How I Invest with David Weisburd
00:06:29 · David Weisburd, Jerry Ye
[00:06:29] S0: This concept of this talent magnet, Elon Musk famously is the greatest talent magnet. He's able to just recruit the very best. At some point, I think he had five of the top rocket engineers at the very early stages of SpaceX. And this had become this consensus view that if you could attract talent, arguably even more important than venture capital. Do you think that it's the fact that these engineers are making the best choices on the companies that's driving that? Or do you think that the engineers themselves, the top engineers are the ones that are building these amazing companies? [00:07:00] S1: A little bit of both. Right? So the team definitely makes a huge difference, but the company itself culture wise also matters too. Right? Like, you could have a company that's attracting top engineers or top product people, and they're churning employees like crazy. Right? So that's actually another signal that you might wanna look at as well. And another example of this is, I think if you just looked at LinkedIn data or talent data, that a couple years ago you saw that Anthropic was probably on average attracting more interesting talent than OpenAI. Right? And if you look at it from a churn perspective, and it might be a reflection of the evaluation increases now, but if you look at churn, Anthropic is churning engineers at a lot lower rate than OpenAI. And so it is a combination of the people and who they're able to attract and what they're able to build, but it's also a reflection of the company and the culture, what they're able to accomplish.
00:07:00 · Jerry Ye, David Weisburd
[00:07:00] S1: A little bit of both. Right? So the team definitely makes a huge difference, but the company itself culture wise also matters too. Right? Like, you could have a company that's attracting top engineers or top product people, and they're churning employees like crazy. Right? So that's actually another signal that you might wanna look at as well. And another example of this is, I think if you just looked at LinkedIn data or talent data, that a couple years ago you saw that Anthropic was probably on average attracting more interesting talent than OpenAI. Right? And if you look at it from a churn perspective, and it might be a reflection of the evaluation increases now, but if you look at churn, Anthropic is churning engineers at a lot lower rate than OpenAI. And so it is a combination of the people and who they're able to attract and what they're able to build, but it's also a reflection of the company and the culture, what they're able to accomplish. [00:08:02] S0: Take me back to this because you noticed the signal, this Anthropic signal before Anthropic became Anthropic today. Tell me about that. What exactly were you seeing in the data?
00:08:02 · David Weisburd, Jerry Ye
[00:08:02] S0: Take me back to this because you noticed the signal, this Anthropic signal before Anthropic became Anthropic today. Tell me about that. What exactly were you seeing in the data? [00:08:12] S1: So I think anyone that was looking at TalentSignal a couple years ago would have seen that on average, Anthropic was hiring people with, you know, more... In terms of, like, a pedigree type signals, like better schools, better experience, and on average, that actually had them surpassing OpenAI at some point. Right? So I have to caveat that this is kind of like an art versus science here because, you know, like, how much weight do you give to, like, a senior manager versus a junior person. Right? [00:08:49] S0: So you're aware of this inflection with Anthropic having better talent before OpenAI. Maybe you could double click on that. Quantify what exactly does that mean? [00:08:59] S1: In terms of what you would see on a profile of a person in terms of a resume, it's, you know, what schools they went to. That's a signal for selectivity. What other companies they worked at, where they went to, right, how many people went there, what churn looked like. And by essentially doing that as well as, you know, understanding how much capitalization those companies had was the outcome, what were the founders, etcetera. You can start putting together a formula for how to rank people, and one that's more technically focused in terms of engineers will show that Anthropic was leading OpenAI three, four years ago.
00:08:59 · Jerry Ye, David Weisburd
[00:08:59] S1: In terms of what you would see on a profile of a person in terms of a resume, it's, you know, what schools they went to. That's a signal for selectivity. What other companies they worked at, where they went to, right, how many people went there, what churn looked like. And by essentially doing that as well as, you know, understanding how much capitalization those companies had was the outcome, what were the founders, etcetera. You can start putting together a formula for how to rank people, and one that's more technically focused in terms of engineers will show that Anthropic was leading OpenAI three, four years ago. [00:09:38] S0: Obviously, talent is arguably the most important factor when it comes to the success of a startup. What's right up there with talent? [00:09:46] S1: Talent is definitely super important because it spans all sectors. And especially for, like, enterprise, that might be the only thing that you have. But for a lot of other sectors, like consumer or gaming, fintech, prosumer, there's actually a lot of signals from alternative data panels, especially the credit card data, web traffic, app traction that you can leverage to identify traction. And some of them are actually so granular that you can actually figure out what retention rates look like as well. [00:10:16] S0: What exactly are you looking for when it comes to revenue?
00:10:16 · David Weisburd, Jerry Ye
[00:10:16] S0: What exactly are you looking for when it comes to revenue? [00:10:19] S1: Might be helpful to take a step back in terms of what I'm talking about for, say, like, card panel data. Right? So there are data providers out there that basically act as, like, the interconnect between your banking account and your brokerage. And they, as a side business, resell this, and a lot of hedge funds buy it. Right? Sometimes this stuff is actually real time, and hedge funds will pay $7.08 figures a year for this. Venture on the other end, because the time horizon is ten years, the ROI is not is not the same. Be happy with, like, a delayed panel. Right? But you're literally talking about three to 5% sample of all US credit card and debit card spend. Right? And with that, not only are you able to figure out, you know, how the corner Starbucks is doing, like, who's coming back, how... What the retention rates are. You're also able to figure out what, you know, OpenAI is looking like in terms of revenue on the consumer side. These are generally consumer type card panels, but there are some that kind of focuses on business transactions as well. And for the consumer side, what you can do is try to identify the companies based on the credit card descriptors, which is similar to what you see in your credit card bill at the end of the month. And for these panels, they generally match the stuff with companies with stock tickers or late stage venture companies. [00:11:49] S0: So hedge funds are using this to figure out earnings data before Yeah. They're announced by the companies.
00:11:49 · David Weisburd, Jerry Ye
[00:11:49] S0: So hedge funds are using this to figure out earnings data before Yeah. They're announced by the companies. [00:11:54] S1: Yeah. So interestingly enough, I remember going to a meeting here in New York that this is almost like a decade ago, and it was it was like a dinner with a bunch of hedge funds, and they basically concluded there was no more alpha from credit card data. That's just like how far advanced they were. Right? Whereas still on the venture side, just because a lot of stuff wasn't tagged, right, because it's only tagged for the long tail, I would say the the head revenue, the long tail was not tagged. So there's actually a lot that goes into it that, you know, you can do any resolution to figure out does this square is this square square cafe? Or is it like square? Or is like... Is there actually like a submerchant under there? And it's still largely untapped. You can't feed this into your LLM. Like, you blow up your context window immediately. Right? And... [00:12:45] S0: And that's because there's so much... Just just so so much data. [00:12:49] S1: Billions or trillions of data data points. Right? And so there's a little bit of work there. But if you do the the grant work there, you can identify, you know, like, up and coming companies, like, what is inflecting? What what has high retention? Right? And a lot of times, you could figure out these companies before it becomes like a cultural phenomenon or before the media picks up on it or other VCs pick up on it.