Hey everyone, welcome to the Lenny's Space podcast, live from A16z. This is Alessio, founder of Colonel Lance, and I'm joined by Twix, editor of Lenny's Space.
Hey, hey, hey. And we're so glad to be on with you guys. Also a top AI podcast, Martin Casado and Sarah Wang. Welcome.
Very happy to be here and welcome.
Yes, we love this office. We love what you've done with the place. The new logo is everywhere now. It still takes a while to get used to, but it reminds me of sort of a callback to a more ambitious age, which I think is kind of...
It definitely makes a statement.
Yeah. Not quite sure what that statement is, but it makes a statement.
Martin, I go back with you to Netifi, and you know, you created software-defined networking and all that stuff. People can read up on your background. Sarah, newer to you. You sort of started working together on AI infrastructure stuff.
That's right. Yeah, seven years ago now.
Best growth investor in the entire industry.
Sarah is Sarah's, and then 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 Shazeer, Mira, Ilya, Fei, and so just these frontier kind of like large AI models. I think you know Sarah's been the broadest investor.
Is that fair? No, I... Well, I was going to say I think it's a really interesting tag team actually, just because a lot of these big C deals, not only are they raising a lot of money, it's still a tech founder bet, which obviously is inherently early stage, but the resources... say the resources one, they just grow really quickly, but then two, the resources that they need day one are kind of growth scale. So the hybrid tag team that we have is quite effective, I think.
What is growth these days? You know, you don't wake up if it's less than a billion or like...
It's actually, 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 pre-monetization, but the dollars are large enough that you need to have a larger fund and the analysis, you know, because you've got lots of users because this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's us or other firms on these large model companies, are like this hybrid between venture and growth.
Yeah, totally. And I think you know stuff like BD, for example, you wouldn't usually need BD when you were seed stage trying to get biz dev, biz dev. Exactly. But like now...
I'm not familiar. What does biz dev mean for a venture fund? Because I know what biz dev means for a company.
Yeah, you know, so a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What sort of partner are you looking at? Is there a go-to-market arm to that? And these are just things on this scale, hundreds of millions, you know, maybe six months into the inception of a company, you just wouldn't have to negotiate these deals before.
Yeah, these large rounds are very complex now. Like in the past, if you did a Series A or a Series B, like whatever, you're writing a 20 to a 60 million dollar 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 times. Listen, I've been doing this for 10 years. I've never seen anything like this.
Yeah. Do you have worries about the circular funding from strategics?
No, listen. As long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.
Exactly. All right. This is like the whole pyramid scheme bubble thing where like as long as you mark 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 raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.
And even in the time of the internet, like the supply and bandwidth overhang, even as massive as it was 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 someone invests in a company that, you know, they'll actually use the GPUs, and on the other side of it is the actual customer. So I think it's a different time.
I think the other piece, maybe just to add on to this, and I'm going to quote Martin in front of him, but this is probably also a unique time in that for the first time you can actually trace dollars to outcomes, right? Provided that scaling laws are holding and capabilities are actually moving forward, because if you can translate dollars into capabilities, a capability improvement, there's demand there, to 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 and marketing, you're investing into R&D to get to the capability, you know, increase, and that's sort of been the demand driver because once there's an unlock there, people are willing to pay for it.
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 two?
There's so many lines that are being crossed right now or blurred, right? So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company? Like it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core R&D. It's a horizontal platform, but it's also an app because it touches the users directly. And then of course, you know, the growth of these is just so high. And so I actually think you're just starting to see a new financing strategy emerge. And you know, we've had to adapt as a result of that. And so there's been a lot of changes. You're right that these companies become platform companies very quickly. You've got ecosystem built out. 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 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. Yeah, I think it's interesting. I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, you raise money for compute, you pour that or you pour the money into compute, you get some sort of breakthrough, you funnel the breakthrough into your vertically integrated application. That could be ChatGPT, that could be Claude Code, you know, whatever it is. You massively gain share and get users. Maybe you're even subsidizing at that point. Depending on your strategy, you raise money at the peak momentum and then you repeat, rinse and repeat. And 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's these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there, but they're competing on the app layer.
Yeah, so this is a really, really important point. So I would say for sure venture and growth, that line is blurry. App and infrastructure, that line is blurry. But I don't think that changes our practice so much. But like where there are very open questions are like does this layer in the same way compute traditionally has? Like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer, but that might not be the case here. It may be the case that you actually can't verticalize on the token string, like you can't build an app, like it necessarily goes down just because there are no abstractions. So those are kind of the bigger existential questions we ask. Another thing that is very different this time than in the history of computer science is in the past if you raised money, then you basically had to wait for engineering to catch up, which famously doesn't scale, like the mythical man-month, it'd 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 year and it's better, right? And it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before. And I think everybody's trying to understand what the consequences are. So I think it's less about like big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.
Yeah. Like at 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 want to spend a million dollars of inference today and get a product out tomorrow.
Yeah. Like we should get to the point where like the friction of like token to product is so low that you can do this, and then you can change the early stage venture model to be much more iterative, and then every round is like either 100k of inference or like 100 million from a16z. There's no like million dollar seed round anymore.
But there's an industry structural question that we don't know the answer to, which involves the frontier models, which is let's take Anthropic. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share, and let's say that a company's building smaller models that, you know, use the bigger model in the background, OpenAI 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 situation where the SOTA models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don't think we've ever seen before, just because we were so bottlenecked on engineering, and it's a very open question.
Yeah. It's almost like bitter lesson applied to the startup industry.
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 sort of postmortem on what happened?
I mean, the actual postmortem is he wanted to go back to Google. 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 the Character thing raises actually a different issue, which actually the frontier labs will face as well. So we'll see how they handle it. But so we invested in Character in January 2023, which feels like eons ago. I mean, three years ago feels like lifetimes ago. And then they did the IP licensing deal with Google in August 2024. And so, you know, at the time, Noam, you know, he's talked publicly about this, right? He wanted to, Google wouldn't let him put out products in the world. That's obviously changed drastically, but he went to go do that. But he had a product attached. The goal was, I mean, it's Noam Shazeer, he wanted to get to AGI. That was always his personal goal. But you know, I think through collecting data, right, and this sort of very human use case that the Character product originally was and still is, was one of the vehicles to do that. The real reason that, you know, if you think about the stress that any company feels before you ultimately go one way or the other is sort of this AGI versus product. And I think a lot of the big, I think, you know, OpenAI is feeling that. Anthropic, if they haven't, you know, felt it, certainly given the success of their products, they may start to feel that soon. And they're real, I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new research, right? Is it or long-term research? Is it toward, you know, near to midterm research? And so in a case where you're resource constrained...
Of course, there's this fundraising game you can play, right? But the fund, the market was very different back in 2023 too. I think the best researchers in the world have this dilemma of, okay, I want to go all in on AGI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to AGI. And so it does make, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right? And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.
I would say that because we're keeping track of all of the things that are different, right? Like, you know, venture, growth, and app infra. And one of the ones is definitely the personalities of the founders. It's just very different this time. I mean, I've been doing this for a decade and I've been doing startups for 20 years. And so, I mean, a lot of people start this to do AGI, and we've never had like a unified north star that I recall in the same way. Like people built companies to start companies in the past, like that was what it was. Like I would 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 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.
You know, as a fraction of founders than we've ever seen. Maybe since like, I don't know, the time of like Shockley and the Traitorous Eight or something like that way back in the beginning of the industry. It's a very, very unusual time of personnel.
Totally, and I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? Very rarely can you see someone get poached for five billion dollars. That's hard to compete with. And then secondly, if you're a founder in AI, you could fart and it would be on the front page of, you know, The Information these days. And so there's sort of this fishbowl effect that I think adds to the deep anxiety that these AI founders are feeling.
Yes, I mean, just to briefly comment on the founder, the sort of talent wars thing. I feel like 2025 was just like a blip. Like I don't know if we'll see that again because Meta built the team. Like I don't know if, I think they're kind of done, and like who's going to pay more than Meta? I don't know.
I agree. So it feels, so it feels this way to me too. It's like basically Zuckerberg kind of came out swinging, and then now he's kind of back to building. Yeah.
Yeah. You know, you got to like pay up to like assemble team to rush the job, whatever. But then now you like, you made your choices, and now they got to ship, right? Like...
I mean, the other side of that is like, you know, like we're actually in the job hiring market. We've got 600 people here. I hire all the time. I've got three open reqs if anybody's interested that's listening to this.
Yeah. On the team, like on the investing side of the team, like, and a lot of the people we talk to have active, you know, active offers for ten 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...
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. 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 eight hundred k, a million at Google. But if I'm getting paid five, six million, that's different.
But on the other hand, there's more strategic money than we've ever seen historically, right? And so the economics, the calculus on the economics is very different in a number of ways. And it's caused a ton of change and confusion in the market. Some very positive, some negative. Like so for example, the other side of the co-founder 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 acqui-hires, right? That 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.
Yeah. 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. And there's maybe not as much risk appetite for things outside of that. I'm curious if you feel like that's true, and what are maybe some of the areas that you think are under-discussed?
I mean, I actually think that we've taken our eye off the ball in a lot of like just traditional, you know, software companies. So like, I mean, you know, I think right now there's almost a barbell, like you're like the hot thing on X or you're deep tech, right? But I, you know, I feel like there's just kind of a long, you know, list of like good, good companies that'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 hundred in a year, you're not interesting. Which is the silliest thing to say. I mean, think of yourself as like an individual person, like your personal money, right? So your personal money, will you put it in the stock market at seven percent, or you put it in this company growing five x in a very large market? Of course, you're going to put in the company five x, but it's just like that we say these stupid things like if you're not going from zero to a hundred, 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 three x net over, you know, the life cycle of a fund, right? So a company in a big market going 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 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, 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, which is maybe an area that we're not investing right now that I think is a question, and we're spending a lot of time in regardless of whether we pull the trigger or not. And it would probably be on the hardware side actually, right? In the robotics, right? Which is, I don't want to say that it's not getting funding because it's clearly, it's sort of non-consensus to almost not invest in robotics at this point, but we spent a lot of time in that space, and I think for us, we just haven't seen the ChatGPT moment happen on the hardware side. And the funding going into it feels like it's already...
Yeah. Yeah. But we also went through the drone, you know, there's a Zipline right out there. Was that the Zipline? The joint with the AV era. And like one of the takeaways is when it comes to hardware, most companies will end up verticalizing. Like if you're investing in a robot company for agriculture, you're investing in an ag company because that's the competition, and that's supply chain. And if you're doing it for mining, that's mining. And so the AD team does a lot of that type of stuff because they're actually set up to diligence that type of work. But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for purpose. And so we kind of like to look at software solutions or horizontal solutions like Applied Intuition, clearly from the AV wave, Deep Math, clearly from the AV wave. I would say Scale AI was actually a horizontal one for, 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, 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 hardware companies. You just, you know, and listen, I mean, it could work this time for sure, right? I mean, if Elon's doing it, he's like, just the fact that Elon's doing it means that there's going to be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just because you have this north star who's Elon with a humanoid, and that's going to like basically will into being an industry. But we've just historically found, like we're huge believers that this is going to happen. We just don't feel like we're in a good position to diligence these things because again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. 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.
Just to clarify, AD stands for...
All right. Yeah. I actually, I do have a related question. First of all, I want to acknowledge also just on the chip side. I recall a podcast where you were on, I think it was the A16z podcast about two or three years ago, where you suddenly said something which really stuck in my head about how at some point, at some point kind of scale, it makes sense to build a custom ASIC per run.
Yeah.
I think you estimated five hundred billion, something.
No, no. A billion dollar training run. A one 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. Because just rough math, if it's a billion dollar training run, then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save twenty percent, which you can save much more than that with an ASIC. Twenty percent, that's two hundred million dollars. You can tape out a chip for two hundred million dollars, 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's how much we leave on the table every single time we do like generic NVIDIA.
Exactly. Exactly. No, it's actually much more than that. You could probably get, you know, a factor of two, which would be five hundred million dollars.
Typical MFU would be like fifty, and that's good.
Exactly. Yeah. So, so yeah, I mean, and I just want to acknowledge like here we are in end 2025, and OpenAI is confirming like Broadcom and all the other like custom silicon deals, which is incredible. I think that, you know, speaking about AD, there's a really like interesting tie in that obviously you guys are head on, which is like the sort of like America First movement or like sort of re-industrialize here and move TSMC here if that's possible. How much overlap is there from AD...
Yeah. To I guess growth and investing in particularly like, you know, US AI companies that are strongly bounded by their compute.
Yeah. Yeah. So I mean, 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 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 going to be like kind of where the majority of dollars go. And listen, there's actually a lot of compounding effects for having a geographic bias, right? You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network. And I mean, I would say the Bay Area is very much back. You know, like I remember during pre-COVID, like it was like almost crypto had kind of pulled startups away from the Bay. 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 invest all over the world. And then I would say like if you take the ring out, you know, one more, it's going to be the 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.
No, no, 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 question is on supply chain and customer base. I would say our customers, our companies are fairly international from that perspective, like they're selling globally, right? They have global supply chains in some cases.
I would say also the stickiness is very different.
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 twenty years. 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 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 chief of staff of like half the AI companies in the Bay Area right now. She's like ops ninja, biz dev, biz ops.
Are you finding much AI automation in your work? Like what is your stack?
Oh, my, in my personal stack.
I mean because like, by the way, it's the reason for this is it's triggering. Yeah, we like, I'm hiring ops people. A lot of partners I know are also hiring ops people, and I'm just, you know, it's an opportunity since you're also like basically helping out with ops with a lot of companies. What are people doing these days? Because it's still very manual as far as I can tell.
Yeah. I think the things that we help with are pretty network-based in that it's sort of like, hey, how do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that. I will say as a growth investor, Claude Code work is pretty interesting. Like for the first time you can actually get one-shot data analysis, right? Which, you know, if you're going to do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight, and the three of us were...
Playing with Claude co-work, we gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like aha moment. That sounds so boring, but you know, that's the kind of thing that a growth investor is like, you know, slaving away on late at night. Um, done in a few seconds.
Yeah. You got to wonder what the whole like Anthropic Labs, which is like their new sort of products studio, what would that be worth as an independent startup, you know, like a lot.
