0:00:03 Today, we’re doing something a little different.
0:00:05 We’re dropping an episode from Impact Theory,
0:00:07 a show hosted by Tom Bilyeu,
0:00:09 featuring a conversation with Ben Horowitz,
0:00:11 co-founder of Andreessen Horowitz.
0:00:13 Ben rarely does interviews like this,
0:00:16 and in this one, he goes deep on AI, on power,
0:00:18 on the future of work, and what it really means
0:00:20 to be a human in a world of intelligent machines.
0:00:24 He breaks down why AI is not life, not consciousness,
0:00:25 but something else entirely.
0:00:28 Why blockchain is critical to preserving trust and truth
0:00:29 in the age of deepfakes.
0:00:32 Why distribution may matter more than code,
0:00:34 and why history tells us this isn’t the end of jobs,
0:00:36 but the beginning of something new.
0:00:37 Let’s get into it.
0:00:42 This information is for educational purposes only
0:00:45 and is not a recommendation to buy, hold, or sell
0:00:47 any investment or financial product.
0:00:49 This podcast has been produced by a third party
0:00:51 and may include paid promotional advertisements,
0:00:53 other company references, and individuals
0:00:54 unaffiliated with A16Z.
0:00:57 Such advertisements, companies, and individuals
0:01:00 are not endorsed by AH Capital Management LLC,
0:01:02 A16Z, or any of its affiliates.
0:01:04 Information is from sources deemed reliable
0:01:05 on the date of publication,
0:01:08 but A16Z does not guarantee its accuracy.
0:01:14 Revolutions don’t always come with banners and protests.
0:01:18 Sometimes, the only shots fired are snippets of code.
0:01:20 This is one of those moments.
0:01:24 AI is the most disruptive force in history,
0:01:26 and it’s no longer a distant possibility.
0:01:28 It is here right now,
0:01:31 and it’s already changing the foundations of power
0:01:32 and the economy.
0:01:34 Few people have been as influential
0:01:36 in shaping the direction of AI
0:01:38 than mega-investor Ben Horowitz.
0:01:40 A pioneer in Silicon Valley,
0:01:42 he spent decades at the center
0:01:44 of every major technological disruption,
0:01:47 including standing up to the Biden administration’s
0:01:49 attempts to limit and control AI.
0:01:50 In today’s episode,
0:01:53 he lays out where AI is really taking us,
0:01:55 the forces that will define the next decade,
0:01:57 and how to position yourself
0:01:59 before it’s too late.
0:02:04 You are in an area making investments,
0:02:05 thinking about some of the things
0:02:07 that I think are the most consequential
0:02:08 in the world today
0:02:09 as it relates to innovation.
0:02:10 But along those lines,
0:02:13 you and Marc Andreessen are all in on AI,
0:02:15 but how do we make sure
0:02:16 that it benefits everyone
0:02:18 instead of making humans obsolete?
0:02:19 To begin with,
0:02:21 we have to just realize,
0:02:22 you know, what AI is
0:02:23 because I think that
0:02:24 because we called it
0:02:25 artificial intelligence,
0:02:26 you know,
0:02:29 our whole industry of technology
0:02:30 has a naming problem
0:02:31 in that we started,
0:02:32 you know,
0:02:34 by calling computer science,
0:02:35 computer science,
0:02:37 which everybody thought,
0:02:39 oh, that’s just like computers.
0:02:42 It’s like the science of a machine
0:02:44 as opposed to information theory
0:02:45 and what it really was.
0:02:47 And then in Web3 world,
0:02:48 which you’re familiar with,
0:02:49 we call it cryptocurrency,
0:02:51 which to normal people
0:02:52 means secret money.
0:02:54 But that’s not what it does.
0:02:55 That’s a good point.
0:02:56 And then I think
0:02:57 with artificial intelligence,
0:03:01 I think that’s also like a bad name
0:03:02 in a lot of ways
0:03:03 and that, you know,
0:03:04 look, the people
0:03:05 who work on in the field
0:03:08 call what they’re building models.
0:03:09 And I think that’s
0:03:11 a really accurate description
0:03:12 in the sense that
0:03:13 what we’re doing
0:03:15 is we’re kind of modeling
0:03:17 something we’ve always done,
0:03:18 which is we’re trying
0:03:19 to model the world
0:03:22 in a way that enables us
0:03:23 to predict it.
0:03:24 And then, you know,
0:03:25 we’ve built much more
0:03:26 sophisticated models
0:03:27 with this technology
0:03:29 than we could.
0:03:30 in the old days,
0:03:31 we had E equals MC squared,
0:03:33 which was like amazing,
0:03:35 but a relatively simple model.
0:03:38 Now we have models
0:03:39 with what,
0:03:41 like 600 billion variables
0:03:42 and this kind of thing.
0:03:43 And we can model like,
0:03:45 what’s the next word
0:03:46 that I should say
0:03:48 and that kind of thing.
0:03:51 So that’s amazing and powerful.
0:03:52 But I would say like,
0:03:53 we need to distinguish
0:03:54 the fact that
0:03:56 it’s a model
0:03:59 that is directed by us
0:04:00 to tell us things
0:04:01 about the world
0:04:02 and do things
0:04:03 on our behalf.
0:04:05 But it’s not a,
0:04:06 it’s not life.
0:04:08 It doesn’t have a free will
0:04:09 and these kinds of things.
0:04:11 So I think default,
0:04:13 you know,
0:04:13 we are the master
0:04:16 and it is the servant
0:04:18 as opposed to vice versa.
0:04:20 The question though
0:04:21 that you’re getting at,
0:04:21 which is, okay,
0:04:24 how do we not get obsoleted?
0:04:26 Like, why do we need us
0:04:27 if we’ve got these things
0:04:28 that can do all the jobs
0:04:29 that we currently do?
0:04:31 And I think, you know,
0:04:33 we’ve gone through that
0:04:34 in the past
0:04:36 and it’s been interesting, right?
0:04:39 So in I think 1750,
0:04:41 over 90% of the jobs
0:04:42 in the country
0:04:43 were agricultural.
0:04:45 And, you know,
0:04:46 there was a huge fight
0:04:47 and a group called
0:04:47 the Luddites
0:04:48 that fought the plow
0:04:49 and, you know,
0:04:50 some of these
0:04:53 newfangled inventions
0:04:54 that eventually,
0:04:55 by the way,
0:04:57 eliminated 97%
0:04:58 of the jobs
0:04:59 that were there.
0:05:01 But I think that
0:05:03 most people would say,
0:05:03 gee,
0:05:05 the life I have now
0:05:06 is better than
0:05:07 the life that I would have
0:05:08 had on the farm
0:05:10 where all I did
0:05:11 was farm
0:05:12 and nothing else
0:05:13 in life.
0:05:15 But, you know,
0:05:15 like,
0:05:16 and if you want to farm
0:05:16 still,
0:05:17 you can.
0:05:18 that is an option.
0:05:20 But most people
0:05:21 don’t take that option.
0:05:23 So the jobs
0:05:24 that we have now
0:05:25 will,
0:05:25 you know,
0:05:26 a lot of them
0:05:27 will go away
0:05:28 and will have,
0:05:29 but will likely
0:05:30 have new jobs.
0:05:30 I mean,
0:05:31 humans are pretty good
0:05:33 at figuring out
0:05:34 new things to do
0:05:36 and new things
0:05:36 to pursue
0:05:37 and so forth.
0:05:40 And, you know,
0:05:40 like including,
0:05:41 like,
0:05:42 going to Mars
0:05:42 and that kind of thing,
0:05:44 which obviously
0:05:45 isn’t a thing today
0:05:46 but could very well
0:05:47 be a thing tomorrow.
0:05:50 So I think that,
0:05:50 you know,
0:05:51 we have to stay creative
0:05:53 and keep dreaming
0:05:54 about, like,
0:05:55 a better future
0:05:56 and how to kind of
0:05:57 improve things for people.
0:05:57 But I think that,
0:05:58 you know,
0:05:58 particularly
0:06:01 for kind of the people
0:06:01 in the world
0:06:03 that are on the struggle bus
0:06:04 who are living
0:06:05 on a dollar a day
0:06:07 or, you know,
0:06:07 kind of subject
0:06:08 to all kinds of diseases
0:06:09 and so forth,
0:06:10 life is going to get,
0:06:10 like,
0:06:11 radically better for them.
0:06:13 When I look at the plow example
0:06:13 and the Luddites
0:06:14 fighting against it,
0:06:15 I think you’ll see
0:06:16 the same thing with AI.
0:06:17 You’re going to get people
0:06:19 that just completely reject it,
0:06:19 refuse to engage
0:06:21 in anything for sure.
0:06:24 But when I look at AI,
0:06:26 what I worry about
0:06:26 is that there will be
0:06:28 no refuge to go to,
0:06:30 meaning if you realize,
0:06:30 oh,
0:06:31 I can’t plow as well
0:06:33 as a plow
0:06:34 or a tractor
0:06:35 or now a combine,
0:06:36 there are still
0:06:37 a lot of other things
0:06:38 that technology
0:06:39 can’t do better than me.
0:06:40 Do you think
0:06:41 there’s an upper bound
0:06:42 to artificial intelligence,
0:06:43 bad name or not,
0:06:45 or do you think
0:06:46 that it keeps going
0:06:47 and it literally
0:06:49 becomes better than us
0:06:50 once it’s embodied
0:06:50 in robotics
0:06:52 at everything?
0:06:54 We are kind of limited
0:06:55 by the new ideas
0:06:56 that we have.
0:06:58 And artificial intelligence
0:06:58 is really,
0:06:59 by the way,
0:07:00 artificial human intelligence,
0:07:01 meaning,
0:07:03 right,
0:07:05 humans looked at the world,
0:07:06 humans figured out
0:07:07 what it was,
0:07:08 you know,
0:07:09 described it,
0:07:09 came up with these
0:07:11 concepts like trees
0:07:11 and,
0:07:12 you know,
0:07:14 air and all this stuff
0:07:16 that it’s not necessarily
0:07:16 real,
0:07:19 just how we decided
0:07:20 to structure the world.
0:07:21 And AI has learned
0:07:22 our structure.
0:07:23 Like,
0:07:24 they’ve learned language,
0:07:25 human language,
0:07:26 which is a version
0:07:27 of the universe
0:07:27 that is not
0:07:29 an accurate version
0:07:29 of the universe.
0:07:30 It’s just our version
0:07:31 of the universe.
0:07:32 But you’re going to
0:07:33 have to go deep on that.
0:07:34 I know my audience
0:07:35 is going to be like,
0:07:36 air seems pretty real
0:07:37 when you’re underwater.
0:07:38 What do you mean
0:07:39 that trees and air
0:07:40 are not necessarily real?
0:07:41 It’s,
0:07:41 look,
0:07:42 it’s a construction
0:07:43 that we made.
0:07:43 You know,
0:07:44 we decided
0:07:46 it is literally
0:07:48 the way humans
0:07:48 have interpreted
0:07:49 the world
0:07:50 in order for humans
0:07:51 to navigate it.
0:07:52 And,
0:07:53 you know,
0:07:55 as is language,
0:07:55 right?
0:07:56 Language isn’t
0:07:57 the universe
0:07:58 as it is.
0:07:59 Like,
0:08:00 if completely objective,
0:08:02 if you had an objective
0:08:02 look at,
0:08:03 you know,
0:08:05 the atoms and so forth
0:08:06 and how they were arranged
0:08:07 and whatnot,
0:08:09 you probably,
0:08:10 you know,
0:08:11 those descriptions
0:08:12 are lacking
0:08:13 in a lot of ways.
0:08:13 They’re not
0:08:14 completely accurate.
0:08:16 They don’t,
0:08:17 you know,
0:08:17 they certainly don’t
0:08:18 kind of predict
0:08:19 everything about
0:08:19 how the world works.
0:08:21 And so,
0:08:23 what machines have learned
0:08:23 or like what
0:08:25 artificial intelligence is
0:08:26 is an understanding
0:08:27 of our knowledge,
0:08:29 of the human knowledge.
0:08:31 so it’s taken
0:08:31 in our knowledge
0:08:32 of the universe
0:08:34 and then it is
0:08:36 kind of,
0:08:36 can refine that
0:08:37 and it can work on that
0:08:39 and it can derive things
0:08:40 from our knowledge,
0:08:42 our axiom set.
0:08:44 But it isn’t actually
0:08:45 observing the world
0:08:46 at this point
0:08:47 and figuring out
0:08:47 new stuff.
0:08:48 So,
0:08:48 you know,
0:08:49 at the very least,
0:08:50 you know,
0:08:52 humans still have to
0:08:55 discover new principles
0:08:56 of the universe
0:08:57 or kind of
0:08:57 interpret it
0:08:58 in a different way
0:08:59 or the machines
0:09:00 have to somehow
0:09:01 observe directly
0:09:02 the world
0:09:02 which they’re not
0:09:03 yet doing.
0:09:06 And so,
0:09:06 you know,
0:09:07 that’s a pretty big role
0:09:08 I would say
0:09:09 but then,
0:09:09 you know,
0:09:10 in addition,
0:09:11 you know,
0:09:11 we direct the world.
0:09:12 I think like Star Trek
0:09:13 is actually a pretty
0:09:14 good metaphor for that.
0:09:15 Like the Star Trek
0:09:16 computer was pretty
0:09:16 badass
0:09:18 but,
0:09:19 you know,
0:09:20 the people in Star Trek
0:09:21 were still like
0:09:22 flying around the universe
0:09:23 discovering new things
0:09:23 about it.
0:09:24 You know,
0:09:24 there was still
0:09:25 much to do
0:09:27 and I think that
0:09:27 it’s always
0:09:29 a little
0:09:31 kind of difficult
0:09:32 to figure out
0:09:33 what the new jobs
0:09:34 that get created
0:09:34 are.
0:09:35 And we’ve had
0:09:37 intelligence for a while,
0:09:37 right?
0:09:38 Like we’ve had
0:09:39 machines that could
0:09:40 do math way better
0:09:40 than us
0:09:42 and,
0:09:43 you know,
0:09:43 I mean,
0:09:44 I can remember
0:09:46 when I was in
0:09:47 junior high school
0:09:49 or junior high
0:09:49 put on a play
0:09:50 about like
0:09:51 how bad it was
0:09:51 that there were
0:09:52 calculators
0:09:52 because nobody
0:09:53 would know
0:09:53 how to do
0:09:54 arithmetic
0:09:54 and then
0:09:56 all the calculators
0:09:56 would break
0:09:57 and then we’d
0:09:57 be stuck.
0:09:58 We’d be trying
0:09:59 to like fly around
0:09:59 the universe
0:10:00 and rockets
0:10:01 but we wouldn’t
0:10:02 be able to do math
0:10:03 and the calculators
0:10:03 would be broken
0:10:04 and we’d be screwed.
0:10:06 So there is always
0:10:07 that fear
0:10:09 and,
0:10:09 you know,
0:10:10 we’ve had computers
0:10:11 that can play games
0:10:12 better than us.
0:10:12 We currently have
0:10:13 computers that can
0:10:14 drive better than us
0:10:15 and so forth.
0:10:16 So we have a lot
0:10:16 of intelligence
0:10:17 out there
0:10:19 but it hasn’t,
0:10:21 you know,
0:10:22 created like
0:10:23 this super dystopia,
0:10:24 you know,
0:10:25 in any degree.
0:10:25 It’s actually
0:10:26 made things better,
0:10:27 you know,
0:10:28 everywhere it’s appeared.
0:10:29 So I would expect
0:10:30 that to continue.
0:10:31 What do you think
0:10:32 though is the limiting
0:10:32 function?
0:10:34 So when I look
0:10:34 at AI,
0:10:36 I always say
0:10:37 unless we run
0:10:38 into an upper bound
0:10:39 where the computation
0:10:40 just can’t allow
0:10:41 the intelligence
0:10:42 to keep progressing,
0:10:45 it seems like
0:10:45 it will become
0:10:46 not only
0:10:47 generalized human
0:10:47 intelligence
0:10:48 and thusly be able
0:10:48 to do everything
0:10:49 that we can do,
0:10:50 it will become
0:10:52 embodied as robotics
0:10:53 and if,
0:10:54 I ran the math
0:10:54 on this once,
0:10:56 Einstein is roughly
0:10:58 2.4 times smarter
0:10:58 than someone
0:11:00 who is definitionally
0:11:00 a moron
0:11:02 and the gap
0:11:03 just between
0:11:04 those two
0:11:06 is so dramatic
0:11:07 the army won’t
0:11:08 even draft
0:11:09 somebody
0:11:10 that is a moron
0:11:11 at, you know,
0:11:11 whatever,
0:11:12 81 IQ
0:11:13 or whatever it is.
0:11:14 because it’s,
0:11:14 they create
0:11:15 more problems
0:11:16 than they solve
0:11:17 even just by being,
0:11:18 you know,
0:11:19 bullet fodder.
0:11:21 So do you think
0:11:22 there is something
0:11:23 that’s going to cause
0:11:25 that upper bound
0:11:27 or you have a belief
0:11:29 about the nature
0:11:30 of intelligence
0:11:31 that will keep
0:11:33 AI subservient
0:11:34 to us?
0:11:35 The smartest people
0:11:36 don’t rule the world,
0:11:37 you know,
0:11:38 Einstein wasn’t in charge
0:11:42 and, you know,
0:11:42 many of us
0:11:43 are like ruled
0:11:45 by our cats
0:11:47 and so,
0:11:48 like,
0:11:49 power and intelligence
0:11:50 don’t necessarily
0:11:51 go together,
0:11:51 particularly when
0:11:52 the intelligence
0:11:53 has no free will
0:11:54 or has no
0:11:55 desire to do
0:11:56 free will.
0:11:56 It doesn’t have will.
0:11:58 You know,
0:11:59 it is kind of a model
0:12:00 that’s computing things.
0:12:02 I think also,
0:12:02 you know,
0:12:03 the whole general
0:12:04 intelligence thing
0:12:06 is interesting
0:12:07 in that,
0:12:10 Waymo’s got a super
0:12:10 smart AI
0:12:11 that can drive a car
0:12:14 but that AI
0:12:14 that drives a car
0:12:16 doesn’t know English
0:12:18 and, you know,
0:12:19 isn’t, you know,
0:12:20 particularly good
0:12:21 at other tasks,
0:12:22 you know,
0:12:23 currently
0:12:24 and then the
0:12:26 chat GPT
0:12:27 can’t drive a car.
0:12:30 And so that’s,
0:12:31 you know,
0:12:33 how much things
0:12:35 generalize particularly
0:12:36 and if you look
0:12:36 at, well,
0:12:37 why is that?
0:12:38 A lot of it
0:12:39 actually has to do
0:12:40 with the long tail
0:12:41 of human behavior
0:12:43 where humans,
0:12:45 you know,
0:12:46 the distribution
0:12:47 of human behavior
0:12:47 is,
0:12:49 it’s fractal,
0:12:50 it’s mantle
0:12:51 broadion or whatever,
0:12:53 it’s not evenly
0:12:54 distributed at all
0:12:56 and so,
0:12:58 you know,
0:12:58 an AI
0:13:00 that kind of
0:13:01 captures all that
0:13:02 turns out to be,
0:13:04 you know,
0:13:05 we’re not so much
0:13:06 on the track,
0:13:07 we’re more on the track
0:13:08 for kind of
0:13:10 really great reasoning
0:13:12 over kind of
0:13:12 a set of axioms
0:13:14 that we came up with,
0:13:15 you know,
0:13:15 in say math
0:13:16 or physics
0:13:18 but not so much
0:13:21 you know,
0:13:21 kind of general
0:13:23 human intelligence
0:13:24 which is,
0:13:25 you know,
0:13:26 being able to
0:13:28 navigate other humans
0:13:30 and the world
0:13:30 in a way
0:13:32 that is productive
0:13:33 for us
0:13:33 is kind of a,
0:13:34 it’s a little bit
0:13:35 of a different
0:13:36 dimension of things.
0:13:37 You know,
0:13:37 yeah,
0:13:38 you can compare
0:13:40 the math capabilities
0:13:41 or the go
0:13:43 playing capabilities
0:13:43 or the driving
0:13:44 capabilities
0:13:46 or the IQ test
0:13:47 capabilities
0:13:48 of a computer
0:13:49 but that,
0:13:51 that’s not really
0:13:52 a human.
0:13:53 I think a human
0:13:54 is kind of different
0:13:55 in a fairly
0:13:56 fundamental way.
0:13:58 So what we end up
0:13:58 doing,
0:13:59 I think is going
0:14:00 to be different
0:14:00 than what we’re
0:14:01 doing today
0:14:02 just like what
0:14:02 we’re doing today
0:14:03 is very different
0:14:04 than what we did
0:14:04 a hundred years ago.
0:14:06 But,
0:14:07 you know,
0:14:08 the not having
0:14:09 a need for us,
0:14:10 I think that,
0:14:10 you know,
0:14:12 these AIs
0:14:13 are tools for us
0:14:15 to basically
0:14:16 navigate the world
0:14:17 and help us
0:14:18 solve problems
0:14:19 and do things
0:14:20 like,
0:14:21 you know,
0:14:22 everything from
0:14:24 prevent pandemics
0:14:25 to deal with
0:14:27 climate change
0:14:28 to that sort of thing,
0:14:29 to not kill each other
0:14:30 driving cars,
0:14:32 which we do a lot of.
0:14:34 You know,
0:14:35 hopefully it doesn’t,
0:14:36 you know,
0:14:38 create more wars,
0:14:38 hopefully it creates
0:14:39 less wars,
0:14:40 but we’ll see.
0:14:41 What I know
0:14:43 about the human brain
0:14:44 maybe tricking me
0:14:46 into painting a vision
0:14:46 of the future
0:14:47 that isn’t going
0:14:47 to come true,
0:14:49 let me put words
0:14:49 in your mouth
0:14:50 and you tell me
0:14:52 if they fit appropriately.
0:14:53 What I hear you saying
0:14:54 is something akin
0:14:55 to the way
0:14:56 that we’re approaching
0:14:57 artificial intelligence
0:14:57 right now,
0:14:58 let’s round it
0:15:00 to large language models,
0:15:02 that is going
0:15:04 to hit an upper bound
0:15:05 where it’s not able
0:15:07 to have insights
0:15:07 that a human
0:15:08 won’t already have,
0:15:10 that they are trapped
0:15:11 inside of the box
0:15:11 that we have created,
0:15:12 what you’re calling
0:15:13 the axioms
0:15:14 by which we navigate
0:15:14 the world.
0:15:15 They get trapped
0:15:16 inside that box
0:15:18 and thusly will never
0:15:19 be able to look
0:15:19 at the world
0:15:20 and go,
0:15:21 I’m not going
0:15:21 to predict
0:15:22 the next frame,
0:15:22 I’m going to render
0:15:23 the next frame
0:15:24 based on what I know
0:15:25 about physics.
0:15:27 And so water reacts
0:15:28 this way
0:15:29 in an earthbound
0:15:29 gravity system
0:15:30 and so it’s going
0:15:31 to splash like this
0:15:32 and it understands
0:15:32 liquid dynamics,
0:15:33 et cetera,
0:15:33 et cetera.
0:15:36 So is that accurate?
0:15:37 Are you saying
0:15:37 that it is trapped
0:15:38 inside of our box
0:15:39 and will never have?
0:15:40 It hasn’t demonstrated
0:15:41 that capability yet
0:15:43 so it hasn’t
0:15:44 walked up to a rock
0:15:45 and said this is a rock,
0:15:46 right?
0:15:47 We labeled it a rock
0:15:49 because that’s our structure.
0:15:50 But a rock
0:15:51 isn’t probably
0:15:54 a more intelligent being
0:15:55 would have called it
0:15:55 something else
0:15:57 or maybe the rock
0:15:58 is irrelevant
0:16:00 to how you actually
0:16:01 can navigate
0:16:02 the world safely.
0:16:05 and kind of figuring
0:16:06 those things out
0:16:07 or kind of adopting
0:16:08 to them
0:16:08 is just not something
0:16:11 that, you know,
0:16:12 it’s trained on our
0:16:14 rendition of the universe
0:16:16 in our kind of
0:16:19 literally like the way
0:16:20 we have described it
0:16:22 using language
0:16:22 that we invented.
0:16:26 and so it is
0:16:29 constrained a bit
0:16:30 to that in nature
0:16:31 currently.
0:16:33 You know,
0:16:34 that doesn’t mean
0:16:34 it’s not like
0:16:36 a massively useful tool
0:16:37 and can do things
0:16:38 and by the way
0:16:39 can derive new rules
0:16:40 from the old rules
0:16:41 that we’ve given it
0:16:42 for sure.
0:16:46 but, you know,
0:16:46 we’ll,
0:16:49 like I think
0:16:49 it’s a bit of a jump
0:16:50 to go,
0:16:51 you know,
0:16:52 it’s going to replace
0:16:53 us entirely
0:16:56 when the whole
0:16:57 discovery process
0:16:58 is something that we do
0:16:59 that it doesn’t do yet.
0:17:01 Okay.
0:17:02 The way that the human
0:17:03 mind is architected
0:17:04 is you have
0:17:05 competing regions
0:17:06 of the brain
0:17:06 like if you cut
0:17:07 the corpus callosum
0:17:08 the part that connects
0:17:09 the left and the right
0:17:09 hemisphere
0:17:10 you can get
0:17:12 two distinct personalities
0:17:13 one that is atheist
0:17:14 for instance
0:17:15 and one that
0:17:17 believes deeply in God
0:17:17 and they’ll argue
0:17:18 back and forth.
0:17:18 I mean,
0:17:19 this is in the same
0:17:20 human brain.
0:17:22 So that tells me
0:17:23 that what you have
0:17:24 is basically
0:17:25 regions of the brain
0:17:26 that get good
0:17:26 at a thing
0:17:28 and then they end up
0:17:28 coming together
0:17:29 to collaborate
0:17:30 and that is
0:17:31 sort of human intelligence
0:17:31 and I’ve heard you
0:17:32 talk about
0:17:32 there’s something
0:17:34 like 200 computers
0:17:36 inside of a single car
0:17:38 so if we already know
0:17:38 that you can
0:17:39 daisy chain
0:17:40 all of these
0:17:40 like it’s
0:17:41 it’s a very deep
0:17:42 knowledge about one thing
0:17:44 but as you daisy chain
0:17:45 then the intelligence
0:17:47 gets what I’ll call
0:17:48 more generalized
0:17:49 you don’t see that
0:17:50 as a flywheel
0:17:51 that is going to
0:17:52 keep going.
0:17:53 You know,
0:17:54 what we can compute
0:17:55 will get
0:17:57 better and better
0:17:57 and better
0:18:00 but having said that
0:18:00 you know,
0:18:01 that doesn’t
0:18:02 say that
0:18:05 like humans
0:18:06 one,
0:18:07 you know,
0:18:08 humans
0:18:09 built the machines
0:18:10 plug them in
0:18:11 give them the batteries
0:18:12 all these kinds
0:18:13 of things
0:18:15 and
0:18:17 you know,
0:18:18 and they’ve been
0:18:19 created to
0:18:20 fulfill our purposes
0:18:21 so
0:18:22 you know,
0:18:23 what it means
0:18:23 to be a human
0:18:24 will probably
0:18:25 will change
0:18:26 like it has been
0:18:26 changing
0:18:28 and kind of
0:18:29 how humans
0:18:29 live their life
0:18:30 will change
0:18:31 but humans
0:18:32 still find things
0:18:32 to do
0:18:32 I mean,
0:18:33 like it’s kind
0:18:33 of like
0:18:34 you know,
0:18:35 like a cheetah
0:18:35 has been able
0:18:36 to run faster
0:18:36 than a human
0:18:37 forever
0:18:38 but we
0:18:38 never watch
0:18:39 cheetahs race
0:18:40 we only watch
0:18:40 humans race
0:18:41 each other
0:18:43 you know,
0:18:44 computers have
0:18:44 played chess
0:18:44 better than
0:18:45 humans for a
0:18:45 long time
0:18:46 but nobody
0:18:47 watches computers
0:18:47 play chess
0:18:48 anymore
0:18:48 they watch
0:18:49 humans play
0:18:49 humans
0:18:49 and chess
0:18:50 is more
0:18:50 popular than
0:18:51 it’s ever
0:18:51 been
0:18:52 and so I
0:18:53 think we
0:18:53 have like
0:18:53 a keen
0:18:54 interest
0:18:54 in each
0:18:54 other
0:18:55 and
0:18:57 how that’s
0:18:57 going to
0:18:57 work
0:18:57 and these
0:18:58 will be
0:18:58 kind of
0:18:58 tools
0:18:59 to enhance
0:19:00 that whole
0:19:00 experience
0:19:01 for us
0:19:01 but I
0:19:01 think
0:19:02 it’s
0:19:03 you know
0:19:06 like a
0:19:07 world of
0:19:07 just
0:19:08 machines
0:19:09 seems
0:19:10 like
0:19:10 that
0:19:10 seems
0:19:11 like
0:19:11 really
0:19:11 unlikely
0:19:12 so
0:19:13 you’ve
0:19:13 got
0:19:13 people
0:19:14 like
0:19:15 Elon Musk
0:19:16 Sam
0:19:16 Altman
0:19:17 who have
0:19:17 both
0:19:18 expressed
0:19:18 deep
0:19:19 concerns
0:19:19 about
0:19:21 how
0:19:22 AI
0:19:24 may
0:19:24 in fact
0:19:25 make us
0:19:25 obsolete
0:19:25 Elon
0:19:26 has likened
0:19:27 he’s
0:19:27 certainly
0:19:27 become
0:19:28 fatalistic
0:19:29 but he
0:19:30 gave a
0:19:31 rant
0:19:31 that I
0:19:31 absolutely
0:19:32 love
0:19:32 that is
0:19:33 AI
0:19:33 is a
0:19:33 demon
0:19:34 summoning
0:19:34 circle
0:19:35 and
0:19:35 you’re
0:19:35 calling
0:19:36 forward
0:19:36 this demon
0:19:36 that you
0:19:37 were just
0:19:37 convinced
0:19:38 you’re going
0:19:38 to be
0:19:38 able to
0:19:38 control
0:19:39 and he
0:19:39 certainly
0:19:40 is not
0:19:40 so sure
0:19:40 and at
0:19:41 one point
0:19:41 and again
0:19:42 I’m fully
0:19:42 aware
0:19:43 that he’s
0:19:43 on his
0:19:44 fatalist
0:19:44 arc
0:19:44 and he’s
0:19:45 just
0:19:45 moving
0:19:45 forward
0:19:45 and he’s
0:19:46 building
0:19:46 as fast
0:19:46 as he
0:19:46 can
0:19:48 it’s
0:19:48 interesting
0:19:49 that both
0:19:50 of them
0:19:50 despite
0:19:51 saying
0:19:51 these
0:19:52 things
0:19:52 are
0:19:52 building
0:19:53 AI
0:19:53 as
0:19:54 fast
0:19:54 as
0:19:54 like
0:19:55 they’re
0:19:55 literally
0:19:56 in a race
0:19:56 with each
0:19:56 other
0:19:57 to see
0:19:57 who can
0:19:57 build it
0:19:58 faster
0:20:01 that they’re
0:20:01 warning
0:20:02 about
0:20:03 what do
0:20:03 you take
0:20:04 away
0:20:04 from that
0:20:04 is it
0:20:04 just
0:20:05 regulatory
0:20:05 capture
0:20:06 on both
0:20:06 of their
0:20:07 parts
0:20:07 is it
0:20:08 is Elon
0:20:09 being
0:20:09 sincere
0:20:10 not that I
0:20:10 need you
0:20:10 to mind
0:20:10 read him
0:20:11 but like
0:20:11 what do
0:20:12 you take
0:20:12 away
0:20:13 in
0:20:14 the fact
0:20:14 that
0:20:14 they’ve
0:20:15 both
0:20:15 warned
0:20:15 against
0:20:16 it
0:20:16 and
0:20:16 they’re
0:20:16 both
0:20:17 deploying
0:20:18 it
0:20:18 as
0:20:18 fast
0:20:18 as
0:20:18 they
0:20:18 can
0:20:19 yeah
0:20:20 it seems
0:20:20 fairly
0:20:21 contradictory
0:20:23 look I
0:20:24 think there
0:20:24 is
0:20:25 like I
0:20:26 won’t
0:20:26 question
0:20:27 either
0:20:27 their
0:20:28 sincerity
0:20:28 at some
0:20:28 degree
0:20:29 but I
0:20:29 do
0:20:29 think
0:20:29 there
0:20:30 are
0:20:32 many
0:20:32 reasons
0:20:33 to warn
0:20:33 about
0:20:33 it
0:20:33 but like
0:20:34 I also
0:20:34 think
0:20:35 that
0:20:36 you know
0:20:37 any
0:20:37 kind
0:20:38 of new
0:20:38 super
0:20:38 powerful
0:20:39 technology
0:20:40 you know
0:20:41 in a way
0:20:41 they’re right
0:20:42 to kind
0:20:42 of warn
0:20:42 about
0:20:42 like
0:20:43 okay
0:20:44 this
0:20:45 thing
0:20:46 if we
0:20:48 you know
0:20:48 if we
0:20:48 don’t
0:20:49 think
0:20:49 about
0:20:49 some
0:20:49 of
0:20:49 the
0:20:50 implications
0:20:50 of
0:20:50 it
0:20:51 could
0:20:51 get
0:20:51 dangerous
0:20:52 and I
0:20:52 think
0:20:52 that’s
0:20:52 a good
0:20:53 thing
0:20:53 like
0:20:53 every
0:20:53 technology
0:20:54 we’ve
0:20:54 ever
0:20:54 had
0:20:54 from
0:20:55 fire
0:20:55 to
0:20:57 you know
0:20:58 from
0:20:58 fire
0:20:59 to
0:20:59 automobiles
0:20:59 to
0:21:00 nuclear
0:21:00 to
0:21:01 AI
0:21:02 has
0:21:02 got
0:21:03 the
0:21:03 internet
0:21:05 has
0:21:05 got
0:21:05 downsides
0:21:06 to
0:21:06 it
0:21:06 they
0:21:06 all
0:21:06 have
0:21:07 downsides
0:21:08 and
0:21:08 the
0:21:09 more
0:21:09 powerful
0:21:09 the
0:21:10 more
0:21:10 kind
0:21:10 of
0:21:12 you know
0:21:12 kind
0:21:12 of
0:21:12 intriguing
0:21:13 the
0:21:13 downside
0:21:13 and
0:21:13 you know
0:21:14 maybe
0:21:14 like
0:21:15 you know
0:21:15 without
0:21:16 the
0:21:16 internet
0:21:16 we probably
0:21:17 would have
0:21:17 never gotten
0:21:18 to AI
0:21:20 and so
0:21:22 maybe that was the downside of the internet
0:21:24 that it led to AI or something like that you could argue
0:21:26 but I think
0:21:27 generally
0:21:33 we would take every technology we’ve invented and keep it because you know net net they’ve
0:21:38 been positive for humanity and for the world and and that’s generally and that’s
0:21:40 why I think they’re building it so fast because I think they know that
0:21:47 all right so anybody with a 17 year old right now is thinking oh my how where do I point
0:21:53 my kid what do I tell them to go study that’s future proof what can we learn about the way you guys are investing at
0:22:01 Andreessen Horowitz that would give somebody an inclination of what you think a 17 year old should be focused on now
0:22:10 yeah you know it’s really interesting I think one of the things what we’re saying in the kind of smartest young people that come out is
0:22:18 they spend a lot of time with AI learning everything they possibly can so I think you want to get very good at like high
0:22:19 curiosity
0:22:21 and
0:22:23 and then learning you know you have
0:22:26 available to you all of human knowledge
0:22:28 in something that will talk to you
0:22:33 and that’s you know that’s an incredible opportunity and I think that
0:22:36 anything you want to do in the world to make the world better
0:22:40 you now have the tools as an individual to do that in a way that
0:22:44 you know if you look at kind of what Thomas Edison had to do
0:22:47 in creating GE and like what that took and
0:22:51 and so forth you know it was a way higher bar to have an impact
0:22:53 whereas now I think
0:22:55 you know you can
0:22:57 very quickly
0:23:02 you know build something or do something that you know just pick a problem
0:23:07 it’s not like you know sometimes in this AI conversation the thing that we ignore is like
0:23:10 well what are the problems that we have in the world
0:23:10 well they
0:23:13 well we still have
0:23:14 cancer and diabetes
0:23:16 and sickle cell
0:23:17 and
0:23:18 and every disease
0:23:19 and we still have
0:23:20 the threat of pandemics
0:23:21 and we still have
0:23:22 climate change
0:23:23 and we still have
0:23:25 you know lots of people who are
0:23:26 starving to death
0:23:27 and we still have malaria
0:23:27 and
0:23:30 and so like pick a problem you want to solve
0:23:32 and now
0:23:34 you know
0:23:36 you have a huge helping hand in doing that
0:23:38 that nobody in the history of
0:23:40 the planet has ever had before
0:23:40 so
0:23:42 I think there’s
0:23:44 really great opportunities along those lines
0:23:47 so that
0:23:47 that would be my
0:23:48 you know
0:23:50 my
0:23:51 best advice
0:23:53 I think is to
0:23:55 to get really good with that
0:23:55 and
0:23:58 and look I think a lot of the things that we’ve learned
0:24:01 or that have been valuable skills traditionally
0:24:02 are going to change
0:24:03 so you really
0:24:04 you know again
0:24:06 want to be able to learn how to do anything
0:24:08 and
0:24:09 and I think that’s probably
0:24:11 going to be key
0:24:13 when I look at
0:24:13 the
0:24:14 things you were just talking about
0:24:15 that feels right
0:24:17 for people that have
0:24:17 the
0:24:18 the inclination
0:24:19 that have the
0:24:20 cognitive horsepower
0:24:22 to go and say
0:24:23 okay I’m going to leverage AI
0:24:25 to extend my capabilities
0:24:26 to tackle the biggest problems in the world
0:24:28 certainly right now in this moment
0:24:28 that
0:24:29 that is the
0:24:30 thrilling reality
0:24:31 that people should focus on
0:24:32 but then
0:24:34 I contrast that with
0:24:36 the deaths of despair
0:24:37 a young
0:24:38 among
0:24:39 largely
0:24:40 young men
0:24:43 we have this problem
0:24:43 in
0:24:44 call it
0:24:45 middle America
0:24:46 where
0:24:47 manufacturing jobs
0:24:48 have gone away
0:24:48 so for that
0:24:49 normal
0:24:50 just sort of everyday person
0:24:51 I want to have a trade
0:24:52 I want to go out into the world
0:24:53 and get something done
0:24:55 is AI going to be
0:24:56 useful to them
0:24:58 or are they going to
0:24:59 get replaced by
0:24:59 robotics
0:25:01 the truth of it is
0:25:02 is there’s only one
0:25:03 robot supply chain in the world
0:25:04 and that’s in China
0:25:05 and
0:25:07 you know
0:25:07 so
0:25:07 like
0:25:08 we all need a robot
0:25:09 supply chain
0:25:11 we need to
0:25:11 manufacture that
0:25:13 so I think there’s going to be
0:25:13 like a real
0:25:16 manufacturing opportunity
0:25:18 coming up
0:25:19 and it’ll be
0:25:20 a different kind of
0:25:20 manufacturing
0:25:22 certainly more will be
0:25:23 automated and so forth
0:25:24 but there will be a lot of
0:25:26 things to learn
0:25:26 in that field
0:25:28 that I think will be
0:25:29 you know
0:25:30 super interesting
0:25:31 and
0:25:33 and you know
0:25:34 likely very very good job
0:25:35 so I
0:25:36 ironically
0:25:37 I would say
0:25:38 like going into
0:25:39 manufacturing now
0:25:40 as a young man
0:25:41 and trying to
0:25:42 you know
0:25:43 kind of figure out
0:25:44 what that is
0:25:45 and get engaged in it
0:25:46 will probably lead to
0:25:47 you know
0:25:48 quite a
0:25:49 good career
0:25:50 you know
0:25:50 maybe in
0:25:52 creating
0:25:53 factories
0:25:53 have become
0:25:55 like insanely valuable
0:25:55 and
0:25:55 and
0:25:56 and kind of
0:25:57 and strategic
0:25:58 to the national
0:25:59 interests as well
0:26:00 that makes sense
0:26:01 so again
0:26:02 at the level of
0:26:03 the guy smart enough
0:26:04 to build the facility
0:26:04 yes
0:26:05 and I recently saw
0:26:06 a video of the
0:26:07 grocery store
0:26:08 of the future
0:26:08 where it is
0:26:10 a huge grid
0:26:11 inside of a
0:26:12 giant facility
0:26:13 and there’s just
0:26:14 like these
0:26:15 bots that
0:26:15 look kind of like
0:26:16 small shopping carts
0:26:17 and they’re just
0:26:18 grid patterning
0:26:19 across all the items
0:26:20 snatching up
0:26:20 whatever you order
0:26:22 so you order online
0:26:22 these things
0:26:23 grab all that stuff
0:26:24 and then they send
0:26:25 it off to you
0:26:26 so for the person
0:26:27 that’s savvy enough
0:26:28 to build that facility
0:26:29 yes
0:26:30 tremendous
0:26:31 but
0:26:33 what I think
0:26:33 I hear you saying
0:26:34 and correct me
0:26:34 if I’m wrong
0:26:35 is that
0:26:36 okay there are
0:26:37 two opportunities here
0:26:37 the opportunity
0:26:38 one is
0:26:39 if you’re the kind
0:26:39 of person
0:26:40 that can leverage
0:26:40 AI to build
0:26:41 that facility
0:26:42 massive opportunity
0:26:44 if you’re the kind
0:26:44 of person
0:26:44 that would traditionally
0:26:45 work at that
0:26:45 factory
0:26:46 something new
0:26:47 is coming
0:26:47 we know that
0:26:48 because looking
0:26:48 back at history
0:26:49 all these technologies
0:26:50 unleash things
0:26:51 we can’t yet see
0:26:52 and so I have faith
0:26:53 in the
0:26:54 we can’t yet see it
0:26:54 but it is coming
0:26:55 yeah
0:26:56 no for sure
0:26:56 look I mean
0:26:58 you know what
0:26:58 like the biggest
0:27:00 in-demand job
0:27:00 in the world
0:27:01 is right now
0:27:02 data labelers
0:27:04 and
0:27:05 like data labeling
0:27:06 wasn’t a job
0:27:08 not long ago
0:27:09 but if you talk
0:27:10 I’ve even heard of this
0:27:11 what is data labeling
0:27:12 yeah
0:27:13 so it’s what
0:27:13 Alex said
0:27:14 it’s what scale
0:27:15 AI does
0:27:16 you know
0:27:16 they pay
0:27:18 armies and armies
0:27:19 and armies of people
0:27:20 to label data
0:27:21 so say hey
0:27:23 this is a plant
0:27:24 or this is
0:27:24 you know
0:27:24 a fig
0:27:25 or whatever it is
0:27:26 for the AI
0:27:28 to then understand it
0:27:29 and then you know
0:27:30 now with the
0:27:32 you know
0:27:33 with the kind of
0:27:34 reinforcement learning
0:27:35 coming back
0:27:36 into play
0:27:37 you know
0:27:38 labeling
0:27:38 you know
0:27:39 that kind of
0:27:40 supervised learning
0:27:41 is still like
0:27:42 very very very important
0:27:44 and I think that
0:27:46 you know
0:27:47 right now
0:27:48 like he’s got
0:27:50 unlimited hiring demand
0:27:51 which is
0:27:51 you know
0:27:52 ironic
0:27:53 for scale AI
0:27:54 to have
0:27:55 unlimited need
0:27:56 for humans
0:27:57 and I think
0:27:58 you know
0:27:59 in manufacturing
0:28:00 there are going to be
0:28:01 jobs like that
0:28:02 and there will be
0:28:02 the kind of physical
0:28:05 well when you go
0:28:06 and you go
0:28:07 into these robot
0:28:08 like the software
0:28:09 companies that are
0:28:10 doing robotics
0:28:12 they have people
0:28:14 managing the robots
0:28:15 right
0:28:15 like they’re training
0:28:16 the robots
0:28:17 humans train robots
0:28:19 to do all kinds
0:28:20 of things
0:28:21 and it turns out
0:28:21 that like
0:28:23 folding clothes
0:28:25 doesn’t necessarily
0:28:27 generalize
0:28:28 to making eggs
0:28:30 they’re like
0:28:31 super different
0:28:32 for robots
0:28:34 and so you need
0:28:35 you know
0:28:36 these robots
0:28:36 trained in all
0:28:37 these kinds
0:28:37 of fields
0:28:38 and so forth
0:28:38 so I think
0:28:38 there’s
0:28:39 you know
0:28:40 there’s a whole
0:28:40 new class
0:28:41 of jobs
0:28:42 that are
0:28:42 a little bit
0:28:43 hard to
0:28:44 anticipate
0:28:46 you know
0:28:47 in advance
0:28:48 but I think
0:28:49 at least
0:28:49 for the next
0:28:50 10 years
0:28:50 I think
0:28:51 the number
0:28:52 of new
0:28:52 jobs
0:28:53 related
0:28:54 to making
0:28:54 these machines
0:28:55 smarter
0:28:56 is going
0:28:57 to increase
0:28:57 a lot
0:28:59 and then
0:29:00 after that
0:29:00 you know
0:29:00 like
0:29:02 I think
0:29:02 there will
0:29:03 be
0:29:04 there just
0:29:05 tend to be
0:29:05 like
0:29:06 throughout
0:29:07 history
0:29:08 so many
0:29:09 needs
0:29:09 for new
0:29:09 things
0:29:10 that we
0:29:10 never
0:29:11 anticipated
0:29:11 like well
0:29:12 I mean
0:29:12 you know
0:29:12 one of my
0:29:13 favorite
0:29:13 examples
0:29:13 is
0:29:14 okay
0:29:15 computers
0:29:17 are going
0:29:18 to kill
0:29:20 the typesetting
0:29:20 business
0:29:21 and they did
0:29:22 everybody knew
0:29:22 that
0:29:23 like that
0:29:23 was coming
0:29:24 nobody
0:29:26 nobody
0:29:26 said oh
0:29:26 and then
0:29:27 there’s
0:29:27 going to be
0:29:28 5 million
0:29:28 graphic design
0:29:29 jobs
0:29:29 that come
0:29:30 out of
0:29:30 the PC
0:29:31 like
0:29:32 nobody
0:29:33 not a
0:29:33 person
0:29:34 predicted
0:29:34 that
0:29:35 so
0:29:35 it’s
0:29:35 really
0:29:36 easy
0:29:36 to figure
0:29:36 out
0:29:36 which
0:29:37 jobs
0:29:37 are
0:29:37 going
0:29:37 to go
0:29:37 away
0:29:38 it’s
0:29:38 much
0:29:39 more
0:29:39 difficult
0:29:39 to
0:29:39 kind
0:29:40 of
0:29:40 figure
0:29:40 out
0:29:40 which
0:29:41 jobs
0:29:41 are
0:29:41 going
0:29:41 to
0:29:41 come
0:29:42 but
0:29:43 like
0:29:43 if
0:29:43 you
0:29:43 look
0:29:43 at
0:29:43 the
0:29:44 history
0:29:44 of
0:29:44 automation
0:29:46 which
0:29:46 is
0:29:46 kind
0:29:47 of
0:29:47 automated
0:29:47 away
0:29:48 everything
0:29:48 we
0:29:48 did
0:29:48 100
0:29:49 years
0:29:49 ago
0:29:50 there’s
0:29:54 and so
0:29:54 you go
0:29:56 okay
0:29:57 and then
0:29:57 you know
0:29:57 like
0:29:58 some of
0:29:58 the
0:29:58 employment
0:29:59 will be
0:30:00 much
0:30:00 more
0:30:01 I think
0:30:01 enjoyable
0:30:02 than the
0:30:02 old
0:30:02 employment
0:30:03 as well
0:30:04 as it
0:30:04 has been
0:30:05 you know
0:30:05 over time
0:30:06 and you
0:30:07 always talk
0:30:07 about
0:30:07 manufacturing
0:30:08 jobs
0:30:08 going
0:30:08 away
0:30:08 but
0:30:09 the
0:30:09 manufacturing
0:30:09 jobs
0:30:10 that
0:30:10 have
0:30:10 gone
0:30:10 away
0:30:10 have
0:30:11 been
0:30:11 the
0:30:11 most
0:30:11 mind
0:30:12 numbing
0:30:12 so
0:30:13 I
0:30:13 think
0:30:13 things
0:30:14 evolve
0:30:14 in
0:30:14 very
0:30:15 very
0:30:15 unpredictable
0:30:16 ways
0:30:17 and
0:30:18 you know
0:30:18 like
0:30:18 I
0:30:18 think
0:30:19 the
0:30:19 hope
0:30:19 is
0:30:19 that
0:30:20 you
0:30:20 know
0:30:20 the
0:30:20 world
0:30:21 just
0:30:21 gets
0:30:22 much
0:30:22 better
0:30:23 but
0:30:23 I’m
0:30:23 not
0:30:24 I’m
0:30:24 not
0:30:25 so
0:30:26 worried
0:30:26 about
0:30:27 kind
0:30:27 of
0:30:28 anticipating
0:30:28 all the
0:30:28 horror
0:30:29 that’s
0:30:29 going to
0:30:29 come
0:30:29 I mean
0:30:29 I think
0:30:30 the main
0:30:30 reason
0:30:30 we’re
0:30:31 making
0:30:31 these
0:30:31 things
0:30:31 is
0:30:32 you
0:30:32 know
0:30:33 the
0:30:33 ways
0:30:34 that
0:30:34 they’re
0:30:34 making
0:30:34 life
0:30:35 better
0:30:36 and
0:30:36 you know
0:30:37 just like
0:30:37 we finally
0:30:38 figured out
0:30:38 a way
0:30:39 for
0:30:39 everybody
0:30:40 like
0:30:40 we
0:30:40 already
0:30:41 have
0:30:42 in
0:30:42 our
0:30:43 hands
0:30:44 everybody
0:30:44 can
0:30:44 get
0:30:45 a
0:30:45 great
0:30:45 education
0:30:46 like
0:30:47 that
0:30:47 whole
0:30:49 inequality
0:30:50 of
0:30:50 access
0:30:50 to
0:30:51 education
0:30:51 is
0:30:52 like
0:30:52 literally
0:30:53 gone
0:30:53 right
0:30:53 now
0:30:54 which
0:30:55 is
0:30:56 pretty
0:30:56 amazing
0:30:57 I mean
0:30:57 it’s
0:30:57 certainly
0:30:58 huge
0:30:59 yeah
0:30:59 nothing
0:30:59 that I
0:31:00 ever
0:31:00 thought
0:31:00 I’d
0:31:00 see
0:31:01 so
0:31:02 hopefully
0:31:04 things
0:31:04 go well
0:31:05 the great
0:31:05 irony
0:31:05 it’s
0:31:06 so
0:31:06 crazy
0:31:07 I
0:31:07 don’t
0:31:07 think
0:31:08 anybody
0:31:08 anybody
0:31:09 saw that
0:31:09 coming
0:31:09 it was
0:31:09 always
0:31:10 going
0:31:10 to
0:31:10 be
0:31:10 it’s
0:31:10 going
0:31:10 to
0:31:10 go
0:31:10 for
0:31:10 the
0:31:11 drivers
0:31:11 it’s
0:31:11 going
0:31:11 to
0:31:12 go
0:31:12 for
0:31:12 all
0:31:12 those
0:31:12 hard
0:31:13 difficult
0:31:13 repetitive
0:31:14 tasks
0:31:14 yeah
0:31:14 it’s
0:31:15 been
0:31:15 very
0:31:16 fascinating
0:31:16 to
0:31:16 see
0:31:16 what
0:31:17 actually
0:31:17 is
0:31:17 in
0:31:18 danger
0:31:18 like
0:31:19 super
0:31:19 creative
0:31:20 jobs
0:31:20 very
0:31:21 much
0:31:21 in
0:31:21 danger
0:31:23 but
0:31:23 yeah
0:31:23 as
0:31:23 you
0:31:23 get
0:31:24 down
0:31:24 I
0:31:24 mean
0:31:24 look
0:31:24 I
0:31:25 think
0:31:25 I
0:31:25 believe
0:31:26 way
0:31:26 more
0:31:26 strongly
0:31:27 than
0:31:27 you
0:31:27 do
0:31:27 that
0:31:28 robots
0:31:28 are
0:31:28 just
0:31:28 going
0:31:28 to
0:31:28 get
0:31:29 better
0:31:29 and
0:31:29 better
0:31:29 and
0:31:29 better
0:31:30 and
0:31:30 better
0:31:31 but
0:31:32 that
0:31:32 could
0:31:32 be
0:31:32 that
0:31:33 I’m
0:31:33 not
0:31:33 as
0:31:33 close
0:31:33 to
0:31:33 the
0:31:34 problem
0:31:34 as
0:31:34 you
0:31:34 are
0:31:35 speaking
0:31:35 of
0:31:35 which
0:31:36 how
0:31:36 the
0:31:37 insights
0:31:37 that
0:31:37 you’ve
0:31:38 had
0:31:38 into
0:31:39 AI
0:31:39 how
0:31:39 are
0:31:39 they
0:31:40 informing
0:31:40 the
0:31:41 investments
0:31:41 that
0:31:41 you
0:31:41 guys
0:31:42 make
0:31:42 the
0:31:43 theory
0:31:43 is
0:31:43 there’s
0:31:44 this
0:31:44 one
0:31:44 like
0:31:44 super
0:31:45 intelligent
0:31:45 big
0:31:46 brain
0:31:46 that’s
0:31:46 going to
0:31:46 do
0:31:47 everything
0:31:47 the
0:31:48 reality
0:31:48 on the
0:31:48 ground
0:31:49 is
0:31:49 even
0:31:50 with
0:31:50 the
0:31:50 state
0:31:50 of
0:31:50 the
0:31:50 art
0:31:51 models
0:31:51 they’re
0:31:51 all
0:31:52 kind
0:31:52 of
0:31:52 good
0:31:53 at
0:31:53 slightly
0:31:53 different
0:31:54 things
0:31:54 right
0:31:54 like
0:31:54 you know
0:31:55 Anthropic
0:31:55 is
0:31:55 like
0:31:55 really
0:31:56 good
0:31:56 at
0:31:56 code
0:31:57 and
0:31:58 Grok
0:31:58 is
0:31:58 really
0:31:59 good
0:31:59 at
0:31:59 like
0:31:59 real
0:31:59 time
0:32:00 data
0:32:00 because
0:32:00 they’ve
0:32:00 got
0:32:00 the
0:32:01 Twitter
0:32:01 stuff
0:32:01 and
0:32:02 then
0:32:02 you
0:32:02 know
0:32:03 OpenAI
0:32:03 has
0:32:04 gotten
0:32:04 like
0:32:04 very
0:32:04 very
0:32:05 good
0:32:05 at
0:32:05 reasoning
0:32:07 so
0:32:09 with
0:32:09 all
0:32:09 of
0:32:10 them
0:32:10 are
0:32:10 doing
0:32:11 AGI
0:32:11 but
0:32:12 then
0:32:12 they’re
0:32:12 all
0:32:12 good
0:32:12 at
0:32:13 different
0:32:13 stuff
0:32:13 which
0:32:14 is
0:32:14 you know
0:32:15 from
0:32:15 an
0:32:15 investing
0:32:16 standpoint
0:32:16 it’s
0:32:16 very
0:32:16 good
0:32:17 to
0:32:17 know
0:32:17 that
0:32:19 because
0:32:20 if
0:32:20 something
0:32:20 like
0:32:20 that’s
0:32:21 not
0:32:21 winner
0:32:21 take
0:32:21 all
0:32:22 that’s
0:32:22 very
0:32:23 that
0:32:23 becomes
0:32:23 like
0:32:24 super
0:32:24 interesting
0:32:25 it
0:32:25 also
0:32:26 is
0:32:26 interesting
0:32:27 for
0:32:28 what
0:32:28 it
0:32:28 means
0:32:28 at
0:32:28 the
0:32:29 application
0:32:29 layer
0:32:29 because
0:32:30 if
0:32:30 the
0:32:32 infrastructure
0:32:32 products
0:32:33 aren’t
0:32:33 winner
0:32:34 take
0:32:34 all
0:32:34 and
0:32:34 then
0:32:35 the
0:32:35 other
0:32:35 thing
0:32:35 about
0:32:35 the
0:32:36 infrastructure
0:32:36 products
0:32:37 that’s
0:32:37 interesting
0:32:37 is
0:32:38 that
0:32:38 they’re
0:32:39 not
0:32:39 particularly
0:32:40 sticky
0:32:41 in the
0:32:42 way
0:32:42 that
0:32:42 kind
0:32:43 of
0:32:44 Microsoft
0:32:44 Windows
0:32:45 was
0:32:45 very
0:32:45 sticky
0:32:46 right
0:32:46 it was
0:32:46 sticky
0:32:47 you build
0:32:47 an application
0:32:48 on Windows
0:32:49 it doesn’t run
0:32:49 on other
0:32:50 stuff
0:32:50 you’ve got
0:32:51 to do a lot
0:32:51 of work
0:32:52 to move
0:32:52 it to
0:32:52 something
0:32:52 else
0:32:53 so you
0:32:53 get this
0:32:53 network
0:32:55 effect
0:32:55 with
0:32:56 developers
0:32:57 then you
0:32:57 go
0:32:58 okay
0:32:58 well
0:32:59 how does
0:32:59 that work
0:32:59 with
0:33:00 state-of-the-art
0:33:00 models
0:33:01 well people
0:33:01 build
0:33:01 applications
0:33:02 on these
0:33:02 things
0:33:03 but
0:33:03 guess
0:33:04 what
0:33:04 like
0:33:05 to move
0:33:05 your
0:33:05 application
0:33:05 to
0:33:06 DeepSeq
0:33:06 you didn’t
0:33:07 have to
0:33:08 they just
0:33:09 literally
0:33:09 took the
0:33:09 open
0:33:11 Python
0:33:11 API
0:33:12 and like
0:33:12 it runs
0:33:13 on DeepSeq
0:33:13 now
0:33:16 so
0:33:17 you know
0:33:17 that
0:33:17 that kind
0:33:18 of thing
0:33:19 really impacts
0:33:20 you know
0:33:20 how you
0:33:20 think about
0:33:21 investing
0:33:21 and like
0:33:22 what is
0:33:23 the value
0:33:23 of having
0:33:24 lead in
0:33:24 application
0:33:25 and then
0:33:25 you know
0:33:26 where is
0:33:26 the mode
0:33:26 going to
0:33:27 come from
0:33:28 and of
0:33:28 course
0:33:28 AI is
0:33:29 also
0:33:29 getting
0:33:29 like
0:33:30 the one
0:33:30 thing
0:33:30 it is
0:33:30 getting
0:33:31 amazingly
0:33:32 good at
0:33:32 is
0:33:32 writing
0:33:33 code
0:33:34 and
0:33:34 so
0:33:35 then
0:33:35 you know
0:33:35 how much
0:33:36 of a
0:33:36 lead
0:33:36 do you
0:33:37 have
0:33:38 in
0:33:38 the
0:33:38 code
0:33:39 itself
0:33:39 versus
0:33:41 you know
0:33:42 kind of
0:33:42 the other
0:33:42 traditional
0:33:43 things
0:33:43 you know
0:33:43 when I
0:33:44 started
0:33:44 in the
0:33:44 industry
0:33:45 the
0:33:46 sales
0:33:46 people
0:33:46 were
0:33:46 in
0:33:47 charge
0:33:48 they
0:33:48 were
0:33:48 kind
0:33:49 of
0:33:49 like
0:33:49 the
0:33:49 big
0:33:50 there’s
0:33:50 a
0:33:50 great
0:33:52 TV
0:33:52 show
0:33:52 called
0:33:53 Halt
0:33:53 and Catch
0:33:53 Fire
0:33:54 and
0:33:55 if you
0:33:55 watch it
0:33:56 like the
0:33:56 thing
0:33:57 that’s
0:33:57 really
0:33:58 stunning
0:33:58 if
0:33:58 you’re
0:33:59 you know
0:33:59 kind
0:33:59 of
0:34:00 coming
0:34:00 from
0:34:01 the
0:34:02 2010s
0:34:03 2020s
0:34:04 world
0:34:04 is
0:34:05 why
0:34:05 are
0:34:05 the
0:34:05 sales
0:34:06 people
0:34:06 so
0:34:06 powerful
0:34:08 but
0:34:09 they
0:34:09 were
0:34:09 the
0:34:09 most
0:34:09 powerful
0:34:10 in
0:34:10 those
0:34:10 days
0:34:11 and
0:34:11 it
0:34:11 was
0:34:12 because
0:34:12 you know
0:34:12 distribution
0:34:13 was the
0:34:14 most
0:34:14 difficult
0:34:14 thing
0:34:16 and
0:34:16 I
0:34:17 you know
0:34:17 I think
0:34:18 distribution
0:34:18 is going
0:34:19 to get
0:34:19 in
0:34:19 very
0:34:19 very
0:34:20 important
0:34:20 again
0:34:23 because
0:34:24 maintaining
0:34:24 a
0:34:25 technological
0:34:25 lead
0:34:25 is
0:34:26 a lot
0:34:26 harder
0:34:27 you know
0:34:28 when the
0:34:28 machine
0:34:29 is writing
0:34:29 the code
0:34:30 and writing
0:34:30 it very
0:34:30 fast
0:34:32 although
0:34:32 it’s not
0:34:33 all the way
0:34:34 where it
0:34:34 can build
0:34:35 like super
0:34:35 complex
0:34:36 systems
0:34:37 but there’s
0:34:37 you know
0:34:38 a bunch
0:34:39 of things
0:34:40 out now
0:34:40 you know
0:34:40 Replet’s
0:34:41 got a
0:34:41 great
0:34:42 product
0:34:42 for it
0:34:43 there’s
0:34:43 a company
0:34:43 called
0:34:44 Lovable
0:34:44 that’s got
0:34:44 one out
0:34:45 in Sweden
0:34:46 that just
0:34:46 builds you
0:34:47 an app
0:34:47 like if
0:34:47 you need
0:34:48 an app
0:34:48 for something
0:34:49 just say
0:34:49 build me
0:34:50 this app
0:34:51 and there
0:34:51 it is
0:34:52 yeah
0:34:53 another thing
0:34:53 in cursor
0:34:54 that you
0:34:54 can select
0:34:56 what model
0:34:56 you want
0:34:57 to use
0:34:58 for whatever
0:34:58 thing that
0:34:58 you’re about
0:34:59 to generate
0:34:59 so the
0:35:00 ability to
0:35:00 go oh
0:35:01 I want
0:35:01 10 of
0:35:01 these
0:35:02 things
0:35:02 I’m gonna
0:35:02 use this
0:35:03 one for
0:35:03 this kind
0:35:03 of code
0:35:04 this one
0:35:04 for that
0:35:04 kind
0:35:04 of code
0:35:05 it’s
0:35:06 really
0:35:06 fascinating
0:35:08 now the
0:35:08 Biden
0:35:08 administration
0:35:09 was super
0:35:09 hostile
0:35:10 towards tech
0:35:12 when you
0:35:13 look at
0:35:14 what’s going
0:35:14 on now
0:35:15 with the
0:35:15 changes
0:35:16 in
0:35:16 regulatory
0:35:18 what do
0:35:19 you think
0:35:19 about the
0:35:20 race
0:35:21 between
0:35:21 us
0:35:21 and
0:35:21 China
0:35:22 were we
0:35:22 headed
0:35:23 down a
0:35:23 dark
0:35:23 path
0:35:24 where
0:35:24 if
0:35:25 that
0:35:26 administration
0:35:26 had
0:35:27 stayed
0:35:27 with that
0:35:27 like we’re
0:35:28 going to have
0:35:28 one or two
0:35:29 companies
0:35:29 we’re going to
0:35:30 control them
0:35:30 that’s going
0:35:30 to be that
0:35:32 is it possible
0:35:32 we could have
0:35:33 lost that race
0:35:34 is that race
0:35:35 a figment
0:35:35 of my
0:35:36 imagination
0:35:36 is that
0:35:36 real
0:35:38 I think
0:35:38 there’s
0:35:40 multiple layers
0:35:41 to the
0:35:43 AI race
0:35:43 with China
0:35:44 and then
0:35:45 you know
0:35:45 the Biden
0:35:46 administration
0:35:47 was
0:35:48 kind of
0:35:48 hostile
0:35:49 in many
0:35:50 ways
0:35:50 but all
0:35:51 for kind
0:35:51 of a
0:35:51 central
0:35:52 reason
0:35:52 I think
0:35:54 so
0:35:55 you know
0:35:56 in AI
0:35:56 in particular
0:35:58 you know
0:35:58 when we
0:35:59 met
0:36:01 and I
0:36:01 should be
0:36:02 very specific
0:36:03 we did
0:36:03 meet with
0:36:04 Jake Sullivan
0:36:04 but he was
0:36:05 very good
0:36:05 about it
0:36:05 we met
0:36:06 with Gina
0:36:06 Raimondo
0:36:07 she was
0:36:07 very good
0:36:08 about it
0:36:09 but we
0:36:10 met with
0:36:10 the kind
0:36:11 of White
0:36:11 House
0:36:13 and
0:36:13 their
0:36:14 you know
0:36:15 their position
0:36:15 was
0:36:16 super
0:36:18 kind of
0:36:18 I would
0:36:18 say
0:36:19 ill-informed
0:36:20 so
0:36:21 they
0:36:21 basically
0:36:22 were
0:36:23 they
0:36:24 walked in
0:36:24 with this
0:36:25 idea
0:36:26 that like
0:36:26 we’ve got
0:36:27 a three-year
0:36:28 lead on
0:36:28 China
0:36:30 and
0:36:30 we have
0:36:31 to protect
0:36:31 that lead
0:36:33 and
0:36:34 there’s
0:36:34 no
0:36:36 and
0:36:36 therefore
0:36:37 we need
0:36:37 to shut
0:36:38 down
0:36:38 open
0:36:38 source
0:36:39 and
0:36:39 that
0:36:39 doesn’t
0:36:40 matter
0:36:41 to
0:36:41 you
0:36:42 guys
0:36:42 and
0:36:42 startups
0:36:42 because
0:36:43 startups
0:36:44 can’t
0:36:44 participate
0:36:45 in AI
0:36:45 anyway
0:36:47 because
0:36:47 they don’t
0:36:48 have enough
0:36:48 money
0:36:50 and
0:36:50 the only
0:36:51 companies
0:36:51 that are
0:36:52 going to
0:36:52 do
0:36:52 AI
0:36:53 are
0:36:54 going to
0:36:54 be
0:36:54 kind
0:36:54 of
0:36:55 ironically
0:36:56 the
0:36:57 two
0:36:57 startups
0:36:58 Anthropic
0:36:58 and Open
0:36:58 AI
0:36:59 that are
0:36:59 out
0:37:00 and then
0:37:00 the big
0:37:00 companies
0:37:00 Google
0:37:01 and so
0:37:01 forth
0:37:01 and
0:37:02 Microsoft
0:37:03 and so
0:37:03 we can
0:37:04 put a
0:37:05 huge
0:37:05 regulatory
0:37:06 barrier
0:37:06 on them
0:37:06 because
0:37:07 they have
0:37:07 the money
0:37:07 and the
0:37:08 people
0:37:08 to deal
0:37:08 with it
0:37:09 and then
0:37:10 that’ll
0:37:10 be
0:37:11 and you
0:37:11 know
0:37:12 in their
0:37:12 minds
0:37:12 I think
0:37:13 they actually
0:37:13 believe
0:37:14 that
0:37:15 that would
0:37:15 be how
0:37:16 we would
0:37:16 win
0:37:18 but of course
0:37:18 you know
0:37:19 in retrospect
0:37:20 that makes
0:37:20 no sense
0:37:21 and it
0:37:21 kind of
0:37:23 it damages
0:37:23 you know
0:37:23 if you look
0:37:24 at China
0:37:24 and what
0:37:24 China’s
0:37:25 great at
0:37:25 then this
0:37:26 goes to the
0:37:26 next thing
0:37:27 so there’s
0:37:27 how good
0:37:28 is your
0:37:28 AI
0:37:28 and then
0:37:30 how well
0:37:30 is it
0:37:31 integrated
0:37:31 into
0:37:33 your
0:37:33 military
0:37:34 and the way
0:37:34 the government
0:37:34 works
0:37:35 and so
0:37:35 forth
0:37:36 and I
0:37:36 think
0:37:36 that
0:37:37 China
0:37:37 being a
0:37:38 top-down
0:37:39 society
0:37:40 their strength
0:37:40 is
0:37:42 you know
0:37:43 that whatever
0:37:44 AI they have
0:37:44 they’re going
0:37:45 to integrate
0:37:45 into it’s
0:37:46 already all the
0:37:46 companies are
0:37:47 highly integrated
0:37:47 into the
0:37:48 government
0:37:49 so you know
0:37:50 they’re going
0:37:50 to be able
0:37:50 to deploy
0:37:51 that and
0:37:52 and we’re
0:37:52 going to
0:37:52 see it
0:37:53 in action
0:37:55 with their
0:37:55 military
0:37:56 very fast
0:37:57 I think
0:37:57 that the
0:37:58 advantage
0:37:58 of the
0:37:59 US
0:37:59 is like
0:38:00 we’re not
0:38:01 a top-down
0:38:01 society
0:38:02 we’re like
0:38:02 a wild
0:38:03 messy
0:38:03 society
0:38:04 but it
0:38:05 means that
0:38:05 all of our
0:38:06 smart people
0:38:07 can participate
0:38:08 in the field
0:38:10 and look
0:38:11 there’s more
0:38:11 to AI
0:38:12 than just
0:38:12 the big
0:38:12 models
0:38:13 as you said
0:38:13 like
0:38:14 you know
0:38:15 how important
0:38:15 is cursor
0:38:16 it’s really
0:38:17 important
0:38:18 if you’re
0:38:18 building stuff
0:38:19 so like
0:38:20 oh you want
0:38:20 to go
0:38:21 build
0:38:23 you know
0:38:24 the next
0:38:25 whatever
0:38:26 thing that
0:38:27 the CIA
0:38:27 needs
0:38:27 or the
0:38:28 NSA
0:38:28 needs
0:38:28 or this
0:38:29 and that
0:38:30 like
0:38:30 you’re
0:38:30 building
0:38:30 that
0:38:30 with
0:38:31 cursor
0:38:31 you’re
0:38:31 using
0:38:31 a state
0:38:32 of the
0:38:32 art
0:38:32 model
0:38:33 but like
0:38:33 if you
0:38:33 had
0:38:34 eliminated
0:38:34 if you
0:38:34 know
0:38:35 if
0:38:35 the
0:38:35 Biden
0:38:36 White
0:38:36 House
0:38:36 had
0:38:36 gotten
0:38:36 their
0:38:36 way
0:38:37 they’d
0:38:37 eliminate
0:38:37 things
0:38:38 like
0:38:38 cursor
0:38:38 they’d
0:38:38 eliminate
0:38:39 startups
0:38:39 being
0:38:40 able
0:38:40 to do
0:38:41 anything
0:38:41 in AI
0:38:43 and so
0:38:44 the advantages
0:38:45 that we have
0:38:45 that we
0:38:45 don’t just
0:38:46 have a model
0:38:47 we’ve got
0:38:47 all this
0:38:47 other stuff
0:38:48 that goes
0:38:48 with it
0:38:50 and then
0:38:50 we have
0:38:51 new ideas
0:38:51 on models
0:38:53 with new
0:38:53 algorithms
0:38:53 and this
0:38:54 and that
0:38:55 and that’s
0:38:55 what the
0:38:55 U.S.
0:38:56 is great
0:38:56 at
0:38:57 and I
0:38:57 think
0:38:57 what China
0:38:58 is great
0:38:58 at
0:38:58 is
0:39:01 by the
0:39:01 way
0:39:01 they’re
0:39:01 very good
0:39:01 at math
0:39:02 people
0:39:02 people
0:39:03 are good
0:39:03 at math
0:39:03 and AI
0:39:04 is math
0:39:04 so
0:39:04 their
0:39:06 models
0:39:06 are good
0:39:07 they also
0:39:07 have
0:39:08 a data
0:39:08 advantage
0:39:09 on us
0:39:10 where they
0:39:10 have access
0:39:10 to the
0:39:11 Chinese
0:39:11 internet
0:39:12 they have
0:39:12 access
0:39:12 to
0:39:13 copywritten
0:39:13 material
0:39:14 which
0:39:15 they do
0:39:15 not
0:39:16 have
0:39:16 the
0:39:17 same
0:39:17 difference
0:39:18 for it
0:39:18 that we
0:39:18 do
0:39:18 in the
0:39:19 U.S.
0:39:19 and so
0:39:19 they’re
0:39:19 able
0:39:20 to
0:39:20 kind
0:39:21 of
0:39:21 get
0:39:21 to
0:39:22 you know
0:39:23 if you
0:39:23 use
0:39:23 DeepSeq
0:39:24 you go
0:39:24 wow
0:39:24 DeepSeq
0:39:25 really is
0:39:25 a great
0:39:25 writer
0:39:26 compared
0:39:27 to a lot
0:39:27 of the
0:39:28 U.S.
0:39:28 models
0:39:28 why
0:39:28 is
0:39:29 that
0:39:29 well
0:39:29 they
0:39:30 train
0:39:30 on
0:39:30 bigger
0:39:30 data
0:39:31 set
0:39:31 than
0:39:31 we
0:39:31 do
0:39:32 and
0:39:33 that’s
0:39:33 amazing
0:39:34 so
0:39:35 it
0:39:35 really
0:39:36 you know
0:39:37 I think
0:39:37 what we
0:39:38 want
0:39:38 is
0:39:38 we
0:39:39 want
0:39:39 to
0:39:39 have
0:39:40 kind
0:39:40 of
0:39:40 world
0:39:41 class
0:39:41 first
0:39:41 class
0:39:41 AI
0:39:42 in
0:39:42 the
0:39:42 U.S.
0:39:42 and I
0:39:43 think
0:39:43 of it
0:39:43 less
0:39:43 as
0:39:44 you know
0:39:45 is it
0:39:45 ahead
0:39:45 of
0:39:45 China
0:39:46 is it
0:39:46 slightly
0:39:46 ahead
0:39:46 of
0:39:46 China
0:39:47 I
0:39:47 think
0:39:47 that
0:39:47 model
0:39:50 you
0:39:52 know
0:39:52 what
0:39:52 we’ve
0:39:52 seen
0:39:53 with our
0:39:53 own
0:39:53 state
0:39:53 of the
0:39:53 art
0:39:54 models
0:39:54 are
0:39:55 very
0:39:55 shallow
0:39:57 and I
0:39:57 think
0:39:57 that’ll
0:39:58 continue
0:39:58 as long
0:39:58 as we’re
0:39:59 able
0:39:59 and allowed
0:40:00 to build
0:40:00 AI
0:40:00 and then
0:40:01 economically
0:40:02 what you’d
0:40:02 like
0:40:02 is you’d
0:40:03 like
0:40:04 you know
0:40:04 to have
0:40:05 a vibrant
0:40:06 AI
0:40:06 ecosystem
0:40:07 coming out
0:40:07 of the
0:40:07 U.S.
0:40:08 so other
0:40:08 countries
0:40:09 you know
0:40:10 who aren’t
0:40:11 state-of-the-art
0:40:12 with this
0:40:12 stuff
0:40:13 adopt our
0:40:14 technology
0:40:14 and you know
0:40:15 we continue
0:40:16 to be strong
0:40:16 economically
0:40:18 as opposed
0:40:18 to everything
0:40:19 goes to
0:40:19 China
0:40:19 and that
0:40:20 was like
0:40:20 a big
0:40:21 big risk
0:40:21 with the
0:40:21 Biden
0:40:22 administration
0:40:22 I think
0:40:24 and you
0:40:24 know
0:40:25 which was
0:40:26 you know
0:40:26 what they
0:40:27 were doing
0:40:27 on AI
0:40:28 was
0:40:29 you know
0:40:30 tough
0:40:30 I would
0:40:30 say
0:40:30 what they
0:40:31 were doing
0:40:31 on kind
0:40:32 of fintech
0:40:32 and crypto
0:40:32 was even
0:40:33 tougher
0:40:35 in that
0:40:35 they were
0:40:36 just trying
0:40:36 to get
0:40:36 rid of the
0:40:37 industry
0:40:37 in its
0:40:38 entirety
0:40:38 you know
0:40:38 with AI
0:40:39 they were
0:40:39 trying to
0:40:42 I would
0:40:42 say they
0:40:43 were extremely
0:40:43 arrogant
0:40:44 in their
0:40:46 in what
0:40:46 they thought
0:40:47 their ability
0:40:47 was to
0:40:47 predict
0:40:48 the future
0:40:50 you know
0:40:50 Mark and I
0:40:51 were in
0:40:51 there
0:40:51 you know
0:40:52 like our
0:40:52 job is
0:40:52 to
0:40:53 predict
0:40:53 it
0:40:53 like this
0:40:53 is our
0:40:54 job to
0:40:54 invest in
0:40:55 the future
0:40:55 to predict
0:40:56 the future
0:40:57 and they
0:40:57 were saying
0:40:58 things that
0:40:59 like were
0:41:01 so arrogant
0:41:01 that we
0:41:02 would never
0:41:02 even think
0:41:03 to say
0:41:03 them even
0:41:03 if we
0:41:03 thought
0:41:03 them
0:41:04 because
0:41:04 we’re
0:41:04 like
0:41:05 we know
0:41:06 that we
0:41:06 don’t know
0:41:07 the future
0:41:07 like that
0:41:09 you know
0:41:09 it’s just
0:41:10 unknowable
0:41:11 there’s too
0:41:11 many moving
0:41:11 parts
0:41:12 and these
0:41:12 things are
0:41:12 really
0:41:13 complicated
0:41:15 all right
0:41:16 well speaking
0:41:16 of the future
0:41:18 fully accepting
0:41:18 that it is
0:41:19 very opaque
0:41:20 and very
0:41:20 difficult to
0:41:21 see
0:41:21 what would
0:41:21 you say
0:41:22 is the
0:41:22 most
0:41:23 controversial
0:41:23 view that
0:41:24 you hold
0:41:24 about the
0:41:25 future
0:41:26 if we
0:41:27 don’t
0:41:28 get to
0:41:29 world class
0:41:30 in crypto
0:41:30 we’re gonna
0:41:32 be you know
0:41:32 AI really has
0:41:34 the potential
0:41:34 to wreck
0:41:35 society
0:41:37 and what I
0:41:37 mean by that
0:41:38 is if you
0:41:39 think about
0:41:40 what is
0:41:42 obviously
0:41:43 clearly gonna
0:41:43 happen in an
0:41:44 AI world
0:41:44 is one
0:41:46 we’re not
0:41:46 gonna be able
0:41:46 to tell the
0:41:47 difference between
0:41:47 a human and
0:41:48 a robot
0:41:49 two
0:41:49 we’re not
0:41:50 gonna know
0:41:50 what’s
0:41:50 real or
0:41:51 fake
0:41:53 three
0:41:54 the level
0:41:55 of security
0:41:56 attacks
0:41:57 on big
0:41:58 central data
0:41:58 repositories
0:41:59 is gonna
0:41:59 get so
0:42:00 good
0:42:01 that
0:42:02 everybody’s
0:42:02 data is
0:42:03 going to
0:42:04 be out
0:42:04 there
0:42:05 and you
0:42:05 know
0:42:06 there is
0:42:06 no safe
0:42:07 haven for
0:42:07 a consumer
0:42:09 and then
0:42:10 finally
0:42:10 you know
0:42:10 for these
0:42:11 agents
0:42:12 and these
0:42:12 bots
0:42:13 actually be
0:42:13 useful
0:42:14 they
0:42:14 actually
0:42:15 need to
0:42:15 be able
0:42:15 to use
0:42:16 money
0:42:17 and pay
0:42:17 for stuff
0:42:18 and get
0:42:18 paid for
0:42:19 stuff
0:42:19 like so
0:42:22 and if you
0:42:22 think about
0:42:22 all those
0:42:23 problems
0:42:23 those are
0:42:24 problems
0:42:25 that are
0:42:25 by far
0:42:26 best solved
0:42:27 by kind
0:42:27 of blockchain
0:42:28 technology
0:42:30 so one
0:42:32 we absolutely
0:42:33 need
0:42:34 a public
0:42:34 key
0:42:34 infrastructure
0:42:36 such that
0:42:37 every citizen
0:42:38 has their
0:42:39 own wallet
0:42:39 with their
0:42:40 own data
0:42:40 with their
0:42:41 own
0:42:41 information
0:42:43 and if
0:42:44 you need
0:42:44 to get
0:42:45 credit
0:42:46 or prove
0:42:46 you’re a
0:42:46 citizen
0:42:47 or whatever
0:42:48 you can do
0:42:49 that with
0:42:49 the zero
0:42:49 knowledge
0:42:49 proof
0:42:50 you don’t
0:42:50 have to
0:42:50 hand over
0:42:51 your social
0:42:51 security
0:42:52 numbers
0:42:52 your bank
0:42:53 account
0:42:53 information
0:42:54 all this
0:42:55 kind of
0:42:55 thing
0:42:56 because
0:42:58 the AI
0:42:58 will get
0:42:59 it
0:43:00 so you
0:43:01 really need
0:43:01 your own
0:43:02 keys
0:43:02 and your
0:43:02 own
0:43:02 data
0:43:03 and there
0:43:03 can’t be
0:43:04 these
0:43:04 gigantic
0:43:06 massive
0:43:06 honeypots
0:43:07 of information
0:43:08 that people
0:43:08 can go
0:43:09 after
0:43:10 I think
0:43:10 that with
0:43:11 deep
0:43:11 fakes
0:43:12 if you
0:43:12 think
0:43:12 about
0:43:13 okay
0:43:14 we’re
0:43:14 going to
0:43:15 have to
0:43:15 be able
0:43:16 to
0:43:16 whitelist
0:43:17 things
0:43:17 we’re
0:43:17 going to
0:43:17 have to
0:43:17 be able
0:43:18 to say
0:43:18 what’s
0:43:18 real
0:43:19 but who
0:43:20 keeps
0:43:20 track
0:43:20 of what’s
0:43:21 true
0:43:21 then
0:43:21 is it
0:43:21 the
0:43:22 government
0:43:23 you know
0:43:24 please
0:43:24 Jesus
0:43:24 everybody
0:43:25 trust
0:43:25 everybody
0:43:25 trust
0:43:26 Trump
0:43:26 now
0:43:27 you know
0:43:27 everybody
0:43:27 trusted
0:43:28 Biden
0:43:28 is it
0:43:29 going to
0:43:29 be
0:43:29 Google
0:43:29 we
0:43:30 trust
0:43:30 those
0:43:30 guys
0:43:31 or is
0:43:31 it
0:43:31 going to
0:43:32 be
0:43:32 the
0:43:32 game
0:43:33 theoretic
0:43:33 mathematical
0:43:34 properties
0:43:34 of the
0:43:35 blockchain
0:43:35 that
0:43:36 can hold
0:43:36 that
0:43:39 and so
0:43:39 I think
0:43:39 that
0:43:40 you know
0:43:40 it’s
0:43:41 essential
0:43:42 that we
0:43:43 regenerate
0:43:43 our
0:43:44 kind of
0:43:45 blockchain
0:43:45 crypto
0:43:46 development
0:43:46 in the
0:43:46 US
0:43:47 and we
0:43:47 get
0:43:47 very
0:43:48 serious
0:43:48 about
0:43:48 it
0:43:49 and
0:43:49 you know
0:43:49 like
0:43:50 if the
0:43:50 government
0:43:50 were to
0:43:51 do
0:43:51 something
0:43:51 I think
0:43:51 it should
0:43:52 be to
0:43:52 start to
0:43:53 require
0:43:54 these
0:43:55 information
0:43:56 distribution
0:43:56 networks
0:43:57 these social
0:43:57 networks
0:43:58 to have
0:43:59 a way
0:43:59 to
0:44:00 you know
0:44:01 verifiably
0:44:01 prove
0:44:02 you’re human
0:44:02 you know
0:44:03 prove where
0:44:04 a piece
0:44:04 of data
0:44:05 came from
0:44:05 and so
0:44:05 forth
0:44:06 and I
0:44:07 think
0:44:07 that
0:44:08 you know
0:44:09 we have
0:44:09 to
0:44:09 you know
0:44:10 have
0:44:10 banks
0:44:12 start
0:44:12 accepting
0:44:13 zero
0:44:13 knowledge
0:44:14 proofs
0:44:15 and you
0:44:15 know
0:44:16 and that
0:44:16 be just
0:44:17 the way
0:44:17 the world
0:44:17 works
0:44:17 we need
0:44:19 a network
0:44:20 architecture
0:44:20 that is
0:44:21 up to
0:44:22 the challenge
0:44:22 of you
0:44:23 know
0:44:23 these
0:44:23 super
0:44:24 intelligent
0:44:24 agents
0:44:25 that are
0:44:25 running
0:44:25 around
0:44:26 we were
0:44:27 talking
0:44:27 before we
0:44:27 started
0:44:28 rolling
0:44:28 that you
0:44:28 guys
0:44:28 have an
0:44:29 office
0:44:29 in DC
0:44:30 and part
0:44:30 of what
0:44:30 you do
0:44:32 is advise
0:44:32 on that
0:44:33 like what
0:44:33 what does
0:44:34 the infrastructure
0:44:35 changes
0:44:35 what do
0:44:35 they need
0:44:36 to look
0:44:36 like
0:44:37 what are
0:44:37 a small
0:44:38 handful
0:44:38 of things
0:44:39 that you
0:44:39 guys are
0:44:40 really
0:44:40 pushing
0:44:41 to see
0:44:41 the government
0:44:42 adopt
0:44:42 to modernize
0:44:43 the way
0:44:43 that
0:44:44 the whole
0:44:44 bureaucracy
0:44:45 works
0:44:46 yeah so
0:44:47 there’s a few
0:44:48 things you know
0:44:48 and one of the
0:44:49 things is because
0:44:50 you know
0:44:51 blockchain
0:44:52 technology
0:44:53 involves money
0:44:55 we do need
0:44:55 right it’s not
0:44:56 like we don’t
0:44:56 need any
0:44:57 regulation
0:44:58 we do need
0:44:58 regulation
0:45:00 and there are
0:45:00 kind of very
0:45:01 specific things
0:45:02 that that
0:45:03 we’re working
0:45:04 with the
0:45:04 administration
0:45:05 to make
0:45:05 sure
0:45:06 are done
0:45:07 in a way
0:45:08 that kind
0:45:09 of creates
0:45:09 a great
0:45:10 environment
0:45:10 for everybody
0:45:11 what do
0:45:12 you guys
0:45:12 hoping
0:45:13 will get
0:45:13 blocked
0:45:13 out
0:45:14 for instance
0:45:14 is that
0:45:14 what you’re
0:45:14 about to
0:45:15 cover
0:45:16 yeah I mean
0:45:17 so like
0:45:18 one of the
0:45:20 first thing
0:45:20 you need
0:45:21 is you know
0:45:22 we do need
0:45:23 electronic money
0:45:23 you know
0:45:24 in the
0:45:26 form of
0:45:26 stable coin
0:45:27 so actual
0:45:27 currency
0:45:29 and
0:45:30 but we need
0:45:31 that to not
0:45:32 like it’s very
0:45:32 bad if one of
0:45:33 those collapses
0:45:36 because then
0:45:36 like the
0:45:37 whole trust
0:45:37 and the
0:45:37 system
0:45:38 breaks down
0:45:38 and so
0:45:38 forth
0:45:39 well why
0:45:39 do we need
0:45:40 this kind
0:45:40 of money
0:45:41 this kind
0:45:42 of internet
0:45:42 native money
0:45:44 well
0:45:45 I’ll give you
0:45:45 an example
0:45:46 so we have
0:45:46 a company
0:45:48 called
0:45:48 Daylight Energy
0:45:50 and what
0:45:50 they do
0:45:50 is
0:45:52 so we’re
0:45:52 running
0:45:52 we’re going
0:45:53 to run
0:45:53 into a big
0:45:54 energy problem
0:45:55 with AI
0:45:55 that I think
0:45:56 most people
0:45:57 probably listening
0:45:58 to this know
0:45:58 about where
0:45:59 AI consumes
0:46:00 a massive
0:46:00 amount of
0:46:01 energy
0:46:01 you know
0:46:02 much more
0:46:02 than Bitcoin
0:46:02 ever did
0:46:03 by the way
0:46:04 which everybody
0:46:04 was all up
0:46:05 in arms about
0:46:07 and you know
0:46:07 so much so
0:46:08 that like
0:46:08 you can’t
0:46:09 really even
0:46:09 get it out
0:46:09 of the power
0:46:10 grid
0:46:10 and I think
0:46:11 Trump has
0:46:11 been smart
0:46:12 about this
0:46:12 saying hey
0:46:12 you probably
0:46:13 need to build
0:46:15 a kind
0:46:17 of power
0:46:17 next to your
0:46:18 data center
0:46:19 because we
0:46:19 can’t be
0:46:20 giving it
0:46:20 to you
0:46:20 from the
0:46:21 central tank
0:46:22 but beyond
0:46:22 that I think
0:46:23 that you know
0:46:23 kind of
0:46:24 individuals
0:46:25 you know
0:46:26 now have
0:46:27 Tesla
0:46:28 kind of
0:46:28 solar panels
0:46:29 and power
0:46:29 walls
0:46:30 and these
0:46:30 kinds of
0:46:31 things
0:46:32 and when
0:46:32 you have
0:46:32 one of
0:46:33 those
0:46:33 you
0:46:33 sometimes
0:46:34 have
0:46:34 more
0:46:34 energy
0:46:35 than you
0:46:35 need
0:46:36 and sometimes
0:46:36 have
0:46:36 less
0:46:37 and wouldn’t
0:46:37 it be
0:46:38 great
0:46:39 if you
0:46:39 could
0:46:40 you know
0:46:40 if there
0:46:41 was a
0:46:41 nice
0:46:42 system
0:46:43 that figured
0:46:43 out who
0:46:44 needed
0:46:44 energy
0:46:45 and who
0:46:46 had
0:46:46 energy
0:46:47 and you
0:46:47 could just
0:46:47 trade
0:46:48 and there
0:46:49 was some
0:46:50 kind of
0:46:50 contract
0:46:51 that said
0:46:51 okay this
0:46:51 is what
0:46:52 you pay
0:46:52 during peak
0:46:53 this is what
0:46:53 you pay
0:46:53 at different
0:46:54 periods
0:46:55 and that
0:46:56 contract
0:46:56 probably
0:46:57 best done
0:46:57 in the
0:46:57 form of
0:46:58 a smart
0:46:58 contract
0:46:59 but a
0:46:59 power wall
0:47:00 is not
0:47:00 a human
0:47:01 so it
0:47:01 doesn’t have
0:47:01 a credit
0:47:02 card
0:47:02 it can’t
0:47:02 get a credit
0:47:03 card
0:47:03 doesn’t have
0:47:03 a bank
0:47:04 account
0:47:04 doesn’t have
0:47:04 a social
0:47:05 security number
0:47:07 but it can
0:47:07 trade crypto
0:47:08 it can trade
0:47:08 stable coins
0:47:11 and so we
0:47:11 need that
0:47:11 kind of
0:47:12 currency
0:47:13 to kind
0:47:14 of facilitate
0:47:16 all these
0:47:16 kind of
0:47:17 automated
0:47:17 agreements
0:47:18 and automated
0:47:19 transfer
0:47:19 of
0:47:22 kind of
0:47:23 wealth
0:47:24 between
0:47:25 entities
0:47:26 in order to
0:47:26 kind of
0:47:27 solve these
0:47:28 big problems
0:47:28 that we have
0:47:29 like energy
0:47:31 and so we
0:47:31 need a
0:47:32 stable coin
0:47:32 bill that
0:47:33 kind of
0:47:33 says okay
0:47:34 look
0:47:35 we need
0:47:36 these
0:47:36 currencies
0:47:37 to be
0:47:37 backed
0:47:38 one for
0:47:39 one with
0:47:39 US dollars
0:47:40 or whatever
0:47:41 it is
0:47:42 so that
0:47:43 you know
0:47:43 we can
0:47:44 have a
0:47:46 system that
0:47:46 works and
0:47:46 is trusted
0:47:47 now there’s
0:47:48 this really
0:47:49 interesting side
0:47:49 benefit to
0:47:50 that which
0:47:51 is if
0:47:51 you look
0:47:52 at treasury
0:47:52 auctions
0:47:53 lately
0:47:55 the demand
0:47:56 for dollars
0:47:56 is not
0:47:57 good
0:47:58 you know
0:47:58 and a lot
0:47:59 of that
0:47:59 is the
0:48:00 two biggest
0:48:01 kind of
0:48:02 lenders
0:48:02 to the
0:48:03 US have
0:48:04 been China
0:48:04 and Japan
0:48:06 and you
0:48:06 know China
0:48:06 has backed
0:48:07 off a lot
0:48:08 and Japan
0:48:09 has backed
0:48:10 off somewhat
0:48:11 and so
0:48:11 the demand
0:48:12 for dollars
0:48:12 has gone
0:48:12 down
0:48:13 we’ve done
0:48:13 things to
0:48:14 also dampen
0:48:15 demand like
0:48:16 you know
0:48:16 when we
0:48:17 sanctioned
0:48:18 Russia
0:48:18 and we
0:48:19 seized the
0:48:19 assets of
0:48:20 the Russian
0:48:20 central bank
0:48:21 you know
0:48:21 there were
0:48:22 other countries
0:48:22 that had
0:48:23 you know
0:48:23 other entities
0:48:24 that had
0:48:24 money there
0:48:26 and their
0:48:26 money got
0:48:26 frozen
0:48:27 and they
0:48:27 couldn’t
0:48:28 access it
0:48:28 and so
0:48:28 that makes
0:48:29 people more
0:48:30 wary of holding
0:48:30 everything in
0:48:31 dollars
0:48:32 so we’ve
0:48:33 done a lot
0:48:34 to dampen
0:48:34 that which
0:48:34 of course
0:48:35 is you
0:48:35 know
0:48:36 fueled
0:48:36 inflation
0:48:37 in the
0:48:38 same way
0:48:38 that increasing
0:48:39 supply
0:48:40 fuels
0:48:40 inflation
0:48:41 killing
0:48:41 demand
0:48:42 fuels
0:48:43 inflation
0:48:44 so here
0:48:44 we would
0:48:44 have this
0:48:45 new
0:48:46 major source
0:48:46 of demand
0:48:47 for dollars
0:48:48 and then the
0:48:49 dollars would be
0:48:49 much more useful
0:48:50 because you can
0:48:50 use them
0:48:51 online as well
0:48:53 and machines
0:48:53 can use them
0:48:54 and so forth
0:48:54 so we
0:48:55 really
0:48:55 and sorry
0:48:56 really fast
0:48:56 for people
0:48:57 that are
0:48:57 trying to
0:48:58 track that
0:48:58 the reason
0:48:59 that that
0:49:01 would increase
0:49:01 the demand
0:49:01 for dollars
0:49:02 is that they
0:49:02 would the
0:49:03 stable coin
0:49:03 would be
0:49:04 backed one
0:49:04 for one
0:49:05 with debt
0:49:06 is that the
0:49:06 idea
0:49:07 well yeah
0:49:07 with
0:49:09 where you
0:49:09 would basically
0:49:10 have
0:49:10 you would
0:49:11 have to
0:49:11 have a
0:49:12 dollar
0:49:12 right
0:49:15 for
0:49:16 every
0:49:16 whole
0:49:17 treasuries
0:49:17 stable coin
0:49:17 yeah you
0:49:18 basically
0:49:18 hold
0:49:18 treasuries
0:49:19 so that
0:49:20 if somebody
0:49:20 wanted to
0:49:21 redeem their
0:49:21 stable coins
0:49:22 they could
0:49:23 and then that
0:49:23 way
0:49:24 you know
0:49:24 kind of
0:49:25 the equivalent
0:49:26 of the
0:49:26 gold standard
0:49:27 in the old
0:49:27 days
0:49:27 you know
0:49:28 when dollars
0:49:28 were trying
0:49:29 to get
0:49:29 credible
0:49:30 you know
0:49:31 we would
0:49:31 need
0:49:31 like
0:49:33 dollars
0:49:34 to be the
0:49:34 gold standard
0:49:35 for the
0:49:35 stable coin
0:49:37 you know
0:49:38 and probably
0:49:38 we should
0:49:39 never back
0:49:40 off of that
0:49:40 maybe we
0:49:40 should never
0:49:41 backed off
0:49:41 of gold
0:49:42 but
0:49:43 you know
0:49:43 it’s easier
0:49:44 when it’s
0:49:44 dollars
0:49:45 because we
0:49:45 did kind of
0:49:46 start to run
0:49:46 out of gold
0:49:47 a bit
0:49:49 so
0:49:50 yeah so
0:49:51 that’s
0:49:51 you know
0:49:51 one thing
0:49:53 then
0:49:54 secondly
0:49:55 there’s
0:49:56 a bill
0:49:57 that went
0:49:58 through the
0:49:58 house
0:49:58 known as
0:49:59 the market
0:49:59 structure
0:50:00 bill
0:50:00 it was
0:50:01 technically
0:50:01 called
0:50:02 fit 21
0:50:03 that’s a
0:50:04 very important
0:50:04 you know
0:50:05 whether it’s
0:50:05 exactly that
0:50:06 or some
0:50:06 form of
0:50:07 that
0:50:08 because
0:50:09 you know
0:50:09 when you
0:50:10 talk about
0:50:11 tokens
0:50:12 which are
0:50:13 this kind
0:50:13 of instrument
0:50:14 that’s very
0:50:15 very important
0:50:16 in blockchain
0:50:16 world
0:50:17 because it’s
0:50:17 the way
0:50:19 that this
0:50:20 amazing
0:50:21 kind of
0:50:21 network
0:50:22 of computers
0:50:23 gets paid
0:50:23 for it
0:50:23 so
0:50:24 you know
0:50:25 who
0:50:26 who pays
0:50:26 the people
0:50:26 for running
0:50:28 the computers
0:50:28 well that’s
0:50:29 paid in the
0:50:29 form of
0:50:30 these tokens
0:50:31 but these
0:50:31 tokens
0:50:33 which can be
0:50:34 created on
0:50:34 blockchain
0:50:36 have
0:50:38 they can be
0:50:39 many things
0:50:41 so you can
0:50:41 create a token
0:50:42 that’s a
0:50:42 collectible
0:50:43 you know
0:50:44 you can
0:50:45 create a
0:50:45 token
0:50:47 that is a
0:50:48 digital property
0:50:48 right
0:50:49 you know
0:50:49 that links
0:50:51 to you know
0:50:51 some piece
0:50:52 of real
0:50:52 estate
0:50:53 or a piece
0:50:53 of art
0:50:53 or so
0:50:54 forth
0:50:55 a token
0:50:55 can be
0:50:57 a you know
0:50:58 pokemon card
0:50:58 a token
0:50:58 could be
0:50:59 a coupon
0:51:00 a token
0:51:00 could be
0:51:01 a security
0:51:02 that represents
0:51:02 a stock
0:51:04 it could be
0:51:04 you know
0:51:05 a dollar
0:51:06 so which
0:51:07 one is it
0:51:08 is a very
0:51:09 kind of
0:51:09 important set
0:51:10 of rules
0:51:11 that doesn’t
0:51:11 exist
0:51:12 and this is
0:51:12 one of the
0:51:14 most insidious
0:51:14 thing that
0:51:15 the Biden
0:51:15 administration
0:51:16 did was
0:51:17 basically say
0:51:18 well everything
0:51:19 is a security
0:51:19 everything’s
0:51:20 a stock
0:51:22 or you know
0:51:22 like some
0:51:23 thing with
0:51:23 asymmetric
0:51:24 information
0:51:25 which kind
0:51:26 of basically
0:51:28 undermines
0:51:28 the whole
0:51:30 power of the
0:51:30 technology
0:51:31 and so it
0:51:32 was basically
0:51:33 a scheme
0:51:34 for them
0:51:36 to kind
0:51:37 of get rid
0:51:37 of the industry
0:51:38 but it was
0:51:39 very very
0:51:40 dark
0:51:43 cynical
0:51:44 way of
0:51:46 legislating
0:51:47 things and
0:51:47 they would
0:51:47 make these
0:51:48 fake claims
0:51:49 about scams
0:51:50 and so forth
0:51:51 but the
0:51:52 market structure
0:51:52 bill is
0:51:53 very very
0:51:53 important in
0:51:54 that way
0:51:54 and by the
0:51:55 way also
0:51:55 you know
0:51:56 another thing
0:51:57 that was in
0:51:57 the original
0:51:58 market structure
0:51:59 bill which
0:52:00 is important
0:52:00 is look
0:52:01 there are
0:52:03 also scams
0:52:04 there are
0:52:04 you know
0:52:05 and we call
0:52:05 it the casino
0:52:07 but you know
0:52:08 like I can
0:52:09 create some
0:52:10 coin like the
0:52:11 Hak Tua girl
0:52:11 did right
0:52:12 like and
0:52:13 she creates
0:52:14 a coin
0:52:15 she kind
0:52:16 of lies
0:52:17 about
0:52:19 you know
0:52:20 her holdings
0:52:20 and says
0:52:21 she’s going
0:52:21 to hold
0:52:21 them but
0:52:22 then sells
0:52:23 them you
0:52:23 know
0:52:24 after people
0:52:25 buy it
0:52:25 in a short
0:52:26 time period
0:52:26 and so forth
0:52:27 and there’s
0:52:28 no part of
0:52:28 the problem
0:52:28 is there’s
0:52:29 no kind
0:52:30 of rules
0:52:30 around that
0:52:31 but in
0:52:31 the kind
0:52:32 of bill
0:52:33 that passed
0:52:33 the house
0:52:34 it said
0:52:34 like you
0:52:35 can create
0:52:35 a token
0:52:37 but if
0:52:37 you hold
0:52:37 it you
0:52:38 can’t trade
0:52:38 it for
0:52:39 four years
0:52:40 that kind
0:52:41 of takes
0:52:41 a lot
0:52:42 of the
0:52:42 ability
0:52:43 to scam
0:52:44 out of
0:52:44 it
0:52:45 and kind
0:52:45 of forces
0:52:46 people to
0:52:46 do things
0:52:47 that are
0:52:47 their real
0:52:48 utilities
0:52:48 or if
0:52:48 it’s a
0:52:49 collectible
0:52:50 you know
0:52:50 if it is
0:52:51 the Hak Tua
0:52:51 collectible
0:52:53 you know
0:52:53 it’s got to
0:52:54 be a real
0:52:54 collectible
0:52:55 where you
0:52:56 don’t just
0:52:56 you know
0:52:57 rug
0:52:58 the users
0:52:59 of it
0:52:59 right away
0:53:00 and so
0:53:01 that you
0:53:01 know
0:53:01 that’s
0:53:02 right away
0:53:04 it’s okay
0:53:04 to do it
0:53:04 later
0:53:04 but
0:53:05 well but
0:53:05 you know
0:53:06 like in
0:53:06 four years
0:53:07 it is
0:53:07 what it
0:53:07 is
0:53:08 right
0:53:08 yeah
0:53:09 no no
0:53:09 I’m just
0:53:09 giving you
0:53:10 a hard
0:53:10 time
0:53:10 I just
0:53:10 know how
0:53:11 that’s
0:53:11 going to
0:53:11 sound
0:53:11 to
0:53:11 people
0:53:12 yeah
0:53:13 yeah
0:53:14 thank you
0:53:15 but you
0:53:15 know
0:53:15 so these
0:53:16 kinds of
0:53:16 things I
0:53:17 think are
0:53:18 are going
0:53:18 to be
0:53:18 really
0:53:18 important
0:53:19 to
0:53:19 making
0:53:19 the
0:53:20 whole
0:53:20 industry
0:53:20 work
0:53:21 and so
0:53:21 we’re
0:53:21 working
0:53:22 you know
0:53:23 on that
0:53:23 you know
0:53:24 trying to
0:53:24 make it
0:53:25 safe for
0:53:25 everybody
0:53:26 but as
0:53:27 I said
0:53:27 it’s just
0:53:28 such a
0:53:28 critical
0:53:29 technology
0:53:34 yes it’s
0:53:34 just going
0:53:34 to be
0:53:35 like a
0:53:35 very
0:53:36 kind of
0:53:37 problematic
0:53:39 you know
0:53:40 it’s going
0:53:40 to be
0:53:40 it’s
0:53:41 cyberpunk
0:53:41 it’s
0:53:44 it’s
0:53:44 a
0:53:44 it’s
0:53:44 a
0:53:45 not
0:53:46 yeah
0:53:46 it’s
0:53:46 a
0:53:47 high
0:53:47 technology
0:53:49 difficult
0:53:50 society
0:53:51 yeah
0:53:51 yeah
0:53:52 it
0:53:53 it was
0:53:54 shocking
0:53:54 to me
0:53:55 the level
0:53:55 of
0:53:56 backlash
0:53:57 that
0:53:57 the
0:53:57 blockchain
0:53:58 web
0:53:58 3
0:53:59 community
0:54:00 got
0:54:01 what
0:54:02 do you
0:54:02 think
0:54:03 drives
0:54:03 that
0:54:03 is
0:54:03 it
0:54:04 just
0:54:04 the
0:54:05 perception
0:54:05 that
0:54:05 it
0:54:06 was
0:54:06 only
0:54:06 scams
0:54:06 and
0:54:07 there’s
0:54:07 nothing
0:54:07 real
0:54:08 like
0:54:08 what
0:54:08 was
0:54:08 that
0:54:08 all
0:54:09 about
0:54:09 so
0:54:09 there
0:54:10 was
0:54:10 multiple
0:54:11 factors
0:54:11 so
0:54:11 the
0:54:11 first
0:54:11 one
0:54:12 is
0:54:12 the
0:54:12 one
0:54:12 that
0:54:12 hits
0:54:13 all
0:54:13 new
0:54:13 technology
0:54:14 where
0:54:15 oh
0:54:16 it’s
0:54:16 a
0:54:16 toy
0:54:18 it
0:54:18 doesn’t
0:54:18 do
0:54:18 anything
0:54:19 new
0:54:19 like
0:54:20 the
0:54:20 old
0:54:20 way
0:54:20 of
0:54:20 doing
0:54:21 things
0:54:21 is
0:54:21 better
0:54:22 and
0:54:24 you
0:54:24 know
0:54:24 we
0:54:24 saw
0:54:24 that
0:54:25 with
0:54:25 social
0:54:25 networking
0:54:26 we
0:54:26 actually
0:54:27 saw
0:54:27 that
0:54:27 with
0:54:27 the
0:54:27 internet
0:54:28 I
0:54:28 think
0:54:29 Paul
0:54:29 Krugman
0:54:29 famously
0:54:30 said
0:54:30 never
0:54:31 have
0:54:31 more
0:54:32 economic
0:54:32 impact
0:54:32 on a
0:54:33 fax
0:54:33 machine
0:54:33 and so
0:54:33 forth
0:54:34 so
0:54:34 that’s
0:54:34 just
0:54:34 kind of
0:54:34 a
0:54:35 normal
0:54:35 thing
0:54:35 that
0:54:35 happens
0:54:36 with
0:54:36 new
0:54:36 technologies
0:54:37 as they
0:54:37 start
0:54:37 out
0:54:57 and
0:54:57 so
0:54:58 you
0:54:58 know
0:54:58 if
0:54:58 you
0:54:59 look
0:54:59 at
0:55:00 even
0:55:00 like
0:55:01 the
0:55:01 iPhone
0:55:02 it
0:55:02 was
0:55:02 a
0:55:03 bad
0:55:03 phone
0:55:04 it
0:55:05 had
0:55:05 a
0:55:05 horrible
0:55:06 keyboard
0:55:06 you
0:55:07 know
0:55:07 if
0:55:07 you
0:55:07 compared
0:55:07 it
0:55:07 to
0:55:08 anything
0:55:08 it
0:55:08 wasn’t
0:55:08 very
0:55:09 powerful
0:55:09 it
0:55:09 had
0:55:10 a
0:55:10 little
0:55:10 itty
0:55:10 bitty
0:55:11 screen
0:55:12 but
0:55:13 it
0:55:13 had
0:55:13 a
0:55:13 feature
0:55:14 that
0:55:15 was
0:55:15 pretty
0:55:15 awesome
0:55:16 which
0:55:16 you
0:55:16 could
0:55:16 put
0:55:16 in
0:55:16 your
0:55:17 pocket
0:55:18 and
0:55:18 it
0:55:18 had
0:55:18 like
0:55:18 a
0:55:19 GPS
0:55:19 in
0:55:19 it
0:55:19 and
0:55:19 a
0:55:19 camera
0:55:20 in
0:55:20 it
0:55:21 and
0:55:21 so
0:55:21 now
0:55:21 you
0:55:21 could
0:55:21 build
0:55:22 Instagram
0:55:22 you
0:55:22 could
0:55:22 build
0:55:23 Uber
0:55:24 which
0:55:24 you
0:55:24 could
0:55:24 not
0:55:24 build
0:55:24 with
0:55:25 the
0:55:25 PC
0:55:25 and
0:55:25 you
0:55:25 still
0:55:26 can’t
0:55:26 build
0:55:26 with
0:55:26 the
0:55:26 PC
0:55:28 and
0:55:28 so
0:55:28 that
0:55:29 was
0:55:29 enough
0:55:30 and
0:55:30 then
0:55:30 eventually
0:55:31 it
0:55:31 started
0:55:31 to
0:55:31 add
0:55:32 the
0:55:32 other
0:55:32 features
0:55:33 and
0:55:33 it’s
0:55:33 an
0:55:33 awfully
0:55:34 powerful
0:55:34 computer
0:55:35 these
0:55:35 days
0:55:36 if
0:55:36 you
0:55:37 look
0:55:37 at
0:55:37 blockchain
0:55:38 it’s
0:55:39 slower
0:55:40 it’s
0:55:40 more
0:55:41 complicated
0:55:41 to
0:55:41 program
0:55:42 like
0:55:42 there’s
0:55:42 a lot
0:55:42 of
0:55:43 issues
0:55:43 with
0:55:43 it
0:55:44 but
0:55:44 it’s
0:55:44 got
0:55:44 a
0:55:51 that
0:55:52 code
0:55:53 you
0:55:53 know
0:55:53 says
0:55:54 there’s
0:55:54 only
0:55:54 21
0:55:55 million
0:55:55 bitcoin
0:55:56 you
0:55:57 can
0:55:57 absolutely
0:55:58 count
0:55:58 on
0:55:58 that
0:55:59 in
0:55:59 a
0:55:59 way
0:56:00 that
0:56:00 like
0:56:00 you
0:56:00 can’t
0:56:01 trust
0:56:01 Google
0:56:02 you
0:56:02 can’t
0:56:02 trust
0:56:03 you
0:56:03 know
0:56:04 Facebook
0:56:04 to
0:56:04 say
0:56:05 like
0:56:05 oh
0:56:05 these
0:56:05 are
0:56:06 our
0:56:06 privacy
0:56:07 rules
0:56:08 like
0:56:08 you
0:56:08 can’t
0:56:08 trust
0:56:09 that
0:56:09 at
0:56:09 all
0:56:10 you
0:56:10 can’t
0:56:10 trust
0:56:10 the
0:56:10 US
0:56:11 government
0:56:11 to
0:56:11 say
0:56:11 they’re
0:56:11 not
0:56:11 going
0:56:11 to
0:56:11 print
0:56:12 any
0:56:12 more
0:56:12 money
0:56:13 like
0:56:13 that’s
0:56:13 for
0:56:13 sure
0:56:14 and
0:56:15 so
0:56:15 you
0:56:15 know
0:56:21 count
0:56:21 on
0:56:22 and
0:56:22 you
0:56:22 don’t
0:56:23 have
0:56:23 to
0:56:23 trust
0:56:23 a
0:56:23 company
0:56:24 you
0:56:24 don’t
0:56:24 have
0:56:24 to
0:56:24 trust
0:56:25 a
0:56:26 lawyer
0:56:26 you
0:56:26 just
0:56:27 have
0:56:27 to
0:56:27 trust
0:56:28 the
0:56:28 game
0:56:28 theoretic
0:56:29 mathematical
0:56:30 properties
0:56:30 of the
0:56:30 blockchain
0:56:32 and
0:56:32 that’s
0:56:32 amazing
0:56:33 so
0:56:33 now
0:56:33 you
0:56:33 can
0:56:34 you
0:56:34 know
0:56:35 program
0:56:35 property
0:56:36 rights
0:56:36 and
0:56:36 money
0:56:37 and
0:56:37 law
0:56:37 and
0:56:38 all
0:56:38 these
0:56:38 kinds
0:56:38 of
0:56:39 things
0:56:39 that
0:56:39 you
0:56:39 could
0:56:39 never
0:56:39 do
0:56:40 before
0:56:41 and
0:56:42 so
0:56:42 I
0:56:42 think
0:56:43 that’s
0:56:43 hard
0:56:43 for
0:56:44 normal
0:56:44 people
0:56:44 to
0:56:45 understand
0:56:46 who
0:56:46 aren’t
0:56:47 deep
0:56:52 and
0:56:52 then
0:56:53 you
0:56:53 know
0:56:53 I
0:56:53 think
0:56:54 the
0:56:54 next
0:56:54 wave
0:56:55 was
0:56:55 you
0:56:55 had
0:56:56 look
0:56:57 you
0:56:57 know
0:56:57 it
0:56:58 was
0:56:58 a
0:56:58 very
0:56:59 odd
0:56:59 thing
0:57:01 with
0:57:01 the
0:57:01 Biden
0:57:01 administration
0:57:02 because
0:57:02 he
0:57:02 wasn’t
0:57:03 really
0:57:04 I
0:57:04 think
0:57:04 it’s
0:57:05 come
0:57:05 out
0:57:05 now
0:57:05 he
0:57:05 wasn’t
0:57:06 really
0:57:06 the
0:57:06 president
0:57:06 he
0:57:07 wasn’t
0:57:07 really
0:57:07 making
0:57:07 any
0:57:08 decisions
0:57:09 you
0:57:09 couldn’t
0:57:10 even
0:57:10 get a
0:57:10 meeting
0:57:10 with
0:57:10 him
0:57:11 if
0:57:11 you
0:57:11 were
0:57:11 in
0:57:12 his
0:57:12 cabinet
0:57:13 and
0:57:13 in
0:57:13 terms
0:57:13 of
0:57:14 domestic
0:57:14 policy
0:57:14 that
0:57:15 was
0:57:15 run
0:57:15 by
0:57:16 Elizabeth
0:57:16 Warren
0:57:17 and
0:57:17 then
0:57:17 the
0:57:17 second
0:57:18 confusing
0:57:18 thing
0:57:18 is
0:57:19 Elizabeth
0:57:19 Warren
0:57:20 is
0:57:20 always
0:57:20 calling
0:57:21 like
0:57:21 people
0:57:21 fascist
0:57:22 her
0:57:23 whole
0:57:24 push
0:57:24 with
0:57:25 fintech
0:57:25 and
0:57:25 crypto
0:57:26 was
0:57:27 to
0:57:27 make
0:57:28 sure
0:57:29 that
0:57:29 she
0:57:29 could
0:57:30 kick
0:57:30 people
0:57:30 out
0:57:30 of
0:57:30 the
0:57:31 banking
0:57:31 system
0:57:31 who
0:57:31 are
0:57:32 political
0:57:32 enemies
0:57:33 and
0:57:33 so
0:57:34 in
0:57:34 order
0:57:34 to
0:57:34 do
0:57:34 that
0:57:35 you
0:57:35 have
0:57:35 to
0:57:35 outlaw
0:57:36 new
0:57:36 forms
0:57:36 of
0:57:37 financial
0:57:38 technology
0:57:39 because
0:57:39 those
0:57:40 would
0:57:40 be
0:57:40 kind
0:57:40 of
0:57:41 back
0:57:41 doors
0:57:41 or
0:57:42 side
0:57:42 doors
0:57:42 or
0:57:43 parallels
0:57:44 to
0:57:45 the
0:57:46 g-sibs
0:57:46 and
0:57:46 the
0:57:46 banking
0:57:46 system
0:57:47 which
0:57:47 she
0:57:48 comprehensively
0:57:49 and I
0:57:49 think
0:57:49 this is
0:57:50 coming out
0:57:50 now
0:57:50 could
0:57:50 kick
0:57:51 people
0:57:51 out
0:57:51 of
0:57:52 and
0:57:52 so
0:57:52 when
0:57:52 you
0:57:53 use
0:57:53 when
0:57:54 it’s
0:57:54 a full
0:57:55 top-down
0:57:55 hierarchy
0:57:56 and you
0:57:56 can
0:57:56 use
0:57:57 private
0:57:57 companies
0:57:58 to enforce
0:57:58 your will
0:57:59 that is
0:58:00 the way
0:58:00 fascism
0:58:01 works
0:58:01 and then
0:58:02 the way
0:58:02 she does
0:58:02 it
0:58:02 is
0:58:03 she
0:58:03 sells
0:58:04 this
0:58:04 fake
0:58:05 story
0:58:05 about
0:58:06 you know
0:58:07 it’s
0:58:08 funding
0:58:08 terror
0:58:08 and it
0:58:09 turns out
0:58:09 like
0:58:09 the
0:58:10 USAID
0:58:10 was
0:58:11 funding
0:58:11 the
0:58:11 terrorist
0:58:11 groups
0:58:12 but
0:58:12 that’s
0:58:12 a
0:58:12 different
0:58:12 story
0:58:13 but
0:58:13 you know
0:58:13 it’s
0:58:14 doing
0:58:14 all
0:58:14 these
0:58:15 nefarious
0:58:15 things
0:58:16 which
0:58:16 you know
0:58:17 was just
0:58:17 a very
0:58:18 unfair
0:58:18 portrayal
0:58:19 and so
0:58:19 then
0:58:20 the whole
0:58:20 industry
0:58:21 got this
0:58:21 reputation
0:58:22 as scammy
0:58:22 and this
0:58:23 and that
0:58:23 and the
0:58:23 other
0:58:24 and then
0:58:24 of course
0:58:25 we had
0:58:26 Sam
0:58:26 Bankman
0:58:26 freed
0:58:27 who
0:58:27 didn’t
0:58:27 do
0:58:27 us
0:58:27 any
0:58:28 favors
0:58:28 by
0:58:30 you know
0:58:30 and this
0:58:31 is another
0:58:32 kind of
0:58:32 though
0:58:33 issue
0:58:33 with what
0:58:34 Elizabeth Warren
0:58:34 did
0:58:34 is she
0:58:35 blocked
0:58:35 all
0:58:36 legislation
0:58:38 and so
0:58:38 the
0:58:39 criminals
0:58:39 were running
0:58:40 free
0:58:40 and the
0:58:41 people
0:58:41 doing
0:58:41 things
0:58:42 that
0:58:42 should
0:58:42 have
0:58:42 been
0:58:42 legal
0:58:43 were
0:58:43 getting
0:58:44 terrorized
0:58:44 by the
0:58:45 government
0:58:46 when they
0:58:46 should
0:58:46 have been
0:58:46 looking
0:58:47 they should
0:58:48 have been
0:58:48 looking at
0:58:49 FTX
0:58:49 they were
0:58:50 looking at
0:58:50 Coinbase
0:58:51 which was
0:58:51 totally
0:58:52 compliant
0:58:52 public
0:58:53 company
0:58:54 you know
0:58:54 begging
0:58:55 for
0:58:55 feedback
0:58:56 tell us
0:58:56 what you
0:58:56 want us
0:58:57 to do
0:58:57 exactly
0:58:59 yeah
0:58:59 that
0:59:00 whole
0:59:00 thing
0:59:00 was
0:59:00 crazy
0:59:01 so
0:59:01 given
0:59:02 that
0:59:02 AI
0:59:02 is
0:59:03 putting
0:59:03 us
0:59:03 on
0:59:03 a
0:59:03 collision
0:59:04 course
0:59:04 with
0:59:05 I
0:59:05 don’t
0:59:05 know
0:59:06 who’s
0:59:06 real
0:59:06 I
0:59:06 don’t
0:59:06 know
0:59:07 what’s
0:59:07 fake
0:59:07 do
0:59:07 you
0:59:08 think
0:59:08 that
0:59:08 blockchain
0:59:09 is
0:59:09 about
0:59:09 to
0:59:09 have
0:59:09 its
0:59:09 day
0:59:10 like
0:59:10 in
0:59:10 the
0:59:10 next
0:59:11 12
0:59:11 to
0:59:12 24
0:59:12 months
0:59:13 or
0:59:13 is
0:59:13 this
0:59:13 still
0:59:14 something
0:59:14 that
0:59:14 it’s
0:59:14 so
0:59:15 embedded
0:59:15 deep
0:59:15 in
0:59:15 the
0:59:16 infrastructure
0:59:16 it’s
0:59:16 going
0:59:16 to
0:59:17 take
0:59:17 a
0:59:17 long
0:59:17 time
0:59:17 to
0:59:18 really
0:59:18 have
0:59:19 its
0:59:19 I
0:59:19 told
0:59:19 you
0:59:19 so
0:59:20 moment
0:59:21 you
0:59:21 know
0:59:21 I
0:59:22 think
0:59:22 it’s
0:59:23 I
0:59:23 think
0:59:23 it’s
0:59:24 within
0:59:24 24
0:59:24 months
0:59:24 for
0:59:25 sure
0:59:25 I
0:59:25 mean
0:59:25 I
0:59:25 think
0:59:26 that
0:59:27 there’s
0:59:27 enough
0:59:28 you
0:59:29 know
0:59:29 there
0:59:29 were
0:59:29 like
0:59:30 actual
0:59:30 like
0:59:30 if
0:59:30 you
0:59:30 like
0:59:31 kind
0:59:31 of
0:59:31 the
0:59:31 last
0:59:31 wave
0:59:31 of
0:59:32 blockchain
0:59:33 there
0:59:33 were
0:59:34 real
0:59:35 technological
0:59:35 limitations
0:59:36 that
0:59:36 made
0:59:36 it
0:59:37 you
0:59:37 know
0:59:38 I
0:59:38 think
0:59:39 we’re
0:59:39 slowing
0:59:39 it
0:59:40 down
0:59:40 from
0:59:40 getting
0:59:40 broader
0:59:41 adoption
0:59:41 so
0:59:42 you
0:59:42 know
0:59:42 very
0:59:43 obvious
0:59:43 usability
0:59:44 challenges
0:59:45 the
0:59:46 fees
0:59:46 were
0:59:46 really
0:59:46 high
0:59:47 the
0:59:47 blockchains
0:59:47 were
0:59:48 slow
0:59:49 so
0:59:49 there
0:59:49 were
0:59:49 just
0:59:49 a
0:59:50 lot
0:59:50 of
0:59:50 use
0:59:50 cases
0:59:50 that
0:59:50 you
0:59:51 just
0:59:51 couldn’t
0:59:51 do
0:59:51 on
0:59:51 them
0:59:53 I
0:59:53 think
0:59:54 that’s
0:59:54 changing
0:59:55 very
0:59:55 very
0:59:55 fast
0:59:56 things
0:59:56 are
0:59:56 you
0:59:56 know
0:59:57 the
0:59:57 chains
0:59:58 are
0:59:58 much
0:59:58 faster
0:59:59 the
1:00:00 layer
1:00:00 two
1:00:00 stuff
1:00:01 makes
1:00:01 them
1:00:02 very
1:00:02 fast
1:00:02 and
1:00:03 cheap
1:00:05 you
1:00:05 know
1:00:05 people
1:00:05 are
1:00:05 doing
1:00:06 a
1:00:06 lot
1:00:07 on
1:00:07 usability
1:00:09 you
1:00:09 know
1:00:09 for
1:00:10 wallets
1:00:10 and
1:00:10 these
1:00:11 kinds
1:00:11 of
1:00:11 things
1:00:11 so
1:00:12 I
1:00:13 think
1:00:13 we’re
1:00:13 getting
1:00:13 pretty
1:00:14 close
1:00:14 and
1:00:14 then
1:00:14 I
1:00:19 world
1:00:19 coin
1:00:20 you
1:00:20 know
1:00:20 to
1:00:21 me
1:00:21 the
1:00:22 difference
1:00:23 between
1:00:23 that
1:00:23 thing
1:00:23 being
1:00:24 very
1:00:24 broadly
1:00:25 adopted
1:00:25 and
1:00:26 where
1:00:26 it
1:00:26 is
1:00:26 now
1:00:26 where
1:00:26 it’s
1:00:27 I
1:00:27 think
1:00:27 half
1:00:27 the
1:00:27 people
1:00:28 in
1:00:28 Buenos
1:00:28 Aires
1:00:29 use
1:00:29 it
1:00:30 daily
1:00:31 so
1:00:31 it’s
1:00:32 widely
1:00:33 adopted
1:00:33 where
1:00:33 it’s
1:00:33 been
1:00:34 legal
1:00:35 I
1:00:35 think
1:00:35 that
1:00:36 if
1:00:36 they
1:00:37 are
1:00:37 able
1:00:37 to
1:00:38 get
1:00:38 integrated
1:00:39 into
1:00:39 some
1:00:39 of
1:00:39 the
1:00:39 big
1:00:40 social
1:00:41 platforms
1:00:42 then
1:00:43 everybody
1:00:44 needs
1:00:44 proof
1:00:44 of
1:00:45 human
1:00:47 and
1:00:49 you
1:00:49 know
1:00:49 like
1:00:49 it
1:00:49 would
1:00:50 make
1:00:50 the
1:00:51 experience
1:00:52 online
1:00:52 so
1:00:52 much
1:00:52 better
1:00:53 if
1:00:53 you
1:00:53 knew
1:00:53 who
1:00:53 was
1:00:53 human
1:00:54 and
1:00:54 who
1:00:54 was
1:00:54 not
1:00:55 and
1:00:55 right
1:00:55 now
1:00:56 like
1:00:56 you
1:00:56 can’t
1:00:57 tell
1:00:57 at
1:00:58 all
1:00:59 and
1:00:59 so
1:00:59 and
1:01:00 that
1:01:00 problem
1:01:00 is
1:01:00 going
1:01:00 to
1:01:00 get
1:01:01 worse
1:01:01 and
1:01:01 then
1:01:01 the
1:01:02 solution
1:01:02 is
1:01:02 really
1:01:02 here
1:01:04 so
1:01:04 I
1:01:05 think
1:01:05 it’s
1:01:05 going
1:01:05 to
1:01:06 start
1:01:06 to
1:01:06 take
1:01:06 off
1:01:06 and
1:01:06 you
1:01:06 only
1:01:07 need
1:01:07 one
1:01:07 or
1:01:08 two
1:01:09 big
1:01:09 use
1:01:10 cases
1:01:10 to
1:01:11 start
1:01:11 getting
1:01:11 the
1:01:12 whole
1:01:12 infrastructure
1:01:13 deployed
1:01:14 and
1:01:14 once
1:01:15 the
1:01:15 infrastructure
1:01:15 is
1:01:16 deployed
1:01:16 I
1:01:17 think
1:01:17 we’ll
1:01:17 certainly
1:01:18 rely
1:01:18 on
1:01:18 it
1:01:19 and
1:01:20 if
1:01:20 you
1:01:20 look
1:01:20 at
1:01:21 actually
1:01:21 the
1:01:21 curve
1:01:22 of
1:01:22 people
1:01:22 who
1:01:22 have
1:01:23 active
1:01:23 wallets
1:01:24 and
1:01:25 the
1:01:25 curve
1:01:25 of
1:01:26 internet
1:01:27 adoption
1:01:27 they’re
1:01:28 pretty
1:01:28 similar
1:01:32 it’s
1:01:32 about
1:01:32 I think
1:01:33 blockchain
1:01:33 is
1:01:33 growing
1:01:34 a little
1:01:34 faster
1:01:34 than
1:01:34 the
1:01:35 internet
1:01:35 did
1:01:36 initially
1:01:37 and
1:01:37 so
1:01:37 I
1:01:37 think
1:01:38 we’ll
1:01:38 get
1:01:38 to
1:01:39 a
1:01:39 place
1:01:39 where
1:01:40 certainly
1:01:40 everybody
1:01:40 in
1:01:40 the
1:01:41 US
1:01:41 will
1:01:41 be
1:01:42 on
1:01:42 it
1:01:44 which
1:01:44 by
1:01:44 the
1:01:44 way
1:01:45 could
1:01:45 be
1:01:46 great
1:01:46 from
1:01:46 a
1:01:46 government
1:01:47 standpoint
1:01:47 you
1:01:47 know
1:01:48 Elon
1:01:48 has
1:01:48 talked
1:01:49 about
1:01:49 putting
1:01:49 all
1:01:49 the
1:01:50 government
1:01:50 payments
1:01:50 on
1:01:50 the
1:01:51 blockchain
1:01:51 which
1:01:51 I
1:01:51 think
1:01:52 would
1:01:52 be
1:01:52 really
1:01:53 good
1:01:53 for
1:01:53 transparency
1:01:55 we never
1:01:55 get into
1:01:56 this weird
1:01:56 situation
1:01:57 now
1:01:57 where
1:01:58 half
1:01:58 the
1:01:58 country
1:01:59 wants
1:01:59 to
1:02:00 tear
1:02:00 down
1:02:01 all
1:02:01 the
1:02:01 government
1:02:02 services
1:02:02 and half
1:02:02 them
1:02:03 wants
1:02:03 to
1:02:03 keep
1:02:03 it
1:02:03 because
1:02:04 nobody
1:02:04 knows
1:02:04 what
1:02:04 the
1:02:05 spending
1:02:05 is
1:02:06 but
1:02:07 that
1:02:07 would
1:02:07 be
1:02:07 great
1:02:08 but
1:02:08 you know
1:02:09 beyond
1:02:09 that
1:02:09 like
1:02:09 if
1:02:10 you
1:02:10 think
1:02:10 about
1:02:11 well
1:02:11 why
1:02:11 is
1:02:11 there
1:02:12 so
1:02:12 much
1:02:12 waste
1:02:12 and
1:02:12 fraud
1:02:14 well
1:02:14 part
1:02:14 of
1:02:14 it
1:02:14 is
1:02:14 you know
1:02:15 like
1:02:16 you know
1:02:17 I
1:02:17 get
1:02:18 taxed
1:02:18 I
1:02:18 give
1:02:19 my
1:02:19 money
1:02:19 to
1:02:19 the
1:02:20 IRS
1:02:21 the
1:02:21 IRS
1:02:22 gives
1:02:22 it
1:02:23 to
1:02:23 Congress
1:02:23 they
1:02:24 you know
1:02:24 do
1:02:24 whatever
1:02:24 they
1:02:24 do
1:02:25 with
1:02:25 it
1:02:25 and
1:02:25 so
1:02:25 forth
1:02:26 and
1:02:27 well
1:02:27 how
1:02:27 does
1:02:27 it
1:02:27 get
1:02:27 to
1:02:27 the
1:02:28 people
1:02:28 who
1:02:28 need
1:02:29 it
1:02:30 you know
1:02:30 that’s
1:02:30 a
1:02:30 very
1:02:31 lossy
1:02:31 process
1:02:32 you know
1:02:32 and
1:02:32 we
1:02:32 don’t
1:02:32 even
1:02:33 know
1:02:33 they
1:02:33 are
1:02:33 and
1:02:33 it’s
1:02:33 very
1:02:34 you know
1:02:35 one of the things we found out during COVID
1:02:38 the government’s not very good at sending people money
1:02:39 it’s good at taking money
1:02:41 it’s not good at sending them money
1:02:41 right
1:02:44 we lost like 400 billion dollars trying to give people
1:02:44 stimulus
1:02:45 ridiculous
1:02:46 yeah
1:02:46 crazy
1:02:47 ridiculous
1:02:48 you know
1:02:48 like
1:02:48 if
1:02:49 everybody
1:02:49 in the US
1:02:50 had
1:02:51 an address
1:02:52 on the blockchain
1:02:54 you could just tell me
1:02:56 okay here’s 10,000 people who need money
1:02:58 please send them
1:02:59 you know
1:03:00 $5,000 each
1:03:02 well probably that’s too much money for me
1:03:02 but
1:03:03 you know
1:03:04 something like that
1:03:05 you know
1:03:07 whatever my tax bill is
1:03:09 or whatever that portion of wealth redistribution is
1:03:11 that would be 100%
1:03:12 zero loss
1:03:13 by the way
1:03:14 I’d feel a lot better about it
1:03:16 because I’d know I’d be helping people
1:03:17 and you know
1:03:17 look
1:03:18 maybe even
1:03:19 somebody would go
1:03:20 hey this is great
1:03:21 thank you
1:03:21 and we’re like
1:03:22 maybe it would bring us
1:03:24 we wouldn’t have this crazy class warfare
1:03:25 because everybody would go
1:03:26 hey we’re all integrated
1:03:26 you know
1:03:27 like I’m helping you
1:03:28 you’re helping me
1:03:30 and then
1:03:31 like if you had that
1:03:32 then you’d fix the whole
1:03:33 kind of
1:03:35 democracy integrity problem
1:03:35 because
1:03:37 everybody could vote
1:03:38 off that address
1:03:39 and by the way
1:03:40 everybody would have an address
1:03:42 because everybody would want the money
1:03:44 so what bigger incentive
1:03:46 to register to vote
1:03:47 than
1:03:48 you
1:03:49 in order to get money
1:03:50 you have to have an address
1:03:52 which registers you to vote
1:03:53 and so that kind of thing
1:03:53 I think
1:03:55 could get us to
1:03:57 just like a much
1:03:58 higher trust
1:04:00 in our own institutions
1:04:01 hmm
1:04:02 yeah
1:04:02 so
1:04:03 wow
1:04:04 speaking of that
1:04:05 I wanted to
1:04:05 absolutely
1:04:06 scream
1:04:08 into the abyss
1:04:08 when I heard
1:04:09 that people couldn’t retire
1:04:10 from the government
1:04:12 faster than the elevator
1:04:13 would lower their records
1:04:15 down into a mine
1:04:16 I was like
1:04:17 what is happening
1:04:19 what do you take away from
1:04:20 why does Elon
1:04:22 want to do this
1:04:22 why is he sleeping
1:04:23 in hallways
1:04:25 um
1:04:26 why
1:04:27 why is he doing this
1:04:28 is it just to get
1:04:28 government contracts
1:04:29 and it’s nefarious
1:04:31 in the way that so many people
1:04:31 think it is
1:04:32 or is there something
1:04:33 positive there
1:04:34 what’s the
1:04:35 what’s the game
1:04:36 I think there’s a
1:04:38 couple of different things
1:04:38 so
1:04:40 one is
1:04:41 the strong thing
1:04:41 is he
1:04:42 he truly believes
1:04:43 that
1:04:44 um
1:04:45 America’s the best
1:04:46 country in the world
1:04:46 you know
1:04:47 he is an immigrant
1:04:48 um
1:04:49 and that
1:04:50 it’s not guaranteed
1:04:51 to stay that way
1:04:52 and
1:04:54 and we have been
1:04:56 in danger of losing it
1:04:57 uh
1:04:58 and
1:04:59 you know
1:05:00 and so the
1:05:01 most important thing
1:05:01 for him to do
1:05:02 in order for his
1:05:03 companies to be relevant
1:05:04 in order for going to Mars
1:05:05 to be relevant
1:05:06 in order for anything
1:05:07 he wants to do in life
1:05:08 to be relevant
1:05:08 is
1:05:09 we’ve got to stabilize
1:05:10 U.S. government
1:05:11 I think that’s the main thing
1:05:12 driving him
1:05:12 you know
1:05:13 so then you say
1:05:13 well how did he get
1:05:14 to that conclusion
1:05:15 that
1:05:16 you know
1:05:17 the whole country
1:05:18 is in jeopardy
1:05:19 and it really
1:05:19 like
1:05:21 it’s a pretty
1:05:22 uh
1:05:22 it was a pretty
1:05:23 interesting thing
1:05:24 to watch
1:05:24 um
1:05:25 because
1:05:26 right in 2021
1:05:28 I think he was a democrat
1:05:29 and he was certainly
1:05:30 pretty apolitical
1:05:32 and uh
1:05:32 I was actually
1:05:34 in a chat group
1:05:34 with him
1:05:35 um
1:05:36 when he got
1:05:37 the idea
1:05:38 or posed
1:05:38 the question
1:05:39 should he buy
1:05:40 Twitter
1:05:41 um
1:05:42 and
1:05:44 a lot of it
1:05:45 stemmed from
1:05:45 um
1:05:46 you know
1:05:47 it started with
1:05:48 the U.S. government
1:05:50 just harassing him
1:05:51 uh
1:05:51 which was a very odd
1:05:52 thing right
1:05:53 like here’s your
1:05:54 I mean I think
1:05:55 you could very well
1:05:56 argue he was our
1:05:57 most productive citizen
1:05:58 he um
1:05:59 was our entire
1:06:00 space program
1:06:02 he advanced
1:06:02 he advanced
1:06:02 the state
1:06:03 of electric
1:06:03 cars
1:06:04 by 20 years
1:06:05 he’s still
1:06:06 like 95%
1:06:06 of there’s
1:06:07 something like
1:06:07 that
1:06:07 percent
1:06:08 of the electric
1:06:09 cars sold
1:06:09 in the U.S.
1:06:12 you know he’s
1:06:13 you know
1:06:14 done the things
1:06:15 with Neuralink
1:06:16 to you know
1:06:17 help people
1:06:18 like uh
1:06:19 use their arms
1:06:20 and legs
1:06:20 who have been
1:06:21 paralyzed
1:06:21 and this kind
1:06:22 of thing
1:06:22 so you know
1:06:23 you’ve got
1:06:24 um
1:06:25 you know
1:06:25 he’s a really
1:06:26 kind of remarkable
1:06:27 person to want
1:06:28 to pick on
1:06:29 but uh
1:06:30 what happened
1:06:31 was uh
1:06:33 because he got
1:06:34 like this PR
1:06:35 for being very
1:06:35 wealthy
1:06:36 um
1:06:37 the Biden
1:06:37 administration
1:06:38 targeted him
1:06:39 um
1:06:39 you know
1:06:40 and again
1:06:40 they’re fascists
1:06:41 so uh
1:06:42 it’s really
1:06:43 a power struggle
1:06:44 always with the
1:06:44 fascists and
1:06:45 anybody who looks
1:06:46 like they’re
1:06:46 becoming powerful
1:06:48 and yeah
1:06:48 some of the
1:06:49 things they did
1:06:50 and you know
1:06:50 one of the ones
1:06:51 that’s talked
1:06:51 about a lot
1:06:52 was um
1:06:54 they sued him
1:06:55 the Biden
1:06:55 Department of
1:06:56 Justice sued
1:06:58 him for uh
1:07:00 discriminating
1:07:01 against refugees
1:07:02 um
1:07:03 but he had a
1:07:04 contract with
1:07:04 the U.S.
1:07:05 Department of
1:07:05 Defense
1:07:06 that required
1:07:06 him to only
1:07:07 hire U.S.
1:07:07 citizens
1:07:09 so he was
1:07:10 breaking the law
1:07:10 either way
1:07:11 uh
1:07:11 and they
1:07:12 they never
1:07:13 dropped the lawsuit
1:07:14 even after it was
1:07:14 pointed out
1:07:15 even after
1:07:16 you know
1:07:16 it was pointed
1:07:17 out by Congress
1:07:18 uh
1:07:18 so it was
1:07:19 clear harassment
1:07:20 and I think
1:07:21 that his
1:07:22 conclusion from
1:07:23 that was
1:07:24 you know
1:07:24 this
1:07:25 we are
1:07:26 like ironically
1:07:27 um
1:07:28 I think his
1:07:29 conclusion was
1:07:29 we’re losing
1:07:30 the democracy
1:07:31 um
1:07:32 you know
1:07:33 we’re going
1:07:34 into this
1:07:34 very very
1:07:35 strange world
1:07:36 uh
1:07:36 where the
1:07:36 incentives
1:07:37 are all
1:07:37 upside down
1:07:38 and
1:07:39 you know
1:07:40 the way
1:07:41 Elon thinks
1:07:42 is
1:07:42 it’s up to
1:07:43 him to
1:07:43 save it
1:07:44 um
1:07:45 and so he
1:07:45 got like
1:07:46 extremely
1:07:46 involved
1:07:47 and then I
1:07:47 think the
1:07:48 more involved
1:07:49 he got
1:07:49 the more
1:07:51 he both
1:07:52 realized like
1:07:53 a lot of the
1:07:53 things really
1:07:54 were dangerous
1:07:54 and then
1:07:55 secondly
1:07:56 that
1:07:57 um
1:07:57 he
1:07:58 personally
1:07:59 uh
1:08:00 would be
1:08:01 somebody who
1:08:01 would know
1:08:01 how to fix
1:08:01 it
1:08:02 and
1:08:03 you go
1:08:03 like
1:08:03 well why
1:08:04 the hell
1:08:04 would Elon
1:08:05 must know
1:08:05 how to
1:08:05 fix the
1:08:06 government
1:08:06 all this
1:08:07 and you
1:08:07 know
1:08:08 this is
1:08:08 the thing
1:08:09 that everybody’s
1:08:09 saying now
1:08:10 uh
1:08:11 and it’s
1:08:11 funny
1:08:11 because I
1:08:12 told this
1:08:13 to um
1:08:14 Andreessen
1:08:15 years ago
1:08:15 uh
1:08:16 was
1:08:17 because I
1:08:18 I’m a big
1:08:19 fan of
1:08:19 Isaac Newton
1:08:20 and you
1:08:20 know like
1:08:21 we always
1:08:21 talked about
1:08:22 like who
1:08:22 is Elon
1:08:22 like
1:08:23 you know
1:08:23 like
1:08:23 what
1:08:24 entrepreneur
1:08:25 comes to
1:08:25 mind
1:08:26 you know
1:08:26 and it
1:08:26 really
1:08:27 wasn’t
1:08:27 maybe
1:08:28 Thomas
1:08:28 Edison
1:08:28 but not
1:08:29 really
1:08:30 um
1:08:31 but Isaac
1:08:32 Newton
1:08:33 uh
1:08:33 whoa
1:08:34 was really
1:08:34 the one
1:08:35 that I
1:08:35 always
1:08:36 thought
1:08:37 he was
1:08:37 most
1:08:37 like
1:08:38 um
1:08:38 you know
1:08:39 because
1:08:39 it’s
1:08:39 like okay
1:08:39 who
1:08:40 can build
1:08:41 like rockets
1:08:41 and cars
1:08:42 and this
1:08:42 and that
1:08:42 and the
1:08:42 other
1:08:43 but the
1:08:43 reason I
1:08:43 thought he
1:08:44 was like
1:08:44 Isaac
1:08:44 Newton
1:08:44 was
1:08:45 at the
1:08:46 end of
1:08:46 Isaac
1:08:46 Newton’s
1:08:46 life
1:08:47 um
1:08:48 but I
1:08:48 think
1:08:48 he was
1:08:49 he was
1:08:49 in his
1:08:49 late
1:08:50 60s
1:08:51 uh
1:08:51 maybe
1:08:52 like
1:08:52 67
1:08:53 68
1:08:53 and this
1:08:54 is you
1:08:54 know
1:08:54 for those
1:08:54 of you
1:08:54 don’t
1:08:55 know
1:08:56 Isaac
1:08:56 Newton
1:08:56 like
1:08:56 he
1:08:56 figured
1:08:57 out
1:08:57 how
1:08:57 the
1:08:57 entire
1:08:58 world
1:08:58 works
1:08:58 and
1:08:59 wrote
1:08:59 it
1:08:59 down
1:08:59 in
1:08:59 a
1:08:59 book
1:09:00 um
1:09:01 you
1:09:01 know
1:09:01 called
1:09:02 Principia
1:09:02 Mathematica
1:09:03 which is
1:09:03 probably
1:09:04 the most
1:09:04 amazing
1:09:05 work
1:09:05 in the
1:09:05 history
1:09:05 of
1:09:05 science
1:09:06 uh
1:09:07 and he
1:09:07 did
1:09:07 it
1:09:08 like
1:09:08 entirely
1:09:08 by
1:09:09 himself
1:09:10 like
1:09:11 he didn’t
1:09:11 even talk
1:09:11 to anybody
1:09:12 at this
1:09:12 you know
1:09:12 at the
1:09:13 time he
1:09:13 wrote it
1:09:13 I think
1:09:14 he was
1:09:14 trying to
1:09:15 figure out
1:09:16 what God
1:09:16 was or
1:09:16 something
1:09:17 like that
1:09:18 um
1:09:19 but
1:09:19 as you
1:09:19 do
1:09:20 but
1:09:20 so he
1:09:20 gets to
1:09:21 be
1:09:21 you know
1:09:21 like
1:09:22 in his
1:09:22 late
1:09:22 60s
1:09:23 and
1:09:23 the
1:09:24 bank
1:09:24 of
1:09:24 England
1:09:24 has
1:09:25 a
1:09:25 crisis
1:09:25 which
1:09:26 is
1:09:26 causing
1:09:27 a
1:09:27 huge
1:09:27 crisis
1:09:27 for
1:09:28 the
1:09:28 whole
1:09:28 country
1:09:29 um
1:09:29 which
1:09:29 is
1:09:30 uh
1:09:30 there’s
1:09:31 a
1:09:31 giant
1:09:32 counterfeiting
1:09:32 problem
1:09:33 so the
1:09:33 currency
1:09:33 is
1:09:34 going to
1:09:34 be
1:09:34 undermined
1:09:35 and
1:09:35 uh
1:09:36 England’s
1:09:36 going to
1:09:36 basically
1:09:37 go
1:09:37 bankrupt
1:09:38 and
1:09:39 so
1:09:39 they
1:09:39 had
1:09:39 no
1:09:40 idea
1:09:40 what
1:09:40 to
1:09:40 do
1:09:41 about
1:09:41 it
1:09:41 um
1:09:41 so
1:09:42 they
1:09:42 call
1:09:42 Isaac
1:09:42 Newton
1:09:43 because
1:09:43 he’s
1:09:43 the
1:09:43 smartest
1:09:44 man
1:09:44 in the
1:09:44 world
1:09:45 of
1:09:45 course
1:09:45 you’re
1:09:45 going
1:09:45 to
1:09:45 call
1:09:46 him
1:09:46 so
1:09:46 Isaac
1:09:47 Newton
1:09:48 67
1:09:48 year
1:09:48 old
1:09:49 like
1:09:50 hermit
1:09:51 physicist
1:09:52 goes
1:09:52 in
1:09:53 and
1:09:53 he
1:09:53 says
1:09:54 okay
1:09:54 I
1:09:54 can
1:09:54 help
1:09:55 with
1:09:55 the
1:09:55 problem
1:09:56 make
1:09:56 me
1:09:56 CEO
1:09:57 of
1:09:57 the
1:09:57 mint
1:09:57 so
1:09:58 they
1:09:58 make
1:09:58 him
1:09:58 CEO
1:09:58 of
1:09:59 the
1:09:59 mint
1:09:59 you
1:09:59 know
1:09:59 kind
1:10:00 of
1:10:00 head
1:10:00 of
1:10:00 doge
1:10:00 whatever
1:10:02 uh
1:10:02 and
1:10:03 he
1:10:04 reorganizes
1:10:04 the
1:10:04 mint
1:10:05 in like
1:10:05 a week
1:10:06 and
1:10:06 then
1:10:06 fixes
1:10:06 the
1:10:07 technology
1:10:07 in a
1:10:07 month
1:10:08 and
1:10:09 completely
1:10:09 makes
1:10:09 it
1:10:10 impossible
1:10:10 to
1:10:10 counterfeit
1:10:11 then
1:10:12 he
1:10:12 becomes
1:10:13 a
1:10:13 private
1:10:13 eye
1:10:15 and
1:10:15 goes
1:10:15 into
1:10:16 all
1:10:16 the
1:10:16 pubs
1:10:16 where
1:10:16 the
1:10:17 counterfeiters
1:10:17 are
1:10:18 arrests
1:10:18 all
1:10:18 of
1:10:18 them
1:10:19 then
1:10:20 he
1:10:20 learns
1:10:20 the
1:10:20 law
1:10:21 and
1:10:21 becomes
1:10:21 the
1:10:22 prosecutor
1:10:23 and
1:10:23 prosecutes
1:10:24 all
1:10:24 the
1:10:24 counterfeiters
1:10:25 and has
1:10:26 100%
1:10:26 conviction
1:10:27 record
1:10:28 and
1:10:28 that
1:10:29 by
1:10:29 the
1:10:29 way
1:10:30 that’s
1:10:30 Elon
1:10:30 so
1:10:32 if
1:10:32 you
1:10:33 I
1:10:33 didn’t
1:10:33 know
1:10:34 that
1:10:34 part
1:10:34 of
1:10:34 his
1:10:34 story
1:10:35 oh
1:10:35 yeah
1:10:35 yeah
1:10:36 so
1:10:36 it’s
1:10:36 an
1:10:37 amazing
1:10:37 thing
1:10:39 and
1:10:39 if
1:10:39 you
1:10:39 look
1:10:40 at
1:10:40 Elon
1:10:40 and
1:10:41 Doge
1:10:41 to
1:10:42 me
1:10:42 the
1:10:42 most
1:10:43 remarkable
1:10:43 thing
1:10:43 about
1:10:44 Doge
1:10:45 is
1:10:46 how
1:10:46 he’s
1:10:47 done
1:10:47 it
1:10:47 so
1:10:48 if
1:10:48 you
1:10:48 or
1:10:49 I
1:10:50 were
1:10:50 to
1:10:51 say
1:10:52 okay
1:10:53 let’s
1:10:53 go in
1:10:54 and
1:10:54 kind
1:10:54 of
1:10:55 get
1:10:55 the
1:10:55 waste
1:10:55 and
1:10:56 fraud
1:10:56 out
1:10:56 of
1:10:56 the
1:10:56 government
1:10:57 we
1:10:57 would
1:10:58 like
1:10:58 audit
1:10:58 the
1:10:59 departments
1:10:59 or
1:10:59 this
1:10:59 and
1:11:00 that
1:11:00 the
1:11:00 other
1:11:00 and
1:11:00 so
1:11:01 forth
1:11:02 no
1:11:02 no
1:11:02 no
1:11:03 like
1:11:03 that’s
1:11:03 how
1:11:03 he
1:11:06 like
1:11:07 how
1:11:07 do
1:11:07 the
1:11:07 checks
1:11:07 go
1:11:08 out
1:11:10 like
1:11:11 how
1:11:11 is
1:11:11 the
1:11:11 system
1:11:12 designed
1:11:12 like
1:11:12 when
1:11:13 does
1:11:13 the
1:11:13 money
1:11:13 leave
1:11:13 the
1:11:14 building
1:11:15 and
1:11:15 then
1:11:15 oh
1:11:16 all
1:11:16 comes
1:11:16 out
1:11:16 of
1:11:17 one
1:11:17 system
1:11:18 let
1:11:18 me
1:11:18 have
1:11:19 access
1:11:19 to
1:11:19 that
1:11:19 system
1:11:20 and
1:11:20 I’ll
1:11:20 look
1:11:20 at
1:11:20 all
1:11:20 the
1:11:21 payments
1:11:22 I’m
1:11:22 not
1:11:22 asking
1:11:22 anybody
1:11:23 what
1:11:23 they’re
1:11:23 spending
1:11:24 I’m
1:11:24 looking
1:11:25 at
1:11:25 what
1:11:25 they’re
1:11:25 spending
1:11:26 like
1:11:26 I’m
1:11:26 getting
1:11:26 to
1:11:26 ground
1:11:27 truth
1:11:27 and
1:11:28 then
1:11:28 I’m
1:11:28 going
1:11:28 to
1:11:28 work
1:11:28 my
1:11:29 way
1:11:29 backwards
1:11:29 from
1:11:30 there
1:11:30 and
1:11:30 he’s
1:11:31 probably
1:11:32 and
1:11:32 you know
1:11:33 so
1:11:34 not only
1:11:34 is he
1:11:35 not
1:11:35 unqualified
1:11:36 he’s
1:11:36 maybe the
1:11:37 only person
1:11:37 qualified
1:11:38 to figure
1:11:38 out
1:11:38 like
1:11:39 how
1:11:46 you know
1:11:46 he’s
1:11:47 just
1:11:47 a
1:11:47 very
1:11:48 unique
1:11:48 individual
1:11:51 he’s
1:11:51 also
1:11:52 a troll
1:11:53 he also
1:11:53 likes
1:11:54 upsetting
1:11:54 people
1:11:55 I get
1:11:55 all
1:11:56 that
1:11:57 but
1:11:58 what
1:11:58 he
1:11:58 brings
1:11:58 to
1:11:58 the
1:11:59 table
1:11:59 is
1:11:59 pretty
1:11:59 interesting
1:12:00 I would
1:12:00 say
1:12:01 I would
1:12:01 say
1:12:02 very
1:12:02 extraordinary
1:12:03 you
1:12:04 have
1:12:04 also
1:12:04 written
1:12:04 about
1:12:05 another
1:12:05 extraordinary
1:12:06 historical
1:12:07 figure
1:12:09 from
1:12:09 the
1:12:10 Haitian
1:12:10 revolution
1:12:11 a guy
1:12:11 named
1:12:12 Toussaint
1:12:16 Tell us
1:12:16 about him
1:12:16 because
1:12:16 there’s
1:12:17 something
1:12:17 about
1:12:17 this
1:12:18 moment
1:12:18 about
1:12:19 being
1:12:19 a
1:12:19 master
1:12:20 strategist
1:12:20 about
1:12:22 using
1:12:22 what you
1:12:23 have
1:12:23 being
1:12:24 creative
1:12:24 that
1:12:25 feels
1:12:25 like
1:12:25 it’s
1:12:26 very
1:12:27 apropos
1:12:27 to
1:12:28 this
1:12:28 moment
1:12:31 what
1:12:31 made
1:12:32 his
1:12:32 story
1:12:32 special
1:12:33 yeah
1:12:33 so
1:12:34 Toussaint
1:12:34 was
1:12:35 another
1:12:35 one
1:12:35 of
1:12:35 these
1:12:36 characters
1:12:36 in
1:12:36 history
1:12:37 like
1:12:37 there
1:12:37 are
1:12:37 certain
1:12:38 I
1:12:39 call
1:12:39 them
1:12:39 like
1:12:39 once
1:12:39 in
1:12:40 every
1:12:40 400
1:12:40 year
1:12:41 type
1:12:41 people
1:12:43 where
1:12:43 you
1:12:43 just
1:12:43 don’t
1:12:43 see
1:12:43 them
1:12:44 that
1:12:44 often
1:12:44 but
1:12:45 so
1:12:46 it
1:12:46 turns
1:12:46 out
1:12:46 like
1:12:46 in
1:12:47 the
1:12:47 history
1:12:47 of
1:12:48 humanity
1:12:50 there’s
1:12:50 been
1:12:51 one
1:12:51 kind
1:12:51 of
1:12:52 successful
1:12:53 slave
1:12:53 revolt
1:12:53 that
1:12:54 like
1:12:54 entered
1:12:55 in
1:12:55 an
1:12:55 independent
1:12:56 state
1:12:57 which
1:12:57 you
1:12:57 know
1:12:57 if
1:12:57 you
1:12:58 think
1:12:58 about
1:12:59 the
1:12:59 history
1:12:59 of
1:13:00 slavery
1:13:00 which
1:13:00 goes
1:13:01 back
1:13:02 thousands
1:13:02 of
1:13:02 years
1:13:02 really
1:13:03 kind
1:13:03 of
1:13:04 from
1:13:04 the
1:13:05 beginning
1:13:05 of
1:13:05 written
1:13:06 history
1:13:06 like
1:13:07 we’ve
1:13:07 had
1:13:07 slavery
1:13:07 so
1:13:08 it’s
1:13:09 like
1:13:09 a
1:13:09 pretty
1:13:10 old
1:13:11 time
1:13:11 construct
1:13:12 and
1:13:13 there’s
1:13:13 a lot
1:13:13 of
1:13:14 motivation
1:13:14 to have
1:13:14 a
1:13:14 revolt
1:13:15 if
1:13:15 you’re
1:13:15 a
1:13:15 slave
1:13:17 but
1:13:17 why
1:13:17 only
1:13:18 one
1:13:18 successful
1:13:18 one
1:13:18 and
1:13:19 it
1:13:19 turns
1:13:19 out
1:13:19 it’s
1:13:20 it’s
1:13:20 really
1:13:21 hard
1:13:23 you know
1:13:24 for
1:13:25 to
1:13:25 generate
1:13:26 an
1:13:26 effective
1:13:27 revolt
1:13:27 if
1:13:27 you’re
1:13:28 slaves
1:13:28 because
1:13:30 slave
1:13:30 culture
1:13:31 is
1:13:32 difficult
1:13:32 because
1:13:32 you
1:13:32 don’t
1:13:32 own
1:13:33 right
1:13:33 if
1:13:33 you
1:13:34 don’t
1:13:34 have
1:13:36 any
1:13:37 sense
1:13:37 of
1:13:38 you know
1:13:38 owning
1:13:38 anything
1:13:39 you don’t
1:13:39 own
1:13:39 your
1:13:39 own
1:13:40 will
1:13:40 right
1:13:40 like
1:13:40 you
1:13:41 are
1:13:42 at
1:13:42 the
1:13:42 kind
1:13:43 of
1:13:43 pleasure
1:13:44 of
1:13:44 who’s
1:13:45 ever
1:13:45 running
1:13:45 things
1:13:46 so
1:13:47 long
1:13:47 term
1:13:48 thinking
1:13:48 doesn’t
1:13:48 make
1:13:49 sense
1:13:50 and
1:13:51 what
1:13:51 it
1:13:52 because
1:13:52 like
1:13:52 why
1:13:53 plan
1:13:53 for
1:13:53 next
1:13:53 week
1:13:54 it
1:13:54 doesn’t
1:13:55 matter
1:13:55 what
1:13:55 you
1:13:55 plan
1:13:56 like
1:13:56 it’s
1:13:56 not
1:13:57 yours
1:13:58 so
1:13:58 everything’s
1:13:58 going to be
1:13:59 very short
1:13:59 term
1:14:00 and short
1:14:00 termism
1:14:01 is
1:14:02 difficult
1:14:02 in a
1:14:02 military
1:14:03 context
1:14:04 because
1:14:06 in order
1:14:06 to have
1:14:06 an effective
1:14:07 military
1:14:07 there needs
1:14:08 to be
1:14:09 a
1:14:09 trust
1:14:10 right
1:14:10 like
1:14:10 a
1:14:10 trust
1:14:12 you know
1:14:12 you have
1:14:12 to be
1:14:12 able
1:14:12 to
1:14:13 trust
1:14:13 people
1:14:13 to
1:14:14 execute
1:14:14 the
1:14:14 order
1:14:14 like
1:14:15 I give
1:14:15 an
1:14:15 order
1:14:16 it’s
1:14:16 kind
1:14:16 of
1:14:16 like
1:14:17 the
1:14:17 Byzantine
1:14:17 generals
1:14:18 problem
1:14:19 to go
1:14:19 back
1:14:19 to
1:14:19 crypto
1:14:21 where
1:14:22 like
1:14:23 I have
1:14:23 to
1:14:23 trust
1:14:23 that
1:14:24 you’re
1:14:24 going
1:14:24 to
1:14:24 do
1:14:24 the
1:14:24 order
1:14:25 you
1:14:25 have
1:14:25 to
1:14:25 trust
1:14:25 that
1:14:26 I’m
1:14:26 giving
1:14:26 the
1:14:26 correct
1:14:27 order
1:14:28 but
1:14:28 trust
1:14:28 is
1:14:28 a
1:14:29 long
1:14:29 term
1:14:29 idea
1:14:29 because
1:14:30 it
1:14:30 comes
1:14:30 from
1:14:30 okay
1:14:31 I’m
1:14:31 going to
1:14:31 do
1:14:32 something
1:14:32 for
1:14:32 you
1:14:32 today
1:14:33 because
1:14:33 I
1:14:33 trust
1:14:33 that
1:14:33 down
1:14:33 the
1:14:34 line
1:14:34 you’ll
1:14:34 do
1:14:34 something
1:14:34 for
1:14:35 me
1:14:35 that
1:14:36 doesn’t
1:14:36 really
1:14:36 exist
1:14:36 in
1:14:47 to
1:14:47 running
1:14:47 a
1:14:48 successful
1:14:48 revolution
1:14:49 and
1:14:49 then
1:14:49 if
1:14:49 you
1:14:49 look
1:14:49 at
1:14:50 Haiti
1:14:50 at
1:14:50 the
1:14:51 time
1:14:53 you
1:14:53 had
1:14:53 the
1:14:54 French
1:14:54 army
1:14:54 the
1:14:55 British
1:14:55 army
1:14:57 and
1:14:57 the
1:14:57 Spanish
1:14:58 army
1:14:58 all
1:14:58 in
1:14:58 there
1:14:59 fighting
1:14:59 for it
1:15:00 so
1:15:00 really
1:15:01 well
1:15:01 developed
1:15:03 the strongest
1:15:04 militaries
1:15:04 of the
1:15:04 era
1:15:05 all
1:15:06 in
1:15:06 that
1:15:06 region
1:15:07 all
1:15:07 very
1:15:08 interested
1:15:08 in
1:15:09 the
1:15:09 sugar
1:15:11 which
1:15:11 was
1:15:12 quite
1:15:12 valuable
1:15:12 at
1:15:12 the
1:15:13 time
1:15:13 so
1:15:14 how
1:15:14 in
1:15:14 the
1:15:14 world
1:15:14 would
1:15:15 you ever
1:15:15 get out
1:15:15 of
1:15:15 that
1:15:17 and
1:15:17 it
1:15:17 turned
1:15:17 out
1:15:18 he
1:15:19 was
1:15:20 probably
1:15:20 the
1:15:21 great
1:15:21 cultural
1:15:22 genius
1:15:22 of
1:15:22 the
1:15:22 last
1:15:23 you know
1:15:25 maybe
1:15:25 in
1:15:25 history
1:15:26 but
1:15:26 certainly
1:15:26 the
1:15:26 last
1:15:27 several
1:15:27 hundred
1:15:28 years
1:15:30 and
1:15:31 he
1:15:32 was
1:15:32 able
1:15:33 because
1:15:34 he
1:15:34 was
1:15:34 a
1:15:35 person
1:15:35 who
1:15:35 although
1:15:35 he
1:15:36 was
1:15:36 born
1:15:36 a
1:15:36 slave
1:15:37 was
1:15:37 very
1:15:38 very
1:15:38 integrated
1:15:38 into
1:15:39 European
1:15:39 culture
1:15:40 because
1:15:40 he
1:15:40 was
1:15:40 so
1:15:41 smart
1:15:42 and
1:15:43 so
1:15:44 the
1:15:44 person
1:15:44 who
1:15:45 ran
1:15:45 the
1:15:45 plantation
1:15:47 kind
1:15:47 of
1:15:48 took
1:15:48 him
1:15:48 to
1:15:48 all
1:15:48 the
1:15:49 diplomatic
1:15:49 meetings
1:15:50 around
1:15:50 and
1:15:50 so
1:15:51 forth
1:15:51 and
1:15:51 he
1:15:52 got
1:15:52 very
1:15:52 involved
1:15:53 and
1:15:53 kind
1:15:53 of
1:15:53 mastered
1:15:55 European
1:15:55 culture
1:15:57 so to
1:15:57 speak
1:15:57 and
1:15:57 the
1:15:57 different
1:15:58 subtleties
1:15:58 around
1:15:59 it
1:16:00 and
1:16:00 he
1:16:01 started
1:16:01 adopting
1:16:01 those
1:16:02 things
1:16:02 and
1:16:02 applying
1:16:03 them
1:16:03 to
1:16:03 his
1:16:04 leadership
1:16:04 and
1:16:05 then
1:16:06 furthermore
1:16:07 incorporated
1:16:08 Europeans
1:16:09 into
1:16:09 the slave
1:16:10 army
1:16:10 so he
1:16:10 would
1:16:11 capture
1:16:12 you know
1:16:13 he
1:16:13 would
1:16:14 defeat
1:16:14 the
1:16:14 Spanish
1:16:15 capture
1:16:16 some
1:16:16 guys
1:16:16 rather
1:16:17 than
1:16:17 kill
1:16:17 them
1:16:17 he
1:16:18 incorporated
1:16:19 the best
1:16:19 leaders
1:16:19 into
1:16:20 his
1:16:20 army
1:16:21 and
1:16:21 he
1:16:21 built
1:16:21 this
1:16:22 very
1:16:23 advanced
1:16:23 hybrid
1:16:24 fighting
1:16:24 system
1:16:24 where
1:16:25 they
1:16:25 used
1:16:25 a lot
1:16:25 of
1:16:25 the
1:16:26 guerrilla
1:16:27 techniques
1:16:28 that
1:16:28 he
1:16:29 had
1:16:29 brought
1:16:29 over
1:16:29 from
1:16:30 Africa
1:16:31 and
1:16:32 then
1:16:32 he
1:16:33 had
1:16:34 combined
1:16:34 that
1:16:35 with
1:16:35 some
1:16:36 of
1:16:36 the
1:16:37 more
1:16:38 regimented
1:16:40 discipline
1:16:41 strategies
1:16:42 of
1:16:42 the
1:16:43 Europeans
1:16:44 and
1:16:44 in
1:16:44 building
1:16:44 all
1:16:44 that
1:16:45 he
1:16:46 ended
1:16:46 up
1:16:46 building
1:16:46 this
1:16:47 massive
1:16:47 army
1:16:48 and
1:16:49 defeated
1:16:49 Napoleon
1:16:50 and
1:16:51 everyone
1:16:51 else
1:16:51 and
1:16:52 it
1:16:52 was
1:16:52 just
1:16:53 quite
1:16:53 a
1:16:54 remarkable
1:16:54 story
1:16:55 about
1:16:55 how
1:16:55 he
1:16:56 figured
1:16:56 everything
1:16:57 out
1:16:57 from
1:16:57 his
1:16:58 principles
1:16:58 and
1:16:58 in
1:16:58 a
1:16:59 way
1:16:59 that
1:16:59 was
1:17:01 very
1:17:02 much
1:17:02 like
1:17:03 Elon
1:17:03 in
1:17:04 that
1:17:04 sense
1:17:05 one
1:17:05 of
1:17:06 the
1:17:06 things
1:17:06 I heard
1:17:06 you talk
1:17:07 about
1:17:10 that
1:17:10 I
1:17:10 thought
1:17:11 was
1:17:11 so
1:17:11 ingenious
1:17:12 was
1:17:12 he
1:17:12 would
1:17:12 basically
1:17:13 use
1:17:13 song
1:17:14 and
1:17:15 sound
1:17:16 as
1:17:16 like
1:17:16 encrypted
1:17:17 language
1:17:18 it’s
1:17:19 really
1:17:19 yeah
1:17:19 so
1:17:20 that
1:17:20 was
1:17:21 like
1:17:21 a
1:17:21 very
1:17:21 cool
1:17:22 thing
1:17:22 so
1:17:23 right
1:17:24 remember
1:17:24 that
1:17:24 this
1:17:24 is
1:17:24 in
1:17:25 the
1:17:25 days
1:17:25 before
1:17:26 telephony
1:17:26 or the
1:17:27 internet
1:17:27 or any
1:17:28 of
1:17:28 these
1:17:28 things
1:17:28 you know
1:17:28 it’s
1:17:29 pre
1:17:29 Alexander
1:17:30 Graham Bell
1:17:30 and all
1:17:30 that
1:17:30 kind
1:17:30 of
1:17:31 thing
1:17:32 and
1:17:33 so
1:17:34 you know
1:17:34 they were
1:17:34 literally
1:17:35 you know
1:17:35 the Europeans
1:17:36 were on
1:17:36 like
1:17:37 you know
1:17:37 notes
1:17:38 carrier pigeons
1:17:40 guys running
1:17:41 you know
1:17:41 back and
1:17:42 forth
1:17:42 and so
1:17:42 forth
1:17:43 and so
1:17:43 as a
1:17:43 result
1:17:45 you know
1:17:45 you kind
1:17:45 of needed
1:17:46 the army
1:17:47 together
1:17:47 in one
1:17:48 place
1:17:48 just
1:17:48 so you
1:17:49 could
1:17:49 communicate
1:17:49 the
1:17:50 order
1:17:52 Toussaint
1:17:53 basically
1:17:54 you know
1:17:55 had these
1:17:57 drummers
1:17:57 and these
1:17:58 songs
1:17:59 which
1:17:59 he could
1:18:00 put on
1:18:00 top of
1:18:00 like
1:18:02 the hill
1:18:02 who could
1:18:03 be very
1:18:03 very loud
1:18:04 and then
1:18:04 he would
1:18:04 separate his
1:18:05 army
1:18:05 you know
1:18:06 into like
1:18:06 six or
1:18:07 seven groups
1:18:09 but in
1:18:10 the song
1:18:11 would be
1:18:11 embedded
1:18:13 the order
1:18:13 of when
1:18:14 to attack
1:18:14 and you
1:18:15 know
1:18:15 when to
1:18:16 retreat
1:18:16 and all
1:18:17 these kinds
1:18:17 of things
1:18:17 so he
1:18:18 had this
1:18:18 like
1:18:18 super
1:18:19 advanced
1:18:20 you know
1:18:20 wide area
1:18:21 communication
1:18:22 system
1:18:23 that nobody
1:18:24 else had
1:18:24 and that
1:18:24 you know
1:18:25 that was a
1:18:26 big advantage
1:18:27 for him
1:18:28 yeah that to
1:18:29 me the reason
1:18:29 that that
1:18:30 comes up
1:18:30 for me
1:18:30 now is
1:18:31 we have
1:18:31 all these
1:18:32 new
1:18:32 technologies
1:18:33 that are
1:18:33 coming
1:18:33 online
1:18:33 and the
1:18:34 person
1:18:34 that’s
1:18:34 going to
1:18:34 be able
1:18:34 to get
1:18:35 outside
1:18:35 that box
1:18:37 and see
1:18:37 something
1:18:38 new
1:18:38 and fresh
1:18:39 is going
1:18:39 to be able
1:18:39 to use
1:18:40 this in
1:18:40 totally
1:18:41 different
1:18:41 ways
1:18:41 and while
1:18:42 in the
1:18:43 final analysis
1:18:43 I think
1:18:44 you and I
1:18:44 see it
1:18:44 very differently
1:18:45 in terms
1:18:46 of AI’s
1:18:46 ability
1:18:47 to ultimately
1:18:48 gobble up
1:18:49 what humans
1:18:49 can do
1:18:50 but right
1:18:51 now
1:18:52 AI is this
1:18:53 incredible
1:18:54 tool that
1:18:54 as an
1:18:54 entrepreneur
1:18:55 for me
1:18:56 it has
1:18:56 been
1:18:58 ridiculously
1:18:59 exciting
1:19:00 to one
1:19:01 see how
1:19:01 much farther
1:19:02 each of my
1:19:02 employees
1:19:03 can push
1:19:03 their own
1:19:04 abilities
1:19:04 by using
1:19:04 AI
1:19:05 and then
1:19:05 it does
1:19:06 not take
1:19:06 much to
1:19:07 prognosticate
1:19:08 out you
1:19:08 know 12
1:19:09 18 months
1:19:10 to understand
1:19:11 where the
1:19:11 tools are
1:19:12 going to be
1:19:12 and how
1:19:12 much more
1:19:13 they’re going
1:19:13 to let
1:19:13 you do
1:19:13 because
1:19:13 we’re
1:19:14 largely
1:19:14 an
1:19:15 entertainment
1:19:15 company
1:19:16 so for
1:19:17 us to
1:19:17 look at
1:19:17 that
1:19:18 and just
1:19:19 the
1:19:20 revolutionary
1:19:21 changes
1:19:21 but you
1:19:22 can’t be
1:19:22 trapped
1:19:23 inside the
1:19:23 old way
1:19:23 of thinking
1:19:24 you’ve got
1:19:25 to like
1:19:25 you said
1:19:25 build up
1:19:26 from first
1:19:26 principles
1:19:27 yeah it’s
1:19:27 a new
1:19:28 creative
1:19:28 canvas
1:19:29 I think
1:19:29 that’s
1:19:29 like a
1:19:30 really great
1:19:30 way of
1:19:30 thinking
1:19:31 about it
1:19:31 in that
1:19:33 it’s
1:19:33 like
1:19:34 well is
1:19:34 your
1:19:35 creativity
1:19:36 going to
1:19:37 be used
1:19:37 on
1:19:39 you know
1:19:40 kind of
1:19:41 frame
1:19:42 by frame
1:19:42 editing
1:19:43 of like
1:19:44 a video
1:19:45 or will
1:19:45 it be
1:19:46 thinking
1:19:46 of like
1:19:47 incredible
1:19:47 new
1:19:47 things
1:19:48 you can
1:19:48 do
1:19:48 in a
1:19:48 video
1:19:49 ad
1:19:50 that
1:19:50 you
1:19:50 could
1:19:50 never
1:19:50 do
1:19:51 before
1:19:51 and
1:19:51 have
1:19:52 the
1:19:52 AI
1:19:52 do
1:19:52 that
1:19:52 for
1:19:53 you
1:19:53 you know
1:19:53 like
1:19:54 and so
1:19:54 it’s
1:19:54 a little
1:19:54 bit
1:19:55 of a
1:19:55 readjustment
1:19:56 of
1:19:57 where
1:19:58 you put
1:19:58 your
1:19:59 creative
1:20:00 energy
1:20:00 into
1:20:01 and
1:20:01 the
1:20:01 things
1:20:01 that
1:20:01 are
1:20:02 possible
1:20:02 and
1:20:02 so
1:20:02 forth
1:20:03 and
1:20:03 I
1:20:03 think
1:20:03 that’s
1:20:04 you know
1:20:04 we’re
1:20:04 really
1:20:05 seeing
1:20:05 that
1:20:05 across
1:20:05 the
1:20:05 board
1:20:10 is
1:20:10 this
1:20:11 going
1:20:11 to
1:20:11 mean
1:20:12 you
1:20:12 know
1:20:12 like
1:20:13 you
1:20:13 don’t
1:20:13 have
1:20:13 human
1:20:14 investors
1:20:14 anymore
1:20:15 and
1:20:15 it’s
1:20:15 actually
1:20:15 been
1:20:16 like
1:20:16 totally
1:20:16 the
1:20:17 opposite
1:20:17 like
1:20:18 instead
1:20:19 of
1:20:19 this
1:20:20 like
1:20:22 painstakingly
1:20:23 collecting
1:20:24 you know
1:20:24 all
1:20:24 all
1:20:25 the
1:20:25 data
1:20:25 needed
1:20:25 to
1:20:26 put
1:20:26 the
1:20:26 investment
1:20:27 memo
1:20:27 together
1:20:28 like
1:20:28 yeah
1:20:28 it
1:20:28 just
1:20:28 does
1:20:29 that
1:20:29 for
1:20:29 you
1:20:29 and
1:20:30 then
1:20:30 you’re
1:20:30 just
1:20:30 thinking
1:20:30 about
1:20:31 like
1:20:31 okay
1:20:31 what
1:20:31 are
1:20:32 the
1:20:32 like
1:20:32 the
1:20:33 really
1:20:33 compelling
1:20:33 things
1:20:34 about
1:20:34 this
1:20:35 or
1:20:35 rather
1:20:36 than
1:20:36 you know
1:20:37 trying
1:20:37 to
1:20:37 track
1:20:38 every
1:20:39 entrepreneur
1:20:40 and
1:20:41 like
1:20:41 great
1:20:41 engineer
1:20:42 in our
1:20:42 database
1:20:43 the AI
1:20:43 is
1:20:43 just
1:20:44 tracking
1:20:44 all
1:20:44 those
1:20:44 people
1:20:45 and
1:20:45 letting
1:20:45 you
1:20:45 know
1:20:45 hey
1:20:46 that
1:20:46 guy
1:20:46 just
1:20:46 updated
1:20:47 his
1:20:47 LinkedIn
1:20:47 profile
1:20:48 or
1:20:54 like
1:20:54 a
1:20:54 much
1:20:55 more
1:20:55 kind
1:20:56 of
1:20:56 fun
1:20:56 part
1:20:56 of
1:20:56 the
1:20:57 game
1:20:58 and
1:20:59 and
1:21:00 so
1:21:00 you know
1:21:00 look
1:21:01 I
1:21:01 would
1:21:02 say
1:21:02 the
1:21:03 best
1:21:03 predictor
1:21:04 of
1:21:04 kind
1:21:04 of
1:21:05 how
1:21:05 things
1:21:05 are
1:21:05 going
1:21:05 to
1:21:06 go
1:21:06 are
1:21:07 more
1:21:07 like
1:21:07 what’s
1:21:08 happening
1:21:08 now
1:21:08 than
1:21:10 like
1:21:10 the
1:21:10 most
1:21:11 dystopian
1:21:11 view
1:21:12 of it
1:21:12 that
1:21:13 we
1:21:13 can
1:21:14 possibly
1:21:14 think
1:21:14 of
1:21:15 which
1:21:15 I
1:21:15 think
1:21:16 is
1:21:16 where
1:21:16 a lot
1:21:16 of
1:21:16 people
1:21:16 go
1:21:17 to
1:21:17 and
1:21:17 like
1:21:17 I
1:21:17 said
1:21:17 I
1:21:17 think
1:21:17 some
1:21:18 of
1:21:18 that’s
1:21:18 the
1:21:18 name
1:21:19 you
1:21:19 know
1:21:20 artificial
1:21:21 intelligence
1:21:21 we
1:21:22 hate
1:21:23 everything
1:21:23 artificial
1:21:24 so
1:21:25 why
1:21:25 do
1:21:25 we
1:21:26 name
1:21:26 it
1:21:26 artificial
1:21:28 that’s
1:21:28 too
1:21:28 true
1:21:29 Ben
1:21:29 I’ve
1:21:30 enjoyed
1:21:30 every
1:21:30 minute
1:21:30 of
1:21:31 this
1:21:31 where
1:21:31 can
1:21:31 people
1:21:31 keep
1:21:32 up
1:21:32 with
1:21:32 you
1:21:33 yeah
1:21:33 well
1:21:33 I
1:21:34 am
1:21:35 B
1:21:35 Horowitz
1:21:35 on
1:21:36 AX
1:21:37 and
1:21:38 you
1:21:39 know
1:21:40 that’s
1:21:40 probably
1:21:40 the
1:21:40 best
1:21:41 thing
1:21:41 we’re
1:21:42 a16z.com
1:21:43 and
1:21:45 hope
1:21:45 you
1:21:45 enjoyed
1:21:45 it
1:21:46 and
1:21:46 that
1:21:46 was
1:21:47 great
1:21:47 fun
1:21:47 good
1:21:48 fun
1:21:48 catching
1:21:48 it
1:21:49 was
1:21:49 indeed
1:21:49 and
1:21:49 then
1:21:49 you
1:21:49 also
1:21:50 have
1:21:51 multiple
1:21:51 books
1:21:52 that
1:21:52 people
1:21:52 can
1:21:52 read
1:21:52 that
1:21:53 are
1:21:53 extraordinarily
1:21:54 well
1:21:55 respected
1:21:55 in the
1:21:55 field
1:21:56 so
1:21:57 also
1:21:57 thank
1:21:57 you
1:21:57 for
1:21:57 those
1:21:58 absolutely
1:22:00 awesome
1:22:00 thanks
1:22:00 all right
1:22:01 well thank
1:22:01 you brother
1:22:02 I appreciate
1:22:02 it
1:22:02 all right
1:22:02 everybody
1:22:03 if you
1:22:03 have
1:22:03 not
1:22:04 already
1:22:04 be
1:22:04 sure
1:22:04 to
1:22:04 subscribe
1:22:05 and
1:22:05 until
1:22:05 next
1:22:05 time
1:22:06 my
1:22:06 friends
1:22:06 be
1:22:07 legendary
1:22:07 take
1:22:07 care
1:22:08 peace
1:22:20 we’ve
1:22:20 got
1:22:20 more
1:22:20 great
1:22:21 conversations
1:22:21 coming
1:22:21 your
1:22:21 way
1:22:22 see
1:22:22 you
1:22:22 next
1:22:22 time