Invest Like the Best with Patrick O'Shaughnessy
00:07:05 · Eric Vishria
But even then, in twenty fourteen, the thesis was the view that AWS was gonna eat everything was massively wrong, not because of all the examples I just mentioned. It was massively wrong because Azure and GCP were irrelevant then, and fast forward to twenty twenty-six and their unbelievable businesses. Is AWS the biggest? I think it's like a forty thirty twenty split. You ended up with an oligopoly 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 smaller players that emerge as hundred billion dollar companies outside of it. So what's the takeaway? The takeaway to me is, oh, there's just a bunch of zero sum thinking and not realizing like how big what if it all works? What if it all works? It all works. And of course, getting the relative winner right matters, and there was roadkill, so it's all those things still matters. I'm not saying spray and pray, I'm not saying that at all, but I'm just saying That one vendor couldn't scale and consume it all, and they just couldn't consume the whole industry. It's different now and like all these things, but just that whole notion right now with what you and I are seeing in AI. Sure feels like it rhymes. Anthropic's gonna do everything. Right. Really? Is that really right? To me, that view that it's just like, "Oh, this one company's gonna eat it all, "doesn't hold. And I would tell you that scaling, while cloud scaled very quickly, cloud didn't scale anywhere close to as quick as what's happening right now. For that company to actually scale and deliver it, scaling in this case requires a ton of infrastructure build out from energy, power, shell. Chips, memory, obviously the algorithms and everything else on top, it feels to me like we're gonna end up with an oligopoly of winners. I really believe there will be these like hundred billion dollar crazy smaller winners.
00:08:58 · Patrick O'Shaughnessy
[00:08:58] S0: It just feels like the same thing is kind of happening. Can you talk about it also from the demand side and compare it to how you watched cloud get adopted in enterprise versus how you're seeing AI get adopted now? So if you go back to like two
00:09:10 · Eric Vishria
[00:09:10] S2: thousand and ten, two thousand and eleven, now you're like three, four years into AWS being offering, enterprises were super skeptical, traditional enterprise, blue chip enterprise. You had digital natives out here, you had new companies forming that were using the cloud. I think kind of famously, Snapchat was built on GCP, and I think at one point in this era, maybe twenty twelve, twenty thirteen, twenty fourteen, Snapchat was like forty percent of GCP. Those kinds of things were happening. That's like Cursor being thirty percent of all the things. Thanks. Hundred percent. Same thing happened. Same exact thing happened. Yeah. Same exact thing happened. Enterprises were very skeptical, I think, of cloud until by twenty fourteen, twenty fifteen, twenty sixteen is when Oh yeah, yeah. Then I think the big banks and financial services and insurance companies and more conservative blue chip enterprises were like, "Oh, wait a minute, this is actually different, and we're gonna have to pay attention. " And it became an issue in recruiting for them because they couldn't get the best developers, 'cause the best developers wanted to work on the easiest platforms, and all these kinds of things happened. So the difference now feels profound to me, because while it is a hundred percent the case that blue chip enterprise AI isn't well absorbed and well adopted and- Not the same thing as going to Cursor and walking the halls of Cursor versus walking the halls of a big New York financial services firm in terms of their AI use. They want it, they wanna figure it out. They're running experiments, they are spending against it, they're trying to figure it out, talking about it, they're not being dismissive about it, and I think they view it probably as more of an opportunity than they did the cloud in terms of the potential impact on their business. I think they probably view it more as a threat than they did. The cloud, and maybe they just also learn lessons from the cloud in terms of what's possible here. I feel that they will continue to try to figure it out and absorb it.
00:11:04 · Eric Vishria, Patrick O'Shaughnessy
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 what the pace of adoption is and what the barriers to adoption are and everything else, there's a ton of opportunity to help the enterprises get there to be one of the things that I tell the companies I work on is, "Hey, let's be their AI sherpa. If we're in that position. To be their AI sherpa where we're crossing both worlds, that's very valuable, and I think that'll continue to work. [00:11:36] S0: What is Sierra teaching you about the adoption of this stuff? It's really interesting contrast to Fireworks where Fireworks is the sort of infrastructure provider, Sierra is feeling its way through what are really cool things that we can do for end consumers, enable companies to do for end consumers, starting with customer service, but I know with Horizon now going beyond that. Again, same question as for Fireworks, what do you know that the world doesn't fully appreciate because of how- You've seen that.
00:12:00 · Eric Vishria
[00:12:00] S2: My partner Peter and Brett, I think this is their third company working together, so it's like a twenty year relationship, which is just like an amazing and great place to start. And one of the things that I think makes Brett so special and the team is they're technologists, but they actually have like lived in enterprise world for a long time and really understand it and everything else. And I think actually he's personified this whole idea of, "Hey, let's be their AI Sherpa. Let's start with customer service, very automatable, and like be their AI Sher Now we have these long running agents with horizons 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, and they're experimenting with 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. And intuitively, they understand that jagged edge of capability and they build around it. You saw the evolution of Cursor from the ide to like tab auto-complete to agentic work over and over and over again. They were Obsoleting their work from six months ago, and it's what Brett Taylor calls like sandcastles. We used to be building castles, now we're building sandcastles and they get washed away. And that's, if you don't think about it that way, yeah, you have to embrace it. Because if you were an artisan and you were like, "Hey, I built these perfect foundation of castle and it was bricks and it was like perfect, and I really care about this and it's gonna be here for a hundred years. You're just not gonna make it. So as you get emergent new properties in the models, which is, you know, 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, they're filling in those gaps and translating it. You can't just be superficially applying things. I think it's actually a complete inversion of how product development used to work to how product development happens now. In product development, you'd also say like, "Hey, product manager, you're trained up.
00:14:02 · Eric Vishria, Patrick O'Shaughnessy
The product manager 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 engineer so that the engineer builds a solution. Like, that was traditional product management. 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 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 beginnings. They really understood the JAGGED capability and built a product that allowed that translation from developer to that JAGGED capability and kept on iterating [00:14:53] S0: on that as the edge changed. It's kind of ironic that- Well, that sounds like the returns to being technical are going up, even as models supposedly are taking away technical edge.