.
    0:09:23 They are willing to test new technology.
    0:09:26 They’re willing to look deeper or undercover
    0:09:28 or in areas that people might have turned away
    0:09:31 or had the incorrect geology approach
    0:09:32 to the wrong model.
    0:09:36 So that attitude to exploration that drives success.
    0:09:38 – And maybe before we jump into
    0:09:40 how technology is changing mining,
    0:09:42 maybe can you share with us
    0:09:45 how has mining exploration changed over the decades?
    0:09:47 One of the stories I’ve always loved in mining
    0:09:50 was there was a story of how someone made
    0:09:52 a huge gold deposit discovery
    0:09:55 based off the cover of a National Geographic Magazine.
    0:09:56 Because just like you said,
    0:09:58 it was being expressed on the surface.
    0:10:02 So one, two decades ago, what did exploration look like then?
    0:10:05 And then we can contrast that to today.
    0:10:07 A lot of the work we’re doing today
    0:10:09 is still the same groundwork.
    0:10:10 We’re still moving onto ground.
    0:10:13 We’re still mapping the geology,
    0:10:15 taking samples of the soil.
    0:10:17 That’s been done for a long time.
    0:10:18 It’s very effective.
    0:10:21 What has changed is the level of technology
    0:10:24 in terms of the geophysical methods that are available.
    0:10:28 So the ability to measure the properties of the rock.
    0:10:29 Is it magnetic?
    0:10:32 Is one rock denser than another rock?
    0:10:34 We can measure that through what we call gravity measurements.
    0:10:36 We can use seismic surveys
    0:10:41 to try and identify the shapes of deposits deep underground.
    0:10:43 And those technologies have advanced dramatically.
    0:10:45 They’ve become airborne.
    0:10:46 So we can fly over deposits.
    0:10:49 So we don’t need to build roads and bridges.
    0:10:51 We can now access the ground easy.
    0:10:53 They’ve become better in their resolution,
    0:10:55 the quality of the data.
    0:10:59 But with that has come significant volumes of data.
    0:11:01 And so the ability to process
    0:11:05 and extract the value out of that data is not a challenge.
    0:11:09 – Tom, tell us how is AI being used for exploration
    0:11:12 in a way that wasn’t true a decade ago?
    0:11:15 – Yeah, I think the types of data that George described
    0:11:17 are what we need to squeeze
    0:11:19 in order to have the insights
    0:11:22 that guide our exploration programs.
    0:11:24 So we might start at the continent scale,
    0:11:27 would say satellite data across all of Australia.
    0:11:30 And we want to be able to find particular rock types
    0:11:32 that might be indicative at the surface
    0:11:34 of deposits that are very deeper.
    0:11:37 So we can use image recognition
    0:11:40 and classification algorithms developed
    0:11:41 for other applications.
    0:11:45 We can adapt those to then apply to our specific instance
    0:11:47 of adding to insights that might lead
    0:11:50 to finding these underground deposits.
    0:11:53 But just one data source is not nearly enough.
    0:11:56 We would also take magnetic data
    0:11:57 across the entire continent.
    0:12:00 We will take whatever kinds of geochemical data,
    0:12:03 government data sets such as geologists
    0:12:04 who’ve walked around, picked up a rock
    0:12:07 and sent that rock in for chemical analysis.
    0:12:10 They’re databases that are available
    0:12:12 but extremely poorly structured
    0:12:14 with multiple analysis methods
    0:12:17 that we need to be able to clean up
    0:12:19 so that we can actually feed it into our algorithms
    0:12:22 and serve it up to our geoscientists.
    0:12:25 We also take geostructural data.
    0:12:27 So what is the topography?
    0:12:29 Where are the mountains and what slopes
    0:12:31 are the different rock layers coming in?
    0:12:33 We can take all that data and use that
    0:12:37 to inform the local interpretation of the geology.
    0:12:41 So that’s going from the continent scale
    0:12:43 to what we call the camp scale
    0:12:46 or roughly 10 kilometers by 10 kilometers
    0:12:49 as we narrow down the search for one of these deposits.
    0:12:52 And then we need a whole different set of algorithms
    0:12:54 in order to go from that 10 kilometer scale
    0:12:57 to where do we actually drill
    0:12:59 to try to answer the question of what’s underground?
    0:13:03 You really can’t tell what’s underground until you drill.
    0:13:06 And the single drill hole can cost up to a million dollars.
    0:13:09 So we want to be able to optimize
    0:13:10 where we place that drill hole.
    0:13:13 So those are some of the types of algorithms
    0:13:17 that we’ve built to give superpowers to our geologists.
    0:13:19 – Quality of the data is so important.
    0:13:21 And you mentioned a lot of the data
    0:13:24 that Cobalt uses previously was very unstructured.
    0:13:28 Give us a taste of just how difficult it was
    0:13:31 to digitize the data to begin with
    0:13:34 and then put it in a usable format.
    0:13:38 – So very talented geologists have been walking
    0:13:41 the surface of the earth for over a hundred years
    0:13:43 and collecting sometimes handwritten records,
    0:13:47 sometimes physical samples that are analyzed,
    0:13:51 sometimes hand drawn maps, sometimes digital maps.
    0:13:55 So they’re all this very diverse set of unstructured data.
    0:13:58 And these are then sometimes in government databases,
    0:14:01 either paper or digital archives,
    0:14:04 or they are with potential joint venture partners
    0:14:06 who might have huge pile of papers
    0:14:09 that go to date back 50 or 100 years.
    0:14:12 And there’s an incredible wealth of information there,
    0:14:14 but information that hasn’t been exploited
    0:14:16 ’cause it’s been locked up in these paper records.
    0:14:18 So yeah, we’ve had teams out scanning
    0:14:20 some of these paper records.
    0:14:23 The job isn’t done once you have the records scanned.
    0:14:26 We really want to get as much information
    0:14:28 out of this raw data as we can.
    0:14:31 And so for example, in Finland,
    0:14:33 there’s a wealth of historic data, but it’s in Finnish.
    0:14:37 And so we need to be able to both translate that.
    0:14:40 And many of the words that people use to describe rocks
    0:14:41 are highly specialized.
    0:14:43 And so we need special translation modules
    0:14:45 to make sure that those come through correctly
    0:14:49 and then extract the structured data
    0:14:50 out of these unstructured reports.
    0:14:53 So for example, somebody may describe a certain rock type
    0:14:55 and a certain grain size on that rock
    0:14:57 and a certain chemistry of that rock.
    0:14:59 And we would like to take that into a table
    0:15:01 that our algorithms can then use.
    0:15:03 – So it’s fascinating to me
    0:15:05 that we basically have all these geoscientists
    0:15:09 that then pair with these data scientists.
    0:15:11 How do these teams work together
    0:15:14 given that they’re coming from such different backgrounds?
    0:15:15 – We’re actually fortunate
    0:15:17 that we live in the world of technology
    0:15:19 and somebody sitting in Silicon Valley
    0:15:23 is working with someone sitting here in Central Africa
    0:15:24 or in Australia.
    0:15:27 And we’ve been very deliberate on making sure
    0:15:29 that our data science software engineering
    0:15:32 and our geoscience teams are highly collaborative
    0:15:36 and are paired in being able to extract the rock
    0:15:39 that we have drilled, upload it into the cloud
    0:15:41 and anyone around the world within Cobalt
    0:15:45 can look with the same precision at the same information
    0:15:48 as if they were standing in front on the ground
    0:15:50 here in South Central Africa.
    0:15:53 We also have the abilities to actually look at the images
    0:15:55 through all different models that we’re building
    0:15:57 to analyze our core.
    0:15:59 And that’s something that we build within the company
    0:16:02 through the tech stack that we have.
    0:16:04 – Even just the languages, the words,
    0:16:07 how do you teach each other about your craft?
    0:16:12 If you are a scientist, a biochemist, a geologist,
    0:16:13 you’re bringing together people
    0:16:15 of all different backgrounds.
    0:16:17 How do you get everyone on the same page?
    0:16:18 – One of the things we kind of do
    0:16:20 with your first drawing, Cobalt,
    0:16:23 we have some sort of nomenclature session
    0:16:27 of what the basics mean in terms of geology and rocks,
    0:16:30 in terms of also the tech and marrying those together,
    0:16:33 like creating our own sort of internal glossary
    0:16:35 that is easy for people from backgrounds
    0:16:36 who have never been on a mine
    0:16:38 or never been around exploration
    0:16:41 and vice versa, people who have never understood
    0:16:44 the different AI models that are used in the US.
    0:16:46 And that starts quite early
    0:16:48 when you’re inducting a new team member.
    0:16:51 If you talk about some of the local staff we have
    0:16:53 down to community members,
    0:16:55 we’ve had indigenous words for copper,
    0:16:56 like the word mukuba,
    0:16:58 and we help teach our North America
    0:16:59 and our South African colleagues,
    0:17:02 like this word mukuba is copper in our local language.
    0:17:04 It’s literally one language in a way.
    0:17:07 We also have a Zambian data scientist on the team
    0:17:09 and they come and spend many, many weeks on site.
    0:17:11 They get to go to a drill rig.
    0:17:13 They get to suggest many new ideas
    0:17:15 with drilling companies, drilling contractors
    0:17:17 who’ve kind of worked in a certain way
    0:17:18 over industry for many, many years.
    0:17:19 So we look at the rig and say,
    0:17:23 how can we be more efficient in extracting the score
    0:17:25 and collecting information
    0:17:29 in a manner that reduces the time to process the information?
    0:17:31 So we’ve been fortunate to work with contractors
    0:17:34 that allow us to trial our hardware
    0:17:37 that Tom and his team build, ship it out to Zambia,
    0:17:39 put it next to a driller,
    0:17:42 do some basic training on how do we capture
    0:17:44 imaging of the core, like 360 imaging of the core
    0:17:46 as it’s coming out of the ground,
    0:17:48 which is really revolutionary.
    0:17:51 Whereas the standard was you have to wait a couple of days,
    0:17:54 take an image, take it right, stitch it together.
    0:17:56 And a data scientist working with a geoscientist
    0:17:59 is also getting training from a geoscientist
    0:18:02 on just the type of lithology that they see in the rock,
    0:18:03 what we’re looking for
    0:18:06 and how do we make better interpretations
    0:18:09 and better predictions in a meaningful way
    0:18:11 through either a mixture of AI
    0:18:12 and HI through all the experience
    0:18:14 of the brilliant geoscientists
    0:18:16 who’ve worked in industry for many, many years.
    0:18:18 – That’s a great point how Cobalt is innovating
    0:18:21 on both the hardware and the software front.
    0:18:24 – That’s right, to feed the algorithms
    0:18:27 and the geoscientists who are going to make
    0:18:31 the next generation of discoveries of these deposits,
    0:18:34 we need to have as many data types as we can
    0:18:38 and put all of that data in front of them.
    0:18:40 So we found some opportunities
    0:18:42 where we could push the state of the art
    0:18:45 to be able to collect more flavors of data more easily.
    0:18:49 And so one of those is with hyperspectral imaging.
    0:18:52 And hyperspectral just means many, many different colors
    0:18:55 all the way from the visible through the infrared.
    0:18:58 And what’s really exciting about hyperspectral imagery
    0:18:59 is that in the infrared,
    0:19:03 you can actually measure the absorption
    0:19:05 of different molecules.
    0:19:09 And so you can actually read out chemistry using light.
    0:19:13 And we implemented that method.
    0:19:15 So hyperspectral imagery on an airborne system
    0:19:19 so that we can fly over the Cobalt claims,
    0:19:22 take terabytes of data with a light aircraft
    0:19:24 and then come back and process that data
    0:19:27 with very high spatial and spectral resolution,
    0:19:31 add together the ground truth that geologists
    0:19:33 who’ve walked the ground and picked up rock samples
    0:19:36 and then scan those with infrared spectroscopy
    0:19:38 combined with this airborne imaging
    0:19:41 to really build a system that hasn’t been built
    0:19:43 in mineral exploration before
    0:19:45 to be able to survey thousands of square kilometers
    0:19:48 and automatically interpret exactly
    0:19:49 what rocks are on the ground.
    0:19:52 So compared to a hand drawn geologic map
    0:19:54 that has a rich history,
    0:19:57 we can actually make a data driven geologic map.
    0:19:58 – That’s great.
    0:19:59 And George, given that you’ve worked
    0:20:03 at more traditional exploration of mining companies before,
    0:20:06 maybe share some examples of how this technology
    0:20:08 or the data has surprised you.
    0:20:10 – Yeah, there’ve been a number of examples.
    0:20:13 Some of the countries have big data sets
    0:20:15 and these are data sets that have been accumulated
    0:20:17 over the years from explorers
    0:20:18 and different parts of the country.
    0:20:21 Each of those explorers was looking for something specific.
    0:20:23 And so they didn’t assay necessarily for everything.
    0:20:26 They didn’t measure every element in every sample.
    0:20:29 They were just targeting a few.
    0:20:32 And now we might be looking for a very specific element
    0:20:34 and it’s only available in a small portion
    0:20:35 of that data set.
    0:20:37 Normally you think, oh, it’s such a pity
    0:20:39 that we have this huge data set
    0:20:41 but we can only use a small part of it.
    0:20:44 Whereas the data scientists will say, well, that’s okay.
    0:20:46 That element that we’re interested in
    0:20:49 has relationships to other elements.
    0:20:53 And we can have a really good idea
    0:20:55 of what the grade of that element would be
    0:20:56 given the grade of all the other elements
    0:20:59 that we know at each of the sample points.
    0:21:01 So we can test it by taking out the examples
    0:21:03 where we do have that grade.
    0:21:05 We can then estimate what that grade would be
    0:21:06 and we compare it.
    0:21:08 And that’s remarkably close
    0:21:09 because we’re not just comparing it
    0:21:11 to one element or two elements.
    0:21:14 We’ll be looking at relationships from numerous elements.
    0:21:16 And so suddenly there’s huge data sets
    0:21:18 that look like at a trunk.
    0:21:20 We’re actually able to use the full value
    0:21:22 and the spread of that data.
    0:21:24 And that allows us to move into areas
    0:21:25 where other people wouldn’t be.
    0:21:26 That’s one way.
    0:21:30 Another way is Tom spoke about data that you digitize, maps.
    0:21:33 If I scan a map, I can look at it on a computer screen,
    0:21:36 but I have to look at it really carefully.
    0:21:38 Now I can just look for a search term
    0:21:42 and the 12 maps that have that term will pop up
    0:21:44 and it’ll show me where that term is on the map,
    0:21:45 straight away.
    0:21:46 And then I’m able to interrogate,
    0:21:49 maybe it’s the name of the drill hole
    0:21:52 or it’s a certain element that I’m looking for
    0:21:55 in a report on a series of maps.
    0:21:57 And suddenly all of this information
    0:21:59 is just so much quicker to interrogate.
    0:22:02 I can spend my time applying my geological training
    0:22:05 and my experience rather than spend my time
    0:22:06 just opening and closing things.
    0:22:09 So that’s a huge advantage to be able to advance
    0:22:11 the search for the new discoveries.
    0:22:13 – With all of this data,
    0:22:16 how do you guys know what to prioritize?
    0:22:17 How do you decide what kind of information
    0:22:18 is more important?
    0:22:22 And then how does that guide the actual work on the field?
    0:22:26 – In a world that is rich in data that becomes the challenge
    0:22:29 is how do you know what to actually focus on?
    0:22:32 Some of that will come from our experience
    0:22:34 with working with data from other projects.
    0:22:37 So the geoscientists will be interacting
    0:22:39 with the data scientists saying in this environment,
    0:22:42 we know that these factors are really critical.
    0:22:44 And this is where the collaboration
    0:22:46 between the geoscientists and the data scientists
    0:22:50 becomes so important that it’s not two separate entities.
    0:22:53 It’s very collaborative and specific.
    0:22:56 And so there’s no off-the-shelf option.
    0:22:58 We’re not developing a tool that we consult
    0:23:01 when exploration company that will help them discover
    0:23:02 in any environment.
    0:23:04 Everything we’re doing is tailored
    0:23:06 to the areas that we’re working.
    0:23:09 – And one key aspect is also modeling the uncertainty
    0:23:10 of what’s underground.
    0:23:12 And there’s incredible uncertainty
    0:23:14 of what’s just under the surface.
    0:23:17 And so by being able to map out the regions
    0:23:20 of high uncertainty or low uncertainty,
    0:23:22 that can also allow us to optimize
    0:23:24 where we collect the next data point.
    0:23:27 And so we can start with these publicly available
    0:23:30 or joint venture style data sets.
    0:23:32 But ultimately we have to go to the field
    0:23:34 and collect our own data.
    0:23:36 – And I guess that uncertainty also brings us
    0:23:38 to the question of with all of this technology,
    0:23:42 how has it improved our accuracy when we do drill?
    0:23:45 Is there a reduction that you see already
    0:23:47 in the number of drills it’s taking
    0:23:49 for us to get more information?
    0:23:52 – So we want to basically quantify uncertainty
    0:23:55 and by being able to drill in an area
    0:23:59 where we’ll maximize the amount of information we get
    0:24:01 that will inform both the geoscience
    0:24:04 and data science team of what is happening
    0:24:07 within the underlying rocks is very different
    0:24:11 than I’ll say the traditional way of from top’s point,
    0:24:14 you have a camp of a 10 by 10 square kilometer
    0:24:17 and you place a grid drill and hope for the best.
    0:24:21 We are very targeted on the areas
    0:24:24 that both the geoscience and data science team
    0:24:26 want to drill a hole to the angle the hole
    0:24:29 should be drilled at to what we’ll call the pierce point
    0:24:33 of where we think the resource may lie.
    0:24:36 And within a 24 month period,
    0:24:39 the level of precision has encouraged a lot of confidence
    0:24:42 particularly on our project in Zambia,
    0:24:43 we’ve moved up to 10 rigs.
    0:24:46 And obviously with each rig,
    0:24:48 we know there’s a level of uncertainty attached to it
    0:24:51 but we want to quantify that as much as possible.
    0:24:54 – So as you talk about drilling for information then,
    0:24:56 does that mean sometimes we’re drilling
    0:24:58 not just to look for the resource
    0:25:00 but we’re drilling whatever hole will give us
    0:25:03 the most information that will either confirm
    0:25:06 or deny a bunch of hypotheses?
    0:25:10 – Yes, and we want to basically falsify the hypotheses quickly
    0:25:12 because you could be drilling into perpetuity
    0:25:15 but to somebody somewhere that’s a cost.
    0:25:19 So even if you drill and you falsify a particular hypothesis,
    0:25:21 you do not find what you’re looking for
    0:25:24 or you affirm what you think was happening in the system,
    0:25:26 that is a lot of information
    0:25:29 that normally in traditional mining exploration,
    0:25:32 someone’s like, oh, we didn’t find what we’re looking for
    0:25:35 but we learned something from that particular hole.
    0:25:38 And that learning takes us back to saying,
    0:25:41 all right, scratch off hypothesis number seven,
    0:25:43 let’s come up with a new hypothesis.
    0:25:46 It’s basically data-driven decision-making.
    0:25:50 – Yeah, a lot of the decisions around how to explore
    0:25:53 would be driven by what is the sort of precedent
    0:25:54 in this environment?
    0:25:56 What grid size would we be drilling?
    0:25:58 What did the neighbors draw?
    0:26:01 What’s been the traditional drill spacing?
    0:26:04 Some of it might be driven by perceived regulatory requirements
    0:26:06 to reach a certain density of data.
    0:26:09 The interesting thing is that as geologists,
    0:26:13 we know that we have a limited understanding
    0:26:14 of what’s deep underground.
    0:26:16 And when you talk to one of the famous things
    0:26:19 about geologists is that they get a room of geologists
    0:26:23 together, you’ll get n plus one number of opinions.
    0:26:24 You know, that’s where the joke is,
    0:26:26 there’s four geologists, there’s five opinions.
    0:26:28 And inherently we know that,
    0:26:31 but that isn’t built into the decision-making process.
    0:26:34 We aren’t building models that account
    0:26:36 for all the different possibilities
    0:26:37 that we know could exist.
    0:26:41 And so typically we will anchor on one model
    0:26:44 and our drilling and our sampling and our exploration
    0:26:47 will focus on that model and we’ll keep testing that model
    0:26:49 until either eventually we realize it’s not working
    0:26:52 or we hope it’s going to hold.
    0:26:55 Whereas at Cobalt, we are happy to hold multiple models
    0:26:57 at the same time.
    0:27:00 And we will test each of those models simultaneously
    0:27:02 ’cause you’re testing the same space.
    0:27:05 But we design the drill holes to maximize
    0:27:08 how many of these models can we effectively test
    0:27:10 with each of these holes that we’re drilling?
    0:27:13 Where are the specific areas that are gonna solve
    0:27:14 some of the problems that we need to know
    0:27:18 about which hypothesis is valid and which one isn’t?
    0:27:19 And so actually building that in
    0:27:22 and being able to show the uncertainty
    0:27:24 and how we are reducing that uncertainty
    0:27:27 as we continue to drill is a really key focus
    0:27:30 and it’s something that’s very unusual in the exploration.
    0:27:34 – One example of applying our AI tools
    0:27:37 to lithium exploration is actually in Canada
    0:27:41 where we knew that the right geologic processes
    0:27:43 had happened within this belt of granite.
    0:27:46 We knew that this is the right flavor of granite
    0:27:48 that might host a lithium deposit,
    0:27:52 but we didn’t know where in hundreds of square kilometers
    0:27:57 with no road access where you had to helicopter in a crew
    0:28:01 to go look at the ground where there might be signs
    0:28:02 of lithium.
    0:28:07 And in order to investigate this very large area,
    0:28:10 the traditional way would be to drop off a field team
    0:28:13 and maybe pick them up at the end of the summer
    0:28:16 and they will have walked across as much terrain
    0:28:19 as they could walk across and found whatever lithium
    0:28:21 they could find at the surface.
    0:28:24 That’s not fast enough or cheap enough.
    0:28:26 And so we knew we could do better than that
    0:28:28 standard practice.
    0:28:31 And so what we did was start with satellite data
    0:28:36 and start with reports across a significant fraction of Quebec
    0:28:40 about where lithium had been found previously.
    0:28:43 And then we matched the satellite imagery
    0:28:48 with those records of lithium-bearing rocks
    0:28:52 and we’re then able to predict where in our claims
    0:28:54 there might be lithium-bearing rocks.
    0:28:56 So then we sent our people out in the field
    0:29:00 to either confirm or deny the existence of lithium
    0:29:02 at the places where our machine learning models were
    0:29:05 predicting there might be lithium.
    0:29:09 And they reported back immediately via satellite link
    0:29:13 and said, guys, you just sent us to a vast field of lichen.
    0:29:14 There aren’t even rocks here.
    0:29:16 Everything’s covered in lichen.
    0:29:18 And that’s not what we were looking for.
    0:29:22 But that led us update our models overnight
    0:29:26 so we could rerun models across 1,000 square kilometers
    0:29:29 overnight with this new ground truth of what our people had
    0:29:30 actually found on the ground.
    0:29:32 And so with the addition of both the false positives
    0:29:35 and the true positives, we were able to improve the accuracy
    0:29:38 of our model prediction by over an order of magnitude.
    0:29:40 And then in the following several weeks
    0:29:43 that we had this helicopter supported field program,
    0:29:45 we were actually able to find spodgamine, which
    0:29:48 is the lithium-bearing mineral that we were looking for.
    0:29:50 So that’s an example of compressing
    0:29:54 what might have taken years of expensive remote field work
    0:29:57 into the period of a week or so by being able to iterate
    0:30:00 and update these machine learning models.
    0:30:03 If I compare what is happening in the oil and gas space
    0:30:06 and how they marry traditional methods and technology,
    0:30:10 how does that compare with the mining space?
    0:30:13 Is mining ahead, behind of oil and gas,
    0:30:15 or are there shared learnings?
    0:30:17 The technology used in the mining industry
    0:30:20 is dramatically behind what we’ve
    0:30:22 used to search for oil and gas.
    0:30:26 And part of our job is to take these amazing technologies that
    0:30:29 have been developed to find fossil fuels
    0:30:32 and then adjust them so that perhaps they’re
    0:30:37 smaller and cheaper so they’re applicable to discovering
    0:30:39 metals rather than oil and gas.
    0:30:42 And so examples of that are directional drilling,
    0:30:46 where in oil and gas now you can precisely control
    0:30:49 the trajectory of exactly where you want to drill.
    0:30:52 This is new for the mining industry.
    0:30:55 We need to make this technology smaller
    0:30:59 so that it’s appropriate for the kinds of brakes that we use.
    0:31:01 Another example is geophysical techniques
    0:31:05 that were initially invented for finding oil and gas.
    0:31:09 We can adjust those to whether that’s seismic imaging,
    0:31:12 whether that’s electromagnetic imaging.
    0:31:13 We can adjust those so that they’re
    0:31:16 suitable for finding the kinds of more bodies we’re looking for.
    0:31:19 So really, it’s taking tools that have been developed
    0:31:21 by other industries, such as oil and gas,
    0:31:23 or in the case of these artificial intelligence
    0:31:26 algorithms, tools that have been developed for image
    0:31:28 processing, we can take those and then apply them
    0:31:30 to the kinds of complex problems that we face.
    0:31:34 Oftentimes, we’re talking about copper, nickel, lithium.
    0:31:38 Does the approach work for other metals underground?
    0:31:42 And how do you guys think about the expanse of what
    0:31:45 this technology can be applied towards?
    0:31:48 Yeah, it definitely has applicability
    0:31:49 across any of the search spaces.
    0:31:51 The processes that are being developed
    0:31:54 are solving data problems.
    0:31:58 They are looking for controls on mineralization.
    0:32:00 And you can adjust those as you’re
    0:32:01 looking at a different commodity.
    0:32:04 So what’s controlling a nickel deposit?
    0:32:06 It’s very different to what’s controlling a lithium deposit.
    0:32:11 This is the beauty of not approaching a commercial software
    0:32:13 vendor to try and solve your problems.
    0:32:16 You still have the data science team and the geoscience team
    0:32:18 working so closely together to say,
    0:32:21 these are the problems in this specific environment.
    0:32:24 Even if you’re thinking of nickel exploration,
    0:32:26 nickel deposits, all of them are different.
    0:32:28 Yeah, the technology really is a toolbox
    0:32:31 that we can apply to different exploration programs
    0:32:34 around the world in multiple mineral commodities.
    0:32:36 And so the same exact tool that you’d
    0:32:39 use in Australia to find lithium is probably
    0:32:43 different from the one that you’d use to find copper in the Arctic.
    0:32:45 But they have many parallels.
    0:32:46 And when I started at Cobold, I was
    0:32:48 new to the mineral exploration industry.
    0:32:51 And I was thinking that there’d be an incredibly sophisticated
    0:32:54 way that society finds the materials
    0:32:56 that we use on a day-to-day basis.
    0:32:59 But actually, the tools in mineral exploration
    0:33:01 are shockingly primitive.
    0:33:03 And we can rapidly move beyond the state of the art
    0:33:05 in mineral exploration.
    0:33:09 I think one anecdote about the state of the industry
    0:33:14 is just how much it relies on looking at rocks,
    0:33:17 where a good geologist can look at a rock
    0:33:20 and tell you from the context of what’s around that rock,
    0:33:22 from the grain size, from the color,
    0:33:25 from the mineralogy of that rock,
    0:33:28 can tell you just so much about what
    0:33:31 has happened over the last billion years at that location.
    0:33:34 And yet that’s all done with the human eye.
    0:33:37 And so it’s said that the best geologist is the one that’s
    0:33:38 seen the most rocks.
    0:33:41 Well, what if we make a system that
    0:33:46 can look at more rocks than any human has ever looked at before
    0:33:50 by just analyzing the imagery of the core that we’ve
    0:33:55 drilled at Mingonba, we now have nearly 100 kilometers
    0:33:58 of rock core that we’ve pulled out of the ground?
    0:34:00 That would take years for a person
    0:34:03 to look at every little part of that core.
    0:34:07 And yet this is perfectly suited to machine learning
    0:34:10 algorithms that can look at millions of images
    0:34:11 in the blink of an eye.
    0:34:15 And so being able to take the real knowledge that
    0:34:19 is embedded in hundreds of years of geoscience
    0:34:25 and then apply new emerging tools to help analyze and extract
    0:34:27 real information from those techniques,
    0:34:29 I think it’s just incredibly promising.
    0:34:30 That’s amazing.
    0:34:32 Lots of times when I’m reading in the headlines now,
    0:34:35 I see this phrase, critical minerals.
    0:34:38 Do you guys have a sense of which critical minerals
    0:34:41 are important to national interest, for example?
    0:34:44 Or how would you define what is a critical mineral?
    0:34:46 Yeah, if you look at all the lists of critical minerals,
    0:34:49 almost every element on the periodic table
    0:34:50 is in one of those lists.
    0:34:53 And so to me, what a critical mineral means
    0:34:57 is something that’s important for our economy
    0:35:00 and our defense industrial base.
    0:35:04 And if you look at where our economy is trending,
    0:35:07 our economy is trending towards electrification,
    0:35:09 towards electrification of everything.
    0:35:11 And so the most important critical minerals
    0:35:16 are those that allow us to electrify the economy.
    0:35:18 And the most important materials for electrifying
    0:35:22 the economy really come down to copper and lithium
    0:35:23 being the main ones.
    0:35:25 And then there’s a long tail of other critical minerals
    0:35:27 beyond that.
    0:35:29 Yeah, I think there’s a lot of minerals
    0:35:32 that can perform a similar role, some just more efficiently
    0:35:33 than others.
    0:35:38 And as supply concerns or prices dictates,
    0:35:40 some can be substituted out.
    0:35:43 But some elements are just so fantastic at what they do
    0:35:47 that they are the critical minerals for electrification.
    0:35:51 Copper is just so good at transmitting electricity.
    0:35:54 Lithium is so good in batteries.
    0:35:58 And it’s really hard to see how you can, on a large scale,
    0:36:00 remove such elements.
    0:36:01 Other considerations are elements
    0:36:05 where the supply is concentrated in very small areas.
    0:36:08 Things that on local or shorter timescales
    0:36:10 might become really critical.
    0:36:11 Yeah, tell me more about those.
    0:36:13 Are they concentrated in specific continents?
    0:36:16 Or is it even at a country level?
    0:36:18 Rare earth elements haven’t always
    0:36:22 been top of the exploration ladder in terms of what people
    0:36:24 are really interested in looking for.
    0:36:26 And so deposits will go through phases
    0:36:29 where you will discover deposits, define them,
    0:36:30 and then prices will drop.
    0:36:32 And those deposits are known about.
    0:36:33 But no one’s interested in them.
    0:36:35 And then there’ll be a price shock.
    0:36:36 And they will not jump back into them.
    0:36:39 And so rare earth deposits are widely spread.
    0:36:43 They are well represented in the geological record.
    0:36:45 But they haven’t been widely explored for.
    0:36:47 So lithium is actually a really good example
    0:36:49 that for many, many, many years, people
    0:36:51 were not that interested in lithium.
    0:36:53 It was used in very niche applications.
    0:36:55 And it’s only since batteries have rarely
    0:36:58 become so critical that suddenly people like, wait a minute,
    0:37:01 where’s the historic database on lithium deposits for?
    0:37:04 It’s small because people haven’t been focused on it.
    0:37:06 And I think some of the rare earth elements
    0:37:09 fall into those categories where for a long time they’ve
    0:37:15 been quite niche and now are becoming a lot more important
    0:37:17 to technologies going forward.
    0:37:18 So traditionally, as a geologist,
    0:37:21 when you’re thinking about what to go explore for before it
    0:37:25 might be in terms of market size or financially,
    0:37:27 does it make sense to go explore for this,
    0:37:29 given where the prices are for those metals?
    0:37:31 And now it seems like there’s another driver, which
    0:37:33 could be national interest.
    0:37:36 Are there certain metals that even if the numbers don’t
    0:37:40 pan out today, there might be other incentives
    0:37:43 to build up more deposits for these other metals?
    0:37:44 Yes.
    0:37:46 And I think in the mining industry,
    0:37:52 there hasn’t been a large transition of sustained demand
    0:37:53 for certain elements.
    0:37:56 And that’s happening and has been in progress now
    0:37:58 for a few years and will continue.
    0:38:01 And that’s quite a change from what’s happened historically.
    0:38:03 We’re always going to need iron ore.
    0:38:04 We’re always going to need copper.
    0:38:08 But in the last few years, other elements
    0:38:10 have suddenly become a lot more critical than they’ve
    0:38:12 been in the past.
    0:38:14 And it only looks like the demand is
    0:38:16 going to grow for those elements.
    0:38:19 And there’s a fundamental shift and a lot of companies
    0:38:22 are refocusing efforts into these elements that
    0:38:24 haven’t had a lot of love from the exploration world
    0:38:26 for a long time.
    0:38:29 It’s more complicated than just a supply and demand
    0:38:30 on an open market.
    0:38:33 There is a country competition where some of those elements
    0:38:36 might not be available.
    0:38:40 And so that also becomes a consideration, definitely.
    0:38:40 Yeah.
    0:38:44 And that country competition and the impact on countries,
    0:38:48 what is Zambia’s response to this new cobalt approach?
    0:38:51 And how has it impacted, also, does Africa as a whole?
    0:38:54 It’s been absolutely positive.
    0:38:57 Definitely a breath of fresh air for all of us
    0:38:59 who’ve been in the sector here.
    0:39:02 And generally, as a continent, there
    0:39:05 is this big drive because we have this huge youthful population
    0:39:07 to drive industrialization.
    0:39:09 This is what many African governments want.
    0:39:12 And an investment like cobalt into Zambia
    0:39:16 is stimulating opportunities, knowing that beyond what we do
    0:39:18 in exploration and eventually mining,
    0:39:21 we’re actually driving the development of the youngest
    0:39:24 population on the continent and a lot of other trade
    0:39:27 and business opportunities, even for US enterprises,
    0:39:30 looking to do business in Zambia and in different parts
    0:39:31 of Africa.
    0:39:36 So it’s a seed that basically is getting in the door,
    0:39:38 meeting the right officials, and stating the intention
    0:39:41 very transparently, very clearly,
    0:39:43 and operating within the bounds of the law
    0:39:45 that we know globally.
    0:39:47 And that’s quite exciting for the country,
    0:39:50 but it’s also getting the neighbors waking up and saying,
    0:39:52 wait a minute, if we integrate more,
    0:39:54 there’s this big project in Zambia.
    0:39:55 How do we participate in it?
    0:39:57 How do we use the Lobito Corridor,
    0:40:01 which we know will be a conduit for some of these minerals
    0:40:04 that will help us drive electrifying the world?
    0:40:07 You’re talking about a continent with a billion people,
    0:40:09 or these billion people in coming decades
    0:40:12 who need all sorts of consumer items and electronics,
    0:40:14 and the US gets the firsthand look into that
    0:40:18 by having access into Africa through the Corridor.
    0:40:21 Just along the corridor, the population of the three countries
    0:40:24 combined is close to the US population as a whole.
    0:40:27 And growing, even as we build these mines,
    0:40:30 how we mine, when we mine,
    0:40:31 and how much material we impact,
    0:40:34 and even how we’re able to look at the environment
    0:40:38 through the mining process is going to rapidly transform
    0:40:40 basically on everything that we’re doing
    0:40:41 with the information we have
    0:40:43 and how we can analyze it statistically.
    0:40:45 So mines of the future are also going
    0:40:46 to be fundamentally different.
    0:40:48 I know we talked about exploration,
    0:40:50 but as earlier said by Connie,
    0:40:53 the image of mining has been poor in past decades,
    0:40:56 and something we want to do is change the image of mining,
    0:40:59 and that change will come to how we do a lot more things
    0:41:01 with more prediction, more precision,
    0:41:04 even as we build out many, many projects,
    0:41:06 and that basically becomes institutionalized
    0:41:08 in the industry.
    0:41:09 – I think from my perspective,
    0:41:13 the industry is perceived as quite an established,
    0:41:18 mature industry, and from a geology perspective,
    0:41:19 a lot of the easy deposits of perception
    0:41:22 as they’ve been found, and that’s only going to get harder,
    0:41:25 and it doesn’t sound like a good marketing touch.
    0:41:29 But the reality is actually this time now
    0:41:33 is an amazing time to be in exploration.
    0:41:36 The ability to take new technologies
    0:41:39 and apply them to solve the problems,
    0:41:42 and knowing that you’re absolutely critical
    0:41:45 to ensuring that we can solve the problems
    0:41:47 that we need to solve globally.
    0:41:49 We don’t do our job properly.
    0:41:52 We’re going to slow down the ability to solve problems.
    0:41:55 – Yeah, that sort of focus has been missing,
    0:41:57 I think, from the industry for a long time,
    0:41:59 and it’s awesome to be part of that now.
    0:42:04 – All right, that is all for today.
    0:42:06 If you did make it this far, first of all, thank you.
    0:42:08 We put a lot of thought into each of these episodes,
    0:42:11 whether it’s guests, the calendar tetris,
    0:42:12 the cycles with our amazing editor, Tommy,
    0:42:14 until the music is just right.
    0:42:16 So if you like what we’ve put together,
    0:42:21 consider dropping us a line at ratethespodcast.com/a16c.
    0:42:23 And let us know what your favorite episode is.
    0:42:26 It’ll make my day, and I’m sure Tommy’s too.
    0:42:28 We’ll catch you on the flip side.
    0:42:31 (upbeat music)
    0:42:34 (upbeat music)
    0:42:37 (upbeat music)

    Critical materials like copper, lithium, and gallium have been mined for decades, but their role in core technologies, geopolitics, and the energy transition have come to a height in recent years.

    In this episode, a16z partner Connie Chan discusses how technology is changing the game of  identification and exploration, together with leading company KoBold and their VP of Geoscience, VP of Technology, and CEO of Africa.

     

    Resources:

    Learn more about KoBold Metals: https://www.koboldmetals.com/

     

    Stay Updated: 

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    Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

  • #214 Outliers: Timothy Eaton and The Original ‘Everything Store’

    AI transcript
    0:00:17 [MUSIC]
    0:00:18 Welcome to The Knowledge Project.
    0:00:21 I’m your host, Shane Parish.
    0:00:25 This podcast helps you master
    0:00:28 the best of what other people have already figured out.
    0:00:29 Today, we’re going to do something
    0:00:31 a little different.
    0:00:33 So far, we’ve focused on interviews,
    0:00:36 but I’ve learned as much from reading biographies
    0:00:38 as from interviewing amazing people.
    0:00:41 That’s why we’re starting lessons from outliers.
    0:00:44 Every other week, we’ll study an outlier
    0:00:47 who did remarkable work, from industrialists
    0:00:50 who reimagined commerce to the irreverent personalities
    0:00:53 who changed the foundations of their fields.
    0:00:57 We’ll explore what they did and how they did it.
    0:01:00 The goal isn’t just to tell interesting stories.
    0:01:03 I want to learn the principles, approaches, and patterns
    0:01:06 that can help me in work and life today.
    0:01:07 I want to know the lessons
    0:01:09 that will help me be a better investor,
    0:01:13 a better parent, a better partner, and a better person.
    0:01:15 The people will cover our heroes
    0:01:17 and we should celebrate them.
    0:01:20 That’s not to say that they’re all going to be perfect,
    0:01:22 but it is to say that we’re not going to throw out the orange
    0:01:25 because there might be a little blemish on the peel.
    0:01:27 We can learn something from everyone.
    0:01:29 Whether you’re a regular listener
    0:01:30 or this is your first time here,
    0:01:32 I hope you’ll join me as we learn
    0:01:35 what’s useful and ignore the rest.
    0:01:38 – The content of this podcast
    0:01:41 is for informational and entertainment purposes only
    0:01:44 and should not be considered professional advice.
    0:01:45 Farnham Street Media Inc.
    0:01:47 disclaim any liability for actions
    0:01:48 taken based on its content.
    0:01:51 (upbeat music)
    0:01:56 – We’re starting our new series,
    0:01:59 Lessons from Outliers, with Timothy Eaton,
    0:02:01 a Canadian name that might not be familiar
    0:02:02 to many listeners today,
    0:02:04 but his innovations fundamentally changed retail
    0:02:07 around the world and how we shop.
    0:02:09 Timothy started his business with an obvious idea
    0:02:11 that wasn’t so obvious at the time.
    0:02:13 Tell the truth about your prices
    0:02:15 and stand behind your products.
    0:02:17 With that and many other innovations,
    0:02:19 he built an empire that would, at its height,
    0:02:23 commend 60% of an entire nation’s department store sales.
    0:02:26 This episode is about how he built that empire,
    0:02:28 the principles that drove its success,
    0:02:31 and the forces that eventually brought it all crashing down.
    0:02:34 Whether you’re building a business, leading a team,
    0:02:37 or trying to understand how great companies rise and fall,
    0:02:39 Timothy Eaton’s story offers timeless lessons
    0:02:43 about innovation, trust, and the true price of success.
    0:02:46 You’ll learn why even the mightiest empires can crumble
    0:02:47 when they forget the principles that built them
    0:02:50 and why success, no matter how massive,
    0:02:54 must be earned and re-earned every day.
    0:02:56 It’s time to listen and learn.
    0:03:02 What Amazon is to the internet age,
    0:03:04 what Walmart was to suburban America,
    0:03:08 Eaton’s was to a rapidly industrializing candidate,
    0:03:10 the everything store of its era.
    0:03:14 In 1869, almost a century before Jeff Bezos was born
    0:03:17 in 50 years before Sam Walton and Ikea
    0:03:20 drew their first breasts, there was another name
    0:03:24 that became synonymous with retailing, Timothy Eaton.
    0:03:25 Like many of today’s entrepreneurs,
    0:03:28 this young Irish immigrant bet against
    0:03:30 how things had already been done.
    0:03:34 Only his innovation wasn’t technology, it was trust.
    0:03:37 He would sell everything to anyone at a fixed price
    0:03:38 with a money-back guarantee.
    0:03:41 His slogan, Good Satisfactory or Money Refunded,
    0:03:45 introduced in 1870, sounds obvious today,
    0:03:49 but in 1870, when every transaction was a battle of wits
    0:03:52 and buyer beware was the universal law of commerce,
    0:03:55 this was as revolutionary as one-click ordering
    0:03:57 would become a century later.
    0:03:59 Eaton’s was the birth of an enterprise
    0:04:02 that would become so interwoven with Canadian life
    0:04:04 that you couldn’t tell the story of one
    0:04:07 without the other for nearly a century.
    0:04:09 But like all stories, this one also has an ending.
    0:04:12 130 years later, the Empire Timothy Eaton
    0:04:16 and three generations of descendants had built, crumbled.
    0:04:19 The factors are many, the big ones being a combination
    0:04:22 of complacency, distraction, and being slow to adapt.
    0:04:25 The heirs didn’t help much either.
    0:04:27 At their pinnacle, the Eatons were so dominant
    0:04:30 that it was regarded as virtually unassailable
    0:04:32 because of enormous competitive advantages
    0:04:33 and financial strengths.
    0:04:35 They commanded as much as 60%
    0:04:39 of all department store sales in the country.
    0:04:42 They made so much money that the government told them
    0:04:44 that they were too profitable.
    0:04:47 Seven years later, they would have only 10%
    0:04:48 of all department store sales
    0:04:51 and eventually seek credit or protection.
    0:04:53 The store that was once everything to everyone
    0:04:56 ended up meaning nothing to anyone.
    0:04:59 Could this have been prevented or predicted?
    0:05:01 Let’s explore the story of one of the world’s
    0:05:03 great merchants to see what we can learn.
    0:05:09 Picture Toronto in 1869, no cars, no electricity,
    0:05:11 no telephones, and most importantly,
    0:05:15 no concept of shopping as we know it today.
    0:05:18 Every purchase was a negotiation, every price a secret,
    0:05:21 every transaction a gamble.
    0:05:23 Shopping wasn’t commerce, it was combat.
    0:05:25 We’re skilled hagglers triumphed
    0:05:28 and the unsophisticated buyer was prey.
    0:05:30 Into this chaos stepped Timothy Eaton
    0:05:33 with $6,500 in cash and what would seem like today
    0:05:35 like the most obvious idea in the world,
    0:05:36 tell the truth about your prices,
    0:05:38 charge everyone the same amount
    0:05:39 and stand behind your products.
    0:05:41 Where others saw haggling as tradition,
    0:05:43 he saw it as friction.
    0:05:45 Where others saw returns as lost profit,
    0:05:48 he saw them as investments in trust.
    0:05:50 Where others saw chaos as inevitable,
    0:05:53 he saw an opportunity for a system.
    0:05:54 Imagine walking into a store
    0:05:57 where the merchant sized you up before quoting a price.
    0:06:00 Charging a banker, double what they’d charge a laborer
    0:06:02 for the same item or returning a defective item
    0:06:06 was met with mockery and shame rather than a refund.
    0:06:09 Where buyer beware wasn’t just a saying,
    0:06:12 it was the fundamental principle of commerce itself.
    0:06:15 This was the world that Timothy Eaton would change forever.
    0:06:18 Timothy Eaton’s origin story
    0:06:20 is less about individual circumstances
    0:06:24 than the collision of forces that would reshape commerce.
    0:06:26 Ireland’s poverty story creating waves
    0:06:28 of ambitious emigrants,
    0:06:31 Canadian railways connecting previously isolated markets
    0:06:34 and an outdated credit-based trading system
    0:06:35 ready to be disrupted.
    0:06:39 Born the ninth child of John and Margaret Eaton
    0:06:41 with his father dying before his birth,
    0:06:43 Timothy’s early life was shaped
    0:06:46 by the harsh realities of 1850s Ireland
    0:06:50 where opportunity was scarce and emigration common.
    0:06:52 While his formal schooling ended at 13,
    0:06:54 his real education came during an apprenticeship
    0:06:57 at a general store in Port Lagone.
    0:06:59 There he mastered retail’s fundamental equations,
    0:07:01 the relationship between inventory and cash flow,
    0:07:03 the tension between credit and risk,
    0:07:06 and the delicate balance between merchant and customer.
    0:07:11 When he joined the exodus of 150,000 Irish emigrants in 1854,
    0:07:14 he brought two crucial assets with him,
    0:07:16 an ironwork ethic and a deep understanding
    0:07:19 of commerce’s flaws and how to fix them.
    0:07:21 Landing in Upper Canada in 1854,
    0:07:25 he arrived at a perfect moment of transformation.
    0:07:27 Railways were connecting isolated markets,
    0:07:29 workers were earning regular wages
    0:07:31 instead of seasonal farm income,
    0:07:33 and for the first time ever ordinary people,
    0:07:35 factory hands, clerks, domestic servants,
    0:07:38 had predictable money to spend.
    0:07:41 While established merchants dismissed these common customers,
    0:07:43 Eaton saw something revolutionary.
    0:07:47 Every butcher boy, snip and snob complained
    0:07:50 one Toronto grocer was excessively given to dress
    0:07:52 and wearing rich things and such foolery,
    0:07:56 but where others saw vulgarity, Eaton saw validation.
    0:07:59 A new middle class with steady income and aspirations.
    0:08:02 The economic crisis of the 1850s
    0:08:04 had exposed the fatal flaw in traditional retail.
    0:08:06 In a credit-based system,
    0:08:09 one failure could trigger a chain reaction of bankruptcies,
    0:08:12 but Eaton’s solution wasn’t to demand cash only sales,
    0:08:13 that would have been impossible.
    0:08:16 Instead, he created a brilliant incentive,
    0:08:18 better prices for cash payments.
    0:08:19 This wasn’t just clever pricing,
    0:08:22 it was behavior engineering at scale.
    0:08:25 Year after year, his book showed increasing cash transactions.
    0:08:28 He was simultaneously teaching customers a new way to shop
    0:08:30 while removing risk from his business.
    0:08:32 More importantly, he was building a system
    0:08:34 that could grow without breaking
    0:08:38 and the most powerful force behind it all, relentlessness.
    0:08:40 Eaton brought the same intensity to retail
    0:08:42 that Edison brought to invention,
    0:08:45 testing, refining and competing daily.
    0:08:47 His early stores became laboratories
    0:08:50 where each transaction taught him something new
    0:08:51 about the future of commerce.
    0:08:53 Here’s what made him different.
    0:08:54 He was a master at observation
    0:08:58 and combining existing ideas in new ways.
    0:09:00 Fixed prices, money-back guarantees,
    0:09:02 direct buying from manufacturers.
    0:09:05 These pieces existed in isolation.
    0:09:08 Eaton’s genius was to weave them together
    0:09:10 into an unstoppable system.
    0:09:12 This pattern is repeated in business history.
    0:09:14 The greatest innovations often come not
    0:09:15 from inventing something new,
    0:09:17 but taking an existing idea
    0:09:21 to its logical conclusion with relentless execution.
    0:09:23 While others treated money-back guarantees
    0:09:24 as marketing gimmicks,
    0:09:27 Eaton built his entire business model around trust.
    0:09:34 When Timothy Eaton opened his Toronto store in 1869,
    0:09:35 it wasn’t just another shop.
    0:09:38 It was a laboratory for testing his theories
    0:09:39 about the future of retail.
    0:09:42 The location was perfect, 178 Yonge Street.
    0:09:44 At a busy intersection where every merchant
    0:09:46 dreamed of setting up shop.
    0:09:48 The price, however, reflected this.
    0:09:52 $6,500 for the existing inventory and Goodwill.
    0:09:54 Demanding every single penny
    0:09:57 that he’d managed to save or borrow.
    0:09:58 The store was tiny.
    0:10:02 It was just 24 feet across by 60 feet deep.
    0:10:04 But what happened inside that small store
    0:10:06 would change retail forever.
    0:10:07 One employee recalled,
    0:10:10 “I have seen Mr. Eaton standing at the end of a counter,
    0:10:13 watching a customer purchase a pair of stockings.
    0:10:15 When she had gone, he would ask whether the goods
    0:10:17 would go any more rapidly if he offered in groups
    0:10:21 of two or three pairs at the price reduced in bulk.”
    0:10:24 Rather than just think he had a better idea,
    0:10:26 he would test it by the afternoon.
    0:10:29 Eaton had discovered discount retailing.
    0:10:31 Lower prices drive higher volume
    0:10:33 and higher volume enables lower prices.
    0:10:35 Though you earn less per item,
    0:10:37 you make more money overall
    0:10:39 because you sell many more items.
    0:10:42 Discount retailing is built on the foundation of fixed prices.
    0:10:45 This virtuous cycle, which Sam Walton and later Jim Senegal
    0:10:47 would turn into billion dollar empires
    0:10:49 at Walmart and Costco was tested behind a counter
    0:10:51 in Toronto a century earlier.
    0:10:53 Timothy Eaton was obsessed with details
    0:10:56 and became a human analytics engine.
    0:10:58 He was constantly fiddling with the status quo,
    0:11:00 trying to make something better, testing his ideas,
    0:11:04 watching, observing, obsessing, experimenting.
    0:11:05 This wasn’t commerce,
    0:11:08 it was the scientific method applied to retail.
    0:11:10 Timothy Eaton got up early every day
    0:11:12 and tried to improve something.
    0:11:15 His famous cash only policy wasn’t born from ideology,
    0:11:17 it came from cold reality.
    0:11:18 In his small town days,
    0:11:21 Timothy knew every customer’s story,
    0:11:23 their harvest prospects, their payment history,
    0:11:26 their family situation and even the latest gossip.
    0:11:28 But in the big city with its flood of strangers,
    0:11:30 he needed a system that would work
    0:11:33 without the personal knowledge of every customer.
    0:11:35 The early days would have broken a weaker man.
    0:11:36 The inventory he inherited
    0:11:38 when he purchased his first store on Young Street
    0:11:42 was what modern merchants would call distressed merchandise.
    0:11:45 Though Timothy’s private letters to his brother James
    0:11:48 used considerably more colorful language to describe it.
    0:11:51 He was forced to sell dresses at 15 cents per yard
    0:11:53 that had cost him 35 cents.
    0:11:55 And even at those ruinous prices,
    0:11:58 the goods moved at the speed of cold molasses.
    0:11:59 However, where others saw losses,
    0:12:01 Timothy saw something different,
    0:12:04 a chance to build trust through transparency.
    0:12:06 Every markdown was advertised openly
    0:12:09 with the original and new prices clearly stated.
    0:12:11 Even when losing money,
    0:12:13 he was gaining something more valuable,
    0:12:15 customer confidence and trust.
    0:12:19 While other stores divided their spaces
    0:12:20 into broad categories,
    0:12:23 Eaton’s created detailed subcategories
    0:12:25 for better information and shopping.
    0:12:26 These were not just departments
    0:12:28 to help customers know where to look.
    0:12:31 There were data streams feeding into an accounting system,
    0:12:33 allowing him to track every item’s movement
    0:12:36 through his store with unprecedented precision.
    0:12:39 Timothy Eaton’s obsession with knowing the details
    0:12:41 no one else was paying attention to
    0:12:44 created an information advantage long before computers.
    0:12:46 When he made buttons, its own category,
    0:12:48 he wasn’t just being organized,
    0:12:50 he was creating detailed data.
    0:12:53 Timothy Eaton could tell you how many buttons he’d sold
    0:12:55 on which day, how fast they moved
    0:12:57 and at which prices and who was buying them.
    0:13:00 His hiring strategy was just a systematic
    0:13:02 but with a twist that was a century ahead of its time.
    0:13:05 Starting with just four employees in 1869,
    0:13:09 two men, a woman and a boy, by 1881,
    0:13:12 he had 36 sales clerks and 12 seamstresses.
    0:13:13 He hired mostly women,
    0:13:15 not just because they cost less at the time,
    0:13:17 but because his fixed price system
    0:13:21 had eliminated the need for aggressive mail haggling.
    0:13:23 It was an early example of how good systems
    0:13:26 could create new opportunities by eliminating bad behavior.
    0:13:28 His marketing targeted factory paydays
    0:13:30 with military precision.
    0:13:33 He didn’t just distribute 40,000 flyers randomly
    0:13:34 all over the city and call it a day.
    0:13:38 No, this was a precisely timed operation.
    0:13:41 Every detail was mapped, which streets to target,
    0:13:43 when workers got paid,
    0:13:46 which neighborhoods had the most weekly wage earners.
    0:13:49 While other merchants chased wealthy customers with carriages,
    0:13:52 Timothy built a system to serve thousands
    0:13:54 with weekly paychecks.
    0:13:56 One employee noted that unlike other stores,
    0:13:59 it was an unusual sight to see a carriage
    0:14:00 at the door of the store.
    0:14:03 He wasn’t betting on the rich few with their carriages,
    0:14:05 he was betting on the many with their weekly paychecks.
    0:14:07 He chose volume over margins,
    0:14:09 the rising power of the working class
    0:14:11 over the carriage riding elite.
    0:14:14 Eaton’s was a place for the working masses,
    0:14:15 not for the privileged elite.
    0:14:20 By 1880, Eaton’s success had created
    0:14:22 every entrepreneur’s favorite problem.
    0:14:25 His business had outgrown its space.
    0:14:26 The store couldn’t expand farther
    0:14:28 without demolishing the church next to it,
    0:14:32 which was a step too far, even for Timothy’s ambitions.
    0:14:35 His solution in 1883 was the kind of bet
    0:14:38 that separates great entrepreneurs from good ones.
    0:14:41 He mortgaged everything, literally every penny he had
    0:14:44 to buy an entire city block for $41,000,
    0:14:48 using $36,000 in borrowed money.
    0:14:49 For those keeping tabs,
    0:14:53 that’s an 87.8% loan-to-value ratio.
    0:14:55 He had no room for error here.
    0:14:57 But what came next was even crazier.
    0:14:59 He announced that he would tear down
    0:15:02 what locals considered the finest block of retail stores
    0:15:06 in the entire city and replace it with something entirely new,
    0:15:11 a single, massive space that would reinvent shopping itself.
    0:15:12 He wasn’t just betting his business.
    0:15:15 He was betting his entire life’s work
    0:15:17 on a vision that only he could see.
    0:15:20 Everyone thought he was crazy, but he went all in.
    0:15:23 He went all in on himself.
    0:15:24 This is the founder’s mentality.
    0:15:27 He believed in his idea, even when others didn’t.
    0:15:29 When Reverend John Potts toured
    0:15:32 the new 25,000-foot location,
    0:15:34 20 times larger than the original store,
    0:15:36 the clergyman was moved to tears.
    0:15:38 He said, “I am so sorry, Mr. Eaton.
    0:15:39 You are ruined.
    0:15:42 What will you do with this great barn of a place?”
    0:15:44 Timothy’s response was six words
    0:15:47 that encapsulated his entire philosophy.
    0:15:50 Fill it with goods and sell them.
    0:15:52 The building itself wasn’t just architecture,
    0:15:54 it was retail innovation.
    0:15:56 Lightwells topped by skylights pierced
    0:15:57 through the building’s core,
    0:16:00 allowing natural light to flood all floors,
    0:16:01 crucial in an era where the upper floors
    0:16:04 were typically dim-lit caves.
    0:16:06 But things that work at one scale
    0:16:07 often break at another.
    0:16:10 What worked when an owner could watch every transaction
    0:16:12 wouldn’t work in a massive operation.
    0:16:15 This is a lesson modern startups keep learning.
    0:16:18 Systems that work for 10 people often break at 100,
    0:16:21 and what works for 100 can collapse at 1,000.
    0:16:23 His nephew, John James Eaton,
    0:16:26 described the chaos of January 1884.
    0:16:28 There was no management.
    0:16:29 Everyone was doing as they like,
    0:16:31 no connection between one another
    0:16:33 and a constant disagreement
    0:16:35 and a constant quarreling between departments.
    0:16:36 It was the kind of crisis
    0:16:39 that either killed a company or transformed it.
    0:16:42 The solution combined family and systems.
    0:16:46 Timothy’s son, Edward Young Eaton became partner in 1888
    0:16:48 while nephew John James was tasked
    0:16:50 with bringing order to the chaos.
    0:16:54 John didn’t just manage, he rebuilt the entire system.
    0:16:57 When he discovered employees spending long breaks
    0:16:58 in the saloon across the street,
    0:17:01 he fired 40 people in a single day.
    0:17:03 Can you imagine that happening today?
    0:17:07 The standards were clear and unwavering.
    0:17:08 Even when fighting internal fires,
    0:17:10 external challenges tested Eaton
    0:17:13 when his Glasgow supplier tried to take advantage of him
    0:17:16 by demanding immediate payment of $6,600
    0:17:17 and refused future credit.
    0:17:20 Timothy didn’t just solve the problem, he eliminated it.
    0:17:22 He created a separate company
    0:17:25 under his son, Edward’s name to handle purchasing.
    0:17:27 Problems are just opportunities.
    0:17:30 This reminds me of the story in Brad Jacobs book
    0:17:32 how to make a few billion dollars.
    0:17:33 And we had Brad on the podcast a while ago,
    0:17:36 so I definitely recommend you check out that episode.
    0:17:41 But at a memorable lunch with his mentor, Ludwig Jesselsen,
    0:17:44 Brad sat down and he started to unload
    0:17:46 all of his problems and frustrations.
    0:17:48 And Jesselsen listened carefully
    0:17:49 and then he put his fork down
    0:17:50 and he just looked at Brad and he said,
    0:17:53 “Look, Brad, if you wanna make money in the business world,
    0:17:55 you need to get used to problems
    0:17:57 because that’s what business is.
    0:18:00 It’s actually about finding problems, embracing
    0:18:02 and even enjoying them because each problem
    0:18:04 is an opportunity to remove an obstacle
    0:18:06 and get closer to success.”
    0:18:11 In 1884, Timothy launched something
    0:18:14 that would change everything, the Wishing Book,
    0:18:16 though farmers called it the Farmers Bible.
    0:18:18 Calling it just a catalog
    0:18:20 would be like calling Amazon just a website.
    0:18:23 Timothy had built a portal to the modern world
    0:18:26 for millions of isolated rural Canadians.
    0:18:29 Imagine being a farmer hundreds of miles from civilization
    0:18:32 where your possibilities end up what you can make or trade.
    0:18:34 Now suddenly you had access to everything
    0:18:38 from parish fashions and English tea sets to German pianos.
    0:18:40 Need an entire house?
    0:18:43 Eatons would ship you every single board and nail
    0:18:45 and window with instructions.
    0:18:48 And everything came with that revolutionary guarantee,
    0:18:51 good satisfactory or money refunded.
    0:18:54 Think about this, before Silicon Valley invented data analytics,
    0:18:57 Eatons was using catalog orders to predict demand.
    0:18:58 Before FedEx existed,
    0:19:01 Eatons had built a delivery network so reliable
    0:19:04 that Canadian towns planned their mail service around it.
    0:19:07 At times of year when the catalog was being released,
    0:19:09 there was coordination between Eatons and the Postal Service
    0:19:11 to employ more delivery people
    0:19:14 and schedule more trains to fulfill the expected demand.
    0:19:18 Eatons would encourage customers to write them with suggestions
    0:19:19 as to what other goods they should carry
    0:19:23 and created new departments based on the feedback they received.
    0:19:26 They were solving tomorrow’s problems a century early,
    0:19:28 supply chain management, predictive analytics
    0:19:29 and last mile delivery.
    0:19:35 Timothy ruled with an iron fist in the velvet glove,
    0:19:37 but both served the system.
    0:19:39 Employees quaked in their boots around him,
    0:19:43 yet the same autocrat would reward initiative generously.
    0:19:46 He wasn’t enforcing rules for rules sake,
    0:19:47 he was maintaining the standards
    0:19:49 that made the entire system work.
    0:19:52 Even as employees dreaded his criticism,
    0:19:54 they understood its purpose.
    0:19:57 Even as they resisted his autocratic style,
    0:19:58 they grew under his talent development.
    0:20:01 His growth philosophy shows in one exchange.
    0:20:04 Mr. M, what do you know about menswear?
    0:20:06 He asked a clerk.
    0:20:09 Mr. Eaton, I don’t know a thing, came the reply.
    0:20:12 Timothy responded, good, then you’ll learn.
    0:20:15 Eatons was becoming dominant,
    0:20:17 but Timothy Eaton wasn’t motivated by money,
    0:20:20 he was motivated by the desire to be the best,
    0:20:21 he was relentless.
    0:20:24 One newspaper described Timothy Eaton this way,
    0:20:28 Mr. Eaton is unique, he is not a man of words or fireworks,
    0:20:30 he is modest and retiring to a fault.
    0:20:34 Indeed, it is difficult for even an expert reporter
    0:20:36 to get half a dozen sentences out of him,
    0:20:38 but he is a man who does things.
    0:20:41 In the language of the motto, he does it now,
    0:20:43 and he seems to do them in such a way
    0:20:45 that they become talked about.
    0:20:48 (gentle music)
    0:20:51 Sometimes history turns on a single moment,
    0:20:55 but decline happens like rust slowly and then suddenly.
    0:20:57 On a crisp autumn day in 1899,
    0:21:00 Timothy Eaton’s horses spooked on the way home.
    0:21:03 The resulting broken hip left him in a wheelchair,
    0:21:05 but the real impact wasn’t physical,
    0:21:08 it forced him to hand over his empire before he was ready.
    0:21:10 The weight of his empire fell to his youngest son,
    0:21:12 John Craig, Jack Eaton.
    0:21:14 Their early conversations about leadership
    0:21:16 contain a lesson in simplicity.
    0:21:18 Jack said to his father,
    0:21:21 “What do I have to say as vice president?”
    0:21:24 And Timothy replied, “Can you say yes and no?
    0:21:25 “Yes, I can do that.
    0:21:28 “Can you decide which one to say at the right time?”
    0:21:30 Well, that might be different,
    0:21:32 but it’s all you have to do.
    0:21:34 In those eight words, can you decide
    0:21:36 which one to say at the right time
    0:21:38 laid Timothy Eaton’s entire philosophy
    0:21:41 of systematic decision-making?
    0:21:44 Timothy Eaton died on January 31st, 1907.
    0:21:47 Jack, who had been running things since 1899,
    0:21:50 would no longer have the wise year of his father.
    0:21:51 Jack would be different.
    0:21:53 Where Timothy had been the system builder
    0:21:55 who changed retail through discipline,
    0:21:58 relentless effort, obsessive attention to detail,
    0:21:59 and adapting to the data,
    0:22:01 Jack would be the showman who’d expand it
    0:22:04 through spectacle and scale.
    0:22:06 Jack looked the part, five nine,
    0:22:07 chestnut hair with gold highlights,
    0:22:10 turning heads in his fond colored coats.
    0:22:11 His blue eyes and ready smile
    0:22:12 couldn’t have been more different
    0:22:14 from his father’s hardened face.
    0:22:17 Jack was the roaring 20s personified
    0:22:19 before anyone knew what that meant
    0:22:20 or why it might be dangerous.
    0:22:24 (upbeat music)
    0:22:26 Jack saw something his father never did.
    0:22:29 Shopping wasn’t just business, it was theater.
    0:22:32 Timothy Eaton built trust through consistency.
    0:22:35 Jack Eaton would build an empire through theater.
    0:22:37 His first act, transforming the entire floor
    0:22:40 of Toronto store into Toyland at Christmas.
    0:22:42 But his master stroke was the Santa Claus parade.
    0:22:44 What started small, which was Santa
    0:22:46 on a packing crate on a wagon,
    0:22:49 became legendary live reindeer shipped from Labrador.
    0:22:51 Massive floats took months to build.
    0:22:53 City streets closed.
    0:22:55 The numbers tell the story.
    0:22:59 15,000 kids riding to Santa by 1919.
    0:23:00 But the real story was bigger.
    0:23:03 Jack had turned shopping into magic.
    0:23:06 The trouble with magic is that it depends on illusion
    0:23:08 where Timothy’s system had been built
    0:23:10 on brutal honesty about what customers wanted
    0:23:12 and what it cost to serve them.
    0:23:14 (upbeat music)
    0:23:16 Jack’s biggest bet was out West
    0:23:18 when he proposed expanding to Winnipeg,
    0:23:20 1500 miles from Toronto.
    0:23:23 His father thought managing it would be impossible.
    0:23:26 The gateway to the Golden West wasn’t just risky,
    0:23:29 it was crazy, but sometimes crazy works.
    0:23:31 After all, everyone told Timothy Eaton
    0:23:33 he was crazy to buy a city block with debt,
    0:23:37 tear it down and build one ginormous store.
    0:23:40 But picture Winnipeg in 1910, 75,000 people
    0:23:42 and more millionaires than Toronto.
    0:23:45 The Hudson’s Bay Company, the only real competitor
    0:23:48 to Eaton’s owned the prime real estate there.
    0:23:49 But Jack saw something bigger.
    0:23:52 Here’s how a local paper described
    0:23:54 how it was done at the time.
    0:23:57 When the decision was reached to locate Winnipeg,
    0:24:00 negotiations were set on foot and carried out silently
    0:24:02 and swiftly until the land required
    0:24:05 for a centuries expansion was acquired.
    0:24:08 There was no noise, no flourish, no trumpets.
    0:24:09 The transaction was simply carried out
    0:24:12 and then came the erection of the store.
    0:24:14 Not only did the Eaton family move in silence,
    0:24:16 but they moved quickly.
    0:24:17 From the time the first sod was turned
    0:24:20 to the opening day was under a year.
    0:24:23 On the first day, tens of thousands of people showed up.
    0:24:25 It would only increase.
    0:24:27 The scale was mind-boggling.
    0:24:30 6,000 people were eating daily in their restaurants,
    0:24:33 from workers’ cafeterias to the oak paneled grow room
    0:24:37 or string quartets played on lunch served with fine china.
    0:24:39 Staff numbers nearly doubled in a week
    0:24:41 from 700 to 1,250.
    0:24:44 First year sales hit 2.5 million,
    0:24:46 numbers that would have seemed impossible
    0:24:49 to Timothy just a decade earlier.
    0:24:51 Keep in mind, this is 1910.
    0:24:54 This is crazy, 2.5 million out of a single location.
    0:24:57 Over 15 years, it grew like a week.
    0:25:00 Three more stories up, two massive mail order buildings.
    0:25:04 By 1919, Eaton’s and Winnipeg covered 21 acres
    0:25:07 and employed 8,000 people.
    0:25:09 Eaton’s wasn’t just a store,
    0:25:11 but a city within a city.
    0:25:16 Jack built his empire by placing pieces on a chessboard.
    0:25:18 1916, a massive warehouse in Saskatoon
    0:25:20 for furniture and farm equipment.
    0:25:23 1917, Regina, standing there during construction,
    0:25:25 Jack pointed west and said something
    0:25:26 that would prove prophetic.
    0:25:28 There’s our future market.
    0:25:30 They framed his footprints in the wet cement,
    0:25:33 a literal impression of the empire building in progress.
    0:25:35 At this point, the catalog had become more than a book.
    0:25:39 At 588 pages and 9,000 illustrations,
    0:25:42 it was becoming the story of a nation itself.
    0:25:44 You could buy anything from 395 fiddles
    0:25:49 to entire houses for $999.77.
    0:25:52 When a town founded by Kennedy Northern Railway in 1917,
    0:25:56 named itself Eaton, later changed to Eatonia,
    0:25:58 it wasn’t just flattery, it was recognition
    0:26:00 that Eaton’s had become woven
    0:26:02 into the very fabric of Canadian life.
    0:26:08 But Jack’s real genius, he didn’t just build stores,
    0:26:10 he built a community.
    0:26:13 Starting in 1911, he created a world inside his company,
    0:26:17 baseball leagues, hockey teams, and cricket clubs.
    0:26:19 Female employees got something unheard of.
    0:26:22 Downtown Toronto clubs with pools, gyms, and libraries,
    0:26:23 he even built a summer camp
    0:26:25 where workers could vacation affordably.
    0:26:28 Then came 1919’s Golden Jubilee,
    0:26:29 the company’s 50th anniversary
    0:26:31 and Jack’s boldest move yet,
    0:26:33 the five and a half day work week.
    0:26:37 Saturday closing year round, not just in summer.
    0:26:39 This wasn’t charity, it was strategy.
    0:26:40 Jack knew something timeless.
    0:26:42 Happy workers build empires.
    0:26:47 By the 1920s, Eaton’s controlled an unprecedented 60%
    0:26:49 of Canadian department store sales.
    0:26:51 When rumors spread of an American buyout attempt,
    0:26:53 Jack’s response became legend.
    0:26:55 There’s not enough money in the whole world
    0:26:56 to buy my father’s name.
    0:26:59 Royal customers didn’t just use the catalog,
    0:27:01 they called it the Bible.
    0:27:03 This was the height of Eaton’s power.
    0:27:06 Below the veneer, however, danger was brewing.
    0:27:09 Jack’s genius for entertainment and expansion
    0:27:10 had a bit of a hidden cause.
    0:27:12 It slowly diverted focus
    0:27:14 from the core principles of excellence and value.
    0:27:18 Less attention to the details and more to theatrics.
    0:27:20 Where Timothy had built an everything store
    0:27:21 by being the best at everything
    0:27:23 and adapting to customers,
    0:27:26 Jack built an empire by being the biggest at everything.
    0:27:29 At first, the difference was too small to notice.
    0:27:32 However, in business, small differences compounded
    0:27:34 both positively and negatively.
    0:27:37 By the 1920s, Eaton’s wasn’t just a store anymore,
    0:27:39 it was an event.
    0:27:40 The mightiest empires can crumble
    0:27:43 when they forget the principles that built them.
    0:27:45 When Jack died in 1922,
    0:27:47 he left behind a dangerous gift,
    0:27:49 a seemingly perfect business.
    0:27:51 The numbers were incredible.
    0:27:55 Sales had exploded from 22 million in 1907
    0:27:57 to 141 million in 1920.
    0:28:00 The catalog alone brought in 60 million.
    0:28:03 They owned 60% of the entire country’s
    0:28:04 department store sales.
    0:28:07 The company was so dominant in the 1920s and early 30s
    0:28:10 that government criticized their profit margins.
    0:28:13 Eaton’s developed the strangest corporate pathology ever,
    0:28:15 a fear of being too successful.
    0:28:18 Greg purchased their former COO, put it perfectly.
    0:28:20 Store managers could actually get in trouble
    0:28:22 for being too successful.
    0:28:23 Think of it with that paradox.
    0:28:24 In a competitive market,
    0:28:27 where survival requires constant reinvestment,
    0:28:29 you could be punished for making the company too much money.
    0:28:30 How Canadian?
    0:28:37 By the 1930s, what had started as a slight drift
    0:28:40 from Timothy’s principles had become a widening gulf.
    0:28:43 Like a ship that’s off course by one degree,
    0:28:45 it’s kind of insignificant at first,
    0:28:48 but leading to an entirely different destination.
    0:28:51 The Great Depression exposed the first cracks
    0:28:53 in what looked like perfect armor.
    0:28:54 While the stores were bleeding money,
    0:28:57 the Queen Street flagship lost 2 million in two years.
    0:28:59 The family kept paying themselves massive dividends
    0:29:03 of $525,555 annually,
    0:29:05 perhaps giving them the illusion
    0:29:06 that they owned a money machine
    0:29:10 when what they really owned required constant reinvestment.
    0:29:12 This was when the cancer started.
    0:29:14 Real estate and credit operations
    0:29:16 generated reliable profits,
    0:29:19 which massed deeper problems in the retail operation.
    0:29:21 People just were not shopping at Eaton’s
    0:29:22 as much as they used to.
    0:29:24 And if there’s any law of retailing,
    0:29:26 you must serve the customer.
    0:29:28 Retailing is not for the faint of heart.
    0:29:31 It’s a difficult business that requires constant vigilance
    0:29:34 as soon as one problem is solved, another services.
    0:29:37 Advantages, even ones that seem insurmountable
    0:29:39 prove temporary at best.
    0:29:40 During the Depression,
    0:29:42 things started to go off track for Eaton’s.
    0:29:44 The company made some unforced errors,
    0:29:46 two of which I want to highlight.
    0:29:49 First, they put much more focus on high-end customers
    0:29:52 and much less focus on the everyday working class.
    0:29:55 Second, they failed to see how automobiles
    0:29:58 drove people out of the core and into the suburbs
    0:30:01 and how that influenced the rise of suburban retail.
    0:30:03 The days when Timothy Eaton courted
    0:30:05 the everyday blue collar customer,
    0:30:06 handing out flyers to workers
    0:30:08 who just got a paycheck were gone.
    0:30:10 The family had fallen into a trap
    0:30:13 that still snares successful companies.
    0:30:16 They started serving customers like themselves, wealthy ones,
    0:30:20 forgetting that their wealth had come from serving everyone else.
    0:30:23 The Toronto College Street store tells the whole story.
    0:30:27 It was opened in 1930, and it was a monument to wealth
    0:30:30 that perfectly symbolized not only the time,
    0:30:33 but how far they’d strayed from Timothy’s principles.
    0:30:36 There was ivory, limestone, marble pillars,
    0:30:39 and even a replica of Mary Antoinette’s
    0:30:40 bedroom on the fifth floor.
    0:30:42 There was one small problem.
    0:30:45 Nobody could afford to shop there.
    0:30:47 The company that had invented modern retail
    0:30:49 and forced all of its competitors to change
    0:30:52 suddenly had a stubborn resistance to change.
    0:30:55 While competitors built suburban stores
    0:30:57 in the late ’20s, ’30s, ’40s, and ’50s,
    0:31:01 Eaton’s clung to downtown like a captain to a sinking ship.
    0:31:04 When Canada’s first mall opened in Vancouver in 1950,
    0:31:07 when Eaton’s executive dismissed it
    0:31:10 with a bit of hubris saying, “It’ll never work.”
    0:31:12 The rise of the automobile meant
    0:31:14 that shopping habits were changing.
    0:31:16 Suburban malls popped up, and with them,
    0:31:18 the rapid growth of discount retailers
    0:31:22 and big box stores based on low prices and high volumes.
    0:31:23 Eaton’s wasn’t the only retailer
    0:31:26 with enormous amounts of capital and fixed assets
    0:31:29 facing sector changing, demographic, and retailing trends,
    0:31:32 but they certainly didn’t do themselves any favors.
    0:31:36 Eaton’s didn’t die from one big mistake.
    0:31:38 They died from 1,000 tiny ones.
    0:31:39 Take credit cards.
    0:31:43 While competitors embraced bank cards in the 1950s,
    0:31:47 Eaton’s clung to their own system until 1981.
    0:31:49 Their logic showcased the ultimate danger
    0:31:50 of inherited success.
    0:31:53 They confused Timothy’s principles with his practices.
    0:31:55 They said Timothy believed in cash only
    0:31:57 so they had to honor his tradition.
    0:31:59 Never mind that Timothy’s real tradition
    0:32:02 was giving customers what they wanted and adapting.
    0:32:05 The catalog story perfectly captures
    0:32:07 how organizations calcify.
    0:32:09 Well, Simpson’s Sears Revolutionized Layouts
    0:32:12 and Photography, Eaton spent months
    0:32:14 debating page sizes.
    0:32:16 Their biggest innovation of the 1960s,
    0:32:18 making the catalogs smaller,
    0:32:20 nine and three quarters by 12 inches,
    0:32:22 changing to eight by 11.
    0:32:23 And their whole reasoning,
    0:32:25 when housewives stacked catalogs,
    0:32:28 they’d put Eaton’s on top, being the smallest.
    0:32:30 This was now their idea of innovation.
    0:32:32 How far have you fallen?
    0:32:34 The core problem is simple.
    0:32:38 Bureaucracy’s optimized for bureaucrats, not for results.
    0:32:41 Reminds me of something Charlie Munger commented on.
    0:32:43 He said, “Bureaucracy is terrible.”
    0:32:45 And as things get very powerful and very big,
    0:32:48 you can get some really dysfunctional behavior.
    0:32:50 The numbers tell the story.
    0:32:53 Simpson’s Sears started from zero in 1952,
    0:32:56 hit 500 million in sales by 1965.
    0:33:00 Eaton’s 700 million just slightly ahead, but losing money.
    0:33:03 The catalog division lost $2 to $10 million annually.
    0:33:05 Same market, same business,
    0:33:08 same target customers, opposite results.
    0:33:12 I wanna talk about the period from 1970 to 1985.
    0:33:16 Complacent institutions become monuments to their own success.
    0:33:18 With less profits, the company invested less
    0:33:19 in its own infrastructure.
    0:33:21 At the same time, competitors started to move
    0:33:23 into Eaton’s core markets with brand new stores.
    0:33:26 And Eaton’s infrastructure was starting to show
    0:33:27 the strains of underinvestment.
    0:33:29 At the same time, the family was living the good life,
    0:33:31 buying helipads and yachts.
    0:33:34 The contrast had me thinking a little bit about Timothy Eaton
    0:33:36 and what he would say looking down on his empire
    0:33:39 that reminded me of something Sam Walton said
    0:33:40 in his book Made in America.
    0:33:43 Some families sell their stock off a little at a time
    0:33:46 to live high and then boom, somebody takes them over
    0:33:48 and it all goes down the drain.
    0:33:49 One of the reasons I’m writing this book
    0:33:51 is so my grandchildren and great grandchildren
    0:33:53 will read it years from now and know this.
    0:33:56 If you start any of that foolishness,
    0:33:57 I’ll come back and haunt you.
    0:33:59 So don’t even think about it.
    0:34:02 I think Timothy Eaton would agree with that.
    0:34:06 By the 1980s, Eaton’s didn’t know what it was anymore.
    0:34:08 Was it upscale mass market?
    0:34:11 The stores were as confused as the strategy,
    0:34:14 ranging from 90,000 square feet to 1 million square feet
    0:34:16 with no clear purpose connecting them.
    0:34:19 Then came George Eaton’s big idea in 1990.
    0:34:21 Every day value pricing.
    0:34:24 No more sales, no more promotions, just like Walmart,
    0:34:26 except Eaton’s wasn’t Walmart.
    0:34:29 They didn’t have Walmart’s obsessive cost control,
    0:34:31 logistic efficiency or customer focus.
    0:34:35 They’d taken Walmart’s strategy without Walmart’s system.
    0:34:38 The disconnect shows in one perfect exchange.
    0:34:40 Bill Hughes and Eaton’s buyer for decades
    0:34:44 accosted George saying, you can’t run Eaton’s like Walmart.
    0:34:46 Oh yes, we can, snapped George.
    0:34:48 We don’t have to advertise.
    0:34:50 Walmart advertises, replies Hughes.
    0:34:53 I wanted to see what Warren Buffett had to say
    0:34:55 about retailing, so I looked it up.
    0:34:57 After all, he had owned two department stores at one point
    0:35:00 and exited them as quickly as possible.
    0:35:02 As if talking about Eaton’s Buffett said,
    0:35:04 he wasn’t talking about Eaton’s,
    0:35:05 but he could have been talking about Eaton’s.
    0:35:07 He said, during my investment career,
    0:35:09 I’ve watched a large number of retailers
    0:35:13 enjoy terrific growth and suburb returns on equity
    0:35:16 for a period and then suddenly nosedive,
    0:35:19 often all the way into bankruptcy.
    0:35:22 His conclusion, a retailer must stay smart day after day.
    0:35:24 It was too hard.
    0:35:27 Munger added his characteristic wit to this commenting
    0:35:28 on their adventures in retailing,
    0:35:31 saying it’s like the story of a man who buys a yacht.
    0:35:33 The two happy days are the days he buys it
    0:35:35 and the day he sells it.
    0:35:36 Retailing can be a good business.
    0:35:39 Of course, Costco is a great example of this,
    0:35:41 which we may cover in a future episode.
    0:35:43 It’s kind of retail with a twist.
    0:35:46 The end of Eaton’s reads like a business school warning label.
    0:35:48 What happens when you adapt slowly
    0:35:50 in a difficult business facing a lot of headwinds
    0:35:53 with a ton of assets that are hard to reposition?
    0:35:56 The giant that once owned 60% of Canadian retail
    0:35:59 had shriveled to less than 10%.
    0:36:01 The company nobody wanted to compete with
    0:36:03 was now the butt end of jokes.
    0:36:05 The company that once made so much money
    0:36:08 the government told them to stop making money
    0:36:10 was now losing money hand over fist.
    0:36:14 In the mid 1990s, Eaton’s entered retail’s deadliest spiral,
    0:36:17 following sales forced inventory cuts,
    0:36:19 driving away customers who expected selection,
    0:36:21 causing more and more sales drop.
    0:36:25 Getting out of this death spiral is like running in quicksand.
    0:36:28 February 1997 brought the final humiliation.
    0:36:30 The company that had revolutionized retail
    0:36:33 by making cash only a virtue now had to beg courts
    0:36:35 for protection from creditors.
    0:36:39 The empire built on paying on cash couldn’t pay its bill.
    0:36:41 The numbers tell the story better than words.
    0:36:46 By 1999 sales had collapsed to 1.6 billion, 1970s levels.
    0:36:48 The years lost 72 million.
    0:36:50 Meanwhile, their old rival Sears Canada
    0:36:52 had soared to five billion.
    0:36:53 Same market, same challenges,
    0:36:55 same opportunities, same customers.
    0:36:57 The difference was simple but profound.
    0:37:00 One company understood that success had to be re-earned daily
    0:37:03 while the other thought it could live off inherited momentum.
    0:37:04 It wouldn’t be long after this,
    0:37:07 however, that Sears would suffer the same fate.
    0:37:08 As Buffett commented,
    0:37:12 you have to be smart every single day in retail.
    0:37:15 Retail is incredibly difficult business.
    0:37:17 Eaton’s didn’t just die.
    0:37:19 It left us a timeless lesson of its success.
    0:37:22 The price must be paid daily.
    0:37:23 It can’t be inherited.
    0:37:27 It can only be earned, re-earned and reinvented.
    0:37:29 Risk and hard work might get you to the top
    0:37:31 but only hard work and constant vigilance
    0:37:33 will keep you at the top.
    0:37:36 The company that had defined Canadian retail
    0:37:37 for generations collapsed
    0:37:41 because it worshiped its past instead of building its future.
    0:37:44 It’s not just a business failure, it’s a warning.
    0:37:47 Even giants fall when they forget yesterday’s success
    0:37:49 doesn’t guarantee tomorrow’s survival.
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    0:37:58 consider joining our membership program
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    So far with The Knowledge Project Podcast, we’ve focused on interviews. But I’ve learned as much from reading biographies as from interviewing amazing people. That’s why we’re starting ‘Lessons from Outliers.’ Every other week, we’ll study an outlier who did remarkable work. From industrialists who reimagined commerce to the irreverent personalities who challenged the foundations of their fields, we’ll explore what they did and how they did it. We can learn something from everyone.  

     

    We’re starting Outliers with Timothy Eaton, a Canadian name that might not be familiar to many listeners today, but his innovations fundamentally changed retail and how we shop. This episode is about how he built that empire, the principles that drove its success, and the forces that eventually brought it all crashing down. Whether you’re building a business, leading a team, or trying to understand how great companies rise and fall, Timothy Eaton’s story offers timeless lessons about innovation, trust, and the true price of success. You’ll learn why even the mightiest empires can crumble when they forget the principles that built them and why success—no matter how massive—must be earned and re-earned daily. 

    01:55 – Introduction

    05:04 – The Vision

    06:16 – Timothy’s Early Years

    09:28 – The System

    12:17 – The Innovation Engine

    14:18 – The Scale Game

    18:08 – The Platform Play

    19:32 – The Leadership Philosophy

    20:48 – The Succession

    22:21 – Retail as Entertainment

    23:14 – The Western Expansion

    25:12 – Building the National Network

    26:05 – Creating the Corporate Family

    26:43 – The Pinnacle of Power

    27:43 – THe Inherited Crown

    28:33 – The Comfortable Plateau

    31:33 – The Weight of Tradition

    33:12 – The Profit Paradox

    34:02 – The Identity Crisis

    34:51 – The Final Chapter

    This podcast is for information purposes only and draws primarily from two excellent books: ‘The Eatons: The Rise and Fall of Canada’s Royal Family’ by Rod McQueen which chronicles the Eaton family history and the company’s journey from beginning to end, and ‘Timothy Eaton and the Rise of His Department Store’ by Joy L. Santiuk, which focuses on the founder’s life. If this story captured your interest, we highly recommend both books for their thorough documentation of what became a Canadian institution for over a century. 

    Newsletter – The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it’s completely free. Learn more and sign up at fs.blog/newsletter

    Upgrade — If you want to hear my thoughts and reflections at the end of the episode, join our membership: ⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠ and get your own private feed.

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  • Raging Moderates: Elon Musk’s Federal Government Takeover

    AI transcript
    0:00:06 I’m going back to university for $0 delivery fee, up to 5% off orders and 5% Uber credits back on
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    0:00:24 and host of the Net Worth and Chill podcast. This is money talk that’s actually fun,
    0:00:28 actually relatable, and will actually make you money. I’m breaking down investments,
    0:00:32 side hustles, and wealth strategies. No boring spreadsheets. Just real talk that’ll have you
    0:00:36 leveling up your financial game. With amazing guests like Glenda Baker. There’s never been
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    0:00:46 that I sold it. This is a money podcast that you’ll actually want to listen to. Follow Net Worth
    0:00:49 and Chill wherever you listen to podcasts. Your bank account will thank you later.
    0:00:59 Are we shaping AI or is AI shaping us? There’s a number of existential risks that confront
    0:01:06 human beings. I think AI just being developed reduces the overall existential risk characteristics.
    0:01:14 I’m Preet Bharara, and this week, Reid Hoffman, entrepreneur, investor, and author of Super Agency,
    0:01:20 What Could Possibly Go Right With Our AI Future, joins me on my podcast Stay Tuned With Preet.
    0:01:26 The episode is out now. Search and follow Stay Tuned With Preet wherever you get your podcasts.
    0:01:35 Welcome to Raging Moderates. I’m Scott Galloway. And I’m Jessica Tarlev.
    0:01:38 Jess, did you watch the Super Bowl? I did. It was so boring.
    0:01:42 Yeah, it wasn’t a good one. We would do for a bad one. Did you stay up?
    0:01:47 You know, I wasn’t planning to. I’m not into sports, and it started at 11.30 p.m. and I made
    0:01:54 this big to-do about, I was basically this axis of evil between shitty fatty food and the diabetes
    0:01:59 industrial complex and that the game is boring and CET. And then, of course, my 14-year-old said,
    0:02:05 “Dad, you want to watch Super Bowl?” I’m like, “Yep, let’s do it.” And so I stayed awake until
    0:02:09 the halftime show, which I thought was awful, by the way. And I get I’m not Kendrick Lamar’s
    0:02:14 audience, but I thought the whole thing was just a giant snooze. What did you think?
    0:02:20 The game itself, I wasn’t much interested. I’m not the biggest NFL enthusiast. I was like the
    0:02:25 side stories. So, you know, I want as much Taylor and Travis as possible. I thought that, you know,
    0:02:30 Trump was the first sitting president to attend a Super Bowl, which kind of surprised me.
    0:02:34 I’m not sure why that hasn’t happened before. So, you know, there were side stories going on
    0:02:41 that were kind of interesting, but the game not so good. The halftime show, I wasn’t wild about.
    0:02:48 You know, it wasn’t Bruno Mars for me was such a good halftime show, which I’m sure is a very
    0:02:52 lame pick in all of this, or Janna Jackson and Justin Timberlake. Actually, a friend who was
    0:03:00 over was at that show and got to see the nipple. So, we talked about that for a while. But overall,
    0:03:04 not great. We had a lot of little kids in the house. We cooked a lot. My husband made a great
    0:03:11 pasta carbonara, rigatoni carbonara, or no, lumaki carbonara. I’m trying to get my noodles
    0:03:18 straight. All in all, whatever, I guess, but raw, raw America. There was one fantastic moment when,
    0:03:23 of course, Taylor Swift got booed. That made me happy. Why is that? Is that wrong? Yeah.
    0:03:27 Is that wrong? I thought that was hilarious. Why? I thought that, I don’t know. It’s like,
    0:03:31 it’s like the Roman Coliseum, except lions. We have Taylor Swift. So, occasionally,
    0:03:37 I think he’d boo against the lions. I don’t know. I found the whole thing. It’s like America,
    0:03:42 where we sell boner pills and opioid-induced contemplation medication while giving young
    0:03:49 men CET, you know, America. I just find the whole thing. I don’t know. I’m too cynical.
    0:03:54 So, when are you moving home? I’m not sure. 17 months, three weeks, and four days is what is
    0:03:58 on my calendar. Not that I’m thinking about it, but I’m looking forward to getting back
    0:04:00 to the States, because, you know, things are going so well.
    0:04:06 Totally. Yeah. I would be desperate to come back at this particular moment. Though,
    0:04:11 I think about moving back to London, and then I think I’ll definitely want to, if we do it,
    0:04:15 that I’ll want to come back to America as well. So, it’s like…
    0:04:18 I don’t think anything makes you feel more… The abuse of relationship, you can’t quit, right?
    0:04:23 Well, I mean, the reality is, if you didn’t know what was going on, I think the reality for most
    0:04:28 Americans, unless you’re a veteran or a beneficiary of SNAP or Head Start, which is a lot of Americans,
    0:04:34 but quite frankly, if you’re in our economic weight class, you can shield yourself from this
    0:04:40 nonsense. And I would argue you’re probably in a beneficiary of it, and not in a good way.
    0:04:45 But what I recognize moving to London, which is in my opinion, the second best city in the world,
    0:04:51 is it is really hard to beat America. And that is, if you like opportunity, if you like a crush
    0:04:59 and a collision of culture, grit, creativity, there’s just nothing like America. And my reductive
    0:05:04 analysis after I say this and it triggers some people molesting the earth for the last 30 years
    0:05:10 is that America is still the best place to make money, and Europe’s the best place to spend it.
    0:05:16 So, when you’re going into your spending years, absolutely spend time in Europe and go to Madrid
    0:05:22 and get a great bottle of wine for 10 bucks, not 80. Check out Munich, which is an amazing city,
    0:05:29 Milan, go to, you know, PSG game in Paris. It’s just, but if you’re looking to advance your career,
    0:05:34 your influence, your impact on the world professionally, everything here, I would
    0:05:40 argue is a kind of medium or second gear. It just can’t get out of second gear. But
    0:05:44 I gotta be honest, I can’t wait to get back. I can’t wait to get back to America.
    0:05:50 Yeah, well, we’ll be thrilled to have you. No, I’m done with that part. But I did notice that
    0:05:58 when I was in grad school, that all of the top performers in my PhD class, all desperate for
    0:06:04 American positions, like couldn’t wait to be able to do it, even with all of the problems with academia.
    0:06:09 But yeah, well, that’s, that’s the joke about Scotland, that some of the finest minds in the
    0:06:13 world, and they all have the same thing in common they left. Anyways, all right, enough of that.
    0:06:17 Today, we’re discussing Elon Musk’s increasing government influence. I don’t know if you’ve
    0:06:22 heard, he’s this very wealthy individual who puts rockets into space but doesn’t live with any of his
    0:06:30 children. He’s this former South African slash Canadian slash naturalized American. Anyways,
    0:06:34 interesting cat, we’re going to talk about him. Reminds me of this very popular guy in the middle
    0:06:38 of the last century, who some people really loved. We’re coming in hot, I guess, right?
    0:06:44 But most people, you know, most people over time found that, found that, well,
    0:06:47 they, it’s interesting, they have the same hand gesture. It appears that they say they have the
    0:06:52 same body language. Anyways, we’re going to talk about Elon Musk’s increasing government influence,
    0:06:57 Trump’s buyout offer to federal workers, and the latest Democrats’ effort to fight back. All right,
    0:07:03 let’s get into it, Joss. I think we’re already in, so go. Go, yeah. I’ve already, I’ve already
    0:07:08 dived in the shallow. You gave away the game already. We’ve already talked about 1930s, so yep.
    0:07:14 Elon Musk’s grip on the executive branch keeps tightening. His Doja crew has been popping up
    0:07:19 at federal agencies, snooping around sensitive systems. And until last week, when a federal
    0:07:23 judge blocked his team from accessing the Treasury Department’s payment system, my understanding
    0:07:28 is every time they run up against a judge, they get blocked. Trump defended Musk’s efforts calling
    0:07:34 it part of his plan to cut wasteful spending and praise Musk for his work. But the AFL-CIO and
    0:07:40 the Department of Justice are hitting back with multiple lawsuits. And Elon got also a new provocative
    0:07:46 time magazine cover that puts him behind Trump’s desk. Interestingly, Republican support from
    0:07:52 us all in the Trump administration is cooling off. An economist you got pulled shows only 26%
    0:07:58 now want him to have a significant influence. That’s down from earlier numbers, Joss. Musk also
    0:08:05 tweeted at me and Kara, funny. Barely noticed my pivot co-host over the weekend accusing us of
    0:08:12 threatening his engineers just for calling out the harm they’re causing. So, look, before I’m
    0:08:17 not going to, I don’t want to get into a back and forth here. What I would say is that the comments
    0:08:22 made were made by me, not by Kara. And I find it sort of telling that he puts Kara’s name first and
    0:08:29 goes after Kara instead of just going after the person who he has or should have a grievance
    0:08:37 with. And that’s me. And anyways, I’ll let you go first. Any thoughts on what’s going on with
    0:08:44 Elon, Joss? Well, I have a lot of thoughts on what’s going on with Elon. I assumed that Kara
    0:08:48 came first in that because she has long covered him. And last week she had a big interview with
    0:08:55 Ezra Klein, which I thought was very impressive talking about her years of covering him and
    0:08:59 being close to him. So that was my assumption as to why that happened. But yes, I have a lot of
    0:09:04 thoughts. I don’t want to steal. If you have things to say about the tweet stuff or maybe you’re
    0:09:08 saving for pivot when you too can talk about it more in depth, but I don’t want to cut you off
    0:09:12 if you’ve got more on that because mine is not about the tweets necessarily. You know, I don’t
    0:09:18 have a lot. At first, I started, this is what happens whenever Elon tweets at me or gets angry
    0:09:23 at me. And that is my phone starts blowing up with, “Are you okay? Is everything okay?” And I’m
    0:09:27 not on Twitter. So I’m shielded from most of the toxicity. And someone sent me a screenshot
    0:09:33 of the tweet and that it had 11,000 comments. And I’m like, “Well, I bet those comments aren’t
    0:09:39 fun to read.” But, I mean, essentially, I start to get worried and I start to get panicked and I
    0:09:44 start to, you know, I start to get anxious. And then I realize, okay, whatever you say about Kara
    0:09:49 and me is we live with our children. We don’t sleep with a loaded gun next to us. We’re not
    0:09:56 severely addicted to a disassociative substance. We’re not making Nazi gestures. And he’s acting
    0:10:02 like these engineers are in Quantanimal Bay when the reality is that probably the most serious thing
    0:10:06 they’re doing other than denying children and veterans their payments, trying to figure out if
    0:10:14 the meme further for Doge should be wearing sunglasses. And just this notion of these billionaire
    0:10:20 tears where he can’t decide if it’s his struggling engineers or just proper grammar, like pick a
    0:10:25 struggle boss. It’s like, well, you don’t have auto correct. I just, I start to read this thing.
    0:10:30 I start to get upset. I start to think about responding. And then I think I don’t want to
    0:10:36 create a sideshow. I want to focus on what I think is important. And that is highlighting
    0:10:42 that we have somebody who was not cleared or approved by government or Congress,
    0:10:49 who is basically hacking into our federal systems. If China did this, it would be an
    0:10:57 act of war without the permission of Congress and shutting off funds to veterans and children
    0:11:04 and the neediest. And I think that’s where we have to remain our focus. So Musk saying mean
    0:11:09 things about me, that’s a sideshow and it really doesn’t matter. It’s not important.
    0:11:14 And I’m not going to, other than I want to stay focused on, you know, when you go into an emergency
    0:11:20 room, there’s a staying called stop the bleeding. And that is if someone comes in with a gunshot
    0:11:24 and they are hemorrhaging blood, they don’t take their PSA or their cholesterol level.
    0:11:31 So my ego and me being butthurt or responding or getting into it with him on Twitter,
    0:11:36 that’s a distraction. We need to stay focused on the fact that we are now in a position where
    0:11:41 we’ve created a series of incentives where when we convict the president of being a felon and he
    0:11:47 gets reelected, he has learned that the American public, as long as they control all three branches
    0:11:53 of government, will not hold him accountable for trespassing or hacking into our most sensitive
    0:11:58 federal systems. Now, if it gets to a judge, it gets pushed back, but they’re kind of in this
    0:12:03 Blitzkrieg moment of let’s ask for forgiveness as opposed to permission. That’s what I want to
    0:12:07 stay focused on. So I’m trying as hard as I can. And this isn’t easy for me, as you know, Jess.
    0:12:15 Yeah. To put my ego aside and focus what limited audience and bandwidth I have on stopping the
    0:12:20 bleeding, if you will, your thoughts. I applaud your maturity. It wasn’t what I expected.
    0:12:26 I had a daunting walk down here this morning. I was like, how do I avoid
    0:12:30 what I was going to happen in the conversation about the tweet? I don’t want to get involved.
    0:12:36 That’s your bag, but I like this attitude. And I think it’s honestly where the American
    0:12:40 public is going to be best served, A, because podcasts are the most important thing in the
    0:12:47 entire world and we can save them all, but B, because the American electorate has basically
    0:12:54 told us that they’re not interested in a lot of the side shows. They voted kind of singularly
    0:13:00 focused either on the economy or on an immigration. And that’s what they deserve to have in their
    0:13:04 conversations. That’s what they deserve to have delivered for them in terms of policy.
    0:13:10 None of those things have happened thus far in the first, I can’t, it’s only three weeks.
    0:13:17 So that’s what’s so crazy that this has only been three weeks since Trump was inaugurated.
    0:13:23 But I think that’s a very mature outlook on this. And I look forward to listening to pivot,
    0:13:28 where I’m sure you two devolve into the immaturity. I’m just going to unshane Kara.
    0:13:33 Just let her loose. Well, she’s better. She’s better at counter punching than me.
    0:13:41 She does have good insults. Yeah. She’s fearless. And I did, I mean,
    0:13:45 it’s interesting, I’m thinking a lot about men emasculated. It is interesting that the Doge
    0:13:52 team is all young men. Yeah. And I do think that at the end of the day, the people responsible for
    0:13:57 this are the president and Elon Musk. And I think these, I’m going to call them kids,
    0:14:02 but these young men, young men are more risk aggressive. Biologically, the prefrontal cortex
    0:14:07 doesn’t catch up until they’re the age of 25 to a woman. It is interesting that there are no women
    0:14:13 as part of this group. But isn’t this what you predicted to some degree in talking about how
    0:14:21 disenfranchised and out of kind of mainstream society, young men were being pushed. So they’re
    0:14:28 looking for community. They’re looking for fun and adventure and that high, wherever they can
    0:14:33 find it. And they’ve spent a lot of time on their computers and they’re really fucking good, right?
    0:14:38 They’re the ones who are going to be able to hack into our system. So it does seem,
    0:14:43 I think calling them hackers is probably wrong. Hackers in a past life and some of them have even
    0:14:52 been fired from past internships or jobs for hacking or working at places where convicted
    0:14:57 hackers have been employed. I mean, this is a motley crew in terms of resumes. And there are a
    0:15:02 lot of FBI agents, former FBI agents who have been speaking out saying, these are not people that
    0:15:07 could pass a conventional clearance, which seems like a problem to me when you’re talking about
    0:15:13 the treasury payment system. But I do think that what we’re seeing in the Doge team is very much
    0:15:18 linked to the world that you have been talking about for the last few years and will be the
    0:15:24 subject of your forthcoming book if you would like to plug that. Thanks for that. So I think
    0:15:29 they should be held accountable if a law has been broken here and that anyone who goes after
    0:15:35 the president or must for laws broken, which I believe they’re trespassing. I believe that they
    0:15:40 have purposely circumvented Congress. We’re in uncharted territory because you don’t know if
    0:15:46 that’s an actual, if that is a civil or criminal offense when the president approves of it. I think
    0:15:53 that’s for courts to decide. But I do think it’s a side show to a certain extent to focus on these
    0:15:57 young men, to be clear, the people accountable for this, the people who are orchestrating this are
    0:16:03 the president and Elon Musk. And to a certain extent, the Democrats, I don’t want to say who
    0:16:06 are enabling it, but have been caught flat-footed and have to figure out a way to strike back.
    0:16:12 And we’re going to talk about that later in the show. But it is interesting. And just to be
    0:16:18 real here about these young men, I was thinking about it. I’ve said a lot. If I was born in 1920
    0:16:23 Germany, I’d probably be wearing a Nazi uniform and probably would have died on a Russian field
    0:16:29 somewhere thinking that I was serving the fatherland. You are a function of where you grow up and in
    0:16:34 what time. And you can see with a lot of young men, these are really talented young men with a lot
    0:16:39 of opportunities. So I don’t feel comfortable grouping them into the bigger swath of young men
    0:16:45 in America who have a lack of on-ramps to a good living, a lack of financial security, a lack of
    0:16:51 prospects, a lack of an ability to meet a potential mate and start a family. These guys are all
    0:16:57 incredibly talented and have a lot of opportunities. And the only lesson someone called me, a
    0:17:00 readership called me, I didn’t go on and I said, “What would your advice be to these young men?”
    0:17:04 And I’m like, “Again, it’s a side show.” But what I would tell any young man is that we’re in a
    0:17:10 high pressure situation. Do what I didn’t do. And that is assemble a kitchen cabinet of people to
    0:17:15 advise you say, “This is what’s going on. Do you have any thoughts for me, whether it’s your parents,
    0:17:21 whether it’s your parents’ friends, whether it’s just friends?” Because I saw being a young man,
    0:17:26 trying to express my manhood is quickly assessing the situation and then making a snap decision and
    0:17:33 trying to talk everybody into me being right, whatever that decision was. It is very hard to
    0:17:38 read the label, especially as a young man when you’re more risk aggressive and quite friendly,
    0:17:42 don’t have incredibly good judgment or reason. You’re not that thoughtful. You’re not that measured
    0:17:48 yet. It is really hard, if not impossible to read the label from inside of the bottle. So the larger
    0:17:54 learning I would want to communicate to all young men is do what I didn’t do. I would have saved
    0:18:00 myself a lot of heartache, a lot of professional missteps, a lot of broken relationships. Had I
    0:18:05 just reached out to people and said, “This is the situation. Do you have any thoughts or advice for
    0:18:11 me?” And you might decide not to change your mind about what you’re doing. But this is, you know,
    0:18:16 when you find yourself in kind of uncharted territory, it’s just a really good idea to check
    0:18:21 in with people from different backgrounds and say, “This is what’s going on. It’s pretty intense. Do
    0:18:27 you have any thoughts?” And I didn’t learn that until I was much older. And I think men have a much
    0:18:34 more difficult time because we conflate strength and masculinity with being decisive as opposed
    0:18:39 to being thoughtful and listening. Yeah, I agree with that. And I think Democrats really suffered
    0:18:45 from a very effective smear campaign of us being the feminine party because we were talking about
    0:18:52 issues that, God forbid, affected women. And men by extension, when someone is pregnant and there’s
    0:18:58 someone who got her pregnant and is sticking around, then it affects you too. So I totally
    0:19:04 agree with that. And I didn’t mean to paint with such a broad brush, but I do think that there’s
    0:19:10 the widest group of young men that you were talking about who are lacking in opportunity and
    0:19:15 lacking in mobility and the chance to make meaningful relationships and to live a full and
    0:19:23 loving and beautiful life that we all want. But then there is also a large contingent of
    0:19:29 these bro types who feel, even though they have been afforded, tons of opportunities
    0:19:33 have had the best education and probably aren’t facing any student debt at the end of this,
    0:19:39 getting internships at places like Palantir at 19 years old who still feel aggrieved.
    0:19:44 And a lot of that is rooted in the fact that they don’t see a ramp to the level of success that
    0:19:50 their boomer parents or late Gen X boomer parents had by their age. I mean, thinking back to how
    0:19:58 enormous it felt if a parent could earn a million dollars in a year and then how stifled people
    0:20:03 who see themselves as upwardly mobile and are living in these big cities and maybe are at a
    0:20:09 big law firm or in banking, when you say, oh, earn a million dollars a year, I’m not going to have
    0:20:13 anywhere close to the life my parents have. My kids are not going to go to private school,
    0:20:21 which is now 55 to $65,000 a year versus the 25,000 when I was growing up as an elder millennial.
    0:20:27 So I wanted to add that. But something I’ve been thinking a lot about, and this is shifting gears
    0:20:33 a little bit, but still about what’s going on with Musk and Co is how much this moment feels to me
    0:20:41 like it did when Trump and the array of lawyers that were fanned out across the country after
    0:20:47 the 2020 election were getting to work to essentially poison pill as big of a swath of the
    0:20:53 population as they possibly could to not believe that Joe Biden had won a free and fair election.
    0:20:59 They did it with vaccine skepticism. Their power is strongest when their supporters are separated
    0:21:05 from the rest of society. And I feel like we’re seeing that moment again. And J.D. Vance tweeted
    0:21:10 over the weekend after the judge ruled about the treasury payments. Not that Scott Besin
    0:21:14 couldn’t access the treasury payment system, but that you couldn’t have individuals that weren’t
    0:21:19 fully vetted having access. And we’ll see what happens. I think today there’ll be an addendum
    0:21:23 to that. But he’s tweeting saying that they’re trying to control the executive’s quote unquote
    0:21:28 legitimate power. And a lot of that as a reference back to the Supreme Court case where they basically
    0:21:34 gave Trump immunity from anything or future presidents, but it was really about Trump.
    0:21:37 And then you had, I don’t know if you saw Christine Noem, the Homeland Security
    0:21:43 Secretary was on with Dana Bash this weekend. People don’t trust the government.
    0:21:47 Right. And then Dana says to her, sorry, I shouldn’t have said Dana, it’s Dana,
    0:21:55 says to her, well, you are the government. And then she’s driveled for 58 seconds after
    0:21:58 that and shows she doesn’t really know what she’s talking about. But she actually did say the
    0:22:04 important part out loud, which is they are creating an environment and have fostered for years now,
    0:22:08 an environment in which people don’t feel that they can trust the government.
    0:22:13 And one of the first things Trump said when he started running for president was I alone
    0:22:20 can fix it. And now it’s I alone plus Elon and JD and whoever is on board for all of this.
    0:22:28 And I’m scared to see society perhaps even further breaking apart along these new lines of
    0:22:33 who thinks that the government does anything good for me and who thinks that there is absolutely
    0:22:42 nothing that of positive note that the government delivers. And that’s hugely dangerous. And I
    0:22:48 don’t know, that’s been the most disturbing part for me that I feel like I’m back in November and
    0:22:52 December of 2020. And I worry we’re not going to get these people back.
    0:22:56 Well, this is a serious issue. And I want to apologize for my Nazi references because they’re
    0:23:02 not funny. Although it is clear that Musk and Trump have made a hard right turn. And also the,
    0:23:07 I don’t know if you’ve driven the new model SS from Tesla. And I saw on Twitter.
    0:23:09 Are you coming up with these on the fly? Or do you have a list of
    0:23:14 Musk Nazi jokes that you like to make? Well, you know, he’s changed his pronouns
    0:23:19 to he and Himmler. But anyways, there’s a lot in there. And I think that
    0:23:26 potentially you have, unfortunately, everything reverse engineers to one key statistic in my view.
    0:23:32 And if we don’t fix it, we’re going to have some form of revolution, famine or war.
    0:23:37 And that happens in every society. And it’s the following. The ultimate social compact is that
    0:23:42 my kids will do better than me. If I work hard, I play by the rules, my kids will do better than
    0:23:48 me. The definition, I used to think the definition of love was caring more about someone than you
    0:23:53 care about yourself. And I’ve broadened that to, you know, you give witness and notice to people’s
    0:23:59 lives. But the people who irrationally love are your children. They’re, you know, I always say
    0:24:02 to my sons, you’re the only people in the world that I want to be more successful than me. And
    0:24:08 I’m embarrassed to say that, but it’s true. And when your kids aren’t doing as well as you are,
    0:24:13 we’re at 30 for the first time in the nation’s history, it’s just a breakdown in the social compact.
    0:24:19 And people want chaos. There’s also because we’ve had what I would argue is the best functioning
    0:24:24 organization. And let me go, I think the most impressive organization in history is a wing
    0:24:28 of the US government. And that’s our military. And I think in the top five is the US government.
    0:24:35 And Mel Robbins, who I think is going to probably displace Joe Rogan, if Stephen Barton doesn’t,
    0:24:42 has this new book out called Let Them. And I’m sort of at the point right now where the people who
    0:24:46 are under the illusion that Trump represents them, the genius of the Republican party is they represent
    0:24:53 the top 1% in corporations. And they’ve convinced the bottom 99 that you should endorse us because
    0:24:57 once you get into the top 1%, you’re going to love it here. And you have more of a chance
    0:25:04 with us. And when Democrats keep spewing out this elitist dribble, and we continue to move
    0:25:09 towards a 30 year old not doing as well as his or her parents, then the parents and the people
    0:25:14 under the age of 30 just want chaos. And what I say around some of this stuff, I’m at the point now
    0:25:21 where it’s like, let them, the states that went for Trump are the states that are the biggest
    0:25:28 takers of federal assistance. So just see what happens when veterans and fairers benefits when
    0:25:34 we disrupt and shut down those people you can’t trust. Okay, let’s see what happens to you and
    0:25:42 dad and your neighbors. And what happens in these rural dark red communities when there is no head
    0:25:49 start. See what happens when you shut down DEI and there is no job opportunity for veterans. Like,
    0:25:54 I’m at the point where it’s like, you got, you know what, you broke it, you own it, you’re going
    0:26:02 to get to find out just how quote unquote incompetent government is, you’re going to find out that
    0:26:06 government is a lot more competent than you had originally thought. And you’re going to get a
    0:26:12 very ugly awakening in my view. And I’m sort of at the point of, all right, it’s time. You really
    0:26:17 want to see what life is like in these red states, the people who are most rapidly for
    0:26:24 Trump who tend to be, who tend to be in rural areas tend to be quite frankly have a larger
    0:26:30 body mass index are more dependent upon Medicare or more dependent on government services. The
    0:26:35 biggest takers from a state perspective are the ones that went hardest towards Trump, which means
    0:26:40 when these payments in these programs get shut down, they’re beginning, this isn’t going to hurt
    0:26:45 us. Yes, I mean, we’re upset about this because I’d like to think we have some fidelity to America
    0:26:50 and the Constitution and want to pay back based on the prosperity we’ve recognized because of this
    0:26:55 incredible system and rule of law and democracy, but quite frankly, this isn’t going to really hurt
    0:27:00 you or me. We’re not, our kids aren’t in snap. We’re not getting veterans a fair payments. We’re
    0:27:06 not getting social security payments, right? We’re not, we’re not dying a malaria in Malawi or
    0:27:11 wherever, right? This won’t affect us. It’s just fascinating though that the people who I think
    0:27:17 are about to get the biggest dose of like, wow, be careful what you ask for are the ones that are
    0:27:22 most rapidly pro-Trump. So my sense is at this point, you know, as Mel Robbins would say, let
    0:27:30 them have at it. You ask for it, you’ve got it, Toyota. That’s definitely the big debate, I think,
    0:27:38 amongst people who voted the way that we did, that balance between wanting to live in a society
    0:27:43 that uplifts everybody, which I feel like is so core to the Democratic Party, and then thinking
    0:27:49 we are never going to have a reversal of these kinds of electoral outcomes unless there is real
    0:27:55 suffering. And that feels like a terrible place to be. I don’t want to be someone
    0:28:02 that wishes a bad economic outcome on anyone. I would love a world in which everyone can succeed
    0:28:08 to the utmost level. But it does seem like there has already been a bit of this. I don’t want to
    0:28:12 go as far as saying buyer’s remorse, but you’re seeing these videos coming up on TikTok. I don’t
    0:28:19 know. There’s a farmer who relies on this cost sharing program that gets funded through the
    0:28:24 Inflation Reduction Act that has been frozen and gone away, and he’s talking about potentially
    0:28:30 losing his farm. He was someone who voted for Trump. Katie Britt, the senator from Alabama,
    0:28:36 is out there talking about how we can’t have the NIH go away, the new policy of getting
    0:28:41 indirect costs down to 15%, which will basically mean that we have to shutter labs that are
    0:28:47 saving us from every disease under the sun and our huge economic boon for the country. I was
    0:28:54 astounded to see that for every dollar that we put into society from the NIH that we get
    0:29:02 $2.46 back, and it generates nearly $93 billion in economic activity in the U.S.,
    0:29:08 and also is what keeps us, the leader of the PAC, our competitiveness. It’s so funny to hear
    0:29:12 Republicans bemoaning how far we’re falling behind all the time, and then they’re like,
    0:29:17 “You know what we’ll do? We’ll get rid of, we’ll slash NIH funding.” That’ll be the way that we’ll
    0:29:24 really show the rest of the world. But in a more personal level, I talked about two or three months
    0:29:30 ago to a college student from the Midwest. He reached out, he watches the Five, is thinking
    0:29:34 about a career in politics, and really interested in political communication. We got on the phone,
    0:29:40 and he emailed me last week, and he said, and he said that I could share this. I’ve really
    0:29:46 appreciated hearing what you have had to say the past week on the Five and your podcast with Scott.
    0:29:50 So, yeah, Scott, you’ve really opened my mind on certain things that are currently going on as
    0:29:54 someone who voted for Trump, and you have been communicating a lot of frustrations I’ve been
    0:29:59 feeling alongside some family and friends who didn’t expect all the chaos. And then he puts in
    0:30:04 parentheses not to mention the tariffs, as our family owns a small business where all our products
    0:30:11 are made in China. So, this is a 20-year-old bro, right, from the middle of the country,
    0:30:17 comes from a conservative family. They love Trump, and it only took, I got this a week ago,
    0:30:23 so it only took two weeks of this level of chaos for him to feel strongly enough that
    0:30:28 he would write that down, right? That’s not a casual comment, and he told me that I could talk
    0:30:32 about it, that I could put that out on air. So, it’s obviously something that’s very emblematic
    0:30:38 of not only what’s going on in his life, but what’s going on in his orbit. Elon Musk’s popularity
    0:30:45 has gone from in 2016, it was plus 29 when he was the SpaceX guy, down to negative 11. So,
    0:30:51 something is happening. There is pushback out there and the realization that, yeah,
    0:30:57 maybe there are cuts we should be making, but this wholesale approach to just move fast and
    0:31:02 break things is not something that works for the public sector. Well, you’re already starting to
    0:31:07 see it, and it kind of goes to, and we’ll talk about this in a bit, potential solutions, but
    0:31:14 basically sales of Tesla cars are diving in the EU. Electric vehicle market declined by 6%
    0:31:23 overall in January. So, there is a structural decline, but sales of Tesla are down 63% in
    0:31:31 France, 44% in Sweden, 38% in Norway, 42% in the Netherlands, and 12% in the UK. And as someone
    0:31:37 who has worked with automobile companies, they measure share in sales and basis points. And
    0:31:43 that is, if year-on-year, you’re down a half a percent, the person running that country is
    0:31:52 sweating. I mean, these are, I mean, these literally are kind of implosions of sales. So,
    0:31:57 it does appear that finally, what, you know, everyone’s been outraged, the lack of outrage on
    0:32:02 the left. It does appear that people, that the bloom is off the road here. People no longer seem
    0:32:08 as a, you know, kind of this provocateur and innovator, but as someone who is a threat and
    0:32:14 that they just don’t need to own his car. I’m curious, with all of these lawsuits and a DOJ
    0:32:20 investigation piling up, how serious do you think the legal threat is to Musk and Doge and what could
    0:32:26 the long-term fallout be other than, obviously, his popularity is going down. But I’ll put forward
    0:32:32 a thesis. When you tell someone you can be a convicted felon and then reelect them,
    0:32:37 he’s essentially decided the incentives and disincentives no longer apply to me that I can
    0:32:43 break the law with impunity. Do you think there is a bridge too far here around these court cases?
    0:32:49 Well, so far, like you mentioned earlier on, the courts have gone against them in all of these
    0:32:55 instances. But the problem is, is that the courts move slower than 20-year-old kids that are in the
    0:33:00 Treasury payment system to some degree, right? It’s already disrupted people’s lives. Funding
    0:33:04 has been cut off, even if it is then getting reinstated. There are all these lawsuits with
    0:33:10 harrowing testimony from people who work for USAID and are stationed abroad and are saying,
    0:33:14 you’re sending me back to America. I haven’t lived there in 15 years. We have no infrastructure.
    0:33:20 We have no family. We have kids with special needs. And if there is a disruption in their care,
    0:33:24 I have notes from their doctors about the long-term suffering that they will endure.
    0:33:30 Nobody in the government seems to care at all about that. So the courts are great. And we’re
    0:33:34 going to have Mark Elias on the podcast in a few weeks. And I’m really interested to talk to him
    0:33:41 about the legal approach to all of this. But they’re moving to some degree too quickly. And I think
    0:33:47 on the fundamental level, what’s most disturbing about all of this is that they are going for
    0:33:52 a two-branch approach to government. I think they don’t care at all about Congress because
    0:33:57 appropriated money means nothing to them. And we should note as well that the government is only
    0:34:03 funded through March 14th. So they’re offering buyouts to people. They have no cash to pay them
    0:34:08 for the next eight months. So Democrats actually have a lot of power in that respect. And some
    0:34:13 of them are already talking about shutting down the government to be able to shut down Elon Musk.
    0:34:18 So I think that they are looking at a world of essentially one and a half branches of government.
    0:34:23 So they want the most powerful presidency or executive that you’ve ever seen in your life.
    0:34:28 And then they want about half of the judiciary. They want the good judges. Which is always how
    0:34:32 Trump talks about things. He says, “Well, of course we want immigration. We just want the good
    0:34:36 immigrants. And of course we want this, but we just want the good ones.” So he wants the judges
    0:34:41 probably that he picks. So he wants Eileen Cannon and the Supreme Court. And the rest of it can
    0:34:49 kind of go to hell. And that’s really new territory for us. Looking at a world in which the executive
    0:34:56 has no interest in having checks and balances with the Congress and who may openly flout
    0:35:04 these judicial decisions. We’ll have to wait and see in terms of how they approach it. Even this
    0:35:10 week will be an interesting incubator for that. But I think that they are so far emboldened even
    0:35:14 from where they were in 2020 when they were getting bad rulings that we could see something
    0:35:15 completely unprecedented.
    0:35:21 Bill O’Reilly recently said that he doesn’t believe Musk has as much power as we think.
    0:35:26 And then Time Magazine in the same week puts him on the cover behind the president’s desk.
    0:35:29 Do you think we’re overestimating Musk’s influence or underestimating it?
    0:35:34 I’m not sure. Listen, Time Magazine wants a good cover. It was a good cover. It definitely pissed
    0:35:39 Trump off even though he said like, “Oh, are they still in business? Who reads that anymore?”
    0:35:43 But he definitely, when he got Time Person of the Year just a month ago was super psyched about
    0:35:49 Time Magazine. I’m sure the answer is somewhere in the middle. I think you don’t want to be the
    0:35:54 person that went out there and took a big swing like Elon Musk actually doesn’t have that much
    0:35:59 power. You can even see from the level of disruption that we’ve had that he has that power and also
    0:36:05 that he swayed the election like this, you know, $290 million, whatever algorithmic changes
    0:36:11 to social media that will probably never understand the true impact of that or the gravity of that
    0:36:17 impact when, I mean, you’re not on Twitter anymore. I still am. I need to be there for work.
    0:36:26 It’s A, assess pool. But B, I can barely find content that I need to be able to do my job.
    0:36:32 Reporters that I follow are not showing up even in the, you know, the for you column.
    0:36:37 It pales in comparison to what it used to be, which was I thought the best news gathering
    0:36:42 site you could always find what you needed right away when you logged on to Twitter. And it’s not
    0:36:46 like that anymore at all. So I’m in the middle on it. What do you think? Are you a Bill O’Reilly
    0:36:51 or Time Magazine? You know, I don’t know. I know that the staff in the White House is more worried
    0:36:57 about trying to calm Trump down when he’s angry because he doesn’t drink or do drugs whereas with
    0:37:04 Musk, you just give him a ketamine infused juice box. There goes the bigger man. That bigger
    0:37:09 man part of the show is over. He’s back. That’s right. Anyways, with that, let’s take a quick
    0:37:19 break. Stay with us. The Republicans have been saying lots of things. Just yesterday,
    0:37:25 their leader said he wants to own Gaza. The U.S. will take over the Gaza Strip and we will do a
    0:37:32 job with it too. We’ll own it. On Monday, the Secretary of State said an entire federal agency
    0:37:36 was insubordinate. USAID in particular, they refuse to tell us anything. We won’t tell you what
    0:37:42 the money’s going to, where the money’s for, who has it. Over the weekend, Vice President Elon Musk,
    0:37:47 the richest man on earth, tweeted about the same agency that, you know, gives money to the poorest
    0:37:53 people on earth. We spent the weekend feeding USAID into the wood chipper. Could gone to some
    0:38:00 great parties, did that instead. But what have the Democrats been saying? People are aroused. I
    0:38:05 haven’t seen people so aroused in a very, very long time. Huh. That’s a weird way to put it,
    0:38:12 Senator. We’re going to ask what exactly is the Democrat’s strategy to push back on Republicans
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    0:39:26 Grammarly.com/podcast. That’s Grammarly.com/podcast. Hey, this is Peter Kafka. I’m the host of
    0:39:32 Channels, a podcast about technology and media. And maybe you’ve noticed that a lot of people are
    0:39:38 investing a lot of money trying to encourage you to bet on sports right now, right from your phone.
    0:39:44 That is a huge change. And it’s happened so fast that most of us haven’t spent much time thinking
    0:39:49 about what it means and if it’s a good thing. But Michael Lewis, that’s the guy who wrote
    0:39:54 Moneyball on the Big Shore and Liars Poker, has been thinking a lot about it. And he tells me
    0:40:00 that he’s pretty worried. I mean, there was never a delivery mechanism for cigarettes as efficient
    0:40:05 as the phone is for delivering the gambling apps. It’s like the world has created less and less
    0:40:09 friction for the behavior when what it needs is more and more. You can hear my chat with Michael
    0:40:18 Lewis right now on Channels wherever you get your podcasts. Welcome back. Over the weekend,
    0:40:22 Trump made history as the first sitting U.S. president to attend the Super Bowl. He also said
    0:40:28 he planned on announcing a 25% tariff on all steel and aluminum imports into the United States.
    0:40:32 Meanwhile, Trump raised eyebrows last week by suggesting he could turn Gaza into the
    0:40:38 Riviera of the Middle East during a press conference with Israeli Prime Minister Netanyahu.
    0:40:43 He floated the idea of U.S. owning Gaza and hit it at relocating 2 million Palestinians
    0:40:49 before walking back comments about deploying U.S. troops. And back home, a federal judge
    0:40:53 temporarily blocked Trump’s federal workers’ bio plan delaying a decision for the 2 million
    0:41:01 eligible employees 65,000 have opted in so far. Trump’s recent comments about the U.S.
    0:41:06 taking over Gaza have sparked a media backlash. I would argue, Jess, that this is another
    0:41:12 just weapon of mass distraction and that it’s just so ridiculous. I don’t know. It’s just sucking
    0:41:19 oxygen out of the room of the real issue, which is this digital coup, if you will. But how might
    0:41:24 this negatively affect the ongoing, in your view, ceasefire between Hamas and Israel or doesn’t have
    0:41:31 any impact on the conflict or the tension in the Middle East? Well, the thing that it does in the
    0:41:36 immediate term, even if he says it’s not something that he was completely serious about and you saw
    0:41:41 that Caroline Levitt, the press secretary, had to walk back his openness to boots on the ground
    0:41:47 the next day in the briefing because that was something that freaked even the biggest warmongers
    0:41:53 out there. They were like, “No, actually, we are not putting boots on the ground in the Middle East,
    0:41:58 but this helps B.B. in the immediate term ensuring up his right flank, which he definitely
    0:42:04 needs if he’s going to be able to stay in power.” And Trump always just has that side door available
    0:42:11 to him where he says, “Well, it was a negotiating tactic. We just needed to see what Egypt and
    0:42:16 Jordan were really made of.” The former deputy prime minister of Jordan said that this was a
    0:42:21 declaration of war on the Arab people. That doesn’t sound good, even if that is the place that you
    0:42:28 were starting at, that you can even think of a world in which you’re going to move two million
    0:42:34 people out of the place that they live. And this is not about defending Hamas, staying in charge,
    0:42:38 or anything of that sort. But just Egypt and Jordan have been very clear about this. Saudi
    0:42:42 got involved and said, “We’re not doing this either.” The Arab world has not been open to
    0:42:48 taking Palestinians for a very long time, which is one of the endemic problems with this conflict.
    0:42:52 And I don’t know what the solution is. We all wish that there was going to be able to be
    0:42:56 a two-state solution with a neat bow and we can’t get there. There are people who have
    0:43:01 dedicated their lives to it, like Tony Blair, for instance, and we have not been able to get there.
    0:43:08 But I think that it’s a little too easy to just say that it’s a distraction or it’s a sideshow
    0:43:13 because it is forcing people to have to deal with it, first of all, which makes a big difference.
    0:43:18 Like Marco Rubio is going to have to say something about it, right? And people like Tom Cotton are
    0:43:22 going to have to say something about it. The administration is going to have Pete Hegseth
    0:43:29 over at Defense. People are going to have to prepare themselves for the fact that his mercurial
    0:43:35 or herky-jerky approach to foreign policy might not go your way all the time. And we did talk about
    0:43:40 it as maybe it’s one of the advantages that our enemies have no idea what he’s actually going to
    0:43:47 do. But there are real implications for this and his words matter as much as we’d like to
    0:43:50 pretend that they don’t. And he says a lot of shit and he throws stuff against the wall and sees
    0:43:58 whether it sticks or not. You cannot, with a serious face, say at this point that Trump is
    0:44:06 not interested in being a modern-day imperialist, at least to some degree. The amount of times
    0:44:10 that he’s talked about, Greenland, he’s talking about the Panama Canal. Now he’s talking about
    0:44:14 whatever he wants to do in the Middle East, which is Jared Kushner’s vision. Jared Kushner said
    0:44:18 there’s a lot of waterfront property that’d be available. And it was interesting, my colleague
    0:44:22 on the five, Jesse Waters, said something about, well, Albania has said that they’ll take people.
    0:44:28 And a couple weeks ago, there was a story in the New York Times about how Albania gave the go-ahead
    0:44:38 for a $1.4 billion luxury hotel investment for Jared Kushner. So it always comes back to money.
    0:44:44 It always comes back to business prospects in all of this. And I don’t think this is something
    0:44:48 that’s going to happen. But I don’t think it behooves us to just kind of push it under
    0:44:52 the carpet or the rug and just say, oh, it’s a complete impossibility.
    0:44:58 So to your point, the premise, it starts from what I think is a legitimate premise. And that is
    0:45:06 the path we’re on now, like, okay, the way I would see right now, the Middle East or the problem
    0:45:12 in Gaza is there’s no moral clarity. And that is when World War II ended,
    0:45:19 we had the Nuremberg trials. And we basically said, this was genocide. And we’re shaming you and
    0:45:24 punishing you. And the world came to some sort of moral clarity. We don’t have that coming out of
    0:45:30 this conflict. And it feels as if all we’re doing is just setting up the exact same thing to happen
    0:45:36 again in two, five or 10 years. That Hamas will rearm, there’ll be people sympathetic, they will
    0:45:45 use aid and their popularity with their domestic population who are feel, you know, understandably
    0:45:52 agreed. And the same thing is going to happen. So the notion that we need to be creative around
    0:45:57 doing something different, that the status quo, the wash, rinse and repeat, we are going to see
    0:46:05 the mother of all shampoo effect here. It’s just going to happen again. The current construct just
    0:46:10 does not work. Now, but this notion of a two-state solution where the two states are either Egypt
    0:46:16 or Jordan, neither, I mean, here’s the problem of relocating these two million residents,
    0:46:20 whether you want to call it ethnic cleansing or some sort of a part, whatever you want to
    0:46:27 term you or relocation or, or condo development or a creative solution. The problem is
    0:46:33 these two million people don’t want to leave. And even more, nobody wants to take them. Albania,
    0:46:39 what’s the population of Albania? I don’t look at the border. You want to see a fortified border?
    0:46:46 Look at the border between Egypt and Gaza. Albania is 2.7 million. And we’re talking about
    0:46:52 two million people who need to go somewhere. What Egypt and Jordan have decided is that
    0:46:57 the elements of this population that they cannot risk incorporating is chaos and violence.
    0:47:06 So this just doesn’t seem like what I’d call a viable solution for anybody. So until we have,
    0:47:14 do we need to be creative? Yes. But I find this, most of those just sort of kind of ridiculous
    0:47:20 that, okay, you’re going to relocate two million people and then put up a bunch of
    0:47:27 residence inns and, and Trump towers and, you know, Western hotels and then invite the rich
    0:47:34 ones back? I don’t, it’s like, okay, walk me through how this logistically actually makes
    0:47:41 any sense. So I don’t see any viable path here. What do you think is the significance of the
    0:47:44 Judge blocking Trump’s federal work or buyout plan and how could this play out in the coming
    0:47:50 weeks? What I think it’s significant, and I already mentioned this, that I know this is Elon’s
    0:47:56 playbook and he does it all the time. He did it with Twitter in 2022 as well, but the cash isn’t
    0:48:00 there to do it. So it is pretty significant. I think there’s a pretty strong case for saying,
    0:48:04 you know, you’re not even talking about money that’s been appropriated. Are you paying out of
    0:48:09 pocket? He’s a very rich guy versus man on the planet. Maybe that’s what he intends to do,
    0:48:13 but going out to people and saying, you can go on that trip you’ve always wanted to take.
    0:48:18 I don’t know. Actually, you have no salary to be able to do that. It’s pretty duplicitous.
    0:48:25 The big question is what orders are they going to abide by and what orders, frankly,
    0:48:32 are they going to be using the younger dogeys to get around? You know, what will be authorized?
    0:48:40 What will the actions from the top level that look pretty normal, right? Post rulings be and
    0:48:49 then what are 20-year-old kids that can hack into anything able to actually do? And I know that it’s
    0:48:55 not necessarily to turn us into a technocratic state. Maybe that is for some people’s vision who
    0:49:02 are involved in this, but I think that because Trump is a lame duck, we have four years essentially
    0:49:08 to break as much as possible that they will be looking to push boundaries in ways we haven’t
    0:49:12 seen before. So while they might have abided by some of these rulings before, they’re going to
    0:49:18 push the envelope even further is my feeling. I mean, I guess the question is their attitude is
    0:49:23 it doesn’t matter if it’s legal or illegal. If we make an offer and people accept it,
    0:49:28 then it’s done. So before you can get caught robbing the bank, just spend the money and enjoy
    0:49:33 yourself. And then if you get caught, okay, we’ll give the money back. There is definitely a kind
    0:49:39 of like a move fast and break things kind of element here and have no regard for institutions
    0:49:44 or process. Just see if you can get away with it. Whenever I see Republicans, I feel like they’re
    0:49:48 sort of like, I can’t believe we’re getting away with this shit. Totally. And it’s like tickling
    0:49:52 their sensors. Isn’t this amazing? And I even, I don’t know if I’m imagining this, but I’m wondering
    0:49:56 if they’re even getting a little bit nervous like, Jesus Christ, I didn’t realize it would be this
    0:50:01 easy. But they are getting upset about things that matter to them. Like I already mentioned,
    0:50:09 Senator Katie Britt, Jerry Moran, Senator Wicker, who are big proponents of USAID programs had to
    0:50:16 go to Rubio and plead with him. Rubio was a fan. Rubio was a fan last year. He was pushing Biden
    0:50:19 to allocate more money. And that links to the problem. And I know we’re going to talk about
    0:50:24 the Democrats next. Well, let’s do that. But first, we have to take one more quick break. Stay with us.
    0:50:32 All right. So here’s the deal. Take a former world number one. That’s me, Andy Roddick,
    0:50:36 adding the journalist who knows everything about tennis and a producer who’s still figuring out
    0:50:40 how to spell tennis. You get served with Andy Roddick, a weekly podcast where we break down
    0:50:45 the game we all love. We cover the biggest stories, talk to the sports biggest stars,
    0:50:48 and highlight the people changing tennis in ways you might not even realize.
    0:50:53 Whether it’s grand slam predictions, coaching changes, off-court drama,
    0:50:58 or the moves shaping the future of the sport, we’ve got it all. This podcast is about having fun,
    0:51:02 sharing insights, and giving fans a real look at what makes tennis so great.
    0:51:08 Catch serve with Andy Roddick on Spotify, Apple podcasts, wherever you listen. Or watch us on
    0:51:20 YouTube. Like, subscribe, follow, all that good stuff. Let’s get started. Welcome back. Before
    0:51:25 we wrap, Democrats are stepping up their strategy. Rallies are gaining momentum. Schumer is calling
    0:51:30 for opposition to every Trump nominee, and Jefferies is making his stance clear in negotiation
    0:51:34 letters. Jess, do you think this is the right approach? Do you think it’ll be effective?
    0:51:40 No. Have you seen these rallies? It’s horrible. So you have, John Stuart did a great
    0:51:47 impression of Schumer of them, where, you know, you have a guy in his 70s who’s not the most
    0:51:54 charismatic. I’m being generous here. Screaming we will win after we just lost everything.
    0:52:01 And then you have Congressman Al Green shaking his cane in the front of the frame. So it’s just
    0:52:07 Schumer’s face with a cane going back and forth. Or Maxine Waters, you know, trying to barge into
    0:52:16 the Department of Education. And I’ve noticed that all of the key speakers at these rallies are
    0:52:23 safe seat Democrats. And that’s really the wrong approach to all of this. I almost want to see
    0:52:29 exclusively people who have run tight competitive races out there talking about what they think
    0:52:34 is the right thing for us to do. Because it’s, you know, it’s all well and good and people are
    0:52:39 great communicators and you be on your social feeds and everyone should be amplifying as much
    0:52:45 of the bad shit that Republicans are doing as possible. But I feel like it just sends the
    0:52:52 message that we haven’t learned that much from the election and that frankly, we’re proliferating
    0:52:58 more hysteria than the average voter is interested in hearing at this point. So it has to be so much
    0:53:04 more targeted. I know how came Jefferies put out a 10 point communications plan. But everyone has
    0:53:10 to, you know, see what their constituents are interested in and what their temperature is
    0:53:15 in terms of Trump versus Elon. There seems to be a big dichotomy there. Jared Golden,
    0:53:22 who is a Democratic Congressman who won in a competitive district, a Trump very rural
    0:53:27 district in Maine, said that all the calls that he’s getting to his office are about Musk.
    0:53:32 People aren’t upset about Trump. They’re upset about Musk. And that’s what he’s leaning into.
    0:53:37 And people have to find kind of their North Star for all of this. But the thing that was driving
    0:53:43 me crazy in terms of the messaging is, I don’t know if you watched after the election, Tony
    0:53:48 Fabrizio, who was Trump’s pollster and Chrysla Savita, who was one of his campaign managers,
    0:53:54 participated in a panel with the Democratic campaign managers as well. And they were talking about,
    0:54:00 you know, what worked, what didn’t work. And they said that Project 2025 was destroying them
    0:54:06 up until a couple months before the election. So it had a 57% negative approval rating.
    0:54:12 And that we were doing a great job hammering it. That all of those, even if we kind of had
    0:54:15 rolled our eyes at it, it had been hugely effective that every time we said, well,
    0:54:20 this is the Trump 2025 playbook. This is what they want. This is what they’re going to do.
    0:54:27 And that has disappeared even as people who wrote Project 2025 are in charge of OMB,
    0:54:34 Brendan Carr at the FCC. And I want that messaging back on mass. I want everyone talking about this
    0:54:41 to say Time Magazine did an analysis. Two thirds of the executive orders that Trump has already
    0:54:47 implemented are out of Project 2025 already. I mean, I was happy to see those Democratic
    0:54:52 Congress people walk over. I think at least, I think they need to be seen doing anything,
    0:54:58 but the optics here, I agree with you. It felt like a senior’s home when they found out water
    0:55:04 aerobics were canceled or jello night had been switched to Thursday. I mean, it just felt, oh,
    0:55:08 God, that’s, that’s how we’re going to win this fight. That’s the army we’re sending in.
    0:55:14 You know, as we try and process this, there’s, I want to move to like, okay, what do we do?
    0:55:18 And there’s, I would argue, and I want to put forward some potential ideas and have you respond to
    0:55:22 them. There’s short-term and there’s long-term. And the first thing you got to do in any sort of
    0:55:27 strategies, you got to determine where’s the soft tissue, what’s the leverage, what are our assets,
    0:55:32 what can be exploited. And he King Jefferies, I thought was actually quite eloquent and honest
    0:55:36 when he said, they control all three branches of government. There’s just not a lot we can do
    0:55:42 from kind of a legislative level. And when the stuff gets to judges, it gets pushed back, but
    0:55:48 they’re, you know, they’re moving at this Blitzkrieg speed. And my view is, okay, I think you go after
    0:55:55 the money, like what they’re doing by hacking and turning off these payment systems or intervening,
    0:56:00 I don’t know what the right term would be. And I think you go after, I think you go after musks,
    0:56:05 financial interests. So it’s already happening in Europe, as I previously mentioned, Tesla sales
    0:56:11 are going down. I think that our congressional representatives and people who think what’s
    0:56:17 going on here is a total subversion of our democracy to make it known that you probably
    0:56:22 shouldn’t sign up for T-Mobile right now, because T-Mobile has just struck a deal with Starlink.
    0:56:28 You probably shouldn’t be thinking about any advertiser on Twitter. That’s an obvious one.
    0:56:34 You should be thinking about, okay, United Airlines has just announced a big deal with Starlink.
    0:56:39 How do you go after the pocketbook? I think that’s really what Musk cares about. It’s already
    0:56:44 happening with Tesla. I don’t see any reason. Should the department, should veterans groups
    0:56:51 be doing anything around Tesla, Starlink, any of his economic interests? I think you go after the
    0:56:57 purse, because that’s what I think these people care about. Over the medium and the long term,
    0:57:03 I think you draft resolutions and say, okay, if this unelected group of people can go in and start
    0:57:11 turning off payments, we’re going to propose turning off payments or anything related to Starlink.
    0:57:18 To a certain extent is a huge beneficiary, and I even wrote a post titled, “Welfare Queen.”
    0:57:22 The notion that he’s trying to cut off payments and claim the government is too big and that
    0:57:26 its large S is wasted. Meanwhile, he’s one of the biggest beneficiaries from this large S.
    0:57:30 Should we be thinking about, one, how do we go after the economic interests
    0:57:36 of Elon Musk to say, we’re not down with this and you circumventing democratic channels
    0:57:42 to implement what you think is right, and we’re going to punish you and your companies? There’s
    0:57:47 nothing illegal. You don’t have to sign up for T-Mobile that’s introducing Starlink. You don’t
    0:57:54 have to fly United Airlines, which is signed a contract with Starlink. Then over the medium
    0:58:00 and long term, I think you just have to tell Republicans, okay, you realize that if you can
    0:58:07 do this, then we can shut off Starlink. We have our own programmers and we’ll find out if a judge
    0:58:13 thinks that’s legal or not. I do think, though, the nuclear option is now on the table, and that is,
    0:58:21 I believe that the Democrats should credibly threaten to get in the way of blocking the extension
    0:58:28 on our debt ceiling such that the next Treasury auction fails, because at the end of the day,
    0:58:33 the reason why the tariffs were rolled back is the leverage in the people that Trump listens to
    0:58:40 are corporations and shareholders, and they called him around these ridiculous Canadian and Mexican
    0:58:45 tariffs and said, do not do this. This will have an immediate impact on the stock market. The adult
    0:58:55 in the room is the stock market and the 10-year bond, and he basically got these illusory symbolic
    0:59:02 concessions and then walked them back. I think if the Democrats say, okay, you want to play
    0:59:10 Russian roulette, we’re going to load the chamber around the upcoming Treasury auction.
    0:59:14 If you want to call all your buddies and tell them that interest rates are about to spike,
    0:59:19 which will take the stock market down, and I’m still trying to figure out if that hurts the
    0:59:26 1%, 1% of America’s population owns 90% of the stocks. I think that the real leverage here is
    0:59:31 around money and is around, you want to shut down the economy, you don’t believe in a democratic
    0:59:36 process, fine, we’re going to shut down the economy, and you’re not going to be able to make the
    0:59:40 interest, the upcoming interest payments, and you’re going to be the president who, for the first time,
    0:59:46 was so offensive, was so non-democratic that we felt we had no choice, but to get in the
    0:59:51 way that you’re about to be the first president where a Treasury auction where America did not
    0:59:57 pay its debts, and let’s see what happens, boss. But I’m trying to think of where we have leverage,
    1:00:01 and those are the only places I can think of because per what Hakeem Jeffery said, us just
    1:00:08 screaming outrage and waving our cane in front of a federal building. That’s not working, right?
    1:00:13 We need to go after the money, and we need to say you’re going to be the president that takes
    1:00:19 the stock market down 8% or 10% on the opening bell next Wednesday after a failed Treasury auction.
    1:00:25 Your thoughts? I love that idea, and I’m sure they’re considering that alongside the negotiations
    1:00:31 that are going to come up in March because the Republicans had these high hopes for one massive
    1:00:36 bill, which seems like a really stupid way to be funding the government anyway. But if you
    1:00:41 put those two things together, that’d be very difficult for Trump to weather, and he will be
    1:00:48 the person in charge if we fail at auction, or if the government shuts down in general,
    1:00:54 though it seems like he does want to furlough employees anyway. But I have an idea that maybe
    1:01:01 could be used as a compliment to this, and I’m hoping that Democrats, I’m loosely calling this
    1:01:05 the Democratic Disruption Plan, because everyone loves this term disruption. It’s become very chic,
    1:01:08 right? And that’s what the Republicans have run on, that we’re just trying to disrupt things. You
    1:01:13 got to shake it up because there’s all of this waste in there that we can cut. And I think that
    1:01:20 we need to counter program with our own doge. And you can go back to the ’90s, and the Clinton
    1:01:24 administration did this. They called it the National Performance Review. I didn’t realize it was as
    1:01:32 successful as it was. It made a 426,000 cuts to the federal workforce going agency by agency,
    1:01:36 and they did it with a lot of precision. They said, “Are there offices that would
    1:01:40 can be closed? Regulations that we can cut? Are there programs that actually don’t need to be
    1:01:47 funded within USAID of the $44 billion that goes out there?” I’m sure there are cuts that we can
    1:01:52 make, but we should be the ones to propose them. So just take all the crap that they’ve been spewing
    1:01:58 and throw it back in their faces. And I think that that would be really effective and show to the
    1:02:02 American public that we are serious about governing, that we do know that there is a huge
    1:02:09 waste, fraud, and abuse problem, but that 66% of US spending is untouchable. And that’s the most
    1:02:14 important part, that we have to be the party out there saying they’re lying to you about cuts that
    1:02:20 they can make. The stuff that they’re talking about are tiny. They’re trimming like a dollar
    1:02:25 off of the budget, but we will never let them touch your social security, your Medicare,
    1:02:31 your Medicaid. And I think that that is a place where we can run and win. And I want to say as
    1:02:36 well, on the Department of Education front, I don’t know if you saw this, the national report
    1:02:46 card test results came out. And it breaks your heart to see stuff like this. And I just…
    1:02:51 So just reference all time low for eighth grade reading levels, is that right?
    1:02:58 Well, since testing began, so 1992. And when the Republicans come out and they say, “I want to
    1:03:03 abolish the Department of Education,” which by the way is in Project 2025, and you look at those
    1:03:12 test results, you understand how average American parents who don’t have optionality to go to a fancy
    1:03:17 private school alternative are being denied vouchers to maybe take a few thousand dollars and put
    1:03:22 their kids in religious schools, which in general have been performing better than the average public
    1:03:27 school, that they say, “You know what? Yeah, burn it all down.” Trump had a great line where he said
    1:03:32 about Linda McMahon, “You know, I don’t want you to have a job for that long,
    1:03:37 because I want you to destroy the Department of Education.” And what they want in the end is for
    1:03:42 private equity to own our education system. That’s what’s already happening. They have been making
    1:03:48 tons of money off of these schools, specifically in the charter school space. But if we are going
    1:03:54 to have public schools under attack at this level and believe strongly in making them better than
    1:03:59 they are certainly, and hopefully taking them into some sort of golden age, we have to play ball
    1:04:03 on the education front. And we have to say, “Abolishing the Department of Education isn’t
    1:04:08 the answer necessarily, but these are our real reform policies.” And that includes going after
    1:04:15 the teachers unions and saying to Randy Weingarten, “No, this all is imperfect. You know, we haven’t
    1:04:20 certainly recovered from the lows of the COVID era and all the damage that was done to those kids,
    1:04:25 not only as academic learners, but as social emotional beings.” But that Democrats aren’t
    1:04:30 going to be afraid to touch that third rail anymore. Someone like Josh Shapiro has done that. He said
    1:04:35 before that he’s for school vouchers, “Wouldn’t you rather live in a world where we have better
    1:04:40 public schools and some kids have the optionality to go to religious schools or private schools
    1:04:44 with the help of the government so that we can save the public school education system?”
    1:04:51 So a lot there. I felt that I got into it a little bit. Is it Ronit Weinberg, the head
    1:04:57 of the National Teachers Union? Randy Weingarten. I got that one close. Yeah. Randy Weingarten.
    1:05:05 You were in the Jewish realm. Something. I was circling the white fish. Anyways,
    1:05:10 I said that I thought that the union she represented was using the kids’ drug meals.
    1:05:16 During COVID. I’m sure she loved that. Well, during COVID, she decided that we have to protect the
    1:05:21 teachers. And she was basically saying, “You’ve got to pay us more and give us a ton of time off.”
    1:05:27 And the reality was that the population of teachers in America is the least vulnerable. It was the
    1:05:32 least vulnerable to COVID. They were young, primarily female, primarily thin. These were
    1:05:38 the least at-risk people in America. And she was using basically kids and their mental health,
    1:05:44 which we found were severely impacted by being out of school, such that she could try and find
    1:05:49 a moment of leverage to get more money for her dues-paying members. I think teachers’ unions,
    1:05:53 and I’m casting a broad brush across all of them, but we have a tendency to
    1:05:58 sanctify all of them, not recognize them. Some of these unions are just bottom line corrupt
    1:06:03 and really don’t seem to care that much about kids, despite their hush, grandmotherly tones.
    1:06:07 Where I would depart a little bit with you, and it sounds like also Governor Shapiro,
    1:06:15 is I am very wary of vouchers, because I think effectively what vouchers do is like everything
    1:06:21 else in our society, the kind of the narrative of let’s shut down the Department of Education and
    1:06:26 let’s take money and just give people choice and give them vouchers. I think theoretically,
    1:06:29 it makes a lot of sense. There are instances where someone would say, “I’d rather take the money,
    1:06:34 put them in a religious school, or I want more choice.” I get it, but effectively on the ground,
    1:06:39 I think what happens is what always happens in our government the last 40 or 50 years,
    1:06:45 it is nothing but a naked transfer of wealth from the poor to the rich, because the reality is the
    1:06:52 majority of rural areas or poor areas don’t have a private school option where they can use the
    1:06:58 voucher. The only reason they have a school is because of federal mandated legislation that they
    1:07:02 have to have transportation, and they have to have a school, and the school has to be funded.
    1:07:08 And all you are doing when you give, say, people a $10,000 voucher, and I was on the
    1:07:13 board of my kid’s school, there would be probably some people who are middle-class who would rather
    1:07:18 have the choice. We charge $22,000, they come up with the $12,000, it would provide access to a
    1:07:24 private school, it’d be good for them. But really what it would be is a $2 million giveaway to the
    1:07:30 other 200 families that can’t afford it, and it would just take income and desperately needed
    1:07:36 resources out of the public schools in that area. So I get it theoretically, but I think on the ground
    1:07:42 all vouchers end up doing is again transfer money from the poorest people in the districts that need
    1:07:49 mandated head start in schools and transportation and food programs to wealthy people who would just
    1:07:54 get, that would be, you know what, that would be just, you have two kids, that would be a $40,000
    1:07:59 gift to you and me. My guess is, I don’t know if you said, do you send your kids to private or public?
    1:08:05 One isn’t in school and one is in a private pre-K. There isn’t public school for her yet.
    1:08:12 So this is what a voucher program would be. It’d be a $30,000 gift from government to you and me.
    1:08:16 I totally understand what you’re saying and I, maybe I got a little ahead of my,
    1:08:22 ahead of my skis or I didn’t mean to say that I want to turn into the party of vouchers. What I
    1:08:28 wanted to say is that if we continue to look so detached from reality that we can, we are telling
    1:08:34 people that these scores are okay with us and we’re going to do nothing about it, which is
    1:08:38 essentially what we’re doing without having our own reform policies, that we’re going to lose people
    1:08:43 forever because there’s nothing that is more valued in society than our children. We have
    1:08:48 Governor Glenn Yonkin in Virginia because Terry McCall have got up there and said,
    1:08:54 you know, the kids, the kids aren’t yours, right? They belong to the teachers when they go. So I,
    1:08:58 I just want to have the conversation about it. I think it can be perhaps done in some sort of
    1:09:06 targeted way, but we look so silly if we just keep saying we’re going to, we’re going to do it the
    1:09:10 same way. We’re just going to keep going down this path. That’s what I meant. But I, I would
    1:09:15 like to see, you want to talk about a way to save government tens, if not hundreds of billions of
    1:09:21 dollars over the next 30 or 50 years, do what Japan does. America has a 40% of its population is
    1:09:26 obese. That is an enormous strain on the well-being, the mental health and our financial system. And
    1:09:32 one of the reasons healthcare costs $13,000 a year here per person at 6,500 in Japan. You know what
    1:09:37 the, we have 40%, 70% of America’s obese are overweight, 40% obese. Do you know what the
    1:09:45 percentage of obese pop, of the population in Japan is obese? 12, four. And here’s where it
    1:09:50 starts. If you wanted, if you wanted to increase the well-being of children in America, you would
    1:09:55 do what Japan does. And that is you’d find the extra money to have a chef at every school. And
    1:10:01 the chef has one mandate. Everything has to be fresh. There are absolutely no processed foods
    1:10:06 allowed in school because this is what we do. We give these kids shitty, sugary, cheap food.
    1:10:10 They get obese because the deal is, okay, we can get them addicted to the food industrial complex
    1:10:15 who basically ran every fucking app last night on the Super Bowl and then hand them over to the
    1:10:20 diabetes pharmaceutical complex. And that’s the axis of evil. North Korea and Iran are nothing
    1:10:24 compared to the food industrial and the diabetes industrial complex in this nation.
    1:10:28 And in Japan, they say, we’re going to spend the monies. Have you seen those interviews with the
    1:10:35 kids coming out of school? What’s your favorite food? Broccoli. Yeah. Right? And they say, your job,
    1:10:40 at three in the morning, these chefs get up at every school, not hugely paid, but a lot of them do it.
    1:10:45 Former chefs, they go to the fresh fish market, they go to fresh, and they have to find fresh food
    1:10:50 every goddamn day. And these kids grow up with a different sense of nutrition. I love the
    1:10:55 idea of thinking out of the box and thinking longterm, but corporate interests get involved.
    1:11:01 And again, this is our school system and our children. What I have found is that America is
    1:11:07 nothing but a platform to transfer money to companies and shareholders who trade and traffic
    1:11:13 in addiction, addiction to food, addiction to opiates, addiction to sex, addiction to Dopa.
    1:11:20 And we use the kids as basically body bags or Dopa bags. I have gotten so far off track,
    1:11:26 Jess. No. Bring me back. Reel me back, Jess. I would just reel you back by saying, as we
    1:11:32 are on the precipice of probably an RFK junior health and human services secretary, that the case
    1:11:37 that you just made is why they should have carved out a role for him at like USDA and had somebody
    1:11:42 who believes in vaccines. He’s great on this. Yeah. He’s great on this. Yeah. I agree. There,
    1:11:46 did I bring you back? I don’t know. Read us out. We got to go. Thank you. Thank you very much. Let’s
    1:11:50 leave it there. That’s it for the episode. Thank you for listening to Raging Moderates, our producers,
    1:11:56 our David Toledo and Cheninye Onike, our technical directors, true boroughs. You can find Raging
    1:12:02 Moderates on its own feed every Tuesday. That’s right. Raging Moderates on its own feed. If you
    1:12:08 want us to keep making this pasta of a podcast, please hit subscribe right now on YouTube or on
    1:12:14 our distinct Raging Moderates feed. Please follow us wherever you get your podcasts without fear or
    1:12:21 favor. That’s right. That’s right. The hit of the show, the one on Fox. That’s what’s coming next,
    1:12:27 and who is the one? That’s right. It’s just Harloff. We are immune. We are super power fucking
    1:12:31 fearless from tweets from the wealthiest man in the world. I’m not going to read any of those
    1:12:37 11,000 comments. You’ve definitely read them all. I have not. I’m not on Twitter. I’m not on X. I’m
    1:12:42 still calling it Twitter. Anyways, and by the way, who sold his Tesla two years ago and before
    1:12:48 he sold it, took a big fat fucking dump in the passenger seat. That’s right. That’s your man.
    1:12:57 Hit subscribe now. Just have a great rest of the week. You too.
    1:13:07 [BLANK_AUDIO]

    Scott Galloway and Jessica Tarlov break down Elon Musk’s growing influence in the government and the legal battles piling up against him and DOGE. They dive into Trump’s latest federal worker buyout plan, his controversial comments on Gaza, and the Democrats’ strategy to push back. 

    Follow Jessica Tarlov, @JessicaTarlov

    Follow Prof G, @profgalloway.

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  • Gemini 2.0: 20x Cheaper Than GPT-4?! (DEEP DIVE) | Logan Kilpatrick

    AI transcript
    0:00:02 (upbeat music)
    0:00:06 – Hey, welcome back to the Next Wave Podcast.
    0:00:07 I’m Matt Wolf.
    0:00:08 I’m here with Nathan Lanz.
    0:00:11 Today we’re joined by Logan Kilpatrick,
    0:00:15 who is the Senior Project Manager over at Google DeepMind.
    0:00:17 And the day we’re recording this episode
    0:00:19 is the same day that Google just released
    0:00:22 a whole bunch of new AI tools.
    0:00:26 Gemini Flash 2.0, Gemini Flash 2.0 Lite,
    0:00:30 Gemini Flash 2.0 Pro, all sorts of really, really cool stuff
    0:00:32 coming out of Google right now
    0:00:34 and Logan’s gonna break it all down.
    0:00:36 And you’re gonna get a pretty grand overview
    0:00:39 of where the AI world is headed according to Google.
    0:00:41 So let’s just go ahead and dive right in
    0:00:43 with Logan Kilpatrick.
    0:00:44 – Thank you so much for joining us.
    0:00:46 It’s probably a really busy day.
    0:00:47 So I really appreciate you taking the time
    0:00:48 to join us today.
    0:00:50 – Yeah, I’m excited to catch up with you both
    0:00:51 and talk about all things Gemini
    0:00:53 and what’s happening in the AI world.
    0:00:55 – Well, so this is actually your second time on the show.
    0:00:58 So we’ve already kind of dove into some of the backstory
    0:01:00 and introduced people to you in a past episode.
    0:01:02 So let’s just jump straight into it.
    0:01:06 Can you break down what is 2.0 Flash, 2.0 Flash Lite,
    0:01:08 2.0 Pro, like what are the differences?
    0:01:10 What’s better about these models
    0:01:12 than what was out prior to them?
    0:01:14 – I think this is an exciting moment for us
    0:01:16 just because of like the amount of effort and work
    0:01:18 that’s gone into bringing Gemini 2.0
    0:01:19 actually into the world.
    0:01:21 And Matt, you were there, Nathan,
    0:01:23 I don’t remember if you were there at I/O last year,
    0:01:27 but we announced 1.5 Flash and the long context
    0:01:29 and a bunch of this other stuff last May.
    0:01:30 So like literally less than a year ago.
    0:01:33 And for the last year-ish, Flash has been this
    0:01:36 like wild success story for us
    0:01:38 of building a model that developers really love.
    0:01:41 And a lot of that is rooted in like the right trade-offs
    0:01:44 of like cost, intelligence, performance, capabilities.
    0:01:46 And if you look at the 1.5 Flash model
    0:01:48 and you think about like, how do we do this better?
    0:01:50 It’s like, you have to make it more powerful.
    0:01:52 You have to give it more capabilities.
    0:01:55 You have to do that all while not making it cost
    0:01:57 a lot more money for developers.
    0:02:00 And it feels like we pulled a rabbit out of the hat
    0:02:01 to a certain extent with 2.0 Flash
    0:02:04 because like the actual cost for developers,
    0:02:06 it was historically like seven and a half cents
    0:02:08 per million tokens.
    0:02:09 Now it’s 10 cents.
    0:02:11 So the blended cost is actually less for this model.
    0:02:13 And we did all that while like,
    0:02:15 this model is actually better than Pro.
    0:02:16 It has all these capabilities.
    0:02:18 It’s natively agentic.
    0:02:21 It has search built into it as code execution built into it.
    0:02:23 – Yeah, it’s just exciting for me as a developer.
    0:02:27 Like I think ultimately you remove the cost barriers
    0:02:29 and all these other things for people to build
    0:02:30 really cool stuff.
    0:02:31 And like that’s what enables the world
    0:02:33 to make really cool products.
    0:02:34 So I’m super excited.
    0:02:36 So that’s the sort of headline for Flash
    0:02:37 is better, faster, cheaper,
    0:02:40 which continues to be sort of the tagline.
    0:02:41 I need to get my Gemini t-shirts
    0:02:43 that say better, faster, cheaper on them.
    0:02:46 – I read that it’s better than GPT-40,
    0:02:47 but like 20 times cheaper.
    0:02:49 Is that like roughly correct?
    0:02:51 – Yeah, which is just crazy to me.
    0:02:53 And I think like we’ve got a lot of work to do.
    0:02:56 I think one of the dimensions of the Gemini story
    0:02:58 is like we continue to put out really great models.
    0:02:59 I think we need to do a great job as well
    0:03:01 of like going and telling the world
    0:03:03 about this technology that we’re building.
    0:03:05 ‘Cause I don’t think people really understand
    0:03:07 and actually for a lot of developers,
    0:03:08 the cost is the reason in many cases
    0:03:10 they don’t build the stuff that they want to build.
    0:03:13 It’s like, I can’t afford to put this thing into production.
    0:03:14 It’s too expensive.
    0:03:17 So I think Flash is really important in that story.
    0:03:18 But we also landed 2.0 Pro.
    0:03:22 We also landed an even cheaper version of Flash Light,
    0:03:23 which sort of has the capabilities
    0:03:24 pared down a little bit,
    0:03:26 but makes it so that we can keep delivering
    0:03:30 on that like frontier cost performance sort of trade often.
    0:03:31 And that model is in preview
    0:03:33 and it’ll go GA in the next few weeks
    0:03:35 as we iron out the last few bugs.
    0:03:38 And I think Pro gives me a lot of excitement
    0:03:43 about those whole narrative of pretraining, being dead.
    0:03:45 An interesting sort of realization
    0:03:47 I had after our conversation with Jack Ray,
    0:03:49 who’s one of the co-leads for the reasoning models
    0:03:53 in DeepMind is there’s this non-linear amount
    0:03:57 of extra effort it takes to make the models
    0:03:59 continue to get better.
    0:04:00 Like you look at like, okay,
    0:04:03 what does 3% mean on some benchmark?
    0:04:06 Like you think 3% and we think of like the normal world
    0:04:10 where 3% is like actually 3% and like in the model world,
    0:04:14 3% is actually like a 25% increase
    0:04:16 in like the amount of efforts that went into this.
    0:04:19 But also that 3% is like the difference
    0:04:22 between unlocking a bunch of capabilities
    0:04:25 and a bunch of use cases that like just didn’t work before
    0:04:28 because like the thing failing 3% to 4% of the time
    0:04:31 versus not is the difference between you putting AI
    0:04:33 into production at your company and not.
    0:04:35 So it actually matters a lot.
    0:04:36 And that’s why I think we continue to push
    0:04:37 on that frontier.
    0:04:41 – So what’s the big difference between like these new models
    0:04:44 and the last ones as far as like how they were created,
    0:04:46 is it like more parameters that are being trained on?
    0:04:49 Obviously, the big narrative like you just mentioned
    0:04:52 with things like deep seek and O3 and things like that
    0:04:55 from open AI are the sort of what happens at inference, right?
    0:04:58 When somebody enters a prompt, it does all of this thinking
    0:05:00 and that’s really what they’re sort of like pushing on
    0:05:02 is like the next sort of breakthrough.
    0:05:03 Like what sort of breakthroughs,
    0:05:06 what changed between the last models and this one
    0:05:07 to make this one so much better?
    0:05:09 – Yeah, there’s two dimensions of this.
    0:05:12 One, it’s a story of the really difficult work
    0:05:14 of doing algorithmic improvements and breakthroughs.
    0:05:16 And I think like the team at DeepMind
    0:05:17 stuff way beyond my understanding
    0:05:20 as far as how they’re able to make this continue to work.
    0:05:23 So I think there’s like core fundamental research
    0:05:24 advancements that are happening.
    0:05:26 And there’s a lot of like data efficiency wins as well,
    0:05:27 which is also exciting.
    0:05:30 But as far as like new capabilities of these models,
    0:05:34 I think the two big ones is when Gemini was first announced,
    0:05:36 it was announced as this model that’s natively multimodal.
    0:05:39 And it was natively multimodal in the sort of input sense
    0:05:42 that it could really understand the videos,
    0:05:45 audios, images that it was being given.
    0:05:47 And that was one of the main differentiators.
    0:05:50 Today, the model’s actually capable of doing that
    0:05:51 except on the output sense,
    0:05:53 which I think was a huge jump for us.
    0:05:54 And it actually requires again,
    0:05:58 a bunch of like non-trivial amount of engineering work
    0:06:00 in order to make the models capable of doing that.
    0:06:02 I had an interesting conversation a few weeks ago
    0:06:04 with someone on our research team,
    0:06:05 which reminded me of this.
    0:06:07 Someone asked a question of like,
    0:06:09 why does it matter if the models are capable
    0:06:12 of natively outputting these multimodal capabilities?
    0:06:14 Like we have really great text-to-speech models.
    0:06:16 We have great speech-to-text models.
    0:06:18 We have great image generation models.
    0:06:20 Like why is it cool that the model can do this natively?
    0:06:23 And there’s all of these really great examples,
    0:06:26 like a calculator versus like, I don’t know,
    0:06:29 an AI model that has access to code execution.
    0:06:31 Like the code execution version can really like solve
    0:06:33 these problems in a really complicated way
    0:06:36 that you wouldn’t otherwise be able to,
    0:06:38 or at least that the effort is required
    0:06:40 as you on the user of the models.
    0:06:43 And I think that’s the world of these custom domain-specific
    0:06:45 models, like image generation and audio generation,
    0:06:48 versus the native capability really feels like
    0:06:50 the model can just do the heavy lifting for you,
    0:06:52 which is really interesting.
    0:06:55 – So right now, can Gemini actually output like an image
    0:06:57 if I give it a prompt to generate an image?
    0:06:59 Does it generate an image right now?
    0:07:00 – Not accessible to everyone yet.
    0:07:01 And I think this is the gap.
    0:07:03 So we have it internally and folks are using it
    0:07:05 in our early access program
    0:07:07 and we should get you both early access
    0:07:08 to play around with it and test it out.
    0:07:11 And we’ll roll it out more broadly soon,
    0:07:12 which I’m excited about.
    0:07:15 But that sort of same line of thinking
    0:07:17 is what takes us to like native tool use as well.
    0:07:19 And like native tool use is available to everyone.
    0:07:22 And it’s like the model was trained,
    0:07:25 knowing how to differentiate questions
    0:07:27 that it should go and search the internet for
    0:07:28 or questions that it needs to use a tool
    0:07:30 like code execution for.
    0:07:33 So you get like all of those like silly examples
    0:07:34 where the model would be like,
    0:07:36 let me try to solve this math problem,
    0:07:38 which I know I’m not going to be able to solve
    0:07:40 just because you asked me to with code execution,
    0:07:42 like it knows it needs to use that tool.
    0:07:44 And there’s a whole bunch of verticals
    0:07:45 where like the performance goes up
    0:07:46 significantly because of that.
    0:07:49 – So Gemini is actually generating the image.
    0:07:51 It’s not going and calling upon like,
    0:07:53 imagine three to generate the image.
    0:07:55 It’s actually Gemini who’s creating that image
    0:07:57 when it does generate an image.
    0:07:57 – Exactly.
    0:07:59 And I’ll push on getting you both access
    0:08:00 after this conversation
    0:08:02 because I think the world knowledge piece
    0:08:04 really highlights like why this matters.
    0:08:06 And there’s like a bunch of examples
    0:08:09 that I played around with of like pictures of a room
    0:08:11 and like having the image change
    0:08:15 based on these like really complex nuanced prompts
    0:08:16 around moving objects in certain ways.
    0:08:19 Like things that if it doesn’t have world knowledge
    0:08:21 and understand like it understands physics
    0:08:22 and understands all these things
    0:08:25 that again require the world knowledge piece.
    0:08:28 And I think it’s actually there’s some interesting trends
    0:08:30 of what is the outcome of like being able
    0:08:32 to take other domain specific models
    0:08:35 and bring them into these LLMs that have world knowledge.
    0:08:38 I think there’ll be some really cool capabilities
    0:08:39 that like we’re not thinking of today
    0:08:41 that this is going to enable,
    0:08:43 which yeah, it gets me excited for people.
    0:08:45 – Yeah, it seems like that’s kind of required
    0:08:46 for this also to work in like everything
    0:08:48 from like gaming to robotics
    0:08:50 to it actually have an understanding of the world.
    0:08:52 – Oh yeah, 100%.
    0:08:54 – Yeah, yeah, I mean, I actually had the opportunity
    0:08:57 to go out to London, go visit the DeepMind offices
    0:08:59 and got to play with Astra on the phone.
    0:09:02 And I mean, that was like the first taste I got
    0:09:07 of like an actual useful AI assistant out in the real world.
    0:09:10 And I don’t know if that was using Gemini 2.0 Flash
    0:09:13 or if that was actually using the full Gemini 2.0 yet
    0:09:17 at the time, but it was definitely a very, very impressive model
    0:09:20 to actually see the tool use in real life
    0:09:21 and just be able to walk around
    0:09:24 and it understand images and understand video
    0:09:26 and understand audio and understand text.
    0:09:29 And it was all built from the ground up
    0:09:33 to understand that stuff as opposed to like look at an image,
    0:09:36 use OCR to figure out the text on the image
    0:09:38 and then pull in the text or listen to the audio,
    0:09:41 transcribe the audio to text and then use the text.
    0:09:43 It’s actually understanding what it’s seeing,
    0:09:46 what it’s hearing, which I think is like one
    0:09:50 of the major differentiators about like what Gemini is doing
    0:09:52 that you don’t see the other models doing yet.
    0:09:54 So it’s super, super impressive.
    0:09:56 – Yeah, I think the other piece of this is
    0:09:58 other than just the raw capabilities
    0:09:59 from a complexity standpoint,
    0:10:01 it means that like developers building stuff,
    0:10:04 you don’t have to go and like do a ton of scaffolding work
    0:10:05 in order to like make this happen.
    0:10:07 It’s like the overall complexity of your application
    0:10:10 when the model is just able to sort of take in a bunch
    0:10:12 of things and put out a bunch of things
    0:10:13 makes life incredibly easy
    0:10:16 and you don’t have to deal with like frameworks
    0:10:17 on frameworks on frameworks.
    0:10:19 So much of the agent world is like,
    0:10:21 hey, the models actually aren’t that good
    0:10:22 at doing some of these things.
    0:10:24 And like the way that we supplant that
    0:10:27 is by like building a bunch of scaffolding and frameworks,
    0:10:29 that’s where a lot of developers are focused today.
    0:10:30 And actually there’s going to be a moment
    0:10:31 where like all of a sudden the model capabilities
    0:10:33 are just like good enough that it kind of works.
    0:10:35 And then people are going to be like,
    0:10:36 well, why do I have all this scaffolding
    0:10:38 that’s doing these things for me
    0:10:40 that the models can just do out of the box now?
    0:10:42 So it’ll be interesting to see how that plays out.
    0:10:45 – Yeah, I know like 2025 is going to be sort of
    0:10:46 the year of the agent, right?
    0:10:49 That term has already been thrown around quite a bit.
    0:10:50 But I also feel like everybody kind of has
    0:10:52 a different definition of an agent.
    0:10:54 You know, some people will look at something
    0:10:56 that like you can build over on make.com or Zapier
    0:10:58 where it’s tying different tools together
    0:11:00 using APIs as an agent.
    0:11:02 But I’m curious, does Google and DeepMind,
    0:11:04 do they actually have like an internal definition
    0:11:06 of an agent that they’re shooting for?
    0:11:08 Do you think we actually have agents now
    0:11:09 based on their definition?
    0:11:11 Where does Google stand on agents?
    0:11:14 – I actually don’t know what our like formal definition
    0:11:15 of agents are.
    0:11:18 I have tried a bunch of the agent products
    0:11:20 and historically haven’t been super impressed
    0:11:21 at what they’re capable of.
    0:11:22 I think we’re just not there yet.
    0:11:25 The thing that I want and I think a lot of users
    0:11:28 want this as well is just the models to be proactive.
    0:11:32 And like all of the products of today that build on AI
    0:11:35 require me to basically change my workflows
    0:11:38 or put in extra work or put in extra effort
    0:11:41 like through this sort of guys of like,
    0:11:42 oh, this is actually going to save you time
    0:11:43 if you do this thing.
    0:11:45 And like really what I want is like the models
    0:11:47 to just be like looking at the stuff
    0:11:48 that I give them access to
    0:11:50 and like coming up with ways to be useful
    0:11:51 and like save me time.
    0:11:54 And like, yeah, it’s going to get some things wrong
    0:11:56 but like I don’t want to have to be the one
    0:11:57 in the driver’s seat all the time.
    0:11:59 And it feels like today’s AI agents,
    0:12:01 again, because the models aren’t good enough
    0:12:03 like have to be proactive.
    0:12:05 Like it requires the proactiveness of the human.
    0:12:08 And I think once that role reversal switches,
    0:12:11 I think that’s where we see like billion agents scale
    0:12:14 deployments like all of a sudden just like happening
    0:12:14 and working.
    0:12:17 And this is also where like things get crazy
    0:12:17 again with compute.
    0:12:19 Cause again, like actually if you look at
    0:12:21 how compute is being used today,
    0:12:23 it’s in a lot of cases like this one to one correlation
    0:12:27 between like a human input and the token output.
    0:12:30 And I think the future is thousands and thousands
    0:12:32 of X more usage of AI happening
    0:12:34 by the agents themselves than by humans,
    0:12:36 which will be fascinating to see play out.
    0:12:40 – Hey, we’ll be right back to the show.
    0:12:42 But first I want to talk about another podcast
    0:12:43 I know you’re going to love.
    0:12:45 It’s called entrepreneurs on fire.
    0:12:46 And it’s hosted by John Lee Dumas
    0:12:49 available now on the HubSpot podcast network.
    0:12:51 Entrepreneurs on fire stokes inspiration
    0:12:54 and share strategies to fire up your entrepreneurial journey
    0:12:56 and create the life you’ve always dreamed of.
    0:12:59 The show is jam packed with unlimited energy,
    0:13:00 value and consistency.
    0:13:02 And really, you know, if you like fast-paced
    0:13:04 and packed with value stories
    0:13:07 and you love entrepreneurship, this is the show for you.
    0:13:09 And recently they had a great episode
    0:13:12 about how women are taking over remote sales
    0:13:13 with Brooke Triplett.
    0:13:15 It was a fantastic episode.
    0:13:16 I learned a ton.
    0:13:17 I highly suggest you check out the show.
    0:13:19 So listen to entrepreneurs on fire
    0:13:21 wherever you get your podcasts.
    0:13:26 – Yeah, no, I think what you described
    0:13:28 is sort of what I envision of an agent
    0:13:29 is almost like predictive.
    0:13:32 Like it sort of figures out what you need before you need it
    0:13:34 and then make suggestions based on that.
    0:13:36 So I think that’s a world that I’m really excited
    0:13:37 to get into.
    0:13:39 But I do want to touch on the word compute
    0:13:41 that you just mentioned for a second
    0:13:43 ’cause obviously there was a bit of like a, you know
    0:13:45 a freak out, so to speak in the US
    0:13:47 when that deep seek model came out
    0:13:49 and everybody thought that, well,
    0:13:52 this deep seek model uses a lot less compute
    0:13:55 than these other models that have been trained.
    0:13:57 So therefore, you know,
    0:13:59 NVIDIA GPUs are no longer necessary.
    0:14:02 And then we saw NVIDIA sort of lose some market share
    0:14:03 as a result of it.
    0:14:05 But I’m just curious, like what are your thoughts
    0:14:06 on the compute?
    0:14:07 Because I saw all of that happening
    0:14:09 and I thought it was so bizarre.
    0:14:11 I was like, this seems like a bullish sign
    0:14:13 for NVIDIA to me, not a bearish sign.
    0:14:15 Like what’s going on here?
    0:14:16 But I’m curious, like what’s your take on
    0:14:17 what happened there?
    0:14:20 – Yeah, there’s a lot of complexity to that story
    0:14:22 and parts of the story that I don’t want to touch on
    0:14:25 but the thing that I do want to touch on is like
    0:14:26 if you look around the world,
    0:14:28 I think this example that I just gave of like
    0:14:31 who is in the driver’s seat of using AI today?
    0:14:34 And like today it’s humans that are in the driver’s seat
    0:14:36 and like we’re just inherently bounded
    0:14:38 by the amount of humans who are using AI
    0:14:39 because, you know, it just takes a while
    0:14:42 for technology to assimilate into culture
    0:14:44 and real use cases and all that stuff.
    0:14:46 You know, we’re on this exponential right now.
    0:14:48 I think as soon as agents start to take off
    0:14:51 that exponential becomes a straight line up into the right.
    0:14:54 Like I think it’s gonna be pretty profound
    0:14:56 because like again, the challenge is
    0:14:59 the human process just doesn’t scale
    0:15:02 and the agent process is going to scale
    0:15:03 which is going to be really interesting.
    0:15:05 Like I have 10,000 emails I haven’t read
    0:15:06 in the last three months.
    0:15:07 – Right, I was thinking emails.
    0:15:08 First thing I was thinking of,
    0:15:10 I wanted to handle all of that for me.
    0:15:11 I don’t want to think about any of it.
    0:15:12 – Yeah, it’s gonna be wonderful.
    0:15:14 And really like go in and find the things
    0:15:16 like I know there’s things that I should be doing
    0:15:18 that would create value.
    0:15:19 – Missed opportunities.
    0:15:20 – 100%.
    0:15:22 And there’s like so many of those things
    0:15:24 where if you also like think about like
    0:15:26 what’s the economic value of all that work?
    0:15:29 Like one of the crazy frames of mind
    0:15:31 that I look at the world through is like,
    0:15:33 you look at the world and like the world is just filled
    0:15:34 with all this inefficiency.
    0:15:36 And like it’s beautiful in many ways,
    0:15:39 but like it’s also this really cool opportunity
    0:15:41 if you can be the one to create something
    0:15:43 that sort of makes people more productive
    0:15:46 and explicitly makes them more productive
    0:15:47 at maybe the things they don’t want to do.
    0:15:49 And I feel like that’s the other part
    0:15:51 of this agent story and this compute story,
    0:15:53 which is a lot of the products
    0:15:55 that I see people building are actually going
    0:15:59 after the things that people really like doing.
    0:16:02 And like maybe like shopping is this sort of
    0:16:03 tongue-in-cheek example of this.
    0:16:05 ‘Cause some people really like shopping
    0:16:05 and some people don’t.
    0:16:07 But like if I told my girlfriend
    0:16:09 that we could never go shopping again together
    0:16:13 and we could never go try out different experiences
    0:16:15 and go check out the vibe of different stores,
    0:16:17 like there’s so much of this is like
    0:16:19 such a fundamental part of the human condition actually
    0:16:22 of like going and seeing these different places
    0:16:24 and like it’s really baked into who we are.
    0:16:27 And that’s such like a traditional example
    0:16:29 of like what agents are going to do for people.
    0:16:30 And it’s odd to me.
    0:16:33 I feel like people have like kind of misconstrued
    0:16:35 what the value creation is going to be
    0:16:36 in some of these examples.
    0:16:37 – I mean, I think that’s like the bias
    0:16:39 from like a, you know, Silicon Valley nerds
    0:16:40 building this stuff, right?
    0:16:41 – Yeah, yeah.
    0:16:42 – I don’t want to shop.
    0:16:43 I just want to be automated for me.
    0:16:46 And it’s like, that’s just like a small subject
    0:16:46 of the world.
    0:16:47 Like, you know, when I shop,
    0:16:49 I just like go in and get what I want.
    0:16:50 There’s like two options.
    0:16:51 Okay, I like this one better.
    0:16:51 I get it.
    0:16:54 But my wife, she just loves checking out stores
    0:16:55 and different shops.
    0:16:56 And like she would hate the idea of like skipping
    0:16:58 all of that would just make no sense to her at all.
    0:17:01 – Yeah, I think there’s an underlying story here,
    0:17:02 which is like, first of all,
    0:17:03 there’s a lot of variance in human preference,
    0:17:07 but also there’s like ways about going about a certain task
    0:17:09 that like make it interesting or not.
    0:17:10 And like, I like shopping too,
    0:17:13 if it’s like me getting to do it on the terms that I want.
    0:17:15 And like, it will be interesting to see like,
    0:17:17 how can agents and some of these products
    0:17:19 like actually help create that experience?
    0:17:21 And this goes back to this deep seek narrative
    0:17:23 around like the value creation
    0:17:24 happening at the application layer.
    0:17:26 And it really does feel like this is true.
    0:17:28 Like if you look back two years ago,
    0:17:29 the narrative was, you know,
    0:17:32 all these companies are just rappers on top of AI.
    0:17:33 There’s no value creation.
    0:17:35 All the creation of the value creations in the tokens,
    0:17:38 like don’t spend your time thinking about these companies.
    0:17:41 And it’s so funny how quickly this like flip-flops
    0:17:43 back and forth between like,
    0:17:45 now all the value creation is at the application layer
    0:17:48 and like LLMs are this commodity thing
    0:17:49 that no one should think about.
    0:17:51 I enjoy watching it all play out.
    0:17:51 It’s fun.
    0:17:52 – So how do you think it’s gonna play out?
    0:17:53 Because like right now,
    0:17:55 Google’s kind of focused on developers, right?
    0:17:57 More than consumers, correct?
    0:17:58 – So on one hand,
    0:18:01 like I spend all my time thinking about developer stuff.
    0:18:02 Google’s got a ton of other people
    0:18:04 who are doing consumer stuff.
    0:18:05 I think a good example of this is like,
    0:18:10 Gemini is both in search and through the Gemini app,
    0:18:13 like, you know, deployed across like billion user scale.
    0:18:15 Like there are literally billions of people
    0:18:18 who are interacting with outputs and the models themselves,
    0:18:20 which is crazy to think about.
    0:18:22 And like it’s a very consumer forward use case
    0:18:23 for those folks.
    0:18:25 And I think it’s also like still incredibly early
    0:18:27 for Gemini in search.
    0:18:29 And there’s some interesting stories around that stuff.
    0:18:32 But I mean, I personally am incredibly bullish
    0:18:35 on the infrastructure layer and like the infra tooling.
    0:18:38 And I think like actually a good example of this.
    0:18:41 And you see this in some of Sam’s recent tweets
    0:18:44 about sort of open AI and the pro subscription
    0:18:46 are sort of good examples of this,
    0:18:48 which is builders at the application layer
    0:18:52 have a lot of tension with building more AI.
    0:18:53 And like actually back to the thread
    0:18:56 of having AI be more proactive.
    0:18:58 This is why I believe in flash so much.
    0:19:00 And I believe in the direction that we’re going
    0:19:02 as far as like reducing the cost for developers
    0:19:04 while continuing to push the performance frontier
    0:19:07 is because the story of AI to me is a story
    0:19:09 of like the actual infrastructure,
    0:19:11 incentivizing developers to not use it.
    0:19:14 Like you literally have an economic incentive
    0:19:16 not to use AI because it costs you money.
    0:19:18 And like the more AI you build into your product,
    0:19:19 the more expensive it is.
    0:19:21 And like the more margin pressure you have
    0:19:23 as an application builder.
    0:19:24 – It’s scary too, right?
    0:19:25 To build an app and like all of a sudden
    0:19:27 you get like a gigantic bill
    0:19:28 because people are using your thing
    0:19:30 and you haven’t figured out how to properly monetize it yet.
    0:19:31 As you know, someone who creates companies,
    0:19:33 it’s kind of intimidating.
    0:19:33 – Yeah, 100%.
    0:19:35 That is like the realist reaction
    0:19:38 and like also just like the truest reaction developers
    0:19:40 have to the cost of the technology.
    0:19:43 So back to the point of like where the value creation happens.
    0:19:46 I think the nice thing for infrastructure providers
    0:19:48 is you have a fixed margin.
    0:19:51 So like, you know exactly how much money you’re gonna make
    0:19:54 by providing some infrastructure at the application layer.
    0:19:56 You’re like, you’re constantly incentivized
    0:20:00 to almost like not add additional stuff.
    0:20:01 And I think this has been the story
    0:20:03 for like the chat GBT plus and pro subscription
    0:20:06 is like they built a subscription for $20 a month
    0:20:08 and they realized, hey, we actually can’t give people
    0:20:09 all of these things anymore.
    0:20:11 We have to make something different.
    0:20:13 And like even at $200 a month,
    0:20:16 it’s like not a break even scenario for them yet.
    0:20:19 So it’s super interesting to see that play out
    0:20:21 and it’s a lot of food for thought
    0:20:22 for people who are building stuff to be like,
    0:20:25 are there new economic incentive mechanisms
    0:20:28 that you can create as you’re building a product
    0:20:31 more so than just like charging a $20 a month subscription.
    0:20:33 And like the one example I can think of of this
    0:20:35 that has made me think is,
    0:20:37 I don’t know if you all are familiar with OpenRouter,
    0:20:40 but it’s a surface that lets you sort of swap
    0:20:41 in and out different language models.
    0:20:44 OpenRouter’s product leaderboard,
    0:20:46 I’m pretty sure they give you discounts on tokens
    0:20:49 and stuff like that for showing you in certain ways
    0:20:51 and some metadata passing back and forth
    0:20:53 so they can understand like how people are generally
    0:20:54 using AI models.
    0:20:57 So like some interesting things like that.
    0:21:00 Alex Atala, who’s the CEO who previously worked on OpenSea,
    0:21:03 he has this quote, which always rings in my head,
    0:21:06 which is like usage is the ultimate benchmark.
    0:21:08 How many people are using your model or your thing
    0:21:10 is like the proof point of success,
    0:21:11 not all these other benchmarks
    0:21:12 that people are chasing after.
    0:21:14 So super interesting platform.
    0:21:15 – Do they actually publish like a leaderboard
    0:21:17 similar to like what LMSYS does?
    0:21:18 – Exactly. – Okay.
    0:21:21 – Checking it out right now at openrouter.ai
    0:21:23 and it’s a forward slash rankings.
    0:21:24 – Oh, cool.
    0:21:26 I’m curious, a little bit of a topic shift here,
    0:21:29 but I know you’re a proponent of open source
    0:21:31 and Google obviously has their Gemma models.
    0:21:34 Are there any updates, any idea of what’s going on with Gemma
    0:21:35 and what we can expect next
    0:21:37 out of the open source side of things?
    0:21:39 – Yeah, I think that this is also the piece
    0:21:42 that makes me excited about what we’re doing at Google,
    0:21:45 which is it really is the exact same research
    0:21:46 that powers the Gemma and I models
    0:21:48 that ends up making the Gemma models.
    0:21:53 And Gemma two was, I think it’s like the second most downloaded
    0:21:55 open source model in existence, which is awesome to see.
    0:21:57 And Gemma three is definitely going to happen.
    0:21:59 I think the timeline is soon.
    0:22:02 So you’ll hear more and y’all should do an episode
    0:22:03 with some of the Gemma folks
    0:22:04 ’cause there’s lots of cool stuff coming.
    0:22:05 – Yeah, for sure.
    0:22:07 – They’ve been doing a lot of like interesting fine tunes
    0:22:08 for different use cases.
    0:22:10 Like they have a version for RAG,
    0:22:12 they have a version for vision.
    0:22:16 I think they’re probably gonna do some agent stuff as well.
    0:22:18 So like there’s lots of really cool explorations happening
    0:22:20 on making those open models.
    0:22:21 – Super cool.
    0:22:23 I wanna talk about Imagine three,
    0:22:26 which I just learned is actually pronounced Imagine three.
    0:22:27 Like I’ve been calling it Imagine.
    0:22:29 I always thought it was like Image Generator.
    0:22:32 So like Image Gen, but I heard you pronounce it Imagine.
    0:22:34 So now I’ll start saying it that way.
    0:22:37 But you mentioned that there’s some updates with that as well.
    0:22:38 What can you tell us about that?
    0:22:39 – Yeah, and you’re in good company.
    0:22:42 Don’t worry, I swear, like 50% of the meetings I’m in,
    0:22:44 I hear Imagine, 50% I hear Image Gen.
    0:22:47 So yeah, there’s no conclusive answer.
    0:22:48 I think it’s Imagine,
    0:22:50 but someone should correct me if that’s not the case.
    0:22:54 So we released the Imagine three model
    0:22:57 across a couple of services late December,
    0:22:58 and then have been doing a bunch of work
    0:23:00 in the last few months to bring that to developers.
    0:23:03 So Imagine three should be available to developers
    0:23:07 in the API and is the frontier Image Generation model
    0:23:10 across like quality and a bunch of like human ranking
    0:23:12 benchmarks, which as an aside comment,
    0:23:15 it’s super interesting that if you look at text models,
    0:23:18 I think one of the reasons the world has so much success
    0:23:20 hill climbing on making models better
    0:23:22 is like there’s a definitive source of truth
    0:23:24 in some of the tasks that the models can perform.
    0:23:26 I think with Image models, it’s like actually not the case.
    0:23:28 It’s really hard to eval
    0:23:30 and like you actually need humans in the loop
    0:23:31 to do a lot of those evals,
    0:23:34 or it’s like artistic and stylistic stuff
    0:23:37 that’s like hard to put a finger on
    0:23:38 which of these two things are better.
    0:23:40 So a lot of those evals use human Raiders
    0:23:41 and human benchmarks.
    0:23:43 So there’s some degree of error,
    0:23:46 but yeah, it’s been exciting to see like the models available
    0:23:47 in the Gemini app.
    0:23:50 It’s available to enterprise customers
    0:23:51 and now it’ll be available to developers
    0:23:52 to build with, which is awesome.
    0:23:55 I think this like gen media future
    0:23:56 is going to be super exciting
    0:23:59 and VO hopefully sometime in the future as well.
    0:24:01 – Yeah, yeah, VO is awesome.
    0:24:02 I have access to it, early access to it.
    0:24:04 It’s super fun to play with.
    0:24:06 Is the best way to use the Imagine 3
    0:24:07 inside of the image effects?
    0:24:09 Is that still kind of like the easiest way
    0:24:11 for a consumer to just go and play around with it?
    0:24:12 – Exactly.
    0:24:13 I think it’s also available for free
    0:24:16 to folks in the Gemini app.
    0:24:17 I think if you ask to generate an image,
    0:24:19 it’ll just do it through the Gemini app,
    0:24:22 but ImageFX gives you a little bit more controllability
    0:24:23 and stuff like that.
    0:24:24 So there’s a few more like features
    0:24:26 that are built into ImageFX.
    0:24:28 So that’s definitely a place that it’s publicly available.
    0:24:29 – Super cool.
    0:24:32 Yeah, I know you and Nathan sort of before we hit record,
    0:24:34 we’re nerding out a little bit about, you know,
    0:24:37 the whole like sort of text application concept.
    0:24:38 He wished there was a way
    0:24:40 that you could have Unity open on the screen
    0:24:42 and then actually have an AI sort of like assist you
    0:24:44 with like where to click and what to do next
    0:24:46 while you’re building a game in Unity.
    0:24:48 And I think both you and I in Unison went,
    0:24:51 “You can do that in AI studio right now.”
    0:24:52 So I thought it might be kind of cool
    0:24:55 to pick up where we left off on that conversation
    0:24:57 and talk about some of the cool stuff
    0:24:59 that’s available inside of AI studio
    0:25:02 that maybe a lot of people don’t even realize exists
    0:25:04 and probably definitely don’t realize
    0:25:06 you could use most of it for free still right now too.
    0:25:07 – Yeah, cool.
    0:25:08 So everything in AI studio is free,
    0:25:10 which I don’t think people realize.
    0:25:12 Like the entire product experience,
    0:25:14 there is no paid version of it.
    0:25:15 There’s a paid version of the API,
    0:25:17 which hopefully developers can scale with
    0:25:18 and do all that fun stuff.
    0:25:22 But all of our latest models end up in AI studio for free,
    0:25:24 including the experience that powers
    0:25:27 this like real-time multimodal live experience,
    0:25:29 which if folks haven’t played around with it,
    0:25:33 aistudio.com/live lets you do things
    0:25:36 like share your screen or show your camera
    0:25:38 and ask all these different questions
    0:25:39 and interact with the model.
    0:25:40 There’s a bunch of different voices.
    0:25:43 There’s a bunch of different modalities to choose from.
    0:25:45 But back to the conversation of like,
    0:25:46 what will agents look like?
    0:25:48 What do we want out of agents?
    0:25:49 One of the limitations for agents
    0:25:51 is you have to build all this scaffolding
    0:25:54 for the agent to be able to see the things that you do.
    0:25:55 Like to see my email and my text
    0:25:57 and my et cetera, et cetera,
    0:25:59 my personal laptop and my work laptop
    0:26:01 or my phone and my watch,
    0:26:03 like incredible amount of work to make that happen.
    0:26:05 Except if you have a camera,
    0:26:07 all of a sudden all of it just works.
    0:26:10 And like you can sort of make the determination
    0:26:12 of being able to show like the information you want to show
    0:26:15 and share the stuff that you want to share.
    0:26:18 It’s definitely more of a showcase of what’s possible.
    0:26:20 And that’s why we put it in the API
    0:26:22 because like, we don’t have the answers ultimately.
    0:26:24 Like developers should go and build these products.
    0:26:25 But I do think Matt,
    0:26:27 you mentioned the Astra experience earlier.
    0:26:29 I think like the multimodal live API
    0:26:32 sort of gets to what Astra does at the core,
    0:26:36 which is like be able to be this co-presence with tools.
    0:26:38 And again, through a simple API, which is really exciting.
    0:26:40 I think the piece that the multimodal live API
    0:26:42 doesn’t have that will build,
    0:26:44 that I think the Astra experience did have
    0:26:46 is this notion of memory,
    0:26:48 which again is like critical for agents.
    0:26:51 Like I don’t want agents to just forget that,
    0:26:54 I prefer sitting in window seats instead of aisle seats
    0:26:55 or whatever it is.
    0:26:57 Like you want all that context to be retained
    0:26:59 as agents are making decisions for you in the future.
    0:27:02 And I think that’s gonna require this sort of memory layer
    0:27:04 which we’re working on building, which is exciting.
    0:27:05 – Yeah, yeah.
    0:27:07 And I mean, project Astra even sort of remembered
    0:27:10 between sessions too, that like I was in London, right?
    0:27:12 So I was talking to it while in London,
    0:27:16 asking for restaurants to go check out things like that,
    0:27:19 close that session, started another session later on.
    0:27:22 And it remembered that I was over in London,
    0:27:24 it remembered all the previous conversations.
    0:27:27 So it wasn’t just like memory in the terms that like,
    0:27:29 you can plug into the custom instructions on open AI
    0:27:32 and it’ll remember your name and stuff like that.
    0:27:35 It was actually like remembering the past conversations
    0:27:37 and bringing in that as additional context,
    0:27:38 which I thought was really cool
    0:27:40 ’cause that’s really helpful to just like remember
    0:27:41 the past conversations you had.
    0:27:43 – I think this is an infrastructure problem.
    0:27:45 And I think we didn’t talk about this explicitly,
    0:27:47 but like one of the other narratives
    0:27:49 over the last like year and a half has been like,
    0:27:51 not enough AI in production.
    0:27:54 You know, it’s kind of this demo toy thing
    0:27:55 and no one really uses it.
    0:27:58 I think a lot of this is ’cause it’s just like taking a while
    0:28:00 for companies to build the infrastructure
    0:28:02 to actually put AI into production.
    0:28:04 And I think memory is this example of,
    0:28:05 there aren’t a bunch of companies
    0:28:07 that are like building this memory as a service.
    0:28:09 And if you are building this, like let’s talk,
    0:28:11 I’d love to hear about it and hear about what you’re building,
    0:28:14 but I think there’s a lot of opportunity still to be built
    0:28:17 around that, around memory, like as a service for folks.
    0:28:18 You could also start to think about like,
    0:28:21 there’s so many interesting ways to explore this.
    0:28:23 Like where does all your personal context
    0:28:24 already live today?
    0:28:26 Like how does that, whoever that provider is,
    0:28:30 plug into the world of where all the other memory services
    0:28:30 are going to be.
    0:28:32 So I think there’s a lot of like really, really interesting
    0:28:36 directions that need to be built for memory specifically.
    0:28:37 – Logan, I’m curious.
    0:28:39 So you were talking about earlier the models
    0:28:40 that a lot of people don’t realize and all of it’s free,
    0:28:41 like an AI studio.
    0:28:44 Like why do you guys hide it in AI studio?
    0:28:46 Recently, I talked to a bunch of different people
    0:28:48 about DeepSeq and they were talking about how amazed
    0:28:49 they were by it.
    0:28:52 And I was like, yeah, but like you can get the same stuff
    0:28:55 but better for free on AI studio right now.
    0:28:57 And they didn’t know.
    0:28:58 And it’s like, there’s a lot of people who don’t know.
    0:29:00 And so I was like, you got to communicate that better
    0:29:02 somehow or like, I think you guys should have like,
    0:29:05 you know, its own website or something like outside of Google,
    0:29:06 like a new product where you guys just like,
    0:29:08 hey, here’s the new frontier
    0:29:10 and here’s what we’re pushing and Google is still there
    0:29:12 and it uses some of the tech, but we have a new thing.
    0:29:14 And that’s that personal opinion, but you know.
    0:29:15 – Yeah, yeah.
    0:29:17 No, I think you’re spot on and for what it’s worth.
    0:29:20 Like I think we get this feedback pretty consistently.
    0:29:23 I think some of this is a factor of just like the state
    0:29:27 of the world and the challenges that we have as a product.
    0:29:30 Like I think in one hand, like we are a developer platform.
    0:29:32 Like we’re not building the front door
    0:29:33 for Google’s AI surfaces.
    0:29:36 Like that’s not the product that I’m signed up to build.
    0:29:37 That’s not the product that like we’re sort of
    0:29:38 directionally building towards.
    0:29:40 We’re really focused on like,
    0:29:43 how do we enable builders to get the latest AI technology?
    0:29:47 The Gemini app formerly barred is the sort of front door
    0:29:49 to Google’s AI technology.
    0:29:51 And I think from a consumer standpoint
    0:29:52 and also from an enterprise standpoint
    0:29:55 and like workspace and other places,
    0:29:57 there’s all this interesting organizational work
    0:29:59 that’s happened at Google over the last couple of years.
    0:30:02 I think like one of the cool stories is like operationalizing
    0:30:05 Google DeepMind from doing this sort of foundational research
    0:30:07 to being an organization that builds
    0:30:09 the world’s strongest generative AI models
    0:30:11 and like actually delivers those to the world
    0:30:12 sort of as a product.
    0:30:15 And then now bringing the product surfaces
    0:30:18 that are the front line of delivering those to the world,
    0:30:21 the Gemini app Google AI studio into DeepMind
    0:30:23 so that we can sort of continue to accelerate.
    0:30:24 Like all of those things to me are like
    0:30:26 directly the right stuff for us to do.
    0:30:27 I agree with you.
    0:30:28 I think we need to put the models
    0:30:30 in front of the world as soon as possible.
    0:30:33 And I think having a single place to do that makes sense.
    0:30:34 And it should probably be the Gemini app
    0:30:37 that probably shouldn’t be AI studio.
    0:30:39 But at the same time I say that like
    0:30:40 we also want to be a surface
    0:30:42 to sort of showcase what’s possible.
    0:30:44 So there’s a lot of like tension points
    0:30:46 but I do think, I fundamentally do think
    0:30:47 we’re going to get there.
    0:30:49 The Gemini app is moving super quickly
    0:30:50 to like get the latest models.
    0:30:52 Like they just shipped 2.0 flash.
    0:30:54 They shipped 2.0 Pro today.
    0:30:55 They shipped the thinking model today.
    0:30:57 So I think that Delta between the Gemini app
    0:30:59 and AI studio is sort of going away.
    0:31:01 Which yeah, I’m excited about
    0:31:03 because like the consistent feedback
    0:31:04 is people don’t like that Delta.
    0:31:06 They want to have a single place to go to
    0:31:07 to sort of see the future.
    0:31:09 – I always saw the AI studio is sort of like
    0:31:12 the playground to test what the APIs are capable of.
    0:31:15 In the same way like open AI has their open AI playground
    0:31:18 and you can kind of go and mess with some of the settings
    0:31:20 and see what the output will look like
    0:31:21 before using it in your own APIs.
    0:31:24 That’s kind of how I always saw the AI studio.
    0:31:26 Because like once you get into it
    0:31:28 if you’re not very technically inclined
    0:31:29 you might get a little overwhelmed
    0:31:32 seeing things like what models should I be using?
    0:31:34 What is the temperature?
    0:31:35 You know, things like that.
    0:31:36 People aren’t necessarily going to know
    0:31:37 like how to play with that.
    0:31:39 Like what should I set my token limit to?
    0:31:40 Things like that.
    0:31:42 I don’t really feel like general consumers
    0:31:43 want to mess with.
    0:31:45 They just want to go to a chat bot
    0:31:46 and ask their question, right?
    0:31:50 It feels very tailored towards developers to me.
    0:31:52 And then the Gemini app feels like, all right
    0:31:53 this is their front user interface
    0:31:55 that they want the general public
    0:31:57 to go be using at least.
    0:31:58 – Yeah, I got a ping yesterday actually
    0:32:01 from someone saying why is the chat experience
    0:32:02 have all this stuff in it?
    0:32:04 Like why are there all these settings and stuff?
    0:32:06 And I was like, you’re in the wrong place.
    0:32:08 Like Gemini app is the place that you need.
    0:32:09 And then they responded right away on Twitter
    0:32:12 and they’re like, yes, this is much better for me.
    0:32:13 I don’t want to see all that complexity.
    0:32:15 But I do think about this a lot
    0:32:17 is like how do people actually show up?
    0:32:19 How are they finding their way into these products?
    0:32:22 I do think the Gemini app is like this very large front door.
    0:32:24 So it tends to capture most of these folks.
    0:32:27 Like it’s literally built into the Google app on iOS
    0:32:28 and all this other stuff.
    0:32:29 Versus like you actually kind of have to do
    0:32:31 a little bit of searching to find AI studio
    0:32:34 which probably makes sense in some cases.
    0:32:35 Awesome.
    0:32:36 – Logan, you said you were super excited
    0:32:38 about text application.
    0:32:39 You were talking about like Lovable, Bolt,
    0:32:41 other companies like that.
    0:32:42 Like what are you excited about in the space
    0:32:44 and where do you think that kind of stuff is going?
    0:32:48 – Yeah, I think just like being able to democratize access
    0:32:51 to people building software and like creating things.
    0:32:52 There’s a ton of people in my life
    0:32:54 and I don’t live in the Bay Area.
    0:32:56 So there’s a disproportionate amount of people
    0:32:57 who aren’t in tech where I live.
    0:33:00 But the proportion of like people with interesting ideas
    0:33:01 I actually think is the same.
    0:33:03 I think it’s just like the actual tools themselves
    0:33:05 that they have to go and execute on those ideas
    0:33:08 that I think is like much less distributed
    0:33:10 in places outside the Bay Area, New York
    0:33:11 and other places like that.
    0:33:15 So I think this frontier of text to app creation
    0:33:18 is gonna be so, so interesting to see play out.
    0:33:19 And yeah, there’s a ton of companies
    0:33:22 that are having like lots of like actual real early
    0:33:24 commercial success and traction today.
    0:33:28 Which I think, again, this is one of those examples
    0:33:31 where like sometimes there’s use cases that don’t work
    0:33:33 and then all of a sudden like the model quality
    0:33:35 just gets good enough for you build the right
    0:33:37 sort of couple of things from a product experience.
    0:33:39 And then all of a sudden it clicks
    0:33:40 and like now this thing is possible.
    0:33:43 And to me it feels like text to app creation
    0:33:46 like has had that moment and it’s now possible.
    0:33:47 And I think it’ll take a while
    0:33:50 and there’ll still be a bunch of other things to hill climb on.
    0:33:52 But I think especially now with like reasoning models
    0:33:54 and the ability for them to like keep thinking
    0:33:57 and writing more code and doing all that work.
    0:33:59 Like I think the complexity of the apps
    0:34:01 is also going to continue to go up on this exponential.
    0:34:03 So and actually replicate just how they’re launched.
    0:34:05 I think today or yesterday of like this
    0:34:07 a similar sort of product of text apps.
    0:34:09 I think there’s more and more players showing up
    0:34:10 in this space.
    0:34:13 I would assume that like probably 50% of products
    0:34:15 or something like that that are building with AI
    0:34:17 have this type of experience.
    0:34:19 And you could think about like, you know
    0:34:20 how does that translate to someone
    0:34:23 who’s doing something very, very domain specific?
    0:34:25 I think there’s a lot of like companies
    0:34:28 that try to build extension ecosystems or connectors
    0:34:31 or like, you know, all these other like side cars
    0:34:32 of their product.
    0:34:34 You can imagine like you just let your users create those.
    0:34:36 Like here’s the sort of generic set of APIs
    0:34:39 that talk to, you know, your email client, for example.
    0:34:42 And like here’s a text box and like go build
    0:34:44 the sort of product experience you want.
    0:34:45 Like it’s sort of in your hands.
    0:34:48 And like that’s a crazy world that you could totally customize
    0:34:49 it however you want to them.
    0:34:50 Yeah, it doesn’t feel that far away.
    0:34:52 My new email client that’s got like, you know
    0:34:54 80s style video game stuff.
    0:34:56 You know, it’s like a mixed end with the email client.
    0:34:57 That’d be so cool.
    0:34:58 You know, I’ve been loving that concept.
    0:35:00 We’ve talked about this a couple of times on the show
    0:35:03 of like, I’ve gotten in the habit now of like,
    0:35:05 when I have like a little problem or a bottleneck
    0:35:07 that I need to solve instead of going and like searching out
    0:35:10 if there’s like a SaaS company that already exists
    0:35:11 that has that product for me.
    0:35:14 If it’s simple enough, I’ll just go prompt
    0:35:15 that software into existence.
    0:35:17 And I have like a little Python script
    0:35:20 that runs on my computer to solve the problem for me, right?
    0:35:22 Like I made a little script where I can input
    0:35:24 my one minute short form videos into it.
    0:35:26 It automatically transcribes them
    0:35:27 and then cleans up the transcription
    0:35:30 and adds like proper punctuation and stuff.
    0:35:31 I created another mini app
    0:35:33 where I can drag and drop any image file.
    0:35:35 Doesn’t matter what type of file format it is.
    0:35:37 It’ll convert it to a JPEG for me
    0:35:39 so I can use it instead of like my video editing.
    0:35:42 And these are probably softwares that exist.
    0:35:43 I could go and hunt them down on the internet
    0:35:46 and maybe pay five bucks a month to use them,
    0:35:49 but I could just go use an AI tool,
    0:35:50 prompt the tool that I need.
    0:35:53 And 15 minutes later, I have something on my desktop
    0:35:56 that I don’t need to go pay anybody else for anymore.
    0:35:57 Maybe it connects to an API,
    0:36:00 like my transcription one connects to the OpenAI whisper API.
    0:36:03 So it is costing me like a penny every time I use it,
    0:36:04 but so what?
    0:36:06 I just love this concept of like
    0:36:08 when I have a bottleneck in my business,
    0:36:10 I can just go like prompt an app into existence
    0:36:12 that solves that bottleneck.
    0:36:16 – Yeah, I think that carried out one step farther
    0:36:18 towards this like infinite app store
    0:36:21 where like truly everyone is creating
    0:36:24 and contributing to this thing and like remixing.
    0:36:26 This is the stuff that gets me excited about the future
    0:36:28 ’cause like there’s so much cool stuff to be created.
    0:36:31 And really, I think that the lens of all of this is like,
    0:36:33 how do you democratize access and make it
    0:36:35 so that anyone can go and build this stuff?
    0:36:36 And as someone who can program,
    0:36:39 but also knows how painful it is in a lot of ways,
    0:36:41 it’s just like so cool that more folks
    0:36:43 are gonna be able to participate in that.
    0:36:44 It’s gonna be awesome.
    0:36:46 – Yeah, coincidentally, someone today
    0:36:48 from my hometown of Alabama like messaged me like,
    0:36:49 hey, I have this idea for an app.
    0:36:50 I get this kind of stuff all the time.
    0:36:52 I have an idea for an app and who can I hire
    0:36:53 to build it and all that stuff?
    0:36:55 And I’m like, I’m about to send him a link to like replit.
    0:36:56 Have you like tried this yet?
    0:36:58 You know, it’s like, just go try that.
    0:37:01 And instead of paying someone $5,000,
    0:37:03 that’s probably like a ton of money for him, right?
    0:37:05 Instead, just go try replit and you know,
    0:37:07 sign up for one month, cancel it if you don’t like that
    0:37:10 to that and then just see what you can get.
    0:37:11 And it’s gonna get better and better.
    0:37:12 Like I’ve tried replit and all of them
    0:37:13 and like they’re pretty good.
    0:37:15 It feels like there’s something that’s like slightly missing,
    0:37:17 but every time I check it, it’s better than the last time.
    0:37:20 And it feels like probably within the next year or two,
    0:37:22 you’re just gonna make any kind of software you want
    0:37:23 just by talking.
    0:37:25 It’s just, that’s gonna be such a magical moment.
    0:37:26 Like in the early days of the internet,
    0:37:27 the internet I feel like was more fun.
    0:37:29 ‘Cause people, there’s all these like different websites
    0:37:31 and different kinds of things or like you’d have Winamp
    0:37:32 and you put a skin on your Winamp.
    0:37:33 But there’s all these different things
    0:37:35 in terms of customization that was happening more
    0:37:36 than there is now on the internet.
    0:37:38 And it feels like this kind of stuff might bring that back
    0:37:39 where like, yeah, the internet,
    0:37:42 you can kind of customize how you interact with the internet
    0:37:44 through creating your own custom software with AI.
    0:37:45 – Yeah, I was just thinking about
    0:37:47 as you were describing like a fun internet,
    0:37:49 I was thinking of my personal website,
    0:37:51 which is like a blank HTML page.
    0:37:53 And like there’s no styling or anything like that.
    0:37:56 But like, if I didn’t have to shoulder the costs like,
    0:37:58 and the LLM on someone’s computer,
    0:37:59 I could just kind of like talk to and say,
    0:38:01 like, you know, remix this site
    0:38:02 and do it in all types of crazy ways.
    0:38:06 Like that would be so fun of like every time someone shows up,
    0:38:08 it’s a different product or it’s a different experience
    0:38:09 to see this content.
    0:38:11 And I think there’s a lot of interesting
    0:38:12 threads to pull on that.
    0:38:15 – Is there anything else happening at Google right now?
    0:38:17 Any other things that you’re working on
    0:38:19 that you’re allowed to talk about?
    0:38:21 Is there any avenues we haven’t gone down
    0:38:22 that you really wanna talk about
    0:38:24 that you’re allowed to talk about, I guess?
    0:38:26 – Yeah, I think the only other thread,
    0:38:28 and we alluded to it a couple of times
    0:38:29 is reasoning model stuff.
    0:38:31 It feels like, and I tweeted this the other day,
    0:38:33 it feels like the GBT2 era for these models.
    0:38:36 Like there’s so much new capability
    0:38:39 and so much progress being squeezed out of the models
    0:38:40 in such a short time.
    0:38:42 And we released our first reasoning model
    0:38:42 back in December,
    0:38:45 right after the Gemini 2.0 flash moment,
    0:38:46 one month later,
    0:38:49 like a normal like six months worth of progress,
    0:38:51 honestly, on like a bunch of the benchmarks
    0:38:53 that matter for this stuff.
    0:38:54 We released an updated version
    0:38:57 like January 21st, a couple of weeks ago.
    0:38:58 And if you look at the chart,
    0:39:02 it’s like literally linear progress up into the right
    0:39:03 across a bunch of the end.
    0:39:06 Like it’s just crazy to think that,
    0:39:08 again, like a month ago, the narrative was like,
    0:39:09 the models are hitting a wall,
    0:39:11 there’s no more progress to be had.
    0:39:14 And it’s funny like how much nuance
    0:39:15 some of the conversation lacks
    0:39:19 because these innovations are like deeply intertwined.
    0:39:21 I was having a conversation earlier today
    0:39:23 about like long context
    0:39:25 and how long context is actually like
    0:39:27 a fundamental enabler of the reasoning models
    0:39:29 because like by themselves,
    0:39:30 the long context innovation,
    0:39:33 like the model’s okay at pulling out
    0:39:34 certain pieces of information.
    0:39:36 Like it can do needle in a haystack well,
    0:39:39 it can find a couple of things and a million tokens,
    0:39:41 but it’s really hard for the models to attend
    0:39:43 to the context of like,
    0:39:44 you know, find a hundred things
    0:39:47 in this million token context window example.
    0:39:49 Reasoning models are the unlock for this
    0:39:50 because the reasoning models,
    0:39:52 the model can really just like continue
    0:39:53 to go through that process
    0:39:55 and think through all the content
    0:39:57 and like really do the due diligence.
    0:40:00 And it’s almost uncanny how similar it is to like,
    0:40:01 how would you go about this?
    0:40:03 Like I couldn’t watch a two hour movie
    0:40:06 and then if you quizzed me on a hundred random little things
    0:40:08 as part of like, I’m not going to get those things, right?
    0:40:10 Like it’s going to be really hard to do that.
    0:40:11 But if you let me go through the movie
    0:40:13 and you know, watching an eye movie
    0:40:17 and like add little inserts and like clip things
    0:40:17 and cut things and do all this.
    0:40:19 So like I’d be able to find those things
    0:40:21 if you asked me those questions again.
    0:40:23 And it feels like that’s kind of what reasoning is doing
    0:40:25 is actually being able to do that.
    0:40:28 So I think we’re super early in this progress
    0:40:29 and it’s going to be a lot of fun
    0:40:32 to see both the progress continue for us.
    0:40:35 But again, through this narrative of how all this innovation
    0:40:38 trickles into the hands of people who are building stuff.
    0:40:39 And like there’s going to be a ton of new products
    0:40:42 that get built, like maybe text to app
    0:40:44 just like it’s 10x better in the next year
    0:40:45 because of reasoning models.
    0:40:48 Like that’s possible, which is just crazy to think about.
    0:40:49 – Yeah, yeah.
    0:40:50 – But like the other models, they almost feel like they’re like
    0:40:53 double checking triple checking themselves in real time.
    0:40:56 It’ll be like sort of starting to give a response
    0:40:58 and then be like, let me actually double check
    0:40:58 what I just said.
    0:41:02 And when it comes to coding, that seems like it’s the ideal
    0:41:03 use case almost, right?
    0:41:05 Cause it can almost look back at its code and be like,
    0:41:07 oh, I think I made a mistake there.
    0:41:09 And sort of continually fix its code
    0:41:11 before it finally even gives you an output,
    0:41:13 which I’ve just found to be really, really cool.
    0:41:17 But also from what I understand, that’s where a lot of the cost
    0:41:18 and the future is going to come in.
    0:41:22 The cost of the inference to do all of this like analysis
    0:41:25 in real time as it’s giving its output.
    0:41:26 – Yeah, and the other thing to think about
    0:41:29 which is interesting is we’re seeing all of this progress
    0:41:33 with the reasoning models and they are doing
    0:41:36 like the most naive version of thinking.
    0:41:38 Like they really are like, if you were to think about
    0:41:40 like the human example of this, like you’re sort of sitting
    0:41:42 in a box like thinking to yourself.
    0:41:44 Like you have no interaction with the outside world.
    0:41:46 You’re not able to like test your hypothesis,
    0:41:50 use a calculator, search the internet, any of those things.
    0:41:52 And like you have to sort of form your thoughts
    0:41:54 independent of the outside world.
    0:41:55 And you imagine what starts to happen
    0:41:57 when you give these things tools.
    0:41:59 And like it really does feel like
    0:42:01 that’s the agentic future that we’ve been promised
    0:42:04 is like all of these tools in a sandbox interacting
    0:42:06 with the model letting it sort of have that feedback loop
    0:42:08 of trying things and seeing what doesn’t work.
    0:42:10 So I couldn’t be more excited about that.
    0:42:12 – Yeah, that’s really interesting to think about.
    0:42:15 So like right now it’s just sort of thinking through things
    0:42:17 and sort of double checking itself, but in the future
    0:42:19 it could actually be working with other tools
    0:42:21 that can also like assist in the double checking
    0:42:25 and things like that and get even smarter in those ways.
    0:42:26 – Yeah, and I think put a different way,
    0:42:28 like to be more extreme, I think it has to do that.
    0:42:30 Like I think the version of the future
    0:42:31 that we’re going towards is like,
    0:42:33 we’re not going to be able to see the progress
    0:42:35 continue to scale unless the models can do that.
    0:42:37 And again, this goes back to this thread of like,
    0:42:39 there’s lots of hard problems to solve in the world,
    0:42:41 like making it so the models can do that efficiently
    0:42:45 and like securely and safely have that sort of sandbox
    0:42:47 to do that type of thinking and work
    0:42:48 is going to have to happen.
    0:42:50 And it’s probably a lot of work that hasn’t been solved today,
    0:42:53 which is interesting and opportunistic.
    0:42:55 – Yeah, it’s crazy to think that like probably soon
    0:42:57 like AI is going to be helping create all those tools as well.
    0:42:59 So that’s when we’ll see things just go exponential.
    0:43:00 – It already is.
    0:43:02 – It’s like whether people with AI or AI itself
    0:43:05 creating the tools and just move that back into the system.
    0:43:06 And it’s going to be wild,
    0:43:08 how fast things are going to get better.
    0:43:10 – All the engineers being powered by cursor.
    0:43:11 It’s crazy, like it’s happening today.
    0:43:14 Like so many people are, I feel this way for myself,
    0:43:15 like I write more software now than I did
    0:43:17 when I was a software engineer
    0:43:20 because I have AI tools
    0:43:22 and I can do all this crazy stuff.
    0:43:22 – Yeah.
    0:43:24 – How far do you think we are from like AI
    0:43:26 actually being able to update its own weights
    0:43:28 based on conversation.
    0:43:31 So it actually learns based on new input
    0:43:33 that it gets through conversations that it has.
    0:43:36 – I think in the small scale example sense,
    0:43:39 you could probably already do this to a certain extent.
    0:43:43 I think in like the like real frontier use cases,
    0:43:44 probably far from that.
    0:43:47 Some of the open AI operator stuff was talking about this
    0:43:51 around like, you know, the need for having evals
    0:43:53 of like basically like creating economic value,
    0:43:57 like actually creating money and where we are in that.
    0:43:58 And like you probably don’t want the models
    0:44:00 to do things that have a high cost today
    0:44:02 because if they get it wrong, it costs you a lot of money.
    0:44:05 And training frontier models is definitely
    0:44:07 on the list of things that would cost you a lot of money.
    0:44:09 If you got that wrong, like you don’t want
    0:44:12 a bunch of training that are just wasted compute.
    0:44:14 Like that’s, you know, millions of dollars
    0:44:15 of potential loss money.
    0:44:17 So I think there’ll be a human in the driver’s seat
    0:44:19 for those things for a while.
    0:44:22 But I do think you can sort of accelerate this,
    0:44:24 you know, small scale feedback loop.
    0:44:26 And I think that’s why small models matter.
    0:44:28 Like this like innovation that’s happening
    0:44:31 of being able to compress the frontier capabilities
    0:44:32 down into small models.
    0:44:35 I think it enables that like rapid iteration loop
    0:44:38 where maybe AI is more a co-pilot in that example.
    0:44:39 – Gotcha.
    0:44:42 Well, cool Logan, this has been absolutely amazing.
    0:44:45 If people want to follow you, what’s the best platform
    0:44:46 to pay attention to what you’re doing
    0:44:49 and to keep up with what Google and DeepMind are up to?
    0:44:51 – Yeah, yeah, I’m on Twitter, I’m on LinkedIn,
    0:44:53 I’m on email.
    0:44:57 So whichever one of those three is easiest to get ahold of me
    0:45:00 would love to chat with folks about Gemini stuff or the like.
    0:45:03 – Yeah, you’re pretty active over on X slash Twitter,
    0:45:04 whatever you want to call it.
    0:45:07 Whenever there’s a new like Google or DeepMind rollout,
    0:45:08 you’re pretty much either tweeting about it
    0:45:09 or retweeting about it.
    0:45:11 So very, very good resource to keep up with
    0:45:14 what’s going on in the world of AI with Google.
    0:45:16 And Logan, thank you so much for hanging out again
    0:45:17 with us today.
    0:45:19 I’m sure we’ll have you back in the future if you want,
    0:45:21 but this has been an absolutely fascinating conversation.
    0:45:23 So thanks again for hanging out.
    0:45:24 – Yeah, this is a ton of fun.
    0:45:25 I’ll see you both at IO, I hope.
    0:45:27 I think hopefully we’ll get the game back together
    0:45:29 and we’ll spend time in person.
    0:45:31 Hopefully at IO, it’s gonna be fun.
    0:45:31 – We’d love to do it.
    0:45:32 Thanks.
    0:45:33 – Thank you.
    0:45:36 (upbeat music)
    0:45:38 (upbeat music)
    0:45:41 (upbeat music)
    0:45:43 (upbeat music)
    0:45:46 you

    Episode 45: How is Google shaping the future of AI with its new Gemini models? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) are joined by Logan Kilpatrick (https://x.com/OfficialLoganK), Senior Project Manager at Google DeepMind.

    In this episode, delve into the details of Google’s latest AI models, Gemini 2.0, Flash 2.0, and Pro versions, as Logan Kilpatrick breaks down the advancements and unique capabilities that set these models apart. They discuss the cost-efficiency that Gemini brings to the table, the concept of reasoning models, and how agents are paving the way for future AI applications. Whether you’re a developer or just intrigued by the progress in AI, this conversation offers insights into what Google’s innovations mean for the industry.

    Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd

    Show Notes:

    • (00:00) Gemini 2.0 Launch Excitement
    • (03:18) Cheaper Flashlight Model Previewed
    • (08:50) Experiencing Gemini AI in London
    • (11:11) AI Agents: Need Proactive Models
    • (14:23) Embracing Inefficiency for Productivity
    • (17:09) AI Infrastructure and Consumer Impact
    • (21:31) Imagen 3 Model Update & Insights
    • (24:18) AI Studio: Free Multimodal Experience
    • (26:53) AI Production and Infrastructure Challenges
    • (31:56) Democratizing App Creation Tools
    • (34:09) DIY Software Solutions
    • (38:51) Reasoning Models Unlock Contextual Understanding
    • (42:45) AI Frontier: Risks and Costs
    • (44:12) AI Updates on Twitter

    Mentions:

    Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw

    Check Out Matt’s Stuff:

    • Future Tools – https://futuretools.beehiiv.com/

    • Blog – https://www.mattwolfe.com/

    • YouTube- https://www.youtube.com/@mreflow

    Check Out Nathan’s Stuff:

    The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

  • Is America broken?

    What do you think of America’s institutions?

    Alana Newhouse, founder and editor-in-chief of Tablet Magazine, says that may be the most important political question in America.

    In an essay published more than two years ago, Newhouse argued that there is a new political divide, one in which your place — and the place of your allies and adversaries — is determined by whether you believe that America’s institutions should be fixed or destroyed. Her argument feels eerily prescient in light of the Trump administration’s recent efforts to dismantle government programs.

    In this episode, which first aired in February of 2023, Alana and Sean debate what that divide means for America’s present and future, and whether it supersedes labels like “left” or “right” and “Democrat” or “Republican.”

    Host: Sean Illing (@seanilling), host, The Gray Area

    Guest: Alana Newhouse (@alananewhouse) editor-in-chief, Tablet and author of “Brokenism.”

    Learn more about your ad choices. Visit podcastchoices.com/adchoices