How I Invest with David Weisburd
00:03:59 · Chaz Englander
[00:03:59] S1: The important thing is is where are we today, but where is this heading? Right? So today, let's make no mistake about it. You know, these application layer products, whether that's in finance, legal, health care, whatever, you know, they are productivity tools for the most part. They're they're giving you, you know, an additional layer of efficiency in your businesses. Right? The question is though, particularly if we think about this from an investing standpoint, is when is this going to be able to deliver a level of insight that wasn't possible pre AI? That's really the question, you know, because at the end of the day productivity is great, But it's all about that alpha from an insight perspective. And in our view, there is absolutely no doubt that 2026 is going to be that year. We think 2025 is the productivity year, 2026 is the year where we're actually going to start to see these systems deliver insight that wasn't possible pre AI. Now, if we're going to play that back a little bit, that's not going to be today no insight, tomorrow insight. It's going to be slightly more incremental than that, right? I'll give you a quite specific example. One of our middle market private equity clients, it's a European client, they're absolutely fantastic. They've pretty much automated 80% of their IC memos, their IC paper, right? A lot of that is going to different data sources and really just data retrieval, a bit of reasoning and producing that in a format that they're used to digesting. Graph, tables, charts, logo in the same formats that they would digest that information from. You know, going to the data room and going to CAPiQ and going to PitchBook and so on, all the areas of information that you would normally go to. But there's two or three pages in that now that are not just generated by AI, but it's kind of like the AI's opinion, right? And these are things like the AI's opinion on the overall management team based on your historical investments. We've noticed that there's a lack of experience over here. There's a lot of experience here, for example. Now, as of today, they glance over that in their IC meeting. It's kind of like this is interesting, we spend five minutes on it and we move on. But one of the things that's clear to them, directly clear to us, is the importance of those two or three pages is only going one way.
00:06:04 · Chaz Englander, David Weisburd
In other words, the AI opinion is only becoming stronger and stronger and stronger. And so I think that's why it's super important that firms it might not be perfect today, right, but you've gotta embed this into your culture and these systems into the way that you think as soon as possible because that future insight is only gonna be unlocked by doing this today. [00:06:23] S0: Tell me about that. Why do you have to prepare today for insights in the future?
00:06:27 · Chaz Englander
[00:06:27] S1: I was thinking coming into this conversation, what would I advise, you know, you know, irrespective of what we do, what would I advise firms to think about today? Would really think about data. I think things like trying to transcribe calls is a is a great example of, like... You know, if you think of what these systems are gonna need in the future, the more data that they have, particularly now because they are data structure agnostic, whether they're calls, emails, files and folders, structured data, it doesn't matter. You wanna try and capture as much data as you possibly can as part of the investing process. I think that's important. The second part of that question is, this is more of a cultural shift than anything else. I think as we've thought about so I should say, our customers are about a third, say asset management in general, a third in the largest consulting firms are on a third in banking. There are thereabouts. There's a few other firms in there now but there are thereabouts. Now, the consistency across all of them, right, so not just on the bi statistics across all of them is this is clearly not a technology problem anymore, Right? This is becoming more and more of a cultural change and a structural change, you know, as to how you think about the organization, how you think about AI from a cultural perspective. But that takes time. And and and, you know, I really would encourage firms to just start and not overthink that initial process and and start.
00:07:42 · David Weisburd
[00:07:42] S0: One of the hardest things of investing is seeing what's shifting before everyone else does. For decades, only the largest hedge funds could afford extensive channel research programs to spot inflection points before earnings and to stay ahead of consensus. Meanwhile, smaller funds have been forced to cobble together ad hoc channel intelligence or rely on stale reports from sell side shops. But channel checks are no longer a luxury. They're becoming table stakes for the industry. The challenges has always been scale, speed, and consistency. That's where AlphaSense comes in. AlphaSense is redefining channel research instead of static point in time reports. AlphaSense channel checks delivers a continuously refreshed view of demand, pricing, and competitive dynamics powered by interviews with real operators, suppliers, distributors, and channel partners across the value chain. Thousands of consistent channel conversations every month deliver clean, comparable signals, helping investors spot inflection points weeks before they show up in earnings or consensus estimates. The best part, these proprietary channel checks integrate directly into AlphaSense's research platform trusted by 75% of the world's top hedge funds with access to over 500,000,000 premium sources from company filings and brokerage research to news trade journals and more than 240,000 expert call transcripts. That context turns raw signal into conviction. The first to see wins, the rest follow. Check it out for yourself at alpha-sense.com/howiinvest. It's interesting because a lot of people use this analogy of data is the new oil, but no one goes upstream of that and thinks about how do we capture more data. You drill it. You drill in the ground to get more oil, and you have to capture the data. If it's truly oil, why aren't you capturing more of it? And why aren't you creating the schema, the processes, and the cultural aspects of becoming a data driven organization?
00:09:27 · Chaz Englander, David Weisburd
[00:09:27] S1: The reason I always use in that example sort of cool transcriptions is because it's kind of in a way obvious but less obvious. Right? You know, these models, they're incredible at interpreting videos, incredible at audio, text. It doesn't matter. Right? And so it's almost like regardless of how you're thinking about AI in the short, mid, or long term, start capturing that data. We did some analysis around what actually goes into the decision making process and where that information comes from. It's something like 70% of all information is coming from calls and meetings. Right? So you wanna try and capture as much of that as as you possibly can. Again, this is this is less relevant to us. It's more just our view on, like, outside of adopting a vertical application, which clearly seems like something that firms should do, what else can they start thinking about doing of agnostic of that choice? And it's it's just capturing more data where they can. The last [00:10:16] S0: time we chatted, you gave me this shocking statistic that you believe that companies will experience 55% productivity gains in the next twelve to eighteen months on the backs of AI. Where are these productivity gains specifically gonna come from?
00:10:16 · David Weisburd, Chaz Englander
[00:10:16] S0: time we chatted, you gave me this shocking statistic that you believe that companies will experience 55% productivity gains in the next twelve to eighteen months on the backs of AI. Where are these productivity gains specifically gonna come from? [00:10:30] S1: So I was saying, well, what even qualifies us for making that statement? A lot of what we're doing if... As we're selling into firms is we're building business cases. So we have to deeply understand where AI is applicable today. The big thing that really no one saw is the progress that happened with reasoning models. Reasoning is effectively a technique. It's very difficult to forecast progress in underlying techniques. Now forecasting progress in the underlying models is theoretically that's a lot more predictable but techniques are more difficult to predict clearly. And so, as we look at that and we look at all the data, really looking at where the time is going more importantly, particularly the more junior members of the team, we're looking at a 50% efficiency, certainly before the end of twenty twenty seven, but that could be a considerable amount of time before that. And and I don't think that's because of the technology. I think that's because of the adoption. [00:11:29] S0: Part of your business processes is you go into organizations and you look for productivity gains that they could have using AgenTeq AI. What's the exercise that you go through? How can an organization figure out where they have the most productivity gains?