How I Invest with David Weisburd
00:43:12 · Jerry Ye, David Weisburd
[00:43:12] S1: I think that goes back to the example I had a little bit, right, where sometimes it's, you know, just hard to even attribute where that came from. So I'm not sure if they saw that from their end in terms of, you know, like NPS or surveys that they sent out to their portfolio. But at a certain stage as well, it's just it's just hard to beat a company or a firm that's actually just purely focused on talent. And for for large companies, right, they have their own in house teams. I think we touched a bit on terms of AI making teams much smaller as well. So maybe hiring itself is also just, like, not as needed. [00:43:52] S0: The distinction between value add and attribution is a fascinating one. It tracks to some of the other conversations going on that I've had with others. Sam Altman's coach, Joe Hudson, talks about this, which the top founders that he's ever coached, and he's coached not just Sam Altman, but some of the top founders in the world, they all have pretty much goldfish memories. They have to make thousands of decisions a day so they don't second guess themselves. They make a decision. They move on. They just compartmentalize on an SDR level so they don't remember. If somebody made an introduction three months ago, they don't remember. And that's not a bug, that's actually a feature. That's what allows them to scale and manage these teams of of tens of thousands of people. That tracks to why these firms might not be getting the the credit for for some of the value add that they're providing. [00:44:39] S1: Yeah. Definitely.
00:44:39 · Jerry Ye, David Weisburd
[00:44:39] S1: Yeah. Definitely. [00:44:39] S0: One of the interesting factors that we see in the startup ecosystem today is these tranche rounds. Sequoia will come in at a billion dollar valuation, and then startups will raise another round at $2,000,000,000 almost, you know, a couple weeks later. What do you think about these tranche rounds? [00:45:00] S1: It's pretty interesting to see those, especially on the AI side. A lot of these are a reflection of revenue growth. It's... They're just scaling so quickly that a round that was priced a couple months ago might be completely mispriced already just five, six months down the road. So I think that makes sense from that perspective. And Venture itself is still sitting on the most dry powder it's had ever. Right? So there's a lot of money as well slashing around. It's just about getting into the right positions. [00:45:31] S0: You've been quantifying venture capital, this art of venture capital, arguably longer than almost anybody going back to 2013 and really built your career on this. If you go back and give yourself one timeless piece of advice when you first started doing this, what would that be?
00:45:31 · David Weisburd, Jerry Ye
[00:45:31] S0: You've been quantifying venture capital, this art of venture capital, arguably longer than almost anybody going back to 2013 and really built your career on this. If you go back and give yourself one timeless piece of advice when you first started doing this, what would that be? [00:45:47] S1: A lot of firms take the if it ain't broke, don't fix it mentality, and it is hard to impact using data from a structural standpoint. Right? You really need top down support from the the GP, unless there's actual KPIs around targets and whether there's outreach, etcetera, it's it's hard to to actually motivate the team to actually go and do the outreach or to to follow-up on flags from the data side. It's actually much easier to target more of the operational side in terms of making the firm more efficient. There's a lot of uncomfortable conversations that come with, you know, just quantifying access or understanding the efficiency of a venture firm. And especially as like an outsider, like, most data people in venture are, coming from like a outsider perspective is hard to... Especially when you deal with attribution as well. And it's a uncomfortable conversation, and it kinda makes it harder than what I initially thought it would be in terms of of of getting adoption through a firm.
00:45:47 · Jerry Ye, David Weisburd
[00:45:47] S1: A lot of firms take the if it ain't broke, don't fix it mentality, and it is hard to impact using data from a structural standpoint. Right? You really need top down support from the the GP, unless there's actual KPIs around targets and whether there's outreach, etcetera, it's it's hard to to actually motivate the team to actually go and do the outreach or to to follow-up on flags from the data side. It's actually much easier to target more of the operational side in terms of making the firm more efficient. There's a lot of uncomfortable conversations that come with, you know, just quantifying access or understanding the efficiency of a venture firm. And especially as like an outsider, like, most data people in venture are, coming from like a outsider perspective is hard to... Especially when you deal with attribution as well. And it's a uncomfortable conversation, and it kinda makes it harder than what I initially thought it would be in terms of of of getting adoption through a firm. [00:46:58] S0: Let's say that you're advising a CEO, one of the top multistage venture capital firms, one of these top 15 firms. And the CEO was bought in of the firm, and they wanted to institute data in the deepest level within their firm on every part of the stack. How would you advise them to execute on that strategy? [00:47:20] S1: I think you need KPIs, like what a true north star is. [00:47:24] S0: You need to make it objective.
00:47:24 · David Weisburd, Jerry Ye
[00:47:24] S0: You need to make it objective. [00:47:26] S1: Exactly. And it has to be objective that's in a way that's grounded by data. And by that, mean, going back to the early example as well of what deals are actually done. Right? And measuring that against, you know, what data flagged, what is the baseline for the firm in terms of, you know, what your associates, principals, GPs did in terms of deal sourcing, etcetera, or decision making. But keeping it at that level in terms of what actually happened, what what ended up being funding. Right? A mistake I've seen a lot of times is trying to figure out, well, let's have the investment team rank each of these. Right? And what I've seen a lot of times is those decisions actually change over time. Right? So this decision, let's say Airtable in 2016, right, it's genius move, but more recently after the acquisition, the outcome was not as good as, you know, a lot of people thought. Right? So those kinds of decisions change over time and actually and actually a lot of investors are turning now too. So you may or may not have the same ground truth label that you're trying to fit your model to. So I'm a 100% advocate of having a North Star or a KPI that is quantifiable in terms of data value add. And then you need that top down support to actually institute this throughout the firm.
00:48:45 · David Weisburd
[00:48:45] S0: There's an old saying from Jason Fried from thirty seven signals, which is don't build a half ass product, build half a product. And I think this is really critical for AI. For anyone building and growing an AI first organization, you need to have... I agree with your point, which needs to be quantitatively. But second to that, you need to have a successful implementation. So a lot of people will come in and say, well, we have sourcing, we have picking, we have winning, we have liquidity, all this stuff. Let's create this whole system, and let's do a nine week off-site, and then let's have a two year implementation. That's not gonna work. What's gonna work is taking something that people care about, which is, are we getting a look at the very top companies? These are top 150 companies. How do you know they're all worth over 100... Over $1,000,000,000? These are the unicorns. This is what the market has determined a 150. How many of these did we look at? Okay. We looked at... We only gotta look at 40 of these 150. In doing a project with AI on that specific thing, a problem that no one could say is not an important problem, a problem that's very clear, a problem that's definable, and then executing on that, and then starting to expand the scope. I think this is just such an underrated aspect as people kind of go between doing these grand projects, which never works. It's like the high speed train in California, which I think they they did, like, one one stop for for, like, $200,000,000,000 versus these... Almost like these startups, these lean startup approach to it, which is get something that works, expand, make that work, expand further on. That's what I've seen as work day in and day out, whether it's a venture capital firm or just a startup implementing AI.