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

0:00In 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, you know, 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.

0:39And 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 people using tools like the super analysts to supplement their analysts or are they disrupting? This debate around kind of labor replacement is one we're going to have for many years into the future.

1:16I 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 worked 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.

1:53They 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. Where 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.

2:35They're getting up to speed on new companies. They're generating new investment ideas. They're prepping for earnings and keeping track through earning 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 a 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 your history?

3:15Our 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.

4:04And 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.

4:51So 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. I think it's one of the biggest distinctions. One of this 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.

5:29people expect all aspects of that to grow at the same pace, but really it comes down to what is the data used for and what is the cost of making it sick? 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.

6:07And 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. Maybe you could solve this through it all. I've been asking people for a couple of years now. Why do LLMs hallucinate? 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.

6:56And that allows these models to generalize extremely well. It also means that the output from the model is probabilistic. It 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, cell-side research analysts. And so what we're trying to do is harness the benefits of those probabilistic models.

7:38That 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 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? 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.

8:16So 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.

9:01That'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 models to improve the quality of the actual context that's fed into these AI models. So we have done a lot of studies showing that the newest frontier models are incredible reasoners.

9:50They're amazing tools, but they're actually really bad searchers. They don't know how to find the right context for a given task. And so we're training our own models to do that extremely well. And you can only do that with sort of control ownership of the data. And then the third reason why data is going to be so important to building agents over the coming years is because the agent output becomes a data set in and of itself. So taking a step back in the question answering age, the chatbot age of AI, you ask a question, you get an answer, and it sort of like goes into the chat thread. Maybe you look at it again.

10:28Maybe it goes away. Today, agents are building investment memos and pitch decks, like real work that itself has value. And so moving forward, the question any agent company should ask is like, should we be preempting the questions our clients are going to ask and actually produce the outputs that are going to be high value to them? And an example of this today is AlphaSense is using our AI to go run channel checks in industry so we can apply. We'll talk about our AI interviewer for in a minute. Let's talk about that. If you guys are not using human beings to interview, you're using AI to interview.

11:06A, what does that mean? And two, how do you do that? So it's a combination. So I mentioned part of the expert transcript library. The majority today is expert analysts at the top buy side firms running interviews themselves. And we built an AI interviewer to supplement that. And so the AI interviewer is an AI agent trained on AlphaSense data. So understands the market, all the information available on any company or any market and uses that to build an interview script to go on a call with a real human expert and interview that person, ask them questions, follow up, drill down into particular topics.

11:44And so we started the first application of that AI interviewer was the channel check data set I started talking about, which is we're applying this AI interviewer to go talk to people across every sector of the economy who understand price and demand signals for these different sectors. And so we're transcribing thousands of calls every quarter and then building an intelligence layer on top that's actually extracting quantitative data ahead of earnings about how a particular company's guidance might ultimately play out. And that AI interviewer is now available to all of our customers as well. And so expert calls have always been a critical part of the diligence process at private equity firms and hedge funds.

12:33And our users are still very much doing their own expert calls, but now they can augment those instead of maybe they're running five calls themselves, but now they can augment that with 20 or 30 or 50 AI calls going deeper on different areas of investigation. And that's creating this content flywheel that's really accelerating the size of that library and all the insights available to our agents. More than 300 ,000 interviews in that library today, adding more than 25 ,000 every quarter and growing fast. And are you also meta-learning on those interviews and learning how to better get information from the interviews?

13:16Absolutely. So when we first built the AI interviewer, we applied it to channel checks because that's a sort of contained problem. It's a pretty structured set of interview questions when you're running a channel check about how much are you buying? What is the demand for a particular product that you see in market? And so we could take a technology that was early and apply it to a really useful case, and it did great. As we improved that system, we evaluated and we now measure the quality of the AI interviewer at or above the quality of our best by-side analysts running interviews. So it's extremely high quality.

13:55That system improves over time through meta-learning and it's pointed at all of the data in AlphaSense. So imagine you were going to give an interview to me and you could read everything ever written on AlphaSense, everything ever written on the space we operate in. That's what the AI interviewer does before every expert called that it does. Going from the early application of AI agents, as you guys are doing, to the future of work and the future of finance, fast forward five years from now, what are investment bankers doing? What are investors doing? What do human beings end up doing? The core roles of these industries will remain critical.

14:37Investment bankers provide advisory services to their clients. Hedge fund analysts figure out where is the edge in a particular market to try to make an investment return. That core, I think, won't change. One of the things we're really excited about, we should talk a little bit about our new super analyst product. But today that super analyst is able to do incredible work delegated by analysts, for example. So all of the data in AlphaSense is flowing through and can be monitored by super analysts so that it can automate work, for example, during earnings, reading all the calls and executing your specific process that you want it to execute on.

15:21And the sort of deliverables that operate in these industries are very document-based, right? It's generating slide decks and investment memos and Excel models and things like that. And it's fantastic for those. As we fast forward a few years, we imagine a world where everyone is building kind of their own custom software that's exactly perfect for their specific process and their specific use case. So we're going to push the boundaries beyond just documents and meetings to a world where everyone is operating on live software that always has access to up-to-date information and is doing exactly sort of built for your specific process.

16:02What becomes valuable in the future? Human judgment is absolutely critical. The ability for a firm to learn from all the decisions they make is really important. Our CEO, Jack, always talks about the enterprise value of a business is the sum of all the decisions it makes. And in the agentic era, the most important thing a business can do is ensure that they've captured the learning that is happening from all of the decisions that your human capital or your agent capital is executing on. And so investing in systems that help you capture that learning and your firm can get better and better and you can apply the judgment on top of what the AI systems are doing is going to remain sort of a critical.

16:55What else outside of human judgment? The world is complex. We see today technology is changing so fast. The business world is evolving. Applications are evolving. And so the ability of humans at sort of every level of a business to act as almost architects, system engineers, on top of all the AI agents is going to remain critical because of that dynamic change that's always present in human society and in the economy. Growing up, I thought managing money meant paying bills and balancing a checkbook. But as you know, that's only a small piece of the financial puzzle. Managing your money takes more than just checking your bank account every once in a while.

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21:09The best conferences do two things well. The content challenges how you think, and the people in the seats are the ones whose opinions actually move markets. Alpha Summit is AlphaSense's annual user conference, and it's built around both. Join me at the Glass House in New York City, October 5th through 7th, for sessions going deep on where AI, data, and human expertise converge. The room will bring together over 1 ,000 institutional investors, corporate decision makers, and capital markets professionals from firms like Goldman Sachs, JP Morgan, and the top PE and hedge funds. If you listen to the show, you're already asking the right questions.

21:44Alpha Summit is where you go to stress test your thinking with the people working through the same problems at the highest level. Register at alphasummit.ai to secure your seat. And as a How to Invest listener, you will get 50 % off. Check the show notes for your exclusive discount code. I look forward to seeing you there. There's a bit of a paradox if every hedge fund in the world is an AlphaSense customer per se, and they have all the same data. How do hedge funds compete against the chain? This has been the question on sort of information systems and investing for decades. We think about the ultimate investment decision is a combination of the investor and AlphaSense or super analyst, the technology they're using, the way you prompt the system, the questions you ask, the choices you make when engaging with AI, what you pointed at, the process you ask it to automate.

22:40That's all the human differentiation. And it's the combination of that plus the agent's ability to look over vast quantities of data and automate these processes. That's what ultimately generates alpha. I also think financial products are themselves products within customers. You might have pensions, endowments, family offices with very different risk tolerances. Sure. And you might look at the same probabilistic model and say, well, I'm going to play it safe and get 8 % return, or I'm going to try to have the best net return, even if there's a lot of volatility. So there's a taste and a product side from it on the investment side as well.

23:20Absolutely. And we think about that a lot with super analysts. We're not bringing one super analyst to market. We're bringing a platform that can be customized for your process. You can bring your own skills. You can integrate super analysts into downstream workflows that integrate your own data, your own perspective to really create that differentiated competitive advantage. Just recently had the president of Peter Thiel's hedge fund. He was there for quite a while. He talked about the systemic risk in markets because of the passive investor. So before you had these active investors that are coming in and making these buy and sell decisions, and now everybody piles into these names and it's created this leverage in the system.

24:05Do you see AI and the symmetry of data leading to more volatile markets? We want to help markets allocate capital more efficiently. That's our mission. And so we do think that using tools like Alphasense, we can contain and reduce risk alongside our users. And so I think the ability to bring really high quality data into decision making, like one of the key value propositions of Alphasense has always been removing blind spots in the investing process or decision-making process. You can get a great answer from AI, but if it's coming from one blog or one analyst, that's much less valuable than a system that's looking across all the sell-side analysts, a bunch of experts, what people inside a company and the company themselves are saying about a topic and being able to wrap all of that into an answer.

25:03That's going to give you a lot more confidence in the decision you're making and increase the likelihood that it's the right decision for your business. I want to go to your data infrastructure. You're using both the large LMs as well as open source in order to run your inference. Talk to me about that. Yeah, that's right. So, you know, we've seen a pretty remarkable difference in what different models are good at. Even the frontier AI models have different characteristics. I would just say no. Well, I think it has to do with the fact that post-training has become a much, much bigger part of the training process.

25:42Whereas a few years back, all of these labs were pre-training on the same data sets. The public internet was the majority of it. And so the models coming out of that pre-training were very similar in what they did. Now, the majority of the GPU spend for these firms is not pre-training, it's post-training. And post-training, you can apply a lot more differentiation in terms of what tasks you're post-training on, what data sets you're acquiring to post-train on. So these models have actually differentiated, which is kind of contra to a lot of predictions that thought they would all sort of commodify and converge.

26:18And so we're seeing, for example, Opus 5 is really, really great at generating high-quality slides, but it's not very good as a context retrieval system, kind of a search model in Alphasense. And so we apply different models based on our evaluation of their strengths and weaknesses. And so you mentioned open source models. We partner with Cerebris, which is a fantastic, really low latency inference provider where we can leverage the best open source models, but at 10 to 20x lower latency than what could be found elsewhere. And so that's really important in trying to build systems that feel conversational and real time.