How I Invest with David Weisburd
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?
00:11:29 · David Weisburd, Chaz Englander
[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? [00:11:42] S1: We do an entirely free, you don't have to sign up, discovery phase. So we will work with a customer and we will identify where we think AI is most applicable today beyond just a chat based interface. As I said, workflow automation. So we do that over a two, three or four week period. And really come the end of that period, we're targeting a number of... And we don't really look at workflows unless it's a 60 plus percent efficiency gain on a single workflow level. The reason we do that is because we really wanna focus on short term ROI to deliver value, you know, quickly. But we do that now with with all of our customers. [00:12:19] S0: Perhaps this is a dumb question, but is this a soft... Is this a software process that's running on machines? Are these virtual machines? How do you actually implement these systems? The user just logs in, presses a button, and the report is generated. How does that mechanically work?
00:12:19 · David Weisburd, Chaz Englander
[00:12:19] S0: Perhaps this is a dumb question, but is this a soft... Is this a software process that's running on machines? Are these virtual machines? How do you actually implement these systems? The user just logs in, presses a button, and the report is generated. How does that mechanically work? [00:12:33] S1: So if you kinda picture it, the way it would work is we have an Excel type interface. So think of, you know, just Excel but powered by AI. You upload a document and what we're doing is we're deconstructing that document into its individual components and we're making... You know, the model is making the best guess as to where you would get that information from, from documents, from fax set, from web filings, from, you know, wherever you may normally get that information from. The user would come in and confirm, would click save, and then that workflow would exist. Right? And they can deploy that workflow to just themselves, to the rest of the team, to the whole group, and then they can obviously, as you go, you can make alterations and then someone can make a copy and, you know, adapt that workflow to them as a specific user if they want to. [00:13:06] S0: Is it fair to compare you as an enterprise OpenClaw of sorts? And how do you look at open source projects like OpenClaw competing with what you're doing? Quite important. That has become very clear in