Bitter Lessons in Venture vs Growth: Anthropic vs OpenAI, Noam Shazeer, World Labs, Thinking Machines, Cursor, ASIC Economics — Martin Casado & Sarah Wang of a16z

a more ambitious age, which I think is kind of Definitely makes a statement. It. Yeah.

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

Yes. There is. 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. Right? So she worked with Noam Jazir, Meera, Ilia, Fei Fei.

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

actually it's actually it's actually very like like no. It's a very interesting time in investing because, like, you know, take, like, the character around. Right?

These tend to be, like, premonetization, 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.

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, you're writing a 20 to a $60,000,000 check and you call it a day. Now you normally have financial investors and 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 very different ties. Listen. I've been doing this for ten years.

It's the I've never seen anything like this. Yeah.

I 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. Alright. This is this is, like, the the whole pyramid scheme bubble thing where, like, as long as you mark to market on, like, the notional value of, like, these deals, fine.

But, like, once it starts to chip away, it really Well, no. It's just like if if as long as there's demand I mean, you know, this this is like, a lot of these sound bites have already become kind of cliches, but they're worth saying it. Right?

Like, during the Internet days, like, we were, raising money to put fiber in the ground that wasn't used. And that's a problem. Right?

Because now you actually have a supply overhang. Mhmm. And even in the the time of the the Internet, like, the supply and and bandwidth overhang, even as massive as it wasn't as as massive as the crash was only lasted about four years.

But we don't have a supply overhang. Like, there's no dark GPUs. Right?

I mean and so, you know, circular or not, I mean, you know, if if someone invests in a company that, you know, they'll actually use the GPUs and on the other side of it is the is the actual customer. So I I I think it's a different time.

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 r and d. It's a horizontal platform, but it's also an app because it touches the users directly. And then, of course, you know, the 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. 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 time scales in which it's happened is pretty phenomenal. And the way 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.

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

Like, it it 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 is in the past, if you raised money, then you basically had to wait for engineering to catch up, which famously doesn't scale.

Like, the mythical man must 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 a year, and it's better. Right?

And and it does it with a team of 20 people or 10 people. So this type of, like, money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before. And I think everybody's trying to understand what the consequences are.

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

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 and then 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 a systemic situation where the Soda models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering. And it's a very open question.

Yeah. It's it's almost like bitter lesson applied to the startup industry. A 100%.

Yeah. It 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?

different You you said it. Yeah. Should talk we should actually talk about this.

Yeah. Yeah. That's Yeah.

Go for it. Take it Well, yeah.

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, 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 I've been doing this for a decade, and I've been doing startups for twenty years. And so, I mean, a lot of people start this to do AGI.

And we've never had, like, a unified north star that I recall in the same way. Like, people built companies to start companies in the past. Like, that was what it was.

Like, I would create an Internet company. Would create an infrastructure company. Like, it's kind of more engineering builders, and this is kind of a different, you know, mentality.

And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider AGI, but others have not. And so, like, there is always this tension with personnel. And so I think we're seeing more kind of founder movement Yeah.

You know, as a fraction of founders than we've ever seen. I mean, maybe since, like, I don't know, the time of, like, Shockley and the trade to Russate or something like that way back to the beginning of the industry. Mean, it's a very, very unusual time of personnel.

Totally.

Yes. I mean, just on briefly comment on the founder, 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 kinda done and, like, who's gonna pay more than Meta? I I don't know.

I I agree. It's so it feels it's gonna so it feel it feels this way to me too. It's like it's like basically Zuckerberg kinda came out swinging and then now he's kinda back to building.

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

But then now now you, like, you you made your choices, now they gotta ship. Right? Like I mean, the The Us other side of that is, 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 reps if anybody's interested listening to this.

For investor? Yeah. On the team.

Like, on the investing side of the team. Like and, a lot of the people we talk to have acting, you know, active offers for 10,000,000 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 Trickles down.

Yeah. It is it's still very active today.

that's different. But on but on the other hand, there's more strategic money than we've ever seen historically. Right?

And so Yep. The economics the the the calculus on the economics is very different in a number of ways, and it's it's caused a ton of change and confusion in the market. Some very positive, some negative.

Like, so for example, the other side of the the cofounder, like, acquisition. You know, Mark Zuckerberg poaching someone for a lot of money is, like, we're actually seeing historic amount of m and a for basically acqui hires. Right?

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

Yep.

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, you're deep tech.

Right? But I I, know, I feel like there's just kind of a long, you know, list of, like, good good companies that'll be around for a long time in very large markets. Say you're building a database.

You know? Say you're building, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but, like, they have a really hard time getting, the attention of investors.

And it's almost become a meme. Right? Which is, like, if you're not basically growing from zero to a 100 in a year, you're not interesting, which is the silliest thing to say.

I mean, think of yourself as, 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 five x in a very large part? Of course, can put in the company five x. So it's just like that we say these stupid things like if you're not growing from zero to a 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, three x net over, you know, the life cycle of a fund.

Right? So a company in a big market growing five x is a great investment. We'd everybody would be happy with these returns, but we've got this kind of mania on these these strong growths.

And so I would say that that's probably the most underinvested sector right now. Boring software. Boring enterprise software.

There's no traditional. Like, no but really good company. AI here.

No. Like, for 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 are great investments from any definition except for, like, random VC on Twitter saying VC on x saying, it's not growing fast enough. What do think?

taking that for granted. Yeah. Yeah.

What are the drone? What's the AVR? And, like, one of the takeaways is when it comes to hardware, most companies will end up verticalizing.

Like, if you're if you're investing in a robot company for an for agriculture, you're investing in an ag company because that's the competition, that's surprising, and that's supply chain. And if you're doing it for mining, that's mining. And so the ADT does a lot of that type of stuff because they actually set up due diligence that type of work.

But for, like, horizontal technology investing, there's very little when it comes to robots just because it's it's so fit for for purpose. And so we kinda like to look at software solutions or horizontal solutions, like Applied Intuition clearly from the AV wave, DeepMap clearly from the AV wave. I would say Scaleai was actually a horizontal one for That's fair.

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

Mhmm. Yeah. I'm curious who these teams are supposed to be that invest in them.

COSLA, GC. I mean, these are all invested in in hardware companies. He's 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 suggest 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. But we've just historically found like, we're a huge believer that this is gonna happen.

We just don't feel like we're in a good position to diligence these things because, again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like, that's like, that competitive equilibrium with a human being is what's important.

No. No. No.

A billion $1,000,000,000 training run. A $1,000,000,000 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 just just rough math. If it's a billion dollar training run, then the inference for that model has to be over a billion.

Otherwise, it won't be solvent. So let's assume it's you could save 20%, which you save much more than that with an ASIC. 20, that's $200,000,000.

You can tape out a chip for $200,000,000.

Yeah. Exactly. Exactly.

No. It's it's actually much more than that. You could probably get, you know, a factor of two, would be $500,000,000.

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 deals with, like, hardware. I mean, they're just set up to to to to to diligence those types of companies.

So it's more of a market segmentation thing. I would say the entire firm, you know, which has been since it's been incepted, you know, has geographical biases. Right?

I mean, for the longest time, like, you know, Bay Area is gonna be, like Great. Where the majority of the dollars go. Yeah.

And and listen. There 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 I remember during pre COVID, like, it was, like, almost crypto had kind of pulled start ups away from the beginning.

Yeah. Yeah. New York was, you know, because it's so close to finance.

Came out like, Los Angeles had a moment because it so close to consumer, but now it's kind of come back here. And so I would say, you know, we tend to be very Bay Area focused historically even though, of course, we invest all over the world. And then I would say, like, if you take the ring out, you know, one more, it's gonna be The US, of course, because we know it very well, and then one ring more is gonna be kind of US and its allies and yeah.

And it goes from there.

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 and 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 twenty I mean, I've been in the Bay Area for twenty five years. So, clearly, I'm just more effective here than I would be somewhere else.

Where 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 kinda be everywhere.

So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the chief of staff of, like, half the AI companies in the in the Bay Area right now.

She's like ops ninja, biz dev, biz ops.

And here is Enthopic. They only focus on this thing, but now they're sort of undercutting and doing the whole Innovator's Dilemma thing on, like, everything else. Yeah.

Yeah. It's very interesting. Yeah.

But there's there's a very open question. So 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, like, your Which way, Western man? Yeah.

Yeah. Yeah. And for me, like like, all the entire industry kind of, like, hinges on, like, two potential futures.

So in in one potential future, the market is infinitely large. There's perverse economies of scale because as soon as you put a model out there, like, it kinda sublimates and all the other models catch up. And, like, it's just, like, software's being rewritten and fractured all over the place, 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, it could be it could be the case that the only reason this is working is because they can raise that next round, and then 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 kinda converge and fragment again. But right now, it's not. Totally.

I think the other by the way, just, a meta point.

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.

If you can raise three times if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter. It doesn't even matter. Do you see what I'm saying?

Like so 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 if they're using it because they're using it.

And, like, unlike in the past where engineering stops me from doing that Mhmm. This is very straightforward. You just train.

So I also thought it was kind of like, you must ask some code AGI general general general, but I think there's also just a possibility that the the the capital markets will just give them the the the ammunition to just go after everybody on top of them.

more thing I think is is under discussed in all of this is, like, to what extent every task is AGI complete? Mhmm. Right?

I code every day. It's so fun. That's 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 health care. It's I mean, it's it's legal.

But it's every it's exactly that. Like, support. Yeah.

That'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, 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 it's it's, you know, the smartest developers. I don't work on it.

It's great. But I think Opus 4.5 is actually very it's got a great bedside manner, and it really it it it really matters if you're building something very complex because, like, it really you know, like, you're you're you're partner and a brainstorming partner for somebody, and I think we don't discuss enough how every task kind of has that quality.

Mhmm. And what does that mean to, like, capital investment and, like, frontier models and sub models? Yeah.

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

No. There's some of them. They didn't even get released.

Magical dev or There was 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, You know? 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.

for for any for anybody listening to this for for for I mean, for you, like, when when you're, like, coding or using these models for something like that, like, actually just, like, be aware of how much of the interaction has nothing to do with coding, and it just turns out to be a large portion of it. And so, like, you're I think, like, like, the best So to ish model, you know, is gonna remain very important no matter what the task is. Yeah.

I help, Fei Fei at World Labs. It's one of the investments, and, and they're building a foundation model that creates three d scenes. Yeah.

We had it under Bob. Yeah. Yeah.

And so these three d scenes are Gaussian splats just by the way that kind of AI works. And so, like, you can reconstruct a scene better with with with radiance fields than with meshes because, like, they don't really have topology. So so they they they produce these beautiful, you know, three d rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.

It's just never you know, it's always been meshes and, like, things like Unreal use meshes. And so I work on a open source library called Spark JS, which is a, a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, you you you need that support.

And and right now, there's kind of a three JS moment that's all meshes. And so, like, it's become kind of the default in three JS ecosystem. As part of that, to kind of exercise the library, I just build a whole bunch of cool demos.

So if you see me on x, you see, like, all my demos and all the world building. But all of that is just to exercise this this library that I work on because it's actually a very tough algorithmics problem to actually scale a library that much. And just so you know, this is ancient history now, but thirty years ago, I paid for undergrad, you know, working on game engines in college in the late nineties.

So I've got actually a and it's very old back I actually have a background in this. And so a lot of it's fun, you know, but but the the the the whole goal is just for this rendering library to to Are you one of the most active contributors, like, their GitHub? Spark.

Js? Yeah. There's only two of us, actually.

So, by the way, so the the the 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, we set it for constant quality. It's almost like hanging out with an old friend, know, and so, like so he he's the core core guy.

He mostly kinda, you know, listen. But, you know, picture fun. It's amazing.

Like, five years ago, you would not have done any of this. And, like, it it brought you back. The active the activation energy was so high because you have to learn all the framework bullshit, and I fucking used to hate that.

And so, like, now I know how to deal with that. Can, like, focus on the algorithmics so I can focus on the scaling.

I I just I see I see some, like, disconnect in there. Yeah. Yeah.

So I think I think, you know, I think I think what she would say is LLMs are great to help with coding. Yes. But, like, that's very different than a model that actually, like, provides They'll they'll never have the the spatial intelligence.

Listen. Our brains clearly listen. Our brains brains clearly have both.

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

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

Right? You know? Like It can be models.

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

Like, to your left, there's a table, like, duck below this thing. Right? I mean, like, the chances that you're gonna, like, not run into something are very low.

Now let me, like, turn on the light and you actually see and you can do distance and, you know, how far something away is and, like, where it is or whatever, then you can do it. Right? Like, language is not the right primitives to describe the universe because it's not exact enough.

So that's all Fei Fei is talking about when it comes to, like, spatial reasoning. It's 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 so I do think at the end of 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. The the investor question was on on World Labs is, well, like, how do I value something like this?

say gonna Be marked to reality. Exactly. So I'm gonna say the venture view, and then she and she could be like, okay.

Know what? You're a little kid. Yeah.

So, like so so these diffusion models literally create something for for almost nothing and something that the the world has found to be very valuable in the past in our real markets. Right? Like like, a two d 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 a $100 in an hour.

The inference cost is a hundredth of a penny. Right? So we've seen this with speech and very successful companies.

We've seen this with two d image. We've seen this with movies. Right?

Now think about three d scene. I mean I mean, when's Grand Theft Auto coming out? It's been six what?

It's been ten years? I mean, how how like But it has been ten years. Yeah.

How much would it cost to, like, to reproduce this room in three d If you if you if you hired somebody on fiber, like, in in any sort of quality, probably 4,000 to $10,000, and then if you had a professional, they're probably $30,000. So if you could generate the exact same thing from a two d image, and we know that these are used, and 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 Yeah. And for listeners, you can do this yourself on your on your own phone with like, the Marble.

Yeah. Marble. 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 do, like, kinda run around and take a whole bunch of pictures, and then you kinda reconstruct it. Yeah.

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. K. Way way off.

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

You know? Like, we're the we're the one that did the thing. And by the time it gets to us, it's gotten so warped and so twisted.

I think this is, everybody's excited. There's a lot of focus. The shot and fried is so high that people just kind of will into being things that didn't exist.

So I'm not know, I don't wanna comment specifically on the thinking machines, but, like, It's an important message to the general audience.

anon counts on Twitter that just seem very confident in what they're saying. Yeah. No.

Yeah. And couldn't be further from the truth. I would I had a couple days stretch where I was like, oh my god.

Twitter is mind poison 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, 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.

You know? Absolutely. You know?

Like, now they're fighting phantoms too. And so, you know, you know, more and more, we're just like and I got this from the cursor guys, I I really appreciate Michael Trolley. He's like, listen.

Heads down. Focus on the business. Yep.

And And they absolutely crushed it. Yeah. Yeah.

And I I think that's right. I think I'll find out how do it 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 nudging in that. It is just the hot topic of the month. We gotta we gotta address the elephant in the room.

a small fraction of the cost, a 100 of 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 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, 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 on the tokens that are going through? Like, everybody has to be on the token path, and everybody has to ask that question. And I've just thought they've been incredibly thoughtful about it.

And one reason is is if you ask, you know, Michael, what type of company are you? They are a developer company for professional developers. That's what they are.

They're a dev tools company. Just focused on coding. And that's a I mean, even if you didn't do AI, that's a you know, they they they 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 we've used? Of course not.

But, you know, they are in a great position to serve great models, they've demonstrated that.

the models go first party. Right? Yeah.

Yeah. What they can do is they can they can Which is the the composer dream. Yes.

Yeah. They can subsidize them the the models. They can subsidize themselves.

Oh, Falco. Falco. 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 we've know, seen this historically. We saw this with the cloud with EC two.

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.

It's such a pleasure. You're welcome back anytime.