Everyone Is Still Undersizing the AI Market | Eric Vishria

I'm very dismissive of this idea that like people are gonna vibe code their own shit, like whatever. Like that isn't the issue. The issue with SaaS companies is every single day that you are hitting your plan, you are destroying equity value. Like, think about that. Our whole careers we learned you lay out a plan, you execute against it like relentlessly and violently, you hit your plan or you exceed your plan and you keep on building that and that's how you build equity value. And now you're getting in here and just like every day you hit that plan, You're fucking up. You're fucking up. You're destroying value.

I love asking you and all your partners this every time we hang out, which is, okay, you've got these singular investments. You don't do that many investments each per year. And then the ones that go on to work, like so far Sierra and Fireworks certainly are, You get to learn so much about the world through the lens of the company. So I'd actually love to do both of those, maybe starting with Fireworks. What do you know or what have you learned about the world and how it's reordering itself based on watching the world through the lens of Fireworks that would maybe be surprising or interesting?

These are 2 trillion, 3 trillion, 4 trillion parameter models. Well, it turns out running those models is damn hard.

Mm-hmm.

And running them efficiently is like super hard. And the way to see this, and everybody can see this, which is everybody from AWS to Azure to GCP to the NeoClouds to the fireworks-based 10-togethers of the world, like all of them, they all run these stock, open-source models that are available in their developer pools and everything else. And like, that's what it is. And the performance difference for a Fireworks versus like a cloud provider is like 5x. And that is just the speed performance. Then you add on top of that the throughput, which is not visible externally.

Yeah.

It's only visible if you look at— if you know the economics of these businesses. And you're like, wait a minute, this is the same open source model with the same NVIDIA hardware and there's a 5x performance difference and a multiple x throughput difference. And the way to just simply understand that is like these companies are paying the margins of the cloud providers and running on top and making money.

Yeah.

And so like, how can that be?

Yeah.

I think my big takeaway on it was like, wow, this stuff is actually really hard to run. Like, it's just really hard to run. And there's a lot of expertise involved in doing that. And it's kind of like this very specific expertise that exists. And it is the kind of thing that when you look at it as an investor from the outside, and we should talk about like early days of AWS, but like when you look at it from the outside, it's like, this is a commodity.

Mm-hmm.

This is just like pass-through resale.

Scale game.

Yeah, scale game, like whatever. Like this doesn't— and you're like, oh, wait a minute. No, it turns out it isn't. It isn't at all.

Do you think that's just a moment in time thing? And I'd love to just hear you. I'd love to really riff on like cloud. You watch cloud very carefully and closely. You know a lot about it and the adoption curve there versus how people use these things and the nature of those 2 businesses in comparison. It's tempting to say like there will be 1 or 2 scale winners like there typically have been in a commodity market.

Totally.

Where cost to serve is everything and scale drives cost to serve down. And like, that's the whole story.

I just, I think the AWS example's so good. Okay, so 2006, you have S3 and EC2 launch, right? Their compute platform and their storage platform. Those are the first 2 AWS offerings in 2006. And you kind of start talking about it in late 2006 or whatever. 2006, 2007, I think the 2007 annual letter, like Bezos talks a lot about, About AWS and why it's important, why it's interesting and everything else. And the investor reaction is just not good. And I think if you put 30 of the smartest investors at that time in a room and ask them like, what's the probability that this AWS business is a good business with durable long-term margins and like super interesting and everything, not commodity? I think you would've gone 0 for 30. with really smart people that you and I know.

Yep.

Who were around at that time in '07. OK, fast forward from '07 to 2014. I joined the venture business in 2014. And a really common narrative in 2014 was, oh my God, AWS is going to eat everything. There's no enterprise opportunity left. It's going to eat databases and infrastructure, but it's going to eat the apps too. And they're going to offer it the cheapest and best. And you got to remember, like, We all had these amazing SaaS and software businesses that we were involved with or investors. And part of the reason people loved them was they were annuities and they ran at like 85% gross margins and everything else. And so it was just like, oh my God, AWS, Amazon can offer things at like 8% gross margin and they're just gonna crush this whole thing. Your market's my opportunity. Yeah, your market's my opportunity. Like the whole thing, the whole narrative. And think about from like 2014 to now. in enterprise. Like, of course you have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out-Amazoning Amazon on Amazon. But it's not just Snowflake. You had Confluent and Elastic and Mongo, Databricks, all of these companies. Amazing. That's the infrastructure layer. Then you have the whole app layer. In the app layer, like think about offerings that they offered at the beginning. Datadog, $100 billion company today. Like they had a competitive offering, right? And they did it. And of course there was tons and tons of roadkill. There was tons of roadkill. They did run over a bunch of stuff. But even then in 2014, The thesis was that the view that AWS was gonna eat everything was massively wrong. Not because of all the examples I just mentioned. It was massively wrong because Azure and GCP were irrelevant then. And fast forward to 2026 and they're unbelievable businesses. So now is AWS the biggest? I think it's like a 40, 30, 20 split.

Yeah.

You ended up with an oligopoly of that. And even outside of those big 3, You have Cloudflare, which is a cloud provider in a different sort, which is another $100 billion company. So you have these smaller players that emerged as $100 billion companies outside of it. So what's the takeaway? The takeaway to me is like, oh, there's just a bunch of zero-sum thinking and not realizing how big—

What if it all works?

What if it all works? It all worked. Of course, getting the relative winner right matters, and there was roadkill, and there were companies. All of those things still matter. I'm not saying it's just spray and pray. I'm not saying that at all. But I'm just saying the market was so big that one vendor could not scale and consume it all. They just couldn't consume the whole industry. Now, again, it's different now and all these things. But if I just like, just that whole notion right now with what you and I are seeing in AI sure feels like it rhymes, right? It's like—

Anthropic's gonna do everything.

Right. It's like, it can't, like, really? Like, is that really right? To me, that view that it's just like, oh, this one company's gonna eat it all, doesn't hold. And I would tell you that scaling, while cloud scaled very quickly, cloud did not scale anywhere close to as quick as what's happening right now. And so just for that company to actually scale and deliver it, scaling in this case requires like a ton of infrastructure buildup, right? From like energy, power, shell, chips, memory, obviously the algorithms and everything else on top. So it just, it feels to me like we're gonna end up with an oligopoly of— of winners and there will be, I really believe they will be these like $100 billion crazy smaller winners. And so like that's, you know, that's like that, it just feels like that the same thing is kind of happening.

Can you talk about it also from the demand side and compare it to how you watched cloud get adopted in enterprise versus how you're seeing AI get adopted now?

So if you kind of go back to like 2010, 2011, like even, now you're like 3, 4 years into AWS being an offering. Enterprises were super skeptical, like super skeptical, like traditional enterprise, blue-chip enterprise. You had digital natives out here, you had new companies forming that were like using the cloud. I think kind of famously like Snapchat was built on GCP. And I think at one point in this era, Maybe 2012, 2013, 2014, like Snapchat was like 40% of GCP. Like it was just like those kinds of things were happening.

That's like Cursor being 30% of all these things.

100%. 100%. Same exact thing happened. Same exact thing happened. Enterprises were very skeptical, I think, of cloud until like they really, you know, by 2014, 2015, 2016, it was like, oh yeah, yeah. You know, then I think the big banks and financial services and insurance companies and more conservative blue chip enterprise were like, oh, wait a minute. this is actually different and we're going to have to pay attention. And it became an issue in recruiting for them because they couldn't get the best developers because the best developers wanted to work on the easiest platforms and all these kinds of things happened. And so the difference now feels really profound to me because while it is 100% the case that blue-chip enterprise AI is not well absorbed and well adopted and not not the same thing as going to Cursor and walking the halls of Cursor versus walking the halls of a big New York financial services firm in terms of their AI use. They want it. They want to figure it out. They're running experiments. They are spending against it. They're trying to figure it out. They're talking about it. They're not being dismissive about it. And I think they view it probably as more of an opportunity than they did the cloud in terms of the potential impact on their business. I think they probably view it more as a threat than they did the cloud. And maybe they just also learned lessons from the cloud in terms of like what's possible here. And so I feel that they will continue to try to figure it out and absorb it. Having said all that, I do think one of the more interesting things, if you go from Silicon Valley out into the world and talk to these companies, these enterprise companies, and you realize like what the pace of adoption is and what the barriers for adoption are and everything else is, I think that like there's a ton of opportunity to help the enterprises get there to be one of the things that I tell the companies I work on is like, hey, let's be their AI Sherpa. Let's be their AI Sherpa. If we're in that position, To be their AI Sherpa where we're crossing both worlds, that's very valuable. And I think that'll continue to work.

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What is Sierra teaching you about the adoption of this stuff? And it's kind of actually, it's really interesting contrast to Fireworks where Fireworks is this sort of infrastructure provider. Sierra is feeling its way through what are really cool things that we can do for end consumers, enable companies to do for end consumers, starting with customer service. But I know with Horizon now, you know, going beyond that. Again, same question as for Fireworks. Like, what do you know that the world doesn't fully appreciate because of how you've seen that business?

Well, I think this is a good— it's a great example. So first of all, like my partner Peter and Brett, I think this is their 3rd company working together. So it's like a 20-year relationship, which is just like an amazing and great place to start. And one of the things that I think makes Brett's so special is, and the team is, they're technologists, but they actually have like lived in enterprise world for a long time and like really understand it and everything else. And I think actually he's personified this whole idea of, hey, let's be their AI Sherpa. Like let's start with customer service. We're very automatable and like be their AI Sherpa. And now we have these long-running agents that can do with Horizon that can do more and more stuff. And one of the things that I think is most interesting about this to me is, Yes, it's an application company on the surface, but they're doing real AI work. They're experimenting with the models and they're building agents and they're very close to the metal of the models and the capability and the harnesses. And they're understanding the jagged edge of AI capabilities, which is very different than the human smooth arc that we understand intuitively. They understand that jagged edge of capability. and they build around it. You saw the evolution of Cursor from like IDE to like tab autocomplete to agentic work like over and over and over again. They were obsoleting their work from like 6 months ago. And it's what Brett Taylor calls like sandcastles, right? We used to be building castles. Now we're building sandcastles that are gonna get washed away. And that's if you don't think of software that way. You have to embrace it. Yeah, you have to embrace it because if you were an artisan and you were like, hey, I built these like perfect foundation of a castle and there's bricks and it was like perfect and I really care about this and it's going to be here for 100 years, you're just not going to make it. As you get emergent new properties in the models, which is— Every 4 weeks. Every 4 weeks, you get new capabilities. They understand that jagged edge. They understand that application of that jagged edge or that valley to their customer base. They're filling in those gaps and translating it. You can't just be superficially applying things. I think it's actually a complete inversion of how product development used to work to how product development happens now. Product development, it always used to be, you'd also say like, hey, product managers, you train, oh, the product manager should understand the technology but really shouldn't be thinking about implementation and shouldn't be specifying implementation and shouldn't be doing this and doing that. The product manager's job is to really understand the customer and translate that problem to the engineers so that the engineers build the solution. Like, that was traditional product management. Like, good luck doing that now. That's a horrible way to do it. You can't do it that way. You actually really need to understand the nuances of model capabilities, what they're great at and where they fail. And you need to understand the customer problem.

Mm-hmm.

And put those together and bridge those gaps to build valuable solutions. I think that's one of the things that Michael and team at Cursor did so well from the very beginning is they really understood the jagged capability and built a product that allowed that translation from developer to that jagged capability and kept on iterating on that as the edge changed.

It's kind of ironic that like all that sounds like the returns to being technical are going up even as models supposedly are taking away technical edge.

Well, I think there's two things. Yeah, 100%. And actually I think there's this weird thing where There was this old discussion about the traditional roles of product manager and designer and engineer and whatever it is. And I think what there really are, are there are people who understand customer problems, people who have taste, and people who understand the jagged edge of AI capabilities and are curious about it. And those are the 3 things.

If you've got those 3, you're going to do well.

If you have those 3 things, you're going to do And it doesn't really matter if you're an engineer or a product manager or a designer, but if you have taste and understanding of customer problems and understanding the jagged edge, you're going to do well.

When we first did this so many years ago now, which is crazy, it'd be fun to revisit some of the ideas that we talked about the first time with SaaS. But you used this term that I've used ever since, which you called it the competitive frontier, meaning like the things that will determine the winners and the losers. My partner Bree has this great idea that stuck in my head, which is that everything is a jump ball. Right now.

Yeah.

And I'm really curious, in addition to this idea of sandcastles versus real castles, what else you're seeing amongst the people that are becoming competitive winners? Is it different? Are the traits different for winners now personality-wise or business strategy-wise or business model-wise versus what you learned in the SaaS era?

I'm very dismissive of this idea that people are going to vibe code their own shit and whatever. That isn't the issue. That isn't the issue at all with SaaS companies. The issue with SaaS companies is the competitive frontier completely shifted, and everything they thought they were building against and that would make them win are not what's going to make them win. Let's take databases. Databases have been a phenomenal area for software for a long, long time. Great margins. Why? You had app developers that would build against these specific database interfaces that existed for that database. You would have more and more data over time. And so migrating an app from one database to another database was a giant project that was like very, very difficult to do. And so these were unbelievably sticky businesses that you could generate a ton of margin in, right? Of course, you got Oracle and SQL Server and like a whole slew of smaller players like that did really, really well in databases. Well, let's think about that in the context of AI. So like now you don't have a developer building against a database interface, you have Claude or Codex building against a database interface. One. Two, the beauty of database interfaces is they're very, very well specified. Well, it turns out AI is very good.

It's very good.

Very good at things that are very, very well specified. Three, agents don't get tired of monotonous work of translating one specification to another. So it turns out that now all of a sudden database migration, which used to be like the number one thing you would not do in software, is like kind of trivial.

Yeah.

Yeah. It's just like, yeah, put some money against it, move it. So what changed? Well, what changes is the criteria to be an amazing database company changed. It isn't that we don't need databases or that everyone's going to build their own database or whatever. Like, that isn't what's going to happen. What's going to happen is the criteria changed. So now you're going to have a ton more applications that start, obviously, as we're seeing everywhere. Like, people are going to experiment a lot more because it's much cheaper to experiment. It's much cheaper to start a new application. So now you need databases that scale from, like, basically zero usage to if it works all the way through. Like, that matters a lot more. Your cost matters a lot more. You want to be able to spin these things up, spin them down, tear them apart. Over and over again. So the iteration speed goes up in what you need on database. Ultimately, I think the cost becomes arbiter of this. So the cost and then this like kind of zero to infinity scaling and transportability and everything else around that become like the arbiters of who wins and who doesn't.

Mm-hmm.

That's really different than like, hey, I specced this database for our user thing and I procured a license and I ran it on this kind of hardware and like, everything else. And so there have been elements, of course, of these things over time, but I think that just criteria changed. And I think one of the big messages to these SaaS companies, you know, a few years ago was like, you have a choice. Get to AI or be worth 3 times revenue. And I think a lot of these public SaaS companies are trading at 6 times or whatever. But keep in mind, in '21, they were going for 30 times, right? Like everybody, right? And so you're like, 3 times, wow. I've grown 4x since then, right? This is the whole multiple compression is a bitch.

Yeah.

I've grown 4x. The multiple has gone down by a factor of 6. So I'm worth less even though I've grown 4x in a few years and gotten to break even and like all these things. So like that was the first message. But I think that even it became really visceral to me where Every single day that you are hitting your plan, you are destroying equity value.

Hmm.

Like, think about that. Our whole careers we learned, like, you lay out a plan, you execute against it, like, relentlessly and violently, you hit your plan or you exceed your plan, and you keep on building that, and that's how you build equity value.

Hmm.

That's what the whole management team's learned. That's what these CEOs learned pre-AI. This is like everything learned. And now you're going in here and just like every day you hit that plan. You're fucking up. You're fucking up. You're destroying value. You're destroying value. And the point of like, you know, saying that to them was to set them free. Another articulation of this from my friend Ann Lee Gates was just like the CEOs who were going through this transitory period and had a business that was at hundreds of millions and they thought they were already They were working on their business from 8:00 AM to 5:00 PM and then trying to do AI from 5:00 to 8:00 in the evenings. And what they needed to be doing— It's the inverse. Was the inverse. And it's so hard to do that because of all of the training and all of the muscle memory and all the inertia and everything that we learned about. We're all hill climbing in a way. And like, you know, the success model was like set the plan, execute the plan, build value, compound value. And it's like, oh no, no, no, stop that. You gotta completely invert. And I think this is a very, very long way to get back to your question of like what the profile or what the mentality of the kind of winners are right now. But if we look at Brendan from Mercor or Lynn from Fireworks or Max or Brett, any of these people. They're so nimble about what the eval is. Like, what are they optimizing against? They're so nimble on all of it. And if you look at every one of those companies, the evolution of the business, it's just the business is constantly evolving.

Mm-hmm.

And they've done such an excellent job at that. And I think that is very, very different than certainly the way I was taught.

What's your sense of the disorienting nature of model progress and how to— you said it earlier, every 4 weeks, I got a new jagged edge I got to figure out. You're one step removed from that as an investor versus as the technical founder with your hands on the metal. How are you behaving differently than you would have 3 years ago or something? Because of the pace.

Any time that I'm talking to a founder about, you know, a problem in their company or what they're doing or a move they're making or anything else, which is what I spend 80% of my day doing, I'm very— this is how we used to do it. This is what we would typically do. This would be the typical readout on this. Like, this would be the old-school readout of why this candidate's better than this candidate. Now, like, let's reevaluate that in the context of today. Let's reevaluate that in the context of an unstable technology substrate. Let's reevaluate that in the context of a business model that's growing this way versus that way. I've really started to question every assumption and every lesson that I learned before, which of it translates and which of it doesn't. That's a huge difference. I'll give you a really concrete example. which has been very disruptive inside of these scaling AI companies. So these scaling AI companies, we've had a lot of leaders come from the prior generation with great experience and everything else come to these scaling AI companies and completely flame out. And you see it across the industry. And so the question is like, why? And these are some of the best leaders, From 4 or 5 years ago. They had all the lessons. They learned it all. They're excellent. But somehow it's not translating. And there's just some impedance mismatch between the AI founders potentially, the needs of the business, and what these people are bringing. And I saw it really abruptly with a particular sales leader who we hired who's like, hey, we can't hit any of these things. Because the way that software sales has been taught forever is a quota capacity model. You have a quota capacity model, each rep does this. In the early days of a company, the quotas are, I don't know, $1.2, $1.5 million. Maybe the ISRs are at $750,000 or $850,000. Then over time, it scales up and enterprise gets to $2.5 million. That's the whole thing. That's how it works. That's how it works. That's how all these financial models are built. You start with the quota capacity model, you take a discount on attainment. This is what we Here's what we can do. Boom, boom, boom. Now, fundamentally, without realizing it, everybody was implementing something that was based on pushing demand, not pulling demand. And for so many of these companies, they're operating here, these customers, and there's a new AI-enabled product that comes along and it's just fucking magic. So these companies are selling magic. Like magic. Well, it turns out if you're selling magic and you're the first one there—

You sell a lot more than 2 million.

You're going to sell a lot more than 2 million. The whole notion of a quota capacity model and that being working, it's not that it doesn't matter. It matters kind of, but it's definitely not the first-order thing or constraint. You have these execs come over with this, like, here's our math and here's the territory assignment and here's what we would do. you do West Coast, and then you do East Coast, and then you do Central, all of these things. It's like, oh wait, no, no, it doesn't work like that at all. One of my big things that I started to realize is, as I'm interviewing these folks and talking to them, it's just like, hey, you need to check everything at the door. Just check it all, which is probably good practice anyway, but check all the baggage. Check everything that you learned and just learn this from first principles. How is it working? What are really the bottlenecks on delivery? What are the bottlenecks on demand? Because it turns out that in a lot of these companies, you have reps doing $10 or $20, $30 million.

I saw $50 million recently.

I mean, like, and so it's like, okay, it turns out that's different.

What is like the best salesperson that you've seen that's doing it in a de novo way doing?

Honestly, the best salesperson in any of these companies is the founder. And what they're doing is bridging the jagged edge to what the customer's capability is. and that's it. It sounds so simple, but it's not. But that's what they're doing. Then the market is just so big. Just like we were talking about with cloud, I think the biggest mistake everybody made was the market. They just undersized the market. It turns out the market's just really, really big, and this market is bigger.

It feels like right in this moment in time, actually just this morning, you and I are in this great group, chat together where the discussion is the demand kind of, demand for intelligence, like, I don't know, seems kind of unlimited. And it seems like the smarter the thing gets, the more demand there is. And that maybe the bottleneck is just capital. Like the world just feels like it needs to take a breath.

Yeah.

Like the RSI kind of concept, if you apply it across technology, doesn't need to breathe. The agents don't get tired.

Right.

But the world feels kind of like, oh man, it sure would be nice to have 3 months just to like digest this a little bit. And for capital to, form and evaluate its prospects and the scale is getting so big. Like, does it feel to you like this can just keep going or are we just going to get tapped out of money that can be invested in these things when it seems like we could consume like any amount of money to build any amount of stuff and serve any amount of inference? Like, how do you feel about the market?

I would say this. I am worried about energy. If you think about the models as translating compute into intelligence. Quite simply, what do models do? They very effectively translate compute into intelligence. What's the demand for intelligence? Well, it seems like a—

A lot.

A lot, right? So then it follows that we will kind of continue to have more and more demand on compute. I'm saying compute broadly, not chips, not storage, not whatever. But then what do we need for compute? We need energy. A lot of energy. There's all this topic of distillation and Chinese open source and all these things. But the bigger thing to me is that I think China's bringing on 10 times as much energy next year as we are in the US. If energy is what you need for compute and is the bottleneck, and there's unlimited demand for intelligence, then it stands to reason that if we have a lot less energy, then we will have a lot less intelligence or a lot less tokens or a lot more expensive tokens. And if we have a lot more expensive tokens, then supply-demand, you're going to end up with less. And that seems very bad. And so to me, I think the energy bottleneck, however that is, and it'll manifest in 20 different ways. gas turbines go up and down, natural gas goes up and down, and solar, whatever, like all these different things, rare earths. That to me is probably more concerning. If I'm thinking about it from a regulatory perspective or government perspective, and I think the administration is doing some things around this, like fostering really investment and development of all energy. And I mean all, like solar, nuclear, gas, like whatever, do it all. Like we should do it all. And it'll work itself out. But again, this is one of these things where yes, one will be relatively better than the other. And I don't know, and I'm not smart enough to predict which one's which, but like it'll all work.

Speaking of compute, I would love to hear the Cerebras story. I haven't heard you tell the full version of this. I think it's— the reason I'm asking about it is I'm deeply interested in compute. I have big investments in compute. And I'm fascinated by it. It's just like the most magical thing to watch happen. It's mind-boggling when you get close to one of these things, what humans have been able to do on these chips and in these systems. I think you invested in 2016 or thereabouts. So I think it was your first foray into like extremely difficult hardware-type investment. Shit's so hard. And I'd love it— now the world is full of opportunities like this, whereas back then it was like a, you know, Really a one-off. Teach me everything you've learned about hardware investing through Cerebras.

Mostly, mostly it's really hard. It's an amazing example to me about like the naivete required. So, you know, the company came in in 2016. It was 5 founders and a deck. I really did not want to go to the pitch, but it's my job because I'm like, why are we going to go to our hardware investment. Like, it's crazy. It'd been 10 years since we'd made a semi-investment. I think basically the team was excellent. And then the kind of first slide was just like, GPUs actually suck for deep learning. They just happen to be 100 times better than CPUs. And as soon as you said it, yeah, you have to remember this is pre-transformer. Like OpenAI is this like weird research lab at this time. Like NVIDIA was worth like $40 billion, not $4 trillion. The TPU hadn't been announced, like none of that. So this is like early, early. But the whole idea, just like as soon as he said it, I was like, oh shit, of course, of course, like why? And I had spent the last 18 months trying to figure out like applications of deep learning and looking at the security thing and looking at this medical imaging thing and like all this other stuff, thinking like, hey, there must be something here that's gonna be really transformed by this stuff. And so anyways, we go through this like whole journey, we end up investing, which was amazing. We just like, we first met on Wednesday, partner meeting on Monday, and had a bunch of meetings in between. And it was like, God, just built a lot of conviction that this was a great swing. And I'll tell you what I understood, and I really just didn't understand so little, but basically there are 3 things that we know how to do to speed up deep learning in hardware still till this day. Increase the number of cores, increase the communication between cores, bring the memory closer to the compute. Those are the 3 things. That's it. Those are the only 3 dimensions that we know in hardware. So my articulation of what they said to me, and honestly all I understood was, let's just take all 3 of those things to their logical maximum. You have a wafer-scale chip. At that time you would have 450,000 cores on it. You'd have something like 20 gig of SRAM on the chip, so you never have to go off-chip to get to memory. Because they were all on the same wafer, the communication between cores is maximized. This is the best you could do on that process. I think the first chip was 7 nanometers or something. You're like, okay, that's it. That's what we do. It turns out that in software, if you have that logical block diagram of why it works and everything else, you're 80% of the way there. It's a matter of go-to-market execution. In hardware, you're like 2% of the way there. There's things like physics and this entire supply chain of vendors. There's of course TSMC, which everyone knows, but it's not just TSMC. There's 30 other vendors that matter in putting all this stuff together and everything else. And so, like, I didn't know any of that and, like, really didn't know. So, you know, fast forward from 2016 to, like, 2019, I think they, they got their first parts back. And, and it's like you go through this, like, bring up, and then it's like, bring up. Oh yes, we got a part back. And then it's like, bring up. Oh shit, another part. Yeah, and then you gotta go through, like, we bring up, and there's like 14 steps of bring up and everything else. And then by, like, 2020, we had our, like, first thing that works. And then you're like, then it's just this march of actually getting it to work. And one of the lessons that I've learned on this stuff is basically you go through all these sims and everything else in hardware and semis in particular, and that basically is your roofline. That's the maximum performance. Best it's ever gonna be. Best it's ever gonna be is what that is. And then every bit of like software and reality and compilers and kernels takes away from that roofline. You might start at 10% of the roofline. Once you bring it up, and then you're like, you know, then these guys are grinding for months and years to like get closer and closer and closer to the roofline. It's really different. It's really hard. I'm actually like astonishingly bullish if I kind of rewind. Part of the reason we made the investment was if you looked at the 4 prior generations of compute that, like in my lifetime, so you had CPUs, you had graphics, networking, mobile, there was a new workload each time. So you had multipurpose compute and it led to the CPU. You had massive parallelism, which led to the graphics processor. Graphics processor offered massive parallelism, which led to graphics. Then with networking, you needed really low latency chips. And so they had low latency chips. And then with mobile, you needed really power efficient chips. And so each case, we ended up with a new $100 billion company. And the question, the first question, going back to 2016 was like, is AI that big a new workload? Because there'd been many, many other attempts for specialized chips for other things that really just didn't end up mattering. Like there's some fine outcomes, but like just didn't really end up mattering.

Big brain, et cetera.

Right, exactly. And so it's just like, well, okay, well you need something that's like a really, really big workload. Okay, so that's one. So we had a lot of conviction on that. And then 2 was the nature of the workload. Did it introduce a new constraint or problem in it? What I learned was like basically the AI workload benefited from the parallelism of GPUs massively. But GPUs didn't solve the core-to-core communication, basically the layers of the network problem. And so you're like, okay, wait a minute. There is a new constraint, which is communications, communication-bound problem. And so then you're like, okay, is AI a new giant workload that is going to have specialized chips? Basically everything that I just said was the entire everything I knew at that time. And I do actually think so that's obviously played out. And like in each prior generation, we obviously, we got Intel, we got Nvidia, we got Broadcom Avago, we got Qualcomm and Arm, like in each of these generations. And like there will be like these giant winners, standalone winners. Obviously the TPU itself is a winner. Trainium is a winner. You've had Groq and Cerebras, Etch. Like it'll keep getting fought out.

Yeah.

And, but I think that will end up being big. And actually, I think there's a new 6th one that's coming, generation, which is— and I'm really excited. We've made an investment that's unannounced in this, but I think that for the first time in a long time, there's actually room for a new CPU approach. The thing that's happening right now, and you see this reflected in all the semi stocks and everything else, is the LLMs, which are running on accelerators and GPUs generate code, the code runs on CPUs. Right now it's running on classic CPUs we've had around forever, but there's a whole bunch of constraints on CPUs that have existed and CPUs have dragged all this baggage forward that you might not need to anymore. I'm actually really excited about About that possible way.

The next category.

The next category.

Does the experience with Cerebras make you want to do a lot more investing in companies?

Fuck no.

But why not?

Like, like the defining question.

In 2019, we're sitting in a board meeting and this thing is melting. It's like fucking melting. OK, we've raised $500 million or something. And it's like, wait, what? Like what? It's melting or it's burning or something. And like, we're looking at it and I'm like, holy shit. I realized like $500 million isn't that much in today's era, but I was just like, we're going to lose all this money. This is not going to work. I talked to Andrew about this. Obviously, just what that team did, insane. Insane in terms of the technical capabilities. Hardware teams are built differently. They're built differently. They're built so differently and I have so much respect and thanks to them for what they've done. Look, joking aside, I think that there are efforts that make you really proud to be a venture capitalist, and because you're funding something that makes a difference and matters. And for me, I'm an investor because that's a means to work with companies, not because I've fundamentally like love investing or something like that. I like working with the companies. That's my favorite part of it. And working with teams like that and companies like that is so special on these like giant ambitious efforts.

Mm-hmm.

And, you know, and I said this before, like well before 2018 or whatever, it was like whether Srebrus worked or didn't, I think it was an effort that was worth venture capital. Like, those are the kind of things you should do. You should try to build something that people have tried for 50 years and have been unable to, but now we think we could do, and there's a reason and application for it and everything else. I love that.

Yeah.

And so I do like those kinds of things, joking aside. And, you know, we do have a robotics company. We actually have a defense company that I'm also really excited about. And then this, like, you know, CPU project that we were talking about. And so like, I think these kinds of things are actually really fun and interesting, good use of venture capital, but they definitely aren't easy.

I love the sort of productive naivete that you described where it's probably a virtue that you didn't know more than you knew, otherwise you wouldn't have done it. Yeah, this is certainly my experience with Etch that like when we first called around asking, like if you asked experts, everyone says, like in any of these fields, you ask experts, they're gonna tell you don't do it. Yeah, like it sucks, it's too hard, base rate's too low. Young people can't do, oh, the 47 reasons. Yeah, all those things, yeah. Is there anywhere where that is, it's just a bridge too far? That could be like, I'm thinking like bio or something like this where you just are unwilling to invest if you're naive.

Actually, like most of the time, all of these like stereotypical statements are correct. Like they're not like correct like 3 times out of 4, they're correct like 19 times out of 20. Maybe 99 times out of 100. The thing that Bruce, one of our founders, always says is just like, what could go right? That we have to ask ourselves is like, what could go right? And do we see that path? And it's like, yeah, young people can't build chips, or you shouldn't do another semi company, or you shouldn't do this, or whatever. All of that stuff is actually totally right, except when it isn't. And so apparently there's a saying that someone said to one of my partners, which was— If it doesn't work, If it doesn't work, it'll be for all of the reasons that your partner said. If it does work, it will be because those reasons didn't matter.

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You and I, I think, are both interested in robotics.

Yes.

It's not controversial that if robotics works, it's going to— it might dwarf what we're currently living through. What do you think has to be true for it to work? Like, obviously it's exciting. I want a robot in my house folding my laundry. It sounds great. But it's kind of one of these classics. It's always 10 years away and it's been that way for a long time. What do you see happening? What has, what has to happen for this to actually be a thing in the near to medium term?

We've had classic robotics forever and they're all over the place and they're on assembly lines and manufacturing lines and all these things where you're doing repetitive tasks in controlled environments. Repetitive tasks in controlled environments. is more or less solved and that'll continue to happen. But having tweaked tasks in real-world environments, that's where you need the AI. That's where you need the AI plus the robots. The trick with it all is you need a model that can do that. Of course, the first problem is there is no internet Scale data to bootstrap the whole thing, right? LLMs are all bootstrapped on the internet, which is a ton of human knowledge. And the equivalent for that for robots doesn't exist. And people are trying different things with videos and simulations and teleoperation. So like there's a lot of different ways. But if you kind of think about, take teleop as an example, Like how much you need to teleop robots to get to internet-scale data. This first step is like getting a good set of data to bootstrap the model. And one of the, I think, insights that you can have is with the internet, there's like a lot of slop data. Like even before AI generated all this stuff, there was also a bunch of junk data. And some data was more valuable than others. Like you may value, okay, certain things on Reddit more valuable than other things. You might value Wikipedia more than other forums. You might value GitHub more than other things. All of the model companies did that. They prioritized data that was more valuable and less valuable and ran that through the model. I think one of the most interesting things that these AI robotics companies are doing are saying, okay, well, let's just go after the high-value data to start. If we go after the high-value data, then we can bootstrap this model. Now, once you do that, then can you, through that pre-training process, get to a place where you can have very small auxiliary examples of data that you add in post-training and all of a sudden it works for that? That's the magic we have with LLMs is you have this giant pre-trained base and then you add a little magic on it. In RL and post-training. You teach it. And you teach it. You teach it a new thing that it wasn't in the pre-training set, and you kind of go from there. And I think the exact same thing is happening in robotics. We're investors in Sunday Robotics, which is going after this exact pipeline.

Such a cool company.

It's a cool company. And, you know, they're doing household robots. But the key part before you get to household and all of that stuff matters much less actually than Like, can you get this training pipeline to work and how do you do that? One of the lessons I learned and I look back, I try to learn from history because it doesn't repeat but it rhymes. If I look at autonomous vehicles as an example, which is they're robots basically, they're AI plus robots. You look at Waymo and you look at Tesla and they were both designed vertically integrated in their own way. You have a Tesla, you had a fleet of Teslas that everybody owned. It was collecting data on the Teslas and it was used to train the models to drive the Teslas. And same thing with Waymo. And I think part of the lessons is they gathered very, very high-quality data. They did their pre-training and then they worked off of that. And that was kind of, that was a simplification to allow you to get to—

Scale.

a complete product or complete solution, which of course is going to continue to improve and ultimately will generalize. I'm sure it'll generalize in some way so you'll be able to strap it onto any car and everything else. So I think the same thing, exact thing is happening in robotics where you have companies like Hyundai and others who are using techniques where they're vertically integrating the robot and the model, the data collection around the robot, Sunday uses gloves that are designed just with the robot hands. So they're perfect. So you get very good data transferability from one to another. You do this pre-training and you have a great pre-training dataset. You have a good model, and then you start adding these examples and you RL and post-train on top and you get cool emergent behavior.

Do you have a most visceral moment?

When we invested in Sunday the first time, We saw them. My partner Peter arranged a demo. We went down into the basement at the Stanford lab, and they had this totally janky cardboard glove thing. You see a few evolutions of it. The last time we saw a demo, we went down in the basement of their now office building, and there was like a dozen robots. Just folding arbitrary laundry. Doing it for free. It wasn't a demo. It was just trial and error. And then they have people, like, taking the clothes and then measuring them to make sure that they were folded properly. And, like, they were, like, creating a rigorous baseline and evaluation criteria. And I was like, oh my God, this is, like, this is happening.

Do you ever worry about how to pick the right customer for these companies? Like, every one of these things folds laundry. Which, like, I don't think anyone likes folding laundry. So it, like, seems like a good use case, but it feels like we don't actually understand the demand for what these things will be used to do. Do you ever worry about that, that we're sort of building solutions that will then be in search of problems?

I don't, and I'll explain why. And it's, like, certainly informed by watching the LLM evolution, which is, like, you know, I think some of the people who were involved in the early LLM at OpenAI really understood that code was going to be important. And obviously the Anthropic team had this perspective that you could get to RSI if you've got code generation going and automating AI research and whatnot. But if you think about it, the first use cases were very much language-oriented. They were very much like essay writing and editing and marketing. I think the first application that really took off was Jasper, which was just like writing marketing copy. And, you know, I think it's gonna evolve a lot, basically. I think like laundry is kind of a good task because it's arbitrary, it's complex, it requires dexterous manipulation, it's done on tech, it isn't time sensitive. So you can like, if it takes 3 times longer, so be it, who cares? It doesn't matter, just let it run all day. So I think it has like some of those properties, but I don't think the task is actually that important. What I think is much more important is, are you pre-training an amazing model and then being able to post-train on top of it and get that flywheel going? And if you get that flywheel going, then, you know, the task capability will just keep— it'll keep multiplying.

Maybe this question will be annoying or slightly uncomfortable for you, but if you ask basically every founder and critically other investors of your type, Almost everyone, if I ask like, who's the best board partner, will say you. Like, you come up way more often than anyone else that I've come across. And I'm curious why you think that is. Like, what it is that you're doing that other people— the incentives are there, like, to do a great job as a board partner. What do you think you're doing on the boards of these companies, partnered with the founders, that's actually different from— other really talented investors who are also nominally doing the same job but don't come up nearly as often when asked that question.

One of the things that I've realized is we each are attracted to different types of entrepreneurs and where we have chemistry. And I think one of the differences when I say I'm really like, I'm an investor second and I try to be a partner first. And here's what I mean. We'll see companies come in— we had one come in yesterday, and it's what I would call an investment-grade opportunity. It's kind of like you can invest, it probably works, you make money, like it's good. And an investor would do that, and a partner wouldn't, because that's not sufficient for a partner. Because unless you have real chemistry with that person where you feel like you're going to be able to work together really effectively, and I'm gonna learn a ton from them, and they're gonna learn something from me, and together we're gonna just feed each other's loops, right?

Mm-hmm.

Unless you feel that way, then you can't be a partner, and so you pass on that, but that's a really important fit element to me, and so it kind of starts with this mutual selection, actually, weirdly, of like, They want to partner with us, and and and I want to partner with them. And like I'm really like looking forward to working with them together. And and I'll give you a really good example of where this like kind of comes into play for me. If I take like Sagi and Benchling, Benchling is you know life sciences SaaS companies absolutely crushed, done really really well. And then of course, you know you have this like biotech crash, and the company. it became hard, like it became grindy. And I joke, the company had never had any churn for the longest time, such that even on their like reports, you know, for every SaaS company you have this like, okay, gross ARR added, churn line, net ARR added, like everybody does the same thing. They never had a churn line, never reported it for the first of like 6 years that I worked with the company. So then they got 7 years of churn in like 12 months. Turns out, like, life sucks when you get 7 years of churn in 12 months. And like through that grind and through it all, and related to the whole like, wait a minute, the goalposts moved, we have to do something different, everything else, they kept like thinking about how to apply AI for these biotech and pharma customers, which they're very close to. How can we make it better for them? How can we apply these models in their world in a way that they're excited about? And and continued to iterate, and like and it was grindy, and I was there for it. And I I just like and that's one of these cases where it's like you you you don't you're excited to work with that person because like one of course you think it's a really special opportunity and there's a way out there's a path and we can find it. But two because of the joy of the game and like the relationship and like everything else like that's part. of it. And I don't know what it is more than that. It's very different. I've seen different models and lots of different models of venture capital work, right? Like Moritz was a writer. Doerr was a sales guy. Gurley was an engineer. Peter's a career venture capitalist. They're all different, right? But for me, I think that working with them, it's like when I call these people, I learn something and they push back on me. And then I ask them questions. And what I've realized is like so much of my job is they know the answer. They know what they want to do. They know the answer. And it's maybe asking questions of them to maybe help solidify their conviction or solidify their articulation of what they want to do and why. And through that process, hopefully we get like 1% better a few times a year. We make a 1% better decision, 2% better decision a few times a year. And if you do that over a decade, that compounds to real results. One of the questions that I ask myself before making an investment is, there are all these people that I care about through my life, like you care about yours. Could I talk one of them into going to this company and honestly, intellectually honestly to myself, explain to them why this could be their life's work. And if I can't do that, I should not invest because it's just not like, it just means that the project is not, for me, it just doesn't line up in that way. And so as long as we have one of those things, it doesn't matter that much what it is. That's what it is to me. It's important. It could make a big dent. If it can make a big dent, and it's a special person, I'd love to work on it.

Are there any other questions like that that you ask yourself before investing? That's a particularly good one.

Yes. The other question, if this person calls me at 9 PM on a Saturday night, will I pick up the phone?

I call that the green button test.

Yeah, right? It's one of these things which is like, if you don't, then—

You just know if it's yes or no.

You just know. For whatever reason, it's a chemistry thing. They have to feel the same way, obviously. And then one of the other ones is like, if it's right, does it matter? Which is different than this first one, but it's like, there's so many things that we could be right on as a business. I looked at one last week and I told the entrepreneur, I was like, I really think that you can build an amazing company here and you just shouldn't raise venture capital. But there's so many things that you can be right on. But they ultimately just don't matter. Like, nobody cares. That's a better way to say it. If we're right, will anybody care? And it's like, if they won't care, then you're just not gonna build enough equity value. And I think that's another useful one.

One of the coolest things that's happening right now is all of that that you just described has higher stakes and more leverage attached to it, which is manifested most simply in more dollars and higher prices. You and I have talked about this notion of what high multiple, meaning like high multiple on invested capital investing is like and what it has been like and what it's moving into.

Yes.

You did something recently, which was you raised a growth fund for the first time in a long time. And I think that is related to this concept of like, these companies need more capital. The prices are higher, the outcomes are bigger. Maybe we can earn the same multiple on a billion-dollar entry price that we could on a $50 million entry price, you know, 10 years ago or whatever. Can you talk through that evolution, talk through the partnership's way of thinking about it and talking about it, like what you believe to be true that results in this decision to do this?

Just go back to like, why did LPs, starting with Swensen and everyone else, like why did they start investing in venture capital? And fundamentally, it wasn't because they thought they could beat the NASDAQ or the index by like 3 percentage points a year, 5 or whatever stupidity. It's because there were situations where venture capital could drive these insane multiples on invested capital. Like, that's from a financial perspective, that's what they were seeking. And for the longest time, for most of the industry's history, 2 things were synonymous: early-stage investing and high cash-on-cash multiples. Like, the way to get high cash-on-cash multiples was to do early stage. And that's it. Those 2 circles in the Venn diagram almost perfectly overlap. And I think the thing that's changed is recently, relatively recently, in the last few years, because outcomes have gotten so much bigger and these markets are bigger and everything else, the circle of high cash-on-cash multiple opportunities is bigger than just early stage. And it's not so, so big that there's a gazillion new companies in there that you can generate 100x's on. That's not true. But there are certainly many outside of early stage where you can generate really high returns. And so that's it. That's what we want to go after. You could argue we're a few years late. I think I'd take that criticism. I think that, but I think that opportunity exists on a go-forward basis. And so, like, we should go do it. And everything else that we represent, which is the high conviction, high commitment, partnership, everything else like that, that has to still be there.

As part of that discussion, what were like the other sides of the debate? Such as like maybe we would've said the same thing in '99 and 2020. As markets get exciting, it seemed like the possibilities, we all do this extrapolation error. What were the counterarguments to like, let's, despite all that, still not do it?

Yeah, I mean, there's all the counterarguments that you would expect. I think the biggest counterargument that really made this time right versus 2 years ago or whatever was you need the team that can do it. It's just a different mentality. There are differences in how you evaluate and think about things. All the other stuff, or there's why not change and stay within your circle of competency, and all those things are true. But to me, that was the biggest one. We had several examples over the last couple of years where we had, I think, the right intuition on a company or an opportunity, And we didn't do it.

Because it was outside the box.

Because it was outside the box. And that's obviously dumb. And I think it is quite different than a lot of, you know, the industry does. We are really chasing these, like, very rare special companies that have, like, very high cash-on-cash opportunities where we think they just can be runaway successes and we can invest Is there any lesson to be pulled from the many, let's call it 20 to 100x's that you personally have observed?

I mean, it's like such a crazy amount of return that like obviously it doesn't pencil in the beginning. Like you can't make something pencil if it was that clear. Like the price would be different. You can't. Yeah. What have the 20 to 100x's taught you in aggregate, if anything?

Work with really special people. You know, it's kind of like, You want to work really hard. You want to work smart and get lucky. Like, it really all has to come together. And there's a lot of things that are like timing dependent, you know, that you have no control over as a company. Like, you know, take this reverse example as a good one, which is like we took it public this time in May, but like we tried to take it public in 2024.

Mm-hmm.

And it would've been, taken public at a much, much lower valuation and it would've been rough and it didn't work out because of CFIUS and all this stuff. So timing matters. And just the advancement of that 18 months made all the difference in the world for a bunch of things that were honestly outside of our control. There were some things that were in our control, like getting inference running and everything else, but there was a lot of stuff that was outside of our control. These are all the classic things, which is like, you gotta focus on what you can control. That's one of the things that's really different than software companies. With software companies, aside from building on AWS or whatever, you pretty much own your whole stack. And so you're really fully in control of your destiny in that way. With hardware companies, you don't. There's an entire supply chain and all this other stuff. HBM is a thing and DRAM is a thing and TSMC is a thing. And a lot of those cross geopolitical borders. And so geopolitics gets involved. And so then that makes that complicated. And so that's a really big difference. You gotta get lucky on the timing and macro and other stuff. But I think it really starts with like working with these like crazy people with unbounded opportunities. And if you work with these crazy special people on unbounded opportunities, you know, then you get lucky from time to time. You're bound to. When I was 20 years old, I was working at an investment bank. Ben Horowitz, Markman had started LoudCloud. It was still in stealth, and you know Ben gave me an offer to be his assistant. And I was talking to this associate who like seemed like this like elder at the time. He's probably 25, but like you know I was talking to this associate at the time, and you know he said this thing to me which I which which really stuck with me. He was like, "Do you golf?" It's like classic banking question. Do you golf? No, I don't fucking golf. But he's like he's like. With golf, you keep on practicing and you keep getting the ball on a 3-par close to the pin, close to the pin, close to the pin, close to the pin. He's like, you keep getting the ball close to the pin and you keep practicing. He's like, that's hard work. That's working smart. That's what you want to keep doing. He's like, getting the hole-in-one, that's luck. I love that framing, and I use it with my kids actually because What it says is like, yeah, there's luck involved and there really is luck involved, but there is actually a way to increase your luck. And the way to increase your luck is get a lot of balls close to the pin and then like, yeah, eventually one will drop. And so I like that mental model. And so I kind of go back to this for companies, which is, and I think in, you know, each of us invests in 1 to 2 companies a year. I think in my 12 years I've invested 18 companies total.

Crazy.

Which is a relatively small number. So it's very high conviction and very high commitment. I have a lot of skin in the game. I believe in these entrepreneurs. I believe in these companies. But if you keep on working with these, like, very special people and these opportunities, magic can happen.

What have you learned about the best reasons and conditions for going public?

Benchmark does these Monday night dinners. You've been once. And so we had a CEO last night of a multi-hundred billion dollar private company, and we had this whole conversation. So it's kind of, it's a little like fresh. I think that ultimately when you go public, you have a range of new opportunities in what you can do. Public trust, actually, weirdly, because there's some transparency that comes with being public, being a public company. You obviously have a currency that you can do things with that ends up being there. You have an unbelievable ability to raise capital, which I think is why the labs will ultimately go. Although I think the trust thing is actually a really important element of why they should go, and it's beneficial to the world and to America if they do go public. It's like, it's like, okay.

See what's going on.

Yeah, see what's going on. Everyone can see it. I think that's like a a really beneficial setup. There's another element of it, which is what does a collegiate athlete want to do? Go pro. They want to play at a higher level. And is it harder? Yeah, it's harder. Is the competition tougher? Yeah, the competition's tougher. They move faster, they're tougher, they're bigger, they're stronger. The stakes are bigger, the stage is bigger, the scrutiny is bigger. All of that's true. It's kind of the same thing with companies. There are a handful, and it really is a handful, 3, 4, whatever, that can get to this tremendous scale without going public because things have gone through their execution and excellence.

Lots of free cash flow.

They have lots of free cash flow, and they've done really well over a really long time. I think that's fantastic and good for them. But in general, for everyone else, get out there. The other thing that I would tell you is there are windows for a particular type of company. So, yes, the SaaS companies that went public in 2021, a whole boat of them have struggled, and it's been tough in the public markets because their stocks ripped to this multiple compression issue. They were trading at 30 times. They've 4x'd in size, but now they're trading at 6 times. It turns out you're under still, right?

Yeah.

That's a tough place to be. Yet I will also tell you that there's 500-something probably SaaS companies that are between $100 million and $500 million that are private. What happens to them? Those employees never got a chance to sell. Those employees don't have annual tenders. Those employees don't have an opportunity to exit like those investors don't have an opportunity to exit. They're stuck. And I don't think they're all going away. And as I said, I don't think they're all getting vibe coded and everything else. But ultimately, the AI natives with their growth rates sucked all the oxygen out of the room and all the interest and the window was missed. And that's tough.

What are the biggest debates right now inside of the partnership? I always love coming here and talking to you guys when there's something interesting going on because you debate, it's healthy, it can be really fun to watch and I learn a lot from it. What are those debates today?

There's a ton of debate around this AI infra apps, well, foundational models, infra apps like ecosystem. Where does value accrue and how does it accrue and why, where are the moats and how do we think about that? But also like the business model innovation. I think one of the things that people don't understand about why SaaS did so well versus traditional software was it wasn't just that it was a better delivery model and everything else. There was actual business model innovation on it. You really did have this subscription element that ended up being fantastic for both the company and the customers. It was a win-win situation. And so I think there's that same thing actually exists in AI and And selling by outcome and that piece of it. But then wrapped up into that debate and discussion is like how much value just accrues to the labs. How much of the value just accrues to the semis? I mean, that's a real discussion. What do you think? I am of the view that it all works, as I said. And I really like, it's a very weird thing. It's like, will the CSPs do well?

Yes.

Yes. Will some of these neo clouds do well? Yes. Will the Fireworks of the world do well? Yes. Will Nvidia do well? Yes. Will these like chip startups do well or, you know, some set of 'em? Yes. Like, are we gonna have edge inference on our phones? Yes. Are we gonna have like near edge inference on PoPs? Yes. Are we gonna have big models in data centers? Yes. There's so much zero-sum thinking, which is just like, okay, how do we cut up this pie? And they're gonna eat this much. And like, oh no, no, no, no, no, Anthropic or whomever is gonna eat 98% of the value and they're gonna do all the drug discovery. And I was like, come on. Like, no, that's not what's gonna happen. Like, and we have patterns like in the not that distant past where it's like this exactly manifested, right? When I say like, I think everything's gonna work, work, and I listed off all these everythings, it's really important to understand that that doesn't mean that every company that's doing every one of those things is going to work. It actually means quite the opposite of that. Most companies in each of those areas are not going to work. It's actually more important than ever to have real differentiation and to really take each of these thoughts to their logical extreme and understand, wait a minute, you've got to go all the way on these things.

Is there anything else that you have your eye on, whether it's in the funding market, in the technology world, like anything at all that you really are watching carefully?

It's actually funny to me that some of the people are so, so smart, and yet they're in this like tech world where they just, they're like kind of reaching these like deterministic almost conclusions, which they of like mass unemployment and like all of these different things. And I'm gonna give you a really concrete example, which I think is just so good. Take Geoff Hinton in radiology. So I think it was 2016 where he was like, we should stop training radiologists. Like AI's gonna do it all better. And like, look, Geoff Hinton's like 3 orders of magnitude smarter than I am, but like, could not have been more wrong. But the actual thing that led him to make that statement or that conclusion was 100% correct, which is if you look at these radiology images, like, we should be able to train AI to do a better job of, like, reading these things than humans. And that's probably true. And actually, I think the studies in areas have shown that to be true. We have an investment in a company called New Lantern, which is approaching this, but the big hurdle and the big thing that it articulated was like, wait a minute, first off, all of the aggregated training dataset doesn't exist anywhere.

Mm-hmm.

What you see is companies going after chest CTs or very specific elements, but your typical radiologist looks at a whole variety of things every single day from X-rays to CTs to MRIs of all parts of the body and everything else. And so an AI climbing in specific areas like chest CTs is like very marginally helpful because it's only doing that one thing, which could be one of 20 things or 40 scans that they read that day. And so, okay, so problem number 1, you don't have the data, just like we talked about in robotics and everything else, to train the AI. Problem number 2, the whole healthcare industry is oriented around reimbursement. cursing doctors for making readouts. And so, like, how is that going to work? And, like, who's— and there's liability associated with that, and there's repercussions of getting something wrong or missing something, and there's medical malpractice and everything else. So, like, how are we going to avoid that, and how are we going to get around that? So that's, that's, like, like, problem number 2 of just, like, real-world stickiness. And so I think in the end, we're going to end up with this application where AI really does help radiologists. It helps radiologists get more and more higher throughput because the AI can do some parts and the radiologist does some parts and they're checking each other and everything else. And you do kind of weirdly end up in this copilot situation for some time. And then you're going to slowly have the AI read more and more of the scans and build up and build up and build up. But the actual duration to get from here to there is going to take a long time. And in ensuing time, we need more radiologists, not less, because, oh, by the way, everyone's getting more imaging than they used to get.

Yeah.

Because the cost of imaging is going down in a Jevons paradox kind of way. And so, like, my point on it is, is like, you have someone very, very smart who really understands the capabilities, really understands what's happening, has the right data, but by not thinking of that data in the real-world application comes to the wrong conclusion. And that's how I think of the unemployment thing. I think it's almost the exact same setup.

Eric, I love talking about markets and governance with you.

So fun.

An absolute blast.

Thank you.

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