Hey everyone, welcome to the Len Space podcast live from A6Z. Uh this is Alessio, founder of Colonel Lance and I'm joined by Twix, editor of L in Space.

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

Very happy to be here and welcome.

Yes, we love this office. We love what you've done with the place. 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 call back to a more ambitious age, which I think is kind of

It definitely makes a statement.

Yeah.

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

Uh Martine, I go back with you to Netifi uh and uh you know, you created software defined networking and all all that stuff. 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. [clears throat]

Hands down. [laughter]

Sarah is Sarah's and then 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 Nome Shazir Mera Ilia Fay and so just these frontier kind of like large AI models. I think you know Sarah's been the the broadest investor.

Is that fair? No, I Well, I was going to say I think it's 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 [laughter] 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 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 [laughter] 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 tends to be like pre-monetization, but the dollars are large enough that you need to have a larger fund and the analysis, you [snorts] 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 bisdev bisdev. Exactly. But like now

I'm not familiar what what does bisdev mean for a venture fund because I know what bisdev means for a company. [clears throat]

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 6 months into the inception of a company, you just wouldn't have to negotiate these deals before.

Yeah, these large rounds are very complex now. Like in the past if you did a series A or a series B like whatever you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors or strategic investors and then the strategic portion always goes with like these kind of large compute contracts which can take months to do and so it's very different ties. Listen, I've been doing this for 10 years. It's the I've never seen anything like this.

Yeah. Do you have worries about the circular funding from Sony Strategics? No, listen. As long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.

Exactly. All right. This is 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, like as long as there's demand. I mean, you know, listen, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it, right? Like during the internet days, like we were um raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.

Mhm.

And even in the time of the the internet, like the supply and and bandwidth overhang, even as massive as it was and as massive as the crash was, only lasted about four years. But we don't have a supply overhang. Like there's no dark GPUs, right? I mean and so you know circular or not I mean you know if if someone invests in a company that um you know they'll actually use the GPUs and on the other side of it is the is the askful customer. So I I think it's a different time. I think the other piece maybe just to add on to this and I'm going to quote Martine in front of him but this is probably also a unique time in that for the first time you can actually trace dollars to outcomes right provided that scaling laws are 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 Martin's point but if that somehow breaks you know obviously that's an important assumption in this whole thing to make it work but you know instead of investing dollars into sales in marketing you're you're investing into R&D to get to the capability um you know increase and that's sort of been the demand driver because once there's an unlock there people are willing to pay for it.

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

what does that look 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 R&D. It's a horizontal platform, but it's also an app because it's um uh touches the users directly. And then of course, you know, the the the growth of these is just so high. And so I actually think you're just starting to see a a 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 time scales in which it's happened is pretty phenomenal. And the where we'd normally cut lines before has blurred a little bit. But but that that that said, I mean, a lot of it also just does feel like things that we've seen in the past like cloud bailed out and the internet bailed 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 GBT, 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 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 this these friendnemy lines that are getting blurred in that a lot of I mean they have billions of dollars of API revenue right and so there are customers there but they're competing on the app layer

yeah so this is a really really important point so I I would say for sure venture and growth that line is blurry app and infrastructure that line is blurry um but I don't think that that changes our practice so much but like where they're very open questions are like does this layer in the same way compute traditionally has like during the cloud is like you know like whatever somebody wins one layer but then another whole set of companies wins another layer but that not might not be the case here. It may be the case that you actually can't verticalize on the token string like you can't build an app like it it 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 mammoth 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 a year and it's better right and and it does it with a team of 20 people or 10 people so this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before and I think everybody's trying to understand what the consequences are. So I think it's less about like big companies and growth and this and more about these more systemic questions that we actually don't have answers to.

Yeah. Like at Colonel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot and you're kind of pot committing to a certain strategy for a certain amount of time. But if you like iteratively spin out companies and products and just throw I I want to spend a million dollar of inference today

and get a product out tomorrow.

Yeah. Like we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the the early stage venture model to be much more iterative and then every round is like either 100k of infrance or like 100 million from a 16Z. There's no there's no like 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 it 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 you know a company's building smaller models that you know use the bigger model in the background open 4.5 but they add value on top of that. Now, if Anthropic can raise three times more every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding. So there could be a systemic there could be a systemic situation where the soda models can raise so much money that they can 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 on engineering and it's a very open question.

Yeah. It's almost like bitter lesson applied to the startup industry

100%. Yeah.

It literally becomes an issue of like raise capital turn that directly into growth use that to raise three times more. And if you can keep doing that, you literally can outspend any company that's built. Not any company, you can outspend the aggregate of companies on top of you and therefore you'll necessarily take their share, which is crazy.

Would you say that kind of happened to character? Is that the the sort of postmortem on what happened? [laughter]

Um,

no.

Yeah, cuz I think

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

[laughter]

But like that's another different that's we should talk we should actually talk about that.

Go for it. Take it take it.

Yeah. I was going to say I think um 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. 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 I mean it's nom shazir 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 The real reason that you know if you think about the the stress that any company feels before um you ultimately go 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 anthropic if they haven't start you know felt it certainly given the success of their products they may start to feel that soon and they're real I think there's real trade-offs right it's like how many when you think about GPUs that's a limited resource where do you allocate the GPUs is it toward the product is it toward new re research right is it or long-term research is it toward um you know near to midterm research and so um in a case where you're resource constrained Um, of course there's this fundraising game you can play, right? But the fund 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 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. I mean, I've been been doing this for a decade and I've been doing startups for 20 years. And so um I mean a lot of people start this to do AGI and we've never had like a unified north star that I recall in the same way like people built companies to start companies in the past like that was what it was like I would create an internet company I would create an infrastructure company like it's kind of more engineering builders and this is kind of a different 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 maybe since like I don't know the time of like Shockley and the traitorous 8 or something like that way back in the beginning of the industry. I It's a very very unusual time of personnel to

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 $5 billion. That's hard to compete with. And then secondly, if you're a founder in AI, you could fart and it would be on the front page of, you know, the information these days. And so there's sort of this fishbowl effect that I think adds to the deep anxiety that that these AI founders are feeling.

Uh yes, I mean just on 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 cuz Meta built the team. Like I don't know if I think I think they're kind of done and like who's going to pay more than Meta? I I don't know.

I I agree. So it feels so it feel 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 got to like pay up to like assemble team to rush the job, whatever. But then now now you like you made your choices and now they got to ship, right? Like

I mean the the 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 wrecks if anybody's interested that's listening to this.

They're 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's still very active today. I mean

yeah you could be an L5 and get an offer in the tens of millions. So I think you're right that it felt like a blip. I hope you're right. Um but I think it's been the steady state is now

got pulled up. Yeah before got pulled up for sure. Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people be like, well, maybe I should just go be a founder instead of like getting paid

800k a million at Google. But if I'm getting paid 56 million, that's different.

But on 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 inc it's caused a a ton of change and confusion in the market. Some very positive, some negative. Like so for example, the other side of the um the co-founder like um acquisition, you know, Mark Zuckerberg poaching someone for a lot of money is like we're actually seeing historic amount of M&A for basically aqua hires, right? That you like, you know, really good outcomes from a venture perspective that are effective aqua hires, right? So I would say it's probably net positive from the investment standpoint even though it seems from the headlines to be very disruptive in a negative way.

Yeah. Um let's talk maybe about what's not being invested in like maybe some interesting ideas that you will see more people build or it seems in a way you know as YC is getting more popular as like X is getting 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 some of the areas uh that you think are under discussed?

I mean I actually think that we've taken our eye off the ball in a lot of like just traditional you know software companies. Um, so like I mean you know I think right now there's almost a barbell like you're like the hot thing on X or you're deep tech, [laughter]

right? But 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 0 to 100 in a year, you're not interesting. Which is the silliest thing to say. I mean, think of yourself as like an individual person like like your personal money, right? So your personal money, will you put it in the stock market at 7% or you put it in this company growing 5x in a very large market? Of course, you're going to put in the company 5x, but 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 when clearly these are good investments for anybody, right? Like our LPs want whatever 3x net over, you know, the life cycle of a fund, right? So a company in a big market going 5x is a great investment. We'd everybody would be happy with these returns, but we've got this kind of mania on these these strong growths. And so I would say that that's probably the most underinvested sector right now. boring software. Boring enterprise software

just traditional but really good.

No AI here like boring 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? 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? in the robotics, 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-conensus 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, we just haven't seen the chat GBT 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 right out there. Was that the zip line? the joint with the AV era and like one of the takeaways is when it comes to hardware um most companies will end up verticalizing like if you're if you're investing in a robot company for an for agriculture you're investing in an egg 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 it's so fit for for purpose and so we kind of like to look at software solutions or horizontal solutions like applied intuition clearly from the AV wave deep math clearly from the AV wave. I would say scale AI was actually a horizontal

one for you know for robotics early on and so that sort of thing were very very interested 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 going to work. Let's keep investing. That seems really hard to predict in a way that it's not.

I mean, CO2, Kla, 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 ju just the fact that Elon's doing it means that there's going to be a lot of capital and a lot of attempts for a long period of time. So, that alone maybe suggests that we should just be investing in robotics just cuz you have this north star who's Elon with a humanoid and that's going to like basically will into being an industry. Um, but we've just historically found like we're huge believers that this is going to happen. We just don't feel like we're in a good position to diligence these things cuz again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. 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.

This is Sarah and I. The AD team is different. They can actually do these types of things.

Uh just to clarify, AD stands for

American Dynamism. All right. Yeah. Uh I actually I do have a related question. First of all, I want to acknowledge also just on the on the chip side. I I I recall a podcast that where you were on. I I I think it was the ACC podcast uh about two or three years ago where you where you suddenly said something which really stuck in my head about how at some point at some point kind of scale it makes sense to build a custom ASIC for per run.

Yes. It's crazy. [laughter]

Yeah.

I think you estimated 500 billion uh something.

No, no. A bill a billion dollar training run. A1 billion dollar training run. It makes sense to actually do a custom if you do it in time. The question now is timeline, not money. Cuz just just just rough math,

if it's a billion dollar training run,

then the inference for that model has to be over a billion otherwise it won't be solvent. So let's assume it's if you could save 20%, which you can save much more than that with an ASIC. 20% that's $200 million. You can tape out a chip for $200 million, right? So now you can literally like justify economically not time warning 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 we do like generic NVIDIA.

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

Typical MFU would be like 50 and that's good.

Exactly. Yeah. Um, so, so yeah, I mean, and I just want to acknowledge like here we are in in end 2025 and OpenAI is confirming like Broadcom and all the other like custom silicon deals which is incredible. I I think that uh, you know, speaking about AD, there's there's a really like interesting tie in that obviously you guys are head on which is like the sort of like America first movement or like sort of re-industrialize here and uh, move TSMC here if that's possible. Um, how much overlap is there from AD

Yeah. 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 to 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 incepted, you know, has geographical biases, right? I mean, for the longest time, like, you know, Bay Area is going to be like kind of where the majority of dollars go 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. Um 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 startups away from the Bay. Yeah. Yeah. New York was, you know, because it's so close to finance came up like Los Angeles had a moment because it was so close to consumer, but now it's kind of come back here. And so I would say, you know, we tend to be very Bay Area focused historically, even though of course we vest all over the world. And then I would say like if you take the ring out, you know, one more it's going to be the US, of course, because we know it very well. And then one ring more is going to be kind of US and its allies. And

yeah, and it goes from there.

Yeah.

Sorry.

No, no, [clears throat] I agree. I think from a but I think from the in 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 our customers are our companies are fairly international from that perspect like they're selling globally, right? They have global supply chains in some cases.

I would say also the stickiness is very different.

Yeah.

Historically between venture and growth like there's so much company building in venture so much. So like hiring the next PM, introducing the customer, like all of that stuff. Like of course we're just going to be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years. So clearly I'm just more effective here than I would be somewhere else. Um 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 kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean Sarah is now the uh chief of staff of like half the AI companies in [laughter] the Bay Area right now. She's like ops ninja, bisdev, bisops. Are are you are you finding much AI automation in your work? Like what what is your stack?

Oh, my in my personal stack.

I mean because like uh by the way it's the the reason for this is it's triggering uh yeah we like I'm hiring ops ops people. Um a lot of ponders I know are also hiring ops people and I'm just you know it's opportunity since you're you're also like basically helping out with ops with a lot of companies. What are people doing these days? Because it's still very manual as far as I can tell.

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 you to the right person so there's not quite an AI workflow for that I will say as a growth investor cloud co-work is

pretty interesting like for the first time you can actually get oneshot data analysis right which you know if you're going to do a customer database analyze [snorts] a cohort retention right that's just stuff that you had to do by hand before and our team the other it was like midnight and The three of us were playing with Claude co-work. We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like aha moment. That sounds so boring, but you know, that's 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 got to wonder what the whole like Enthropic Labs, which is like their new sort of products studio,

what would that be worth as an independent uh startup, you know, like [laughter]

a lot.

Yeah, true.

You got to 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 against open AI and like that is like red alert at open

I think they've been pretty clear they're enterprise focused

they have been but like here's fair publicly

it's enterprise focused it's coding right and then and but here's cloud cloud co-work and and here's like well uh they apparently they're running

Instagram ads for cloudi on you know for people right and so like Uh it it's kind of like this the disruption thing of uh you know OPI has been doing consumer been doing the just pursuing general intelligence in every modality and [clears throat] here is enthropic 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.

It's very interesting.

Yeah. I mean there's there's a very open so for me there's like do you know that meme where there's like the guy in the path and then there's like a path this way and there's a path this way and like one which 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 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 is 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. we'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 going to have to slow down. That's going to catch up to them at some point in time. But we don't really know.

Yeah.

Does that make sense? Like I mean it could be 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 in 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 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 going to be a plethora it's not an oligopoly you know if you fast you know if you if you rewind time even before that GPT4 was number one for 9 months 10 months it's a long time right um and of course now we're in this era where it feels like an igopoly 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 a good way and bad way is that the capability progress is actually not slowing down. And so until that happens, right, like you don't know what it's going to look like.

But I 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 save them. Who knows?

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

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

It doesn't even matter. You see what I'm saying? Like so, so I have an API business. My APA 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 GI or not. And I will know what they're using it because they're using it. And like unlike in the past where engineering stops me from doing that, this is very straightforward to just train. So I also thought it was kind of like you must ask some AGI general general general. I think there's also just a possibility that the that the capital markets will just give them the the the ammunition to just go after everybody on top of them. I I 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 asmmptoted to you know we can call it AGI or whatever you know actually Ali goatsy 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 the legal profession or whatnot and maybe that's not a great one because the models are 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

sorry what am I 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 core question yeah

and like

when I'm talking to these models it's not just code I mean it's everything right like I you know Like it's it's healthare, it's legal,

but it's every it's exactly that like 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 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 codeex in my experience is for sure better than Opus 4.5

for coding. Like it finds the hardest bugs that I work in with like is you know the smartest developers I know 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 and it it really matters if you're building something very complex because like it really, you know, like you're you're you're a partner and a brainstorming partner for somebody and I think we don't discuss enough how every task kind of has that quality

and what does that mean to like capital investment and like frontier models and subm models

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

Some of them didn't even get released. magic.dev or

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 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 got to 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 GPT5 and a GP5 codeex like that's the The way I call it is one for RZ and one for Tis. [laughter] Um and and then like someone internally open was like yeah

that's a good way to favor.

That's so funny.

Uh but maybe maybe it collapses down to ris and that's it. [laughter] It's not like 100 dimensions. It's two dimensions.

Yeah. Yeah.

Like in exactly Benai manner versus coding.

Yeah. [laughter] as it is. Yeah.

I I THINK FOR FOR ANY for anybody listening to this or 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 sodtoish model you know is going to remain very important no matter what the task is.

Yeah. Uh, speaking of coding, uh, I I'm going to be cheeky and ask like, what actually are you coding? Because obviously 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, Fei at World Labs. Uh, it's one of the investments and, um, and they're building a foundation model that creates 3D scenes.

Yeah, we had our under. Yeah.

Yeah. Yeah. And so these 3D scenes are Gaussian splats just by the way that kind of AI works. And so like you can reconstruct a scene better with with with radiance fields than with meshes because like they don't really have topology. So so they they they produce these just beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great. It's just never, you know, it's always been meshes and like things like Unreal use meshes. And so I work on a open source library called SparkJS, 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 3JS ecosystem as part of that to kind of exercise the library. I just build a whole bunch of cool demos. [laughter] 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 '9s. So, I've got actually a back it's very old background, but I actually have a background in this. And so, a lot of it's fun, you know, but but the the the whole goal is just for this rendering library to to

Are you one of the most active contributors the their GitHub

SparkJS? Yeah, there's only two of us, actually. [laughter] SO, YES.

NO. SO, BY THE WAY, so the the Yeah. Yeah. the primary developer is a guy named Andreas Sunquist who's an absolute genius. He and I did our our PhDs together and so like um we studied for Compton calls together. It's almost like hanging out with an old friend, you know, and so like so he he's the core core guy. I did mostly kind of you know this venture fund.

It's amazing like 5 years ago you would not have done any of this and it brought you back.

The act the activation energy was so high you have to learn all the framework [ __ ] and I [ __ ] used to hate that and so like now I don't have to deal with 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 investor question which is like the irony is Fei actually doesn't believe that LM can lead us to spatial intelligence and here you are using LLMs to like help like achieve spatial [laughter] intelligence. I I just 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

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

Okay. 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 vague sort of abstract reasoning thing and one formal formal thing um and that's what DeepMind had in 2024 with alpha proof of geometry and now they just use deep think and just extended thinking tokens and it's one model and it's and it's

in LM.

Yeah. Yeah. Yeah.

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

Yeah. So so let me step back. I mean at the end of the day at the end of the day these things are like nodes in a graph with weights on them, right? You know like they can be models like you distill it down. But let me just talk about the two different substrates. Let's let me put you in a dark room like totally black room

and then let me just describe how you exit it. Like to your left there's a table like duck below this thing, right? I mean like the chances that you're going to like not run into something are very low. Now let me like turn on the light and you actually see and you can do distance and you know how far something away is and like where it is or whatever then you can do it right like language is not the right primitives to describe the universe because it's not exact enough. So that's all fave fee is talking about when it comes to like spatial reasoning is like you actually have to know that this is 3 ft far like that far away. It is curved. You have to understand you know like the actual movement through space.

Yeah.

So I do I listen I do think at the end 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 the investor question was on on world labs is well like how do I value something like this? [laughter] What what what work does do you do? I'm just like FA is awesome. Justin's awesome and you know the other two coound co-founders but like the the the tech everyone's building cool tech but like what's the value of the tech and this is the fundamental question.

Let me let me just for like let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture you know like venture is always like kind of wild west type. You're you paid to dream and she has to like actually

I'm going to say I'm reality.

Exactly. So I'm going to say the venture be she can be like okay you little kid. Yeah. So like so so these diffusion models literally create something for for almost nothing and something that the the world has found to be very valuable in the past in our real markets right like like a 2D image. I mean that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean to create a you know to turn me into whatever like an image would cost a h 100red bucks in an hour the inference cost us a hundredth of a penny right so we've seen this with speech and very successful companies we've seen this with 2D image we've seen this with movies right

now [snorts] think about 3D scene I mean I mean when's grand theft auto coming out

it's been 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 something on fiber like in in any sort of quality probably $4,000 to $10,000 and then if you had a professional probably $30,000. So if you could generate the exact same thing from a 2D image and we know that these are used 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 math.

Yeah. And for listeners, uh, 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 or 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 the back of the table under the table the like like the images it doesn't see the generator stuff is very different than reconstruction that it fills in the things that you can't see.

Yeah. Okay. So, all right. So now the

No, I mean I love that the adult perspective.

Um well no I was going to say these are very much a tag team. So we 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 neolab but every foundation model 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 [laughter] that about you. No, they don't. They don't.

They say that we're big. We're in everything. But um you know, if you think about Ilia, 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, at Meera 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, OpenAI or or actually just Deep Mind would be number one and everything, right? There's clear value to specialization. It's like 11 Labs.

There have been so many audio models that have hit the market, they're still freaking number one, right? And so if you think about and they've created a ton of value um for their customers, for their investors, you know, for their team. Um and so if you think about those two put together, right, that's sort of the foundation of our thesis when we back uh these foundation model uh companies. Um of course the valuations, you know, they sound astronomical when you think about current revenue, the numbers. Um you know, there's they're 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 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 SAS 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 ekking to whatever it is um and and by the way great companies not 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't me like my our listeners will roast us if we don't if we mention Thinky and not discuss what happened. Uh, I mean obviously founder split happens. Um, but like I guess is the thesis unchanged is is like um you know like what's what's going on? I think

yeah um we're more excited than ever about them. Um they have some things that we're not going to do breaking news on a a pod. uh 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 going to 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 and the story is Tink uh, is Tinker is custom models RL. Um, yeah. 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 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

industrywide ever. Like I I guarantee you for any of these gossipy things, I guarantee you it's way off. Okay. Way way like like the general sentiment and like and what happens is like we've got this crazy game of telephone right now where there's always like seeds of truth but it gets so warped by the time like we hear all the time rumors about stuff that we're directly involved in. Like we're literally on the board, you know, like we're the one that did the thing and by the time it gets to us it's gotten so warped and so twisted. I think this is like everybody's excited. There's a lot of focus. the shot on fried is so high that people just kind of will into being things that didn't exist. Um, so I'm not, you know, I you don't want to comment specifically on the thinking machines, but like

it's an important message to the general audience.

I will tell you if you hear something on X, 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 anon counts on Twitter that just seem very confident in what they're saying. I couldn't be further from the truth. I I had a couple day stretch where I was like, "Oh my god, Twitter is mind poison." And I love

we talk to each other all the time cuz we actually know cuz we're there. Like we're there saying 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

fighting 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 cursory guys which I I really appreciate Michael Troll he's like listen heads down focus on the business and and he absolutely crushed it. Yeah.

Yeah. And I I think that's right. All founders should do that right now because the noise is so hot.

Yeah. No, 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 the hot topic of the moment. We got to got to address the elephant in the room. Um uh cursor, right? Obviously, you guys are big investors. Uh 2025, I would say it's curs's year. [laughter] I mean maybe decade but uh uh just like I I think you know I was just going back to the discussion about how AGI would just kind of consume everything cuz like the one like the kind of the shining example of like here's how you build application layer that's a rapper

but a extremely damn good one.

Uh and I guess just 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 interesting thing about cursors they actually for you know a small fraction of the cost 100 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 there there's two approaches you start with a foundation model and then you verticalize up or you start with the app and all of the product data and you go down and they're the ones that are doing that. I think any company that's doing an app has to ask the margin question 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 they are a developer company for professional developers that's what they are they're a dev tools they're just focused on coding and that's a hu 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 going to 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 going to have to go into here is actually I think um what I've been calling agent labs which are people who build on top of all the other models 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 model labs but agent labs the difference is the delta between token intelligence which keeps going down and human costs which keep going up.

Yeah. Yeah.

And so the margin should be higher.

There they they they they should be the the the caveat to that is if the models go first party right what they can do is they can they can

which is the the composer dream. Yes.

Yeah. they can subsidize themsel the models they can subsidize themselves cloud 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 unus we saw this with the operating system it's not unusual but it's playing out very very quickly

yeah thank you for joining us that's all the time we have today such a pleasure

you're welcome back anytime

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

Thank you for having us.

Great. Thank you.