# AI Token Economics: Reshaping Business Models & Capital Allocation

**Podcast:** web3 with a16z crypto
**Published:** 2026-07-27

## Transcript

For the first time, it's easier to think about token spend more like headcount.
The question no longer becomes how much work can we do, but rather how much money we can be allocated to the token budget in order to get work done.
The absolute numbers have gone from maybe a few thousand a month to a few hundred thousand a month, and in theory could go to a few million a month.
And so how we think about that is super interesting.
People have speculated since the AI started getting good that we're going to see at least like billion dollar companies run by like one guy, a bunch of agents.
Maybe we'll see variations of that where we see much leaner companies.
We also might see businesses that are like much more horizontal.
If everyone...
Hello and welcome to the A16Z Crypto Show.
I'm Robert Hackett, an editor here at A16Z Crypto, and I'm here with two of my colleagues, Guy Ouellette, GP at A16Z Crypto, and Noah Citron, Head of Engineering.
And today we're going to talk about AI and the way that it's changing business.
So...
I'd like to toss it to you, Guy.
You know, everybody sort of understands that AI makes things more efficient.
Maybe it makes people more productive.
It makes things cheaper.
That's sort of like the basic surface level understanding the debate that's going on right now.
But you're thinking also about how the nature of the company might change in this new world.
Tell us a little bit about that.
Well, Noah and I were having a discussion yesterday, which was a lot of the inspiration for this.
And we started by talking about...
how many tokens each person on his team on the engineering team are consuming which i think was a good jumping off point for basically a discussion on which sort of work is productive and how to think about uh like firm formation you know we as a venture capital firm want to invest large amounts of money in companies that need a large amount of money and will then you know hopefully be fantastically successful and uh occasionally we'll the object failures.
I think there's a whole new opportunity or space in the market for a sort of company.
that needs variable headcount that can be run in a much more asset and headcount light fashion today because of AI models.
It does not need the equivalent of something like venture capitals.
In fact, not a particularly good fit for it because the terminal outcome, the success case, is not a big enough success for most venture outcomes to make sense.
And so we're talking a little bit about the financing models for that is something like convertible debt.
Do these end up looking a bit like a modernized form of private equity or a bit like a builder form of pod shops as opposed to a quant and a trading form of that?
But I think an interesting place to start is just by asking the question of for a long time, it was hard to spend a lot of money on AI inference on models.
It has become much easier to spend a lot of money on AI inference.
And we were talking a bit about that shift for your team and essentially how much everyone is spending.
which I thought was like an interesting number.
And that's because a lot of the leading model, there are newer models being released that allow you to use a lot more computation.
They're a lot more computation heavy and so they are a lot more expensive, but they do a lot more.
And there's a lot more of that complexity they can handle.
I think the fundamental change was that, you know, a year ago or even less than a year ago, six months ago, we really didn't even have to think much about our token spend, mostly because our ability to scale tokens was limited.
So we were able to...
you know, spend, you know, almost within our subscription limits for the most part, maybe going a bit into the API pricing, but, you know, not really a significant spend.
But in the past, maybe three months, we've noticed that You know, our ability to get more work done has basically started to get more linear or at least linear for a little bit longer with the amount of money we're willing to bring on tokens.
So now the question no longer becomes how much work can we do, but rather, you know, how much money can we allocate to the token budget in order to get work done?
And, you know, this is this kind of changes the paradigm a lot.
So I think for the first time, like it's easier to think about token spend more like headcount, right?
Where.
An engineering organization can scale headcount, you know, assuming you have like unlimited capital, you can continue to scale headcount and barring any organizational inefficiencies, you can just keep, keep, keep scaling that and get more worth.
And then you have to, you know, answer the question of how much work to do is valuable, right?
For like our given business units, like, you know, where can we allocate resources to actually increase revenue?
This was not the way we used to think about it, but now it is absolutely the way we think about it.
So, you know.
we see you know for the first time like i i you know talk to my team about how much uh they spend on tokens and every once in a while i i hear you know on some given week where there was some heavy work being done and i go oh that that could cause a problem i had never been put in that situation before um so you know now we're trying to grapple with you know both how do we manage those costs, but also how do we decide how much we should spend?
When is it too much?
Yeah, I think there's a very interesting concept here, which like the concept of a P&L mostly comes from finance, that someone is like running a book of business and they have a profit and loss statement.
And it seems like...
that was never a concept for an engineering team.
Like even if you were an engineering manager, you didn't run a PNL, you like got a certain amount of headcount and you were asked to do a certain amount of things.
And now in many senses, if you are the one controlling token spend, like you are running some form of PNL.
you have at least a loss in the form of the tokens that you're spending.
And maybe there's a way to attribute the work being done by the token spend to some profit or income stream for the business itself.
And I think like that shift specifically is very interesting.
There's been a whole discussion about return on token.
Like if I'm spending this much in tokens, what am I getting back?
And I think that's...
Ben, because exactly as you were saying, it was very hard to spend a lot of money on models and none really cared about the return on token or the return on investment.
Now people do because the absolute numbers have gone from maybe a few thousand a month to a few hundred thousand a month.
And in theory could go to a few million a month.
And so how we think about that is super interesting.
And it also then like implies we don't do a particularly good job, I think, like in the economy broadly quantifying the.
ROI of each individual person's contribution or labor to like the broader organization.
I think in most tech companies, it's very hard to A-B test.
Like what if we had this one person and we took them out of the org for a week?
How would that impact the overall P&L and the overall productivity of the company?
So many tech businesses are fundamentally network effects businesses where you get to a certain scale and you almost kind of can't screw it up.
as opposed to more traditional financial businesses where they often explicitly require someone to take a few weeks off to see how their book and their P&L is affected.
Oftentimes this is done for regulatory reasons to see if they're marking their book correctly, but it has super interesting implications for basically A-B testing the quality of token spend or A-B testing the quality of work, so to speak.
And so I think this is...
Obviously interesting in the context of an engineering team trying to build something, but also very interesting in the context of like these sort of micro businesses where maybe it's a sole proprietorship or a small team building a company that they entirely own, largely a software product.
but not going to be a venture scale outcome.
Like how do you finance that token spend?
I think it's a really interesting opportunity for crypto and for blockchains if you're able to underwrite like a specific return on token and then make micro loans or, you know, sort of small convertible equity or debt investments.
Like that, I think is a super interesting space.
It's probably a very interesting like emerging manager space from an investment perspective.
It's very much not our focus.
You know, we have a sizable fund and want to make large investments in companies that will either conquer the world or, or ultimately not succeed, unfortunately.
But I think there's like a whole sort of liminal space for uh, lots of smaller businesses.
And this is a lot of what we were talking about yesterday.
You're right in like the description of a finance company where, you know, a team might have a book and have some P and L and then you can like pretty easily quantify how good of a job they do.
But then when I look at how we do that on, on our team, you know, I, I kind of have a reasonable sense for what our token spend is on, for example, a given week or given month, talk to everyone on my team and see, you know, what they've been doing and can make kind of the, the qualitative assessment of, you know, was that spend worth it?
But unfortunately, like this is.
for a large swath of business is probably going to be the way that it does work.
I think the way I would segment these two things is there is the category of businesses where you can look at your token spend like headcount.
And for those category of businesses, you...
kind of a very fuzzy sense of return on token you have to do what i do but then there's a separate category this is where uh you don't look at tokens been like headcount but you look at it as like um some some other kind of cost in your business unit that you can scale up or down quite rapidly and in that case i you know i i think like you said there's probably like a more limited subset of businesses where that makes sense for but you can do uh some interesting things and i think some of the things we were talking about is you know, are there like subsets of, you know, businesses that look more like the headcount business where you can actually like start to try and quantify return on token for like more limited subset of things.
So, you know, example we had talked about was like an A-B test.
So, you know, even though we may have like, for example, some network effect business, like a social media company, these businesses love to run A-B tests and, you know, their goal is, you know, we will try two different features and just measure, you know, how does retention return get affected by that?
And, you know, now we can instead basically point tokens at something and say, okay, let's allocate a token budget for, you know, A-B tests and, you know, we'll build some potentially like very autonomous agent system that is constantly making hypotheses of like, okay, what can we A-B test next?
And implement it, deploy it, you know, potentially without even a human in the loop and then measure, you know, how do your metrics change, how does your retention rate, how does your returns change?
change uh in those different a b tests but also now the the additional unit you get is you know how much did it actually cost you to implement that feature and we can start running those pipelines and just you know continually measure is this single pipeline actually going to be continuously improving the business and i suspect this is uh maybe a good way for us to kind of pipe in this, you know, more P&L like analysis into a business that like looks more like a business where you view your engineering work as headcount.
Yep.
There's sort of like two categories here of being legible to tokens or like legible to capital as a proxy via tokens.
One of which is the classic like CAC LTV concept of if I put dollars in, I get dollars out.
And I think a lot of...
Customer acquisition cost and long-term value.
Yeah.
Essentially like...
if i acquire a customer this is how much it costs me and for as long as they're using my product this is my payback period so uh in that sense from the the investing perspective you kind of have like a implicit irr on on that and a lot of classic web 2 businesses that did very well had like wonderful cac to ltv ratios and you could you know throw a ton of cash at them and so they raised a lot of money and they they grew very quickly i think that's like the classic notion of being sort of legible to capital from a product sense.
I think the extent to which you're now like legible to tokens would be the ability for the product itself to change and the like CAC to LTV ratio to improve with the number of tokens you're spending on improving the product.
And so in some sense, you've made this even more quantitative.
And I wonder the extent to which this is just like a whole new domain of of statistics.
Like in some sense, astrophysics and sports analytics and like quant trading are all the same job.
And I wonder the extent to which software engineering is now just like another domain of the quant.
I think if that's true, there are lots of interesting implications, both for very big firms and for small firms, especially for smaller firms.
I think maybe you now have this invention of the software engineering pod shop.
where you do have small engineering teams that like do actually own a P&L for a specific product and get a slice of the finance way of saying this would be like get some balance sheet where you get money that you can spend on tokens to improving your product, trying to improve your CAC to LTV and then, you know, putting money into that itself.
I feel like you did not like this idea or this analogy of like the software engineering pod shop.
So tell me where that breaks down and where that's wrong.
Also, just break down what is a pod shop for people who aren't familiar.
It's a concept borrowed from the hedge fund world.
Yes.
Maybe you could just elaborate briefly on that.
I think in finance, there's kind of this classic evolution of the principle or the.
the person that starts a firm is very good at one thing.
So maybe you're like a good trader to start with and you trade a small set of assets and you like move to trading a wider set of assets.
And then you sort of graduate not from doing the investing, but to picking other people that can do the investing.
And this has been the path of many successful managers and hedge funds in the last couple of decades.
You sort of like start with a game, you move to a specific, this is like the classic, you know, poker.
trading manager selection, like now I run a pod shop.
And the notion of a pod shop is basically there's one overarching fund that picks a bunch of portfolio managers or individual investors who each run their own teams, and they try to come up with alpha or signals, essentially, that can be traded by the firm overall.
And the compensation model is a bit different from classic 2 and 20 investing, where the pod shop or the PM will will get in many cases something like half of the returns from the the alpha the signals they they generate you know the overarching firm or team or company gives you often data supporting infrastructure uh a talent pipeline oftentimes mentoring and and sort of help and training uh but also importantly a balance sheet they give you money to to trade and one of the questions is if i have a hundred dollars and i have five pms each running their own pod like who gets how much of that that dollar And this is a very simple, straightforward, obvious question when there's like one manager either running a software engineering org or like one manager running a P&L in a trading context.
This is like then its own sort of meta domain of investing or trading or engineering where you have to allocate resources amongst people that can use them effectively or not.
If you believe that building a software engineering product is becoming more quantitative and software engineering products that are legible to tokens will do very, very well.
then that seems to be a super important skill that regardless of if you're at a big company or a small company, in the same way you used to get allocated a headcount budget and your headcount budget was relatively static.
That's not true with tokens.
You can like drastically increase your token budget or reduce it overnight.
Yeah, it's sort of in this direction.
That's why I think there's an interesting analogy between something like a pod shop and a software engineering org.
If...
you know, you sort of believe that a lot of the spend in a software engineering org will go to tokens and engineers can be significantly augmented and improved with the AI models, which I feel like is just true and everyone takes for granted, but maybe someone will disagree with me.
So the Podshop model is effectively you put a bunch of teams on the field, you say, go win by whatever means possible.
And then the ones who do win, you give more resources toward and it's a little bit evolutionary.
kind of darwinian in that way yeah i think there's specifically the interesting uh pattern that someone starts with like a a lower stakes game like they master chess or poker and then they move into a bit more of an amorphous structure like trading and they become very good at that and then they sort of graduate to not doing the thing themselves but selecting other people that are very good at doing the thing And I do wonder the extent to which being an excellent software engineer or software engineering manager will in the future be more of a talent business where you are really good at selecting people that are good at spending tokens well, as opposed to spending tokens well yourself or even writing code yourself.
It reminds me very much of the same reckoning that came for the advertising industry.
There's that famous line, you know, I know I'm wasting half the money I spend.
I just don't know which half.
But then you got digital advertising and it became very clear, you know, you have you can target hyper target certain demographics and actually see how much return you're getting on your spend.
It seems like it's that same kind of concept now being applied to engineering and tech teams.
Yeah, but I do think I have maybe some comments and pushback on the pod shop analogy is that I think one is that depending on the business, it's very hard to...
You know, you may be able to measure like your kind of your gross P&L, but it's not necessarily easy to understand how much each kind of pod would contribute to it because, you know, unlike a pod shop where it's, you know, very obvious, like everyone gets a book, they run their own trading strategy and they get a P&L from that.
When you kind of centralize it more to being that you have each of the pods, but they're all, you know, working on different parts of the same overarching product, you know.
what does each one contribute to the P&L and how do you measure that?
So I think there are maybe, you know, businesses where, or engineering organizations where each pod can actually be a separate business unit, in which case this makes a lot of sense, right?
Like you can imagine like almost doing like a AI engineering P fund, right?
Where you're, you know, buying or building and scaling a bunch of like small normal businesses.
And, you know, each pot is its own business, in which case, like measuring the P&L is really easy.
And then you can do this thing where you kind of direct the token funds in that direction.
But if you're instead saying, I have one product that has, you know, like one engine that generates revenue, but there's, you know, a hundred different pieces of that product that work together to keep that revenue generation engine healthy, you know, it's much harder to actually measure that, which makes the allocation problem harder.
And I think this is why it like it keeps going back to.
headcount problem, right?
Like where, you know, I think that engineering organizations already have this like similar dynamic to pod shops where, you know, you have a bunch of engineering managers in your organization and, you know, you need to decide, you know, how big each of their teams are going to be, what their budget is going to look like.
And, you know, we...
qualitatively look at the performance and decide, okay, you know, this guy's great, so he's going to be promoted or we're going to allow him to hire more people or give him a higher token with it, right?
I think most businesses are going to be stuck doing it that way.
Potentially, the more interesting thing is not the legibility of Return on Token because I don't think the existence of AI makes Return on Token that much more legible.
But I do think it allows us to scale up and scale down our token usage much more aggressively, which may in turn cause us to be able to kind of have slightly better analysis on our return on token because we can do these things like ABA tests where we can very briefly be like, all right, we're going to allocate this guy a ton of tokens and see what he does with it, which you can't necessarily do if you've had counts without potentially breaking your organization.
Yeah.
Yeah, I think there are probably lots of businesses that have the natural...
requirement or path of being like five people and then like needing or wanting to be 100 people.
And then like once again, needing or wanting to be five people.
That specifically probably influences a lot how you think about the shape of a business and also how you think about financing the business.
Lots of people raise money because they need to hire a headcount and you're sort of committing to a specific headcount for a long period of time.
And there's no idea that like that headcount will reduce after we've built something.
Especially in a venture context, like a startup is momentum, where it just kind of keeps getting bigger and the ball keeps rolling.
And that is very much the idea.
But I think there's maybe a different sort of company that needs for a brief period of time, a lot of money to spend on tokens and then builds a very repeatable, retentive engine that you can put money into in the CAC to LTV sense and receive money out of.
I could totally see there being either individual companies like this or, you know, holding companies, fund structures to build a bunch of these different things.
And that seems like a skill set that's probably translatable across these businesses.
So I think that itself is a very interesting opportunity for, you know, new funds, new managers, new investments.
And I also think that like maps perfectly to what we see working on chain today.
which is the ability to do a very granular, small-scale, either loans or investments in a programmatic sense to companies or agents that...
are doing something relatively deterministically.
It's very much like not a Wall Street model and it's very much like not a classic Silicon Valley venture capital model.
And I think that specifically is super interesting to me today.
I think this brings up an interesting point though that maybe the better analogy to use on how tokens affects businesses is not to look at pod shops, but to look at consulting businesses, right?
Because you talk about how some businesses...
you know, might need a bunch of energy devoted into one area for a period of time and then need to scale it down.
This is, of course, like really hard to do organizationally, which is, you know, people argue all the time about why is it that businesses hire consultants in the, you know, it's a big, big question.
But I think one of the like standard answers is like, I have this like very particular problem that I need to solve and I need to solve this problem right now.
And I don't, and it's a one-time problem, right?
And I'll, I'll throw the consultants at it.
Right.
And I can say, like, maybe I'll just throw the AI at it.
I think there are like two reasons that management consultants really exists often.
And I'm sure there are things outside of this.
Like one of them is justification.
Like the core team already knows they want to do something, but they need some in theory, third party, unbiased, you know, entity to confirm what they already want to do.
And they're willing to pay a very large amount of money for that to exist.
It's like, you know, I'm actually willing to pay a lot for consensus that we get everyone in the company to agree to do this.
Sort of like a highly paid scapegoat.
Yeah, 100%.
Like there's a whole rant on Rene Girard in there.
There's a whole rant on like monarchy that we'd rather have a bad king as long as everyone agrees that he's the king.
So that's actually super valuable.
And then I think there's also this like management consulting idea of sort of talent laundering, where if you have a very boring business that no super highly credentialed person would want to go work at directly out of college, but everyone wants to go work at McKinsey because that's a very high status.
So those people are willing to take a high status job that actually probably pays them a little bit less than going to work for the low status company directly.
And it's essentially like a recruiting arbitrage.
I think if you live in a world now of being able to use the models directly, you like kind of don't need that recruiting arbitrage.
It's much more interesting to just like go build the business yourself coming out of some.
you know, incubator call it, whether it's a college or, or another company that's known for developing talent.
It's like one of the classic things like, oh, we hire a consultant to help us justify why we need to do a layoff.
But if you ask a consultant why, you know, what their job is, it is not that cynical answer.
It's more aligned with the answer I gave, right?
So regardless of why, I don't, I actually don't think that's a cynical answer at all.
I think like one of the hardest things to do is to get people to agree.
I would actually argue like if the management consultant literally doesn't do anything other than getting everyone to agree on what was probably the right path anyway, that's an incredibly valuable service.
So I would like push back on this being cynical.
But I do agree there are some like consulting services where it's like, yeah, we need a point solution for a thing for a time and we hire a consultant and that works very well.
Yeah, so let me rephrase that.
And then rather there is a different version of consulting, which maybe some consultants do, which is the boring answer of like.
I have a problem I need to solve, so I'm going to hire these people to temporarily solve it.
They're, you know, kind of contractors.
Yeah, well, I think there's also attacks are been there, like depending on how these people are, are they employees?
Are they not employees?
Like, you know, that's also certainly a part of this.
But if you do it as like a valid thing that businesses need is like the temporary workforce, which maybe consulates provide, maybe contractors provide, you know, a new entrant to that space is that tokens provide.
Yep.
You were trying to make this point for like 10 minutes and I missed it, which is that like, you know, the consultants are variable labor and tokens are variable labor.
And so now we can like scale consulting.
Actually, maybe the entire world is now consulting.
Exactly.
And, you know, consultants have the maybe one problem that.
If you look at the archetype for a consultant, it's like all kind of like this like finance managerial style of person.
So it means that when you hire a consultant, there is a pretty like small slice of like, you know, these point problems that you can attack with them, you know, but when it comes to AI that.
that slice is much larger, right?
Because it's, it's much more general intelligence.
So now we can maybe expand actually like the TAM of these like consulting style businesses to being like, it's not clear you would ever hire McKinsey because you have like an engineering project that you need to get done.
Maybe you'd hire a dev shop.
I mean, I think management consulting is a lot of what I was saying and like systems integrators are a lot of what you were saying and we call them.
But it's kind of the same problem in the case of the variable labor case rather than the scapegoat case.
Yeah, 100%.
I don't think tokens help you get to agreement at all, other than maybe people are willing to outsource their thinking to a model.
I think saying that the machine god says do this is actually maybe a valid way to get some consensus.
Yeah, that's fair.
That's maybe a cultural or like a social question as to how quickly are people just willing to outsource their thinking to one of these models?
Apparently quite quickly.
Yes.
Yeah, that does seem to be empirically true.
I would have thought it would take longer.
So, I mean, Noah, with what you're describing, like this whole world becomes consulting.
Are you seeing an opportunity for new businesses to arise in this domain?
Or are you just saying that?
like the AI models as they exist today eat those businesses?
It's possible that there's new business or new ways businesses get run.
You know, people have speculated for, you know, since the AI started getting good that we're going to see, you know, at least like billion dollar companies run by like one guy with a bunch of agents.
Maybe we'll see that, but maybe, maybe we'll see like variations of that where we see much leaner companies.
We also might see businesses that are like much more horizontal because You know, something I noticed in my own engineering work is that it's no longer efficient to do things, particularly single threaded.
Some of the best engineers are people who are able to focus on kind of one problem for a very long time and think very sequentially.
And then the best managers are people who can kind of focus on many problems and constantly context switch.
I think in engineering, that's changing because if I were to do it sequentially, I'd be spending a lot of my time.
sitting waiting for the uh for claw or codex to kind of churn through a problem and that's just not a very good allocation of my time if i have like uh you know intelligence that may be you know as good as me but much cheaper than me uh why would i wait while the you know the bar is kind of spinning so instead i i find myself like literally working on multiple like very different things at the same time in fact it's sometimes you know if you can nicer to to work on different projects at the same time because at least you know, you can avoid, you know, them overlapping a little bit too much.
I mean, you can still obviously, you know, work on 10 different things in the same projects, just like any engineering organization does.
But, you know, it's becoming much more viable for like one person to think about 10, you know, totally different projects, which I think means that like, you can have, you know, the thing I was describing before is like this like PE style thing where you have like one company that might own 10 like boring businesses.
And now you can have like one person who's really good with AI who is basically working on all of them in parallel, which I think is a little bit different.
And it changes the archetype who's going to be good at that thing, right?
Because before you have like one person who should devote all of their time to being the best that they can at running that one business.
And now you're going to have one person who is really good at managing talent.
It's turning, I think engineers are more and more so becoming.
I wouldn't say engineers are becoming managers because there is still a lot of, at least today, there's still a lot of technical knowledge that goes into managing your LLM.
But it's a totally different style of thinking now where it used to be sit and think very sequentially and very deep and hard on one line of reasoning to becoming thinking on many lines of reasonings in parallel.
I think it's an interesting question.
Like there are all these AI transformation PE things now.
And we've been talking a lot about like some form of private equity.
So it's like what catalyzed private equity to begin with, especially back in the like the late 70s, early 80s.
I think it's some combination of like Lotus Notes or just spreadsheets made it a lot more legible to in a day with a computer analyze a business instead of you needed a floor of people in two weeks.
to figure out whether or not you could buy a business.
The classic LBOs also used a lot more leverage than you use today.
And so I think just in the same way that that was probably the time for this sort of thing to happen.
And their transformation in many cases was taking businesses that were not well managed and putting the classic professional managerial class in there and just trying to run them more effectively.
I think today people think of private equity as being very like, we buy a company and then we trim the fat.
That was not.
like the original sort of orientation, I think, of private equity.
It was much more so these businesses are now legible to someone that would like to buy them because we have spreadsheets.
And what if we changed slightly how they are operated and we moved from sort of the original operators or maybe their kids to sort of professional managers?
And I think that worked very well for a time, but it's...
you know, we've been doing this for like 40 or 50 years now, and it's probably time to move beyond that.
In the same way, like that time period probably catalyzed the specific sort of movement.
It seems relatively straightforward to say that now that you have LLMs, that will make a whole new set of businesses much more legible either to acquisition or to formation to begin with.
Like maybe the modern version of private equity is not buying existing companies, but, you know, using an LLM to very quickly recreate a very similar product and compete with them.
And then I think there's the question of like, what is the operations transformation, where, you know, maybe in 1982, you would have hired someone from Harvard Business School and like, you know, had them run the company now.
Today, it seems like the modern version of business school is getting AI psychosis.
It's like how the sort of person that actually can say, this is my return on tokens and very effectively use those tokens.
I think like that's a super interesting opportunity or skill set and will have like lots of ramifications on financial markets.
And my hope is that...
Because the velocity of spend, both on tokens and in dollar terms is much, much higher, this will end up being something that just kind of by default happens in stable coins.
That if I want to change my subscription, if I want my agent to act economically, I will just end up doing so on a blockchain, almost, you know, at least in my opinion, doing so in stable coins.
And people will not think of this as crypto.
They will think of this as like AI transformation.
But I think under the hood of a lot of what has been built in crypto over the last many years will very likely be used to enact this transformation.
So I'm curious your take on this.
And then I have another question, which hopefully we'll argue about.
I think the stablecoins are really like the new financial stack.
I think we're seeing something interesting happen where in the past, a lot of times with the businesses we try and look at are, you know, businesses that like weren't possible without crypto or like are sort of like incredibly uniquely enabled with crypto.
I think now we're seeing something slightly different happen where there are more and more businesses that aren't actually like, you know, could use some traditional financial rail or could use crypto.
And it actually wouldn't necessarily matter.
But then you actually just look at, you know.
making that choice and be like, that's actually easier for me to use crypto, right?
I can kind of very quickly integrate a blockchain, get some stable coins on it rather than get some making partners and go deal with, you know, Terribles, ACH Bulls and the like.
So I think, you know, we're going to start seeing more and more like just it not being like, oh my God, there's this new business that is like so cool because of crypto, but rather being like, we have a new business and we need to do some financial stuff and like.
you know, crypto is the way we do this just because it's like the new stack for the same reason why, like when you go to the cash register, you don't just like pay in cash and have someone calculate your change, but you tap your credit card.
It's just like the, you know, obvious choice.
It's not the, it's not the thing that made the bodega uniquely able to, to, to scale.
It was just, it's what they do because it's the obvious answer.
So, you know, how AI plays into it.
I mean, I think.
you know it's going hopefully i think makes it easier for businesses to kind of do that transformation because obviously the new businesses i think will make the choice of like let's use stable coins let's use crypto just because it's it's the obvious answer but you have these businesses that have like entrenched relationships and code bases that are built on on the old system and i think ai is probably going to be useful in us swapping them out just because again if it's the obvious answer um but we already have something that works almost as well then it's not worth spending a lot of money to rip it out.
But if the tokens are cheap, then maybe it is worth it.
Especially if it's like, oh, I'll get a tiny improvement on my margins, but if my business isn't at some massive scale where it's worth totally redesigning something, you wouldn't do it, but totally redesigning something is increasingly becoming free.
Yeah, I think there's the old quip of like science advances one dead scientist at a time, maybe like the accounting department advances one retired CFO at a time.
And so existing companies will probably take longer to adopt these things, which is, I think, in many cases, why especially new, newly formed small companies or teams that want very high leverage in headcount terms will just end up using stable coins and on-chain finance.
I think there's some...
analogy here to how in many parts of the developing world, they, they like never used cash.
They never used credit cards.
They just moved immediately to contactless payments.
And yet in the U S like you still carry cash around very occasionally.
And you almost certainly like tap your credit card, uh, quite often.
So I think there's probably a dynamic like that here as, as well.
So I think there's a very interesting question.
Many people may have seen, I think this is two or three months old, the Financial Times graph of GDP growth year over year in the US going back to like 1850.
And it more or less looks like 2% year over year.
There's some years where it's 4%.
There's some years where it's negative slightly, but it basically looks like a linear extrapolation over the last 150 years.
And then the...
Forward projection is either like GDP skyrockets or it falls off a cliff and goes to zero.
It's either like utopia or death, which I think is an interesting way of considering how public markets are thinking about AI.
But the implication to this is why has GDP growth not inflected or not fallen off a cliff as a result of the AI models?
It seems like there's relative consensus that we got a form of AGI at the end of 2025.
The models have been very performant for a number of years now.
And I think the null hypothesis here is that GDP growth will increase with time.
It just takes time for technology to diffuse throughout society.
I do think there are other interesting hypotheses or conclusions for that.
Maybe we'll just start with a question of like, when do you think we will see?
macroeconomic indicators begin to show increasing productivity from LLMs?
I think when it comes to how many years it's going to be, who knows?
I have no idea.
Pick a quarter in a year.
Totally guessing like five years, right?
Because I think these things take time because there are many reasons why you might see productivity grow from AI.
One is that business that exists, with the labor it has starts consuming a bunch of tokens and they uh they produce more value right and and that's gdp growth i hope that we see that right but there's you know i think a open question of like how much can a given business scale right like you know if you give a social media company 10 times more money you know how can they sell us 10 times more social media i don't i don't think so one explanation for this is What causes GDP growth?
What causes the ability to do more with less?
It's technology.
So you could look at roughly 2% GDP growth year over year, and you could say, well, that is the pace of inventing new technology.
Or you could say that is the pace at which society is able to integrate new technology.
And maybe the barrier has not been we can't create more new technology, but that 2% year over year in economic terms is just roughly...
like the level of change that society can adapt to at any given point in time.
That is more of like a human or a societal or an organizational thing as opposed to a technological thing.
You know, the idea that 2% year over year is very similar to Moore's law in that like we've had all of these incredible, wonderful inventions and technological changes that have continued to enable 2% year over year.
That that's like, you know, a modern miracle.
And AI will just like continue along the 2% year over year.
And I think that's originally an Andre Carpathia argument, which is like very intellectually consistent and I think interesting.
I think there's another form of this, which is one reason you wouldn't see drastic changes in macroeconomic data would be there are a lot of people that are being more productive at their corporate job.
but where that job actually was not productive at all to begin with.
I would be quite skeptical that large corporations will actually make significant headcount reductions because I think the incentives just don't make a lot of sense if you're sort of part of the professional managerial class.
Like if you're the CEO and you fire half of your company and you have a bad quarter or two, like you will probably get fired.
And so I would suspect you only see the sort of drastic headcount changes where there is like a sole proprietor of the business.
I think it's not happenstance that where this happened at Twitter and acts with Elon, like he owned the business outright.
No one was going to fire him.
And, you know, maybe you'd call this founder mode, but I just think the incentives are very unlikely that we will see like drastic layoffs at sort of big corporations.
I think it's just much more likely that you will have stronger competition from newer firms that are run in a different way.
And then eventually you will have like this new class of transformation or I think you would just call it private equity by shorthand, but that's probably not going to be correct.
It like ends up transforming those larger businesses.
I have two more theories now on this, right?
My first one is it could also be that where AI has massively transformed software businesses because it's like the easiest thing to start scaling up on.
And maybe it is just that there is like not that much more.
output to squeeze out of the software side and it's going to take time for us to figure out how do we apply ai to massively scale businesses that still have productivity to squeeze out of them right so you know maybe it's simply that you know the software companies are you know doing the most they can do right they can't sell us more social media there are other sectors or new sectors that we have much more work to do right like robotics right that is going to take much more time and maybe maybe we'll see you know five years from now as AI gets deployed across much more broadly and in more cases, we'll see the GDP growth explode.
Yep.
My second theory is that maybe GDP isn't a real number.
I think it's like, what's the kind of saying about once something becomes a measure, it no longer makes any sense.
Goodhart's law, I think.
Yeah, Goodhart's law.
So I think GDP and inflation are the things that central banks look at and they point their levers to make sure that both those things are the right number.
And, uh, maybe it's just that like, you know, the actual productivity of the economy is, is super high, but the central bank pulled the levers to make sure it's the, it's the percentage that they feel comfortable with.
And like no amount of technology is going to make that measure change because, you know, you have so much power by being able to change rates or, or, I think that's correct for the CPI and for inflation.
Like I, I just, I think that that's totally fake.
If you look at any of the inflation adjusted numbers, I don't know that there's a better measure than, than GDP.
Uh, And I'm, I'm like, I've become probably for worse, but for better, for worse, just like maniacally obsessed with this concept of productivity.
Like, and everyone is very interested in the idea that AI will make you more productive.
Anish, one of our partners had a really interesting post recently that most people don't want to be more productive, don't want to save time.
They want to spend time, which is like one interesting conclusion for AI making everything more productive.
But the other question would be like, Even if you do want to be more productive, how do you define productivity?
Is that in terms of more dollars created?
And then to your point, does monetary policy make it very hard to understand like whether more dollars being created is actually good?
Like, does this mean more production of information or technology?
Like, I think that's very hard to measure.
You could look at a number of patents, number of PhDs granted, like all of these different metrics, which I think are probably skewed.
Like we have, I think it's something like 10 times more physicists.
If you just looked qualitatively, I think you would say physics is not advanced, you know, very much after World War II.
This is really a yearning for a better definition of productivity, like how to say at a broader macro perspective, like what is the value of the level of intelligence that we have created?
Like we turn sand into thought, which is a miracle, but the sand into how much thought and how are we using that thought?
Is it also possible, too, that maybe we are actually in a massive recession and we just have no idea because AI picked up all the slack?
I've heard this argument before that the economy is still wrecked by the actor effects of shutting it down for a little while from COVID and that there's a bunch of measures that still don't do very well.
And maybe it's that AI picked up the slack and the...
large swaths of the economy are not doing very well.
So it looks like 2%, but it was actually, that should be down 10% and it's up 12%.
Yeah.
I mean, I think a lot of the vibe session, this idea that if you look at the economic indicators, they seem to be doing pretty well.
But if you talk to people, they seem to be sad about the state of the economy.
So I think this is just like a second order effect of the internet, that people are like deeply mimetic animals.
And if you can see how all of the...
people on earth that are doing the best are doing at every second of your day, all of the time, you will try to compare yourself to them, which is a horrible idea.
And then you will feel sad and you should be inspired by the classic quote that true nobility is not superiority to your fellow man, but to your former self.
Like this is just sort of a psychological thing that you have to grapple with for better, for worse.
I do think it's interesting that if you look at why have AI models gotten better, they've gotten better because you can throw a ton of data and compute at them.
And, you know, the entire world has learned the bitter lesson that the size of the model is directly proportional to the performance.
And like, what are the inputs to, you know, data and compute?
The most important input is probably energy.
In the U.S.
for roughly 20 years, call it between 2000 and 2020 to make the numbers course, there was a very slow growth in energy demand from, you know, call it 1920.
through, depending on how you define it, the 80s or the 90s, something like that, you had like 7% year over year growth in demand for energy or energy generation.
And so I think one implication of this is like we ran out of things to do.
If you look at many of the productivity measures in society, things like self-reported happiness or infant mortality or GDP, like they're strongly correlated just with the amount of energy a society consumes.
And there was not kind of a marginal buyer.
for megawatt or kilowatt hour.
Like we didn't have anything to do with the power.
And now we do, which I think is an incredible change and essentially means we can deploy as much new energy generation capacity as we want.
And like, this is what has been happening in the US and in the West broadly.
It's just like building as much generation as we can, as many data centers as we possibly can.
And that I think is very positive, but you could totally say that just the CapEx spend on AI has been, you know, papering over or covering up.
other areas of the economy not performing the way they would have wanted.
I think my argument for that would be until you have a new thing to put the money into, it won't change.
That like if someone invents fusion or, you know, we have time travel or whatever, like your next sci-fi technology is like you then would probably would see like a narrative rotation and the unit economics of like AI would probably become more important, but it seems relatively unlikely in a short term that we'll like have a narrative rotation.
And so.
Yeah, I think we're probably in a pretty good position there.
We probably have no way of measuring this, right?
But you can, you know, if you don't like GDP as the thing you measure, we should like measure like the gross intelligence in our country or planet.
But how do you measure intelligence?
I'm not claiming that this is something that is easier possible to measure, but simply that like it, imagine you had that measure, like it could be somewhat informative that like, it could just be that, you know, we're in our little bubble and we see AI everywhere.
But it could be that the intelligence in the world has actually only grown by a couple percent because, you know, we just haven't scaled this enough.
And if the intelligence is only scaled by 2%, maybe the GDP should only scale by 2%.
Yeah, I mean, I think it's very hard to measure intelligence directly because the world seems to want things at given points in time and like people deliver them.
And sometimes we, I think, make the mistake of saying that like the person and their incredible intelligence is the reason this happened.
I would point to the...
creation of the Turing machine and the lambda calculus at almost exactly the same time by two totally different people who never met each other all the way across the world.
That's just like the world wants this to happen at a given point in time.
And you can make whatever spiritual, religious, like information, theoretic conclusion you want for that.
But I think there's like a time for things and then people materialize them.
So I think it's very hard to quantify intelligence in any of those like outcome factors.
And then I don't know if you believe in IQ tests, but uh they just don't seem to be particularly effective yeah like how do you quantify intelligence so so i i'm not saying that like we should try and quantify it i'm saying that like you know imagine there is some theoretical quantification of this and and it could be that like we just haven't increased the amount of intelligence that we have right like you you know you can make a very simple model about like you know for economies that are like you know full of skilled labor that like we kind of just convert intelligence into gdp Maybe.
And then like, maybe it's just that like, we haven't increased our amount of intelligence very, very much.
And yeah, we may totally never be able to measure that.
Or it may like, it may not even like mean anything to measure that.
But if you imagine the made up measure, it could just be that that hasn't increased by very much because we have a whole bunch of human brains and not so many data centers.
And, you know, at some point we'll have more data centers and brains.
And at that point we see GDP explosion.
We've talked about this before.
If you look at the correlation between IQ and earning, uh, IQ and income, like the correlation goes away above roughly 125 or 130 IQ.
I would argue the models are definitely smarter on an IQ test basis than 125 or 130.
Five, six, ultra measured at 136, according to Twitter yesterday.
So there we go.
I think a super interesting question is like, as the martyrs, as the models get smarter, do they, uh, become more economically productive?
And if you look at people, your.
projected earnings as a result of IQ do not go up if you get smarter than the models currently are.
So like what is strongly correlated with high earning potential?
I think the shortest answer or my argument would be some form of grit or tenacity or agency, which I think has become an overloaded term, which inherently like these models do not have because they do not have desires.
It's just math.
I'm referring to intelligence like a gross unit, right?
Like when you talk about IQ increasing, it's like one person gets smarter.
I'm talking about like you can think of like one person versus 10 people being like approximately 10 times the amount of intelligence, assuming they're all kind of just as smart.
And like, you know, that AI can be like part of that measure of like it, you know, the more like each token like gives you some amount of like intelligence, more tokens equals more gross intelligence in the world.
Yeah.
One is definitely good if everyone is smarter.
That seems obvious.
And two, if everyone has access.
to these models, and I think they will, this will be an incredibly democratizing force where you basically like slam shut this dispersion in IQ.
And it probably becomes much more important not to be particularly smart, but to be particularly gritty or agentic or to have strong desires for things, which is strange and weird because the Western economy has been selecting for intelligence and like the leadership and elite for hundreds of years now.
And maybe overnight, at least in the the scheme of time, uh, we will like start to select for something else entirely.
I think that is like, that's a very interesting question.
If now the amount of intelligence we have is just a result of how many data centers we've built and not the number of people we have or the quality of the education system or something else.
Uh, yeah, it's going to be a fun time.
So you had started by posing to Noah.
What is the year, the quarter that you're going to start seeing these macroeconomic indicators show up, but.
You didn't answer it yourself.
I think it would be very hard to say.
I'd say like Q4 27, Q1 28, something like that.
I don't have a good justification for that.
That's just, that's a, that's a vibes based assessment.
The other thing I'll say, which we probably should have caveated to begin with, I think like the most obvious counter argument to the discussion we've just been having is like the, the CapEx spend from AI, like obviously shows up in GDP.
I think that was something like half of GDP growth in 2025.
So like, yes, that is, that is true.
The question is much more like when do the, when does inference from the models themselves, not, you know, the dollars we're putting into the ground show up in GDP growth, but like the technology itself enable people to, to do things more, more efficiently.
And I think the answer probably is just time.
Like it's probably a smattering of the other things we're saying, which are more interesting, but it's probably just things, things take time.
And if we started to see this, you know, really happen at the end of last year, it doesn't seem unrealistic for it to take, you know, two or three years for that to properly diffuse throughout society.
One final question for you guys.
Guy, when you're describing this opportunity that AI opens up, you described it in relation to traditional venture bets where you're trying to swing for the fences and create the biggest possible.
business and outcome possible.
But in contrast to that, you're saying AI enables these much smaller, nimbler sort of opportunities for businesses.
You called them micro businesses.
Maybe they even get funded by micro loans through crypto.
Does that not seem though like a lessening of the ambition of business building?
Like how, yeah, how do you square?
I still think there will be like these giant, you know, network effect, very successful technology businesses.
And that.
AI gives you much stronger leverage and the ability to build those sorts of things.
The set of comments was not excluding that.
It was more, I feel like there's this category of things that is not talked about enough or a lot.
There seems to usually be like a default path for smart, interesting, ambitious people in the Western world.
And, you know, for a long time, that was finance.
And that has definitely become tech.
And it seems like the default now, if you want to start a new thing, is just to raise venture capital.
The meaning of venture capital has definitely changed in the last 20 years.
And so the comments are all more just to really say, like, what is venture capital?
Venture capital is the idea that I need a lot of money to do something very ambitious that if it works, which is very unlikely, will have an incredibly...
you know, successful payoff, both in terms of how it changes society and how valuable the company becomes.
And that like, that's not the only sort of business that you can build.
That, you know, the American economy is composed of a very large number of very small businesses that in their aggregate are incredibly important.
And there are really interesting opportunities to build those sorts of things.
even if you come from like a you know harvard stanford google etc background like you do not necessarily have to go try to build something that's worth a trillion dollars like you can have a very fulfilling and interesting and like a creative to society life building something at a smaller scale that probably wasn't possible before And I think that's interesting from the perspective of how you think about the landscape of investments and investment managers.
I also think that's interesting from the perspective of a lot more people being in charge of their own destiny instead of trying to get, you know.
a job at a big company or build a big company yourself, you have a much greater distribution of, you know, smaller companies and sole proprietorships that all interact with each other well.
And I think that is the sort of bottoms up distributed decentralized world is very much the one that we've been excited about and building in for a number of years, uh, and, or investing and not you've been building in, uh, I used to be an engineer and definitely am not anymore.
So I think I think it's more that just the idea that if you believe in a world of smaller firms, more firms, more coordination between them, higher velocity of commerce, like it all rhymes very well with kind of a change in how people think about starting new businesses and the small businesses that people have been interested in starting previously.
It's like usually you come out of some.
like M7 MBA program and you buy an HVAC installer, I would just like advertise to you that they're all on a spreadsheet and it's probably not a good idea anymore.
And like you should spend 18 hours a day with Claude and that will probably be a better outcome for you and your business and for society.
I also think like we should actually be stoked about.
These things that you were calling unambitious businesses, having more of them and being able to do them more efficiently, we should be incredibly stoked about.
Because I think you can split the economy in two pieces.
You can think of it, there are businesses that work very well under what one might call the perfect model of capitalism, and there are businesses that don't.
The businesses that do, they're businesses that are small.
There are many of them.
They have no product differentiation.
They have no advertising.
And when you have those businesses, Capitalism does one thing very, very, very well.
What it does is it makes the prices go very, very low.
It makes the margin be tiny.
And we actually, we love it when there are businesses like that.
This is why food is so cheap, right?
Because like food is for the most, or, you know, like.
Corn or rice, right?
It's for the most part, like undifferentiated.
There are like many small producers.
Of course, there are some very large producers as well that have some scale effects, but that portion of the economy is constantly like squishing itself to make prices lower.
And we should be excited about potentially like more of the economy looking like that.
On the other hand, we shouldn't be, we shouldn't say that the other category is bad because the other category is the part that almost to a certain extent breaks capitalism because they have like massive scale effects and network effects and they are widely differentiated from one another.
And these businesses, maybe rather than cynically calling them the businesses that break capitalism, you might call them like the innovation economy.
They're businesses that are like net new.
And we see these incredibly large companies kind of grow out of them.
Now, I think we need both these things, right?
We need the innovation economy to have new things.
And then we need an economy of businesses that are commoditizing themselves so that people can buy things for cheaply.
Now, what we want to do, though, is that as the innovation side of the economy grows, we have these new massive businesses.
We want this second thing to happen is that we actually want some force that converts some of these kind of new businesses that are breaking capitalism and, you know, are therefore able to extract high margin into businesses that kind of go back into that perfect model where they break up into many businesses and their margins get compressed, they get commoditized, maybe less excited to invest in.
But, you know.
We'll continue investing in the part of the innovation economy as these companies get created, which is, you know, I think what a VC's job is.
But then we also, you know, need some force that converts those businesses, you know, once they've grown and, you know, returned to their investors and then all of these great things and hopefully change the world.
We want to convert them into businesses that have low margins and, you know, provide things to people at a low price.
And I think people leveraging AI is actually a great thing and we should be super excited about that.
Very well put.
Capitalism is a process of creative destruction, and you need both of those in equal measure.
We covered a lot of ground today.
There's a lot to chew on.
We started with token spend, went into different models for companies, you know, pod shops versus this sort of like PE style stuff that you were talking about, the nature of consulting and how that's going to change, GDP, whether it even means anything, and what happens next there.
So this has been great.
Guy, Noah, thanks so much for coming on.
Thank you.