Everyone Is Still Undersizing the AI Market | Eric Vishria

Eric Vishria 0:00 I, I'm very dismissive of this idea that, like, people are gonna vibe code their own shit. Like, and whatever. Like, it-- that, 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 s- 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 turning in here just like every day you hit that plan-

Patrick O'Shaughnessy 0:32 You're fucking up. [laughs]

Eric Vishria 0:33 ... you're fucking up. You're destroying value. [upbeat music]

Patrick O'Shaughnessy 0:48 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 of investments each per year. And then the ones that go on to work, like so far, Sierra and, and Fireworks certainly are. You get to learn so much about the world through the lens of the company.

Eric Vishria 1:03 Yeah.

Patrick O'Shaughnessy 1:04 So I'd actually love to do both of those, maybe starting with Fireworks. What, 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 or-

Eric Vishria 1:17 These are two trillion, three trillion, four trillion parameter models. Well, it turns out running those models is damn hard.

Patrick O'Shaughnessy 1:27 Mm.

Eric Vishria 1:28 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 hand 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, and, like, that's what it is. And the performance difference for a Fireworks versus, like, a cloud provider is, like, five X.

Patrick O'Shaughnessy 2:10 Mm.

Eric Vishria 2:12 And that is just the speed performance. Then you add on top of that the, like, throughput, which is not visible externally.

Patrick O'Shaughnessy 2:19 Yeah.

Eric Vishria 2:19 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 five X performance difference and a multiple X throughput difference?" And, you know, and, 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.

Patrick O'Shaughnessy 2:47 Yeah.

Eric Vishria 2:47 And so, like, how, how can that be?

Patrick O'Shaughnessy 2:49 Yeah.

Eric Vishria 2:49 And so what-- uh, 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, um, and, and there's a lot of expertise involved in doing that, and it's kind of like this very specific expertise that, that exists. And it, 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.

Patrick O'Shaughnessy 3:16 Mm.

Eric Vishria 3:16 This, this, this just is like pass-through resource.

Patrick O'Shaughnessy 3:18 Scale game.

Eric Vishria 3:19 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."

Patrick O'Shaughnessy 3:24 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.

Eric Vishria 3:31 Yeah.

Patrick O'Shaughnessy 3:31 You know a lot about it, and the adoption curve there versus how people use these things and the, the

Patrick O'Shaughnessy 3:36 nature of those two businesses in comparison. It's tempting to say, like, there will be one or two scale winners-

Eric Vishria 3:41 Yeah

Patrick O'Shaughnessy 3:41 ... like there typically have been in a commodity market-

Eric Vishria 3:43 Totally

Patrick O'Shaughnessy 3:43 ... where cost to serve is everything, and scale drives cost to serve down, and, like, that's the whole story.

Eric Vishria 3:48 I, I just-- I think the AWS example is so good. Okay, so two thousand six, you have S3 and EC2 launch, right? Their compute platform and their storage platform. Those are the first two AWS offerings in two thousand six, and you kinda start talking about it in late two thousand six or whatever. Two thousand six, two thousand seven, um, I think the two thousand seven annual letter, like, Bezos talks a lot about, about, um, 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 thirty of the smartest investors at that time in a room-

Patrick O'Shaughnessy 4:20 Mm

Eric Vishria 4:21 ... and asked 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 have gone zero for thirty-

Patrick O'Shaughnessy 4:34 Mm

Eric Vishria 4:34 ... with really smart people that you and I know-

Patrick O'Shaughnessy 4:37 Yeah

Eric Vishria 4:37 ... who were, who were around at that time in '07. Okay, fast-forward from '07 to twenty fourteen. I joined the venture business in twenty fourteen, and a really common narrative in twenty fourteen was, "Oh my God, AWS is gonna eat everything. There's no enterprise opportunity left."

Patrick O'Shaughnessy 4:52 [laughs]

Eric Vishria 4:53 "It's gonna eat databases and infrastructure, but it's gonna eat the apps too, and they're gonna offer it the cheapest and best." And you know, and you gotta remember, like, we all had these, like, 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, eighty-five percent gross margins and, like, everything else. And so it was just like, oh my God, AWS, Amazon can offer things at, like, eight percent gross margin, and, like, they're just gonna crush this whole thing.

Patrick O'Shaughnessy 5:20 Your margin's my opportunity.

Eric Vishria 5:20 Your-- Yeah, your margin's my opportunity. Like, the whole thing, whole narrative. And, and think about from, like, twenty fourteen to, to now in enterprise, okay? Like, of course, you have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out Amazoning Amazon on Amazon.

Patrick O'Shaughnessy 5:43 [laughs] Right.

Eric Vishria 5:44 Okay? But it's not just Snowflake. You had Confluent and Elastic and Mongo, Databricks.

Patrick O'Shaughnessy 5:49 Mm.

Eric Vishria 5:50 All of these companies. Okay, 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, hundred billion dollar company today. Like, they had a competitive offering, right? And, and they did it. And, 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 twenty fourteen, the thesis was the, 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.

Patrick O'Shaughnessy 6:30 Mm.

Eric Vishria 6:30 And fast-forward to twenty twenty-six, and they're unbelievable businesses.

Patrick O'Shaughnessy 6:33 Mm.

Eric Vishria 6:34 So now is AWS the biggest? I think it's like a forty-thirty, twenty split.

Patrick O'Shaughnessy 6:37 Yeah.

Eric Vishria 6:38 You ended up with an oligopoly of, of that. And even outside of those big three, you have Cloudflare, which is a cloud provider in a different sort, which is another hundred billion dollar company. So you have these like smaller players that emerge as hundred billion dollar companies-

Patrick O'Shaughnessy 6:53 Mm

Eric Vishria 6:54 ... outside of it. So what's the takeaway? The takeaway to me is like, oh, there's just a bunch of like kinda zero-sum thinking and not realizing like how big-

Patrick O'Shaughnessy 7:05 What if it all works? [laughs]

Eric Vishria 7:06 What if it all works? And so it all worked. And of course, like, getting the relative winner right matters, and there was roadkill, and there were comp... So it's all those things still matter. So I'm not saying like, it's just like, you know, like spray and pray. I'm not saying that at all. But I'm just saying like, the market was so big that one vendor could not scale and consume it all.

Patrick O'Shaughnessy 7:28 Mm.

Eric Vishria 7:28 And they just couldn't consume the whole industry. Now, I just like, again, like, it's different now and like 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 it's-

Patrick O'Shaughnessy 7:43 Antropic's gonna do everything.

Eric Vishria 7:45 Right. It's like it can-

Patrick O'Shaughnessy 7:46 Every business.

Eric Vishria 7:46 Like, really? Like, is that, is that really right?

Patrick O'Shaughnessy 7:49 Mm.

Eric Vishria 7:49 To me, that view that it's just like, "Oh, this one company's gonna eat it all," does-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.

Patrick O'Shaughnessy 8:03 Mm.

Eric Vishria 8:03 And so f- just for that company to actually scale and deliver it, scaling in this case requires like a ton of infrastructure buildup.

Patrick O'Shaughnessy 8:13 Mm.

Eric Vishria 8:13 Right? From like energy, like power, shell, you know, chips, memory, um, obviously the algorithms and everything else on top. So it just, it feels, it feels to me like we're gonna end up with an oligopoly, um, of, of winners, and there will be... I, I really believe there will be these like hundred billion dollar crazy smaller winners.

Patrick O'Shaughnessy 8:39 Mm.

Eric Vishria 8:40 Um, and [laughs] so like that's, you know, that's, uh, like that, this just feels like that, uh, the same thing is kinda happening.

Patrick O'Shaughnessy 8:45 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?

Eric Vishria 8:53 So if you kinda go back to like two thousand and ten, two thousand and eleven, like even, you know, three, now you're like three, four years into to AWS being an offering, enterprises were super skeptical. Like, super skeptical, like traditional enterprise, blue, blue chip enterprise. Um, you had digital natives out here. You had new companies forming that were like using the cloud, you know, I think kinda famously like Snapchat, uh, was built on GCP.

Patrick O'Shaughnessy 9:21 Mm.

Eric Vishria 9:21 And I think at one point in this era, maybe twenty twelve, twenty thirteen, twenty fourteen, like Snapchat was like forty percent of GCP. Like, it was just like those kinds of things were happening.

Patrick O'Shaughnessy 9:30 That's like Cursor being thirty percent of all these things.

Eric Vishria 9:33 Hundred percent. Hundred percent.

Patrick O'Shaughnessy 9:33 Same thing.

Eric Vishria 9:34 Same exact thing happened.

Patrick O'Shaughnessy 9:34 Yeah.

Eric Vishria 9:35 Same exact thing happened. Enterprises were very skeptical, I think, of cloud until like they really... You know, by twenty fourteen, twenty fifteen, twenty sixteen, 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. We-- This is actually, you know, different, and we're gonna have to pay attention." And, you know, it became an issue in recruiting for them because they couldn't get the best developers, 'cause best developers wanted to work on the easiest platforms, and all of these kinds of things happened. And so the difference now feels really profound to me because while it is a hundred percent the case that blue chip enterprise AI is not like well-absorbed and well-adopted and not, not the same thing as going to Cursor and, you know, walking the halls of Cursor versus, um, walking the halls of, you know, a big New York financial services firm in terms of their AI use, they want it.

Patrick O'Shaughnessy 10:32 Mm.

Eric Vishria 10:32 They wanna 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, um, than they did the cloud in terms of the potential impact on their business. I think they probably view it more as a threat-

Patrick O'Shaughnessy 10:50 Mm

Eric Vishria 10:50 ... than they did the cloud, and maybe they just also learned lessons from the cloud in terms of like what, what's possible here. And so I, I feel that they will continue to, 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-

Patrick O'Shaughnessy 11:25 Mm

Eric Vishria 11:25 ... to help the enterprises get there to be... One of the things that I, I tell the companies I work on is like, "Hey, let's be their AI Sherpa."

Patrick O'Shaughnessy 11:33 Mm.

Eric Vishria 11:33 "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.

Patrick O'Shaughnessy 11:41 Mm.

Eric Vishria 11:41 And I think that'll continue to work. [upbeat music]

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Patrick O'Shaughnessy 12:50 What is, what is Sierra

Patrick O'Shaughnessy 12:52 teaching you about the adoption of this stuff? And it's kind of actually, it's really interesting contrast to Fireworks, where Fireworks is the sort of infrastructure provider.

Eric Vishria 13:00 Mm-hmm.

Patrick O'Shaughnessy 13:00 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-

Eric Vishria 13:07 Yep

Patrick O'Shaughnessy 13:08 ... starting with customer service, but I know-

Eric Vishria 13:09 Yeah

Patrick O'Shaughnessy 13:09 ... with Horizon now, you know, going beyond that. Again, same question as for Fireworks, like what, what do you, what do you know that the world doesn't fully appreciate because of how you've seen that business grow?

Eric Vishria 13:17 Well, I, 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 third 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, you know, Brett so special is, um, 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-

Patrick O'Shaughnessy 14:40 Yeah

Eric Vishria 14:40 ... six months ago.

Patrick O'Shaughnessy 14:41 Yeah.

Eric Vishria 14:41 You know, and it's what Brett Taylor calls like sandcastles, right? We, we used to be building castles. Now we're building sandcastles that can-

Patrick O'Shaughnessy 14:47 Oh, that's great. Yeah

Eric Vishria 14:47 ... get washed away. And that's... If you don't think of software that way-

Patrick O'Shaughnessy 14:50 Gotta embrace it. Yeah

Eric Vishria 14:50 ... Yeah, you have to embrace it, because if you're an artisan and you're like, "Hey, I built this like perfect foundation of castle, and it was just bricks, and it was like perfect, and I really care about this, and it's gonna be here for 100 years," you're just not gonna make it. So as you get emergent new properties in the models, which is, you know-

Patrick O'Shaughnessy 15:06 Happening every four weeks

Eric Vishria 15:06 ... you get a new... every four 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, and they're, 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-

Patrick O'Shaughnessy 15:27 Mm

Eric Vishria 15:27 ... to how product development happens now.

Patrick O'Shaughnessy 15:30 Mm.

Eric Vishria 15:30 And product development, you know, it always used to be, say, you'd also say like, "Hey, product managers, you're trained." Oh, the product managers shouldn't, you know, should understand the technology, but like really shouldn't be thinking about implementation and shouldn't be specifying implementation and shouldn't be doing this and doing that, and the product manager's job is to really understand the customer and translate that problem to the engineers so that the engineers build a 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-

Patrick O'Shaughnessy 16:03 Mm

Eric Vishria 16:03 ... 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.

Patrick O'Shaughnessy 16:25 It's kind of ironic that like all that sounds like the returns to being technical are going up-

Eric Vishria 16:31 Totally

Patrick O'Shaughnessy 16:31 ... even as models supposedly are taking away technical edge. [chuckles]

Eric Vishria 16:34 Well, yeah, yeah, yeah. I think there's two things-

Patrick O'Shaughnessy 16:36 It's a funny conundrum. [chuckles]

Eric Vishria 16:36 Uh, uh, yeah, 100%. And, and actually, I think there's this weird thing where there was this whole discussion about like the traditional roles of product manager and designer and engineer and like 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.

Patrick O'Shaughnessy 17:01 Mm.

Eric Vishria 17:01 And like, those are the three things.

Patrick O'Shaughnessy 17:03 If you've got those three, you're gonna do well.

Eric Vishria 17:04 If you're gonna be... If you have those three things, you're gonna do great.

Patrick O'Shaughnessy 17:06 Yeah.

Eric Vishria 17:07 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 gonna do well.

Patrick O'Shaughnessy 17:18 We, 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

Patrick O'Shaughnessy 17:35 right now.

Eric Vishria 17:35 Yeah.

Patrick O'Shaughnessy 17:36 And I'm really curious, in addition to this like idea of sandcastles versus real castles, what else you're seeing amongst the people that are becoming competitive winners? Like, 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?

Eric Vishria 17:55 I, I'm very dismissive of this idea that like people are gonna vibe code their own shit and like, and whatever. Like it... That, 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 it would make them win are not what's gonna make them win. Let's take databases. Like database, 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, and 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 cloud or, or codecs building against a, a, a database interface, one. Two, the beauty of database interfaces is they're very, very well specified.

Patrick O'Shaughnessy 19:09 Hmm.

Eric Vishria 19:10 Well, it turns out AI is very good- [chuckles]

Patrick O'Shaughnessy 19:12 It's very good at that.

Eric Vishria 19:12 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.

Patrick O'Shaughnessy 19:20 Hmm.

Eric Vishria 19:20 So it turns out that now all of a sudden, database migration, which used to be like the, like number one thing you would not do in software-

Patrick O'Shaughnessy 19:30 Hmm

Eric Vishria 19:30 ... is like kind of trivial.

Patrick O'Shaughnessy 19:33 It's money. Yeah.

Eric Vishria 19:34 Yeah. It's just like, yeah, puts 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 gonna build their own database or whatever. Like, that isn't what's gonna happen. What's gonna happen is the criteria changed. So now you're gonna have a ton more applications that start, obviously, as we're seeing everywhere. Like, people are gonna 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 wanna 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 the 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.

Patrick O'Shaughnessy 20:35 Hmm.

Eric Vishria 20:35 That's really different than like, "Hey, I spec-ed this database for our user thing, and I, you know, procured a license, and I ran it on this kind of hardware," and like everything else. And, 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 three times revenue."

Patrick O'Shaughnessy 21:06 Hmm.

Eric Vishria 21:07 And I think a lot of, a lot of these public SaaS companies are trading at six times or whatever. But keep in mind, in '21, they were going for thirty times.

Patrick O'Shaughnessy 21:14 Yeah.

Eric Vishria 21:15 Right? Like everybody, right? And so you're like three times, wow.

Patrick O'Shaughnessy 21:19 It's a big drawdown. [chuckles]

Eric Vishria 21:19 I've grown four X since then, right? This is... Oh, multiple compression is a bitch.

Patrick O'Shaughnessy 21:23 Yeah. [chuckles]

Eric Vishria 21:23 I've grown four X. The multiple has gone down by a factor of six, so I'm worth less even though I've grown four X in a few years.

Patrick O'Shaughnessy 21:30 Yeah, yeah.

Eric Vishria 21:31 And gotten to breakeven 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.

Patrick O'Shaughnessy 21:44 Hmm.

Eric Vishria 21:46 Like, think about that. Our whole careers, we learned, like you, 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.

Patrick O'Shaughnessy 22:02 Hmm.

Eric Vishria 22:02 That's what the whole management team's learned. That's what these CEOs learned, like pre-AI. This is like everything learned. And now you're in here just like every day you hit that plan.

Patrick O'Shaughnessy 22:10 You're fucking up. [chuckles]

Eric Vishria 22:11 You're fucking up. You're destroying value. You're destroying value. And, and the point of like, you know, saying that to them was to set them free. Another articulation of this from my, my friend Anne Lee Skates, was, was just like the, the CEOs who were going through this transitory period and had a business that was at a hundreds, hundreds of millions, and they thought they, you know, they're all ready. 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-

Patrick O'Shaughnessy 22:40 Hmm

Eric Vishria 22:40 ... in the evenings. And what they needed to be doing-

Patrick O'Shaughnessy 22:44 Is the inverse. [chuckles]

Eric Vishria 22:44 ... was the inverse.

Patrick O'Shaughnessy 22:45 Yeah.

Eric Vishria 22:46 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.

Patrick O'Shaughnessy 23:03 Mm-hmm.

Eric Vishria 23:03 And it's like, oh, no. No, no. Stop that.

Patrick O'Shaughnessy 23:05 Mm-hmm.

Eric Vishria 23:05 You're, you're... 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, the kinda winners are right now. But if we look at, you know, Brendan from Recore or Lynn from Fireworks or, or Max or Brett, any of these people, they are so nimble about what the eval is.

Patrick O'Shaughnessy 23:32 Hmm.

Eric Vishria 23:33 Like, what is... What are they optimizing against? They are so nimble on all of it.

Patrick O'Shaughnessy 23:38 Hmm.

Eric Vishria 23:38 And if you look at every one of those companies, the evolution of the business, it's just the business is constantly evolving.

Patrick O'Shaughnessy 23:45 Hmm.

Eric Vishria 23:45 And they've done such an excellent job at that. Um, and I think that is very, very different than certainly the, the way I was, like taught.

Patrick O'Shaughnessy 23:58 Well, what's your sense of the disorienting nature of model progress and, like how to... Y- we were talking, you said earlier, four, every four weeks, like I got a, a new jagged edge I gotta figure out.

Eric Vishria 24:09 Yeah, yeah.

Patrick O'Shaughnessy 24:09 Y- you're one step removed from that as an investor versus as the f- technical founder, you know, with your, with your hands on the metal. How are you behaving differently, uh, than you would have, you know, three years ago or something because of the pace?

Eric Vishria 24:20 Any time that I'm talking to a, 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 eighty percent of my day doing, I'm very... This is how we used to do it.

Patrick O'Shaughnessy 24:34 Mm-hmm.

Eric Vishria 24:35 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 is 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 on, you know, in the context of, like, a business model that's growing this way versus that way. And, and, and, and so I've really started to question like every assumption and every lesson that I learned before, like which of it translates and which of it doesn't.

Patrick O'Shaughnessy 25:09 Mm.

Eric Vishria 25:09 And, and so like that's a huge difference. I'll give you a really concrete example, it which is, which has been very, um, disruptive inside of, um, these like 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. Like you, and you see it across the industry. And so the question is like, why? And it's, these are, these are some of the best leaders from four or five years ago. They had all the lessons, they learned it all, you know, they're excellent. But somehow it's not translating. And, and there's just like some impedance mismatch between like the AI founders potentially, the n-needs of the business, and what these people are bringing. And I saw it like really abruptly with, with a particular sales leader who we hired, who's like, "Hey, we can't hit any of these things 'cause the way that software sales has been taught forever is a quota capacity model."

Patrick O'Shaughnessy 26:10 Mm-hmm.

Eric Vishria 26:11 You have a quota capacity model, each rep does this. In the early days of a company, the quotas are, I don't know, one point two, one point five million. You know, maybe the ISRs are at seven fifty or eight fifty, and then over time it scales up, and enterprise gets to two point five million, and that's the, the whole thing is-

Patrick O'Shaughnessy 26:25 How it works.

Eric Vishria 26:25 That's how it works. That's how all these financial models are built. You start with a quota capacity model, you take a discount on attainment. This is how, this is what we can do. Boom, boom, boom. Now, like fundamentally, without like realizing it, everybody was implementing something that was based on pushing demand, not pulling demand.

Patrick O'Shaughnessy 26:46 Mm.

Eric Vishria 26:47 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.

Patrick O'Shaughnessy 26:55 Mm.

Eric Vishria 26:56 So these companies are selling magic.

Patrick O'Shaughnessy 26:58 Mm.

Eric Vishria 26:59 Like magic. Well, it turns out if you're selling magic, and you're the first one there-

Patrick O'Shaughnessy 27:04 Sell a lot more than two million [laughs]

Eric Vishria 27:05 ... you're gonna sell a lot more than two million. And so the whole notion of a quota capacity model and that being working, like it, it's not that it doesn't matter, it matters kind of, but it's definitely not the first order thing or constraint. And so you have these like execs come over with this, just like, "Here's our math and here's the territory assignment and here's what we would do. And you know, first you do West Coast, and then you do East Coast, and then you do Central," all of these things. And it's like, oh wait, no, no, it doesn't work like that at all.

Patrick O'Shaughnessy 27:30 Mm.

Eric Vishria 27:31 And so one of my big things that I started to realize is as I'm interviewing these folks and, and, and talking to them is just like, "Hey, you need to check everything at the door. Just like 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. Like 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 ten or twenty, thirty million.

Patrick O'Shaughnessy 27:59 I saw fifty recently.

Eric Vishria 28:01 I mean, like, and so it's like, okay. [laughs]

Patrick O'Shaughnessy 28:04 Yeah.

Eric Vishria 28:04 It turns out that's different.

Patrick O'Shaughnessy 28:06 What is like the best salesperson that you've seen that's doing it in a de novo way doing?

Eric Vishria 28:10 Honestly, the best salesperson at 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. And it's like, I... It sounds so simple, but it's not. But, but that's what they're doing.

Patrick O'Shaughnessy 28:22 Mm.

Eric Vishria 28:22 And, um, and then the, you know, the market is just so big. Like, just like we were

Eric Vishria 28:28 talking about with cloud, I think the biggest mistake everybody made was the market, they just undersized the market.

Patrick O'Shaughnessy 28:33 Mm.

Eric Vishria 28:33 And it turns out the market's just really, really big.

Patrick O'Shaughnessy 28:35 Mm.

Eric Vishria 28:36 And this market is bigger.

Patrick O'Shaughnessy 28:38 It feels like right in this moment in time, actually, just this morning, you and I are in this great, this great group, um, chat together where the discussion is the demand kind of s- demand for intelligence, like I don't know, s-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, the world just feels like it needs to take a breath.

Eric Vishria 28:56 Yeah.

Patrick O'Shaughnessy 28:56 Like the RSI kind of concept, if you apply it across technology, doesn't need to breathe, the agents don't get tired.

Eric Vishria 29:02 Right.

Patrick O'Shaughnessy 29:03 But the world feels kind of like, oh, man, it sure would be nice to have three months just to like digest this a little bit-

Eric Vishria 29:08 Yeah

Patrick O'Shaughnessy 29:08 ... and for capital to form and, and, and evaluate its prospects, and the scale is getting so big. Like, does it feel to you like this can just keep going, or ed- are we just gonna 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?

Eric Vishria 29:32 I, I, I would say this: I am worried about energy. If you think about the models as translating compute into intelligence, like quite simply, what do models do? They c- 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 con-continue to like have more and more demand on compute. I'm saying compute broadly, not-

Patrick O'Shaughnessy 29:58 Yeah

Eric Vishria 29:58 ... not chips, not storage, not whatever. But then like what do we need for compute? We need energy. Like a lot of energy. There's all this topic of distillation and, and, and Chinese open source and all these things. But the bigger thing to me is that I think China's bringing on ten 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-

Patrick O'Shaughnessy 30:36 Mm

Eric Vishria 30:36 ... or a lot less tokens, or a lot more expensive tokens. And if we have a lot more expensive tokens than supply demand, you're gonna end up with less. And that seems very bad. And so to me, I think the energy bottleneck, however that, that is, and it'll manifest in twenty different ways. You know, gas turbines go up and down, natural gas go up and down, and solar, whatever, like, all these different things, rare earths. Like, that to me is, is probably more concerning.

Patrick O'Shaughnessy 31:07 Mm.

Eric Vishria 31:07 And if I'm thinking about it from a, a regulatory perspective or government per- perspective, and I think the administration is doing some things around this, like, you know, fostering really investment and development of all energy.

Patrick O'Shaughnessy 31:23 Mm.

Eric Vishria 31:23 That-- And, and I, I mean all. Like, solar, nuclear, gas, like, whatever. Do it all.

Patrick O'Shaughnessy 31:27 Yeah.

Eric Vishria 31:28 Like, we should do it all.

Patrick O'Shaughnessy 31:29 Yeah.

Eric Vishria 31:30 And, um, and it'll work itself out, you know. 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.

Patrick O'Shaughnessy 31:40 Speaking of compute, I would love to hear the Cerebras story. I, I haven't heard you tell the full version of this. I think it's... The reason I'm asking about it is, um, I'm deeply interested in compute.

Eric Vishria 31:49 Sure.

Patrick O'Shaughnessy 31:49 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, uh, and in these systems. I think you invested in 2016 or thereabouts. Um, so I think it was your force- first foray into, like, extremely difficult hardware type investment.

Eric Vishria 32:10 It's so hard.

Patrick O'Shaughnessy 32:10 And, and I'd love it... Now, the world is full of opportunities like this.

Eric Vishria 32:15 Yeah.

Patrick O'Shaughnessy 32:15 Whereas back then, it was like a, you know, a, a, really a one-off. T-teach me everything you've learned-

Eric Vishria 32:20 [laughs]

Patrick O'Shaughnessy 32:20 -about hardware investing through Cerebras.

Eric Vishria 32:22 Mostly, mostly it's really hard. [laughs] It's an amazing example to me about, like, the naiveté required.

Patrick O'Shaughnessy 32:30 Mm.

Eric Vishria 32:30 So, you know, the, the, the company came in in 2016. It was five founders and a deck. Um, I really did not wanna go to the pitch, um, but it's my job. 'Cause I'm like, "Why are we gonna go to hardware investment? Like, this is crazy." Um, it'd been ten years, eh, since we'd made a semi investment. I, I think basically the team was excellent.

Patrick O'Shaughnessy 32:50 Mm-hmm.

Eric Vishria 32:50 And then the kinda first slide was just like, "GPUs actually suck for deep learning. They just happen to be a hundred times better than CPUs."

Patrick O'Shaughnessy 32:57 Mm.

Eric Vishria 32:58 And as soon as he 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, forty billion, not four trillion. The TPU hadn't been announced. Like, none of that.

Patrick O'Shaughnessy 33:12 Mm-hmm. Mm.

Eric Vishria 33:12 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?"

Patrick O'Shaughnessy 33:21 Mm-hmm.

Eric Vishria 33:21 And I'm-- I had been-- spent the last eighteen 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, um, 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, got... Just built a lot of conviction, um, that this was a great swing. And I'll tell you what I understood, and I really just didn't-- understood so little. But basically, there are three things that we know how to do to speed up deep learning and 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 three things. That's it. Those are the only three dimensions that we know in hardware. So my articulation of what they said to me, and, like, honestly, all I understood, was if w- let's just take all three of those things to their logical maximum. You have a wafer scale chip. At that time, you would have four hundred and fifty thousand cores on it. You'd have something like twenty gig of SRAM on the chip, so you never have to go off chip to get to memory. And because they were all on the same wafer, the communication between cores is maximized. So this is the best you could do-

Patrick O'Shaughnessy 34:40 Mm

Eric Vishria 34:40 ... on that process. And I think first, the first chip was seven nanometer or something. And so, like, you're like, "Okay." Like, that's it. Like, that's what we, we do. And it turns out that in software, if you have, like, that kind of, like, logical block diagram of, like, why it works and everything else, you're kinda eighty percent of the way there.

Patrick O'Shaughnessy 35:01 Mm.

Eric Vishria 35:01 And it's a matter of, like, go to market execution.

Patrick O'Shaughnessy 35:04 Mm.

Eric Vishria 35:04 In hardware, um, you're, like, two percent of the way there. [laughs]

Patrick O'Shaughnessy 35:08 [laughs]

Eric Vishria 35:08 Like, there's things like physics a- and, [laughs] you know, and, and this entire supply chain of vendors and that, like, you know, like, there's of course TSMC, which everyone knows, but it's not just TSMC. There's thirty other vendors-

Patrick O'Shaughnessy 35:20 Sure

Eric Vishria 35:20 ... that, like, matter in, like, putting all this stuff together and everything else. And so, like, I didn't know any of that, and, like, really didn't know this. So, you know, fast-forward from 2016 to, like, 2019, I think they, they got their first parts back.

Patrick O'Shaughnessy 35:34 Mm-hmm.

Eric Vishria 35:35 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."

Patrick O'Shaughnessy 35:39 Like, oh, shit. No, no. [laughs]

Eric Vishria 35:40 And then it's like bring up. [laughs] Yeah, then you gotta go through, like, wait, bring up, and there's like fourteen steps of bring up and everything else. And then by, like, 2020, we had our, like, first thing that, like, works. And then you're like... Then it's just this march of, like, actually getting it to work.

Patrick O'Shaughnessy 35:58 Mm-hmm.

Eric Vishria 35:58 And, you know, 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, and semis in particular, and that, that basically is your roof line. Like, that's the maximal performance.

Patrick O'Shaughnessy 36:13 Best it's gonna be. Yeah.

Eric Vishria 36:14 Best it's ever gonna be is, like, what that is. And then every bit of, like, software and reality and compilers and kernels takes away from that roof line. You might start at ten percent of the roof line.

Patrick O'Shaughnessy 36:25 Mm.

Eric Vishria 36:25 Once you bring it up-

Patrick O'Shaughnessy 36:27 Mm

Eric Vishria 36:27 ... and then you're like, g- g- g... You know, then these guys are grinding for, for months and years to, like, get closer and closer and closer to the roof line. It's really different, and it's really hard. I'm, I'm, I'm actually, like, astonishingly bullish if I kinda rewind Part of the reason we made the investment was if you looked at the four prior generations of compute, um, that like in my lifetime, so you had CPUs, you had graphics, networking, mobile, there, there was a new workload each time. So you had multipurpose compute and it loaded to the CPU. You had massive parallelism, which, you know, 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, with mobile, you needed really power efficient chips. And so each case we ended up with a new hundred billion dollar company.

Patrick O'Shaughnessy 37:21 Mm.

Eric Vishria 37:22 And the question, the first question, you know, going back to 2016 was like, is AI that big a new workload?

Patrick O'Shaughnessy 37:30 Mm.

Eric Vishria 37:30 Because there'd been many, many other attempts for specialized chips for other things that like really just didn't end up mattering.

Patrick O'Shaughnessy 37:37 Yeah.

Eric Vishria 37:37 Like there, there's some fine outcomes, but like just didn't really end up mattering.

Patrick O'Shaughnessy 37:39 Like Goran, et cetera.

Eric Vishria 37:40 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 the... And we, we, we had a lot of conviction on that. And then two 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 like, you're like, okay, wait a minute. Like 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? You know, basically everything that I just said was the entire, everything I knew at that time. And I, I do actually think so that, that's obv- obviously played out and, you know, like in each prior generation, we obviously, we got Intel, we got Nvidia, um, we got Broadcom and Avago, we got Qualcomm and Arm, like, you know, in each of these generations. And like you're... There will be like these giant winners, standalone winners. Obviously the TPU itself is, is a winner. Training is a winner. Um, you know, you've had Groq and Cerebras, you know, Etch, like it'll keep getting fought out.

Patrick O'Shaughnessy 38:53 Yeah.

Eric Vishria 38:53 Um, and you know, but I think that will end up being big. And actually I think there's a new sixth one that's coming, um, 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.

Patrick O'Shaughnessy 39:13 Mm.

Eric Vishria 39:14 The thing that's happening right now, and, and you, you see this reflected in all the semi stocks and everything else, is the LLMs which are running on, you know, accelerators and GPUs generate code. The code runs on CPUs.

Patrick O'Shaughnessy 39:28 Mm.

Eric Vishria 39:29 And 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. And so I'm actually like really excited about, about that possibility.

Patrick O'Shaughnessy 39:48 The next category.

Eric Vishria 39:49 The next category.

Patrick O'Shaughnessy 39:50 Does the experience with Cerebras make you want to do a lot more investing in companies?

Eric Vishria 39:55 Fuck no. [laughs] Fuck no.

Patrick O'Shaughnessy 39:56 But why not? Like, like the defining-

Eric Vishria 39:58 Because in, in 2019 we're sitting in a board meeting and this thing is melting. It's like fucking melting. Okay. And we've raised five hundred million dollars or something, and it's like, wait, what? Like what? It's melting or it's burning or something. And, and like we're looking and I'm like, holy shit.

Patrick O'Shaughnessy 40:16 Mm.

Eric Vishria 40:17 Like, you know, and I realize like five hundred million dollars isn't that much in today's era, but like, I was just like, we're gonna lose all this money. Like, this is just not gonna work. And, you know, and, and I'm, and talked to Andrew about this obviously, just what that team did, insane.

Patrick O'Shaughnessy 40:32 Mm.

Eric Vishria 40:32 Like in- insane in terms of the technical-

Patrick O'Shaughnessy 40:34 These hardware teams are built differently.

Eric Vishria 40:35 They're built differently. They, they're, they're built so differently and I have so much respect and thanks to them, um, for what they've done. Look, I, I, I, you know, joking aside, I think that there are efforts that make you really proud to be a venture capitalist and like to... Because you're funding something that like makes a difference and matters.

Patrick O'Shaughnessy 40:58 Mm-hmm.

Eric Vishria 41:01 And for me, I- I'm an investor because like that's a means to work with companies, not because I like fundamentally like love investing or something like that. I like the working with the companies. That's my, 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.

Patrick O'Shaughnessy 41:24 Mm.

Eric Vishria 41:24 And um, you know, and I said this before, like well before 2018 or whatever, it was like whether Cerebras worked or didn't, I think it was an effort that was worth venture capital. Like it, it, now that's the kind of thing you should do. You should try to build something that people have tried for fifty 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.

Patrick O'Shaughnessy 41:46 Yeah.

Eric Vishria 41:47 And, and so I, 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, that I'm also really excited about. And, and then this like, you know, CPU project that we're talking about. And so like I, I think these kinds of things are actually really fun, um, and interesting and good use of venture capital, but they, they definitely aren't easy.

Patrick O'Shaughnessy 42:06 I love the, the sort of productive naivete that you described where it's probably a virtue that you didn't know more than you knew-

Eric Vishria 42:13 Oh, 100%

Patrick O'Shaughnessy 42:13 ... otherwise you wouldn't have done it.

Eric Vishria 42:14 Yeah.

Patrick O'Shaughnessy 42:15 It's just really 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."

Eric Vishria 42:23 Yeah.

Patrick O'Shaughnessy 42:23 Like, "It sucks. It's too hard. Base rate's too low."

Eric Vishria 42:25 Yeah. Yeah.

Patrick O'Shaughnessy 42:26 "Young people can't do it." There'd be like forty-seven reasons.

Eric Vishria 42:27 Yeah. All those things. Yeah.

Patrick O'Shaughnessy 42:28 Um, 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.

Eric Vishria 42:39 Actually, like most of the time, all of these like stereotypical statements are correct.

Patrick O'Shaughnessy 42:43 Mm.

Eric Vishria 42:44 Like, they're, they're not like correct like three times out of four. They're correct like nineteen times out of twenty.

Patrick O'Shaughnessy 42:50 Yeah.

Eric Vishria 42:50 Maybe ninety-nine times out of a hundred. The thing that, um, 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, you know, young people can't build chips, or you shouldn't do another semi company, or you shouldn't do this, or like whatever. Like, all of that stuff is actually totally right.

Patrick O'Shaughnessy 43:10 Mm-hmm.

Eric Vishria 43:11 Except when it isn't. And so apparently, there's a saying that, um, someone, someone said, uh, to one of my partners, which was, "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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Patrick O'Shaughnessy 45:10 You can request a demo at ridgeline.ai. You and I, I think, are both interested in robotics.

Eric Vishria 45:16 Yes.

Patrick O'Shaughnessy 45:17 Uh, it's not controversial that if robotics works, it's gonna-- 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, it's kinda one of these classics that's always ten 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?

Eric Vishria 45:41 We've had classics robotics forever-

Patrick O'Shaughnessy 45:44 Yeah

Eric Vishria 45:44 ... and they're all over the place, and they're on assembly lines and manufacturing lines and all those things. It's where you're doing repetitive tasks in controlled environments. Repetitive tasks in controlled environments is, like, more or less solved and, like, the-- you know, that'll continue to happen. But having tweaked tasks in real world environments, that's where you need the AI. Like, that's where you need the AI plus the, plus the robots. And the trick with it all is you need a model that can do that.

Patrick O'Shaughnessy 46:15 Mm.

Eric Vishria 46:15 And of course, the problem, the first problem is there is no internet scale data-

Patrick O'Shaughnessy 46:21 Mm

Eric Vishria 46:21 ... to bootstrap the whole thing, right? LLMs are all bootstrapped on the internet, um, which is a ton of human knowledge, and the equivalent for that for robots doesn't exist. Um, and people are trying different things with videos and simulations and, um, and teleoperation. So, like, there's a lot of different ways, but if you kinda think about, take teleop as an example, like, how much you need to teleop robots to get to an 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, like, other forums. You might value GitHub more than other things. And, and all of the model companies did that, right? They prioritized data, um, that was more valuable and less valuable and, and ran that through the model. So 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."

Patrick O'Shaughnessy 47:35 Mm-hmm.

Eric Vishria 47:36 And if we go after the high-value data, then we can kind of bootstrap the, this, this model. Now, once you do that, then can you, through the 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 p- pre-trained base, and then you add a little magic on it, um, in RL and post-training.

Patrick O'Shaughnessy 48:09 You teach it.

Eric Vishria 48:10 And, and you teach it. You teach it a new thing that it wasn't in the pre-training set, and, and you kinda go from there. And I think the exact same thing is happening in robotics. We're investors in, in Sunday Robotics, um, which is going after this exact pipeline.

Patrick O'Shaughnessy 48:22 Such a cool company.

Eric Vishria 48:23 It's a cool company, and the-- and the-- you know, they're doing household robots, but the, the key part before you get to household and all that stuff matters much less actually than, like, can you get this, like, training pipeline to work, and how do you do that? And one of the lessons I learned, and I look back, I try to learn from history 'cause, you know, it doesn't repeat, but it rhymes. And, and if I look at autonomous vehicles as an example, which is- They're robots, basically. They're, 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 gather very, very high-quality data. They did their pre-training, and, and then they worked off of that, and that was kind of... That was a simplification to allow you to get to a complete product or a complete solution, which of course is gonna continue to improve and ultimately will generalize, I'm sure, it'll generalize in some, 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, um, you have companies, uh, like Sunday and others who are using techniques where they're vertically integrating the robot, um, and the model, the data collection around the robot. Um, Sunday uses gloves that are designed just with the robot hands, so they're, they're, 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 your RL and post train on top, and you get cool emergent behavior.

Patrick O'Shaughnessy 50:14 Do you have a most visceral moment?

Eric Vishria 50:16 When we invested in Sunday the first time, um, we saw, we saw them. My partner Peter, uh, arranged a demo, and we went down into the basement at the Stanford lab, and they had, like, this t-totally janky cardboard glove thing. And you, you know, you see a few evolutions of it. And, um, the last time we, we saw a demo, we, like, went down in the basement, um, of their now office building and, and you see... And there was like a dozen robots just folding arbitrary laundry. It wasn't, it wasn't a demo. It was just trial and error, and then they have people, like, taking, taking the clothes and then measuring them to make sure that they were folded properly and, like, and they were, like, creating a rigorous baseline and evaluation criteria. And I was like, "Oh, my God, this is, like, this is happening."

Patrick O'Shaughnessy 51:05 Do you ever worry about, uh, how to pick the right customer for these companies? Like ev-every one of these things folds laundry, which, like, I don't think anyone likes folding laundry. So it, like, seems like a good, uh, 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 searches of problems.

Eric Vishria 51:28 I don't, and, uh, and I'll explain why, and it's, it's, it's, like, certainly informed by watching the LLM evolution-

Patrick O'Shaughnessy 51:35 Uh-huh

Eric Vishria 51:35 ... which is like... You know, I think some of the people who were involved in the early LLM, um, at, at OpenAI, like, really understood that, like, code was gonna be important and, and obviously, you know, the Anthropic team had this perspective that you could get to RSI if you got code generation going and, and automating AI research and whatnot. But if you think about it, like, the first use cases were very much language-oriented. They were very much, like, essay writing and editing and, and, um, and, like, marketing. Like, I think the first application that really took off was, 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 ti- it isn't time sensitive, so you can, like... If it takes three 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, I don't think the task is actually that important. Um, 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? If you get that flywheel going, then, you know, the task capability will just keep, it'll keep multiplying.

Patrick O'Shaughnessy 52:52 Maybe this question will

Patrick O'Shaughnessy 52:53 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, than anyone else that I've come across. And I'm curious why you think that is. Like, what, what it is that you're doing that other people-- The incentives are there, like, to, 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, when asked that question?

Eric Vishria 53:34 One of the things that I've realized is we each are attracted to, like, different types of entrepreneurs and, like, where we have chemistry. And I think one of the differences when I say I'm like, I'm really, like, I'm an investor second, and I try to be a partner first. And, and here's what I mean. We'll, we'll see companies come in. We, we had one come in yesterday, and it's an, what I would call an investment grade opportunity. It's kinda like you can invest, it probably works, you make money. Like, it's, it's good. And an investor would do that, and a partner wouldn't because that's not sufficient for a partner. Because, like, unless you have real chemistry with that person, where you feel like you're gonna be able to work together really effectively and, and I'm gonna learn a ton from them, and they're gonna learn something from me, and, and together we're gonna just, like, feed each other's loops, right? Unless you feel that way, then, like, you can't be a partner. And, and so you pass on that. But, like, that's, that's a really important, like, fit element to me. And so it kinda starts with this, like, mutual selection actually, weirdly, of like, they wanna partner with us, and, and, and I wanna 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, kinda comes into play for me. If I take, like, Sagi and Benchling Benchling is, you know, life sciences SaaS. Company's absolutely crushed, done really, really well, and then of course, you know, you have this, like, biotech crash, and the company, it, it became hard. Like, it became grindy. And I, and I drove... 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.

Patrick O'Shaughnessy 55:32 Mm.

Eric Vishria 55:32 Never reported it for the first of, like, six years that I worked with the company. So then they got seven years of churn in, like, twelve months.

Patrick O'Shaughnessy 55:40 Huh.

Eric Vishria 55:40 Turns out, like, life sucks when you get seven years of churn in t- twelve months. And, like, through that grind and through it all, and related to the whole, like, wait a minute, the goalpost 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 'cause, 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, I don't know what it is more than that. I... It's, it's very different. I've seen different models and, and lots of different models of venture capital work, right? Like, Moritz was a writer. Dor was a sales guy. Like, you know, Gurley was an engineer. Like, Peter's a, a career venture capitalist. Like, they're all different, right? Um, a-and but for me, I think, like, 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.

Patrick O'Shaughnessy 57:10 Mm.

Eric Vishria 57:11 They know what they wanna do. They know the answer, and it's maybe asking questions of them to maybe help solidify their conviction or solidify their articulation-

Patrick O'Shaughnessy 57:24 Mm

Eric Vishria 57:24 ... of what they wanna do and why. And, and, and through that process, hopefully we get, like, you know, one percent better a few times a year. We make a one percent better decision, two percent better decision a few times a year. And if you do that over a decade, that compounds to real results.

Patrick O'Shaughnessy 57:43 Mm.

Eric Vishria 57:43 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 of 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?

Patrick O'Shaughnessy 58:06 Mm.

Eric Vishria 58:07 And if I can't do that, I should not invest.

Patrick O'Shaughnessy 58:09 Mm.

Eric Vishria 58:09 Because it's just not... Like, it just means that the project is not... For me, it, it just doesn't line up in that, in that way. And so as long as we have one of those things, it doesn't matter that much what it is to me.

Patrick O'Shaughnessy 58:22 Mm.

Eric Vishria 58:22 It's just, it's important. It could make a big dent, and if it can make a big dent and it's a special person, I, I'd love to work on it.

Patrick O'Shaughnessy 58:31 Are there any other questions like that that you ask yourself before investing? That's a particularly good one.

Eric Vishria 58:35 Um, yes. Um, the other question, if this person calls me at nine PM on a Saturday night, will I pick up the phone?

Patrick O'Shaughnessy 58:41 I call that the green button test.

Eric Vishria 58:42 Yeah, yeah, yeah. Right? It's just like, it's like it's one of these things which is like, if you don't, then, then that-

Patrick O'Shaughnessy 58:46 You just know if it's a yes or a no, yeah.

Eric Vishria 58:47 You just know. Like, for whatever reason, it's just, it's a chemistry thing.

Patrick O'Shaughnessy 58:51 Yeah.

Eric Vishria 58:51 And they have to feel the same way, obviously. And then, you know, one of the other ones is, like, if, if it's right, does it matter? Which is different than this, like, first one, but it's like, you know, there's so many things that we could be right on as a business. I, I, I looked at one last week, and I told the entrepreneur, I was like, "I, I don't... I really think that you can run, build an amazing company here, and you just shouldn't raise venture capital."

Patrick O'Shaughnessy 59:16 Mm.

Eric Vishria 59:16 But there's, like, so many things that you can be right on that but they ultimately just don't matter. Like, nobody cares. That, that's a better way to say it. If we're right, will anybody care?

Patrick O'Shaughnessy 59:25 Right, yeah.

Eric Vishria 59:26 And it's like, if they won't care, then you're just not gonna build enough equity value.

Patrick O'Shaughnessy 59:30 Mm.

Eric Vishria 59:31 Um, and I think that's a, that's another useful one.

Patrick O'Shaughnessy 59:33 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-

Eric Vishria 59:39 Yes

Patrick O'Shaughnessy 59:39 ... which is-

Eric Vishria 59:40 Yes

Patrick O'Shaughnessy 59:40 ... manifested most simply in more dollars-

Eric Vishria 59:42 Yes

Patrick O'Shaughnessy 59:42 ... um, and higher prices. You and I have talked about this notion of, um, what high multiple inve- meaning, like, high multiple on invested capital investing is like and what it has been like and what it's moving into.

Eric Vishria 59:54 Yes.

Patrick O'Shaughnessy 59:54 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-

Eric Vishria 1:00:02 Yes

Patrick O'Shaughnessy 1:00:02 ... 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 fifty million dollar entry price, you know, ten years ago or whatever. Can you talk through that evolution, talk through the partnerships, like, way of thinking about it and talking about it, like, what you believe to be true that, that results in this decision to do this?

Eric Vishria 1:00:23 Just go back to, like, why did LPs, you know, starting with, 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, three percentage points a year, or five or whatever stupidity. Like, it's because there were, 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, two 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 like, that's it. Like, those two circles in the Venn diagram like almost perfectly overlap.

Patrick O'Shaughnessy 1:01:13 Mm-hmm.

Eric Vishria 1:01:14 And I think the thing that's changed is recently, relatively recently, in the last few years, um, 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 like there's like a gazillion new companies in there that you can generate hundred X's on. That's not true. But, um, there are, there are certainly many outside of early stage where you can generate really high, high returns. And so that's what... that's it. Like, that's what we wanna go after. You could argue we're a few years late. I, I, I think I take that criticism. I think that, um, but I think that opportunity exists for, on a go-forward basis.

Patrick O'Shaughnessy 1:02:06 Mm-hmm.

Eric Vishria 1:02:07 Um, and so, like we should go do it. And, uh, you know, everything else that, that we represent, which is the high conviction, high commitment, you know, partnership, everything else like that, that has to still be there.

Patrick O'Shaughnessy 1:02:21 As part of that discussion, what, what, what were like the other sides of the debate, such as like maybe we would've said the same thing in 'ninety-nine and twenty twenty. As markets get exciting-

Eric Vishria 1:02:31 Yeah

Patrick O'Shaughnessy 1:02:31 ... it seemed like the possibilities, we all extr- do this extrapolation error.

Eric Vishria 1:02:36 Yeah.

Patrick O'Shaughnessy 1:02:36 Um, what were the counterarguments to like, let's, despite all that, still not do it?

Eric Vishria 1:02:40 Yeah. I mean, there are all the counterarguments that you would expect. I, I think the biggest counterargument that really made this time right versus two years ago or whatever, um, was you need the team that can do it. Like it's just a different mentality. Like, there, there are differences in how you evaluate and think about things. All, all the other stuff are there's, there's, you know, why not change and stay within your circle of competency, and all those things are true.

Patrick O'Shaughnessy 1:03:02 Mm-hmm.

Eric Vishria 1:03:03 Um, but, but to me, that, that was like the biggest one.

Patrick O'Shaughnessy 1:03:06 Mm-hmm.

Eric Vishria 1:03:06 And we had a s- several examples over the last couple years where we had, I think, the right intuition on a company or an opportunity, and we didn't do it-

Patrick O'Shaughnessy 1:03:20 'Cause it was outside the box

Eric Vishria 1:03:21 ... because it was outside the box, and that's, that's obviously dumb. And I think it is quite different than a, a lot of, um, you know, the industry does. We're, we, we are really chasing these like very rare special companies that have like very high cash on cash opportunities-

Patrick O'Shaughnessy 1:03:39 Mm-hmm

Eric Vishria 1:03:39 ... um, where we think there just can be runaway successes and, and we can invest in them.

Patrick O'Shaughnessy 1:03:43 Is there any lesson to be pulled from the many, let's call it twenty to one hundred Xs 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-

Eric Vishria 1:03:57 Yeah, you can't

Patrick O'Shaughnessy 1:03:58 ... would be different.

Eric Vishria 1:03:58 You can't.

Patrick O'Shaughnessy 1:03:59 Um-

Eric Vishria 1:03:59 Yeah

Patrick O'Shaughnessy 1:03:59 ... what, what has, what have the twenty to one hundred Xs taught you in aggregate, if anything? [laughs]

Eric Vishria 1:04:05 Work with really special people.

Patrick O'Shaughnessy 1:04:07 Hmm.

Eric Vishria 1:04:07 You know, it's kind of like you wanna work really hard, you wanna work smart, and, 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 the Cerebras 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 twenty twenty-four.

Patrick O'Shaughnessy 1:04:33 Mm-hmm.

Eric Vishria 1:04:33 And it would've been taken public at a much, much lower valuation and, and like, you know, it would've been, um, rough and it didn't, it didn't work out because of Celsius and all this stuff. So timing matters. Like, and, and you know, just the advancement of that eighteen months like 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 like there was a lot of stuff that was outside of our control. These are all the classic things where it's just 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, you know, aside from like building on AWS or whatever, you pretty much own your whole stack, and so you're really fully in control of your, your destiny in that way. With hardware companies, you don't. Like there's an entire supply chain and all this other stuff.

Patrick O'Shaughnessy 1:05:18 HBM is a thing, yeah.

Eric Vishria 1:05:18 Yeah, HBM is a thing, and DRAM is a thing, and TSMC is a thing, and, and a lot of those cross geopolitical orders, and so geopolitics gets involved and, and so then that makes that complicated. And so that's a, a really big difference. You gotta get lucky on the timing and macro and, and, and other stuff. But I think it really starts with like working with these like crazy people with unbounded opportunities.

Patrick O'Shaughnessy 1:05:40 Mm-hmm.

Eric Vishria 1:05:40 And if you work with these crazy special people on unbounded opportunities, you know, then, then you, you get, you get lucky, uh, from time to time. You're bound to. I, I-- This... When I was twenty years old, I was working at an investment bank. Ben Horowitz, Mark Benn, um, had started Loudcloud. It was still in stealth and, you know, Ben gave me an offer to be his assistant. Um, and I was talking to this associate who like seemed like this like elder at the time. He was probably twenty-five. Uh, but like, [laughs] you know, talking to this associate at the time, and, you know, he, he said this thing to me, which I, which, which really has stuck with me. He was like, "Do you golf?" And it was like classic banking question, "Do you golf?" No, I don't fucking golf. But he's like, he's like, "You know, with golf, you, you keep on practicing and you like keep getting the ball on a three-par, like close, close to the pin, close to the pin, close to the pin, close to the pin." And he's like, "You keep getting the ball close to the pin and you keep practicing." And he's like, "That's hard work. That's like working smart. That's like, that's what you wanna keep doing." He's like, "Getting the hole in one, that's luck." I kinda love that framing, and I, I use it with my kids actually, 'cause 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.

Patrick O'Shaughnessy 1:06:55 Mm-hmm.

Eric Vishria 1:06:55 And the way to increase your luck is get a lot of balls close to the pin.

Patrick O'Shaughnessy 1:06:58 Mm-hmm.

Eric Vishria 1:06:58 And then like, yeah, eventually one will drop.

Patrick O'Shaughnessy 1:06:59 Mm-hmm.

Eric Vishria 1:07:00 And, and so I like that, that mental model, and so I kinda go back to this for, for companies, which is in... I think in, you know, each of us invests in one to two companies a year. I think in my 12 years, I've invested in 18 companies total

Patrick O'Shaughnessy 1:07:14 Crazy

Eric Vishria 1:07:15 ... which is a relatively small number. Um, so it's very high conviction and very high commitment. It's, 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,

Eric Vishria 1:07:30 very special people and these opportunities, magic can happen.

Patrick O'Shaughnessy 1:07:34 What have you learned about the best reasons and conditions for going public?

Eric Vishria 1:07:39 Benchmark does these Monday night dinners. You've, you've been once or, like... And so we had a, a CEO last night of a, a multi-hundred billion dollar private company.

Patrick O'Shaughnessy 1:07:49 Oof.

Eric Vishria 1:07:49 And we had this whole conversation, so it's kind of, it's, 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. You have public trust, actually, weirdly, 'cause there's some transparency that comes with being public, being a public company. You obviously have a currency that you can do things with, um, that ends up being there. You have an unbelievable ability to raise capital, which I think is why the labs will ultimately go.

Patrick O'Shaughnessy 1:08:19 Mm-hmm.

Eric Vishria 1:08:19 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, is like, it's like, okay-

Patrick O'Shaughnessy 1:08:28 See what's going on.

Eric Vishria 1:08:28 Yeah, see what's going on. Everyone can see it, and I think that's, like, a really beneficial setup. There's another element of it, which is what does a collegiate athlete wanna do? Go pro.

Patrick O'Shaughnessy 1:08:37 Mm.

Eric Vishria 1:08:38 They wanna 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's bigger. All of that's true. It's kinda the same thing with companies.

Patrick O'Shaughnessy 1:08:56 Mm.

Eric Vishria 1:08:56 There are a handful, and it really is a handful, three, four, whatever, that can get to this, like, tremendous scale without going public because things have gone through their execution and excellence.

Patrick O'Shaughnessy 1:09:08 Yeah. Lots of free cash flow.

Eric Vishria 1:09:09 They have lots of free cash flow, and they've done really well over a really long time, and I think that's fantastic. Um, and you know, good for them. But, you know, in general, for everyone else, like, get out there.

Patrick O'Shaughnessy 1:09:21 Mm.

Eric Vishria 1:09:22 And then the other thing that I would tell you is there are windows for a particular type of company. So, like, yes, the SaaS companies that went public in 2021, a whole boat of them have struggled, and it's been tough i- in the public markets 'cause their stocks ripped to this thir- this multiple compression issue. They were trading at 30 times. They 4X'd in size, but now they're trading at six times. It turns out you're under still, right? Like, that's, that's a tough place to be. Um, 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, what happens to them? Those, 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.

Patrick O'Shaughnessy 1:10:14 Mm.

Eric Vishria 1:10:15 And, you know, 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, you know, the AI natives with their growth rates sucked all the oxygen out of the room and all the interest, and the window was missed.

Patrick O'Shaughnessy 1:10:30 Mm.

Eric Vishria 1:10:31 And that's tough.

Patrick O'Shaughnessy 1:10:33 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, y- you know, you, you debate. It's healthy. It's can be, can be, um, really fun to watch.

Eric Vishria 1:10:45 Yeah.

Patrick O'Shaughnessy 1:10:45 And I learn a lot from it. What are those debates today?

Eric Vishria 1:10:48 There's a ton of debate around, in this AI infra apps, you know, well, foundational models, infra apps, like ecosystem, where does value accrue-

Patrick O'Shaughnessy 1:10:58 Mm

Eric Vishria 1:10:58 ... and how does it accrue?

Patrick O'Shaughnessy 1:10:59 Where are the moats?

Eric Vishria 1:11:00 And like, why, where are the moats?

Patrick O'Shaughnessy 1:11:01 Yeah. Yeah.

Eric Vishria 1:11:01 And, and how do we think about that? Um, but also, like, the business model innovation. I think one of the things that's, people don't understand about, like, 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, right? Like, you, you really did have this, like, subscription element that ended up being fantastic, um, for both the company and the customers. Like, it was a win-win situation. And so I think there's, like, that same thing actually exists in AI and, you know, selling by outcome, um, and, and that piece of it. Um, but then, you know, wrapped up into that debate, um, and discussion is, like, how much value just accrues to the labs?

Patrick O'Shaughnessy 1:11:48 Yeah.

Eric Vishria 1:11:48 How much of the value just accrues to the semis?

Patrick O'Shaughnessy 1:11:50 Mm.

Eric Vishria 1:11:51 I mean, that's, that's a real discussion.

Patrick O'Shaughnessy 1:11:52 What do you think?

Eric Vishria 1:11:53 Like, I am of the view that it all works [laughs] as I said. And I, I really... Like, it's a very weird thing. It's like-

Patrick O'Shaughnessy 1:12:02 Mm

Eric Vishria 1:12:02 ... will the CSPs do well?

Patrick O'Shaughnessy 1:12:04 Yes.

Eric Vishria 1:12:04 Yes.

Patrick O'Shaughnessy 1:12:05 [laughs]

Eric Vishria 1:12:06 Will some of these NeoClouds 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 them? Yes. Like, are we gonna have edge inference on our phones? Yes.

Patrick O'Shaughnessy 1:12:23 Mm.

Eric Vishria 1:12:23 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, 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."

Patrick O'Shaughnessy 1:12:42 Yeah.

Eric Vishria 1:12:42 Like, no.

Patrick O'Shaughnessy 1:12:43 Mm.

Eric Vishria 1:12:43 That's not what's gonna happen.

Patrick O'Shaughnessy 1:12:44 Mm.

Eric Vishria 1:12:44 Like, and, 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, and I listed off all these everything's, I, it's really important to understand that that doesn't mean that every company that's doing every one of those things is gonna work. It actually means quite the opposite of that. Like, m- most companies in each of those areas are not gonna work, and it's actually more important than ever to have real differentiation and to, to, like, really, like, take each of these thoughts to, to their logical extreme and understand, like, wait a minute, you gotta go all the way-

Patrick O'Shaughnessy 1:13:17 Mm

Eric Vishria 1:13:17 ... um, on these things and really be differentiated on it.

Patrick O'Shaughnessy 1:13:20 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, that you really are watching carefully?

Eric Vishria 1:13:28 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, and I'm gonna, I'm gonna give you a really concrete example, which I think is just so good. Take Jeff Hinton in, in radiology. So I think it was twenty sixteen where he was like, "We should stop training radiologists, like AI's gonna do it all better." And like, look, Jeff Hinton's like three orders of magnitude smarter than I am, but like could not have been more wrong.

Patrick O'Shaughnessy 1:14:15 Mm.

Eric Vishria 1:14:16 But the actual thing that led him to s- make that statement or that conclusion was a hundred percent correct.

Patrick O'Shaughnessy 1:14:22 Mm.

Eric Vishria 1:14:22 Which is if you look at his radiology images, like yeah, we should be able to train AI to do a better job of like reading these things than humans, and that's probably true.

Patrick O'Shaughnessy 1:14:33 Mm.

Eric Vishria 1:14:33 Um, and, and, and actually I think the studies and areas have shown that to be true. And 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.

Patrick O'Shaughnessy 1:14:50 Mm.

Eric Vishria 1:14:50 And so like what you see is like companies going after like chest CTs or like 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 twenty things or tw- or forty scans that they read that day.

Patrick O'Shaughnessy 1:15:18 Mm.

Eric Vishria 1:15:18 And so okay, so problem number one, you don't have the data, just like we talked about in robotics and everything else to, to, to train the AI. Problem number two, the whole healthcare industry is oriented around reimbursing doctors for making readouts. [laughs]

Patrick O'Shaughnessy 1:15:31 Yes.

Eric Vishria 1:15:31 And so like how is that gonna 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 mal- malpractice and everything else. So like how are we gonna avoid that, and how are we gonna get around that? So that's, that's like, like problem number two, just like real-world stickiness.

Patrick O'Shaughnessy 1:15:48 Mm.

Eric Vishria 1:15:49 And so I think in the end we're gonna end up with this application where AI really does help radiologists. It helps radiologists get more and more higher, 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 co-pilot situation, um, for, for some time. And then you're gonna 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 gonna take a long time, and in the ensuing time we need more radiologists, not less, because, oh, by the way, everyone's getting more imaging than they used to get-

Patrick O'Shaughnessy 1:16:27 Yeah

Eric Vishria 1:16:28 ... because the cost of energy 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.

Patrick O'Shaughnessy 1:16:48 Mm.

Eric Vishria 1:16:48 And that's how I think of the unemployment thing. I think it's just... it's almost the exact same setup.

Patrick O'Shaughnessy 1:16:53 Eric, I love talking about markets and-

Eric Vishria 1:16:55 So fun

Patrick O'Shaughnessy 1:16:55 ... things with you. An absolute blast.

Eric Vishria 1:16:58 Thank you.

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