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

David Weisburd (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 have, 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?

Chris Ackerson (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, etc. 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 AlphaSense. You know, 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.

David Weisburd (1:03)

Are people using tools like the Super Analyst to supplement their analysts or are they disrupting them?

Chris Ackerson (1:10)

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 AlphaSense through its paces over many months, and they were able to prove that AlphaSense could increase average coverage by up to 15 percent, 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.

David Weisburd (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?

Chris Ackerson (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. They're reading 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.

David Weisburd (2:58)

A lot of people are looking at using horizontal players like an open AI 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?

Chris Ackerson (3:15)

Our 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. You know, any error rate is going to be a blocker to real adoption. And that's why people are still spending so much time hand crafting, 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 filing, for example, where that data was sourced from.