The a16z Show - AI’s Capital Flywheel: Models, Money, and the Future of Power

Episode Date: February 19, 2026

a16z's Martin Casado and Sarah Wang join Latent Space hosts Alessio Fanelli and Swyx to discuss what makes this AI investment cycle unlike anything in the history of venture capital. They cover why the lines between venture and growth, apps and infrastructure are blurring, how frontier model companies can raise more than the aggregate of everyone built on top of them, and why the industry-wide gap between perception and reality has never been wider. Follow Alessio Fanelli on X: https://x.com/FanaHOVA Follow Swyx (Shawn Wang) on X: https://twitter.com/FanaHOVA Follow Martin Casado on X: https://twitter.com/martin_casado Follow Sarah Wang on X: https://twitter.com/sarahdingwang   Listen to more from Latent Space: https://www.youtube.com/@LatentSpacePod Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I mean, every industry is talent wars, but not at this bag of tooth, right? Very rarely can you see someone get poached for $5 billion? That's hard to compete with. 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 the silliest thing to say. 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 could be a systemic situation where the soda models can raise so much money that they can outpay anybody that bills on. on top of them, which would be something I don't think we've ever seen before, just because we were so bottlenecked on engineering. During the internet buildout, investors put money into fiber that nobody used. Four years of supply overhang followed. This time, there are no dark GPUs. Every dollar going into compute has demand on the other side.

But something else is different. A model company can raise capital, drop a model in a year with a team of 20, and produce something with immediate demand. If Frontier Labs can raise three times more than the aggregate of every company built on top of them, they may consume the entire application layer. Or the market fragments and value accrues the company's closest to the end user. Nobody knows which path wins. In this conversation previously aired on the Latenspace podcast, Martin Casado and Sarah Wang, general partners at A16Z, speak with Alessio Finnelli and Sean Wang about the capital flywheel, talent wars, and why

boring software is underinvested in whether every task is AGI complete. Hey everyone, welcome to the Latien Space podcast live from A16Z. This is Alessio, Foundro Kernelance, and I'm joined by Twix, editor of Laton Space. Hey, hey, hey, and we're so glad to be on with you guys. Also, a top AI podcast, Martin Casado and Sarah Wang, welcome. Very happy to be here and welcome. Yes. We love this office.

We love what you've done with the place. The new logo is everywhere now. It's still getting, it takes a lot to get used to, but it reminds me of sort of a callback to a more ambitious age, which I think is kind of... Definitely makes a statement. Yeah. Not quite sure what that statement is what it makes a statement. Martin, I go back with you to Netlify.

And, you know, you create a software-defined networking and all that stuff. People can read up on your background. Sarah, I'm newer to you. You sort of started working together on AI infrastructure stuff. That's right. Yeah, seven years ago now. Best growth investor in the entire industry. Oh, same more.

Hands down. Yeah, Sarah is. I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive investment thesis around AI models, right? So she worked with Noam Chazir, Mira, Ilya, Pha, and so just these frontier kind of like large AI models, I think, you know, Sarah's been the broadest investor. Is that fair?

No, well, I was going to say, I think it's been a really interesting tag team, actually, just because a lot of these big C deals, not only are they raising a lot of money. It's still a tech founder bet, which obviously is inherently early stage, but the resources... So many. Well, I was going to say the resources,

one, they just grow really quickly, but then two, the resources that they need day one are kind of growth scale. So 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 very, like...

No, it's a very interesting time in investing because I take like the character around, 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 because this stuff has such high demand requires, you know, more of a number 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 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. BISDF? BISDF, exactly. But like now...

I'm not familiar with what does BISDF mean for a venture fund, because I know what BISDF means for a company. Yeah, you know, so 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 sort of partner are you looking at? Is there a go-to-market arm to that? 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, 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 a very different time. Listen, I've been doing this for 10 years. I've never seen anything like this. Yeah.

Do you have worries about the circular funding from some of these strategics? Listen, as long as the demand is there Like the demand is there Like the problem of the internet The demand wasn't there Exactly. All right, this is like the whole pyramid scheme bubble thing Where like it's obviously marked to market

On like the notional value of like these deals fine But like once it starts to chip away It really Well no, as long as there's demand I mean you know 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 Raising money to put fiber in the ground that wasn't used. That's a problem, right? Because now you actually have a supply overhang. And even in the time of the internet, like the supply and bandwidth overhang, even as massive as it was,

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 someone invests in a company, you know, they'll actually use the GPUs and on the other side of it is the ask for customers.

So I think it's a different time. I think the other piece, maybe just to add on to this, and I'm going to quote Martine 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, right, provided that scaling laws are holding and capabilities are actually moving forward. Because if you can translate dollars into capabilities,

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

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, Open AI is now at the same size as some of the cloud providers were early on. Like, what does that look like? Like, how much information can you feed off each other between the two.

There's so many lines that are being crossed right now are 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? 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 touches the users directly. And then, of course, you know, the growth of these is just so high. And so I actually think

you're just starting to see 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. You're right that these companies become platform companies very quickly. You've got ecosystem built out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And then where we'd normally cut lines before is blurred a little bit. But 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. Yeah, I think it's interesting. 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, you raise money for compute, you pour the money into compute,

you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GPT, 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. depending on your strategy, you raise money at the peak momentum, and then you repeat, rinse and repeat.

And that wasn't true even two years ago, I think. And so it's sort of just tying into fundraising strategy, right? And hiring strategy, all of these are tied. I think the lines are blurring even more today, where everyone is, but of course these companies all have API businesses. And so there are 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 would say for sure, venture and growth, that line is blurry, app and infrastructure, that line is blurry. 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 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 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 man must have taken 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 year and it's better, right? 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. too. Yeah. Like at Colonel, as 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 pot committing to a certain

strategy for a certain amount of time. But if you could like iteratively spend out companies and products and just throw, I want to spend a million dollar

of inference today and get a product out 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 early stage venture model to be much more iterative. And then every round is, like, either 100K of inference or, like, 100 million from a 16-Z. There's no, there's not, like, $8 million C round anymore.

But there's a, there's an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take Anthropic. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that a company is building smaller models that use the bigger model in the background. 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. It's like imagine like a star that's just kind of expanding.

So there could be a systemic, there could be a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of them, which would be something I don't think we've ever seen before. Just because we were so bottlenecked on engineering. And it's a very open question. Yeah, it's almost like bitter lesson applied to the startup industry. 100%. It literally becomes an issue of, like, raise capital, turn that directly to growth, use that to raise three times more. And if you can keep doing that, you literally can outspend any company that's built.

Not any company. You can outspend the aggregative companies on top of you, and therefore you'll necessarily take this year, which is crazy. Would you say that kind of happens a character? Is that the post-mortem on what happened? No. Yeah, because I think... I mean, the actual post-mortem is he was,

wanted to go back to Google. Yeah, exactly. By like, that's another different. You said it, yeah. We should actually talk about this. Yeah. Go for it. I was going to say, I think 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 so we invested in character in January 20203, which feels like eons ago. I mean, three years ago feels like lifetimes ago. But, and then they did the IP licensing deal. with Google in August, 2024. And so, 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 he went to go do that. But he had a product attached. The goal was, oh, I mean, it's Noam Shazir. He wanted to get to AGI. That was always his personal goal. But, you know, I think through collecting data, right, in this sort of very human use case that the character product originally was and still is, was, was,

one of the vehicles to do that. I think the real reason that, you know, if you think about the stress that any company feels before you ultimately go on one way or the other is sort of this AGI versus product. And I think a lot of the big, I think, you know, opening eye is feeling that anthropic, if they haven't 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 tradeoffs, 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 research, right?

Is it long-term research? Is it toward near to mid-term research? And so in a case where your resource constrained, of course, there's this fundraising gave you can play, right? But the market was very different back in 2023, too. I think the best researchers in the world have this dilemma of, okay, I want to 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, 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 we're keeping track of all of the things that are different, right? like, you know, venture growth and app infra. And one of the ones is definitely the personalities of the founders. It's just very different this time.

I mean, I've been doing this for a decade and I've been doing startups for 20 years. And so, 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 would have created Internet company. I'd an integrated infrastructure company. Like, it's kind of more engineering.

builders and this is kind of a different 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 as a fraction of founders than we've ever seen and maybe since like I don't know the time of like Shockley in the Trader's 8 or something like that way back to the beginning of the industry it's a very very, very unusual time of personnel.

Totally. And I think it's exacerbated by the fact that talent wars, I mean, every industry is talent wars, but not at this bag of tooth, right? 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 and 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 the deep anxiety that that these AI founders are feeling. Yes. I mean, just on a briefly comment on the founder,

the sort of talent wars thing, I feel like 2025 was just like a blip. Like I don't know if we'll see that again. Because meta built the team. Like, I don't know if I think, I think they're kind of done and like who's going to pay more than meta. I don't know.

I agree. So it feels, it feels this way to me too. It's like, because like basically Zuckerberg kind of came out swinging and then now he's kind of back to building. Yeah. Yeah, you know,

you got to like pay up to like assemble team to rush the job. whatever, but then now you made your choices and now they got a shit, right? I mean, the other side of that is like, you know, like we're actually in the job hiring market. We've got 600 people here. I hire all the time. I've got three open wrecks. If anybody's interested that's listening to this. For investor? Yeah, on the team. Like on the investing side of the team, like, and a lot of the people we talk to have acting, you know, active offers for 10 million a year or something like that. And like, you know, we pay really, really well. And just to

see what's out on the market is really, is really, you know, is really. remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars. But like the inflated, um, trickles down. Yeah, it's still very active today. I mean. Yeah. You could be an L5 and get an offer in the tens of millions. Yeah, easily. So I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is not elevated. Everything got pulled up. Yeah. Exactly. 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 $800K, a million at Google. But if I'm getting paid $5, $6 million, that's different. But on the other hand, there's more strategic money than we've ever seen historically, right? And so the economics, the calculus on the economics is very different in a number of ways. And it's caused a ton of change in confusion in the market. Some very positive, some negative. Like, so, for example, the other side of the,

the co-founder, like, acquisition, you know, Mark Zuckerberg poaching someone for a lot of money is like we're actually seeing historic amount of M&A for basically aqua hires, right? They, you know, really good outcomes from a venture perspective that are effective aqua 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. 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 seems in a way, you know, as YCA has gotten more popular, it's 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. And there's maybe not as much risk appetite for things outside of that. I'm curious if you feel like that's true and what are maybe at some of the areas. that you think are under-discussed. I mean, I actually think that we've taken our eye off the ball and a lot of like just traditional, you know, software companies. So, like, I mean, you know, I think right now there's almost a barbell. Like you're like the hot thing in the next, you're a deep tech.

Right? But, 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, 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 the attention of investors. 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 the silliest thing to say. I mean, think of yourself as like an individual person, 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 part? Of course, you can put it in the company 5x. So it's just like we say these stupid things like if you're not going from zero to 100, but like those like who knows what the margins of those are.

When clearly there's a good investment for anybody, right? Like our LPs want whatever, 3x net over, you know, the life cycle of a fund, right? So a company in a big market going 5X is a great investment. Everybody would be happy with these returns. But we've got this kind of mania on these strong growths. And so I would say that that's probably the most underinvested sector right now. Boring software, boring enterprise software.

Just traditional, like, really good cover. No, yeah, I here. Well, the AI, of course, is pulling them into use cases, but that's not what they're. They're not on the token paths, right? Let's just say that. Like, they're softened, 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, VCI on X saying, like, it's not growing past enough. What do you think? Maybe I'll answer a slightly different question, but adjacent to what you asked, which is maybe an area that we're not 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.

And it would probably be on the hardware side, actually. Right? And the robotics sector, right? Which is, I don't want to say that it's not getting funding because it's clearly, it's sort of non-consensus to almost not invest in robotics at this point. But we spent a lot of time in that space. And I think for us, we just haven't seen

the chat GPT moment happen on the hardware side. And the funding going into it feels like it's already taking that for granted. Yeah, yeah. We also went through the drone, you know, there's a zip line

right right out there. Was that? Oh, yeah, yeah, yeah. Oh, yeah, yeah, there's a zipline. What's the AVI era? And like, one of the takeaways is when it comes to hardware, most companies will end up

verticalizing. Like, if you're investing in a robot company for agriculture, you're investing in an ag company because that's the competition and that's surprising and that's a 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. because they're actually set up to diligence that type of work. But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for purpose. And so we kind of like to look at software solutions or horizontal solutions,

like applied intuition clearly from the AV wave, deep map clearly from the AV wave. I would say scale AI was actually a horizontal one for, you know, for robotics early on. So that sort of thing, we're very, very interested. But the actual like robot interacting with the world is probably better for different teams. me, 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 going to work, let's keep investing. That seems really hard to predict in a way that it's not. I think CO2, COSLA, GC. I mean, these are all invested in Harvard 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, just the fact that Elon's doing it means that there's going to be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just because you have this North Star who's Elon with a humanoid, and that's going to, like, basically will into being an industry. But we've just historically found, like, we're a huge believer that this is going to happen. We just don't feel like we're in a good position to diligence these things because, again, robotics companies tend to, be vertical. 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 quartet type investors. And this is Sarah and I. The AD team is they can actually do these types of things. Just to clarify, AD stands for American Dynamism. All right. Yeah, yeah. I actually do have a related question that, first of all I want to acknowledge also just on the on the chip side.

I recall a podcast that where you are on, I think it was the ACCC podcast. About two three years ago where you suddenly said something which really stuck in my head about how at some point, at some point kind of scale, it makes sense to build a custom basic for per run.

Yes, it's great. I think you estimated 500 billion or something. A billion dollar training run. A one billion dollar training run, it makes sense to actually do a custom basic if you can do it in time. The question now is timeline, not money. Because 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 save much more than that with an ASIC, 20%, that's $200 million, you can tape out a chip for $200 million, right? So now you can literally like justify economically,

not timeline-wise, that's a different issue, an ASIC per model. Because that's how much we leave on the table every single time. We do it like generic and video. Exactly, exactly. No, it's actually much more than that. You could probably get, you know, a factor of two,

which would be $500 million. Typical MFF would be like 50. Yeah, yeah, yeah. And that's good. Exactly. Yeah. So, yeah. I mean, and I just want to acknowledge, like, here we are in N-2020-5 and opening eyes confirming

like Broadcom and all the other, like, Customs Silicon Deals, which is incredible. I think that, you know, speaking about AD, there's a really, like, interesting tie-in that obviously you guys are hit on, which is like these sort of like America First Movement or like sort of re-industrialized here and move TSM here, if that's possible. How much overlap is there from AD to, I guess, growth and, uh, investing in particularly like, you know, U.S. AI companies that are strongly bonded by their compute. Yeah, yeah.

So, I would view, I would view AD is 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, sell or deals with like hardware. I mean, they're just set up 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, it's been, Incepted, you know, has geographical biases, right? I mean, for the longest time, we're like, you know, Bay Area is going to be like

where the majority of the dollars go. Yeah. And listen, there's actually a lot of compounding effects for having a geographic bias, right? 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.

And, I mean, I would say the Bay Area is very much back. You know, like, I remember during pre-COVID. Like, it was like almost crypto had kind of pulled startups away from the Bay Area. Yeah. Yeah. New York is, you know, because it's so close to finance. Came out like Los Angeles. had a moment because they're 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, very 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, one more, it's going to be the U.S., of course, because we know very well. And then one ring more is going to be kind of U.S. and its allies. And, yeah, and it goes from there. Yeah.

Sorry. No, no, I agree. I think from a, but I think from the intern, that's sort of like where the companies are headquartered, maybe your question's on supply chain and customer base. I would say our customers or our companies are fairly international from that perspective. 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 going to 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 that would be somewhere else.

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 they can kind of be everywhere. So there's kind of less of that stickiness. This is definitely in the AI time. I mean, Sarah is now the chief of staff of like half the AI companies in the Bay Area right now. She's like, Ops Ninja, biz dev, biz ops. Are you finding much AI automation in your work?

What is your stack? Oh, in my personal stack? I mean, because, like, by the way, the reason for this is it's triggering, yeah, like, I'm hiring offs people. A lot of founders I know are also hiring OS people. And I'm just, you know, it's an opportunity since 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. Yeah.

I think the things that we help with are pretty network based in that it's sort of like, hey, how do I shortcut this process? Well, let's connect you the right person. And so there's not quite an AI workflow for that. I will say as a growth investor, Claude Co-Work is pretty interesting. Like for the first time, you can actually get one-shot data analysis, right? Which, you know, if you're going to 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 the three of us were playing with Claude Co-Work. 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, you know, that's the kind of thing that a growth investor is like, you know, slaving away on late at night, done in a few seconds. Yeah. You got to wonder what the whole like enthropic labs,

which is like their new sort of products studio. What would that be worth as an independent startup, you know? Like a lot. Yeah. True. Yeah. You got to hand it to them. I mean, to me,

incredibly well. Yeah. I mean, to me, like, you know, Anthropic, like building on code. I think it makes sense to me. The real pedal to the metal, whatever the phrase is, is when they start coming after consumer against Open AI. And that is like red alerted open AI.

Oh, I think they've been pretty clear their enterprise focus. They have been. But like here's like enterprise focus. It's being pretty clear publicly. It's enterprise focus. It's coding. Right. And then and but here's like, well, they're apparently they're running Instagram ads for

Cloud AI on, you know, for people. They have the mom to talk about. And so like It's kind of like this disruption thing of You know Open eyes been doing Consumer had been doing Just pursuing general intelligence

In every modality And here is Anthropay they're only focus on this thing But now they're sort of undercutting And doing the whole Innovators dilemma thing on like Everything else It's very interesting

Yeah I mean there's a very open question So for me there's like Do you know that meme Or there's like the guy in the path And there's like a path this way There's a path this way Which way Western man

Yeah Yeah yeah Yeah. And for me, like all the entire industry kind of like hinges on like two potential futures. So in in one potential future, the market is infinitely large. There's perverse economies of scale because 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, because they're perfectly general. And like, so this would be like 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. And nobody knows the end. answer. My belief is if you actually look at the numbers of these companies, so if you look at the numbers of these companies, if you look at like the amount they're making and how much 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, the gross margin

negative. So part of me thinks that a lot of them are kind of borrowing against the future and that's going to have to slow down. That's going to catch up to them at some point in time. But we don't really know. Yeah. Does that make sense? It could be the case that the only reason this is working is because they can raise that next round and then you can train that next model because 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 converge your fragment together. But right now it's not.

Totally. I think the other, by the way, just a meta point, I think the other lesson from the last three years is, and we talk about this all the time because we're on this Twitter X bubble. But, you know, if you go back. to, let's say, March 24, that period. It felt like a, I think an open source model with like a, you know, benchmark leading capability was sort of launching on a daily basis at that point. And so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's going to be a plethora. It's not an oligopoly. You know, if you fast, you know,

if you rewind time even before that, GPT4 was number one for nine months, 10 months. It's a long time, right? And of course, now we're in this era where, it feels like an oligopoly, maybe some very steady state shifts. And, you know, it could look like this in the future, too. But it's so hard to call. And I think the thing that keeps, you know, us up at night 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's going to look like.

But 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 at the end, at some point, the market will rationalize it and just nobody knows what that will look like.

Yeah. Or like the drop in price of compute will save them, who knows. 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 GI as some specific task, and now you can like depreciate the model over a longer time. I think now right now there's like no old model.

No, but let me just change that mental. 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.

See what I'm saying? Yeah, yeah. So I have an API business. My API business is 60% margin or 70% margin or 80% market. It's a high margin business. So I know what everybody is using. If I can raise more money

than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not. And I will know if they're using it because they're using it. And like, unlike in the past where engineering stops me from doing that, is this very straightforward you just train. So I also thought it was kind of like, you must have asymptote, AGI, general, general, but I think there's also just a possibility that the capital markets will just give them the ammunition to just go after everybody on top of them.

I do wonder, though, to your point, 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. Actually, Ali Goadsey talks about this. Like, we're already at AGI for a lot of functions in the enterprise. That's probably, 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 if you're looking about the legal profession or whatnot, and maybe that's not a great one because the models are getting better

on that front too, but just something where it's a bit sad. 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. One more thing I think is under-discussed in all of this is like, to what extent every task is AGI complete? I code every day. It's so fun.

That's a core question, yeah. And like, what I'm talking to these models, it's not just code. I mean, it's everything, right? Like, you know, like it's it's health care. It's legal. But it's exactly that. Yeah, that's for support. It's everything.

Like, I'm asking these models 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, that's having a full conversation with me while I engineer. And so it could be the case that like the most, 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, is, you know, the smartest developers I don't work on it. It's great. But I think Opus 4.5 is actually very, it's got a great bedside manner. And it really, it really matters if you're building something very complex

because, like, it really, you know, like, 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. And what does that mean to like capital investment in like frontier models and sub models? Like what happened to all this special coding models? Like none of them worked, right?

So there's some of them. They didn't even get released. Magic. Goddev. There was a whole host. We saw a bunch of them and like there's this whole theory that like there could be a and I think one of the conclusions is like there's no such thing as a coding model. You know? Like that's not a thing.

Like you're talking to another human being and it's good at coding but like it's got to be good at everything. Minor disagree only because I'm pretty, like, have pretty high confidence that basically Okunai will always release a GPT5 and a GPT5 codex. Like, that's the coding one. Yeah, yeah, yeah. The way I call it is one for Riz and one for Tiz. And then, like, someone internal open I was like, yeah.

That's a good way to frame. That's so funny. But maybe it collapses down to Riz and Tiz and that's it. It's not like 100 dimensions. It's two dimensions. Yeah, yeah, yeah, yeah. Like in exactly Bitsai Manor versus coding. Hey, yeah.

Oh, hi. Yeah. I think for anybody listening to this, for, I mean, for you, like, when you're, like, coding or using these models for something like that, like, 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, I think, like, like, the best Soto-ish model, you know, is going to remain very important no matter what the task is.

Yeah. Speaking on coding, I'm going to be cheeky and ask, like, what I should. are you coding because obviously you could code anything and you're also a busy investor and a manager of the giant team. What are you coding? I help Faefe at World Labs.

It's one of the investments. And they're building a foundation model that creates 3D scenes. Yeah, we add our in a pod. 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 radiance fields than with meshes because they don't really have to So 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 to use meshes. And so I work on a open source library called SparkJS, which is a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, you need that support. And right now there's kind of a 3JS moment.

that's all meshes. And so it's become kind of the default in 3JS 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 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 in the late 90s. So I've got actually a bad, It's very old. I'm actually on the background in this. So a lot of it's fun, you know, but the whole goal is just for this rendering library to... Are you one of the most active contributors to their GitHub?

SparkJ is? Yeah, there's only two of us on. So yes. No, so by the way, so the primary developer is a guy named Andrea Sunquist, who's an absolute genius. He and I did our PhDs together. And so, like, we said it for constant quality. it's almost like hanging out with an old friend, you know. And so like, he's the core, core guy.

I did mostly kind of, you know, the Stai. I'm a venture fund. It's amazing. Like five years ago, you would not have done any of this. And it brought you back. The activation energy was so high because you had to turn all the framework bullshit, man. I fucking used to hate that.

And so, like, now I know how to deal with that. I can like focus on the algorithmics. So I can focus on the scaling. Yeah, yeah. And then I'll observe one irony and then I'll ask a serious investor question, which is like, the irony is, Fei actually doesn't believe

the LMs can lead us to spatial intelligence and here you are using LLMs to help, like, help, like, achieve spatial intelligence. I just see, I see some, like, disconnect in there. Yeah. Yeah, so I think, I think, you know, 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. They'll never have the spatial intelligence.

And listen, our brains clearly, listen, our brains, brains clearly have both. 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. I would say that the one data point I recently had against it is the deep mind

IMO gold where so typically the typical answer is that this is where you start going down the neurosymbolic path right like one sort of very sort of abstract reasoning thing and one formal formal thing

and that's what deep mine had in 2024 which alpha proof of geometry and now they just use deep think and just extend the thinking tokens and it's one model and it's in all of them yeah yeah yeah And so that was my indication of like, maybe you don't need a separate system.

Yeah, 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. If you distill it down. But let me just talk about the two different substrates. 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 going to, 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 Faye Faye 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, like the actual movement through space. Yeah. So I do think of the end of these models are definitely converging as far as models, but 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 space reasoning is just showing them. Yeah. Yeah, yeah, right. Got it. The investor question was on World Labs is, well, like, how do I value?

with something like this. What work does do you do? I'm just like, Fife's awesome, Justin's awesome, and you know, the other two co-founders,

but like the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question. Let me just for like these, let me just maybe give you a rough sketch on the diffusion models.

I should love to hear Sarah, because I'm a venture person. I mean, so like kind of Wild West. You're, you paid the dream and she has to like actually be marked to reality. So I'm going to say the venture

being, And she can be like, okay, you little kid, yeah. So these diffusion models literally create something for almost nothing and something that the world has found to be very valuable in the past our real markets, right? 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, to turn me into whatever, like an image would cost $100

bucks in an hour. The influence costs a hundredth of a penny, right? So we've seen this with speech and very successful companies. We've seen this with 2D image. We've seen this with movies, right? Now, think about 3D scene. I mean, when's Grant Theft Auto coming out? It's been six, what?

It's been 10 years. I mean, how, like, awesome. How much would it cost to, like, to reproduce this room in 3D? If you hire somebody on fiber, like in any sort of quality, probably $4,000 to $10,000, and then if you had a professional, it would 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. And for listeners, you can do this yourself on your own phone with like the marble. Yeah, marble. But also there's many Nerf apps where you just go on your iPhone and do this. Yeah, yeah, yeah. And in the case of marble, though, 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. Things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the, like...

Meaning it has to fill in stuff. Yeah, the back of the table, under the table, the back, like the images that doesn't see. The generator stuff is very different than reconstruction that it fills in the things that you can't see. Yeah. Okay, so... All right. So now the... No, no, I mean, I love that.

the adult perspective. Well, no, I was going to say, these are very much tag team. So we started this pod with that premise, and I think this is a perfect question to even build on that further because it truly is. I mean, we're tag teaming all of these together. 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 we're not investing in every, you know, now the term is neolab, but every foundation model, any company, any founders try to build a foundation model, we're not, 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 say that we're big, we're in everything. But, you know, if you think about Elia, right, he's at SSI. He's sort of been behind almost every foundational breakthrough for the last 15 years. If you think about, you know, the thinking machines team, right?

at Mira and John, right? John is the godfather of reinforcement learning. And so 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. And, you know, I think, you know, obviously we talked about talent wars. We do think at this particular moment in time, there are particular people that can move needles. Clearly, other companies believe that too. otherwise they wouldn't be willing to pay such crazy prices for single individuals. So that's one.

And then two, we don't think it's a zero-sum game, right? Like if that were true, open AI or actually just deep mind would be number one and everything, right? There's clear value to specialization. It's like 11 laps. There have been so many audio models that have hit the market. They're still freaking number one, right? And so if you think about, and they've created a ton of value for their customers, for their investors, you know, for their team, And so if you think about those two put together, right, that's sort of the foundation of our thesis when we back these foundation model companies.

Of course, the valuations, you know, they sound astronomical when you think about current revenue, the numbers. You know, there's sort of, 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 around that they typically raise, or typically of around that they raise. But I think the thing that gets us excited about backing them is that the revenue growth has typically followed the capability breakthrough. So it sort of ties back to that question of the cyclical nature. Like, are you just funding it and you raise more funding? 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, but their product went GA in a few weeks, tens of millions of revenue, right? 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. And by the way, great companies, not at all diminishing what they've accomplished. But the fact is to get that revenue growth that quickly, it's not just the two companies that people talk about. 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. Our viewers are going to, so first of all, thank you for that overall take. I think like, it's important to hear you guys' perspective because the rest of us are just kind of looking at headlines and not knowing how to make sense of any of this. We can mention, like, our listeners will roast us if we, if we mention thinking and not discuss what happened. I mean, obviously,

founder split happens. But like, I guess this is the thesis on change is, is like, You know, like what's going on and thinking? Yeah. We're more excited than ever about them. They have some things that we're not going to do breaking news on a pod. You know, obviously they should share themselves. But they've, you know, I think when you bring a team of that caliber together, there's special things that happen.

And I think 2026 is going to be a big year for them. Obviously, you know, some of the themes that we talked about before even with just the media news start, like the whole something happens and then it's everywhere instantly. You know, I think that's a tough situation for any company to be in. But to come out of that stronger than ever, I think that we're more bullish about thinking than, you know, even before. And obviously... And the story is Tinker, it's custom models RL.

Yeah, is that what we're aiming for? Yeah, and a bunch of stuff we can't talk about here. Yeah, absolutely. But no, that team is cooking. And, you know, I think they'll be just fine from, 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 industry-wide ever. Like, I guarantee you for any. any of these gossipy things, I guarantee you it's way off. Okay. Way way out. Like the general sentiment.

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 were directly involved in. Like we're literally on the board. You know, like 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 shot on fried is so. high that people just kind of will into being things that didn't exist so I'm not you know I don't want to comment specifically on the thinking machines but like it's an important message to the general audience I will tell you if you hear something I'd ask

like the chances that it's you know it is accurately representing but it's saying to is very very low yeah I have never lost so much faith in the anon counts on Twitter that just seem very confident in what they're saying and could it be further for

from the truth. I had a couple days stretch where I was like, oh my God, Twitter is mind poison. And I love X. But we twice we tell it all the time because we actually know because we're there. Like we're there singing these things and like, you know, Sarah will like text me, you know, like whatever. It's like ridiculous. So for us it's like this ridiculous. But the problem is as we realize that things start taking on a life of their own and then people assume that they're real 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, now they're fighting phantoms too. And so, you know, more and more we're just like, I got this from the cursor, guys, which I really appreciate Michael Trull. He's like, listen, heads down, focus on the business.

And they absolutely crushed it. Yeah. And I think that's right. I'll find you should do that right now because the noise is so hot. Yeah. Now, that team's been back to business for weeks, the thinky team. So, yeah. Yeah.

Well, thank you for acknowledging in that. It's just the hot topic of the moment. We've got to address the elephant in the room. Cursor, right? Obviously, you guys are big investors. 2025, I would say it's Cursus Year. I mean, maybe decade. But just like, I think, you know,

just going back to the discussion about how AGI would just kind of consume everything. Chris is just like the one, like, kind of the shiny example of like, here's how you build application layer. That's a rapper. Yeah. But an extremely damn good one. And I guess just like the general analysis,

I guess, development and what it means for everyone? Is there a cursor in every industry to be built? Yeah, so the interesting thing about cursors, they actually, for a small fraction of the cost, a hundred the cost or less, developed an almost soda 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'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, which is like how do I extract margin 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 if you ask, you know, Michael, what type of company are? They are a developer company for professional developers. That's what they are. They're a dev tool.

They're just focused on coding. And that's a huge, I mean, even if you didn't do AI, that's a, you know, they, they acquired graphite. I mean, like, you know, so 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 get the application, but to start building your own models.

Are these going to be the only models of people use? Of course not. but they are in a great position to serve great models and they've demonstrated that. Yeah, my sort of thesis which we're now going to have to go into here is actually I think

what I've been calling agent labs which are people who build on top of all the other models we'll probably have a better time with the margins because they price against the end user hours spent or like human labor whereas models get commodity

price per token. And so margin-wise We know inference economics for model labs. But Asian labs, the difference is the delta between token intelligence, which keeps going down, and human costs which keep going out. Yeah, yeah, yeah. And so the margin should be higher. They should be.

The caveat to that is if the models go first party, right? Yeah, yeah. What they can do is they can... Which is the composer dream. Yeah, they can subsidize themselves. They can subsidize themselves. Cloud code. cloud code.

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 or the C2. So this is not unusual. We saw this with the operating system. 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's such a pleasure. You're welcome back anytime. And thank you for being so open and also like just leading the industry in so many areas. It's really inspiring to see. So thank you for having us. Thank you.

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our substack at A16Z.com. Thanks again for listening, and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include pay promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16Z, or any of its affiliates.

Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy. Thank you.