How I Invest with David Weisburd
00:00:00 · David Weisburd, Joseph Coyne
[00:00:00] S0: So I did invest in Anthropic at a $4,000,000,000 valuation. How would you underwrite it today? Have a [00:00:05] S1: look at the implied forward capture of the market. Right? So how big is the market likely to be, and then what percentage are you expecting to capture as Anthropic vis a vis or relative to OpenAI and some unknown entrants. So I think that's hard to handicap on the who's coming next, right, conversation. [00:00:23] S0: It's unknown unknown. Who's gonna have the idea or the resources? [00:00:27] S1: That's the unknown unknown. But you have other folks that are devoting a lot of resources to the space that have billions of dollars and reasons to be highly defensive. The Googles, the Metas, the Amazons, etcetera.
00:00:27 · Joseph Coyne, David Weisburd
[00:00:27] S1: That's the unknown unknown. But you have other folks that are devoting a lot of resources to the space that have billions of dollars and reasons to be highly defensive. The Googles, the Metas, the Amazons, etcetera. [00:00:39] S0: I'll tell you the bold case. Virtue of Lucas Wisher, he runs growth for Code two, Bain, he talked about the three s curves of adoption. So the first s curve, the early adopters in terms of LMs from a revenue standpoint have What's crazy about that is that there's only 50,000,000 developers in the world out of 8,000,000,000 people. And on top of that, only a small percentage of developers have actually implemented. If you look at worldwide kind of engineers, then you have all of enterprises, then you have consumers. And then you could argue a fourth S curve, which is agents. So some smart people would argue that your TAM is not actually 8,000,000,000, it's 8,000,000,000 people and their agents talking to each other. So in terms of adoption curve, we started this conversation talking about we're in the first inning of AI. It's reported that precede rounds today on average $100,000,000 valuation. How do you underwrite? [00:01:33] S1: I think [00:01:37] S0: So, Joe, you've been in and around venture for nearly two decades with firms like Bain Capital Ventures, Samsung. And you believe that AI is in its first inning. Why?
00:01:37 · David Weisburd, Joseph Coyne
[00:01:37] S0: So, Joe, you've been in and around venture for nearly two decades with firms like Bain Capital Ventures, Samsung. And you believe that AI is in its first inning. Why? [00:01:49] S1: Well, I think specifically, enterprise AI is really just starting to scratch the surface of what it can do within enterprise. What we've seen at large enterprises, I e the customers of our portfolio companies, is that they're either scared to adopt or slow to adopt or have high failure rates when they adopt AI. So I think because of all we're seeing from our customer base through the portfolio lens, I think there's so much more to come, and I'm excited to see what it can do. [00:02:14] S0: Which one of those is is the real issues being scared to adopt or being slow to adopt or just being poor poor at adopting today?
00:02:14 · David Weisburd, Joseph Coyne
[00:02:14] S0: Which one of those is is the real issues being scared to adopt or being slow to adopt or just being poor poor at adopting today? [00:02:21] S1: It's a new muscle for a lot of enterprises. I mean, they've gone through data transformation over the many years. They've built sort of database architecture. And now as they start to use that foundational data to adopt AI best practices, it's all new for them. And so they either need external help. It could be a startup, could be a growth company, could be a consultant. But internally, their resources are generally pretty poor right now from what we've seen. 95% of enterprise AI deployments are failing these days, and that's... I think that's per Deloitte. So that only has a lot of room to scale, a lot of room to grow. And, obviously, if you've done something a few times, as in deployed AI across different verticals or different datasets inside of an enterprise, you only get better over time. [00:03:03] S0: That number you said from Deloitte, ninety five percent of pilots fail. I haven't heard that number, but I experienced it firsthand. And you're see... You're seeing these hundreds of enterprises implementing AI over and over every day. What are the best practices in terms of the five percent that do succeed?
00:03:03 · David Weisburd, Joseph Coyne
[00:03:03] S0: That number you said from Deloitte, ninety five percent of pilots fail. I haven't heard that number, but I experienced it firsthand. And you're see... You're seeing these hundreds of enterprises implementing AI over and over every day. What are the best practices in terms of the five percent that do succeed? [00:03:19] S1: If they come from a deeply technical background that's already been doing deep data analysis and has great data infrastructure plus security, then they have the foundation of success. Right? So that's like already tipping the scale in the favor of tech companies that are pretty well poised to do that. Some of the legacy companies, legacy industries that are lower tech, we've seen implementations where once we get under the covers, the data is pretty unruly. And so getting the data to a point where from an infrastructure standpoint, it's ready to adopt AI is half the battle. Now, I think once we cross that chasm, a lot of these AI best practices can start to proliferate and accelerate. [00:03:57] S0: What does it mean to have good data versus bad data when it comes to implementing AI on the enterprise level?
00:03:57 · David Weisburd, Joseph Coyne
[00:03:57] S0: What does it mean to have good data versus bad data when it comes to implementing AI on the enterprise level? [00:04:02] S1: You see in large enterprises, right, they're... The larger the enterprise, the more complex, generally speaking, and they have lots of data silos. That means data tagging fidelity across different fields and tags can be different. And so when you try to run enterprise grade AI on top of it, you have all sorts of sometimes conflicting tags and data structures, which have to be resolved before you can really do it in a meaningful way. And so those are some of the data transformation projects that are foundational to then running great AI and and analytics on top of enterprise. [00:04:32] S0: Something I hear over and over about AI implementation is that it's mostly a cultural issue. Yeah. Sure. There's data. There's processes, but starts at the culture level. How much does culture play a role in terms of enterprises being able to implement AI within the organization?