How I Invest with David Weisburd
00:59:56 · David Weisburd
[00:59:56] S1: I had this very conversation with Matt Harris, who's at Bain Capital Ventures. One of the benefits of the podcast is I get to get investment and tax planning advice. I invest in the series c and Anthropic, and I just asked him, like, what do you think about Anthropic at 2,000,000,000,000? Like, what should I do with my position? And he used a very simple heuristic, which is if you look at the revenue multiples and you just take away the last three zeros, so now you say 2,000,000,000 valuation. Instead of 100,000,000,000 in revenue, you say a 100,000,000 revenue. It seems pretty reasonable. So we just get such sticker shock on some of these numbers that sometimes you just think it just can't be more. That there's there's no more room. And maybe there is or not room at at 2,000,000,000,000, but, certainly, if there's companies going public at 2,000,000,000,000, at a billion dollars, there is still... Depending how you define power law, there is still this power law, some nonzero chance of a power law.
01:00:50 · Brijesh Jeevarathnam
[01:00:50] S0: Absolutely. And and there was a... Was it 70 or 80,000,000,000 round in Anthropic and a 180,000,000,000 round in Anthropic, right? If this company goes out next month, knock on wood, hopefully, right? And trades well, even though we're looking at like 10X plus returns for those early investors, right? Ballpark. That might've been unthinkable a few years ago considering the ingoing entry valuation. But I do want to separate the cyclical from the structural. I think there are elements of the market we're in today that are no doubt cyclical. We're going to see kind of not just up into the right, but maybe some kind of, I'll call it normalization in the coming quarters and years. But if I step back far enough, the structural element of the venture aperture growing in both dollars and in terms of what it funds and magnitude and so on, that is a structural change in our mind and that'll keep kind of over a long time, keep going up. As I said before, eating the universe of publicly listed companies, starting from a small base, still small. We might look back in ten years from now and look back at this moment in time and be very wrong about the upset of these companies in either direction, right? We might have been very conservative. If I think about twenty sixteen, ten years ago, there were some things that seemed kind of peaking in the twenty sixteen, seventeen, eighteen era. Some of that was true, as 2022 proved, and some of that has not been true. So, it's hard to really prognosticate and say we're at a structural maxima. I think we're at a local maxima potentially. I don't know. But I think there's still long term growth in the private capital markets for sure.
01:02:58 · David Weisburd
[01:02:58] S1: I have to ask, what ending of the AI boom are we in?
01:03:03 · Brijesh Jeevarathnam
[01:03:03] S0: It's funny you ask me that because that's one of my favorite questions to ask our most trusted GPs. So, will paraphrase what they tell me. I've gotten various answers, including some that say, Oh, I think we're still in the kind of warmups. But I don't think a single person has told me it's going to post the fifth inning. So, in other words, it's still broadly kind of first half in people's consensus, whether it's kind of still pregame warmups or first or second inning. I like to sometimes go back and watch a presentation or a new product release from OpenAI or Anthropic and see what those very smart people, Sam Altman, Dario say. And what I find interesting is with the passage of time, even they cannot quite foresee where this is all going, right? And my point there is if the best minds in the world in AI cannot quite kind of prognosticate where we are in the AI cycle, generally optimistic, of course, but only directionally correct, I think we can all just say, well, we don't know what we don't know, but likely we still have a lot of room to go from here. One rubric that I like to talk about is, and this came from a few of our GPs, when there's a seminal moment in a particular technology, and maybe the best example is Netscape, the first kind of real public browser, I think it was 'ninety three, early '90s. There were many companies that were started in the internet 1.0 era, but the best ones came arguably kind of four to seven ish years out. And this is despite the .com issues. I'm talking about the Amazons and the Googles of the world, right? Even eBay.
01:04:53 · David Weisburd, Brijesh Jeevarathnam
[01:04:53] S1: Facebook as well. Think it was like the twelfth or thirteenth social network. [01:04:56] S0: Yes, exactly. Right? So, it came after the Friendster, MySpace era, right? Exactly. And if we then say, Look, history definitely rhymes, even if it doesn't repeat. And if we then say OpenAI and ChatGPT, that moment in fourth quarter of twenty twenty two, let's call it about four years ago from today, was that seminal AI moment. It certainly was the moment when AI burst into our consciousness, collective consciousness in a very, Oh my God, it's here kind of way. Could argue that we are just getting started with the Google, Amazon, your example, Facebook phase of this company creation, right? Only time will tell, but I feel it won't be a smooth journey necessarily, but there is a lot more to come in terms of company creation building around this mega wave. And I think it's easy to say it's all AI all the time, but there's a lot more to it in my opinion. I think AI is building on the prior waves that came before it. I think Sequoia did a good job recently talking about why is AI scaling so quickly? For example, distribution channels, right? With our smartphones and so on and so forth. I think AI is a wave built on all the prior waves that came before it that is creating a pace of change in terms of adoption by customers, in terms of monetization and really growth at scale. Anthropic OpenAI are good examples of this, that we've never seen before. And I don't think these are late innings. I think there's more to come.
01:06:35 · David Weisburd, Brijesh Jeevarathnam
[01:06:35] S1: A variation of this very question I like to ask people is if AI is a bubble, how's that possible? Like what part of the thesis would be wrong? And what people settle on is there's no way... There's no intrinsic value in the technology. People might be off on valuations or might be overly bullish, but there's no way this is a binary bubble like the eyeballs were for the .com bubble. There's just no possible way that there's no business use case for AI because everybody experiences the the productivity gains. [01:07:06] S0: Right. And and I certainly see that point. And at this moment in time, if one kind of uses your analogy and then adds like the dark fiber part of it, like, you know, was it ever lit up kind of thing. At this point in time, the demand for compute seems to be far outstripping the supply into the, at least the foreseeable future. And from a broad use case standpoint, it appears to be still very early in the AI wave. So, one could argue there's real legs to this potentially. And of course, you and I both know the most dangerous words in investing is this time it's different, but there are enough parameters to say that there is a lot more adoption and therefore monetization and therefore value creation to come from AI.