E433: AlphaSense’s Chris Ackerson on AI, the Future of Finance & Finding Alpha
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How is AI reshaping the role of financial analysts?
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?
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 Offsense. 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.
Are people using tools like the super analysts to supplement their analysts or are they disrupting?
This debate around 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%, 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.
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
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.
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 your history?
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. 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
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Chapters
7 chapters
1
How is AI reshaping the role of financial analysts?
0:00–6:38
2
What productivity gains are customers seeing with AlphaSense?
6:38–12:20
3
Why do vertical‑focused AI models outperform generic LLMs in finance?
12:20–19:00
4
What causes LLM hallucinations and how does AlphaSense mitigate them?
19:00–24:31
5
How does AlphaSense’s proprietary expert‑transcript library add value?
24:31–30:16
6
What is the AI Interviewer and how is it used for channel checks?
30:16–34:31
7
What will the work of investment bankers and hedge‑fund analysts look like in five years?
34:31–36:46
Speakers
1 identifiedMore from How I Invest with David Weisburd
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