Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z

Hey everyone, welcome to the Lately in Space Podcast live from a16z. Uh, this is Alessio, founder of Kernel Labs, and I'm joined by Twix, editor of Lately in Space.

Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also, a top AI podcast, uh, Martin Casado and Sarah Wang, welcome.

Very happy to be here.

Very happy.

And welcome.

Yes. Uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now.

Yeah, yeah. Yeah, yeah.

It's, it's still getting... Takes a while to get used to, but it reminds me of, like, sort of a callback to a more ambitious age-

Right. Yeah, yeah, yeah

... which I think is kind of-

Definitely makes a statement

... nice, right. But, yeah.

Yeah, yeah. Not quite sure what that statement is, but it makes a statement.

Uh, Martin, I go back with you to Netlify.

Yep.

Uh, and, uh, you know, you created software-defined networking and all, all that stuff.

Yeah.

Uh, people can read up on your background.

Yep.

Sarah, newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.

That's right, yeah.

Um-

Seven, seven years ago now.

Best growth investor in the entire industry.

Oh, say more.

Hands down. Uh, Sarah's, Sarah's... I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive, um, investment thesis around AI models, right? So she worked for Nome Shazir, Mira, Ilya, Fei Fei.

And so just these frontier kind of, like, large AI models, I think, you know, Sarah's been the, the broadest investor.

Mm.

Is that fair?

No, I, well, I was gonna say, I think it's a-

But, uh-

... really interesting tag, tag team, actually. Just 'cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage, but-

Cursor, PFAL

... the resources-

So, so many. Yeah, all the, uh-

Well, I was gonna say, the resources-

Like, all of them

... one, they just grow really quickly, but then two, the resources that they need day one are kind of growth scale. So I think the hybrid tag team that we have is quite effective, I think.

What is growth these days? You know, you don't wake up if it's less than a billion or, like ...

It's actually, it's actually very, like, like... No, it's a very interesting time in investing because like, you know, take like the character round, right? These tend to be like pre-monetization, but the dollars are large enough that you need to have a larger fund and the analysis, you know, because you've got lots of users 'cause this stuff has such high demand, requires, you know, more of a numbers sophistication.

And so most of these deals, whether it's us or other firms on these large model companies, are like this hybrid between venture and growth.

Yeah, totally. And I think, you know, stuff like BD, for example, you wouldn't usually need BD when you were seed stage trying to get product market fit.

Are we talking about BizDev?

BizDev, exactly.

Okay, yeah.

But, like, now sort of-

I, I'm not familiar, what, what does BizDev mean for a venture fund? Because I know what BizDev means for a company. That's-

Yeah, you know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at?

Is there a go-to-market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe six months into the inception of a company. You just wouldn't have to negotiate these deals before.

Yeah, these large rounds are very complex now. Like in the past, if you did a series A or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now, you normally have financial investors or strategic investors-

Yeah

... and then the strategic portion always still goes with, like, these kind of large compute contracts, which can take months to do and so it's, it's very different times. This- I've been doing this for 10 years, it's the...

I've never seen anything like this.

Yeah. Uh, do you have worries about the circular funding from some of these strategics?

I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.

Exactly. All right. This, this is like the- ... the whole pyramid scheme bubble thing where, like, as long as you mark to market on like the notional value of like these deals, fine, but like once it starts to chip away, it really collapses

Well, no, like as, as, as long as there's demand... I mean, you know, listen, this is... Like a lot of these sound bites have already become kind of cliches, but they're worth saying it, right? Like during the internet days, like we were, um, raising money to put fiber in the ground that wasn't used, and that's a problem, right?

Because now you actually have a supply overhang.

Mm-hmm.

And even in the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was and as, as massive as the crash was, only lasted about four years. But we don't have a supply overhang.

Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um, you know, they'll actually use the GPUs and on the other side of it is the, uh, is the actual customer.

So I, I think it's a different time.

I think the other piece, maybe just to add onto this, and I'm gonna quote Martin in front of him, but this is probably also a unique time in that for the first time you can actually trace dollars to outcomes.

Yeah.

Right? Provided that scaling laws are, are holding-

Yes

... um, and capabilities are actually moving forward. Because if you can put translate dollars into capabilities, uh, capability improvement, there's demand there, to Martin's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work.

But, you know, instead of investing dollars into sales and marketing, you're, you're investing into R&D to get to the capability, um, you know, increase, and that's sort of been the demand driver. Because once there's an unlock there, people are willing to pay for it.

Yeah.

Is there any difference in how you build the portfolio now that some of your growth companies are like the infrastructure of the early stage companies? Like, you know, OpenAI is now the same size as some of the cloud providers were early on.

Like-

Mm-hmm

... what does that look like? Like how much information can you feed off each other between the, the two?

There's so many lines that are being crossed right now or blurred, right? So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?

Mm-hmm.

Like it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core R&D, it's a horizontal platform, but it's also an app because it's, um, uh, touches the users directly. And then of course, you know, the, the, the growth of these is just so high.

And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that. And so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly.

You've got ecosystem build out. And so none of this is necessarily new, but the timescales at which it's happened is pretty phenomenal. And the way we'd normally cut ... lines before has blurred a little bit. But, but that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build-out and the internet build-out as well.

Yeah. Um, yeah, I think it's interesting. Uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is... And this builds off of your other question. Um, you raise money for compute, you pour that, or you, you pour the money into compute, you get some sort of breakthrough, you funnel the breakthrough into your vertically integrated application.

That could be ChatGPT, that could be Cloud Code, you know, whatever it is. You massively gain share and get users. Maybe you're even subsidizing at that point, um, depending on your strategy. You raise money at the peak momentum, and then you repeat, rinse and repeat.

Um, and so... And that wasn't true even two years ago, I think.

Mm-hmm.

And so it's sort of to your... Just tying it to fundraising strategy, right? There's a... And hiring strategy. All of these are tied. I think the lines are blurring even more today, where everyone is... And they, and but of course, these companies all have API businesses, and so there are these, these frenemy lines that are getting blurred in that a lot of...

I mean, they have billions of dollars of API revenue, right? And so there are customers there, but they're competing on the app layer.

Yeah, so this is a really, really important point. So I, I would say for sure venture and growth, that line is blurry. App and infrastructure, that line is blurry. Um, but I don't think that changes our practice so much.

But, like, where the very open questions are, like, does this layer in the same way compute traditionally has? Like, during the cloud is, like, you know, like, whatever. Somebody wins one layer, but then another whole set of companies wins another layer.

But that not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like, you can't build an app. Like, it, it-

Mm

... necessarily goes down just because there are no abstractions. So those are kind of the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is in the past, if you raised money, then you basically had to wait for engineering to catch up, which famously doesn't scale.

Like, the Mythical Mammoth, it took a very long time. But, like, that's not the case here. Like, a model company can raise money and drop a model in a, in, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people.

So this type of, like, money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before, and I think everybody's trying to understand what the consequences are.

So I think it's less about, like, big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.

Yeah. Like, at Kernel Labs, one of our ideas is, like, if you had unlimited money to spend productively to turn tokens into products, like, the whole early-stage market is very different. Because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of plot committing-

Yeah

... to a certain strategy for a certain amount of time. But if you could, like, iteratively spin out companies and products and just sort of, "I, I want to spend a million dollar of inference today and get a product out-

Yeah

... tomorrow."

Yeah.

Like, we should get to the point where, like, the friction of, like, token to product is so low that you can do this, and then you can change the-

Right

... the early-stage venture model-

Right

... to be much more iterative. And then every round is, like, either 100K of inference or, like, 100 million from a16z.

Yeah.

There's no, there's no, like, $8 million-

You know, like-

... C round anymore.

Right. But, but, but, but there's a, there's a, the, an industry structural q- question that we don't know the answer to, which involves the frontier models, which is let's take Anthropic. Uh, let's say Anthropic has a state-of-the-art model that has some large percentage of market share.

And let's say that, uh, uh, uh, you know, uh, a company's building smaller models that, you know, use the bigger model in the background. You know, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it.

And if that's the case, they can expand beyond everything built on top of it. And it's like, imagine, like, a star that's just-

Mm

... kind of expanding. So there could be a systemic, there could be a, a systemic situation where the soda models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don't think we've ever seen before, just because we were so bottlenecked in engineering.

And this is a very open question.

Yeah. It's, it's almost like bitter lesson applied to-

Yeah

... the startup industry.

Yeah, 100%.

Mm-hmm.

Yeah. It, it literally becomes an issue of, like, raise capital, turn that directly into growth, use that to raise three times more.

Yeah.

Exactly.

And if you can keep doing that, you literally can outspend any company that's built... The, not any company. You could outspend the aggregate of companies on top of you, and therefore you'll necessarily take their share.

W-

Which is crazy.

Would you say that kind of happened to Character? Is that the, the sort of postmortem on what happened?

Um-

No.

Yeah. Yeah, 'cause I think Char- so the-

I mean, the actual postmortem is he wanted to go back to Google.

Yeah, exactly.

But, like-

That's, that's another different... That's another-

You, you said it, yeah.

We should, we should talk, we should actually talk about this. Yeah.

Yeah.

Yeah, let's talk. Go for it. Take it, take it wherever you want.

Well, you know, I was gonna say, I think, um, the, the, uh, the, the Character thing raises actually a different issue, which actually the Frontier Labs will face as well, so we'll see how they handle it. But, um, so we invested in Character in January 2023, which feels like eons ago.

Yeah.

I mean, three years ago feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August 2024. And so, um, you know, at the time, Noam, you know, he's talked publicly about this, right?

He wanted to... Google wouldn't let him put out products in the world. That's obviously changed drastically. But, um, he went to go do that. Um, but he had a product attached. The goal was al- ... I mean, it's Noam Shazeer.

He wanted to get to AGI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case that the Character product originally was and still is, um, was one of the vehicles to do that.

Um, I think the real reason that, you know- If you think about the, the stress that any company feels before, um, you ultimately go on one way or the other, is sort of this AGI versus product. Um-

Yeah

... and I think a lot of the big- I think, you know, OpenAI is feeling that. Um, Anthropic, if they haven't start- you know, felt it, certainly given the success of their products, they may start to feel that soon.

And they're real... I think there's real trade-offs, right? It's like how many- when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re- research, right?

Is it to- or long-term research? Is it toward, um, you know, near to mid-term research? And so, um, in a case where you're resource-constrained, um, of course, there's this fundraising game you can play, right? But the fun- the market was very different back in 2023, too.

Um, I think the best researchers in the world have this dilemma of, "Okay, I wanna go all in on AGI," but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to AGI.

And so it does make, um... You know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right? And certainly, if you don't have that progress, you can't continue this fly- you know, fundraising flywheel.

I would say that because, because we're keeping track of all of the things that are different, right? Like, you know, venture growth and, uh, app infra, and one of the ones is definitely the personalities of the founders. It's just very different this time.

You know, been, been doing this for a decade, and I've been doing startups for 20 years. And so, um, I mean, a lot of people start this to do AGI, and we've never had, like, a unified North Star that I recall in the same way.

Like, people built companies to start companies in the past. Like, that was what it was. Like, I wanna create-

Yeah

... an internet company. I wanna create an infrastructure company. Like, it's kind of more engineering builders, and this is kind of a different, you know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider AGI, but others have not.

And so, like, there is always this tension with personnel. And so I think we're seeing more kind of founder movement-

Yeah

... you know, as a fraction of founders than we've ever seen. I mean, maybe since, like, I don't know, the time of, like, Shockley and the Traitorous Eight or something like that, way back to the beginning of the industry.

I mean, it's a very, very unusual time of personnel.

T- totally. And it- I think it's exacerbated by the fact that talent wars... I mean, every industry has talent wars, but not at this magnitude, right?

No, yeah.

Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in AI, you could fart, and it would be on the front page of, you know, the information these days.

And so there's sort of this fishbowl effect that I think adds to-

Yeah

... the deep anxiety that, that these AI founders are feeling.

Hmm.

Uh, yes. I mean, just on a briefly comment on the founder, uh, the sort of talent wars thing, I feel like 2025 was just, like, a blip. Like, I, I don't know if we'll see that again, 'cause Meta built the team.

Like, I don't know if- I think, I think they're kind of done, and, like, who's gonna pay more than Meta? I, I don't know.

I, I agree.

Right?

It's what feels- it's gonna- so it feel- it feels this way to me too.

Yeah.

It's like, it's, like, basically Zuckerberg kind of came out swinging, and then now-

Yeah

... he's kind of back to building. Yeah.

Yeah, you know, you gotta, like, pay up to, like, assemble the team to rush the job, whatever.

Yeah.

But then now, now you, like, you, you made your choices, and now they gotta ship, right? Like...

I mean, the, the us- other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time. I've got three open recs if anybody's interested that's listening to this.

For investor?

Yeah, on t- on the team.

Yeah.

Like, on the investing side of the team.

Yeah.

Like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that, and, like, you know, and we pay really, really well, and just to see what's out on the market is really, is really remarkable.

And so I would just say it's actually... So you're right, like, the really-

Yeah

... flashy one, like, I will get someone for, you know, a billion dollars, but, like, the inflated, um, uh-

Trickles down

... yeah, is, is still very active today. I mean...

Yeah. You could be an L5 and get an offer in the tens of millions.

Oh, yeah. Yeah, easily.

It's-

Yeah

... so I think you're right that it felt like a blip. Hope, I hope you're right. Um, but I think it's been e- it- the steady state has now been-

Everything got pulled up. Yeah, yeah.

Exactly.

The floor got pulled up, for sure. Yeah.

Yeah. And I think that's breaking the early-stage founder math, too. I think before a lot of people would be like, "Well, maybe I should just go be a founder instead of, like, getting paid-

Yeah

... 800K, a million at Google," but if I'm getting paid five, six million, that's different.

But on, but on the other hand, there's more strategic money than we've ever seen historically-

Right

... right?

Mm-hmm.

And so-

Yeah

... the economics, the, the, the, the calculus on the economics is very different in a number of ways, and, uh-

Crazy

... it's cr- it's causing like a c- a, a, a ton of change and confusion in the market, some very positive, some negative. Like, so for example, the other side of the, um, the co-founder, like, um, acquisition, you know, Mark Zuckerberg poaching someone for a lot of money is, like, we're actually seeing historic amount of M&A for-

Mm

... basically acqui-hires, right? That you, like, you know-

Yeah

... really good outcomes from a venture perspective that are effective acqui-hires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.

Yeah. Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build. Or it, it seems in a way, you know, as YC has gotten more popular, as, like, X has gotten more popular, there's a startup school path that a lot of founders take, and they know what's hot in the VC circles, and they know what gets funded.

Yeah.

Uh, and there's maybe not as much risk appetite for things outside of that. Um, I'm curious if you feel like that's true, and what are maybe, yeah, some of the areas, uh, that you think are under-discussed?

I mean, I actually think that we've taken our eye off the ball in a lot of, like, just traditional, you know, software companies. Um, so you, like... I mean, you know, I think right now there's almost a barbell.

Like, you're like the hot thing on X, you're deep tech.

Mm-hmm.

Right? But I, I, you know, I feel like there's just kind of a long, you know, list of, like, good- Good companies that'll be around for a long time in very large markets. Say you're building a database. You know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever.

There's some good companies out there right now, but, like, they have a really hard time getting, um, the attention of investors. And it's almost become a meme, right? Which is like, if you're not basically growing from zero to 100 in a year, you're not interesting, which is just the silliest thing to say.

I mean, think of yourself as, like, an individual person. Like, like, your personal money, right? So your personal money, will you put it in the stock market at 7% or you put it in this company growing 5X in a very large market?

Of course you're gonna put it in the company 5X. So it's just like the- we say these stupid things like, if you're not growing from zero to 100, but, like, those, like, who knows what the margins of those are.

I mean, clearly these are good investments-

So true

... for anybody, right? Like, our LPs want whatever, 3X net over, you know, the life cycle of a fund, right? So a, a company in a big market growing 5X is a great investment. We'd... Everybody would be happy with these returns.

But we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most under-invested sector right now.

Boring software. Boring enterprise software.

Just traditional, like really good company.

No, no AI here.

No, like, boring-

Like-

Well, well, the AI, of course, is pulling them into use cases.

Yeah, yeah.

But that's not what they are. They're not on the token path, right?

Yeah.

Let's just say that.

Yeah.

Like, they're software, but they're not on the token path. Like, these are, like, they're great investments from any definition except for, like, random VC on Twitter saying, VC on X saying, like, "It's not growing fast enough." What do you think?

Yeah. Maybe I'll answer a slightly different question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing right now that I think is a question and we're spending a lot of time in, regardless of whether we pull the trigger or not.

Um, and it would probably be on the hardware side, actually.

Robotics.

Right, in the robotics sector.

Robotics, yeah.

Right? Which is, it's, I don't want to say that it's not getting funding, 'cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point.

Yeah.

But, um, we spent a lot of time in that space, and I think for us, we just haven't seen the ChatGPT moment happen on the hardware side. Um-

Yeah.

And the funding-

It feels-

... going into it feels like it's already taking that for granted.

Yeah, yeah. But we also went through the drone, you know, um, era.

There's a zip line right, right out there.

What's that?

The zip line.

Yeah, yeah. There's a zip line.

Oh, yeah. There's a zip line, yeah.

There's a drone, uh, there was the Aviora. And, like, one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing. Like, if you're, if you're investing in a robot company for an a- for agriculture, you're investing in an ag company, 'cause that's the competition and that's the pricing and that's the supply chain.

And if you're doing it for mining, that's mining. And so the AD team does a lot of that type of stuff, 'cause they actually set up due diligence that type of work. But for, like, horizontal technology investing, there's very little when it comes to robots-

Mm-hmm

... just because it's so fit for, for purpose. And so we kind of like to look at software solutions or horizontal solutions, like Applied Intuition clearly from the AV wave, DeepMap clearly from the AV wave. I would say Scale AI was actually a horizontal one for-

That was fair

... you know, for robotics-

Yeah

... early on. And so that sort of thing we're very, very interested, but the actual, like, robot interacting with the world is probably better for a different team. Yeah, yeah.

Mm-hmm.

Yeah. I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, "Yeah, robotics, it's important, and, like, people should invest in it." But then when you look at, like, the numbers, like, the capital requirements early on versus, like, the moment of, okay, this is actually gonna work-

Mm

... let's keep investing, that seems really hard to predict in a way that it's not-

I mean, Coat-

... with the-

Coatu, Coastlight-

Yeah

... GC. I mean, these are all invested in, in hardware companies. You just, you know... And listen, I mean, it could work this time for sure, right? I mean, if Elon's doing it, he's... Like-

Right

... ju- just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe sug- suggests that we should just be investing in robotics.

Just 'cause you have this north star who's Elon with a humanoid, and that's gonna, like, basically-

Yeah

... will into being an industry. Um, but we've just historically found, like, we're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things, 'cause again, robotics companies tend to be vertical.

Mm-hmm.

You really have to understand the market they're being sold into. Like, that's, like, that competitive equilibrium with a human being is what's important. It's not like the core tech. And, like, we're kind of more horizontal core tech type investors.

Uh, this is Sarah and I.

Yeah.

The AD team is different.

Yeah, yeah.

They can actually do these types of things.

Uh, just to clarify, AD stands for?

American Dynamism.

All right.

Yeah, yeah.

Yeah.

So-

Uh, I actually, I do have a related question. Uh, first of all, I want to acknowledge also, just on the, on the chip side-

Yeah

... I, I recall a podcast that where you were on, I f- I, I think it was the a16z podcast, uh, about two or three years ago where you, where you s- suddenly said something which really stuck in my head about how at some point, at some point k- kind of scale, it makes sense to build a custom ASIC-

Yes

... for, per run.

Yes. It's crazy. Yeah. We're here. We're here.

And I think you estimated 500 billion, uh, something like-

No, no, no. A billion, a billion dollar training run. A $1 billion training run, it makes sense to actually do a custom ASIC if you can do it in time. The question now is timeline-

Yeah

... not money. 'Cause just, just, just rough math. If it's a billion dollar training run, then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC.

20%, that's $200 million, you can tape out a chip for $200 million, right?

Yeah.

So now you can literally, like, justify economically, not timeline-wise, that's a different issue, an ASIC per-

Yeah

... model. Which is great.

'Cause that, that's how much we leave on the table every single time we, we d- we do, like, generic NVIDIA. Uh-

Yeah, exactly, exactly.

Yeah.

No, it's, it's actually much more than that. You could probably get, you know, a factor of two, which would be $500 million.

Yeah. Typical MFU would be, like, 50 or whatever.

Yeah, yeah. Yeah.

And that's good.

Exactly. Yeah, 100%.

Um, so, so yeah. I, I mean, and, and I just want to acknowledge, like, here we are in, in tw- end 2025, and Opening Eyes confirming, like, Broadcom and all the other-

Yeah

... like, custom silicon deals-

Yeah

... which is incredible.

Yeah.

I, I think that, uh, you know, speaking about AD, there's, there's a really, like, interesting tie-in that obviously you guys are hit on, which is, like, these sort of, this sort of, like, America first movement or, like, sort of reindustrialize here and, like-

Yeah

... uh, move TSMC here, if that's possible. Um, how much overlap is there from AD-

Yeah

... to, I guess-

Nice

... growth and, uh, investing in particularly, like, you know, US AI companies that are strongly li- bounded by their compute?

Yeah, yeah. So, I mean, I, I would view, I would view AD as more as a market segmentation than, like, a mission, right? So the market segmentation is it has kind of regulatory compliance issues or government, you know, sale, or it deals with, like, hardware.

I mean, they're just set up to, to, to, to, to- To diligence those types of companies. So it's more of a market segmentation thing. I would say the entire firm, you know, which has been since it's been in- incepted, you know, has geographical biases, right?

I mean, for the longest time we're like, you know, Bay Area is gonna be, like-

Great

... kind of where the majority of the dollars go.

Fantastic. Yeah.

And, and listen, there, there's actually a lot of compounding effects for having a geographic bias, right? You, you know, everybody's in the same place. You've got an ecosystem. You're there. You've got presence. You've got a network. Um, and, uh, I mean, I would say the Bay Area's very much back.

You know, like I, I remember during pre-COVID, like, it was like almost crypto had kind of pulled startups away from-

Miami. Yeah

... the Bay Area. Yeah. Yeah. Uh, New York was, you know, 'cause it's so close to finance, came up. Uh, like Los Angeles had a moment 'cause it was so close to consumer. But now it's kind of come back here.

And so I would say, you know, we tend to be very Bay Area focused historically, even though of course we invest all over the world. And then I would say, like if you take the ring out, you know, o- one more, it's gonna be the US, of course, because we know it very well, and then one ring more it's gonna be kind of US and its allies, and-

Hmm

... yeah, and it goes from there.

Yeah.

Sorry.

No, no, I agree. I think from a... But I think from the inter- That, that's sort of like where the companies are headquartered. Maybe your question's on supply chain end customer base. Uh, I, I would say our customers are, or our companies are fairly international from that perspe- Like, they're selling globally, right?

They have global supply chains in some cases.

I would say also the stickiness is very different-

Yeah

... historically between venture and growth. Like, there's so much company building in venture, so much. So like hiring the next PM, introducing the customer, like all of that stuff, like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years.

I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where-

Yeah

... I think, I think for some of the later stage rounds, the companies don't need that much help. They're already kind of pretty mature historically. So like they can kind of be everywhere, so there's kind of less of that stickiness.

This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh- ... in the Bay Area right now. She's like ops ninja, biz dev, biz ops.

Are, d- are you, are you finding much AI automation in your work? Like, what, what is your stack?

Oh, am I... In my personal stack?

I mean, so because like, uh, by the way, the, the, the reason for this is it's triggering, uh, yeah, we are, like I'm hiring ops for, o- ops people. Um, a lot of founders I know are also hiring ops people, and I'm just, you know, it's opportunity since you're, you're also like basically helping out with ops with a lot of companies.

What are people doing these days? Because it's still very manual as far as I can tell.

Hmm. Yeah. I think the things that we help with are pretty network based, um, in that it's sort of like-

So it's email, superhuman

... hey, how do I shortcut this process? Well, let's connect you to the right person. So there's not-

Yeah

... quite an AI workflow for that. I will say as a growth investor, Claude Cowork is pretty interesting.

Yeah.

Like, for the first time you can actually get one-shot data analysis, right? Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other...

It was like midnight, and th- three of us were playing with Claude Cowork. We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like aha moment. That sounds so boring, but, uh, you know, that's a, that's the kind of thing that a growth investor is like, you know, slaving away on late at night, um, done in a few seconds.

Yeah. You gotta wonder what the whole, like Anthropic Labs, which is like their new sort of products studio-

Yeah

... what would that be worth as an independent, uh, startup, you know? Like

A lot.

Yeah. Yeah. True.

Yeah, you gotta hand it to them. They've been executing incredibly well.

Yeah. I, I, I mean, and to me, like, you know, Anthropic like building on Cloud code, I think, uh, it makes sense to me. The, the real, um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI, and like that is like red alert at OpenAI.

Oh, I think they've been pretty clear they're enterprise focused.

They have been.

They've been-

But like here is-

They've been pretty clear publicly

... like en- it's enterprise focused, it's coding, right?

Yeah.

And then, and but here's Cloud, Cloud Cowork.

Mm.

And, and here's like, well, w- uh, they apparently they're running Instagram ads for Claude AI on, you know, for, for people to-

Like the mom to child

... have the chatbot. Right. And so like-

Crazy.

I- it's kind of like this, the disruption thing of f- uh, you know, mo- OpenAI's been doing consumer, been doing the, just pursuing general intelligence in every mo- modality.

Yeah.

And here is Anthropic, they only focus on this thing, but now they're sort of c- undercutting and doing the whole innovator's dilemma thing on like everything else.

Mm.

Yeah.

It's very interesting.

Yeah, but there's, there's a very open que- So, so, so for me there's like... Do you know that meme where there's like the guy in the path and then there's like a path this way-

Mm

... there's a path this way, and like one-

Which way, Western man? Yeah.

Yeah, yeah.

Yeah, yeah. And for me, like, like all, the entire industry kind of like hinges on like two potential futures. So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale, 'cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like it's just like software's being rewritten and fractured all over the place, and there's tons of upside, and it just grows.

And then there's another path which is like, well, maybe these models actually generalize really well and all you have to do is train them with three times more money. That's all you have to do, and it'll just consume everything beyond it.

And if that's the case, like you end up with basically an oligopoly for everything, like you know-

Mm

... because they're perfectly general and like... So this would be like-

Mm-hmm

... the, the AGI path would be like these are perfectly general, they can do everything, and this one is like this is actually normal software. The universe is complicated, you've got... And nobody knows the answer. My belief is if you actually look at the numbers of these companies...

So generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive. You're like, "Oh, that's really working."

But if you look at, like the current training that they're doing for the next model, they're gross margin negative. So part of me thinks that a lot of them are kind of borrowing against the future, and that's gonna have to slow down.

That's gonna catch up to them at some point in time.

Yeah.

But we don't really know.

Yeah.

Does that make sense? Like, I mean, it could be-

Yeah, yeah, yeah, yeah

... it could be the case that the only-

Yeah

... reason this is working is 'cause they can raise that next round, and they can train that next model, 'cause these models have such a short ... life. And so at some point in time, like, you know, they won't be able to raise that next round for the next model, and then things will kind of-

Yeah

... converge and fragment again. But right now it's not.

Totally. I think the other, by the way, just, um, a meta point, I think the other lesson from the last three years is, and we talk about this all the time 'cause we're on this Twitter/X bubble. Um, but-

Very cool

... you know, if you go back to, let's say March 2024, that period, it felt like a, I think an open source model with an f- like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.

And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly. You know, if you fast, you know, if you, if you rewind time even before that, GPT-4 was number one for nine months, 10 months.

It's a long time, right? Um, and of course now we're in this era where it feels like an oligopoly. Um, maybe some very steady state shifts and, and, you know, it could look like this in the future too, but it just, it's so hard to call.

And I think the thing that keeps, you know, us up at night i- in a good way and bad way, is that the capability progress is actually not slowing down. And so until that happens, right, like you don't know what it's gonna look like.

But I, I would, I would say for sure it's not converged. Like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently. Which you can do that for a period of time, but, but you know, at the end, at some point the market will rationalize it and just nobody knows what that will look like.

Yeah.

Or, or like the drop in price of compute will, will, will save them. Who knows?

Mm.

Yeah. Yeah, I think the models need to asymptote to specific tasks. You know, it's like, okay, now Opus 4.5 might be AGI at some specific task, and now you can like depreciate the model over a longer time. I think now, n- right now there's like no old model.

No, but let, but let me just change that mental. That's, that used to be my mental model. Let me just change it a little bit. If you can raise three times... If you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.

It doesn't even matter. Do you see what I'm saying? Like, so-

Yeah, yeah

... so I have an API business. My API business is 60% margin or 70% margin, or 80% margin. It's a high margin business. So I know what everybody's using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm AGI or not.

And I will know that they're using it 'cause they're using it. And like unlike in the past where engineering stops me from doing that-

Mm-hmm

... this is very straightforward to use as trained. So I also thought it was kind of like you must asymptote AGI general, general, general, but I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of them.

I, I-

Yeah

... do wonder though, to your point, um, if there's a certain task that getting marginally better isn't actually that much better. Like we've asymptoted to, you know, we can call it AGI or whatever. You know, actually Ali Ghodsi talks about this, like we're already at AGI for a lot of functions in the enterprise.

Um, that's probably tho- for those tasks you probably could build very specific companies that focus on just getting as much value out of that task that isn't coming from the model itself. There's probably a rich enterprise business to be built there.

I mean, could be wrong on that, but there's a lot of interesting examples. So right, if you're looking about the legal profession or, or whatnot, and maybe that's not a great one 'cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from services, it comes from implementation, right?

It comes from all these things that actually make it useful to the end customer.

Mm.

Sorry, one more, uh, uh, one more thing I think is, is under discussed in all of this is like to what extent every task is AGI complete.

Mm-hmm.

Mm. Yeah.

I code every day. It's so fun. And like-

That's a core question, yeah

... and like when I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's-

It's healthcare, it's-

I mean it's, it-

... legal

... but it's every... It's exactly that.

Yeah, like customer support.

I mean-

Yeah

... it's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history. Like it's having a full conversation with me while I, I engineer.

And so it could be the case that like-

Yeah

... the most A, you know, AGI complete... Like I'm not an AGI guy. Like I think that's, you know... But like the most AGI complete model will always win independent of the task. And we don't know the answer to that one either.

Yeah.

But it seems to me that like, listen, Codex in my experience, is for sure better than Opus 4.5 for coding. Like it finds the hardest bugs that I work in, with like, it's, it's, it's, you know, the smartest developers that don't work on it.

It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner- ... and it really, it, it, it really matters if you're building something very complex because like it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.

And I think we don't discuss enough how every task kind of has that quality.

Mm-hmm.

Mm.

And what does that mean to like capital investment and like frontier models and sub models?

Yeah.

Like what happened to all the special coding models? Like none of them worked, right?

So does some of them, they didn't even get released.

Yeah.

Magic of dev or-

There was a whole, there's a whole host. We saw a bunch of them and like there was this whole theory that like there could be a... And I think one of the conclusions is, is like there's no such thing as a coding model.

Yeah.

Like that's not a thing. Like you're talking-

Yeah

... to another human being and it's, it's good at coding, but like it's gotta be good at everything.

Uh, minor disagree only because I, I'm pretty, like have pretty high confidence that basically OpenAI will always release a GPT-5 and a GPT-5 Codex. Like the, that, that's the coding model.

Yeah, yeah, yeah.

Yeah, yeah, yeah.

The way I call it is one for rizz and one for tizz. Um, and, and then like someone internal at OpenAI was like, "Yeah."

That's a good way to frame it.

That's so funny.

Uh, but maybe, maybe it collapses down to rizz and tizz and that's it.

Yeah.

It's not like 100 dimensions.

It doesn't life, yeah.

It's two dimensions.

Yeah, yeah, yeah, yeah, yeah, yeah.

And like, and exactly bedside manner versus coding.

Yeah, yeah, yeah.

Yeah. Yeah. It's rizz and tizz, yeah.

I, I think for, for any-

Hilarious

... for any, for anybody listening to this, for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that, like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it.

And so like you're-

Mm

... I think like, like the best Soto-ish model, you know, is gonna remain very important no matter what the task is.

Yeah. Uh, speaking of coding, I, I'm gonna be cheeky and ask, like what actually are you coding? Because obviously you, you could code anything and you're obviously a busy investor and a manager of the good- Giant team. Um, what are you gonna-

I help, um, uh, Fei-Fei at World Labs. Uh, it's one of the investments and, um, and they're building a foundation model that creates 3D scenes.

Yeah, we had her on the pod, yeah.

Yeah, yeah. And so these 3D scenes are Gaussian splats just by the way that kind of AI works, and so, like, you can reconstruct a scene better with, with, with radiance fields than with meshes 'cause, like, they don't really have topology.

So, so they, they, they produce these just beautiful, you know, 3D-rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great. It's just never... You know, it's always been meshes and, like, things like Unreal use meshes.

And so I work on a open source library called Spark.js, which is a, uh, a JavaScript rendering library for Gaussian splats, and it's just-

Yeah

... because, you know, um, you, you, you need that support and, and right now there's kind of a Three.js moment that's all meshes, and so, like, it's become kind of the default in Three.js ecosystem. As part of that, to kind of exercise the library, I just build a whole bunch of cool demos.

So if you see me on X, you see, like, all my demos and all the world-building. But all of that is just to exercise this, this library that I work on, because it's actually a very tough algorithmics problem to actually scale a library that much.

And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college-

Ah

... in the late '90s, so I've got actually a ba- it's very old background, but I actually have a background in this. And so a lot of it's fun, you know, but, but the, the, the whole goal is just for this rendering library to, to work.

Are you one of the most active contributors to their GitHub?

Spark.js?

Yeah, yeah.

There's only two of us on it.

Okay.

So, so yes.

Yeah.

No, so by the way, so the, the pri- the pri- yeah. Yeah, so the primary developer is a guy named Andreas Sundquist, who's an absolute genius. He and I did our, our, our PhDs together, and so, like, um, we set it for comps and quals together.

It's almost like hanging out with an old friend, you know? And so, like, so he, he's the core core guy.

Yeah.

I do mostly kind of, you know, the side-

But, you know-

... I write the venture fund

... it's amazing, like, five years ago you would not have done any of this, and like-

No, it's-

... it brought you back

... the active-

You're so back

... the activation energy was so high-

Yeah

... 'cause you had to learn all the framework bullshit, and I fucking used to hate that. And so, like, now I don't have to deal with that. I can, like, focus on the algorithmics and I can focus on the scaling and I-

Yeah, yeah. And then, uh, I'll observe one irony, and then I'll ask a serious, uh, investor question, uh, which is, like, the irony is Fei-Fei actually doesn't believe that LLMs can lead us to spatial intelligence, and here you are using LLMs to, like, help- ...

like, achieve spatial intelligence. I, I just also see, I see some, like, disconnect in there.

Yeah. Yeah, so I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding-

Yes

... but, like, that's very different than a model that actually, like, provides, like-

Yeah

... that spatial-

You'll, you'll never have the-

Like the-

... the spatial intelligence she wants.

Yeah, listen, our brains clearly... Listen, our brains, brains clearly have both. Our, our brains clearly have a language reasoning section, and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.

Okay. And y- y- like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neurosymbolic path, right?

Like, one, uh, sort of very sort of abstract reasoning thing and one formal, formal thing. Um, and that's what DeepMind had in 2024 with AlphaFold, AlphaGeometry, and now they just use Deep Think and just extend the thinking tokens, and it's one model, and it's, and, and it's an LLM.

Yeah. Yeah, yeah, yeah, yeah.

And so that, that was my indication of, like, maybe you don't need a separate system.

Yeah. So, so let me step back. I mean, at the end of the day- ... at the end of the day, these things are, like, nodes in a graph with weights on them, right? You know, like-

It can be modeled

... like, if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room, like totally black room, and then let me just describe how you exit it.

Like, to your left there's a table. Like, duck below this thing, right? I mean, like, the chances that you're gonna, like, not run into something are very low. Now let me, like, turn on the light and you actually see, and you can do distance and, you know, how far something away is and, like, where it is or whatever.

Then you can do it, right? Like, language is not the right primitives to describe the universe because it's not exact enough. So that's all Fei-Fei is talking about when it comes to, like, spatial reasoning is, like, you actually have to know that this is three feet far, like, that far away.

It is curved. You have to understand, you know, the, like, the actual movement through space.

Yeah.

So I do, I... Listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language, which, you know, that would be like describing to somebody, like, what to do, and the other one is actually just showing them, and the spatial reasoning is just showing them.

Yeah. Yeah, yeah, right. Got it. Got it. Uh, the in- the investor question was on, on World Labs is, well, like, how do I value something like this? What, what, what work does, do you do? I'm just like, Fei-Fei's awesome, Justin's awesome, and you know, the other two cofound- cofounders, but, like, the, the, the tech, everyone's building cool tech, but, like, what's the value of the tech?

And this is the fundamental question of-

Well, let me, let me just for like these-

Yeah

... let me just m- maybe give you a rough sketch on the diffusion models. I actually would love to hear Sarah, 'cause I'm a venture per- I'm, you know, so, like, venture's always, like, kind of Wild West type stuff.

You, you, you're, you paint a dream, and she has to, like, actually-

She has to basically- So I'm gonna say, I'm gonna say-

The mark to reality.

Exactly. So I'm gonna say the venture view-

Okay, sure

... and then she, and she can be like, "Okay, you little-"

His dream

... "you little kid." Yeah. So, like, so, so these diffusion models literally create something for, for almost nothing, and something that the, the world has found to be very valuable in the past in our real markets, right? Like, like a 2D image, I mean, that's been an entire market.

People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, um, a, to turn me into a whatever, like an image would cost 100 bucks and an hour.

The inference cost is a hundredth of a penny, right? So we've seen this with speech in very successful companies. We've seen this with 2D image. We've seen this with movies, right? Now think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out?

Soon.

It's been six, what, it's been 10 years. I mean, how, how, like, like-

It has been 10 years, yeah

... honestly, how much would it cost to, like, to reproduce this room in 3D? If you, if you, if you hired somebody on Fiverr, like in, in any sort of quality, probably $4,000 to $10,000. And then if you had a professional, probably $30,000.

So if you could generate the exact same thing from a 2D image, and we know that these are used, they're using Unreal and they're using Blender, they're using movies, and they're using video games, and they're using all... So if you could do that for, you know, less than a dollar, that's four or five orders of magnitude cheaper.

So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.

Yeah. A- and for listeners, uh, you can do this yourself on your, on your own phone with like, uh, the Marble.

Yeah, Marble.

Uh, or, but also there's many Nerf apps where you just-

Yep

... go on your iPhone and, and do this.

Yeah, yeah, yeah. And, and in the case of Marble, though, it w- what you do is you literally give it in... So most Nerf apps you like kind of run around and take a whole bunch of pictures, and then you kind of reconstruct it.

Yeah.

Um, things like Marble, just the, the whole generative 3D space will just take a 2D image and it'll reconstruct all the, like, like-

Meaning it has to fill in, uh, stuff that-

Yeah

... stuff it can't see

Like the back of the table, under the table, the back.

Yeah.

Like, like the images it doesn't see. So the generative stuff is very different than reconstruction, that it fills in the things that you can't see.

Yeah. Okay, so.

Yeah.

All right. So now the, now-

No, no, I mean I love that

... now the adult, now the adult perspectives.

Um, well, no, I was gonna say, these are very much a tag team. So we s- we started this pod with that, um, premise, and I think this is the perfect question to even build on that further, 'cause it truly is.

I mean, we're tag teaming all of these together. Um, but I think every investment fundamentally starts with the same, maybe the same two premises. One is at this point in time, we actually believe that there are N of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?

So, uh, we're not investing in every, you know, now the term is Neo Lab, but every foundation model, uh, any, any company, any founder who's trying to build a foundation model. We're not, um, contrary to popular opinion, we're not invested in all of them, right?

We have a very specific thesis-

I don't think people say that about you. No, they don't, they don't.

They say that we're big, we're in everything. But, um, you know, if you think about Ilya, right, he's at SSI. He's sort of been behind a- almost every foundational breakthrough for the last 15 years.

15 years.

Um, if you think about, you know, the Thinking Machines team, right? Amira and John, right? John is the godfather of reinforcement learning. And so, um, I go through this because, you know, if you think about for each of the bets that we've made, it goes back to one of, to a very specific thesis about that person, the team they've assembled, and what they've done in a prior life.

Um, and you know, I, I, I think, you know, obviously we talked about talent wars. Um, we do think at this particular moment in time, there are particular people that can move needles. Um, clearly, uh, other companies believe that too, otherwise they wouldn't be willing to pay such crazy prices for single individuals.

So that's, that's one. And then two, we don't think it's a zero-sum game, right? Like, if that were true, OpenAI or, or actually just DeepMind would be number one in everything, right? There's clear value to specializations, like ElevenLabs.

There have been so-

Oh my God, yeah

... many audio models that have hit the market.

So far. Yeah, yeah.

They're still fricking number one, right? And so if you think about... And they've created a ton of value, um, for their customers, for their investors, you know, for their team. Um, and so if you think about those two put together, right, that's sort of the foundation of our thesis when we back, uh, f- these foundation model, uh, companies.

Um, of course, the valuations, you know, they sound astronomical when you think about current revenue, the numbers. Um, you know, there's, there's sort of the... I would, one, I would say that's the market out there because they are raising larger dollars.

They have compute needs, right? That's 80% of a round that they typically raise, or typically of, of a round that they raise. Um, but I think the thing that gets us excited about backing them is that the revenue growth has typically followed the capability breakthrough.

So you sort of ties back to that question of the cyclical nature. Like, are you just funding it and then you raise more funding? Um, when there's a real capability breakthrough, the demand is there. And so the revenue growth is much faster than we've ever seen once it's turned on.

There's a company, I can't share the name, um, but their product went GA in a few weeks, tens of millions of revenue, right? We have SaaS comp-

I, I've seen this myself, yes.

Absolutely.

Absolutely.

We have SaaS companies that, you know, have been in business for seven years, and they get to the same level seven years later, and the growth is, you know, eking to whatever it is. Um, and, and by the way, great companies, n- not at all, um, diminishing what they've accomplished.

But the fact is, to get to that revenue growth that quickly, it's not just the two companies that people talk about, it's, it's really a lot of these, you know, sort of every domain has a specialist, and we think if you can win that, you become very large very quickly, and that's actually played out in the numbers.

Yeah. Uh, our, our viewers are going to, uh... So first of all, thank you for that overall take. I think, like, it's important to hear you guys' perspective because n- the rest of us are just kind of looking at headlines and not knowing how to make sense of any of this.

Um, we can mention, like my, our listeners will roast us if we don't, if we mention Thinky and not discuss what happened. Uh, I mean, obviously founder split happens. Um, but like, I guess is the thesis unchanged? Is, is like, um, you know, like, uh, what's, what's going on at Thinky?

Yeah. Um, we're more excited than ever about them. Um, they have some things that we're not gonna do breaking news on a, a pod, uh, that, you know, obviously they should share themselves. But, um, they've... You know, I think when you bring a team of that caliber together, there's special things that happen and, um, I think 2026 is gonna be a big year for them.

Um, obviously, you know, some of the themes that we talked about before, even with just the media news stor- like the whole something happens, and then it's everywhere instantly, um, you know, I think, uh, that's a, uh, that's a tough situation for any company to be in.

Um, but to come out of that stronger than ever, I think that, you know, we're, we're more bullish about Thinky than, um, you know, even before. And, um, obviously-

And it, and the story is Tin- uh, is Tinker. It's custom models RL. Um, yeah. Is that, is that what, is that what we're aiming for with this?

Yeah, and a bunch of stuff we, we can't talk about here.

Okay.

Yeah.

All right, cool.

Yeah, absolutely. But no, that team is cooking and, um, you know, I think, um, they'll, they'll be just fine from, uh... They'll, they'll recover from the events in January.

Yeah. I will say

This is the furthest... So we have a very privileged position on the boards of these companies, and like I will say, I've never seen the perception of the truth be further from the truth-

Oh

... industry-wide ever. Like, I, I guarantee you-

It's like-

... for any of these gossipy things, I guarantee you it's way off.

Okay.

Way, way. Like, like the general sentiment. And like, and what happens is like we've got this crazy game of telephone right now where there's always, like, seeds of truth, but it gets so warped by the time. Like, we hear all the time rumors about stuff that we're directly involved in.

Like, we're literally on the board, you know, like we're the- ... we're the one that did the thing. And by the time it gets to us, it's gotten so warped and so twisted. I think this is like everybody's excited, there's a lot of focus, the schadenfreude is so high that people just kind of will into being things that didn't exist.

Um, so I'm not, you know, I, you know, I don't want to comment specifically on the Thinking Machines, but, like-

It, it's an important message to the general audience

... I, I, I will tell you-

Yes

... if you hear something on X, like the chances that it's, you know, it is accurately representing what it's saying to you is very, very low.

Yeah.

I have never lost so much faith in the Anon- Anon counts on Twitter-

I know. I know

... that just seem very confident in what they're saying.

I know, yeah.

And couldn't be further from the truth.

I know.

I w- I had a couple day stretch where I was like, "Oh my God, Twitter is mind poison." And I love X, but-

No, but we talk to each other all the time 'cause we actually know 'cause we're there. Like, we're there-

Right

... seeing these things and like, you know, Sarah will like text me, you know, like whatever. Like it's like ridiculous. So for us it's like, it's like this ridiculous... But the problem is, is we realize that things like- things start taking on a life of their own, and then people assume that they're real and, and everything.

And so I think it's very tough for founders because, you know, it's tough enough fighting the real battle, you know-

Absolutely

... but now you have to-

Yeah. Actually building

... like, like fighting-

Yeah

... now you're fighting phantoms too. And so, you know, you know, more and more we're just like... And, and I got this from the Cursor guys, which I, I really appreciate Michael Trul. He's like, "Listen, heads down, focus on the business."

Yeah.

And, you know-

And they absolutely crushed it. Yeah.

Yeah. And I, I think that's right.

Yeah, absolutely.

I think all founders should do that right now 'cause the noise is so high.

Yeah. No, that team's been back to business for, for weeks, the Thinky team, so yeah.

Yeah. Well, thank you for acknowledging that. Uh, uh, it's, it's just, uh, the hot topic of the moment.

Oh, absolutely.

We gotta, gotta address the elephant in the room. Um, uh, Cursor, right? You- obviously you guys are big investors. Uh, 2025, I would say is Cursor's year.

Right.

And I mean, maybe decade. But- ... uh, just like I, I think, you know... Uh, just going back to the discussion about how AGI would just kind of consume everything.

Yeah.

Cursor's like the one, like the kind of the shiny example of like, here's how you build application layer that's a wrapper-

Yeah

... but an extremely damn good one.

Yeah.

Uh, and, uh, I guess just what like the, the general analysis, I guess, of, of Cursor's development and what it means for everyone. Like, is there a Cursor in every industry to be built?

Yeah. So the, the interesting about Cursor is they actually for, you know, a small fraction of the cost, a hundredth of the cost or less, developed an almost SOTA model, which for a period of time was the most popular coding model in the world, right?

Which is really crazy to think about. So I think they're just kind of doing it in reverse, right? So there, there, there's two approaches. You start with a foundation model and then you verticalize up, or you start with the app and all of the product data and you go down, and they're the ones that are doing that.

I think any company that's doing an app has to ask the margin question-

Mm-hmm

... which is like, how, how do I extract margin on, on, on the tokens that are going through? Like, everybody has to be on the token path, and everybody has to ask that question, and I've just thought they've been incredibly thoughtful about it.

And one reason is, is if you ask, you know, Michael, what type of company are you? They are a developer company for professional developers. That's what they are. They're a dev tools company. They're just focused on coding. And that's a hu- I mean, even if you didn't do AI, that's a ma- you know, they, they, they, um, they acquired Graphite.

I mean, like-

Yeah

... you know, listen, we were investors in GitHub. Like, we know how big this market is, so that's a massive market even without becoming a model company. But they've also been quite successful in doing their own models, and so I think it just shows you that if you are focused, you have a large use case, there's a huge opportunity not only to ge- get the application, but to start building your own models.

Are these gonna be the only models people use? Of course not. Um, but you know, they are in a great position to serve great models, and they've demonstrated that.

Yeah. My, my, uh, sort of, uh, thesis, which we're not gonna have to go into here, is actually, I think a- um, what I've been calling agent labs, which are people who build on top of, uh, all the other models-

Yeah

... um, will probably have a better time with the margins because they, they price against the end user hours spent or, like, human labor-

Yeah

... whereas models get commodity price per token.

Yeah.

And so-

Mm

... margin-wise, we know inference economics for, uh, mo- uh, model labs.

Yeah.

But agent labs, uh, the difference is the delta between token intelligence, which keeps going down, and human costs, which keep going up.

Yeah, yeah.

And so the, the margin should be higher.

They sh- they, they, they- ... they, they, they should be. The, the, the, the caveat to that is if the models go first party, right?

Yeah, yeah.

And what they can do is they can, they can sub- they-

Which is the, the composer dream. Yes.

Yeah. They can subsidize thems- no, the models. They can subsidize-

Oh, sorry. Yeah

... themselves.

Oh, Cloud Code.

Cloud Code.

Cloud Code, yeah.

They can subsidize themselves, and then they can charge the third party more. And it's a very delicate dance because you're kind of competing with your own customers. And so, you know, we've seen this historically. We saw this with the cloud, with EC2.

Like, so this is not unusual. We saw this with the operating systems. It's not unusual, but it's playing out very, very quickly.

Yeah. Thank you for joining us. That's all the time we have today.

It was such a pleasure.

You're welcome back anytime. Um-

Yeah

... and thank you for being so open and also, like, just leading the industry in so many areas. Uh, it's, uh, really inspiring to see, so thank you so much.

Thank you so much.

Yeah. Thank you for having us.

Great. Thank you.