E433: AlphaSense’s Chris Ackerson on AI, the Future of Finance & Finding Alpha

How I Invest with David Weisburd

Chris Ackerson, SVP of Product at AlphaSense, discusses the transformative impact of vertical AI on financial research and analyst productiv

Key takeaways

• AlphaSense leverages an AI interviewer to conduct channel checks, expanding their expert transcript library by 25,000 interviews per quarter.
• Vertical AI can deliver 3x higher quality answers at 3x lower cost compared to general-purpose models in financial contexts.

Main topics

• Vertical AI vs Horizontal AI
• LLM Hallucinations and Error Rates

Notable quotes

All AI companies will become data companies.

Conclusion

The future of finance lies in augmenting human judgment with highly ac

Transcript preview

Speaker 1 (0:00) In software engineering, there's been this paradox where the more effective AI has become, the more there's been a demand for engineers because now they're kind of all 100x engineers. Do you see the same thing playing out in finance? Or do you see the analyst layer being disrupted completely? Speaker 2 (0:17) We do. What we're seeing is that as our systems get more and more capable, we're able to take a lot of the grunt work, the manual work. that analysts were staying up late at night executing, and now they're able to focus on higher value activities like meeting with clients, corporate action events, et cetera. And what that really means, as companies get more productive, they're able to cover more companies, launch more products, accelerate their roadmaps. We're seeing that internally at Offsense. You mentioned software engineering. Our roadmap is just accelerating as our engineers, our product managers are getting more efficient with AI. The competitive landscape is heating up. And so we're seeing the same thing with our client base. Are Speaker 1 (1:03) people using tools like the super analysts to supplement their analysts or are they disrupting? Speaker 2 (1:09) This debate around kind of labor replacement is one we're going to have for many years into the future. I think we're seeing the ability for our customers to do much more, which means they're going to invest more in their people. But the roles are changing and adjusting. I work with a major investment bank that had divested a portion of its business. And so they wanted to challenge AlphaSense to see if they could use us to increase the coverage of all of the rest of their. bankers. And so they put Alphacent through its paces over many months, and they were able to prove that Alphacent could increase average coverage by up to 15%, making their bankers much more efficient. Now, what they do with those savings is up to the bank. They may hire many more bankers because they can cover that many more companies. They can deliver better advisory services to their clients, or they may do different things. But there's no question that the roles And the responsibilities, the work being executed is going to evolve as these AI systems get better and better. Speaker 1 (2:12) And you mentioned you see your customers doing more and more business. What are some early case studies where you've seen people using AlphaSense and tools like Agentic AI in order to increase their business? Speaker 2 (2:26) Let's take the hedge fund space, for example, where the job of an analyst is to track an industry, a sector. They're getting up to speed on new companies. They're generating new investment ideas. They're prepping for earnings and keeping track through earnings season. Read-throughs that are impacting all the companies in their portfolio. All of these are areas where AI is ripe to drive significant efficiencies. And so we're seeing the same across every sector that we serve. Speaker 1 (2:58) A lot of people are looking at using... horizontal players like OpenAI and Anthropic to solve their investment needs, and you guys are verticalized. How do you differentiate your process from a horizontal AI? And how has that evolved over years? Our Speaker 2 (3:16) thesis has always remained consistent from the beginning, which is that by aggregating and controlling high quality data and building AI purpose-built to understand and make sense of that data, we could deliver higher accuracy. at lower cost, with more trust into the market. And we're seeing that very much today. One of those leading AI labs just last week published data showing that the error rates using kind of the leading market data MCPs were between 5 % and 9%. That is a non-starter for a serious investment professional. Speaker 2 (3:59) Any error rate is going to be a blocker to real adoption. And that's why people are still spending so much time handcrafting, verifying all the information, because these systems just don't work at the level of a professional analyst. And so the way we solve that problem is we aggregate all of the data. We index all of the information. We enrich it, understanding. with AI reading every line of every document that flows through the system, exactly what the data is saying, the connections and relationships across disparate sources, and then integrating that into the agentic systems so that they know exactly what information to pull for the particular task at hand. So we're able to dramatically reduce the error rates, the hallucination rates. And then the ability for the end user to actually verify that information is dramatically faster and more efficient when the data is actually integrated into the system. And you can see down to the individual line and the individual, you know, filing, for example, where that data was sourced from. I Speaker 1 (5:14) think it's one of the biggest distinctions, one of the governor on AI growth is. how critical the information is. On one side, you might have hospital systems where people literally die if you have even a 5 % error rate. On the other side, you're researching treats for your dogs. People expect all aspects of that to grow at the same pace, but it really comes down to what is the data used for and what is the cost of making it stick? Speaker 2 (5:38) That's exactly right. And we've done the work to test this in AlphaSense. We've built evaluation harnesses that put... all of our AI systems through their paces, building very detailed and specific rubrics for all of the real-world tasks that our users are executing in AlphaSense and running automated evals and then also human evals. We have financial analysts that we've hired at AlphaSense to actually spot check and prove that these evals are accurate. And what we see is that by leveraging frontier AI models inside the AlphaSense harness with access to the AlphaSense data and tools, we can deliver 3x higher quality answers at 3x lower cost. And that's what the vertical integration really enables. This episode is presented by Juniper Square, the operations partner for private markets. Speaker 1 (6:31) Maybe you could solve this through it all. I've been asking people for a couple of years now, why do LLMs hallucinate? Speaker 2 (6:37) LLMs hallucinate because by definition, they're probabilistic. They're trained on millions of documents, data points, reinforcement loop traces that essentially encode all of that information into a compressed set of model weights. And that allows these models to generalize extremely well. It also means that the output from the model is probabilistic. depends on the data it's seen. These models are trained on the entire internet. The internet, as we all know, is full of information that you can't trust and great information as well. And so at AlphaSense, everything that comes out of our AI systems is grounded in data that we curate, which is authoritative, trusted information. I mentioned some of it earlier, what companies are actually saying, interviews with experts, sell-side research analysts. And so what we're trying to do is harness the benefits of those probabilistic models. That is the fact that they can be creative, they can think outside the box, and they can do things that sort of classical machine learning can't do, but ground them in data you can really trust and take into an investment committee or to a CFO in a client meeting. I Speaker 1 (7:54) want to go to that data. It seems to be one of the most valuable aspects for a vertical AI company. How's AlphaSense using data specifically? Speaker 2 (8:03) People talk a lot about data. I still think it's dramatically underappreciated how critical it is. Let me take a step back and talk a little bit about the data sets that we aggregate. So I'll talk about our expert transcript library. So this is a data set where the best investors, the best buy side analysts in the world are doing interviews with experts in industry. So people that used to work at companies, former executives, suppliers, customers of companies. and asking them questions that are going to lead to real investment decisions. All of that is proprietary insight. The vast majority of knowledge in the world isn't written down. It's in your head. It's in my head. And so our expert transcript library is uncovering that insight and transcribing it, making it available to consume by our users and now our AI agents to influence the answers, the outputs that... come out of our system. That's an example of proprietary data that you can't find in any other AI system, any AI system that's pointed at the public internet. So the ability to find proprietary insight is one of the key reasons why sort of control of data is so important in our space. The second critical reason is the ability to actually drive high quality. lower those error rates and hallucination rates. So at AlphaSense, we're constantly training our search models, our AI