AI transcript
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0:01:34 Today’s number 30.
0:01:38 That’s the percentage of U.S. travelers who now use generative AI tools to plan trips.
0:01:39 That’s a true story.
0:01:45 When I enter a foreign country and have to fill out a visa form and it says profession, I put chaos.
0:01:47 Boom!
0:01:47 That’s right.
0:01:48 That’s how I roll, Ed.
0:01:51 I’m an agent of chaos coming in.
0:01:54 Listen to me.
0:01:55 Markets are bigger than us.
0:01:58 What you have here is a structural change in the wealth distribution.
0:01:59 Cash is trash.
0:02:01 Stocks look pretty attractive.
0:02:02 Something’s going to break.
0:02:03 Forget about it.
0:02:05 I have so many travel stories, Ed.
0:02:09 When I was a first-year analyst right out of UCLA at Morgan Stanley.
0:02:11 I don’t know if you know this, but I’m not, like, really good with details.
0:02:15 And before there was GPS, there was maps.
0:02:16 And I lived in L.A.
0:02:21 And my other analyst, Don Larson, we had to go to Stanford for a recruiting trip.
0:02:23 So we’re bombing to the airport.
0:02:27 I take a right turn on La Cienega instead of a left turn.
0:02:30 And finally, Don catches up with me and says, you’re going the wrong way.
0:02:33 Turn around, get to the airport, and we see the plane pull away.
0:02:38 And then we, there’s one every 30 or 60 minutes of San Francisco.
0:02:43 We’re at Stanford lecturing, talking about how great Morgan Stanley fixed income was, which was a total lie.
0:02:45 So we went up there and started lying to people.
0:02:46 It’s the job.
0:02:48 And then a woman comes in and says, is Donald Larson here?
0:02:50 And they said, yeah.
0:02:52 And Don went out and he came back in and he was all upset.
0:03:00 His father had had a heart attack and because the plane we were, we missed went down.
0:03:07 And it was, you probably, you’re too young to remember this, but it changed aviation history because a disgruntled employee got on the plane with a gun.
0:03:11 And employees up until that point didn’t have to go through metal detectors, pilots, and crew.
0:03:16 And he shot the pilot and the plane crashed and everyone on board died.
0:03:16 Oh, my God.
0:03:20 And because I turned to right, we missed the plane instead of a left.
0:03:21 Oh, my God.
0:03:25 I thought to myself, does anyone I know know that I’m even up here?
0:03:28 And it was no, so I didn’t make any calls.
0:03:36 And of course, my friend, Dennis, my roommate from the fraternity, was expecting me and called my mom.
0:03:41 And my mom called my assistant and my assistant looked it up and said, yeah, he was on that flight.
0:03:50 And so I called my mom and my mom had friends over because they thought I had gone down on this plane and she thought she was hallucinating.
0:03:53 Not a hallmark story here.
0:03:56 Not a hallmark story here.
0:04:01 But anyways, Ed, people think I’m inconsiderate because I’m late all the time and I get lost a lot.
0:04:03 I don’t mind missing stuff and being a little bit late.
0:04:04 It’s worked out for me.
0:04:06 It’s worked out for me.
0:04:09 You’re focused on the important stuff.
0:04:10 That’s what matters.
0:04:13 But, yeah, that is crazy.
0:04:17 I’m just imagining you driving a car right now, which I can’t picture.
0:04:20 And I wonder, are you a good driver?
0:04:23 Because I know you don’t drive anymore, really.
0:04:24 I’m a great driver because I grew up in L.A.
0:04:29 And I started driving literally at the age of 15 and a half.
0:04:30 I got my learner’s permit.
0:04:38 And then back at California Dreaming Culture in California was, I’m not exaggerating, I got my driver’s license on my 16th birthday.
0:04:46 And it just freaks me out that my son right now is technically, what is he, nine months away from driving, which makes no sense.
0:04:51 But, yeah, when you’re in L.A., you just drive everywhere all the time.
0:04:56 So it wasn’t that I was especially deaf to driving, but you just get very well practiced.
0:04:57 When’s the last time you drove?
0:04:58 That’s really interesting.
0:04:59 It’s probably been a couple years.
0:05:00 Yeah, I don’t drive.
0:05:01 But I can drive stick.
0:05:03 I can drive a big rig.
0:05:03 Wow.
0:05:04 I love cars.
0:05:06 When you grow up in California, you love cars.
0:05:12 I just don’t – I hate shoelaces, passwords, keys, and cars because they all demand things from me.
0:05:17 Also, most of my relationships are now starting to ask for something in return, which is really bumming me out.
0:05:21 That’s not why we’re here.
0:05:29 The key term is service, specifically acts of service from you to me.
0:05:30 And I pay for everything.
0:05:32 That’s the deal.
0:05:35 Talk to me about cars, Ed.
0:05:35 Do you own a car?
0:05:36 I don’t own a car.
0:05:37 My girlfriend does, though.
0:05:43 And we’ve – so I drive around with her car, and it really is sort of a game changer.
0:05:44 What kind of car does she have?
0:05:45 Subaru Outback.
0:05:49 So she’s a lesbian.
0:05:50 Yeah, exactly.
0:05:51 Sorry, Ed.
0:05:53 Claire, should we tell them?
0:05:56 I had the same reaction in my head.
0:05:58 And let me get it.
0:06:01 She doesn’t want to have kids, but you’re going to get a German Shepherd puppy.
0:06:07 Do you want my car history?
0:06:07 Yes.
0:06:11 My first car was the best gift I have ever received, hands down.
0:06:13 Best material item that has meant more to me than anything.
0:06:15 And I have a lot of nice material items.
0:06:21 When I was 15 – when you lived in California, if you didn’t have a car, you had no social life.
0:06:23 There was no – there was no Subway.
0:06:24 There was no Uber.
0:06:27 We had the RTD, which was just awful.
0:06:31 And so if you wanted to have any social life, you had to have a car.
0:06:37 And my friend Adam got a Fiat Spider, and then he bought an Austin Healey Mark 7.
0:06:39 He was like fucking James Bond.
0:06:41 He was this good-looking guy in a leather coat.
0:06:43 I didn’t have the money for a car.
0:06:48 And my mom borrowed money to buy an Acura, and she gave me her lime green Opel Manta.
0:06:49 And I remember the day she came home.
0:06:51 We used to practice driving it.
0:06:56 She’d come home and go into the underground garage in our apartment complex and honk the horn.
0:06:58 And I’d run down, and she’d teach me how to drive stick.
0:07:06 And on my 16th birthday, she came home in this new, like, bad Acura, and she came up to me and put her hands on my shoulders and said,
0:07:08 you’re a handsome man who owns his own car now.
0:07:10 And she gave me the keys to her Opel Manta.
0:07:11 That’s nice.
0:07:12 Oh, I’m going to cry.
0:07:13 Isn’t that nice?
0:07:13 Yeah.
0:07:15 Anyways, I had that.
0:07:16 Then I had a Renola car.
0:07:17 Then I had a rabbit.
0:07:22 Speaking of closeted heterosexuals, convertible rabbit.
0:07:26 My girlfriend in college dated a guy with a convertible rabbit.
0:07:27 I’m like, okay, should we tell her?
0:07:40 Anyways, and then out of business school, hit it pretty early, got the Lexus GS300, which was the bad Lexus that never had a market, but it was a Lexus, and I was super excited.
0:07:51 Then I had three BMW 7 Series in a row, including Jason Stabbers, who used to work with us here at Prop G, used to house it for me.
0:07:55 And he calls and says, I’m afraid we’re on vacation.
0:07:57 We used to go to Hawaii because we lived on the West Coast.
0:08:01 Well, I got in a terrible auto accident.
0:08:05 I ran the car into the side of, I think it was Grace or San Ains Church.
0:08:06 He swerved out of the way.
0:08:12 Jason totaled your BMW.
0:08:16 Yeah, Jason Stabbers told, he totaled my first 7 Series.
0:08:18 He doesn’t bring that up much anymore.
0:08:19 You know, we do employee reviews.
0:08:20 You’re about to get yours tomorrow.
0:08:23 By the way, we’re asking you for money back.
0:08:24 You’re not getting a bonus.
0:08:31 But I remember, I couldn’t wait to do his review because I had it as the first bullet point in his review.
0:08:33 Total boss’s car.
0:08:38 Anyway, so he goes, I’ve had this terrible accident.
0:08:40 Da-da-da, your car’s gone.
0:08:41 I’m like, just to stop right there.
0:08:43 I’m like, the important question is the following.
0:08:44 How was the car?
0:08:49 Ed, are we done with banter?
0:08:50 Let’s call it there.
0:08:51 We’ve got a big interview to get into here.
0:08:58 All right, let’s get into our conversation with Tom Lee, co-founder, managing partner and head of research at Fundstrat Global Advisors.
0:09:00 Tom, thank you for joining us.
0:09:02 Great to see you and Merry Christmas.
0:09:06 All right, let’s pass right into it.
0:09:12 You’ve been vocal that investors are still underestimating how strong 2026 can be.
0:09:14 Why are you so bullish on 26?
0:09:18 I think the economy and stocks have been suppressed for the past few years.
0:09:25 Part of it is, of course, that we’ve seen six, what I call, extinction events take place in markets.
0:09:39 Everything from COVID to the bullwhip supply chain effect as the economy restarted to the fastest inflation cycle in history and then followed by the fastest Fed hikes in history.
0:09:51 And then we’ve, of course, had a very controversial administration which put tariffs in place in April of this year that caused a miniature bear market.
0:09:55 And then we’ve even had U.S. bombing Iran’s nuclear facilities.
0:10:09 I think all of these collectively have made investors very nervous about what I call investing in full risk because these are what six black swans that happened in four years.
0:10:18 And I think on top of that, we’ve had a Fed that has not really given a green light about monetary easing.
0:10:27 And I think the Fed’s reluctance has actually suppressed business, quote, animal spirits because the ISM has been below 50 for more than three years now.
0:10:35 So I think that’s all been a business cycle that has been pretty good, but not one that has been really expansionary.
0:10:37 And I think that starts to happen next year.
0:10:54 It feels as if the market has become so concentrated or dependent or circling around a small number of stocks that to be bullish on 26 sort of mandates that you’re – well, tell me if you think this is true – indicates that you’re bullish on the Magnificent 10 and AI stocks.
0:10:55 Is that necessarily true?
0:10:58 And are you bullish on the Magnificent 10?
0:11:08 Yeah, I mean, I think 2026 is going to look a lot like this year, meaning we are probably going to have many months where the market is actually down year to date.
0:11:12 You know, I mean, this year we were down double digits at one point before the market recovered.
0:11:23 I think that plays out next year, but for the reason I previously stated, I think that we end up a bullish outcome despite all the skepticism.
0:11:36 And it does require large-cap tech and AI stocks to still produce earnings growth and not have a lot of P.E. reduction so that you still get positive return.
0:11:52 But I think the rest of the stock markets or the other 490 or so can actually perform well because if the Fed is cutting and interest rates are coming down and the business cycle is sort of really starting, that’s good for other stocks.
0:12:00 As you say, we’ve seen a lot of these black swan events, things that you would think would freak the markets out and make people worried.
0:12:11 And yet, I look at what has happened in the markets and my view of it is, it’s not necessarily that it’s suppressing sentiment.
0:12:15 To me, it almost looks like the market is deciding to shrug everything off.
0:12:28 So when you describe how, you know, perhaps these black swan events have made investors perhaps have less, lower risk appetite, to me, I’m almost kind of taking the other side of that in my head.
0:12:31 I’m like, it seems as if these things happen, these things that are concerning.
0:12:35 One example would be what we’re seeing with these AI circular deals.
0:12:42 And it seems to me that the market is kind of swanning it away, shrugging it off, and the market continues to climb.
0:12:47 And that’s what we’ve seen this year and we saw the previous year and we saw the year before that.
0:12:53 We continue to have this bull market, despite what many would say are really concerning events.
0:12:56 So how would you think about that?
0:12:57 How would you respond to my concerns there?
0:13:03 You’re almost kind of mirroring what we’re observing, but just with a different take.
0:13:07 I mean, our take is markets climb a wall of worry.
0:13:14 You know, historically, when there’s a lot of skepticism, stocks can rise.
0:13:18 In fact, you know, markets actually peak on good news.
0:13:21 You know, they don’t peak when people are bearish.
0:13:25 Markets peak when everyone’s bullish and it no longer responds to good news.
0:13:28 Just like markets bottom on bad news.
0:13:33 And I think many people are skeptical, but stocks have risen.
0:13:37 It doesn’t really mean markets are shrugging off the concerns.
0:13:39 That’s one way to interpret it.
0:13:45 My other interpretation is, you know, there is a wall of skepticism.
0:13:52 And I mean, maybe that’s just the, it is like, maybe we’re just talking about the same sides, just the same thing.
0:13:58 And, but, you know, like if, if someone asked me, are we, is it worrisome?
0:14:01 You know, I was a technology analyst in the 90s.
0:14:04 So I covered wireless stocks starting in 93.
0:14:12 And I witnessed the bubble that was created, you know, a decade in the making, really two decades in the making.
0:14:24 And by 99, not only were, was there no skepticism, there was, you know, excessive entitlement.
0:14:28 Investors were expecting stocks to do explosively.
0:14:35 And, you know, a 20% upside wasn’t satisfactory and valuations were already elevated and expanding.
0:14:49 So I’d say that if I was trying to compare this to the bubble of the 90s and, you know, and wireless was a central cast character in that internet infrastructure build, we’re, I don’t really see the echoes of that today.
0:14:58 I think it’s so interesting what you say there about, you know, we’re looking at the same things and we’re drawing slightly different conclusions about what that means for markets.
0:15:06 And we had a similar conversation with, actually, I mean, you were the chief equity strategist at J.P. Morgan.
0:15:09 We spoke with their head of investment strategy, Michael Semblist.
0:15:11 And we were talking about a lot of these issues.
0:15:19 And where he ultimately landed was, he was quite bearish or bearish-ish.
0:15:24 And I just want to play you what he said and get your reaction, see what you think.
0:15:34 It would be kind of shocking if you didn’t have some kind of profit-taking correction in 2026 at some point on the order of 10% to 15%.
0:15:38 It would be, I’d be, I’d be really surprised not to see that.
0:15:44 So that’s his base case is some sort of correction, 10% to 15%.
0:15:50 Looking at your 2026 outlook, you’ve got S&P price target of $7,700.
0:15:59 We’re at $68.50, so that would imply, you know, a little over 10% rise next year.
0:16:02 So two very different views.
0:16:05 Where do you land compared to his view?
0:16:07 What do you think he might be overlooking?
0:16:09 And where do you differ, do you think?
0:16:14 Our outlook actually does call for a drawdown next year.
0:16:18 So very similar to this year of probably closer to 20%.
0:16:26 So I think we are going to have another miniature bear market next year, but then we’re going to recover.
0:16:28 I mean, let’s take 2025.
0:16:37 Let’s say that at the end of last, at the end of 2024, and actually we did talk about, you know, the idea of a drawdown in 2025.
0:16:44 But let’s say that someone plays the clip, so let’s say it’s Michael Semblis, but you don’t, let’s just pretend he’s saying at the end of 2024.
0:16:47 And he says the mark will be down 10% to 15%.
0:16:54 That doesn’t rule out where we are by the end of 2025 because, in fact, we did have a drawdown.
0:17:03 And I think I’m not, again, saying, I actually think Michael and I are pretty aligned in the sense that I think there is going to be a drawdown next year.
0:17:10 He says he wouldn’t be surprised, but to me, it doesn’t mean that’s the end of the actual bull market.
0:17:13 And in fact, I think stocks fully recover.
0:17:23 So the wall of worry here that we’re talking about and that you outline in your outlook, you’ve got several elements in there.
0:17:28 You’ve got, and these are the things that you describe as people are worried about, investors are worried about.
0:17:35 So politically divided nations, social unrest, Supreme Court overturns tariffs, new Fed chair.
0:17:39 And then you have two ones here that I really agree with.
0:17:42 I feel like I’d love to have you dive in on.
0:17:45 AI valuations, which I think a lot of people are concerned about.
0:17:51 And then also 20% equity returns in the past three years, i.e.
0:18:02 Each year for the past three years, the stock market has risen by an average of 20%, which may imply maybe we’re running out of steam at some point.
0:18:06 Could you just unpack what your concerns are in that wall of worry?
0:18:11 And do you think those would be the trigger of such a correction?
0:18:14 Let’s start with the one that you just mentioned.
0:18:18 The stock market, you know, we’re up 16%.
0:18:25 So I think if we rally three percentage points, we’ll be three years of 20% gains back to back.
0:18:32 And it’s actually more common than we realize.
0:18:44 In fact, when we look at the last 65 years, you know, it’s happened in 20 different, I think it’s happened 20 times in different countries and multiple times in the U.S.
0:18:45 I’m sorry, 12 times.
0:18:48 It means a lot of good news is priced in.
0:18:54 I mean, of course, you know, stocks being up 20% a year, three years in a row, it’s definitely pricing in a lot of good news.
0:18:59 So to me, I do think that we have to consolidate those gains.
0:19:05 And that’s why I think a drawdown next year makes perfect sense to me.
0:19:15 But because there isn’t a lot of leverage in the economy, you know, household sector has not really borrowed money.
0:19:16 It’s been expensive to borrow money.
0:19:22 And even margin debt, it’s risen, but it hasn’t risen parabolically.
0:19:25 It’s actually essentially tracked S&P gains.
0:19:34 So it’s not like people are borrowing faster than the market’s been going up, especially if you look at a five-year CAGR.
0:19:45 So I would be in the camp that as long as the economy is holding up, that drawdown is going to be viewed as a buying opportunity.
0:20:03 Now, on AI valuations, it makes perfect sense for someone to say a lot of the valuations for AI are probably absurd because this is the nature of an exponential sector, right?
0:20:16 If we look at an industry that could grow parabolically for 10 years, all of the future value is in the latter half of those years, right?
0:20:21 So it’s – and then we’re trying to discount that back to today.
0:20:25 And so stocks are going to look absurdly expensive.
0:20:40 And more importantly, investors make a common mistake, which is that they assume that the existing universe of companies are going to be the central cast characters over the next 10 years, which is not the case.
0:20:52 So the reason valuations don’t make sense today is that, one, of all the AI stocks, I’d say it’s probably safe to say only 10% are going to be good investments.
0:20:54 Maybe it’s even generous, maybe 5%.
0:21:00 And, of course, there’s going to be a new emergence set of new players.
0:21:02 And, in fact, the economic model might change.
0:21:05 But it doesn’t mean it’s a bad investment.
0:21:09 And we’ve highlighted this as generational traits in past reports.
0:21:28 For instance, like if you look at the internet, if you bought the internet basket in 99, okay, so you bought it near the peak, and you held it to today, you actually still outperformed the S&P 500, even though 99% of the stocks went to zero.
0:21:34 So it wasn’t – it was a bad investment if you tried to pick a winner.
0:21:37 But it wasn’t so bad if you held it as a basket.
0:21:45 So I think AI, it’s probably going to be fair to say 90% of the stocks are going to be – do way worse than people expected.
0:21:46 They were too optimistic.
0:21:48 But I think as a basket, it’s probably going to outperform.
0:21:50 That all makes sense to me.
0:21:51 I’m with you.
0:22:01 But it seems to be a little bit more nerve-wracking when we realize that a lot of the AI companies are the largest companies in the world.
0:22:02 It’s the big tech companies.
0:22:06 I mean, I think Google is an AI company at this point, or an AI stock.
0:22:08 Meta, NVIDIA.
0:22:12 I mean, these are the largest, most valuable companies in the world.
0:22:16 And the market really depends on their performance.
0:22:39 So when I think about the idea that, you know, many of these companies and the expectations that have been pinned to the AI cycle, the fact that that could affect some of the largest, most valuable companies in the world, where we’re seeing the highest concentrations in those small companies, the higher concentration than we’ve ever seen in history.
0:22:42 To me, that makes it scarier, what you just said.
0:23:01 So I guess my question is, do those companies, do the big tech companies, the Magnificent Seven, do they count in your analysis of AI valuations being too high and the possibility that perhaps we might lose out or that the value won’t actually show up for many of these companies?
0:23:06 I might even just add to your concern, because there’s a lot of capex here, too.
0:23:13 So that, these are, you know, a lot of the mag-7 used to be asset-light businesses.
0:23:23 You know, they, the remarkable equity, these are rent-seeking model of them, was that they could create growth with very little spending.
0:23:27 I mean, R&D spending was there, but really capex was not there.
0:23:43 But today, as you know, AI is extremely capital-intensive and it’s energy-intensive, and it’s only justifiable if it’s replacing real work somewhere else.
0:23:50 Then you can justify it, because now it’s creating assets to replace future opex, you know?
0:24:02 I’m going to give you a spin about what’s happening that is not disagreeing with what you’re saying, but it’s probably observing a change in the reality, okay?
0:24:26 By the way, we wrote about that in 2018, that if you go back to 1930 and you just use simple demography, okay?
0:24:40 So, the population tables, whenever the population growth rate grows faster than the prime-age workforce, which means you have compounded labor deficit, you’ve always had a technology cycle.
0:24:46 That is 1948 to 67, in 1991 to 99.
0:24:55 In both of those periods, the population growth rate was growing fast, which is demand faster than worker supply.
0:25:06 And we entered the third epoch, or era, of labor shortage, which started in 2018, and it’s going to last to 2035.
0:25:16 So, then technology spend is necessity, because you don’t have as much labor available, so there’s going to be less wage spend.
0:25:38 Now, if I substituted the word and called this, instead of the word banks, tech companies, I called them financial institutions, we would not be saying there’s a financial institution bubble, because for every level, for every unit of GDP growth, there’s a unit of financial spend.
0:25:41 I mean, it’s literally the other part of the ledger.
0:25:44 And, in fact, the financial industry has all circular spending.
0:25:45 I mean, think about this.
0:25:50 Real estate is valued as a separate asset, but every company needs real estate just to run a business.
0:25:51 So, why are we valuing real estate?
0:25:56 Like, in a GDP sense, real estate should be an interim product, not a final product.
0:26:11 So, I think tech is becoming so central to the economy, especially because of labor shortage, that we’re—when we see tech intensity growing, people are flagging that as a bubble.
0:26:16 Whereas, I’m actually just pointing out it’s actually out of economic necessity.
0:26:26 But it becomes a bubble if the multiple we’re applying to the tech streams don’t justify higher valuation.
0:26:31 I think tech earnings are probably more valuable than Costco, for instance.
0:26:32 Yeah, agreed.
0:26:33 Or Walmart, right?
0:26:39 But, you know, Walmart trades at 37 times board earnings, and Costco trades at 50 times.
0:26:41 So, NVIDIA trading at 27.
0:26:48 I mean, is there a bubble in Costco and Walmart because NVIDIA’s at 27 times earnings?
0:26:49 100%.
0:26:52 And we’ve looked at that Costco valuation.
0:26:53 It’s crazy.
0:26:54 I totally agree.
0:26:59 But then I go back to what—something that Aswath Damodaran said when he joined us on the podcast.
0:27:03 And he said he can’t see value anywhere.
0:27:07 He thinks everything is overvalued when he looks at the stock market.
0:27:09 So, that’s the other side.
0:27:14 And as Scott and I have discussed, you know, we’re not necessarily in agreement with him on all of that.
0:27:18 But that, I think, becomes a concern.
0:27:38 And then to the circular dealmaking and the CapEx point, I think the concern, unlike the financial institutions, is like, we haven’t seen the AI product proven itself yet in terms of its ability to provide the value that we’re pricing in, I guess, is the problem.
0:27:51 We haven’t seen that these data centers, one, I mean, are even going to be necessary to keep the workforce and the labor market going, as you say, to keep our economy growing at a fast clip.
0:28:04 Therefore, it seems that we’re making giant, giant predictions with not that much evidence, which, you know, we could call it a bubble or we can just call it what I said, which is we don’t really know what’s happening.
0:28:06 And yet we’re spending tons of money.
0:28:14 And so, if there becomes a moment where suddenly everyone says, wasn’t what we thought it was, then that could be quite damaging to portfolios.
0:28:16 Yeah, 100% agree.
0:28:24 Because, by the way, anything that is relying on future growth, none of us is an expert on the future.
0:28:28 I mean, that’s, right, like that there’s many roads to the future.
0:28:38 One thing I just want to point out, Benjamin Graham’s book, The Intelligent Investor, which I did read, I don’t know if you remembered his rule of thumb about what a proper P.E. is.
0:28:39 No, please.
0:28:43 It’s 12 times plus two times the growth rate.
0:28:45 That is in his book.
0:28:52 I’m just saying, when someone says they’re a value investor and they’re saying stocks are expensive, I know they didn’t read his book because I read the book.
0:28:59 But, you know, as you know, that’s because he didn’t believe things could grow 10% a year.
0:29:04 You know, that’s three, like 10% is a lot of growth back then.
0:29:06 Yeah, that was a pre-digital economy.
0:29:13 The second point I would make is when I did wireless in the 90s, okay, now I was in my 20s.
0:29:21 I was a senior analyst at the age of 23, so I was really lucky to be very young and actually a senior equity analyst.
0:29:28 But when I was covering wireless, the industry only had 34 million cell phones in 1993.
0:29:38 And the industry telecom services was dominated by long distance and local telephony, these things called the Bell operating companies.
0:29:41 And they made all their money from two businesses.
0:29:47 The Bells made the biggest profit maker for the telephone companies was the directory business.
0:29:50 And number two was local business telephony.
0:29:56 They made more money selling local exchange service to Chase than they did from any other business.
0:30:05 So when wireless was happening in the 90s, as me in my 20s, my imagination was ignited.
0:30:09 And, you know, I talked about how, you know, you could do so many more things with cell phones.
0:30:17 And we joked about how it would have changed the path of like the Revolutionary War, right, if Paul Revere had a cell phone.
0:30:25 But most of the money managers were in their 40s and 50s, and all the experts were in their 40s and 50s.
0:30:30 And they mostly thought cell phones was an expensive yuppie toy.
0:30:32 They said the economics didn’t make sense.
0:30:36 You could never fit that much traffic on cellular waves.
0:30:40 And all the money was in long distance and local.
0:30:53 So the telephone companies and the long distance providers, including MCI, would do everything to protect their existing businesses and use regulatory strengths to make sure cellular never really grew.
0:30:59 Now, look back, that was, of course, the wrong bet.
0:31:06 And remember, cellular companies had to build, they had to spend $50 per pop to build out a cellular system.
0:31:14 So if you took any city of 10 million people, you had to spend $50 per person just to build a basic system.
0:31:16 It was enormously expensive.
0:31:19 And cell phone penetration was 6%.
0:31:21 You had to make a bet that you would have a lot of penetration.
0:31:27 I think that I’m seeing people make the same arguments against AI.
0:31:33 And I think one of the things we have to say is, which lens are you using?
0:31:45 If you’re using it through the lens of a 40-year-old, really wealthy person, no new technology looks interesting to you because you’re more interested in protecting your wealth and incumbency.
0:31:48 But young people are the ones who change the world.
0:31:50 Look at Chase Institute.
0:31:54 Credit card spending growth only comes from people under age 50.
0:31:58 And what are young people doing with AI?
0:32:03 I mean, my daughter is, one of my daughters is in college, my youngest.
0:32:14 They have adapted to open AI and chat GBT in a way that I can’t even fathom.
0:32:20 And so those people represent the future vintage of AI adoption.
0:32:26 Just like cellular adoption in the 90s, it was 70% of 20-year-olds had a cell phone.
0:32:29 And it was like 5% of 60-year-olds.
0:32:35 So, of course, all my clients who are in their 50s and 60s said, you know, who needs a cell phone?
0:32:38 They didn’t realize that those 20-year-olds become 60.
0:32:42 And that 70 became 90.
0:32:43 And soon everybody had a cell phone.
0:32:53 So, I think we have to be more – we have to think about how the 14-year-olds using these models compared to us.
0:32:57 Because we’re already – you know, we’ve already lived our lives and we’ve established our regimes.
0:33:02 And our – so, it doesn’t mean that AI stocks are correctly valued.
0:33:07 I’m just saying we have to really understand that the future change is coming from young people.
0:33:10 We’ll be right back after the break.
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0:35:41 We’re back with Prof G Markets.
0:35:45 You said something, Tom, that really stuck out to me.
0:35:50 You said that we’re in this labor shortage cycle from 2018 that will last through 2035.
0:35:57 And you can’t avoid the catastrophizing around the destruction in the labor force from AI.
0:36:02 Do you still believe we’re going to be in a cycle of labor shortage, even with AI?
0:36:08 I vacillate because there’s times where I’m like, when I read a book like The Coming Wave,
0:36:14 I panic and I realize like, wow, like we need to re-educate society.
0:36:31 But one thing that gives me hope is that we did at Fundstrat study another technological wave that wiped out at least 20% of the labor force in the 20th century, which was frozen foods.
0:36:44 So what many people don’t realize is that when Charles Birdseye created Flash Frozen, which, by the way, was a venture backed by Goldman Sachs.
0:36:45 It was a VC backed company.
0:36:46 I love this.
0:36:50 And by the way, his name is Birdseye because he was an ornithologist.
0:36:51 He actually was studying birds.
0:37:04 But he found that the Inui tribe in Alaska had kept their fish super fresh because they were putting it in a frozen saltwater solution that flash froze the fish.
0:37:12 If we look at the labor tables from the 20s, 40% of the U.S. labor force was employed on farms.
0:37:18 It was literally, we spent, most of the economy was people working on farms.
0:37:25 And most of the service sector that was defined back then were household servants, people working for someone else.
0:37:37 Food was over 25% of the wallet prior to frozen foods being widely, you know, mass market because most food spoiled on the way to the supply chain.
0:37:40 And so grocery aisles were mostly fresh.
0:37:44 And what was frozen back then had a freezer burn.
0:37:45 It was terrible.
0:37:52 So Flash Frozen allowed suddenly the cost of food to drop dramatically because you had less spoilage.
0:37:58 And the number of people working on a farm today is down to, what, 2% of the U.S. workforce?
0:38:17 So Flash Frozen was really the key innovation that brought down the cost of food from 20% of the wallet to, what is it, 5% or 6% today, and reducing farming labor from more than, I think it was 40% of the peak, down to 2%.
0:38:24 An economist in 1920, okay, let’s just pretend on CNBC in 1920, there is none.
0:38:37 But let’s say there was a CNBC in 1920, and these economists were saying, frozen food, if it comes along and it’s going to wipe out 95% of all farmers, this is going to wipe out the U.S. economy.
0:38:41 The U.S. economy can’t survive frozen food.
0:38:44 And instead, it freed up time, right?
0:38:50 And it created, it allowed people to be repurposed, and it created a completely new labor force.
0:39:01 So I, so Scott, to your point, I think that there is an adverse outcome, but then when I look at past episodes of huge labor disruption, it’s actually had positive outcomes.
0:39:11 Every technology thus far, it’s followed the cycle you’re talking about, some short-term destruction of labor, and then profits and innovation get reinvested, and we reinvent ourselves.
0:39:15 But I want to, we’re about the same age, Tom, I want to walk down memory lane.
0:39:20 In the 90s, I think you were a telco analyst with Kidder and then Solomon, is that right?
0:39:21 Yeah, that’s right.
0:39:28 And I was raising money for internet companies, the internet company that started e-commerce companies in the 90s.
0:39:32 And I can’t help, but this smells a lot like teen spirit.
0:39:48 I feel like I’ve been to this movie, and I have this certain muscle memory, and I might be wrong, but I’m curious if you would, if you think my timeline trues up with your, where you think we are, and that is, the economists perfectly called the dot bomb.
0:39:51 They said how it would happen, how it would unwind.
0:40:04 They were exactly right, but they called it a 97, and the NASDAQ doubled between 97 and 99, and what you said also that really struck was that the market seems to be climbing a wall of worry.
0:40:14 And that is, in 97 and 98, we were just very anxious, these things are overvalued, there’s no way we can sustain this, and the markets kept going up.
0:40:27 And then in 99, we had this zeitgeist where all the short sellers, all the long hedge, all the hedge funds, I mean, Julian Robinson just like threw in the towel and gave up and said, I can’t predict this market.
0:40:38 And then there were all these articles, and I remember one specifically in the Wall Street Journal saying, maybe we have moved to a new economic model that the internet has ushered in, and we should be thinking about things differently.
0:40:42 And then, wham, the market crashed.
0:40:56 So if I were to look back and try and equate this to the 90s and the internet, you know, the internet timeline, it feels like we’re more like 97 and 98, a ton of catastrophizing that might indicate a surge up.
0:41:04 My sense is when prices are, PE multiples are crazy, which I would argue they are right now, they go insane before they crash.
0:41:13 Does this timeline, A, is that even useful to think about, this economic history, and does that timeline sort of where 97, 98, not 99, true up with what you’re thinking?
0:41:14 It does, Scott.
0:41:20 We both have experience of 35 years or more in markets and in technology.
0:41:30 And we have to keep in mind that the median tenure of a portfolio manager today managing a fund is nine years.
0:41:38 So they’ve experienced the markets really only since 2015, you know, being generous.
0:41:49 So to them, the 90s is only a legend that their bosses talked about or things that they heard and it’s become second and third hand stories.
0:42:09 Now, when I was a wireless analyst, there was so much meat to the stories in the 90s, like in the 97 period, like real things happening, TDMA, adoption, the quality of the customers were good.
0:42:12 There was real spending taking place.
0:42:16 It wasn’t startups paying for everything, you know.
0:42:26 But by the late 90s, the customer quality had already essentially, you exhausted the post-pay world.
0:42:28 You had to suddenly go into a prepaid model.
0:42:33 And, you know, technology, we called it bleeding edge.
0:42:39 There was innovation coming, but there wasn’t apps and services to support it, like, you know, picture messaging.
0:42:42 And, you know, these were still years away.
0:42:56 So I’d say that there is going to be a moment where, like, we’ve all gotten so accustomed to stocks going up that we insisted that that’s the new regime.
0:42:58 Like, that’s what you’re talking about in, like, 2000, right?
0:43:00 That’s when everyone capitulated.
0:43:03 But that’s not what I encounter today.
0:43:13 You know, when we talk to our institutional investor clients, this is a market that’s frustrating to them because they don’t really want to be buying and buying expensive stocks.
0:43:16 There’s a lot of discipline in place today.
0:43:23 And I think that that discipline is the reason markets are climbing a wall of worry because I think there’s, as you know, a lot of cash on the sidelines.
0:43:25 Sentiment is still really bearish.
0:43:29 And a lot of people are claiming that we’re at a top.
0:43:35 But again, I just, you know, in my experience, you know, people are not bearish at the tops.
0:43:37 They’re bullish at the tops.
0:43:39 And I don’t really find that many bullish people.
0:43:48 When people talk about the types of jobs that AI will, quite frankly, destroy, I think they’re describing the analysts at Fundstrat.
0:43:52 So tell me what is actually happening on the ground at Fundstrat.
0:43:54 How are you using AI?
0:44:00 And what, if any, impact is it having or do you think is going to have on your human capital?
0:44:14 Wall Street itself has actually been a victim of technology because, you know, first, many investment firms have been using essentially versions of AI systems for a long time, right?
0:44:17 They’ve been investing in quant systems and models.
0:44:24 And the sell-side firms have invested in technology to replace labor constantly.
0:44:33 I don’t think there’s been a year in my career on Wall Street that money wasn’t being spent to actually reduce the labor intensity of the job.
0:44:45 I mean, I remembered when I was at J.P. Morgan, you know, in the 90s and the early 2000s, trading occupied a huge percentage of the cash equities business real estate.
0:44:46 You know, it was a couple floors.
0:44:54 And then one day went to electronic systems and, like, the number of traders went to, like, you know, a tenth of a floor.
0:45:02 So I think that’s the nature of Wall Street, that every job is eventually automated away.
0:45:08 And so value capture is shifting around.
0:45:13 In the 90s, when I started, equity research was a back office job.
0:45:19 You know, it wasn’t, like, because I went to Wharton undergrad and I graduated, I wanted to get into research.
0:45:22 Firms weren’t really actively hiring for research.
0:45:24 Research was an apprenticeship industry.
0:45:29 You had to, like, find a job and find an analyst that would hire you and take you in.
0:45:38 Of course, research has become a much more important business today as other parts of Wall Street became commoditized.
0:45:43 But, you know, when I graduated in the 90s, traders were the highest paid people, the sales trader.
0:45:45 They were, like, the masters of the universe.
0:45:48 And, of course, now it’s just computer code.
0:45:52 So I think you’re exactly right.
0:46:03 In the future, a mediocre research person is not going to be any better than a mediocre open AI or LLM, right?
0:46:08 So Wall Street needs to constantly evolve.
0:46:12 I know that at our company, we are using AI at so many levels.
0:46:17 It’s not just research, and we are using it to ingest data.
0:46:34 But it’s really how we manage our data now and even how we manage our customer service experience, you know, because Fundstrate has 11,000 RAA and family offices plus around 400 hedge fund clients.
0:46:41 So we – but the way we manage them and identify their needs, we are using AI.
0:46:55 So I would say I have not found any of the AI models that have been shown to us and that we trial because everybody wants Fundstrate to adopt one of their models has not been good at stock picking.
0:46:59 We actually run three ETFs.
0:47:06 Fundstrate Capital has granny shots, GRNY, GRNJ, which is a small mid-cap version, and GRNI.
0:47:13 But GRNY has a one-year of history, has outperformed the S&P by 800 basis points this year.
0:47:22 And none of the AI systems that have been shown to us have actually outperformed our own process for stock picking.
0:47:26 What would you say makes a great researcher and a great analyst?
0:47:28 And then I want to get into crypto.
0:47:31 So this is one of my final AI questions.
0:47:42 But when you talk about AI is going to replace the analyst, what are the kinds of skills that makes you irreplaceable as an analyst?
0:47:51 What kinds of things should white-collar workers, working professionals in general, be trying to work on and be trying to hone in order to not be replaced by AI?
0:47:55 AI is very good at looking at the past.
0:48:03 So if you need to build a model, you need to recall data, even say, give me the last 12 times something happened.
0:48:07 That is AI.
0:48:14 But as you know, to do true training, then you need to have it work in the future.
0:48:20 Now, future has not a binomial outcome.
0:48:23 It’s multiple forward scenarios.
0:48:27 And the probabilities are unknown of each future event.
0:48:28 Okay?
0:48:40 So you’re dealing with so much uncertainty that I don’t know how a probabilistic way to give you a single-point answer would ever work.
0:48:49 I mean, if you give me an example, someone will say this is the fair value of a stock and they say this is the PE and this is the E.
0:48:57 And I can never understand that because I’m always wondering, which E are you using?
0:48:57 And you know what I mean?
0:49:02 Like, because price is today, but then which forward metric and then how do you discount it?
0:49:11 How do you explain to AI that you have to look at 10 years of future earnings, but you don’t know which future earnings will actually matter the most?
0:49:13 And then how do you assign the weights?
0:49:23 I mean, that’s really what we do at Fundstrat is we’re constantly assessing the probabilities of future events and then deciding we do have to pick a direction, right?
0:49:25 Then we say this is the path we’re going to take, but it’s a guess.
0:49:41 So I think that the best qualities of a researcher, at least in my opinion, are you do need to be unemotional, but you also need to know the difference between conviction and being stubborn.
0:49:49 And stubborn is riding something when and believing in something when all the facts have changed.
0:49:56 Conviction is basically riding through the volatility and it’s not easy to tell the difference until history has already passed.
0:50:04 And I think, you know, the third thing that’s really important in markets is to know what’s already priced in.
0:50:18 And I don’t know if AI is going to really have a good sense for, like, if all, if AI is the only thing managing money in the future, I think a human will be the market because all the AI systems will be predictable.
0:50:19 You know what I mean?
0:50:21 And then you can spoof them all.
0:50:34 So that’s really where I think human judgment matters because, you know, I’m constantly surveying our clients and I’m in constant touch with them.
0:50:42 So I kind of know where the money is and what the bets are and how they react to the Fed.
0:50:56 And I don’t know how you can program AI unless it is, well, they’ll get very good, but they really have to think not just on a specific outcome, but on a series of future outcomes.
0:50:59 And that’s what we’re always sort of obsessing over.
0:51:02 We’ll be right back.
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0:53:29 We’re back with Prof G Markets.
0:53:33 Speaking of future outcomes, last month, I want to talk about crypto.
0:53:38 Last month, you said you think Bitcoin could go as high as $200,000 in January.
0:53:42 We’re currently at $94,000.
0:53:48 It’s been a rough couple of months for Bitcoin and for crypto.
0:53:52 Where do you stand on Bitcoin right now?
0:53:55 Do you still believe that we could hit $200,000 in January?
0:53:58 What do you make of what’s happening in the crypto markets?
0:54:00 Well, crypto’s had a rough year.
0:54:03 It was actually having a great year until October 10th.
0:54:07 Because on October 10th, Bitcoin was up 36% for the year.
0:54:11 And now it’s currently, I think it’s like flat for the year.
0:54:14 So it lost a lot of its gains.
0:54:24 I’m still very optimistic because crypto still has its best years ahead.
0:54:28 But crypto should have had a good year this year.
0:54:32 And it was on track to until there was a liquidity crisis on October 10th.
0:54:39 That was a bigger wipeout in terms of liquidation than any event in history.
0:54:43 The most recent one before this would have been 2022 with FTX.
0:54:48 And that wipeout pales in comparison to what happened on October 10th.
0:54:52 But in 2022, it took eight weeks before crypto.
0:54:54 The smoke cleared.
0:55:01 The leverage wipeout was enough in the mirror that crypto prices began to recover.
0:55:06 This week is the eighth week since that crisis.
0:55:09 And I think crypto prices are beginning to actually recover.
0:55:15 That’s why I think Bitcoin can double from here by the end of January.
0:55:19 Now, many people don’t expect it because of the four-year cycle.
0:55:22 And that’s going to be the big question.
0:55:28 If Bitcoin breaks $125 in January, there is no four-year cycle.
0:55:31 What did happen on October 10th?
0:55:34 Like, everyone knows crypto markets got hit.
0:55:38 It seems unclear to people what actually happened.
0:55:43 And as you say, it was one of the largest liquidation events that we’ve seen in the history of crypto.
0:55:44 What happened?
0:55:48 I’m going to give you what we’ve been able to piece together.
0:55:58 And I would say that it’s like a 90% correct because, you know, there’s going to be 10% you don’t know if there was something else.
0:56:03 Just to clarify, is the reason we don’t know what’s happening because of the anonymity of crypto?
0:56:04 We don’t know who owns which wallets.
0:56:06 And so it’s harder to track what’s actually happening.
0:56:08 Well, it’s that.
0:56:20 And plus, you know, you know, if when we look at liquidation events like in for in in a stock market, 90% of what someone will explain is probably correct.
0:56:28 But 10% like 90% it would be 90% to correct say that the February to April decline was largely due to the Trump tariffs.
0:56:35 But the other 10% would be like, well, stocks were already expensive and they were overdue for a correction.
0:56:38 So that’s that’s what I mean by 90-10.
0:56:39 So give us your 90%.
0:56:40 What do you think happened?
0:56:45 On October 10th, there was two things that happened.
0:56:54 One was a triggering event, which was Trump announced, you know, a re-escalation of tariffs with China, like a tripling of proposed tariffs.
0:57:07 And because markets were closed, historically, crypto is what reflects reaction to a macro event when you’re already after market hours.
0:57:11 Like if the S&P was open, it probably wouldn’t have been gut punched as hard.
0:57:12 But crypto prices fell.
0:57:21 Now, crypto prices falling, system can handle that because, you know, crypto is a hyper volatile by nature.
0:57:26 So large swings in prices shouldn’t really overload the system.
0:57:33 And even volume shouldn’t overload the system because crypto trades, you know, there’s so much automated trading.
0:57:44 However, there was an algorithm in place that actually what I call a glitch happened on a specific exchange.
0:57:48 Many people use leverage in crypto.
0:57:51 I mean, it attracts leverage trading.
0:57:56 But people put up collateral so they can do leverage trading.
0:57:58 One of the collaterals is stable coins.
0:57:59 OK.
0:58:05 And stable coins are pretty safe collateral because, hey, if it’s Tether, it trades at a dollar.
0:58:07 It’s pretty safe collateral.
0:58:10 And so you can borrow a lot against safe collateral.
0:58:18 And if it was USDC, Circle, that’s also a really stable coin because it’s backed by a dollar.
0:58:23 However, there was a popular stable coin called USDA.
0:58:26 It was an algorithmic stable coin.
0:58:39 And on one particular exchange, because of the shock of Friday, internal prices, like people bid ask of that stable coin actually got out of whack.
0:58:46 Suddenly, the price went to 65 cents, even though it’s supposed to be essentially worth a dollar.
0:58:52 So, but within one exchange that the quoted price dropped to 65 cents.
0:59:01 Well, that meant that all the collateral for every account that used that particular stable coin to borrow money was now in deficit.
0:59:03 OK.
0:59:13 Even if it was just one dollar that traded at that price, it was already putting every single piece of collateral at risk into deficit.
0:59:17 So then something called ADL was triggered, automatic deleveraging.
0:59:25 And so in one exchange, everybody who had who would use that stable coin as collateral basically got wiped.
0:59:26 Their accounts liquidated.
0:59:29 Well, what were those accounts long?
0:59:36 They were long altcoins and all these different cryptos that suddenly got dumped on to spot exchanges.
0:59:47 So then on all these other exchanges, suddenly some cryptos, some altcoins suddenly went down 99% because there was a lot of selling from ADL.
0:59:55 But it triggered a domino effect of all these other exchanges triggering other ADLs because spot prices of all these alts dropped.
1:00:08 So that to me was a glitch because it was like an illiquid quote that didn’t represent a true VWAP triggered an ADL that triggered a cascade of ADLs.
1:00:13 And that led to millions of accounts being literally zeroed out.
1:00:26 It won’t happen again, I’m sure, because in the future, I’m sure they will use a composite set of prices or if there’s a variance between what’s quoted internally versus on spot or if it’s a volume-based measure.
1:00:31 So that’s why I don’t think it would happen again, but that’s what happened on October 10th.
1:00:31 Okay.
1:00:33 We finally got our answer.
1:00:36 I’ve asked this question to many people and I never get a proper answer.
1:00:41 My takeaways from that are that, you know, it seems vulnerable.
1:00:56 This asset class that is supposed to be a hedge in a lot of ways, we are learning in various ways is extremely vulnerable to what seems to be almost nonsensical mechanical glitches.
1:00:59 Which, I guess, that is my takeaway.
1:01:03 In 2020, the price of oil went negative.
1:01:05 Right?
1:01:10 I mean, oil is the most liquid commodity in the world and it traded at a negative price.
1:01:26 So these glitches happen in all markets, but it does happen in crypto because it’s a gigantic place where people are trying to experiment and create, you know,
1:01:31 what they view as free from censorship and interference.
1:01:36 But, of course, there’s – every event is going to bring something new.
1:01:38 And you’re 100% right.
1:01:40 You know, it’s terrible that it happened.
1:01:47 But, again, I remembered oil was negative, you know, and people actually were able to buy negative oil.
1:01:51 They were paid to, like, take oil.
1:01:53 I guess I’ll wait for a negative stock price.
1:01:57 I’d love the opportunity to get, yeah, negative stock.
1:01:58 Remember bonds?
1:02:00 Corporate bonds had a negative yield in Europe.
1:02:07 You were actually paid to own – like, an issuer was paid to issue a bond.
1:02:13 Can you identify any sectors or geographies that you think are dramatically over or undervalued right now?
1:02:25 What I think is small caps because there is real earnings growth now coming, but there is no money flows.
1:02:33 So small caps are a whole group that professional investors can afford to ignore because no one else is buying them.
1:02:36 You know, the amount of active money in small caps is, like, at record lows.
1:02:46 I think financials are also dramatically undervalued, Scott, because – well, this is where we could be wrong.
1:02:50 But in my view, I think the financial sector is actually becoming a tech sector.
1:03:06 That as money is becoming more digital and as AI implementations are heavily, heavily taking place in financial services, it’s going to make – it’s creating an advantage for the companies, the companies who issue capital.
1:03:17 And companies like J.P. Morgan probably, as we just discussed earlier, could really dramatically reduce their dependence on humans, which is their largest expense.
1:03:26 So I think financial companies are increasingly going to look like tech companies, and their multiples may become more like tech multiples.
1:03:34 So that, to me, is one group that in the future could have a 30 P.E., even though they used to trade at 10 times earnings.
1:03:39 We’ve been talking with a lot of different people in the markets.
1:03:44 We’ve been talking with renowned professors, investment strategists, economists.
1:03:46 We talk with a lot of people.
1:03:51 Most of them are somewhat bearish right now.
1:03:56 You are one of the only real bulls that we’ve talked with.
1:04:00 I’ve seen you described online as a perma-bull.
1:04:02 That was what Bloomberg called you.
1:04:06 What do you think about that label?
1:04:14 And how is it that you are bullish right now in a sea of bears?
1:04:16 And what do you think that says about who you are?
1:04:20 I was first called a perma-bull in 2009.
1:04:26 In fact, it was major newspapers that were using it as a mocking term.
1:04:28 Here’s what’s interesting.
1:04:32 16 years later, what was the right call to be?
1:04:34 The optimists have won.
1:04:45 And yet today, if I had to say what proportion of investors are bullish versus bearish, it’s really risen in the last year.
1:04:50 In fact, it’s kind of close to the 2009 levels.
1:04:56 I think people are already betting on the fact that we’re in a bear market.
1:05:07 Now, many people were convinced of that in 2022 because of the Fed hikes, and they just never changed their views three years later.
1:05:19 But as you know, what made people a lot more bearish is also because President Trump is a very unpopular president.
1:05:20 He’s a very divisive figure.
1:05:21 I’m a registered independent.
1:05:28 So I have never tried to let politics be involved in how I view markets.
1:05:38 But I can’t help notice that I think that political lens plays into many of our clients’ views around markets that they tend to view.
1:05:42 When Biden was president, there were a lot of people who were critical of Biden.
1:05:46 I thought the economy—I’ve cared about the economy.
1:05:47 I thought the economy was fine.
1:05:52 I think the economy’s still doing fine under Trump.
1:06:01 So that’s kept me—I use that as one level of coding that I think has kept people bearish.
1:06:11 But, you know, I think America, as long as it’s a place of innovation, and we are because we’re at the center of AI, I think it’s pretty bullish.
1:06:15 But you guys have raised the key point.
1:06:20 I mean, there’s a chance that this AI is a disaster for labor markets.
1:06:26 And if it is, the U.S. will be the least scathed, but everyone’s going to go down.
1:06:31 Tom Lee is the co-founder, managing partner, and head of research at Fundstract Global Advisors,
1:06:33 a leading independent research firm.
1:06:40 He has more than 25 years of experience in equity research and has been top-ranked by institutional investor every year since 1998.
1:06:48 Prior to co-founding Fundstract, he served as J.P. Morgan’s chief equity strategist from 2007 to 2014.
1:06:51 Tom, I wish we could do this for three hours.
1:06:52 Maybe next time we will.
1:06:54 Really appreciate your time.
1:06:55 Thank you.
1:06:56 Thanks, Tom.
1:06:56 Good to see you.
1:06:57 Yeah, next time.
1:07:08 Scott, what did you think?
1:07:11 Yeah, I have a lot of respect for Tom.
1:07:19 Not just because I think he’s a great analyst and does the work, but I just appreciate how measured he is.
1:07:22 He basically says, yeah, that’s a good point.
1:07:23 You could be right.
1:07:38 When these guys go on CNBC and sort of talk their own book and say, no, the market’s going up 20% next year and this is why, he’s very measured around, this is what I think, but I don’t know.
1:07:42 And he acknowledges the other side.
1:07:45 He just strikes me as very reasonable and tempered.
1:07:59 And, you know, I can kind of see why institutions like his research because I think he’s probably got a track record of sort of, you know, I hope most of this is right, but I know some of it’s wrong and buyer beware.
1:08:01 It just strikes me as the adult in the room when he’s making these recommendations.
1:08:05 Like, I think it’s good that we had the bull come on before the end of the year.
1:08:07 We’ve had a lot of bears on.
1:08:10 I think all of them have presented really.
1:08:11 He and Josh Brown.
1:08:12 Josh Brown’s a kind of a bull.
1:08:15 Well, we’ll definitely get him on in 2026.
1:08:21 But, yeah, I think I love his analysis of frozen foods.
1:08:31 I think that was a great example of a technology that had real impacts on the labor force, but ultimately a freed up time and it left us with more productive things to do.
1:08:37 I don’t think that that means that we’re not going to see an impact on individuals’ lives.
1:08:39 I mean, he talked about how, you know, frozen foods happen.
1:08:42 Everyone thought that farmers would go out of business and then we were fine.
1:08:46 It’s like some of those farmers did go out of business and some of those farmers were not fine.
1:08:56 But long term, as an investor, yes, the idea of these technologies creating short term destruction in the labor market shouldn’t worry you too much.
1:09:05 I think the question is, you know, he was talking about his younger, his children using AI and young people using AI, which I think is a fantastic point.
1:09:09 We should be really looking at how are young people using it.
1:09:13 I think the question is, what exactly is the market pricing in?
1:09:23 Because it’s hard to tell if the market is underestimating the potential or overestimating the potential.
1:09:27 And it seems that we haven’t really reached a consensus on this.
1:09:29 And perhaps that’s just the way markets work.
1:09:30 We can’t know.
1:09:33 But I think that is sort of my big question.
1:09:43 It’s like, you know, just how optimistic or pessimistic or neutral on AI is the market really based on current prices.
1:09:55 And then I think the final point that I think was a good point to end on for the end of the year is he says, you know, yes, he expects or he could easily see a 10 to 15 percent correction next year.
1:09:57 And I think that was a little bit scary hearing that.
1:10:01 But then he also points out it’s a long time a year, 52 weeks.
1:10:07 So, he expects that actually we’ll see a rally after then.
1:10:10 So, I think it was a good way to end the year.
1:10:11 Appreciate his perspective.
1:10:13 And we’ll definitely want to talk to him again.
1:10:22 This episode was produced by Clyde Miller and Alison Weiss and engineered by Benjamin Spencer.
1:10:26 Our research team is Dan Shalon, Isabella Kinsel, Chris Nogonohue and Miel Saverio.
1:10:30 Drew Burrows is our technical director and Catherine Dillon is our executive producer.
1:10:32 Thank you for listening to Prof. G Markets and Prof. G Media.
1:10:38 If you like what you heard, give us a follow and join us for a fresh take on markets on Monday.
1:10:57 You have me in kind reunion
1:11:04 As the world turns
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0:01:34 Today’s number 30.
0:01:38 That’s the percentage of U.S. travelers who now use generative AI tools to plan trips.
0:01:39 That’s a true story.
0:01:45 When I enter a foreign country and have to fill out a visa form and it says profession, I put chaos.
0:01:47 Boom!
0:01:47 That’s right.
0:01:48 That’s how I roll, Ed.
0:01:51 I’m an agent of chaos coming in.
0:01:54 Listen to me.
0:01:55 Markets are bigger than us.
0:01:58 What you have here is a structural change in the wealth distribution.
0:01:59 Cash is trash.
0:02:01 Stocks look pretty attractive.
0:02:02 Something’s going to break.
0:02:03 Forget about it.
0:02:05 I have so many travel stories, Ed.
0:02:09 When I was a first-year analyst right out of UCLA at Morgan Stanley.
0:02:11 I don’t know if you know this, but I’m not, like, really good with details.
0:02:15 And before there was GPS, there was maps.
0:02:16 And I lived in L.A.
0:02:21 And my other analyst, Don Larson, we had to go to Stanford for a recruiting trip.
0:02:23 So we’re bombing to the airport.
0:02:27 I take a right turn on La Cienega instead of a left turn.
0:02:30 And finally, Don catches up with me and says, you’re going the wrong way.
0:02:33 Turn around, get to the airport, and we see the plane pull away.
0:02:38 And then we, there’s one every 30 or 60 minutes of San Francisco.
0:02:43 We’re at Stanford lecturing, talking about how great Morgan Stanley fixed income was, which was a total lie.
0:02:45 So we went up there and started lying to people.
0:02:46 It’s the job.
0:02:48 And then a woman comes in and says, is Donald Larson here?
0:02:50 And they said, yeah.
0:02:52 And Don went out and he came back in and he was all upset.
0:03:00 His father had had a heart attack and because the plane we were, we missed went down.
0:03:07 And it was, you probably, you’re too young to remember this, but it changed aviation history because a disgruntled employee got on the plane with a gun.
0:03:11 And employees up until that point didn’t have to go through metal detectors, pilots, and crew.
0:03:16 And he shot the pilot and the plane crashed and everyone on board died.
0:03:16 Oh, my God.
0:03:20 And because I turned to right, we missed the plane instead of a left.
0:03:21 Oh, my God.
0:03:25 I thought to myself, does anyone I know know that I’m even up here?
0:03:28 And it was no, so I didn’t make any calls.
0:03:36 And of course, my friend, Dennis, my roommate from the fraternity, was expecting me and called my mom.
0:03:41 And my mom called my assistant and my assistant looked it up and said, yeah, he was on that flight.
0:03:50 And so I called my mom and my mom had friends over because they thought I had gone down on this plane and she thought she was hallucinating.
0:03:53 Not a hallmark story here.
0:03:56 Not a hallmark story here.
0:04:01 But anyways, Ed, people think I’m inconsiderate because I’m late all the time and I get lost a lot.
0:04:03 I don’t mind missing stuff and being a little bit late.
0:04:04 It’s worked out for me.
0:04:06 It’s worked out for me.
0:04:09 You’re focused on the important stuff.
0:04:10 That’s what matters.
0:04:13 But, yeah, that is crazy.
0:04:17 I’m just imagining you driving a car right now, which I can’t picture.
0:04:20 And I wonder, are you a good driver?
0:04:23 Because I know you don’t drive anymore, really.
0:04:24 I’m a great driver because I grew up in L.A.
0:04:29 And I started driving literally at the age of 15 and a half.
0:04:30 I got my learner’s permit.
0:04:38 And then back at California Dreaming Culture in California was, I’m not exaggerating, I got my driver’s license on my 16th birthday.
0:04:46 And it just freaks me out that my son right now is technically, what is he, nine months away from driving, which makes no sense.
0:04:51 But, yeah, when you’re in L.A., you just drive everywhere all the time.
0:04:56 So it wasn’t that I was especially deaf to driving, but you just get very well practiced.
0:04:57 When’s the last time you drove?
0:04:58 That’s really interesting.
0:04:59 It’s probably been a couple years.
0:05:00 Yeah, I don’t drive.
0:05:01 But I can drive stick.
0:05:03 I can drive a big rig.
0:05:03 Wow.
0:05:04 I love cars.
0:05:06 When you grow up in California, you love cars.
0:05:12 I just don’t – I hate shoelaces, passwords, keys, and cars because they all demand things from me.
0:05:17 Also, most of my relationships are now starting to ask for something in return, which is really bumming me out.
0:05:21 That’s not why we’re here.
0:05:29 The key term is service, specifically acts of service from you to me.
0:05:30 And I pay for everything.
0:05:32 That’s the deal.
0:05:35 Talk to me about cars, Ed.
0:05:35 Do you own a car?
0:05:36 I don’t own a car.
0:05:37 My girlfriend does, though.
0:05:43 And we’ve – so I drive around with her car, and it really is sort of a game changer.
0:05:44 What kind of car does she have?
0:05:45 Subaru Outback.
0:05:49 So she’s a lesbian.
0:05:50 Yeah, exactly.
0:05:51 Sorry, Ed.
0:05:53 Claire, should we tell them?
0:05:56 I had the same reaction in my head.
0:05:58 And let me get it.
0:06:01 She doesn’t want to have kids, but you’re going to get a German Shepherd puppy.
0:06:07 Do you want my car history?
0:06:07 Yes.
0:06:11 My first car was the best gift I have ever received, hands down.
0:06:13 Best material item that has meant more to me than anything.
0:06:15 And I have a lot of nice material items.
0:06:21 When I was 15 – when you lived in California, if you didn’t have a car, you had no social life.
0:06:23 There was no – there was no Subway.
0:06:24 There was no Uber.
0:06:27 We had the RTD, which was just awful.
0:06:31 And so if you wanted to have any social life, you had to have a car.
0:06:37 And my friend Adam got a Fiat Spider, and then he bought an Austin Healey Mark 7.
0:06:39 He was like fucking James Bond.
0:06:41 He was this good-looking guy in a leather coat.
0:06:43 I didn’t have the money for a car.
0:06:48 And my mom borrowed money to buy an Acura, and she gave me her lime green Opel Manta.
0:06:49 And I remember the day she came home.
0:06:51 We used to practice driving it.
0:06:56 She’d come home and go into the underground garage in our apartment complex and honk the horn.
0:06:58 And I’d run down, and she’d teach me how to drive stick.
0:07:06 And on my 16th birthday, she came home in this new, like, bad Acura, and she came up to me and put her hands on my shoulders and said,
0:07:08 you’re a handsome man who owns his own car now.
0:07:10 And she gave me the keys to her Opel Manta.
0:07:11 That’s nice.
0:07:12 Oh, I’m going to cry.
0:07:13 Isn’t that nice?
0:07:13 Yeah.
0:07:15 Anyways, I had that.
0:07:16 Then I had a Renola car.
0:07:17 Then I had a rabbit.
0:07:22 Speaking of closeted heterosexuals, convertible rabbit.
0:07:26 My girlfriend in college dated a guy with a convertible rabbit.
0:07:27 I’m like, okay, should we tell her?
0:07:40 Anyways, and then out of business school, hit it pretty early, got the Lexus GS300, which was the bad Lexus that never had a market, but it was a Lexus, and I was super excited.
0:07:51 Then I had three BMW 7 Series in a row, including Jason Stabbers, who used to work with us here at Prop G, used to house it for me.
0:07:55 And he calls and says, I’m afraid we’re on vacation.
0:07:57 We used to go to Hawaii because we lived on the West Coast.
0:08:01 Well, I got in a terrible auto accident.
0:08:05 I ran the car into the side of, I think it was Grace or San Ains Church.
0:08:06 He swerved out of the way.
0:08:12 Jason totaled your BMW.
0:08:16 Yeah, Jason Stabbers told, he totaled my first 7 Series.
0:08:18 He doesn’t bring that up much anymore.
0:08:19 You know, we do employee reviews.
0:08:20 You’re about to get yours tomorrow.
0:08:23 By the way, we’re asking you for money back.
0:08:24 You’re not getting a bonus.
0:08:31 But I remember, I couldn’t wait to do his review because I had it as the first bullet point in his review.
0:08:33 Total boss’s car.
0:08:38 Anyway, so he goes, I’ve had this terrible accident.
0:08:40 Da-da-da, your car’s gone.
0:08:41 I’m like, just to stop right there.
0:08:43 I’m like, the important question is the following.
0:08:44 How was the car?
0:08:49 Ed, are we done with banter?
0:08:50 Let’s call it there.
0:08:51 We’ve got a big interview to get into here.
0:08:58 All right, let’s get into our conversation with Tom Lee, co-founder, managing partner and head of research at Fundstrat Global Advisors.
0:09:00 Tom, thank you for joining us.
0:09:02 Great to see you and Merry Christmas.
0:09:06 All right, let’s pass right into it.
0:09:12 You’ve been vocal that investors are still underestimating how strong 2026 can be.
0:09:14 Why are you so bullish on 26?
0:09:18 I think the economy and stocks have been suppressed for the past few years.
0:09:25 Part of it is, of course, that we’ve seen six, what I call, extinction events take place in markets.
0:09:39 Everything from COVID to the bullwhip supply chain effect as the economy restarted to the fastest inflation cycle in history and then followed by the fastest Fed hikes in history.
0:09:51 And then we’ve, of course, had a very controversial administration which put tariffs in place in April of this year that caused a miniature bear market.
0:09:55 And then we’ve even had U.S. bombing Iran’s nuclear facilities.
0:10:09 I think all of these collectively have made investors very nervous about what I call investing in full risk because these are what six black swans that happened in four years.
0:10:18 And I think on top of that, we’ve had a Fed that has not really given a green light about monetary easing.
0:10:27 And I think the Fed’s reluctance has actually suppressed business, quote, animal spirits because the ISM has been below 50 for more than three years now.
0:10:35 So I think that’s all been a business cycle that has been pretty good, but not one that has been really expansionary.
0:10:37 And I think that starts to happen next year.
0:10:54 It feels as if the market has become so concentrated or dependent or circling around a small number of stocks that to be bullish on 26 sort of mandates that you’re – well, tell me if you think this is true – indicates that you’re bullish on the Magnificent 10 and AI stocks.
0:10:55 Is that necessarily true?
0:10:58 And are you bullish on the Magnificent 10?
0:11:08 Yeah, I mean, I think 2026 is going to look a lot like this year, meaning we are probably going to have many months where the market is actually down year to date.
0:11:12 You know, I mean, this year we were down double digits at one point before the market recovered.
0:11:23 I think that plays out next year, but for the reason I previously stated, I think that we end up a bullish outcome despite all the skepticism.
0:11:36 And it does require large-cap tech and AI stocks to still produce earnings growth and not have a lot of P.E. reduction so that you still get positive return.
0:11:52 But I think the rest of the stock markets or the other 490 or so can actually perform well because if the Fed is cutting and interest rates are coming down and the business cycle is sort of really starting, that’s good for other stocks.
0:12:00 As you say, we’ve seen a lot of these black swan events, things that you would think would freak the markets out and make people worried.
0:12:11 And yet, I look at what has happened in the markets and my view of it is, it’s not necessarily that it’s suppressing sentiment.
0:12:15 To me, it almost looks like the market is deciding to shrug everything off.
0:12:28 So when you describe how, you know, perhaps these black swan events have made investors perhaps have less, lower risk appetite, to me, I’m almost kind of taking the other side of that in my head.
0:12:31 I’m like, it seems as if these things happen, these things that are concerning.
0:12:35 One example would be what we’re seeing with these AI circular deals.
0:12:42 And it seems to me that the market is kind of swanning it away, shrugging it off, and the market continues to climb.
0:12:47 And that’s what we’ve seen this year and we saw the previous year and we saw the year before that.
0:12:53 We continue to have this bull market, despite what many would say are really concerning events.
0:12:56 So how would you think about that?
0:12:57 How would you respond to my concerns there?
0:13:03 You’re almost kind of mirroring what we’re observing, but just with a different take.
0:13:07 I mean, our take is markets climb a wall of worry.
0:13:14 You know, historically, when there’s a lot of skepticism, stocks can rise.
0:13:18 In fact, you know, markets actually peak on good news.
0:13:21 You know, they don’t peak when people are bearish.
0:13:25 Markets peak when everyone’s bullish and it no longer responds to good news.
0:13:28 Just like markets bottom on bad news.
0:13:33 And I think many people are skeptical, but stocks have risen.
0:13:37 It doesn’t really mean markets are shrugging off the concerns.
0:13:39 That’s one way to interpret it.
0:13:45 My other interpretation is, you know, there is a wall of skepticism.
0:13:52 And I mean, maybe that’s just the, it is like, maybe we’re just talking about the same sides, just the same thing.
0:13:58 And, but, you know, like if, if someone asked me, are we, is it worrisome?
0:14:01 You know, I was a technology analyst in the 90s.
0:14:04 So I covered wireless stocks starting in 93.
0:14:12 And I witnessed the bubble that was created, you know, a decade in the making, really two decades in the making.
0:14:24 And by 99, not only were, was there no skepticism, there was, you know, excessive entitlement.
0:14:28 Investors were expecting stocks to do explosively.
0:14:35 And, you know, a 20% upside wasn’t satisfactory and valuations were already elevated and expanding.
0:14:49 So I’d say that if I was trying to compare this to the bubble of the 90s and, you know, and wireless was a central cast character in that internet infrastructure build, we’re, I don’t really see the echoes of that today.
0:14:58 I think it’s so interesting what you say there about, you know, we’re looking at the same things and we’re drawing slightly different conclusions about what that means for markets.
0:15:06 And we had a similar conversation with, actually, I mean, you were the chief equity strategist at J.P. Morgan.
0:15:09 We spoke with their head of investment strategy, Michael Semblist.
0:15:11 And we were talking about a lot of these issues.
0:15:19 And where he ultimately landed was, he was quite bearish or bearish-ish.
0:15:24 And I just want to play you what he said and get your reaction, see what you think.
0:15:34 It would be kind of shocking if you didn’t have some kind of profit-taking correction in 2026 at some point on the order of 10% to 15%.
0:15:38 It would be, I’d be, I’d be really surprised not to see that.
0:15:44 So that’s his base case is some sort of correction, 10% to 15%.
0:15:50 Looking at your 2026 outlook, you’ve got S&P price target of $7,700.
0:15:59 We’re at $68.50, so that would imply, you know, a little over 10% rise next year.
0:16:02 So two very different views.
0:16:05 Where do you land compared to his view?
0:16:07 What do you think he might be overlooking?
0:16:09 And where do you differ, do you think?
0:16:14 Our outlook actually does call for a drawdown next year.
0:16:18 So very similar to this year of probably closer to 20%.
0:16:26 So I think we are going to have another miniature bear market next year, but then we’re going to recover.
0:16:28 I mean, let’s take 2025.
0:16:37 Let’s say that at the end of last, at the end of 2024, and actually we did talk about, you know, the idea of a drawdown in 2025.
0:16:44 But let’s say that someone plays the clip, so let’s say it’s Michael Semblis, but you don’t, let’s just pretend he’s saying at the end of 2024.
0:16:47 And he says the mark will be down 10% to 15%.
0:16:54 That doesn’t rule out where we are by the end of 2025 because, in fact, we did have a drawdown.
0:17:03 And I think I’m not, again, saying, I actually think Michael and I are pretty aligned in the sense that I think there is going to be a drawdown next year.
0:17:10 He says he wouldn’t be surprised, but to me, it doesn’t mean that’s the end of the actual bull market.
0:17:13 And in fact, I think stocks fully recover.
0:17:23 So the wall of worry here that we’re talking about and that you outline in your outlook, you’ve got several elements in there.
0:17:28 You’ve got, and these are the things that you describe as people are worried about, investors are worried about.
0:17:35 So politically divided nations, social unrest, Supreme Court overturns tariffs, new Fed chair.
0:17:39 And then you have two ones here that I really agree with.
0:17:42 I feel like I’d love to have you dive in on.
0:17:45 AI valuations, which I think a lot of people are concerned about.
0:17:51 And then also 20% equity returns in the past three years, i.e.
0:18:02 Each year for the past three years, the stock market has risen by an average of 20%, which may imply maybe we’re running out of steam at some point.
0:18:06 Could you just unpack what your concerns are in that wall of worry?
0:18:11 And do you think those would be the trigger of such a correction?
0:18:14 Let’s start with the one that you just mentioned.
0:18:18 The stock market, you know, we’re up 16%.
0:18:25 So I think if we rally three percentage points, we’ll be three years of 20% gains back to back.
0:18:32 And it’s actually more common than we realize.
0:18:44 In fact, when we look at the last 65 years, you know, it’s happened in 20 different, I think it’s happened 20 times in different countries and multiple times in the U.S.
0:18:45 I’m sorry, 12 times.
0:18:48 It means a lot of good news is priced in.
0:18:54 I mean, of course, you know, stocks being up 20% a year, three years in a row, it’s definitely pricing in a lot of good news.
0:18:59 So to me, I do think that we have to consolidate those gains.
0:19:05 And that’s why I think a drawdown next year makes perfect sense to me.
0:19:15 But because there isn’t a lot of leverage in the economy, you know, household sector has not really borrowed money.
0:19:16 It’s been expensive to borrow money.
0:19:22 And even margin debt, it’s risen, but it hasn’t risen parabolically.
0:19:25 It’s actually essentially tracked S&P gains.
0:19:34 So it’s not like people are borrowing faster than the market’s been going up, especially if you look at a five-year CAGR.
0:19:45 So I would be in the camp that as long as the economy is holding up, that drawdown is going to be viewed as a buying opportunity.
0:20:03 Now, on AI valuations, it makes perfect sense for someone to say a lot of the valuations for AI are probably absurd because this is the nature of an exponential sector, right?
0:20:16 If we look at an industry that could grow parabolically for 10 years, all of the future value is in the latter half of those years, right?
0:20:21 So it’s – and then we’re trying to discount that back to today.
0:20:25 And so stocks are going to look absurdly expensive.
0:20:40 And more importantly, investors make a common mistake, which is that they assume that the existing universe of companies are going to be the central cast characters over the next 10 years, which is not the case.
0:20:52 So the reason valuations don’t make sense today is that, one, of all the AI stocks, I’d say it’s probably safe to say only 10% are going to be good investments.
0:20:54 Maybe it’s even generous, maybe 5%.
0:21:00 And, of course, there’s going to be a new emergence set of new players.
0:21:02 And, in fact, the economic model might change.
0:21:05 But it doesn’t mean it’s a bad investment.
0:21:09 And we’ve highlighted this as generational traits in past reports.
0:21:28 For instance, like if you look at the internet, if you bought the internet basket in 99, okay, so you bought it near the peak, and you held it to today, you actually still outperformed the S&P 500, even though 99% of the stocks went to zero.
0:21:34 So it wasn’t – it was a bad investment if you tried to pick a winner.
0:21:37 But it wasn’t so bad if you held it as a basket.
0:21:45 So I think AI, it’s probably going to be fair to say 90% of the stocks are going to be – do way worse than people expected.
0:21:46 They were too optimistic.
0:21:48 But I think as a basket, it’s probably going to outperform.
0:21:50 That all makes sense to me.
0:21:51 I’m with you.
0:22:01 But it seems to be a little bit more nerve-wracking when we realize that a lot of the AI companies are the largest companies in the world.
0:22:02 It’s the big tech companies.
0:22:06 I mean, I think Google is an AI company at this point, or an AI stock.
0:22:08 Meta, NVIDIA.
0:22:12 I mean, these are the largest, most valuable companies in the world.
0:22:16 And the market really depends on their performance.
0:22:39 So when I think about the idea that, you know, many of these companies and the expectations that have been pinned to the AI cycle, the fact that that could affect some of the largest, most valuable companies in the world, where we’re seeing the highest concentrations in those small companies, the higher concentration than we’ve ever seen in history.
0:22:42 To me, that makes it scarier, what you just said.
0:23:01 So I guess my question is, do those companies, do the big tech companies, the Magnificent Seven, do they count in your analysis of AI valuations being too high and the possibility that perhaps we might lose out or that the value won’t actually show up for many of these companies?
0:23:06 I might even just add to your concern, because there’s a lot of capex here, too.
0:23:13 So that, these are, you know, a lot of the mag-7 used to be asset-light businesses.
0:23:23 You know, they, the remarkable equity, these are rent-seeking model of them, was that they could create growth with very little spending.
0:23:27 I mean, R&D spending was there, but really capex was not there.
0:23:43 But today, as you know, AI is extremely capital-intensive and it’s energy-intensive, and it’s only justifiable if it’s replacing real work somewhere else.
0:23:50 Then you can justify it, because now it’s creating assets to replace future opex, you know?
0:24:02 I’m going to give you a spin about what’s happening that is not disagreeing with what you’re saying, but it’s probably observing a change in the reality, okay?
0:24:26 By the way, we wrote about that in 2018, that if you go back to 1930 and you just use simple demography, okay?
0:24:40 So, the population tables, whenever the population growth rate grows faster than the prime-age workforce, which means you have compounded labor deficit, you’ve always had a technology cycle.
0:24:46 That is 1948 to 67, in 1991 to 99.
0:24:55 In both of those periods, the population growth rate was growing fast, which is demand faster than worker supply.
0:25:06 And we entered the third epoch, or era, of labor shortage, which started in 2018, and it’s going to last to 2035.
0:25:16 So, then technology spend is necessity, because you don’t have as much labor available, so there’s going to be less wage spend.
0:25:38 Now, if I substituted the word and called this, instead of the word banks, tech companies, I called them financial institutions, we would not be saying there’s a financial institution bubble, because for every level, for every unit of GDP growth, there’s a unit of financial spend.
0:25:41 I mean, it’s literally the other part of the ledger.
0:25:44 And, in fact, the financial industry has all circular spending.
0:25:45 I mean, think about this.
0:25:50 Real estate is valued as a separate asset, but every company needs real estate just to run a business.
0:25:51 So, why are we valuing real estate?
0:25:56 Like, in a GDP sense, real estate should be an interim product, not a final product.
0:26:11 So, I think tech is becoming so central to the economy, especially because of labor shortage, that we’re—when we see tech intensity growing, people are flagging that as a bubble.
0:26:16 Whereas, I’m actually just pointing out it’s actually out of economic necessity.
0:26:26 But it becomes a bubble if the multiple we’re applying to the tech streams don’t justify higher valuation.
0:26:31 I think tech earnings are probably more valuable than Costco, for instance.
0:26:32 Yeah, agreed.
0:26:33 Or Walmart, right?
0:26:39 But, you know, Walmart trades at 37 times board earnings, and Costco trades at 50 times.
0:26:41 So, NVIDIA trading at 27.
0:26:48 I mean, is there a bubble in Costco and Walmart because NVIDIA’s at 27 times earnings?
0:26:49 100%.
0:26:52 And we’ve looked at that Costco valuation.
0:26:53 It’s crazy.
0:26:54 I totally agree.
0:26:59 But then I go back to what—something that Aswath Damodaran said when he joined us on the podcast.
0:27:03 And he said he can’t see value anywhere.
0:27:07 He thinks everything is overvalued when he looks at the stock market.
0:27:09 So, that’s the other side.
0:27:14 And as Scott and I have discussed, you know, we’re not necessarily in agreement with him on all of that.
0:27:18 But that, I think, becomes a concern.
0:27:38 And then to the circular dealmaking and the CapEx point, I think the concern, unlike the financial institutions, is like, we haven’t seen the AI product proven itself yet in terms of its ability to provide the value that we’re pricing in, I guess, is the problem.
0:27:51 We haven’t seen that these data centers, one, I mean, are even going to be necessary to keep the workforce and the labor market going, as you say, to keep our economy growing at a fast clip.
0:28:04 Therefore, it seems that we’re making giant, giant predictions with not that much evidence, which, you know, we could call it a bubble or we can just call it what I said, which is we don’t really know what’s happening.
0:28:06 And yet we’re spending tons of money.
0:28:14 And so, if there becomes a moment where suddenly everyone says, wasn’t what we thought it was, then that could be quite damaging to portfolios.
0:28:16 Yeah, 100% agree.
0:28:24 Because, by the way, anything that is relying on future growth, none of us is an expert on the future.
0:28:28 I mean, that’s, right, like that there’s many roads to the future.
0:28:38 One thing I just want to point out, Benjamin Graham’s book, The Intelligent Investor, which I did read, I don’t know if you remembered his rule of thumb about what a proper P.E. is.
0:28:39 No, please.
0:28:43 It’s 12 times plus two times the growth rate.
0:28:45 That is in his book.
0:28:52 I’m just saying, when someone says they’re a value investor and they’re saying stocks are expensive, I know they didn’t read his book because I read the book.
0:28:59 But, you know, as you know, that’s because he didn’t believe things could grow 10% a year.
0:29:04 You know, that’s three, like 10% is a lot of growth back then.
0:29:06 Yeah, that was a pre-digital economy.
0:29:13 The second point I would make is when I did wireless in the 90s, okay, now I was in my 20s.
0:29:21 I was a senior analyst at the age of 23, so I was really lucky to be very young and actually a senior equity analyst.
0:29:28 But when I was covering wireless, the industry only had 34 million cell phones in 1993.
0:29:38 And the industry telecom services was dominated by long distance and local telephony, these things called the Bell operating companies.
0:29:41 And they made all their money from two businesses.
0:29:47 The Bells made the biggest profit maker for the telephone companies was the directory business.
0:29:50 And number two was local business telephony.
0:29:56 They made more money selling local exchange service to Chase than they did from any other business.
0:30:05 So when wireless was happening in the 90s, as me in my 20s, my imagination was ignited.
0:30:09 And, you know, I talked about how, you know, you could do so many more things with cell phones.
0:30:17 And we joked about how it would have changed the path of like the Revolutionary War, right, if Paul Revere had a cell phone.
0:30:25 But most of the money managers were in their 40s and 50s, and all the experts were in their 40s and 50s.
0:30:30 And they mostly thought cell phones was an expensive yuppie toy.
0:30:32 They said the economics didn’t make sense.
0:30:36 You could never fit that much traffic on cellular waves.
0:30:40 And all the money was in long distance and local.
0:30:53 So the telephone companies and the long distance providers, including MCI, would do everything to protect their existing businesses and use regulatory strengths to make sure cellular never really grew.
0:30:59 Now, look back, that was, of course, the wrong bet.
0:31:06 And remember, cellular companies had to build, they had to spend $50 per pop to build out a cellular system.
0:31:14 So if you took any city of 10 million people, you had to spend $50 per person just to build a basic system.
0:31:16 It was enormously expensive.
0:31:19 And cell phone penetration was 6%.
0:31:21 You had to make a bet that you would have a lot of penetration.
0:31:27 I think that I’m seeing people make the same arguments against AI.
0:31:33 And I think one of the things we have to say is, which lens are you using?
0:31:45 If you’re using it through the lens of a 40-year-old, really wealthy person, no new technology looks interesting to you because you’re more interested in protecting your wealth and incumbency.
0:31:48 But young people are the ones who change the world.
0:31:50 Look at Chase Institute.
0:31:54 Credit card spending growth only comes from people under age 50.
0:31:58 And what are young people doing with AI?
0:32:03 I mean, my daughter is, one of my daughters is in college, my youngest.
0:32:14 They have adapted to open AI and chat GBT in a way that I can’t even fathom.
0:32:20 And so those people represent the future vintage of AI adoption.
0:32:26 Just like cellular adoption in the 90s, it was 70% of 20-year-olds had a cell phone.
0:32:29 And it was like 5% of 60-year-olds.
0:32:35 So, of course, all my clients who are in their 50s and 60s said, you know, who needs a cell phone?
0:32:38 They didn’t realize that those 20-year-olds become 60.
0:32:42 And that 70 became 90.
0:32:43 And soon everybody had a cell phone.
0:32:53 So, I think we have to be more – we have to think about how the 14-year-olds using these models compared to us.
0:32:57 Because we’re already – you know, we’ve already lived our lives and we’ve established our regimes.
0:33:02 And our – so, it doesn’t mean that AI stocks are correctly valued.
0:33:07 I’m just saying we have to really understand that the future change is coming from young people.
0:33:10 We’ll be right back after the break.
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0:35:41 We’re back with Prof G Markets.
0:35:45 You said something, Tom, that really stuck out to me.
0:35:50 You said that we’re in this labor shortage cycle from 2018 that will last through 2035.
0:35:57 And you can’t avoid the catastrophizing around the destruction in the labor force from AI.
0:36:02 Do you still believe we’re going to be in a cycle of labor shortage, even with AI?
0:36:08 I vacillate because there’s times where I’m like, when I read a book like The Coming Wave,
0:36:14 I panic and I realize like, wow, like we need to re-educate society.
0:36:31 But one thing that gives me hope is that we did at Fundstrat study another technological wave that wiped out at least 20% of the labor force in the 20th century, which was frozen foods.
0:36:44 So what many people don’t realize is that when Charles Birdseye created Flash Frozen, which, by the way, was a venture backed by Goldman Sachs.
0:36:45 It was a VC backed company.
0:36:46 I love this.
0:36:50 And by the way, his name is Birdseye because he was an ornithologist.
0:36:51 He actually was studying birds.
0:37:04 But he found that the Inui tribe in Alaska had kept their fish super fresh because they were putting it in a frozen saltwater solution that flash froze the fish.
0:37:12 If we look at the labor tables from the 20s, 40% of the U.S. labor force was employed on farms.
0:37:18 It was literally, we spent, most of the economy was people working on farms.
0:37:25 And most of the service sector that was defined back then were household servants, people working for someone else.
0:37:37 Food was over 25% of the wallet prior to frozen foods being widely, you know, mass market because most food spoiled on the way to the supply chain.
0:37:40 And so grocery aisles were mostly fresh.
0:37:44 And what was frozen back then had a freezer burn.
0:37:45 It was terrible.
0:37:52 So Flash Frozen allowed suddenly the cost of food to drop dramatically because you had less spoilage.
0:37:58 And the number of people working on a farm today is down to, what, 2% of the U.S. workforce?
0:38:17 So Flash Frozen was really the key innovation that brought down the cost of food from 20% of the wallet to, what is it, 5% or 6% today, and reducing farming labor from more than, I think it was 40% of the peak, down to 2%.
0:38:24 An economist in 1920, okay, let’s just pretend on CNBC in 1920, there is none.
0:38:37 But let’s say there was a CNBC in 1920, and these economists were saying, frozen food, if it comes along and it’s going to wipe out 95% of all farmers, this is going to wipe out the U.S. economy.
0:38:41 The U.S. economy can’t survive frozen food.
0:38:44 And instead, it freed up time, right?
0:38:50 And it created, it allowed people to be repurposed, and it created a completely new labor force.
0:39:01 So I, so Scott, to your point, I think that there is an adverse outcome, but then when I look at past episodes of huge labor disruption, it’s actually had positive outcomes.
0:39:11 Every technology thus far, it’s followed the cycle you’re talking about, some short-term destruction of labor, and then profits and innovation get reinvested, and we reinvent ourselves.
0:39:15 But I want to, we’re about the same age, Tom, I want to walk down memory lane.
0:39:20 In the 90s, I think you were a telco analyst with Kidder and then Solomon, is that right?
0:39:21 Yeah, that’s right.
0:39:28 And I was raising money for internet companies, the internet company that started e-commerce companies in the 90s.
0:39:32 And I can’t help, but this smells a lot like teen spirit.
0:39:48 I feel like I’ve been to this movie, and I have this certain muscle memory, and I might be wrong, but I’m curious if you would, if you think my timeline trues up with your, where you think we are, and that is, the economists perfectly called the dot bomb.
0:39:51 They said how it would happen, how it would unwind.
0:40:04 They were exactly right, but they called it a 97, and the NASDAQ doubled between 97 and 99, and what you said also that really struck was that the market seems to be climbing a wall of worry.
0:40:14 And that is, in 97 and 98, we were just very anxious, these things are overvalued, there’s no way we can sustain this, and the markets kept going up.
0:40:27 And then in 99, we had this zeitgeist where all the short sellers, all the long hedge, all the hedge funds, I mean, Julian Robinson just like threw in the towel and gave up and said, I can’t predict this market.
0:40:38 And then there were all these articles, and I remember one specifically in the Wall Street Journal saying, maybe we have moved to a new economic model that the internet has ushered in, and we should be thinking about things differently.
0:40:42 And then, wham, the market crashed.
0:40:56 So if I were to look back and try and equate this to the 90s and the internet, you know, the internet timeline, it feels like we’re more like 97 and 98, a ton of catastrophizing that might indicate a surge up.
0:41:04 My sense is when prices are, PE multiples are crazy, which I would argue they are right now, they go insane before they crash.
0:41:13 Does this timeline, A, is that even useful to think about, this economic history, and does that timeline sort of where 97, 98, not 99, true up with what you’re thinking?
0:41:14 It does, Scott.
0:41:20 We both have experience of 35 years or more in markets and in technology.
0:41:30 And we have to keep in mind that the median tenure of a portfolio manager today managing a fund is nine years.
0:41:38 So they’ve experienced the markets really only since 2015, you know, being generous.
0:41:49 So to them, the 90s is only a legend that their bosses talked about or things that they heard and it’s become second and third hand stories.
0:42:09 Now, when I was a wireless analyst, there was so much meat to the stories in the 90s, like in the 97 period, like real things happening, TDMA, adoption, the quality of the customers were good.
0:42:12 There was real spending taking place.
0:42:16 It wasn’t startups paying for everything, you know.
0:42:26 But by the late 90s, the customer quality had already essentially, you exhausted the post-pay world.
0:42:28 You had to suddenly go into a prepaid model.
0:42:33 And, you know, technology, we called it bleeding edge.
0:42:39 There was innovation coming, but there wasn’t apps and services to support it, like, you know, picture messaging.
0:42:42 And, you know, these were still years away.
0:42:56 So I’d say that there is going to be a moment where, like, we’ve all gotten so accustomed to stocks going up that we insisted that that’s the new regime.
0:42:58 Like, that’s what you’re talking about in, like, 2000, right?
0:43:00 That’s when everyone capitulated.
0:43:03 But that’s not what I encounter today.
0:43:13 You know, when we talk to our institutional investor clients, this is a market that’s frustrating to them because they don’t really want to be buying and buying expensive stocks.
0:43:16 There’s a lot of discipline in place today.
0:43:23 And I think that that discipline is the reason markets are climbing a wall of worry because I think there’s, as you know, a lot of cash on the sidelines.
0:43:25 Sentiment is still really bearish.
0:43:29 And a lot of people are claiming that we’re at a top.
0:43:35 But again, I just, you know, in my experience, you know, people are not bearish at the tops.
0:43:37 They’re bullish at the tops.
0:43:39 And I don’t really find that many bullish people.
0:43:48 When people talk about the types of jobs that AI will, quite frankly, destroy, I think they’re describing the analysts at Fundstrat.
0:43:52 So tell me what is actually happening on the ground at Fundstrat.
0:43:54 How are you using AI?
0:44:00 And what, if any, impact is it having or do you think is going to have on your human capital?
0:44:14 Wall Street itself has actually been a victim of technology because, you know, first, many investment firms have been using essentially versions of AI systems for a long time, right?
0:44:17 They’ve been investing in quant systems and models.
0:44:24 And the sell-side firms have invested in technology to replace labor constantly.
0:44:33 I don’t think there’s been a year in my career on Wall Street that money wasn’t being spent to actually reduce the labor intensity of the job.
0:44:45 I mean, I remembered when I was at J.P. Morgan, you know, in the 90s and the early 2000s, trading occupied a huge percentage of the cash equities business real estate.
0:44:46 You know, it was a couple floors.
0:44:54 And then one day went to electronic systems and, like, the number of traders went to, like, you know, a tenth of a floor.
0:45:02 So I think that’s the nature of Wall Street, that every job is eventually automated away.
0:45:08 And so value capture is shifting around.
0:45:13 In the 90s, when I started, equity research was a back office job.
0:45:19 You know, it wasn’t, like, because I went to Wharton undergrad and I graduated, I wanted to get into research.
0:45:22 Firms weren’t really actively hiring for research.
0:45:24 Research was an apprenticeship industry.
0:45:29 You had to, like, find a job and find an analyst that would hire you and take you in.
0:45:38 Of course, research has become a much more important business today as other parts of Wall Street became commoditized.
0:45:43 But, you know, when I graduated in the 90s, traders were the highest paid people, the sales trader.
0:45:45 They were, like, the masters of the universe.
0:45:48 And, of course, now it’s just computer code.
0:45:52 So I think you’re exactly right.
0:46:03 In the future, a mediocre research person is not going to be any better than a mediocre open AI or LLM, right?
0:46:08 So Wall Street needs to constantly evolve.
0:46:12 I know that at our company, we are using AI at so many levels.
0:46:17 It’s not just research, and we are using it to ingest data.
0:46:34 But it’s really how we manage our data now and even how we manage our customer service experience, you know, because Fundstrate has 11,000 RAA and family offices plus around 400 hedge fund clients.
0:46:41 So we – but the way we manage them and identify their needs, we are using AI.
0:46:55 So I would say I have not found any of the AI models that have been shown to us and that we trial because everybody wants Fundstrate to adopt one of their models has not been good at stock picking.
0:46:59 We actually run three ETFs.
0:47:06 Fundstrate Capital has granny shots, GRNY, GRNJ, which is a small mid-cap version, and GRNI.
0:47:13 But GRNY has a one-year of history, has outperformed the S&P by 800 basis points this year.
0:47:22 And none of the AI systems that have been shown to us have actually outperformed our own process for stock picking.
0:47:26 What would you say makes a great researcher and a great analyst?
0:47:28 And then I want to get into crypto.
0:47:31 So this is one of my final AI questions.
0:47:42 But when you talk about AI is going to replace the analyst, what are the kinds of skills that makes you irreplaceable as an analyst?
0:47:51 What kinds of things should white-collar workers, working professionals in general, be trying to work on and be trying to hone in order to not be replaced by AI?
0:47:55 AI is very good at looking at the past.
0:48:03 So if you need to build a model, you need to recall data, even say, give me the last 12 times something happened.
0:48:07 That is AI.
0:48:14 But as you know, to do true training, then you need to have it work in the future.
0:48:20 Now, future has not a binomial outcome.
0:48:23 It’s multiple forward scenarios.
0:48:27 And the probabilities are unknown of each future event.
0:48:28 Okay?
0:48:40 So you’re dealing with so much uncertainty that I don’t know how a probabilistic way to give you a single-point answer would ever work.
0:48:49 I mean, if you give me an example, someone will say this is the fair value of a stock and they say this is the PE and this is the E.
0:48:57 And I can never understand that because I’m always wondering, which E are you using?
0:48:57 And you know what I mean?
0:49:02 Like, because price is today, but then which forward metric and then how do you discount it?
0:49:11 How do you explain to AI that you have to look at 10 years of future earnings, but you don’t know which future earnings will actually matter the most?
0:49:13 And then how do you assign the weights?
0:49:23 I mean, that’s really what we do at Fundstrat is we’re constantly assessing the probabilities of future events and then deciding we do have to pick a direction, right?
0:49:25 Then we say this is the path we’re going to take, but it’s a guess.
0:49:41 So I think that the best qualities of a researcher, at least in my opinion, are you do need to be unemotional, but you also need to know the difference between conviction and being stubborn.
0:49:49 And stubborn is riding something when and believing in something when all the facts have changed.
0:49:56 Conviction is basically riding through the volatility and it’s not easy to tell the difference until history has already passed.
0:50:04 And I think, you know, the third thing that’s really important in markets is to know what’s already priced in.
0:50:18 And I don’t know if AI is going to really have a good sense for, like, if all, if AI is the only thing managing money in the future, I think a human will be the market because all the AI systems will be predictable.
0:50:19 You know what I mean?
0:50:21 And then you can spoof them all.
0:50:34 So that’s really where I think human judgment matters because, you know, I’m constantly surveying our clients and I’m in constant touch with them.
0:50:42 So I kind of know where the money is and what the bets are and how they react to the Fed.
0:50:56 And I don’t know how you can program AI unless it is, well, they’ll get very good, but they really have to think not just on a specific outcome, but on a series of future outcomes.
0:50:59 And that’s what we’re always sort of obsessing over.
0:51:02 We’ll be right back.
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0:53:29 We’re back with Prof G Markets.
0:53:33 Speaking of future outcomes, last month, I want to talk about crypto.
0:53:38 Last month, you said you think Bitcoin could go as high as $200,000 in January.
0:53:42 We’re currently at $94,000.
0:53:48 It’s been a rough couple of months for Bitcoin and for crypto.
0:53:52 Where do you stand on Bitcoin right now?
0:53:55 Do you still believe that we could hit $200,000 in January?
0:53:58 What do you make of what’s happening in the crypto markets?
0:54:00 Well, crypto’s had a rough year.
0:54:03 It was actually having a great year until October 10th.
0:54:07 Because on October 10th, Bitcoin was up 36% for the year.
0:54:11 And now it’s currently, I think it’s like flat for the year.
0:54:14 So it lost a lot of its gains.
0:54:24 I’m still very optimistic because crypto still has its best years ahead.
0:54:28 But crypto should have had a good year this year.
0:54:32 And it was on track to until there was a liquidity crisis on October 10th.
0:54:39 That was a bigger wipeout in terms of liquidation than any event in history.
0:54:43 The most recent one before this would have been 2022 with FTX.
0:54:48 And that wipeout pales in comparison to what happened on October 10th.
0:54:52 But in 2022, it took eight weeks before crypto.
0:54:54 The smoke cleared.
0:55:01 The leverage wipeout was enough in the mirror that crypto prices began to recover.
0:55:06 This week is the eighth week since that crisis.
0:55:09 And I think crypto prices are beginning to actually recover.
0:55:15 That’s why I think Bitcoin can double from here by the end of January.
0:55:19 Now, many people don’t expect it because of the four-year cycle.
0:55:22 And that’s going to be the big question.
0:55:28 If Bitcoin breaks $125 in January, there is no four-year cycle.
0:55:31 What did happen on October 10th?
0:55:34 Like, everyone knows crypto markets got hit.
0:55:38 It seems unclear to people what actually happened.
0:55:43 And as you say, it was one of the largest liquidation events that we’ve seen in the history of crypto.
0:55:44 What happened?
0:55:48 I’m going to give you what we’ve been able to piece together.
0:55:58 And I would say that it’s like a 90% correct because, you know, there’s going to be 10% you don’t know if there was something else.
0:56:03 Just to clarify, is the reason we don’t know what’s happening because of the anonymity of crypto?
0:56:04 We don’t know who owns which wallets.
0:56:06 And so it’s harder to track what’s actually happening.
0:56:08 Well, it’s that.
0:56:20 And plus, you know, you know, if when we look at liquidation events like in for in in a stock market, 90% of what someone will explain is probably correct.
0:56:28 But 10% like 90% it would be 90% to correct say that the February to April decline was largely due to the Trump tariffs.
0:56:35 But the other 10% would be like, well, stocks were already expensive and they were overdue for a correction.
0:56:38 So that’s that’s what I mean by 90-10.
0:56:39 So give us your 90%.
0:56:40 What do you think happened?
0:56:45 On October 10th, there was two things that happened.
0:56:54 One was a triggering event, which was Trump announced, you know, a re-escalation of tariffs with China, like a tripling of proposed tariffs.
0:57:07 And because markets were closed, historically, crypto is what reflects reaction to a macro event when you’re already after market hours.
0:57:11 Like if the S&P was open, it probably wouldn’t have been gut punched as hard.
0:57:12 But crypto prices fell.
0:57:21 Now, crypto prices falling, system can handle that because, you know, crypto is a hyper volatile by nature.
0:57:26 So large swings in prices shouldn’t really overload the system.
0:57:33 And even volume shouldn’t overload the system because crypto trades, you know, there’s so much automated trading.
0:57:44 However, there was an algorithm in place that actually what I call a glitch happened on a specific exchange.
0:57:48 Many people use leverage in crypto.
0:57:51 I mean, it attracts leverage trading.
0:57:56 But people put up collateral so they can do leverage trading.
0:57:58 One of the collaterals is stable coins.
0:57:59 OK.
0:58:05 And stable coins are pretty safe collateral because, hey, if it’s Tether, it trades at a dollar.
0:58:07 It’s pretty safe collateral.
0:58:10 And so you can borrow a lot against safe collateral.
0:58:18 And if it was USDC, Circle, that’s also a really stable coin because it’s backed by a dollar.
0:58:23 However, there was a popular stable coin called USDA.
0:58:26 It was an algorithmic stable coin.
0:58:39 And on one particular exchange, because of the shock of Friday, internal prices, like people bid ask of that stable coin actually got out of whack.
0:58:46 Suddenly, the price went to 65 cents, even though it’s supposed to be essentially worth a dollar.
0:58:52 So, but within one exchange that the quoted price dropped to 65 cents.
0:59:01 Well, that meant that all the collateral for every account that used that particular stable coin to borrow money was now in deficit.
0:59:03 OK.
0:59:13 Even if it was just one dollar that traded at that price, it was already putting every single piece of collateral at risk into deficit.
0:59:17 So then something called ADL was triggered, automatic deleveraging.
0:59:25 And so in one exchange, everybody who had who would use that stable coin as collateral basically got wiped.
0:59:26 Their accounts liquidated.
0:59:29 Well, what were those accounts long?
0:59:36 They were long altcoins and all these different cryptos that suddenly got dumped on to spot exchanges.
0:59:47 So then on all these other exchanges, suddenly some cryptos, some altcoins suddenly went down 99% because there was a lot of selling from ADL.
0:59:55 But it triggered a domino effect of all these other exchanges triggering other ADLs because spot prices of all these alts dropped.
1:00:08 So that to me was a glitch because it was like an illiquid quote that didn’t represent a true VWAP triggered an ADL that triggered a cascade of ADLs.
1:00:13 And that led to millions of accounts being literally zeroed out.
1:00:26 It won’t happen again, I’m sure, because in the future, I’m sure they will use a composite set of prices or if there’s a variance between what’s quoted internally versus on spot or if it’s a volume-based measure.
1:00:31 So that’s why I don’t think it would happen again, but that’s what happened on October 10th.
1:00:31 Okay.
1:00:33 We finally got our answer.
1:00:36 I’ve asked this question to many people and I never get a proper answer.
1:00:41 My takeaways from that are that, you know, it seems vulnerable.
1:00:56 This asset class that is supposed to be a hedge in a lot of ways, we are learning in various ways is extremely vulnerable to what seems to be almost nonsensical mechanical glitches.
1:00:59 Which, I guess, that is my takeaway.
1:01:03 In 2020, the price of oil went negative.
1:01:05 Right?
1:01:10 I mean, oil is the most liquid commodity in the world and it traded at a negative price.
1:01:26 So these glitches happen in all markets, but it does happen in crypto because it’s a gigantic place where people are trying to experiment and create, you know,
1:01:31 what they view as free from censorship and interference.
1:01:36 But, of course, there’s – every event is going to bring something new.
1:01:38 And you’re 100% right.
1:01:40 You know, it’s terrible that it happened.
1:01:47 But, again, I remembered oil was negative, you know, and people actually were able to buy negative oil.
1:01:51 They were paid to, like, take oil.
1:01:53 I guess I’ll wait for a negative stock price.
1:01:57 I’d love the opportunity to get, yeah, negative stock.
1:01:58 Remember bonds?
1:02:00 Corporate bonds had a negative yield in Europe.
1:02:07 You were actually paid to own – like, an issuer was paid to issue a bond.
1:02:13 Can you identify any sectors or geographies that you think are dramatically over or undervalued right now?
1:02:25 What I think is small caps because there is real earnings growth now coming, but there is no money flows.
1:02:33 So small caps are a whole group that professional investors can afford to ignore because no one else is buying them.
1:02:36 You know, the amount of active money in small caps is, like, at record lows.
1:02:46 I think financials are also dramatically undervalued, Scott, because – well, this is where we could be wrong.
1:02:50 But in my view, I think the financial sector is actually becoming a tech sector.
1:03:06 That as money is becoming more digital and as AI implementations are heavily, heavily taking place in financial services, it’s going to make – it’s creating an advantage for the companies, the companies who issue capital.
1:03:17 And companies like J.P. Morgan probably, as we just discussed earlier, could really dramatically reduce their dependence on humans, which is their largest expense.
1:03:26 So I think financial companies are increasingly going to look like tech companies, and their multiples may become more like tech multiples.
1:03:34 So that, to me, is one group that in the future could have a 30 P.E., even though they used to trade at 10 times earnings.
1:03:39 We’ve been talking with a lot of different people in the markets.
1:03:44 We’ve been talking with renowned professors, investment strategists, economists.
1:03:46 We talk with a lot of people.
1:03:51 Most of them are somewhat bearish right now.
1:03:56 You are one of the only real bulls that we’ve talked with.
1:04:00 I’ve seen you described online as a perma-bull.
1:04:02 That was what Bloomberg called you.
1:04:06 What do you think about that label?
1:04:14 And how is it that you are bullish right now in a sea of bears?
1:04:16 And what do you think that says about who you are?
1:04:20 I was first called a perma-bull in 2009.
1:04:26 In fact, it was major newspapers that were using it as a mocking term.
1:04:28 Here’s what’s interesting.
1:04:32 16 years later, what was the right call to be?
1:04:34 The optimists have won.
1:04:45 And yet today, if I had to say what proportion of investors are bullish versus bearish, it’s really risen in the last year.
1:04:50 In fact, it’s kind of close to the 2009 levels.
1:04:56 I think people are already betting on the fact that we’re in a bear market.
1:05:07 Now, many people were convinced of that in 2022 because of the Fed hikes, and they just never changed their views three years later.
1:05:19 But as you know, what made people a lot more bearish is also because President Trump is a very unpopular president.
1:05:20 He’s a very divisive figure.
1:05:21 I’m a registered independent.
1:05:28 So I have never tried to let politics be involved in how I view markets.
1:05:38 But I can’t help notice that I think that political lens plays into many of our clients’ views around markets that they tend to view.
1:05:42 When Biden was president, there were a lot of people who were critical of Biden.
1:05:46 I thought the economy—I’ve cared about the economy.
1:05:47 I thought the economy was fine.
1:05:52 I think the economy’s still doing fine under Trump.
1:06:01 So that’s kept me—I use that as one level of coding that I think has kept people bearish.
1:06:11 But, you know, I think America, as long as it’s a place of innovation, and we are because we’re at the center of AI, I think it’s pretty bullish.
1:06:15 But you guys have raised the key point.
1:06:20 I mean, there’s a chance that this AI is a disaster for labor markets.
1:06:26 And if it is, the U.S. will be the least scathed, but everyone’s going to go down.
1:06:31 Tom Lee is the co-founder, managing partner, and head of research at Fundstract Global Advisors,
1:06:33 a leading independent research firm.
1:06:40 He has more than 25 years of experience in equity research and has been top-ranked by institutional investor every year since 1998.
1:06:48 Prior to co-founding Fundstract, he served as J.P. Morgan’s chief equity strategist from 2007 to 2014.
1:06:51 Tom, I wish we could do this for three hours.
1:06:52 Maybe next time we will.
1:06:54 Really appreciate your time.
1:06:55 Thank you.
1:06:56 Thanks, Tom.
1:06:56 Good to see you.
1:06:57 Yeah, next time.
1:07:08 Scott, what did you think?
1:07:11 Yeah, I have a lot of respect for Tom.
1:07:19 Not just because I think he’s a great analyst and does the work, but I just appreciate how measured he is.
1:07:22 He basically says, yeah, that’s a good point.
1:07:23 You could be right.
1:07:38 When these guys go on CNBC and sort of talk their own book and say, no, the market’s going up 20% next year and this is why, he’s very measured around, this is what I think, but I don’t know.
1:07:42 And he acknowledges the other side.
1:07:45 He just strikes me as very reasonable and tempered.
1:07:59 And, you know, I can kind of see why institutions like his research because I think he’s probably got a track record of sort of, you know, I hope most of this is right, but I know some of it’s wrong and buyer beware.
1:08:01 It just strikes me as the adult in the room when he’s making these recommendations.
1:08:05 Like, I think it’s good that we had the bull come on before the end of the year.
1:08:07 We’ve had a lot of bears on.
1:08:10 I think all of them have presented really.
1:08:11 He and Josh Brown.
1:08:12 Josh Brown’s a kind of a bull.
1:08:15 Well, we’ll definitely get him on in 2026.
1:08:21 But, yeah, I think I love his analysis of frozen foods.
1:08:31 I think that was a great example of a technology that had real impacts on the labor force, but ultimately a freed up time and it left us with more productive things to do.
1:08:37 I don’t think that that means that we’re not going to see an impact on individuals’ lives.
1:08:39 I mean, he talked about how, you know, frozen foods happen.
1:08:42 Everyone thought that farmers would go out of business and then we were fine.
1:08:46 It’s like some of those farmers did go out of business and some of those farmers were not fine.
1:08:56 But long term, as an investor, yes, the idea of these technologies creating short term destruction in the labor market shouldn’t worry you too much.
1:09:05 I think the question is, you know, he was talking about his younger, his children using AI and young people using AI, which I think is a fantastic point.
1:09:09 We should be really looking at how are young people using it.
1:09:13 I think the question is, what exactly is the market pricing in?
1:09:23 Because it’s hard to tell if the market is underestimating the potential or overestimating the potential.
1:09:27 And it seems that we haven’t really reached a consensus on this.
1:09:29 And perhaps that’s just the way markets work.
1:09:30 We can’t know.
1:09:33 But I think that is sort of my big question.
1:09:43 It’s like, you know, just how optimistic or pessimistic or neutral on AI is the market really based on current prices.
1:09:55 And then I think the final point that I think was a good point to end on for the end of the year is he says, you know, yes, he expects or he could easily see a 10 to 15 percent correction next year.
1:09:57 And I think that was a little bit scary hearing that.
1:10:01 But then he also points out it’s a long time a year, 52 weeks.
1:10:07 So, he expects that actually we’ll see a rally after then.
1:10:10 So, I think it was a good way to end the year.
1:10:11 Appreciate his perspective.
1:10:13 And we’ll definitely want to talk to him again.
1:10:22 This episode was produced by Clyde Miller and Alison Weiss and engineered by Benjamin Spencer.
1:10:26 Our research team is Dan Shalon, Isabella Kinsel, Chris Nogonohue and Miel Saverio.
1:10:30 Drew Burrows is our technical director and Catherine Dillon is our executive producer.
1:10:32 Thank you for listening to Prof. G Markets and Prof. G Media.
1:10:38 If you like what you heard, give us a follow and join us for a fresh take on markets on Monday.
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1:11:04 As the world turns
1:11:09 And the love flies
1:11:13 In love, love, love, love
1:11:19 Support for this show comes from Odoo
1:11:22 Running a business is hard enough
1:11:23 So, why make it harder
1:11:26 With a dozen different apps that don’t talk to each other?
1:11:28 Introducing Odoo
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1:11:40 And the best part?
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1:11:48 That’s why over thousands of businesses have made the switch
1:11:49 So, why not you?
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1:11:52 At odoo.com
1:11:55 That’s O-D-O-O dot com
Ed Elson and Scott Galloway are joined by Tom Lee, co-founder, managing partner, and head of research at Fundstrat Global Advisors, to break down why he’s so bullish on 2026. He also shares his outlook on Bitcoin, explains how his company is leveraging AI, and highlights the sectors he thinks are currently undervalued.
Visit fundstrat.com/tom for complimentary access to Tom Lee’s Fundstrat research.
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