# AI Capital Flywheel and Market Fragmentation

**Podcast:** AI + a16z
**Published:** 2026-02-24

## Transcript

I mean, every industry has talent wars, but not at this magnitude.
Very rarely can you see someone get poached for five billion dollars.
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 a hundred in a year, you're not interesting, which is just 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 builds on top of them, which would be something I don't think we've ever seen before.
Just because we were so bottled up than engineering.
During the internet build out, investors put money into Fiverr 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, Martin Casado and Sarah Wang, general partners at A16Z, speak with Alestio Fanelli and Sean Wang about the Capital Flywheel, this is Alessio, founder of Kernelance, and why boring software is underined by Twix, editor of Laden Space.
Hey task is AGI complete.
Uh, and we're so glad to be on with you guys.
Also a top AI podcast.
Uh Martin Casado and Sarah Wang.
Welcome.
Very happy to be here and welcome.
Yes.
Uh we love this office.
We love what you've done with the place.
Uh the new logo is everywhere now.
It's it's still getting takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of definitely makes a statement.
Yeah.
Not quite sure what that statement is, but it makes a statement.
Uh Martin, I go back with you to Netlify.
Yep.
Uh, and uh, you know, you create a software defined networking and all that stuff.
Uh people can read up on your background.
Sarah, newer to you.
Uh you you sort of started working together on AI infrastructure stuff.
That's right.
Yeah, seven, seven years ago now.
Best growth investor in the entire industry.
Oh, say more hands down.
Sarah is there's I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive um investment thesis around AI models, right?
So she worked with Nam Jazir, Mira, Ilya, Feife, 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, I well, I was gonna say, I think it's been a really interesting tag tag team, actually, just because the a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage, but the resources.
Well, it's gonna 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 I think the hybrid tag team that we have is quite effective, I think.
What is growth these days?
You know, you don't wake up if it's less than a billion or like it's actually very it's actually very like like no, it's a very interesting time in investing because like you know, take like the character around, right?
These tends 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 and growth.
Yeah totally and I think you know stuff like BD for example you wouldn't usually need BD when you were seed stage trying to get it we're talking about biz dev biz dev, exactly but like now I'm not familiar with what what what is bizdev mean for a venture fund because I know what busdev means for a company.
Yeah you know so a a good example is I mean we talk about buying compute but there's a huge negotiation involved there in terms of okay do you get equity for the compute what what sort of partner are you looking at?
Is there a go-to-market arm to that um and these are just things on this scale hundreds of millions you know maybe six months into the inception of a company you just wouldn't have to negotiate these deals before yeah these large rounds are very complex now like in the past if you did a series A or a series B like whatever you're writing a 20 into a $60 million dollar 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 it's a very different ties.
Listen, I've been doing this for 10 years.
This is the I've never seen anything like this.
Yeah.
Do you have worries about the circular funding from some of these strategics?
I mean listen, as long as the demand is there, like the demand is there.
Like the problem with the internet is the demand wasn't there.
Exactly.
All right.
But like once it starts to chip away, it really is.
Well no, it's like as long as there's demand.
I mean, you know, listen, this is it, like a lot of these sound bites have already become kind of cliches, but they're worth saying it, right?
Like during the internet days, like we were um raising money to put fiber in the ground that wasn't used.
And that's a problem, right?
Because now you actually have a supply overhang.
Mm-hmm.
And even in the the time of the the internet, like the supply and and bandwidth overhang, even as massive as it was and as as massive as the crash was, only lasted about four years.
But we don't have a supply overhang.
Like there's no dark GPUs, right?
I mean, and so you know, circular or not, I mean, you know, if if someone invests in a company the um, you know, that'll actually use the GPUs and on the other side of it is the is the askful customer.
So I I I think it's a different time.
I think the other piece, maybe just to add on to this, and I'm gonna quote Martin in front of him, but this is probably also a unique time in that for the first time you can actually trace dollars to outcomes, right?
Provided that scaling laws are are holding um and capabilities are actually moving forward.
Because if you can put translate dollars into capabilities, uh 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 you're investing into RD to get to the capability um, you know, increase, and that's sort of been the demand driver because once there's an unlock there, people are willing to pay for it.
Yeah.
Is there any difference in how you build the portfolio now that some of your growth companies are like the infrastructure of the early stage companies?
Like, you know, open AI is now 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 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 RD, it's a horizontal platform, but it's also an app because it's um uh touches the users directly.
And then of course, you know, the the the growth of these is just so high.
And so I actually think you're just starting to see a a new financing strategy emerge, and you know, we've had to adapt as a result of that.
And so there's been a lot of changes.
Um, you're right that these companies become platform companies very quickly.
You've got ecosystem built out.
And so none of this is necessarily new, but the timescales in which it's happened is pretty phenomenal.
And the 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 that, or you you pour the money into compute, you get some sort of breakthrough.
You funnel the breakthrough into your vertically integrated application.
That could be 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, um, depending on your strategy.
You raise money at the peak momentum and then you repeat, rinse and repeat.
Um, and so and that wasn't true even two years ago, I think.
And so it's sort of to your just tying it to fundraising strategy, right?
There's a and hiring strategy, all of these are tied.
I think the lines are blurring even more today, where everyone is, and they but of course these companies all have API businesses, and so there are this these frenemy lines that are getting blurred in that a lot of I mean, they have billions of dollars of API revenue, right?
And so there are customers there, but they're competing on the app layer.
Yeah, so this is a really, really important point.
So I I would say for sure, venture and growth, that line is blurry, app and infrastructure, that line is blurry.
Um, but I don't think it 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 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 and 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 does a scale like the mythical man with it take a very long time but like that's not the case here.
Like a model company can raise money and drop a model in a in in a year and it's better, right?
And 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 a kernel 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 spin out companies and products and just throw, I I want to spend a million dollars 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 right the early stage venture model to be much more iterative.
And then every round it's like either 100k of imprint or like a hundred million from A6CZ.
There's no there's not like a million dollar C round anymore.
But but but but there's a there's a the 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 uh uh uh you know uh a company is building smaller models that you know use the bigger model in the background and you 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 outpay anybody that builds on top of them, which would be something I don't think we've ever seen before.
Just because we're so bottlenecked on engineering.
And it's a very open question.
Yeah, it's uh 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 the not any company, you can outspend the aggregate of companies on top of you, and therefore you'll necessarily take their share.
Which is crazy.
Would you say that kind of happened to character?
Is that the the sort of post mortem on what happened?
Um.
Yeah, because I think so.
I mean, the actual post mortem is he wanted to go back to Google.
But like that's another different thing.
We should actually talk about that.
Go for it.
Take it up to get it over.
I was gonna say, I think um the the the character thing raises actually a different issue, which actually the Frontier Labs will face as well.
So we'll see how they handle it.
But um, so we invested in character in January 2023, which feels like eons ago.
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 um, he went to go do that.
Um, but he had a product attached.
The goal was oh, I mean, it's gnome shazier.
He wanted to get to AGI.
That was always his personal goal.
But, you know, I think through collecting data, right, and this sort of very human use case that the character product originally was and still is, um, was one of the vehicles to do that.
Um I think the real reason that, you know, if you think about the the stress that any company feels before um you ultimately go on one way or the other, is sort of this AGI versus product.
Um, and I think a lot of the big I think you know, OpenAI is feeling that um enthropic.
If they haven't started, 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 gave 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 um, you know, I think it sets up an interesting dilemma for any startup that has it trouble raising up until that level, right?
And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.
I would say that because because we're keeping track of all of the things that are different, right?
Like, you know, venture growth and uh app infra.
And one of the ones is definitely the personalities of the founders.
It's just very different this time.
You know, I've been doing this for a decade and I'm bending startups for 20 years, and so um, I mean, a lot of people start this to do AGI.
And we've never had like a unified North Star that I recall in the same way.
Like people built companies to start companies in the past.
Like that was what it was.
Like I would have created an internet company, I would have created an infrastructure company.
Like it's kind of more engineering builders, and this is kind of a different, you know, mentality.
And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider AGI, but others have not.
And so like there is always this tension with personnel.
And so I think we're seeing more kind of founder movement.
Yeah, you know, as a fraction of founders than we've ever seen.
I mean, maybe since like I don't know, the time of like Shockley and the trade at Dr.
Seate 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 it I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right?
Very rarely can you see someone get poached for five billion dollars.
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 that these AI founders are feeling.
Uh yes.
I mean, just on a briefly comment on the founder, uh, the sort of talent wars thing.
I feel like 2025 was just like a blip.
Like I I don't know if we'll see that again.
Because Meta built the team.
Like, I don't know if I think I think they're kind of done, and like who's gonna pay more than Meta?
I I don't know.
I I agree.
So it feels it's gonna so it feels it feels this way to me too.
It's like 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 gotta like pay up to like assemble team to rush the job whatever but then now now you like you you made your choices and now they got a ship right like the the us other side of that is like you know like we're we're actually in the job hiring market.
We've got 600 people here I hire all the time I've got three open recs if anybody's interested that's listening to this investor yeah on the team like on the investing side of the team like and um a lot of the people we talk to have acting you know active um offers for 10 million a year or something like that and like you know and we pay really really well and just to see what's out on the market is really is really remarkable.
And so I would just say it's actually so you're right like the really flashy one like I will get someone for you know a billion dollars but like the inflated um trickles down yeah it is 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 it's so I think you're right that it felt like a blip.
Hope I hope you're right.
Um but I think it's been it the steady state is now they got pulled up yeah yeah 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 yeah 800k a million at Google, but if I'm getting paid five, six million, that's different.
But on the but on the other hand, there's more strategic money than we've ever seen historically, right?
And so the economics the the the the calculus on the economics is very different in a number of ways.
And uh it's incre it's caused a a c uh a a a ton of change and confusion in the market.
Some very positive, some negative.
Like, so for example, the other side of the um the co-founder like um acquisition, you know, Mark Zuckerberg poaching someone for a lot of money is like there's 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 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, uh and there's maybe not as much risk appetite for things outside of that.
Um I'm curious if you feel like that's true and what are maybe yeah, some of the areas uh that you think are under discussed.
I mean, I actually think that we've taken our eye off the ball on a lot of like just traditional, you know, software companies.
Um, so you like, I mean, you know, I think right now there's almost a barbell.
Like you're like the hot thing in the next or Deep Tech.
Right.
But I I, you know, I feel like there's just kind of a long, you know, list of like good, good companies that'll be around for a long time in very large markets.
Say you're building a database, you know, say you're building um, you know, kind of monitoring or logging or tooling or whatever.
There's some good companies out there right now, but like they have a really hard time getting um the attention of investors.
And it's almost become a meme, right?
Which is like if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just the silliest thing to say.
I mean, think of yourself as like an individual person, like like your personal money, right?
So your personal money, will you put it in the stock market at 7%, or you put it in this company growing 5x in a very large part.
Of course, you can put it in the company 5x.
So it's just like that, 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 lifecycle of a fund, right?
So a company in a big market growing 5x is a great investment.
We'd everybody would be happy with these returns, but we've got this kind of mania on these these strong growth.
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 company.
No, like no AI here.
Well, like a whole well, well, the AI, of course, is pulling them into use cases, but that's not what they are.
They're not on the token path, right?
Let's just say that.
Like they're software, but they're not on the token path.
Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on X saying like it's not growing fast enough.
What do you think?
Yeah.
Maybe I'll answer a slightly different question, but adjacent to what you asked, um, which is maybe an area that we're not uh investing right now that I think is a question and we're spending a lot of time in, regardless of whether we pull the trigger or not.
Um, and it would probably be on the hardware side, actually, right?
And the robotics like it, right?
Which is it's I don't want to say that it's not getting funding because it's clearly uh it's it's sort of non-consensus to almost not invest in robotics at this point.
But um, we spent a lot of time in that space.
And I think for us who just haven't seen the Chat GPT moment happen on the hardware side.
Um, and the funding going into it feels like it's already taking that for granted.
Yeah, yeah.
But we also went through the drone, you know.
Um there's a zip line right out there.
Was that?
Oh, yeah, there's a zipline.
What's the A V around?
Like one of the takeaways is when it comes to hardware, um most companies will end up verticalizing.
Like if you're if you're investing in a robot company for an ad for agriculture, you're investing in an ag company because that's the competition and that's surprising and that's 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 just so fit for for purpose.
And so we kind of like to look at software solutions or horizontal solutions, like applied intuition, clearly from the A V wave, deep math clearly from the A V 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 in.
But the actual like robot interacting with the world is probably better for a different team.
Yeah, I agree.
Yeah.
I'm curious who these teams are supposed to be that invest in them.
I feel like everybody's like yeah robotics it's important and like people should invest in it.
But then when you look at like the numbers like the capital requirements early on versus like the moment of okay this is actually gonna work let's keep investing that seems to really hard to predict in a way that it's not I mean code CO2, Kosla, GC, I mean these are all invested in in hardware companies.
You just you know and listen I mean it could work this time for sure right I mean if Elon's doing it he's like just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time.
So that alone maybe 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 gonna like basically will into being an industry.
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 is different.
They can actually do these types of things.
Just to clarify AD stands for American dynamism.
All right.
Yeah, yeah.
I actually I do have a related question.
First of all, I want to acknowledge also just on the on the chip side.
Yeah.
I I recall a podcast that where you were on, I I I think it was the ACCZ podcast.
Uh about two or three years ago where you where you was 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.
Yeah.
I think you estimated 500 billion uh something.
No, no, no.
A billion a billion dollar training run, a one billion dollar training run, it makes sense to actually do a custom ASIC if you can do it in time.
The question now is timeline, yeah, not money.
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 solved.
And so let's assume it's if you could save 20%, which you say much more than that with an ASIC.
20% that's 200 million dollars, you can tape out a chip for 200 million dollars, right?
So now you can literally like justify economically, not timeline-wise, that's a different issue.
An ASIC per model, which is because that that's how much we leave on the table every single time we we do we do like generic NVIDIA.
Exactly, exactly.
No, it's it's actually much more than that.
You could probably get you know a factor of two, which would be 500 million dollars.
Typical MFE would be like 50.
And that's good.
Exactly, yeah, 100%.
Um so yeah, I mean, and I just want to acknowledge like here we are in in 2025, and OpenAI is confirming like Broadcom and all the other custom silicon deals, which is incredible.
I I think that uh, 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 this sort of like America first movement or like sort of reindustrialized here and then uh move TSMC here if that's possible.
Um how much overlap is there from AD to I guess growth and uh investing in particularly like you know US AI companies that are strongly bounded by their compute.
Yeah, yeah.
So I mean I I would view I would view AD as more as a market segmentation than like a mission, right?
So the market segmentation is it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware.
I mean, they're just set up to to diligence those types of companies.
So it's a more of a market segmentation thing.
I would say the entire firm, you know, which has been since it's been in thecepted, you know, has geographical biases, right?
I mean, for the longest time, we're like, you know, Bay Area is gonna be like where the majority of the guys go.
Yeah.
And and listen, there's 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.
Um, and uh, I mean, I would say the Bay Area is very much back, you know.
Like, I I remember during pre-COVID, like it was like almost crypto had kind of pulled startups so the Bay Area.
Yeah, yeah.
New York was uh, 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 best all over the world.
And then I would say, like, if you take the ring out, you know, one more, it's gonna be the US, of course, because we know very well, and then one remote is gonna be Kenny Us 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 that's sort of like where the companies are headquartered.
Maybe your questions on supply chain and customer base.
Uh I I would say if our customers are 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 gonna be stronger where we have our network and we've been doing business for 20 years.
I mean, been in the Bay Area for 25 years.
So clearly, I'm just more effective here than I would be somewhere else.
Um I think I think for some of the later stage rounds, the companies don't need that much help.
They're already kind of pretty mature historically.
So, like they can kind of be everywhere.
So there's kind of less of that stickiness.
This is definitely in the AI time.
I mean, Sarah is now the uh chief of staff of like half the AI companies in uh the Bay Area right now.
She's like ops ninja, biz dev, biz ops.
Are you do are you are you finding much AI automation in your work?
Like what is your stack?
Oh, am I in my personal stack?
I mean, it's because like uh by the way, it's uh the the the reason for this is it's triggering uh yeah, we are like I'm hiring off people, um, a lot of ponderers, I know are also hiring all people, and I'm just, you know, it's an opportunities since you're you're also like basically helping out with ops with a lot of companies.
What are people doing these days?
Because it's still very manual as far as I can tell.
Yeah.
I think the things that we help with are pretty network-based.
Um, in that it's sort of like, hey, how do I shortcut this process?
Well, let's connect to the right person.
So there's not quite an AI workflow for that.
I will say, as a growth investor, Claude Cowork is pretty interesting.
Like for the first time, you can actually get one-shot data analysis right, which, you know, if you're gonna do a customer database, analyze a cohort retention, right?
That's just stuff that you had to do by hand before.
And our team the other, it was like midnight, and the three of us were playing with cloud 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 uh, you know, that's a that's the kind of thing that a growth investor is like, you know, slaving away on late at night, um, done in a few seconds.
Yeah.
You gotta wonder what the whole like anthropic labs, which is like their new sort of products studio, what would that be worth as an independent uh startup, you know?
Like a lot.
Yeah.
True.
No, you gotta hand it to them.
They've been executing incredibly well.
Yeah.
I I mean to me, like, you know, enthropic, like building on cloud code, I think uh it makes sense to me.
The the real um pedal to the metal, whatever the the the phrase is, is when they start coming after consumer with uh against open AI, and like that is like red alert at OpenAI.
I think they've been pretty clear they're enterprise focused.
They have been.
But like here is there publicly.
It's enterprise focused, it's coding.
Right.
And then and but here's cloud cloud cowork.
And and here's like, well, we uh they're apparently they're running Instagram ads for cloud AI on it, you know, for for people.
Right.
And so like it's kind of like this the disruption thing of uh, you know, mo open AI has been doing consumer it's been doing the just pursuing general intelligence in every modality.
And here is enthopy they 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 there's a very open question so for me there's like do you know that meme where there's like the guy in the path and there's like a path this way there's a path this way and like one Western yeah yeah and for me like like all the entire industry kind of like hinges on like two potential futures.
So in in one potential future um the market is infinitely large there's perverse economies of scale 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 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.
You've got and nobody knows the answer.
My belief is if you actually look at the numbers of these companies.
So generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they they spent training the last model, they're gross margin positive.
You're like, oh, that's really working.
But if you look at like the current training that they're doing for the next model, they're gross margin negative.
So part of me thinks that a lot of them are kind of borrowing against the future, and that's gonna have to slow down.
That's gonna catch up to them at some point in time.
But we don't really know.
Yeah.
Does that make sense?
Like 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 um 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.
Um, but you know, if you go back to let's say March 2024, that period, it felt like a I think an open source model with an F like a you know, benchmark leading capability was sort of launching on a daily basis at that point, and um, and so that you know, that's one period, suddenly it's sort of like open source takes over the world.
There's gonna be a plethora, it's not an oligopoly.
You know, if you fast you know, if you if you were wind time even before that, GPT-4 was number one for nine months, ten months, it's a long time, right?
Um, and of course, now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts.
And and you know, it could look like this in the future too, but it just it's so hard to call.
And I think the thing that keeps, you know, us up at night, in 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 gonna look like.
But I I would I would say for sure it's not converged.
Like for sure, like the systemic capital flows have not converged.
Meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time, but but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.
Yeah.
Or or like the drop in price of compute will will will save them.
Who knows?
Yeah.
Yeah.
I think the models need to asymptote to specific tasks.
You know, it's like, okay, now Opus 4.5 might be a GI as some specific task, and now you're gonna like depreciate the model over a longer time.
I think now not right now, there's like no old model.
No, but let me but let me just change that mental.
That's that used to be my mental model.
But 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?
Like so, 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 A G I or not.
And I will know that they're using it because they're using it.
And like, unlike in the past where engineering stops me from doing that, it's just very straightforward, you just train.
So I also thought it was kind of like you must ask some total AGI general, general, general, but I think there's also just a possibility that the the the capital markets will just give them the ammunition to just go after everybody on top of them.
I I do wonder though, to your point, um, if there's a certain task that getting marginally better isn't actually that much better.
Like we've asked some toted to, you know, we can call it AGI or whatever.
You know, actually Ali Goatesy talks about this.
Like we're already at AGI for a lot of functions in the enterprise.
Um that's probably though for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't coming from the model itself.
There's probably a rich enterprise business to be built there.
I mean, could be wrong on that, but there's a lot of interesting examples.
So right, if you're looking about the legal profession or or whatnot, and maybe that's not a great one because the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from services, it comes from implementation, right?
It comes from all these things that actually make it useful to the end customer.
So one more thing I think is is under-discussed in all of this is like to what extent every task is AGI complete.
Right.
I code every day.
It's so fun.
That's a poor question, yeah.
And like when I'm talking to these models, it's not just code.
I mean, it's everything, right?
Like, I, you know, like it's it's healthcare, it's legal.
But it's everything it's exactly that.
I think I mean for yeah.
It's everything.
Like, I'm asking these models to yeah, to understand compliance.
I'm asking these models to go search the web.
I'm asking these models to talk about things I know in the history.
Like, like that's having a full conversation with me while I I engineer.
And so it could be the case that like the most a you know, AGI complete, like I'm not an AGI guy, like I think that's you know, but like the most AGI complete model will always win independent of the task.
And we don't know the answer to that one either.
Yeah.
But it seems to me that like listen, codecs, in my experience, is for sure better than Opus 4.5 for coding.
Like it finds the hardest bugs that I work in with, like, it's is you know, the smartest developers I don't work on it.
It's great.
Um, but I think Opus 4.5 is actually very it's got a great bedside manner.
And it really matters if you're building something very complex because like it really, you know, like you're you're you're you're a partner and a brainstorming partner for somebody.
And like I think we don't discuss enough how every task kind of has that quality.
And what does that mean to like capital investment and like frontier models and submodels?
Yeah, like what happened to all the special coding models?
Like none of them worked, right?
So do some of them didn't even get released.
Magical dev or whatever.
There's a whole there's 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 is like there's no such thing as a coding model.
Yeah.
Like that's not a thing.
Like you're talking to another human being and it's it's good at coding, but like it's gotta be good at everything.
Uh minor disagree only because I I'm pretty like have pretty high confidence that basically OpenAI will always release a GPT 5 and a GPT-5 codex.
Like that that's the code.
The way I call it is one for Riz and one for Tiz.
Um and then like someone internally, OpenAI was like, yeah, that's that's a good way to feel it.
That's so funny.
Uh but maybe it collapses down to reason tis, and that's it.
It's not like a hundred dimensions, yeah.
It's two dimensions.
Yeah, yeah, yeah, yeah.
Like in exactly Beside Manor versus coding.
Yeah, yeah, yeah.
I think for anyone for anybody listening to this or for for I mean for you, like when you're like coding or using these models for something like that, like actually just like be aware of how much of the interaction has nothing to do with coding.
And it just turns out to be a large portion of it.
And so, like, you're I think like like the best Sotoish model, you know, is gonna remain very important no matter what the task is.
Yeah.
Uh speaking on coding, uh, I'm gonna be cheeky and ask what actually are you coding?
Because obviously you you could code anything, and you're obviously a busy investor and a manager of like a giant team.
Um, what are you coding?
I help um uh Fayfe at World Labs.
Uh it's one of the investments, and um, and they're building a foundation model that creates 3D scenes.
Yeah, we add our underpod.
Yeah, yeah, yeah.
And so these 3D scenes are Gaussian splats, just by the way that kind of AI works.
And so like you can reconstruct a scene better with with with radiance feels than with meshes, because like they don't really have topology.
So they they they produce these just beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.
It's just never, you know, it's always been meshes and like things like Unrealities meshes.
And so I work on an open source library called Spark.js, which is a uh a JavaScript rendering library ready for Gaussian splats.
And it's just because you know, um, you you you need that support.
And and right now there's kind of a 3JS moment that's all meshes, and so like it's become kind of the default in 3ds ecosystem.
As part of that, to kind of exercise the library, I just build a whole bunch of cool demos.
So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this this library that I work on, because it's actually a very tough algorithmics problem to actually scale a library that much.
And just so you know, this is ancient history now, but 30 years ago, I paid for undergrad, you know, working on game engines in college in the late 90s.
So I've got actually a bad it's very old background, but I actually have a background in this, and so a lot of it's fun, you know, but but the the the whole goal is just for this rendering library to are you one of the most active contributors to their GitHub?
Spark JS?
Yeah, there's only two of us, actually.
So yes, no, no, so by the way, so the the pri the pri yeah, yeah.
So the primary developer is a guy named Andreas Sunquist, who's an absolute genius.
He and I did our our our PhDs together, and so, like, um, we said it for constant quality.
It's almost like hanging out with an old friend, you know, and so, like, so he he's the core core guy.
I knew mostly kind of yeah, let's say fun.
It's amazing.
Like, five years ago, you would not have done any of this, and I guess it brought you back.
The active the actual 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 don't have to deal with it.
I can like focus on the algorithmics and I can focus on the scaling and I can yeah, yeah.
And then uh I'll observe one irony, and then I'll ask a serious uh investor question, uh, which is like the irony is Fayfe actually doesn't believe that LLMs can lead us to spatial intelligence, and here you are using LLMs to like help like achieve spatial intelligence.
I just I see some like disconnect in there.
Yeah, yeah.
So I think I think you know, I think I think what she would say is LLMs are great to help with coding.
Yes, but like that's very different than a model that actually like provides.
They'll never have the spatial intelligence.
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, and you like I I would say that the the one data point I recently had uh against it is the deep mind uh IMO gold, where so uh typically the the typical answer is that this is where you start going down the neurosymbolic path, right?
Like one uh sort of veg sort of abstract reasoning thing and one formal formal thing.
Um, and that's what DeepMind 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 it and it's an LLM.
Yeah, yeah, yeah, yeah.
And so that that was my indication of like maybe you don't need a separate system.
Yeah, so so let me set 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 models.
Like if you distill it down, but let me just talk about the the two different substrates.
Let's let me put you in a dark room, like totally black room, and then let me just describe how you exit it.
Like to your left, there's a table, like duck below this thing, right?
I mean, like the chances that you're gonna like not run into something are very low.
Now let me like turn on the light and you actually see and you can do distance and you know how far something away is and like where it is or whatever, then you can do it, right?
Like language is not the right primitives to describe the universe because it's not exact enough.
So that's all Faye Fei is talking about when it comes to like spatial reasoning.
Is like you actually have to know that this is three feet far, like that far away, it is curved, you have to understand, you know, the like the actual movement through space.
Yeah.
So I do I listen, I do think at the end of these models are definitely converging as far as models, but there's there's there's different representations of problems you're solving.
One is language, which you know that would be like describing to somebody like what to do, and the other one is actually just showing them.
And the spatial reasoning is just showing them.
Yeah, yeah, yeah.
Right.
Got it, got it.
Uh, the in the investor question was on on World Labs is well, like, how do I value something like this?
What what what work does they do you do?
I'm just like Faye Fe's awesome, Justin's awesome, and you know, the other two founder co-founders, but like the the the tech, everyone's building cool tick.
But like, what's the value of the tech?
And this is the fundamental question.
Let me let me just for like these, let me just be maybe give you a rough sketch on the diffusion models.
I actually love to hear Sarah, because I'm a venture person.
Um, yeah, so like Venture is always like kind of wild west.
You paid the dream, and she has to like actually use it.
I'm gonna say I'm gonna think about it.
Exactly.
So I'm gonna say the venture view in the show.
And she can be like, okay, you dream you little kid.
Yeah.
So like so these diffusion models literally create something for for almost nothing, and something that the the world has found to be very valuable in the past in our real markets, right?
Like a 2D image, I mean, that's been an entire market.
People value them.
It takes a human being a long time to create it, right?
I mean, to create a, you know, um to turn me into a whatever, like an image would cost a hundred bucks 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, I mean, when's Grand Theft Auto coming out?
Six, what it's been 10 years.
I mean, how how like how much would it cost to like to reproduce this room in 3D?
If you if you if you hire somebody on Fiverr, like in in any sort of quality, probably 4,000 to $10,000.
And then if you had a professional probably $30,000.
So if you could generate the exact same thing from a 2D DMS, 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 and for listeners, um, you can do this yourself on your on your own phone with like uh the marble.
Uh or but also there's many NERF apps where you just go on your iPhone and and do this.
Yeah, yeah, yeah.
And and in the case of Marble, though, it would what you do is you literally give it in so most NERF apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.
Yeah.
Um things like marble, just 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 uh like the back of the back of the table under the table that I like 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 adult perspective.
Um, no, I was gonna say these are very much a tag team.
So we said we started this pod with that um 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.
Um, but I think every investment fundamentally starts with the same, maybe the same two premises.
One is at this point in time, we actually believe that there are N of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?
So uh we're not investing in every, you know, now the term is neo lab, but every foundation model, uh any any company, any founder is trying to build a foundation model.
We're not um contrary to popular opinion, we're not invested in all of them, right?
We have a very specific thesis.
I don't think people say that about you.
No, they don't, they don't.
They say that we're big, we're and everything.
But um, 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.
Um, if you think about, you know, the thinking machines team, right?
Amira and John, right?
John is the godfather of reinforcement learning.
And so um, I go through this because, you know, if you think about for each of the bets that we've made, it goes back to one of to a very specific thesis about that person, the team they've assembled, and what they've done in a prior life.
Um, and you know, I I think you know, obviously, we talked about talent wars.
Um, we do think at this particular moment in time, there are particular people that can move needles.
Um clearly uh other companies believe that too, otherwise they wouldn't be willing to pay such crazy prices for single individuals.
So that's that's one.
And then two, we don't think it's a zero-sum game, right?
Like if that were true, open AI or or actually just DeepMind would be number one in everything, right?
There's clear value to specializations, like 11 labs.
There have been so many audio models that have hit the market, they're still frickin' 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.
Um, you know, there's there's sort of that I would one, I would say that's the market out there because they are raising larger dollars.
They have compute needs, right?
That's 80% of a round that they typically raise, or typically of of a round that they raise.
Um, but I think the thing that gets us excited about backing them is that the revenue growth has typically followed the capability breakthrough.
So you sort of ties back to that question of the cyclical nature, like are you just funding it and then you raise more funding.
Um when there's a real capability breakthrough, the demand is there.
And so the revenue growth is much faster than we've ever seen once it's turned on.
There's a company, I can't share the name, um, but their product went GA in a few weeks, tens of millions of revenue, right?
We have 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.
Um and and by the way, great companies, not at all um diminishing what they've accomplished.
But the fact is to get to that revenue growth that quickly, it's not just the two companies that people talk about.
It's it's really a lot of these, you know, sort of every domain has a specialist, and we think if you can win that, you become very large very quickly.
And that's actually played out in the numbers.
Yeah.
Uh our our viewers are going to uh so first of all, thank you for that overall take.
I think like it's important to hear you guys' perspective because the rest of us are just kind of looking at headlines and not knowing how to make sense of any of this.
Um we can mention like my our listeners will roast us if we don't if we can mention Thinky and not discuss what happened.
Uh I mean, obviously founder split happens.
Um, but like I guess is the thesis on change, is is like um, you know, like uh what's what's going on in Thinky.
Yeah, um we're more excited than ever about them.
Um they have some things that we're not gonna do breaking news on a pod uh that you know, obviously they should share themselves, but um they've you know I think when you bring a team of that caliber together, there's special things that happen, and um I think 2026 is gonna be a big year for them.
Um obviously, you know, some of the themes that we talked about before, even with just the media news storm, 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 Tink uh is Tinker, it's our custom models RL.
Um yeah, I is that is that what is that what we're aiming for?
Yeah, and a bunch of stuff we we can't talk about here.
Yeah, cool.
Yeah, absolutely.
But no, that team is cooking, and um, you know, I think um they'll they'll be just fine from uh they'll they'll recover from the events in January.
Yeah, I will say this is the furthest.
So we have a very privileged position on the boards of these companies, and like I will say, I've never seen the perception of the truth be further from the truth.
Oh, industry-wide ever.
Like, I I guarantee you, for any of these gossipy things, I guarantee you, it's way off.
Okay.
Way, way way out.
Like the general sense of it, and like, and what happens is like we've got this crazy game of telephone right now where there's always like seeds of truth, but it gets so warped by the time.
Like we hear all the time rumors about stuff that we're directly involved in.
Like we're literally on the board, you know, like we're the 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 shot and freight is so high that people just kind of will into being things that didn't exist.
Um I'm not, you know, I didn't you know I don't want to cover specifically on the thinking machines, but like it's an important message to the general audience.
Like the chances that it's, you know, it is accurate representing what it's saying to is very, very low.
Yeah.
I have never lost so much faith in the a non-counts on Twitter that just seem very confident in what they're saying.
And could it be further from the truth?
I would I had a couple days stretch where I was like, oh my God, Twitter is mind poisoned.
And I love it.
But we talk to each other all the time because we actually know because we're there, like we're there seeing these things, and like, you know, Sarah will like text me and all like whatever.
Like, it's like ridiculous.
So for us, it's like it's like this ridiculous thing.
But the problem is is we realize that things like things start taking on a life of their own, and then people assume that they're real and and everything.
And so I think it's very tough for founders because you know it's tough enough fighting the real battle, you know.
Absolutely.
You know, but like fighting phantoms too.
And so, you know, you know, more and more we're just like, and I got this from the cursor, you guys, which I I really appreciate Michael Troll.
He's like, listen, heads down, focus on the business.
Yeah.
And he absolutely crushed it.
Yeah.
Yeah.
And I think that's right.
All founders should do that right now because the noise is so hot.
Yeah.
Now that team's been back to business for for weeks, the thinky team.
So yeah.
Yeah.
Well, thank you for indulging in that.
Uh it's just a the hot topic of the moment.
We gotta address the elephant in the room.
Um, uh cursor, right?
You obviously you guys are big investors.
Uh 2025, I would say it's curses year.
I mean, maybe decade, but uh uh just like I I think you know, uh just going back to the discussion about how AGI would just kind of consume everything, because there's like the one like the kind of the shiny example of like here's how you build application layer that's a wrapper, yeah, but an extremely damn good one.
Yeah, uh and uh I guess just the what like the the general analysis, I guess, of of curses development and what it means for everyone.
Like, is there a cursor in every industry to be built?
Yeah, so the the interesting thing about cursors, they actually, for you know a small fraction of the cost, a hundredth 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 there's 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 how do I extract margin on on the tokens that are going through?
Like everybody has to be on the token path and everybody has to ask that question.
And I've just thought they've been incredibly thoughtful about it.
And one reason is is if you ask, you know, Michael, what type of company are you?
They are a developer company for professional developers.
That's what they are.
They're a dev tool center.
They're just focused on coding.
And that's a huge I mean, even if you didn't do AI, that's a m you know, they they they um they acquired graphite.
I mean, like, you know, listen, we were investors in GitHub, like we know how big this market is.
So that's a massive market, even without becoming a model company.
But they've also been quite successful in doing their own models.
And so I think it just shows you that if you are focused, you have a large use case.
There's a huge opportunity not only to get the application, but to start building your own models.
Are these gonna be the only models people use?
Of course not.
Um, but you know, they are in a great position to serve great models and they've demonstrated that.
Yeah, my my uh sort of uh thesis, which we're not gonna have to go into here is actually I think it um what I've been calling agent labs, which are people who build on top of uh all the other models, yeah, um, will probably have a better time with the margins because they they price against the end user hours spent or like human labor, whereas models get commodity price per token.
Yeah.
And so margin-wise, we know inference economics for uh model labs, but agent labs uh the difference is the delta between token intelligence, which keeps going down and human costs, which keep going up.
Yeah, yeah, yeah.
And so that the margin should be higher.
There's they they they should be.
They the the caveat to that is if the models go first party, right?
Yeah, what they can do is they can they can compose a dream.
Yeah, yeah, they can subsidize themselves.
The models, they can subsidize themselves.
Oh, cloud code 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 with EC2.
Like, 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.
Um and thank you for being so open and also like just leading the industry in so many areas.
Uh, it's uh really inspiring to see.
So thank you for having us through.
Thank you.
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