I mean, every industry has talent wars, but not at this magnitude.
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.
During the internet build-out, 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 to the company's closest to the end user.
Nobody knows which path wins.
In this conversation previously aired on the Latent Space podcast, Martine Cassato and Sarah Wang, general partners at A16Z, speak with Alessio Finelli and Sean Wang about the Capitol flywheel talent wars and why boring software is underinvested and whether every task is AGI complete.
Hey, hey, hey.
And we're so glad to be on with you guys.
Also a top AI podcast.
Martín 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 takes a while to get used to, but it reminds me of like 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, but 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, hands down.
Yes, there is.
I mean when it comes to AI companies.
Sarah, I think, has done the most kind of aggressive investment thesis around AI models.
So she worked with Noam Chazir, Mira, Ilya, Feifei.
And so just these frontier kind of like large AI models, I think 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, 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, you know, 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 more of a number of 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.
Are we talking about BizDev?
BizDev, exactly.
What does BizDev mean for a venture fund?
Because I know what BizDev 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 tie.
I've been doing this for 10 years.
I've never seen anything like this.
Yeah.
Yeah.
Do you have worries about the circular funding from some of these strategics?
No, 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 is like the whole pyramid scheme bubble thing where like, as long as you mark the 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 listen, this is Like a lot of these soundbites have already become kind of cliches, but they're worth saying, right.
Like during the internet days, like we were 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.
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.
Right.
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 actual customer.
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 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 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 Martine'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 investing into RD to get to the capability 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.
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.
Yeah.
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 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?
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, right?
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 build out.
So none of this is necessarily new, but the timescales in which it's happened is pretty phenomenal.
And then where we'd normally cut lines before, is 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 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, 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 so, and that wasn't true even two years ago, I think.
And so it's sort of to your, just tying it to fundraising strategy, right?
There's an hiring strategy.
All of these are tied and 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 science 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 month, 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 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 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 pot committing to a certain strategy for a certain amount of time.
But if you could like iteratively spin out companies and products and just throw, I want to spend a million dollar of inference today and get a product out tomorrow.
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 A16C.
There's no, like, $8 million C round anymore.
But there's a...
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 you know a company is building smaller models, that you know use the bigger model in the background, open 45, 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.
Imagine like a star that's just kind of expanding.
So there could be a systemic...
There could be a systemic situation where the SOTA 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 it's a very open question.
Yeah, it's almost like bitter lesson applied to the startup industry.
100%, yeah.
It literally becomes an issue of like raise capital, turn that directly into 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 aggregate of companies on top of you and therefore you'll miss your tick this year, which is crazy.
Would you say that kind of happens to character?
Is that the sort of postmortem on...
What happened?
No.
Yeah, because I think… I mean the actual post-mortem is he wanted to go back to Google.
Yeah, exactly.
But like… That's another different… You said it.
We should actually talk about this.
Go for it.
Take it everyone.
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 invest in character in january 2023, which feels like eons ago, 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, gnome 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 Chazir.
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 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, OpenAI is feeling that.
Anthropic.
If they haven't, 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 research right is it?
Or long-term research?
Is it toward um, you know, near to midterm 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 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, right?
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 create an internet company.
I would create an infrastructure company.
Like it's kind of more engineering builders.
And this is kind of a different...
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 happening, 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 Trader Joe's 8 or something like that way back in the beginning of the industry.
I mean, it's a very, very unusual time of personnel.
Totally.
And I think it's exacerbated by the fact that talent wars I mean every industry has talent wars, but not at this magnitude.
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 the deep anxiety that these AI founders are feeling.
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 they're kind of done and who's going to pay more than Meta?
I don't know, I agree.
So it feels this way to me too.
It's 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 a team to rush the job, whatever.
But then now you made your choices and now they got to ship, 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 recs if anybody's interested that's listening to this.
For investor?
Yeah.
Yeah, on the team, like on the investing side of the team.
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 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... 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.
Yeah.
So I think you're right that it felt like a blip.
I hope you're right.
But I think it's been the steady state.
Everything got pulled up.
Yeah.
Exactly.
Yeah.
For sure.
Yeah.
Yeah.
And I think that's breaking the early stage founder math too.
I think before a lot of people were 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 five, six 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 constantly changing.
A ton of change and confusion in the market.
Some very positive, some negative.
Like.
So, for example, the other side of 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 MA for basically, aqua hires right that you like, 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 um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you will see more people build, or it seems in a way, you know, as YC 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 some of the areas 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.
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 year, deep tech.
Right.
But I, you know, I feel like there's just kind of a long, you know, list of like good, good companies that will 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 data, 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 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.
I mean, clearly these are good investments 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 growing 5x is a great investment.
We'd everybody 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.
Really good card.
No AI here.
Well, the AI, of course, is pulling them into use cases, but that's not what they are.
They're not on the token paths, right?
Let's just say that.
They're software, but they're not on the token path.
These are great investments from any definition, except for random VC on Twitter saying, VC on X saying 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, 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.
Well.
Right.
And the robotics sector right, which is it's 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, but we also went through the drone, you know... There's a zip line right out there.
What's that?
Oh, yeah, there's a zip line, yeah.
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 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 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 math clearly from the AV wave.
I would say scale AI was actually a horizontal solution.
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 a different team.
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 going to work.
Let's keep investing.
That seems really hard to predict in a way that it's not.
I mean, KOTU, COSLA, GC.
I mean, these are all invested in hardware companies.
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 basically will into being an industry.
But we've just historically found... 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 equilibria 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.
And this is Sarah and I. The AD team, they can actually do these types of things.
Just to clarify, AD stands for?
American Dynamism.
I actually do have a related question.
First of all I want to acknowledge, also just on the chip side, I recall a podcast where you were on I think it was the ACCC podcast about two or 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 ASIC for per run.
Yes, it's crazy.
I think you estimated $500 billion or something.
No, no, no.
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, not money.
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 like generic NVIDIA.
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 MFU would be like 50.
Yeah, yeah, yeah.
And that's good.
Exactly, yeah.
$100 million.
So yeah, I mean and I just want to acknowledge, like here we are in 2025 and opening eyes confirming, like Broadcom and all the other, like custom 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 this sort of like America first movement or like sort of re-industrialize here and move TSMC here, if that's possible.
How much overlap is there from AD to, I guess, growth and investing in particularly, like you know, US AI companies that are strongly bounded by their compute?
Yeah, yeah.
So I would view AD as more of 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 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 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.
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.
New York was, you know, because it's so close to finance, came out like Los Angeles had a moment because 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 vest 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 US, of course, because we know very well.
And then one ring more is going to be kind of U.S. and its allies.
And, you know, and it goes from there.
Yeah.
No, no, I agree.
I think from a, but I think from the, that's sort of like where the companies are headquartered.
Maybe your questions 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.
Yeah, historically between venture and growth.
Like there's so much company building and 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 I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else.
But 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 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?
Like what is your stack?
Oh, in my personal stack?
I mean, because like, by the way, the reason for this is triggering.
Yeah, like I'm hiring ops people.
A lot of founders I know are also hiring ops people.
And I'm just you know it's 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.
So there's not quite an AI workflow for that.
I will say, as a growth investor, cloud co-workers 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 and 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.
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 Anthropic 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.
You got to hand it to them.
They've been executing incredibly well.
Yeah.
I mean, to me, like, you know, Anthropic, like, building on CloudCo, I think it makes sense to me.
The real pedal to the metal, whatever the phrase is is when they start coming after consumer right against OpenAI and that is red alert at OpenAI.
I think they've been pretty clear they're enterprise-focused.
They have been.
It's enterprise-focused, it's coding.
But here's CloudCodeWork.
And here's like, well, apparently they're running...
Instagram ads for quality eye on, you know, for people.
Right.
And so like it's kind of like this the disruption thing of you know, opening has been doing consumer been doing just pursuing general intelligence in every modality.
And here is a topic that 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 where there's like the guy on the path and there's like a path this way, there's a path this way.
Which way, Western man?
Yeah, yeah, yeah.
And for me, like the entire industry kind of like hinges on like two potential futures.
So 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 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 could do everything.
And this one is like, this is actually normal software.
The universe is complicated.
And nobody knows the answer, right?
My belief is if you actually look at the numbers of these companies.
So just 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, 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 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?
I mean, it could be the case that the only reason this is working is because they can raise that next round and they 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 and fragment again.
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 if you go back to, let's say, March 2024, that period it felt like an open source model with a benchmark leading capability was sort of launching on a daily basis at that point.
And And so 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 rewind time even before that, GPT-4 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 just 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 you know, 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, a sound 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?
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 is 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 because they're using it.
And unlike in the past where engineering stops me from doing that, this is very straightforward.
You just train.
So I also thought it was kind of like you must ask the AGI general, 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.
You know, actually, Ali Godsi talks about this.
Like, we're already at AGI for a lot of functions in the enterprise.
Yeah.
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 at 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 saturated
Yeah. 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...
When I'm talking to these models, it's not just code.
I mean, it's everything, right?
Like, you know, like it's healthcare, it's legal.
But it's exactly that.
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, it's having a full conversation with me while I engineer.
And so it could be the case that like the most 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 45 for coding.
Like it finds the hardest bugs that I work in with.
Like you know, The smartest developers I know work on it.
It's great.
But I think Opus 4.5 has got a great bedside manner.
And it really matters if you're building something very complex, because 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 capital investment and frontier models and sub-models?
What happened to all the special coding models?
None of them worked, right?
Yeah.
Some of them didn't even get released.
There's a whole host.
We saw a bunch of them and there's this whole theory that there could be, And I think one of the conclusions is 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 OpenAI will always release a GPT-5 and a GPT-5 codecs.
Like that's the coding one.
The way I call it is one for RIS and one for TIS.
And then like someone in TurnOn OpenAI was like, yeah.
That's a good way to frame it.
That's so funny.
But maybe it collapses down to reason twos and that's it.
It's not like a hundred dimensions.
It's two dimensions.
Yeah, yeah, yeah.
Like in exactly the same manner versus coding.
Yeah, yeah, yeah.
Oh my god.
That's reason twos.
I think…
For anybody listening to this when you're coding or using these models for something like that, actually just 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.
I think the best Soto-ish model is... is going to remain very important no matter what the task is.
Speaking of coding, I'm going to be cheeky and ask, what actually are you coding?
Because obviously you could code anything and you're obviously a busy investor and a manager of a giant team.
What are you coding?
I help Feifei at World Labs.
It's one of the investments.
And they're building a foundation model that creates 3D scenes.
Yeah, we had her in the 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 like they don't really have topology.
So...
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 use meshes.
And so I work on a open source library called SparkJS, which is a JavaScript rendering library for Gaussian splats.
And it's just because you need that support.
And right now, there's kind of a 3.js moment.
That's all meshes.
And so it's become kind of the default in 3.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 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 back... It's very old.
I actually have a background in this.
And 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.
SparkJS?
Yeah.
There's only two of us.
So yes.
No, so by the way, so the... Yeah.
Yeah, so the primary developer is a guy named Andreas Sundqvist, who's an absolute genius.
He and I did our PhDs together.
And so, like, we studied for Compton Quality.
It was almost like hanging out with an old friend, you know?
And so, like, so he's the core, core guy.
I did mostly kind of, you know, this type of 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 learn 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 and 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-Fei actually doesn't believe that LLMs can lead us to spatial intelligence.
And here you are using LLMs to help achieve spatial intelligence.
I see some disconnect in there.
Yeah.
So I think what she would say is LLMs are great to help with coding, but that's very different than a model that actually provides spatial intelligence.
And listen, our 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.
So typically, the typical answer is that this is where you start going down the neurosymbolic path, right?
Like one sort of abstract reasoning thing and one formal thing.
And that's what DeepMind had in 2024 with Alfred Proof, Alfred Geometry.
And now they just use DeepThink and just extended thinking tokens.
And it's one model and it's in LLM.
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, these things are like nodes in a graph with weights on them, right?
They 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 language, 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, like the actual movement through space.
Yeah, so I do.
I do think at 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, got it.
The investor question was on World Labs is, well, like, how do I value something like this?
What work do you do?
I'm just like, Feifei's awesome, Justin's awesome, and, you know, the other two co-founders.
But like, the tech, everyone's building cool tech.
But what's the value of the tech?
And this is the fundamental question.
Let me just maybe give you a rough sketch on the diffusion models.
I actually love to hear Sarah, because I'm a venture person.
Ventures are always kind of Wild West type stuff.
You paid to dream, and she has to actually be marked to reality.
So I'm going to say the venture view, and she can be like, okay, you little kid.
So these diffusion models literally create something for almost nothing, and something that the world has found to be very valuable in the past are 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 a whatever, like an image would cost $100 in 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, when's Grand Theft Auto coming out?
It's been six, what, it's been 10 years?
I mean, how... It has been 10 years, yeah.
How much would it cost to reproduce this room in 3D?
If you hire somebody on Fiverr, like 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 database and we know that these are used, and they're used in Unreal, and they're used in Blender, they're used in movies, and they're used in video games and they're used in 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 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 So most Nerf apps.
You kind of run around and take a whole bunch of pictures, and then you kind of reconstruct it.
Yeah.
Things like Marble, just the whole generative 3D space will just take a 2D image and it'll reconstruct all the Meaning it has to fill in.
Yeah, like the back of the table, under the table, like the images it doesn't see.
So the generative stuff is very different than reconstruction in 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.
Now the adult perspective.
Well, no, I was going to say, these are very much a 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 Neo Lab but every foundation model, any company, any founders trying 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 don't.
They say that we're big, we're in everything, but...
You know, if you think about Ilya, 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 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 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, OpenAI or actually just DeepMind would be number one in everything, right?
There's clear value to specialization.
It's like 11 labs.
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 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, these foundation model, uh companies um, 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 a round that they typically raise or typically of a round 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 then 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...
I've seen this myself, yes.
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.
And by the way, great companies, not at all... 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 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.
Our viewers are going to... So first of all, thank you for that overall take.
I think 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.
Our listeners will roast us if we mention Thinky and not discuss what happened.
I mean, obviously, founder split happens.
But I guess this is unchanged.
What's going on at Thinky?
Yeah.
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.
Obviously, they should share themselves.
But I think when you bring a team of that caliber together, there are special things that happen.
And I think 2026 is going to be a big year for them.
Obviously some of the themes that we talked about before.
Even with just the media news store, like the whole, something happens and then it's everywhere instantly.
Um, you know, I think, uh, that's a, 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 the story is Tinker, it's Custom Models RL.
Um 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 of these gossipy things, I guarantee you it's way off.
Way, way off.
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 we're 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 he 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.
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 at X like the chances that it's you know it is accurately representing what 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.
Could it be further from the truth?
I had a couple days stretch where I was like, oh, my God, Twitter is mind poison.
And I love it because we actually know, because we're there, like we're there 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, that 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, Absolutely.
Finding 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 he absolutely crushed it.
Yeah.
And I think that's right.
I think all founders should do that right now because the noise is so hot.
Now that team's been back to business for weeks, the Thinky team.
Yeah.
Well, thank you for acknowledging 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, is Cursor's year.
I mean, maybe a decade.
But just like I think you know, just going back to the discussion about how AGI would just kind of consume everything,
Because just like the one, like kind of the shining example of like here's how you build application layer.
That's a wrapper, but an extremely damn good one.
And I guess, just like the general thing analysis, I guess, of Cursor's development and what it means for everyone.
Like, is there a Cursor in every industry to be built?
Yeah.
So the interesting thing about Cursor is they actually for you know, a small fraction of the cost, a hundred of 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?
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 you?
They are a developer company for professional developers.
That's what they are.
They're a dev tool.
So 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 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 people use?
Of course not.
But, you know, they are in a great position to serve great models and they've demonstrated that.
Yeah, my sort of thesis, which we're not 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, will 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 agent labs.
The difference is the delta between token intelligence, which keeps going down, and human costs, which keep going up.
And so the margin should be higher.
They should be.
The caveat to that is, if the models go first party right yeah, what they can do is they can, which is the composer dream.
Yeah, they can subsidize the models, they can subsidize themselves, 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 EC2.
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.
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.
Thank you so much.
Thank you for having us.
Great.
Thank you.
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