00:16:08 · Aydin Senkut
[00:16:08] S0: want to read? I think a lot of it and even the lab part of it, like for instance, like we have a company that is not biology in the hard science part called Periodic Labs. We keep talking about these AI models that they can do amazing things. But you know one thing that we found out is that when it comes to the physical world, one of the things that is missing is how do you even test if this works? Let's say that the AI model came up with a new element like we have a new superconductor or we have a new chip material. Like you can do things 100 times cheaper. Like how much of an impact it would have? Great. Well, AI models can come up with it and the same thing can happen in biology. The problem is you don't know if it works. You can't just take a human and test on them like the way you do software testing on an app. So what I love and what we learned with Periodic is they basically spend years building an infrastructure where they can do more than a thousand experiments a day. And so they took over a biotech space. So And what I'm excited about biology is there are a lot of things right now we cannot scale because the real world verification and testing does not scale. And all we can still do is test things on animal, which by the way doesn't even reflect to people. So to me, one of the very interesting examples, my wife and I support this professor at Stanford that basically took advantage of that you can grow brain cells from skin cells, basically make like replica human brains and actually test on them for different things. They were able to cure like one of the first diseases. So there is like this new era of biology coming that people don't even imagine how cool it's going to be. In terms of just like we're gonna cure so much disease. And one of the only ways it's gonna be possible though is that AI is also gonna change the testing environment and how we basically verify that these things actually work.
00:17:49 · Eric Newcomer
[00:17:49] S1: On our third bucket, at Newcomer we surveyed venture capitalists a couple of weeks ago and asked the hottest category we weren't paying attention to. And overwhelmingly, they said robotics. So there's clearly this emergent list, that survey. There's a lot of points that robotics is hot. You're in the venture capital business. We're going talk in a second about building a fund. You need to make returns off these investments. Like even if you say, Okay, robotics, clearly there's something there. What's your analysis of what part of robotics is going to actually generate a huge return to investors?
00:18:22 · Aydin Senkut
[00:18:22] S0: So the one thing that I will say, we've been investing in robotics for almost fifteen years. Up until a year and a half ago or two years ago, everything we invested in robotics was an absolute abject failure. Like not even partial failure, like abject failure. Because what happened is we never had a moment where robots learn things the way humans do. Like you can't just basically program a robot only to take a step or to lift the glass and all of that. And more importantly, we learn things by looking, observing, whatever. There is some way that our brain has evolved to essentially pick things up really fast. And you see like the babies, it's amazing how they learn. For the first time, we have a way of making that happen in robotics. So it's infinitely scalable. So this is a very important moment. The reason why I'm calling that out is it's easy to say something is hyped or exciting but the more interesting question is why. Why and why now? And so that's a really important reason. And then the next thing is when you think about... I just did this simple calculation that just U. S, Western Europe, Japan alone 190,000,000 low skilled jobs. By the way, these are only jobs humans are willing to do. And there might be like another billion jobs that we didn't even imagine because humans would never do something like that. And so when you basically think about that as like, I don't know, like to pick your number, 10,000 a year, 20,000 a year, we're talking the market is so gigantic that for one of our robotics investments, I made this calculation that even 0.2% of that market would be 10,000,000,000 a year. Right? So like 10,000,000,000 a year at 10x multiple, that's a $100,000,000,000 company. So this is not a VC pipe dream of I'm optimistic this company is going to do great. And to me, the other big thing was the validation of some of the worst, most critical companies. The question is, can you have robots in the field in the toughest environments? And if those companies say these are good enough that I'm going to deploy them. And we are just starting to see that. That to me is a very important validation. We're not talking about household robots. We're talking mission critical, the biggest, most critical companies in the world. So the combination of all these things, I'm super excited.
00:20:26 · Aydin Senkut, Eric Newcomer
By the way, this doesn't even get to the bottom of like, hey, if we're going to build that many robots where are all the parts going to come from? Manufacturing industry is going to go through a 100 exchange. All of these robots will need actuators and energy and other stuff. We're going to need... If you thought inference market is big enough just wait until these robots come along with Asians. It's going be like an incredible explosion of demand. So that's why I'm super excited. And one of the other interesting approaches we have at Felicis is people look at a wave and it's this company and we're like, wait, no. This wave has like six, seven different ways it's going to play out. The robots themselves will be great. There's going to be data providers for the robots. There's going to be like AI and inference for it. There's going to be manufacturing wave. All these robots will need parts. There's energy that needs to come. There was robot deployment. There were like maybe 10 areas, not just the robots themselves, but like between the models and all of that. I just think like we are not even like top of the tip of the iceberg of how [00:21:28] S1: far it's kind of multiple robot builders, like if they're sort of competitors, but you think the market is large enough, can you invest in multiple companies?
00:21:28 · Eric Newcomer, Aydin Senkut
[00:21:28] S1: far it's kind of multiple robot builders, like if they're sort of competitors, but you think the market is large enough, can you invest in multiple companies? [00:21:37] S0: So what we try to do is we are not a fan of investing in competing companies, at least at the moment of investment. So this is the reason why I've articulated what I just did. So instead what we do is look, we know that this wave is going to be big. How many different ways can we play it? Right? Like I do not have the crystal ball of what company is going to be the eventual winner. I think we have a pretty good inkling. What we want to do is play... [00:21:59] S1: Can you say which one? [00:22:00] S0: Mean, think obviously the main robotics company... Mean, at what happened with OpenAI and Anthropic. So if you're doing... That's going to be a great place and there is going to be, I think, an OpenAI and Anthropic version in robotics. But I think the data of that business could be equally big because OpenAI and Anthropic use publicly available data for the most part. But for robotics, the data doesn't exist. I mean, even though now we are looking at videos and trying to learn from it, but still I think that's going to be a humongous, stellar business. So our philosophy is identify the big wave, identify hyper demand and then try to play it in every possible different perspective that you can.
00:22:00 · Aydin Senkut, Eric Newcomer
[00:22:00] S0: Mean, think obviously the main robotics company... Mean, at what happened with OpenAI and Anthropic. So if you're doing... That's going to be a great place and there is going to be, I think, an OpenAI and Anthropic version in robotics. But I think the data of that business could be equally big because OpenAI and Anthropic use publicly available data for the most part. But for robotics, the data doesn't exist. I mean, even though now we are looking at videos and trying to learn from it, but still I think that's going to be a humongous, stellar business. So our philosophy is identify the big wave, identify hyper demand and then try to play it in every possible different perspective that you can. [00:22:39] S1: If you were giving advice to these Emergent founders, I mean, there's such different categories, some not even seed, like now we're going to throw in the spotlight on them. Know it's hard to give advice across sort of a bunch of different types of companies, but what would be a piece of advice you'd give those founders?