Transcript
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The speaker opens by contrasting explosive AI demand with falling token costs, then introduces the core idea that intelligence is becoming cheap and abundant. He argues that this changes how we should think about AI’s economic impact.
Speaker A
I can see all these incredible ways to use AI and I use a gajillion tokens and I can see how I could use a gajillion more.Everyone uses many tokens as me.We are so short.Like it's going to be this huge thing that's going on forever and everyone, and everyone who's in this build out is going to make a fortune, right?On the other hand, the cost per token is going to collapse.And so maybe actually like we're totally overbuilt, right?And like I can tell, I can tell both stories, like with equal certainty.
Speaker B
You called me saying it's just intelligence was sort of the theme of our conversation.Describe that like simple idea.
Speaker A
There's this idea that if intelligence starts like an amoeba and it gets better and better and then it's like a monkey and then it's like a person and then it's like Einstein and then it goes zoom and like every.And it becomes God.I think that that's something of an intellectual mistake.What people have wrong there is they're missing what are sort of the real limits of what intelligence can do.And maybe another way to put it is like if you think thinking about intelligence as like an unbounded scalar, like the height of a tree isn't correct, what you should really think about intelligence as is more like a ratio, like the roundness of a ball.And what the ratio is calculating is intelligence is a gadget and we use it to make predictions about the universe.And so we start with this thin scattering of facts about the universe where we have relatively few of them and the world is in.
The universe is complex and we use intelligence to interpolate between them.
So as your intelligence goes up, your ability to sort of color in the spaces gets better.And some domains are really amenable to that, right?In mathematics you can solve it like those sorts of things, and in those areas you kind of get to perfect.But for most of the interesting stuff, that is most of the questions in the world, the mathematical term is they're complex dynamic systems.So like what that basically means is they're kind of like weather in the sense that you can't just guess your way, you can't make a closed form answer.
You can try to simulate it, but if you simulate it, invariably your simulation will have some degree of error in your initial conditions or in each step going forward, because at a minimum your steps, for example, are going to be truncated.And so what happens is your error will build.And invariably after the simulation runs for some period of time, you'll be off by some amount.And so the point is that you can't actually, like, for mixing cream into your coffee or predicting the weather or thinking about how groups of people who are all very complicated are going to interact with each other.Intelligence just isn't that powerful.
And so my sense is that what you get is you get this sort of increasing intelligence to the point where you kind of are making all the predictions that something that's super smart could make, which it turns out is a relatively small set of all the interesting things you'd want to know about the world.To get past that, you then need to go out and get more information about the world.And that just takes time.You have to do experiments, you have to touch reality.But one interesting corollary to that is that the zoom actually kind of becomes.
One of the things you can turn your intelligence to is reducing the cost of intelligence, which we're seeing.So what you actually end up getting is like, everyone gets unlimited, really great, like, very round ball, so to speak, intelligence for essentially free.And obviously that's a sort of a limit statement.It won't actually be free.But the point is that, you know, that what we're seeing is that, like, really good enough intelligence is rapidly falling in price.And it turns out really good enough intelligence will solve the vast majority of questions where people kind of need to use intelligence.
Speaker C
Yeah.
He explains why even very advanced intelligence cannot solve everything, using complex dynamic systems like weather as the key example. The section emphasizes that many problems require touching reality, not just better reasoning.
Speaker B
Can you say more about why this notion of like a complex adaptive or dynamic system means that actually even perfect intelligence or whatever intelligence we have in a bottle 10 years from now, or pick your horizon, will still be limited in its applications?
Speaker A
Yeah.So like nonlinear dynamic systems.So the famous story here and how these were discovered and this came into the branch of mathematics, became chaos theory is back in the day, there was a guy who was building a weather forecasting model back on big computers that took up rooms and that sort of thing.He runs the model and it makes predictions of pressure and temperature over some span of some forecasting period.And he's like, cool.So then just to make sure it's still working, they reset it and they run it again.And it starts out basically the same results, but then the results start to diverge.And by sort of the third, fourth, fifth day of the forecast, the results were entirely different.The guy thought some version of like, darn, I must have a bug in my system, and begins the process of trying to figure out why the computer isn't producing the same results in repeated runs.
And it turned out that the technician who had loaded the data into the computer in the second run had rounded off at some decimal, like 1 decimal less all of the initial conditions for pressure and temperature and such.You would have thought that, okay, maybe that would have produced a small but consistent error over the whole forecast period.But what had actually happened was that small error through all of the repeated steps.You take your current conditions, you calculate your next set of conditions they compound, and you had an entirely different forecast by some small distance into the forecasting period.Unrecognizable weather.One model said it was 70 degrees and sunny, and the other model said it was 20 degrees and pouring.People called it like the butterfly effect because at least in principle, you know, butterfly could flap its wings in Africa and like that could lead to a hurricane in North America or whatnot.
And it just turns out that like many systems that we're interested in, like have this characteristic.You can know everything about pressure and temperature, but you can't model.You can't necessarily like write down an equation that you can solve easily that would tell you that there's going to be a hurricane.You have to like model it.And the model in turn is going to be grained at some level unless you actually go to the step of actually having the whole universe.And so ultimately, in the limit, if you want to simulate the whole universe, what you actually need is the whole universe.
You don't actually do that.What you actually do is there are many, many circumstances where simulation is super useful for approximations.And in those circumstances you can use intelligence to solve them, but there are many more where it doesn't work.And so intelligence is limited in that the sort of fundamental unknowableness, I guess, about the universe.
Speaker B
So back to this notion of it's just intelligence.
The discussion shifts to how ubiquitous AI agents will change companies, consumers, and software businesses. He argues that the best investments will be in the complements to intelligence, such as data, attention, and physical scale.
Speaker B
What does that mean about where you think we're going?From a more practical standpoint, we're all
Speaker A
going to have a team of 200 IQ people working full time in our pocket.And that team will have itself, will have a team that it can deploy of many more.And they're going to be trying to figure out all the little optimizations that we're too lazy to do.
And every company will make similar teams available to their employees in order to do their jobs.There's just a lot of businesses where that's not good for them.I think there's a variety of ways in a lot of companies, I think that what happens is consumer satisfies, Consumers are boundedly rational at present.They only have so much cognitive space.And so you sort of go to a consumer and you say, look, we'll take your money, we won't steal it, we'll pay you a decent interest rate, you don't have to think about it and you can go about your life.
And the consumer doesn't say, look, if I think about this every day, I could save $400 a year.So therefore I'm going to think about this every day.The consumer says something like, sounds good enough, I'm going to move on.But with an agent, you would think the agent will continuously optimize for them.And there's also companies where what they produce fundamentally is some sort of artifact that is just some sort of crystallized intelligence.I'm by far not the first person to realize that AI could be bad for certain software companies.But just this idea that what an AI model is, it's kind of like this general purpose intelligence tool.And what many software tools are kind of like crystallized and hardened pieces, like projections of intelligence.And so as I've been thinking about companies in investments, I found it helpful to sort of assume everyone has really great intelligence and ask what's left.
And so the line I've been using when people talk about some version of the pushback is often some version of, but you don't understand how hard this is.You don't understand how complicated this is.And you don't understand how many smart people have had to think for so long about this.And my sort of hand wavy and sort of approximately right response is, okay, sure, but that's all just intelligence, right?And to follow this logic all the way through to the end is if intelligence becomes cheap and ubiquitous, what you want to own are the complements to intelligence.The complements to intelligence are their data, attention, love, affection, whatever sort of emotional attachment you want.
It's physical proximity, it's scale, it's a little cynical, but it's regulatory capture.It's these sorts of things where for some businesses what happens is their finite ability to deploy intelligence, be it economics, just the cost of it.They're only a certain size company and they don't have access to 10,000 of Google's best engineers for 10 years.Or be it just the organization is behind or whatever, like the ability to deploy way more intelligence to your business and your competitors, to deploy more intelligence to their business and your customers, to use lots of intelligence, it can work out to be better for you.Or if intelligence is a barrier to you serving your customers, like the lack of it.
Or it can be bad for you.If intelligence, if Your customers.Lethargy is kind of a fundamental element of your, of your economic value.
Speaker B
Maybe pick like Meta or something as like the perfect example of a company that I think you believe owns perfect complements to ubiquitous cheap intelligence.Just to like bring the example to life.
Speaker A
I'm actually going to set aside for a moment like Muse and Agents and that whole thing and focus on business.And the core business at Meta, right, is personalized tv.
Speaker B
Personalized tv, yeah.
Speaker A
It used to be social media, but now it's like it's personalized TV and it turns out that people really like watching Instagram or whatnot.It's discovered that I love dark British humor and it's great if you take the really high.
People talk about the 10,000 foot or whatever.Let's talk about Meta from space.The highest view, they have 250 billion in revenue and they spend $250 billion a year.Some of it gets accounted as various costs, some of it gets accounted for as capex.It doesn't really matter, just they spend it all.
The question you might ask is like what do they spend it on?And a really reductionist view is that they spend it all on intelligence.And what do I mean by that?Well, they're buying data centers.What is a data center?Well, it's kind of an intelligent producing tool.What else are they doing?Well, they're actually literally buying inference.Right?That's just intelligence.
What else are they doing?Well, they're paying really smart people to write algorithms.That's intelligence.They're writing code.Also intelligence, Right, like they're serving videos.That's intelligence adjacent.Sure, intelligence.And so in some highly reductionist sense, and you shouldn't take these things literally, but I'm just kind of trying to highlight the really big directions here.All they buy is intelligence.And if you think forward with the fact that intelligence, the price of intelligence is falling by, you know, like a factor of 100 or something over the next five, five years, first principles, their costs kind of go away.
And that's just thinking about the cost side of things.On the revenue side of things, there are all of the creators.You are a creator.How much more quality creative can every creator that makes content for Meta make?If they have access to intelligence, that's 1/100th the cost and 10,000 times more effective or whatever.The point is probably better then Meta in turn.Maybe they don't take their costs to zero.Maybe they spend more or more than zero and they do a far better job of picking the videos to show people next which should make the content more better targeted and more enjoyable.And then of course, on the ad side, advertisers could make far better designed, far better targeted personalized ads that then meta in turn can target much better.And advertisers can also build kind of much more slippery funnels or whatever to convert.
The whole point of all this is that maybe instead of taking their cost to zero, with much better intelligence and much lower prices, maybe they can sell a few more ads.All of this is downstream of what they possess, which is not destroyed by ubiquitous intelligence, which is data and attention.They just have this enormous amounts of data about all these people and how they view videos and whatnot, and they have an enormous amount of attention.And these things compound each other.And so I don't think that the availability of cheap and ubiquitous intelligence is going to cause people not to be interested in watching funny 20 second videos.
And so if anything, they can win some time on the margin from other things people do with their time.I don't actually own it, but a company that benefits in theory from having a lot more intelligence is Grindr.
Basically, Grindr is the gay dating app, but it has been constrained over its life by the fact that it's just not that big a company.It turns out they've got lots of attention and they've got lots of data, but their ability to build all the things that you could imagine building on what ultimately amounts to a finite size, team and budget and ability to create things has meant in some ways that Grindr can't be all the things you could imagine it could be.Again, I don't actually own any Grindr or whatever, but I study lots of companies and let's just sort of occurred to me that this is another example of something where here the mental model is like, what if you had 10,000 of Google's best engineers to go work on your problem for five years?What could you do?
He evaluates frontier AI labs as businesses, arguing that many uses of frontier models will commoditize as cheaper models improve. The conversation explores whether demand for intelligence can keep expanding fast enough to support the buildout.
Speaker A
And Grindr is one where maybe they could do more.
Speaker C
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Speaker B
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Speaker B
want to Talk about the Labs so back to the original point, I think lots of great investors would say intelligence, specifically frontier intelligence, is not a commodity.
Historically, the conversation has been about the gap in intelligence between closed source and open source.Increasingly, the conversation is about the gap between what's publicly available from the labs and what's privately used by them inside their own businesses.They seem to have figured out how to stop or slow distillation.Lots of people would say that there's this escape velocity thing happening and that the frontier token is going to be way more valuable and useful and and that the labs themselves can then distill their own models and provide the best version of the cheap intelligence too.I'm curious for you to riff on the Labs as companies through this sort of mind of an investor.
Speaker A
If they have models that are better and the ability to produce adequate intelligence at way lower cost than everybody else and nobody can copy it, then it's going to be a great business.I guess I have a couple of thoughts.One is, as I've watched models get deployed, it feels like one way to think about it is like you start out with these very inadequate models that are basically able to do a very small subset of jobs.Then what happens is the models get better and they work their way kind of up the stack.And with each successive step up the frontier model has unlocked a larger call addressable market.
But behind it, what's happening is that once the models get saturated, once the sort of evals or whatever gets saturated, you get this.You would never use a Frontier model for a chatbot in a customer service application.You'd be an idiot.And so what's happening is those areas, they clearly commoditize.And so there's this wave of hard tasks where you really need a Frontier model and then behind that you don't.
And so the friends I have who are big Frontier Labs companies, bulls or whatever, and obviously these companies financials aren't public or whatever and they might have an information advantage over me, but would say something like, don't worry, we can also along the whole efficient frontier, produce cheaper tokens.Maybe they can right now, maybe they can't right now.Maybe they will in a few years, maybe they won't.I find this to be very hard to think about.Certainly the trend has been rapid commoditization of these things.If you were to look at the rise of Chinese models and sort of the step, every step and at a minimum the cost has been collapsing.So even if they do have a position where the tokens necessary to do tasks that are well behind the frontier, it might not be a very big market because it just doesn't cost that much in the scheme of things to deliver and it is competitive even if they are a cost leader in a shrinking commodity market.
Then the other thing is that I think that programming was this huge use of models.As you push forward, the question becomes like for the next, next, next, like layers of intelligence, do you still have bigger and bigger places to go after?And the bull story would be some version of like, yes, like people will pay anything for a model that can like figure out how to cure cancer.
Maybe that's true, but I think figuring out to go to our prior conversation, I think figuring out how to cure cancer requires touching reality.And I'm not sure that more IQ gets you that we don't believe that there's some super incredible use cases where you can just charge an enormous amount for the neck for like more intelligence.Then you start getting to this point where the next step up in terms of you go coding and then maybe it's like, I don't know, something else.But the smaller niches, it's like this diamond shaped demand frontier where initially you get wider and wider uses and then actually it starts to get more and more rarefied where you actually how often do you really need a 300 IQ?One might start to ask.And look, these things are all going to, we're going to find out.And I don't want to be too dogmatic, but it strikes me as like totally reasonable that what we end up with is some version of like there are very few use cases for like super elite intelligence.
There are lots of use cases for like very good intelligence.That very good intelligence is highly competitive and commoditized.And if these frontier labs can somehow become like cost leaders in doing that, there's a margin to make in being like a cost leader.
But then there's also just rampant deflation.And then this question becomes how big is the market?If good enough intelligence is very, very cheap, then how much good enough intelligence are people going to buy?
Speaker B
I'll just keep playing the devil's advocate.So the two things people might say in this case are Javant's paradox, that as it gets cheaper, we're just going to want to use more of it.I'm curious what you think about that.The second would be, historically the new uses of intelligence have typically been discovered by highly intelligent people.My good friend says intelligence creates its own demand.What about that?Like, what about the fact that maybe it's diamond shaped for humans?If humans are driving the new questions that the intelligence is set after to answer.But what if the thing itself is creating the new questions and we trained it to do that really effectively and just sort of like explore in an open ended way in the way that like all of humanity would be doing, led by the people with, you know, 150/ IQ or something.
Speaker A
Historically, Jevons paradox is just a sort of narrow example of price elasticity, demand.And in general all markets are U shape, right?So initially when the product is terrible, there's no demand and then the product gets better and people will pay a lot for, but they won't buy very much.
And then as the price goes down, the size of the market will rise because it becomes you have Jevons paradox essentially.But then you get to a point where further price decreases, don't actually drive enough demand and the market shrinks.And eventually of course, if you take the price to zero, then demand goes to the size of the market goes to zero.One example that I've thought about is the cost of fiber optic communications or just communications.The cost of moving information used to be super high and people moved very little of it.
And then through a whole series of innovations, the cost of moving information went down a lot and the amount of information moved went up a ton.And the market grew a lot for a long time.And then market stopped growing and the cost of moving information kept falling and the market kept shrinking.And nowadays Cogent Communications, I think routes 30 or 40% of all IP traffic across the Internet.And it's through one of its divisions, which is not even its biggest division.And Cogent Communications is like a few billion dollar market cap company.This is just not a very big industry.Now I'm not saying that we're going to get quite that small in terms of intelligence, but Jevons paradox is a statement about a particular spot on a curve which doesn't necessarily have to be true.
The speaker examines Jevons paradox, price elasticity, and why cheaper intelligence may not create infinite demand. He stresses that discovery still depends on experiments and physical constraints, even when models become much better.
Speaker A
And it remains as your other question about can more intelligence create more desire for intelligence?I mean, I have a couple of thoughts on that certainly, maybe, but I started out by saying you need to touch reality to answer questions, right?
And so if you wanted to, if you had a model and it was going to think more about physics, it could certainly think lots of interesting thoughts about physics.But eventually it's going to need to do experiments.And that's not to say we couldn't also do experiments, but now we're bound by like our ability to physically do experiments.And so I think there's going to be this incredible time where we sort of, there's going to be so many cool discoveries which we're already starting to read about where like this paper was written and this paper was written and this paper was written.And if somebody had read all three papers and thought about it, they would have realized this.
But nobody had.But it turns out the models read all the papers and they'll have to like figure that out.And there's going to be, we're going to, there's going to be all kinds of great stuff that falls out of that.But at some point we're kind of finished that and then it's going to be about like, hey, there's some way to think about things that are outside of what we know.And we have a hypothesis, but a model, even a super smart model won't be better at guessing that necessarily.
We'll be better at guessing that than the smartest person.But the smartest person's odds are super low anyway.So it'll be able to come up with great ideas for experiments to do and they'll be worth doing, but that will limit our ability to expand.The other thing I just think about is my own use of AI on a lot of levels.One, just to go back to the commoditization thing, I've totally commoditized my use of models.For what it's worth, it's because I'm lazy about how I do a lot of my building for stuff.I'll tell a Grokbot to build things because I don't want to use up all my Grokbot wheat tokens.I then have IT run all of them.I subscribe to all the things and have it use all this and I'll have it build using those subscriptions on a server.And so I actually don't know what model I'm using anymore.
I used to know which this model is better.This one's worse.I no longer know.All of this is being optimized on the back end for me.And all I know is that stuff is getting built and it's not using up my subscriptions.And so we're good.
Speaker B
There's this batch of companies that I'm curious what you think about, which is effectively real world experimentation.Companies that create loop.That's automatic.There's this happening in robotics.There's this happening in several places in bio labs and automated wet labs, automated testing labs.Any commentary on that?Super great idea, right brand of company?
Speaker A
Yeah, super great idea.Closing the loop in the physical world is going to be great and it's going to accelerate the process of learning.
And this is a great idea.But again, there are speed limits.I guess my.The thing I keep coming back to is some version of this is amazing.And I'm super, in a sense, AI pilled in the sense of what I think these are things are going to do and they're useful for.
But I'm also sort of cognizant of the idea that things bump up against.The way I like to tell this to people is I said, look, you're really smart, so you must understand being smart has limits.Being smart has limits.And so the ability to just think your way to solutions is limited.And so if you're going to build an automatic biology testing laboratory and you're going to connect it to a computer and the computer is going to come up with an idea and then it's going to run an experiment and it's going to iterate.That's awesome.But if that experiment requires breeding mice and mice need a year to grow up and die, that experiment will take time.And it might be a great experiment, you might learn a lot, and that's great.Still going to take time.And the lab's only so big.
And so you might have 5,000 great ideas.You're going to pick 20, and the model may be better.Your odds, your odds of that experiment working might even be 1%.Turns out that might be 10 times better than the average researcher.And that's amazing.
It's still 1%.None of this is to be pessimistic.This is like super amazing.Like discovering all the things that we should know because it's already in our corpus that we forgot is awesome.Discovering all the things and better directing our research is awesome.
Automating that research to move faster is awesome.We will learn.That will push the frontier.But this is not like suddenly the model looks around and says, you guys missed it.Here's some new world that you guys can't comprehend or whatnot.
I don't think that's where we're headed.
Speaker B
It seems like the market has been, especially in private markets and the leading edge of investors thinking about AI, very focused on investing in supply related stuff.Like how are we going to build and serve all the intelligence versus what I'll call demand related stuff.Someone watching their personalized TV on their meta app or whatever.What do you think about that first batch of companies as investments?
Speaker A
Super hard.I've joked I've been willfully ignorant about them and probably I'm sort of worse off for it.But there's this big build out and then you're trying to forecast inference demand and I can't forecast supply or demand within several orders of magnitude.This idea of how much more inference will people use as intelligence gets better and cheaper is a really, really hard thing.And equally hard is how much better and cheaper inference is going to get.
And then even if you could somehow guess at those things, then there's the whole value chain and you sort of need to be at the bottleneck in order to capture the rents.And then of course finally you get to your actual competition with your competitors in that particular piece of the value chain and like, it's just a super hard thing.I think about my use of AI as a consumer.You know, increasingly I'm doing stuff like instead of like having the AI chatbot like talk to my daughter like you know, or you know, text with my daughter about how to, you know, do math.Now I have it make like a three blue one brown style video explainer so that you know, she can see like a nice 5 minute or 10 minute video on like, you know, powers or area or you know, circumference or something.
It's super cool.I don't think I would do that if it was going to be $5.Right.And so there's this thing where like there's real price elasticity.The point I'm Trying to make is like if a lot of demand comes from the idea that like, you know, it's one thing like I'll film myself working out with my Muse app and it'll like show me tip pointers on my form and I'll do that for very little money.But I won't do that if the price point is real.And I'm probably much less sensitive to the price than like most people.So I think there's like AI demand is going to be really sensitive to the price, which intrinsically I think does limit in some sense like the amount in total consumers are going to be willing to spend on this.
The discussion turns to why so much talent and capital are flowing into AI infrastructure despite uncertainty. He frames the buildout as a massive capital cycle with real economic tradeoffs and potential overinvestment.
Speaker B
Maybe this is a time to talk about capital cycles or something.It sure does seem like the smartest people in the world.And many or most of the best entrepreneurs in the world are heavily personally invested in this supply build out thing.You know, if you watch SpaceX's investor day yesterday, like this is basically the theme of it.Arguably maybe the greatest entrepreneur of all time is dedicated to effectively doing this, building out the data center, supply, et cetera.This is just like a historical question, like if that's all true and there's so much uncertainty about supply and demand and there are other ways to go build businesses, products, make money, why do you think so many of like the best entrepreneurs, the smartest people are so completely dedicating their lives to the build out of the supply?
Speaker A
This is a huge infrastructure buildout of a huge and important technology.
And it would be impossible for there to be a huge infrastructure build out of a huge and important technology if there weren't a strong price signal and a ton of talent going at it.So you may have your causality backwards.Obviously these things work both ways.But could you imagine a telling of the story where nobody interesting who'd made no money was building out the AI?Right, that's probably the best answer.
And to be clear, this is a huge and important project.It's only happening because super smart and talented people are focusing their lives on it.It's great that they are, they will have and deserve to make lots of money.It's just very, very hard from where I sit.And there are probably people who are smarter and figured it out, but from where I sit it is very, very hard to figure out where one would invest along this and whether or not you'll make any money doing it.
It's not to say that there isn't plenty of opportunity potential there.I've just decided to focus my attention more on the longer term equilibrium results that come from what the world goes to instead of betting on which railroad tycoon is going to make money and how many railroads we need.I'm trying to think about, okay, once you have railroads, I'd love to have the foresight to figure out that CPG brands come next.That's kind of the way I've been trying to think about it.
Speaker B
Can you riff a little bit about what you've learned about capital cycles and innovation cycles like this?I would love your perspective on having studied so much market history.If we're in the middle of one of these, maybe the biggest one ever, what does history teach us about these cycles and how they work that's interesting to you?
Speaker A
You could think about American society right now as like a household that's like saving money for a house, right?Like we're building a house and we're all kind of feeling pinched, right?Because all these resources are dedicated to like building out like this big AI infrastructure.All these electricians and plumbers and everything else are going to do these tasks.And as a consequence, there's higher costs elsewhere in the economy.And undoubtedly we're going to get a lot of value for it and we're going to keep pushing it because no one really knows we're going to keep pushing it until eventually we overdo it.Along the way, people have to make tons of money because otherwise people won't keep pushing it.And because things have lead times and because it's sort of a winner's curse, invariably the last person to stop will regret it.
And it's just so hard, right?It's just so hard.And then you combine that with the fact that there's just so much dynamism in the technology here, which I think was less true for sort of other historic, like infrastructure buildouts.But, like, you know, there's just so much change in terms of how you do these things.You know, I mean, other people have made the point, which I don't think is necessarily wrong, that like even going, you know, we talked about the models, but even at like the CHIPS level or whatever, that, you know, the potential for AI to commoditize, even things like
Speaker B
Nvidia or sort of recursive self improvement everywhere, like at the.
Speaker A
Yeah, like everywhere.And a lot of it ends up being like, just intelligence.Like Nvidia, quite literally, like makes flat files that they send to Taiwan, right?So like now their flat files are very hard to make, but, like, what they have is a lot of people who think really hard about it, right?
And so in theory you could make them.And also then the question becomes at what point?What if you step back a little from the frontier then the cost of making something comes way down in that world.
Speaker B
Would your model be that physical and scale are the things that you'd want in a business that you were to back on the supply side of this equation?
Speaker A
Tsmc, I don't want to carry myself around as some sort of TSMC expert.
Certainly what they do has better attributes.I think in that regard.They've also got the same of interesting problem where they did this super hard thing that requires all this process power built over many years and is super hard to replicate and much less amenable to an AI just because it is so physical and because it's all these little things that are many of which aren't even legible to an AI.It's like some guy who is in the system kind of knows that you have to do this because there's so much demand and because they haven't been able to meet it, they've basically caused every one in the world to seek to copy their moat.That's like not a great fact.
But I think more generally sort of stepping outside the AI build out.Like if you think about businesses, we talked about the meta example.Just the idea of businesses that have physical proximity or physical scale might benefit if they can use intelligence to serve their customers better or have lower costs as a consequence.
Speaker B
I want to go back through the natural complements to intelligence.Just have you explain why it's a complement to intelligence so then people can go away and think about okay, what companies have this feature that might be interesting.
Good example to go talk about physical proximity.How would you think about Doordash or Uber or these businesses that have local density effects that matter a lot.Does intelligence being ubiquitous sort of naturally magnify all of the scale winners of this type where physical proximity is some is some sort of key advantage.
Speaker A
Well, so if you're the low cost producer of something, I'll give a sort of stylized example.So like if everyone has an AI agent then what happens is many decisions get offloaded to the I agent because the AI agent helps you like optimize, helps you pick up all the like pennies and dollars and nickels and stuff that you like we're leaving on the ground.
And so let's take the example of.So I think about consumer choices in like a spectrum, right?Like on the one hand is like maximizing my bank rate, bank deposits.This is something where I think I can almost Entirely Defer to the AI's judgment.As long as it's FDIC insured, I just want the highest rate.
On the other hand, end of the spectrum might be like picking my favorite sweater.This is something where it's just all of my judgment.The AI can source me things to look at, but in the end I need to decide.I can't really delegate that judgment to it.And there's a spectrum.And so let's just pretend for a moment that garbage collection is something where I can defer the judgment.You might say, no, I really care about the guy and I trust the service and he's driving to my house or whatever.But let's just set all that aside for a second.So right now, if I'm in the waste collection business, I might be great at it, but it's still really hard to get people to bother to switch.They sort of have, like, I doubt you know what you pay for garbage collection, like off the top of your head.
Many people do, but like, I doubt you do.And so what happens is you have to do all this work to get people to pay attention, to bother to switch garbage collection companies.Even if you're offering a better service at a lower price.You could imagine that their agent could talk to my agent, be like, hey, I'll save you 50 cents a month.And the agent will say, will the garbage go away?And I'm like, yes, the garbage will go away and no problem, and I'll change.So the first order effect is that you move to the low cost provider.But the general market effects are actually sort of interesting too, because now let's say that there's a street, and before, it would be more efficient for society if all of the people on my street had the same waste garbage collector.Because the guy could drive up the street and pick up the trash and he could offer all of us a lower price and make more money.
But it's really hard for us to coordinate that because what happens is all of us are just lazy about switching garbage collectors.And so we all kind of have one.And the guy with two guys on the street can't call everyone else up and say, hey, I've got two guys on the street.Everyone else has just one.I'm the low cost producer.Let me roll this up.
A second order effect might be that the guy who has two people on my street can go to everyone else on the street and say, hey, I'll offer you 50 cents a lower price in the garbage, and everyone will switch.Now you'd get this local.Everyone moves to low cost producer who in turn gets to be a lower cost producer that actually creates in some sense a better business where the low cost guy wins a bunch more volume at much lower margins.
He explores how AI agents could change competition, pricing, and business structure, especially for local and low-cost services. The section also considers how always-on optimization could make prices more dynamic and markets more efficient.
Speaker A
So a lot of companies have mixed as you study them, a lot of companies have mixed effects that point in different directions because of these things.
Speaker B
What do you think the impact of agents is going to be on your world?
What will be your role?How will it be different in five years?Will the best agent be able to build a portfolio of Carvana and Hilton grand vacations and Meta in the same way that you would because you trained it how you think?
Speaker A
Well, hopefully better in terms of what I do.I think that in the long term we'll probably get competed out.
Speaker B
Pricing risk as the thing you do.
Speaker A
Yeah.When Computer Aided Chess came along, there was this long span of time where freestyle chess was better, where the human working with the computer program was able to out compete either the human or the computer by itself.And this is just because humans thought about things differently and computers thought about things differently and they were synergistic.And I think in investing for a
Speaker B
while, we're in that era now.
Speaker A
We're in that era.And that's because there are things that I understand in the world in ways that the model just doesn't by virtue of being a person and having lived life.And there's information I have about the world that just isn't in models.Because as much information is on the Internet, not everything is actually on the Internet.
While that's true, which is going to I think be a while, the ability of me to use models to assist me and then to make judgments, we'll make better judgments working together.You could imagine a circumstance though in the future where that gap diminishes to the point where it doesn't matter anymore.And then models, I don't think models will be able to predict the future.As we talked about, the future is in some ways very intrinsically unpredictable.But the model's distribution of outcomes may be the best that you can do.And that adding a human to the forecast would be kind of like adding my judgment to whether or not it's going to rain in seven days.Cliff, I don't really care what you think.I'm using the forecast.This is going to be better.However you fudge it, it's just going to make it worse.
That day may come.It's an interesting moment there.There's a lot to investing.You think about judging management or something, but Maybe, maybe the models will be able to judge management, they'll be able to interview them, they'll be able to figure it out.And then, and then it gets really interesting.Like, how do you raise capital?You'll be like, you know, I'd like to run an amusement park and model say, like, sure, here are like six locations that have a 51% chance of an amusement park working.And you know, as long as you're like really committed to it and I've assessed you and you have like a 51.2% chance of like succeeding because you're actually slightly below average, right?And you're like, okay, well what can I do better than average?And I was like, well, you know, have you thought about like running a, you know, go karting park?
But fortunately I don't think it's going to affect my career that much because I think it'll take a while.
Speaker B
What about replacing price as the organizing principle and signal that coordinates economic activity?
Speaker A
Price is a miracle, right?Because it's a signal wrapped in an incentive.
Speaker B
What does that mean?
Speaker A
So price is an incentive.It causes you to want to buy more or less of something because it goes up or down.And it's also a signal because it says something about the relative value of something and the scarcity of something and the cost to produce something.It's like a miracle of a mechanism to coordinate things.And I think you'd be really hard pressed to find a better system.
Even with like unlimited intelligence.It'd be pretty hard.
Speaker C
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Speaker B
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Speaker C
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Speaker B
can do more with the team you already have.Maybe another way of asking the question is kind of like investing.If ubiquitous intelligence gets so much better and so much cheaper and in some way it can replicate what you do and you're really good at pricing risk.A friend of mine, Dan was saying there's sort of three levels to this.There's I put in a prompt and I get something back.That's level one.Level two is I set up a watchtower.So I set up my instinct every night runs like a set of tasks and it happens in the background on a schedule.And level three, which produces the most use of intelligence is just always on proactive figuring out its own way to use its own intelligence.And that just goes on forever.And it's sort of unlimited in how much it can do.
If I think about that level three, applying to everything like what things should be priced, efficiencies, optimization, all this kind of stuff.Does that add something interesting to the
Speaker A
price sharpens markets There's a lot of stickiness to prices.It's a well known macroeconomic phenomenon.It's like price stickiness.There's a lot of lazily priced stuff.Think about deposits and such.I mean it's a really competitive world out there, but it's actually not as competitive as in theory like it could be.So I think if everyone has agents who are working on their behalf and constantly sort of optimizing, I think the price of garbage collection will move around a lot more as all the input costs of garbage collection evolve.
And every garbage collecting company is constantly rerunning its financials and trying to figure out its true marginal cost and bid every customer where it could serve for more than marginal cost.And every agent is switching to the lowest cost provider.That's just a much the price of garbage.You won't just pay $60 a month for 12 months and then have the guy come back to you and say it's a 5% increase, it's going to be 63 next year and and you don't even notice and you just like they're just picking up your garbage.Your garbage collection will vary by day and one week it'll be whatever time it there'll be all this interesting, much more accurate, much more rapidly Moving prices, I would imagine.
The speaker praises U.S. capital markets for rapidly redeploying resources into AI and supporting large-scale innovation. He argues that America’s risk-taking culture and institutions help explain its dominance in financing new technologies.
Speaker B
Have you seen anything interesting in capital markets?Innovation?There was this awesome economist story talking about the behemoth that is American capital markets and how good it is at adapting to new stuff and creating new, new instruments, new ways of financing things.And that, that's been as much a part of the American story as the technology innovations that typically happen to cause, you know, like there's this amazing interrelationship between Wall street and Silicon Valley would be one way of thinking about it.Anything interesting percolating for you in markets, in capital markets, in ways of financing things, things like this?
Speaker A
Well, I mean, it's fun to take a moment to pause and maybe congratulate ourselves as a, as capital markets for what we're doing with AI.
People were worried about this, a decline in American dynamism.I think we just redeployed, what is it, 3% of GDP right into this highly speculative venture, basically on the turn of a dime.It's just this remarkable thing.Of course, who knows, maybe it should be two, maybe it should be six.But it is amazing that we can redeploy resources at this scale this quickly.
And there's no one else in the world that's been able to do it.
Certainly the inability to move investment in productive resources can be a binding constraint on productivity growth, even with, especially with rapid technological progress.And so at least with regards to capital, and obviously we talk about capital, but the capital investments are tied to people doing different things.Americans are doing different things today.Every day people are waking up and going to someplace in Pennsylvania and building a data center.That wouldn't be happening if it wasn't for this whole infrastructure we have that's somehow been able to move this enormous amount of capital.It's incredible as deep innovation.I think the fundamental idea that people are taking risk on speculative adventures and sort of slicing up the pie in different ways is sort of irreducible.I think people come up with lots of different names for what are essentially the same things over time.I don't think that's like deeply different, but it is incredible.
I should also note that like, I'm sure we'll discover all kinds of like fraud and bad behavior and stuff eventually, like when, when like the water goes out.
But I think given the magnitude of this, I suspect that it's so clean, like it's amazing.Like if you read about like the railroad build out or something, like the amount of waste and fraud and whatever, invariably there is some.But this is like there's a lot of people Trying to basically do the right thing here and the governance parts that seem to kind of, we just take for granted, like it's incredible.America's capital markets and the capital markets in general, I think are just like incredibly underappreciated.Like the idea that any person can sort of buy $100,000 of stock and get a portfolio of well run, well motivated teams building companies that are basically doing mostly the right things and do it basically instantly and basically no cost, and then sell it as soon as they're done is amazing.And then the fact that all those companies and other ways people invest can then allocate society's resources pretty well in the scheme of things is just, you know, just incredible.
Like the whole system is just very underrated.
Speaker B
Why has America so dominated this, do you think?
Speaker A
My pet theory, and everyone has their own theories, is some sort of interplay between genetics, cultural evolution and institutional evolution.So basically like the people who came to the United States were the subset of people who were most interested in taking risk because going to the United States was very risky.
And I think that risk taking has a meaningful, like genetic component.And so I think that that tendency is just more common amongst, you know, Americans.When you're in a place where risk taking is more the norm.There's also a whole bunch of like other things that come from that.Like it's more the norm.And then that leads to buildups of cultural and institutional things that reinforce that it's understood that people can take risk and fail.That leads to bankruptcy laws that make sense, that leads to labor laws that support that, that leads to venture capital that supports that, and all of those things reinforce that.And then that leads to success stories.So you get kind of this whole soup.But that's my pet theory.
Speaker B
So if there's a moon base, at some point you're going to be bullish on that community's ability to do really well economically.
Speaker A
Yeah, actually that's a great way to put it.The United States has been persistently like innovative in like a very amazing way.And so if there's a moon base, I wouldn't be shocked if it's ours.Even though the Chinese are brilliant and numerous and the Europeans exist,
He connects higher rates to society saving for future AI infrastructure, then discusses his concentrated portfolio and how he would advise portfolio companies. The advice centers on broad internal AI adoption and building systems that give durable advantage.
Speaker C
When you
Speaker B
see rates go up, you see that great long term chart of rates, let's say the 10 year rate in the US and it just looks like this forever, basically our whole lives.And I was going comment on that chart.
Speaker A
Well, I mean there's so much, and I'm certainly no macroeconomics expert, in order to Spend too much time thinking about it.But I'll make a couple of observations.
One, obviously is just in general, when society has like your society and you're making a giant investment in the future, means you're gonna have to consume less today.Like fundamentally, all of the resources going into this AI build out are about the future.Are about the future.And fundamentally, like the plumber building that today is not making consumption today.Like he could be making today's present consumption, instead he's making a data center which is future consumption.
So one piece of a very complex puzzle that I have thought a bit about is this idea that rates are higher because we're inducing people to save for the future.And the way you do that is you pay them.
Speaker B
Simple people would kill me if I didn't ask about your portfolio a little bit, which is so interesting to me.It's like 80% Carvana Vacations, 5% Meta is basically your portfolio.But rough justice, it's like five things and one thing is 80%.
If you think about your portfolio and those companies which obviously know extremely well and you were advising them on how to think about using AI to transform their businesses, what's that speech look like?
Speaker A
Well, it starts by making sure that everyone is vibe coding, starting at the board and the CEO and everybody needs to start building some, they need to have hackathons.And of course I brought this to Carvanha and they said, yeah, we call them pizza parties.And I don't mean the developers, I mean everybody needs to have hackathons.And it's not so much that you're going to build hugely useful software necessarily that comes out of these hackathons, but it's that you're going to teach everybody what can be done and that's going to change their expectations, I think, for
Speaker B
what can be done.
Speaker A
And then the other thing, the second thing which I tell them is the CEO needs to basically turn to the head of IT and say something to the effect of so I want my Grokbot Metamuse agent, whatever, to be able to access every system in the company on a read only basis.Everything I could look up if I asked somebody, I want to be able to get that in a prompt.The guy's going to say, that's really hard.We have umpteen trillion problems with that.And he's going to say, that's my point.I want that.
That's a big process and it's system by system and all the rest.But if you can get to that, then that gets you read only.
Speaker B
Access to the starting line.
Speaker A
Yeah, to the starting line that gets you read only access.And then the next logical thing is a CEO or whatever needs to use this everywhere.And everyone is going to get jealous and you're going to say, oh, you can have it too, but we need to work out your permissions.
And then that in theory percolates down and now you've got permission to read only access.And then invariably someone is going to say something like, I'd really like to put data into the system.And you're going to say, that's fantastic.We got to work out, write permissions.And then someone's going to say, I'd love to change a system because we just did this vibe coding thing and I'm confident we could do it.
And you're going to say, that's also fantastic.We need to work out a process by which we're going to manage changes to the system.
What I just described is, sure, it's like four bullet points, but that's like impossible.Years of work, it's impossibly hard.So the question is, are you in a business where you, you have time for that to happen before someone who
Speaker B
starts, before the jump ball, someone else that starts from Go meets you?
Speaker A
Exactly.
And so it's good to have a really good business.
Speaker B
It seems like one of the conclusions from that is it's the best time ever to start companies, both because of capital market conditions that people will fund at fairly low cost of capital, highly experimental things, but also just because with these tools, if you start without the baggage, you can go so far so fast.You think that's roughly right?
Speaker A
I mean, yeah, it's the best time to start a company until tomorrow.Right.So I agree.It's amazing.I'd also just say that, like, just thinking from my perspective, right, like, you know, people are such a pain in the butt, right?Like as a, if you're running a company, like they're such a pain in the butt.And the idea that you have all these agents and stuff, it makes it so you can do so much more, like with so many fewer people.
Of course, you know, one of the challenges always is like, if you can sort of easily do something, then so can everybody else.So there has to be some wedge by which the thing you build produces like enduring value.Does it have a path where you end up with some sort of like lock in or proprietary data in this world where all the agents are happily changing providers every day, like, what gives you any enduring economics?But then of course, then there's the further thing, right, Is that obviously if you do the thing where it seems like there's an obvious path to enduring economics, then that's a bad thing to do because everyone is trying to do that.So it's very competitive and the really great outcomes are the ones that make no sense.
You shouldn't do it at all.But then it turns out there is a path where the world turns.
Speaker B
What do you think are the most underrated sources of future competitive advantage then?Like if ultimately good businesses need sustainable edge, sustainable competitive advantage to have great profits.Anything underrated?
Speaker A
Underrated.
Speaker B
We talked about attention and data and physical proximity.These are examples of things that might be super or more valuable in the future than they are today because of cheap intelligence.Is there any type of.Maybe it's regulatory capture.
Are there any types of edge that you think are underrated?
Speaker A
I get the sense that a lot of these are appropriately weighted.
I guess.One interesting question I've had, I'll have two of these.One is, does this make margins?If you think of corporate margins in aggregate, do they go up or down like this, right?Do they go up or down?
And I think everything we're talking about was sort of argued down.There's many businesses where they make margin
Speaker B
because more consumer surplus, more consumer service everywhere.
Speaker A
What's interesting is if you go back and read a lot of the research reports from the 90s when the Internet was coming along, they said the same thing.So the base rate here might not be great.Like maybe we're wrong, but I think the main reason they were often wrong was like, was this aggregation economics, the Ben Thompson aggregation economics, Right?So maybe absent that, maybe it wouldn't have been so wrong.And I do think that like a lot of aggregation theory breaks down when.Because that basically relies on the idea that consumers only look at the first thing on Google.But it turns out if your agent looks at everything on search, then being ranked first doesn't matter.
All the core things like quality and price and everything else, service become more salient.But the other thing is that maybe another downstream effect of this is that you'll be able to build more complicated businesses.Right now a lot of industries are more horizontal in structure because the complexity of running your slice is very high and that makes it hard to do what's above and below you and not have undue complexity.With AI agents and basically cheap and ubiquitous code and intelligence, you could build the whole vertical stack.People are trying to do this, I think in other countries, but you can have a full stack pet Care business that does everything from make pet food all the way up through pet insurance and pet hospitals and pet vets and all these things that historically were multiple layers, but now, because you can solve the coordination and reduce the principal agent problems by having AI do it, maybe those sorts of businesses are possible and then maybe those businesses are higher margin in the end and sort of more efficient and more consumer surplus.
Carvana is sort of an example of that.Built before, this was easy to do, but maybe people can do it in other verticals, in which case maybe margins go up and there's consumer surplus surplus.
The conversation examines whether AI will compress or expand margins and how it may affect income distribution. He argues that cheap intelligence could both flatten rents and create new forms of concentrated advantage.
Speaker A
The other one is one thing too is just how this affects wealth, income distribution, right?I can tell two stories.On the one hand, I think a lot of people have talked about this idea that it concentrates income distribution.You've got all these people who made money on various labs and whatever data Nvidia, and this idea that there are increasing returns to small groups of people.But the counter argument is if we have for cheap and ubiquitous intelligence, a couple of things happen.One is there's all these rent takers in the economy.Think you're the software company that charges bowling alleys, like, you know, whatever, right?And those guys all get smoked, which sucks for them, but it does lower the cost of bowling.
And so then that increases living standards for everyone who bowls, right?And that's flattening because my spending on bowling is small proportion to my income.But like people who make less money spend more proportion to their income on bowling.And so that's a flattening effect on income.If all these little rent pieces go away, the decline in corporate profits, all the money that the average American is giving to bank of America goes away from bank of America.The capitalists lose, the average consumer wins.So that's leveling.The other piece is there's a whole professional class who basically you could define their role as intelligence.Intelligence, right.They take big corpuses of information and apply them to certain circumstances in kind of a fairly like, you know, straightforward way.
And if those all get commoditized, one, everyone gets the best ubiquitous, which is one, good for everybody, but two, particularly good for people who can't afford the best right now or any.But then three, those individuals, they earned these extra rents because they were smarter.They then have to drop to their next best alternative.And in theory, then that might flatten the curve.Now, the counter argument to that might be something like, well, yes, but returns to initiative and ambition and intelligence could go up because now I can do so much more because These models are like a bicycle for the mind on steroids.
They, they give people who are smart and have initiative the ability to accomplish so much more.Maybe that actually causes increasing returns to that subset of people and it steepens the curve.Truman asked for a one handed economist.I seem to very distinctly have two hands on everything.But I think that's what makes they're big interesting questions.They're super interesting questions.And I find that a lot of situations I look at the, there's a lot of cross currents like on the one hand, everything we started the conversation about the AI build.On the one hand I can see all these incredible ways to use AI and I use a gajibillion tokens and I can see how I could use a gajillion more.And obviously I'm in the far right end of the distribution.Everyone uses as many tokens as me.We are so short.It's going to be this huge thing that's going on forever and everyone who's in the build that's going to make a fortune.
On the other hand, the amount I get per token is like going to the moon and, and it turns out I'm a pretty unusual guy in a lot of senses.And people are going to adapt more slowly and the cost per token is going to collapse.And so maybe actually we're totally overbuilt.Right.And I can tell both stories with equal certainty.
Speaker B
I have a good friend who has been running this very cool experiment where if you break investing down into picking what to own and then how much to own of each thing that you've picked.Overly simplified but kind of interesting way of thinking about it that he's been running this experiment where he continues to pick the things to own but then runs his own portfolio construction as he would have in the past in one lane and in the second lane is just completely handing that off to an AI harness thing that he's built and says you do all the portfolio construction but stick with the stuff that I've picked.And what he's found so far is that that is drastically outperforming his version of portfolio construction which itself is quite sophisticated.
The simple argument that he would make is well, AIs are just really good at optimization and they've read every paper on portfolio construction and all this kind of stuff and bet sizing and Kelly criterion and they know it all.And that in the future perhaps we make that transition where the investor's job is just picking what to own and not how much of a of each to own.And I ask you this question, what you think about this idea and the future because you have such an unusual portfolio where again 80% of it is in one specific asset.And my guess is if you were in lane two, the AI would weight your portfolio very differently than you have chosen to do it.
Speaker A
I'm underweight,
Speaker B
So I'm dying to know what you think about, about this idea.Underweight carvana should be 90 timescales.
Speaker A
Probably are interesting on that to think about too.As I think about how AI is going to encroach into this business, I think it's going to start at shorter time scales and work its way up to longer time scales.Because one of the challenges is that there's no verification loop.
Your friend's ability to say that is based on results over, I mean certainly not 20 year results.I strongly suspect that like the march of AI into markets and I mean it already started, right?Like I don't know how Renaissance technologies did it, but like I assume seems
Speaker B
like they might have discovered a transformer for everybody else.I'm not kidding.
Speaker A
It wouldn't shock me if people who invest over shorter time scales, starting with seconds and working their way out to minutes, hours, weeks, months, find themselves handing more and more, delegating more and more to models.Makes perfect sense and that you could definitely imagine bifurcating that between trying to enrich the model.I think other people do things like this already and this is just a more sophisticated version.But you can have a fully sort of quantitative strategy and then you can enrich it with what you think is qualitative kind of human designed analysis.And then you can have a quantitative strategy to incorporate the qualitative.I think many of these multi strats do these sorts of things and what he's doing is kind of just a slightly different flavor of that.It makes perfect sense.There are so many ways to be successful and models have just added more dimensions of freedom.
Speaker B
But what about your own portfolio?Say a word more about how you feel comfortable having a position.Forget the company that it is that's that big in the face of so much research that would suggest that's a crazy way to build a portfolio.
Speaker A
A lot of that research I think is more looking at price changes in terms of securities prices and less focused on thinking about it the way someone who owns a company might think about it.We're talking about Carvana.Were it not for my sense that Carvana's infrastructure and brand and everything else is just so differentiated that there are few scenarios where it works out really badly, then I wouldn't feel so comfortable owning so much of it.It might just be as simple as that.
Thinking over that portfolio approach would have worked.Absolutely.But certainly if you discovered Walmart or Home Depot or Fastenal or something back in the day, you wouldn't have taken full advantage of it.
And that's kind of my sense with Carvana.If you read history, of course all the people who did really great tended to be very concentrated.But of course the history is literature.You don't read the history of the Barbara should bias.
Speaker B
Yeah.
Speaker A
You don't read the history of the people who didn't.I also think that a lot of that literature is sort of what most people who are looking for average or slightly better than average results should do.And it's totally the right thing to do.But that's not what I'm looking for.I like Carvana a lot.
It wouldn't upset me any if stock went up a bunch and I felt compelled to diversify.That would also be okay.It's just more of a trade off between expected returns and sort of knowingly taking an unusual risk.
The final section focuses on how education should change when models can do so much. He argues that kids still need foundational skills so they can direct models effectively, while also learning to use AI for more ambitious work.
Speaker B
What do you think about education?You and I have kids of roughly similar ages.
Comment a little bit on how you're thinking about their educations, the institutions that have driven education in this country that have been really important in that whole path dependent story you've described.What do you think the present and future of education looks like or should look like?
Speaker A
I've been like talking to the head of my kid's school about this and more and more it seems to be that his thinking and my thinking are heading in this direction of like the way you're educating kids is getting more bimodal in terms of what you're looking for.Right.Let me step way back.
What is the benefit of intelligence, personal intelligence, like your own intelligence in a world where there's infinite intelligence for models and you might say, oh, it's useless, like, doesn't matter.Like someone with a 50 IQ can navigate the world just as well as someone with a 150 IQ.And I don't think that's quite right and intuitively doesn't feel right.But I think to nail down where it is, is that the more intelligence you have and the more crystallized intelligence you have, which would be said of education, the better you are at communicating with a model and telling a model what you want.So like I would have a very tough time like having a deep conversation with a model to like create, leading like quantum physics experiments Because I don't know anything about quantum physics.
And the model could try very hard to explain things to me, but in the end, it would be explaining things in crayon and Nerf.And like, it just like I.We just, like, it would just be very hard to get to leading edge and like, have a.And get like, the best out of what the model has to offer.Whereas in an area where I do have some expertise, the model and I can communicate kind of effectively and I can direct the model to my will much better.
And so there's a super important to direct a model to do your will, which I think is going to be an important skill in the future.You do have to understand something about the world, and smarts help for that, and education helps for that.You can't ask it to assume it's a stochastic process unless you know what a stochastic process is.Now, certainly it could teach you what one is, but that's a lot of friction.Instead of this figuring this all out, you don't have to work out the math, but you have to know what it is.
When I've been thinking about education, there's this idea that you're teaching your kids a bunch of foundational skills that build on one another.Where I think in the end, the sum total of those, they have to be able to do them kind of without models, not because they're going to do them without models in the future, but because if they don't know how to do multiplication, it's going to be really hard to understand matrix multiplication.And if they don't know what matrix multiplication is at a high enough level, they don't have to be able to do it really well, but they have to be able to understand what the model is doing when it's doing it.And that's so they can direct the model.And so, increasingly, I'm of the view that we should spend a bunch of time with kids basically separated from these models, teaching them basic skills that we think are important.
And what those are is a really hard thing.But the idea being that, yes, model can do it, but you need to know it so that you can tell the model what to do.And then on the flip side, some English teacher is like, I don't like models because, like, the kids just cheat on their Shakespeare essays.I'm like, you're not being ambitious enough.Right?If the kids are cheating on Shakespeare
Speaker B
essay, give them something harder to do.
Speaker A
Give them something a hundred times harder and more ambitious and have them use models to their fullest extent, have them bring the work product back and then make sure that they didn't just give a prompt to the model, do the homework, make sure they own it, ask them why it's like this.Right.Force them to justify it.Right.And I think that's another really important thing because kids need to be good at using them.And they also, I think frankly, just need to be taught that they can do, like, anything.
Speaker B
You and I get to talk every so often on the phone.Every time I wish it was recorded so that other people could hear it because I learned so much.
And I think in this particular conversation today, which is sort of a version of a call we had several months ago, it's just such a valuable set of food for thought about the future of intelligence and what that means for markets and companies and is a little bit not in opposition to, but a different perspective than the most common one that you're hearing, which is sort of unlimited.You know, unlimited build out forever, short everything forever, no cap on the demand for intelligence.And I've loved talking to you about it both on the phone and today.So thank you so much for your time.
Speaker A
Thank you.
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