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

What happens to financial analysts when AI can do the work they used to spend all night doing? Chris Ackerson is SVP of Product at AlphaSense, where he focuses on applying information retrieval, natural language processing, deep learning, and recommendation systems to search and discovery. We discuss how AI is changing the analyst role, why vertical AI can outperform general-purpose models in financial research, and why proprietary data may become one of the most important competitive advantages in AI. Chris also explains why LLMs hallucinate, how AlphaSense is using AI to conduct expert interviews, where humans still create investment alpha, and why the future of financial research may look less like software and more like an AI teammate that never sleeps.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00David Weisburd?

  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?

• 0:17Speaker 01

  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. 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.

• 0:59Speaker 01

  The competitive landscape is heating up, and so we're seeing the same thing with our client base.

• 1:03David Weisburd?

  Are people using tools like the super analysts to supplement their analysts? Or are they disrupting them?

• 1:09Speaker 01

  This debate around kind of labor replacement is one we're going to have for many years into the future. Yeah, 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 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%, you know, making their bankers much more efficient. Now, what they do with those savings is up to the bank.

• 1:53Speaker 01

  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.

• 2:12David Weisburd?

  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?

• 2:26Speaker 01

  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.

• 2:58David Weisburd?

  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?

• 3:15Speaker 01

  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.

• 3:58Speaker 01

  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 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,

• 4:40Speaker 01

  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.

• 5:14David Weisburd?

  I think it's one of the biggest distinctions, why 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 you in 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 them sick?

• 5:39Speaker 01

  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.

• 6:22Speaker 01

  And that's what the vertical integration really enables.

• 6:25David Weisburd?

  This episode is presented by Juniper Square, the operations partner for private markets.

• 6:32David Weisburd?

  Maybe you could solve this for it all. I've been asking people for a couple of years now, why do LLMs hallucinate?

• 6:38Speaker 01

  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, you know, set of model weights. And 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

• 7:20Speaker 01

  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.

• 7:54David Weisburd?

  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?

• 8:03Speaker 01

  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

• 8:49Speaker 01

  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.

• 9:30Speaker 01

  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. They'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

• 10:13Speaker 01

  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, maybe 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.

• 10:56Speaker 01

  So we can apply, we'll talk about our AI interviewer for in a minute.

• 11:01David Weisburd?

  Talk about that. If you guys are not using human beings to interview, you're using AI to interview. A, what does that mean? And two, how do you do it?

• 11:08Speaker 01

  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've 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. And so we started, the first application of that AI interviewer was the channel check data set I started talking about,

• 11:52Speaker 01

  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.

• 12:33Speaker 01

  And 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.

• 13:08David Weisburd?

  And are you also meta-learning on those interviews and learning how to better get information from the internet?

• 13:15Speaker 01

  Absolutely. 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 buy-side analysts running interviews. So it's extremely high quality. That system improves over time through meta-learning,

• 13:59Speaker 01

  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.

• 14:16David Weisburd?

  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?

• 14:31Speaker 01

  The core roles of these industries will remain critical. Like investment 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,

• 15:16Speaker 01

  reading all the calls and executing your specific process that you want it to execute on. The sort of deliverables that operate in these industries are very like 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

• 15:58Speaker 01

  and is doing exactly sort of built for your specific process.

• 16:02David Weisburd?

  What becomes valuable in this future?

• 16:05Speaker 01

  Human judgment is absolutely critical. The ability for a firm to learn from all the decisions that 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.

• 16:48Speaker 01

  And you can apply the judgment on top of what the AI systems are doing is going to remain sort of a critical.

• 16:55David Weisburd?

  What else outside of human judgment?

• 16:57Speaker 01

  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.

• 17:28David Weisburd?

  If you run a business, you probably start your morning with way too many tabs open. You're checking revenue, ad performance, email, inventory, spreadsheets. And before you've made a single decision, you've already spent your morning just figuring out what happened yesterday. That's what I like about Perplexity Computer. Instead of jumping between every dashboard yourself, Perplexity Computer could connect to the tools you already use and build a command center for your business. It could pull together things like revenue, ad spend, margins, inventory, and email performance, then show you what changed, where you might be losing money, and what you should look at next. But what makes Perplexity Computer really interesting is that it could actually work on projects. It could help rewrite an email flow, build an ad campaign, or draft outbound outreach. And unlike most AI tools, you're not locked in into one model.

• 18:12David Weisburd?

  Perplexity Computer orchestrates leading AI models behind the scenes and automatically chooses the right one for each step. For a limited time only, you could try Perplexity Computer for free. Head to pplx.ai/invest. That's pplx.ai/invest to try Perplexity Computer today. Most AI tools are still answering questions while Perplexity Computer is completing full projects. If you run a business, you probably start your morning with way too many tabs open. You're checking revenue, ad performance, email, inventory, spreadsheets. And before you've made a single decision, you've already spent your morning just figuring out what happened yesterday. That's what I like about. Perplexity Computer. Instead of jumping between every dashboard yourself,

• 18:53David Weisburd?

  Perplexity Computer could connect to the tools you already use and build a command center for your business. It could pull together things like revenue, ad spend, margins, inventory, and email performance, then show you what changed, where you might be losing money, and what you should look at next. But what makes Perplexity Computer really interesting is that it could actually work on projects. It could help rewrite an email flow, build an ad campaign, or draft outbound outreach. And unlike most AI tools, you're not locked in into one model. Perplexity Computer orchestrates leading AI models behind the scenes and automatically chooses the right one for each step. For a limited time only, you could try Perplexity Computer for free. Head to pplx.ai/invest. That's pplx.ai/invest to try Perplexity Computer today.

• 19:38David Weisburd?

  Most AI tools are still answering questions while Perplexity Computer is completing full projects. One thing that I've learned from talking to hundreds of investors, founders, and business leaders is that planning for the future isn't just about growing assets. It's also about protecting the people who matter most. Life comes with a lot of important milestones. Maybe you're buying a home, starting a family, planning for college, or simply thinking more intentionally about your financial future. As those responsibilities grow, it's worth making sure you protect those you love. Not sure what kind of life insurance you may need? Concerned about how much it may cost? Think life insurance is too complicated? Your local State Farm agent can help answer those questions. They can help you explore your options for life insurance and choose coverage that you can adjust as your family's needs change over time. Financial protection for your loved ones when it matters most.

• 20:22David Weisburd?

  Learn more about life insurance at statefarm.com. Like a good neighbor, State Farm is there. Thing I've learned from talking to hundreds of investors is that great investment firms aren't built on investment returns alone. The firms under are great at the things most people don't see. Their operations, their relationship with LPs, and the quality of information they use to make decisions. And here's what AI has changed. Every firm now has access to the very same models, so the intelligence isn't the edge anymore. The edge is what you could feed it. A firm with its fund operations and data in one connected record can actually put AI to work. A firm running on disconnected systems simply can't. That's why thousands of GPs run their funds on Juniper Square. Juniper Square puts your fund operations, data, and administration together in one connected record.

• 21:07David Weisburd?

  That means less time managing disconnected systems and more time investing, working with LPs, and building your firm. This episode is brought to you by Juniper Square, the operations partner for private market GPs. Learn more at junipersquare.com/HowIInvest. That's junipersquare.com/HowIInvest. The 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.

• 21:48David Weisburd?

  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 this show, you're already asking the right questions. Alpha 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 HowIInvest 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 have the same data, how do hedge funds compete against each other?

• 22:31Speaker 01

  This has been the question on sort of information systems in investing for decades. We think about the ultimate investment decision is a combination of the investor and, you know, AlphaSense or SuperAnalyst, the technology they're using. The way you prompt the system, the questions you ask,

• 22:52Speaker 01

  the choices you make when engaging with AI, what you pointed at, the process you ask it to automate. That'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.

• 23:14David Weisburd?

  I also think financial products are themselves products within customers. You might have pensions, endowments, family offices with very different risk tolerances.

• 23:24Speaker 01

  Sure.

• 23:24David Weisburd?

  And you might look at the same probabilistic model and say, well, I'm gonna play it safe and get 8% return, or I'm gonna 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.

• 23:38Speaker 01

  Absolutely. And we think about that a lot with, you know, SuperAnalyst. We're not bringing one SuperAnalyst to market. We're bringing a platform that can be customized for your process. You can bring your own skills. You can integrate SuperAnalyst into downstream workflows that integrate your own data, your own perspective, to really create that differentiated competitive advantage.

• 24:03David Weisburd?

  So recently I 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. Do you see AI and the symmetry of data leading to more volatile markets?

• 24:30Speaker 01

  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, you know, contain and reduce risk alongside our users. And so, yeah, 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 blogger, one analyst, that's much less valuable than a system that's looking across all the sell-side analysts,

• 25:12Speaker 01

  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. That'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.

• 25:29David Weisburd?

  I want to go to your data infrastructure.

• 25:33David Weisburd?

  You're using both the large LMs as well as open source in order to run your inference. Talk to me about that.

• 25:39Speaker 01

  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.

• 25:51David Weisburd?

  How would you explain that?

• 25:53Speaker 01

  Well, I think it has to do with, you know, the fact that post-training has become a much, much bigger part of the training process. Whereas a few years back, all of these labs were training, you know, 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 are, have actually differentiated, which is kind of contrary to a lot of predictions that thought they would all sort of commodify and converge.

• 26:37Speaker 01

  And so we're seeing, for example, you know, 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 Cerebrus, 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.

• 27:19Speaker 01

  And so all of our tools, whether it's SuperAnalyst or our Generative Grid, are leveraging multiple models in their pipelines that are optimized for these different tasks. And we'll probably talk about this in a minute, but we're starting to train our own frontier models as well, where we think we can add a lot of value in certain tasks.

• 27:39David Weisburd?

  I want to underline something that you mentioned, which is the LMs themselves have different data sets. And this confuses a lot of people. I've been helping one of our portfolio companies go through an M&A or capital raising event right now. And I've been talking to a lot of the heads of investment banks. And they have all said the same thing, which is a large majority of their work right now, at least in the tech sector, is helping the large LM companies buy companies for data sets.

• 28:04Speaker 01

  Sure.

• 28:04David Weisburd?

  Most people don't know that this is going on, but this is a big part of transactions today. It's helping these large LMs gain more and more data and more and more proprietary data so that they could start to differentiate against themselves as well.

• 28:17Speaker 01

  That's right. All AI companies will become data companies. And we're seeing that with the proliferation of companies, you know, that are like a mercure, that are helping people build reinforcement learning environments with experts helping to build data sets. Recently bought the data remains of Spirit Airlines was an interesting acquisition. And so we're seeing that more and more, and it just underscores the criticality of differentiated data and why we feel so good about the data that we've aggregated over more than a decade in business.

• 28:51David Weisburd?

  And get back to what you were just saying, which is you're training your own data and you're doing your own post-training data as well. Tell me about that.

• 29:00Speaker 01

  AlphaSense has trained our own AI models for almost a decade now. Prior to kind of the generative AI explosion, the post-ChatGPT era, all these models needed to be post-trained to make them effective. And so we have lots of kind of search-specific models throughout the system that are helping to find the right content, to understand a user's question, to rank sources. And those models have learned over many years, like what are the most relevant sources of information, the most authoritative content for any given task? And then as we move forward, we're training frontier models to actually power SuperAnalyst, for example, end-to-end.

• 29:46Speaker 01

  When you look at SuperAnalyst, for example, it's a little different than maybe some of the experiences people have with AI agents where you're really kind of engaging with the LLM pretty directly. In a context like automating financial research, actually, the vast majority of the work is the research loop. It's like 80% of the cost and something like 70% of the kind of influence on quality is how effectively you can research across all the tools and data sets and AlphaSense and find the right context for, you know, an earnings preview or a pitch deck or a strategy memo, whatever you're building. And so that research loop, we think, you know, we've got incredible data and tools to build frontier

• 30:28Speaker 01

  systems that can run that research loop much more effectively than can a frontier AI model that really doesn't understand all of that.

• 30:36David Weisburd?

  Another way that LLMs are weighted, so they have some weight to all the different sources and through running your own data, you're able to weight them slightly different in order to get an information edge on even the LLMs themselves.

• 30:49Speaker 01

  The frontier LLMs, the way that they're trained to search is very much sort of web search frame where you just kind of spray and pray. You just search a ton of stuff. You're looking to try to find a diamond in the rough. You know, I think that works for web search generally. It doesn't work effectively for financial research where having a ton of low value or noisy content is expensive. It costs a lot of money to run these research loops. Two, it lowers quality. It's sort of context pollution as you're pulling in irrelevant information. It lowers latency. The tasks take longer to execute. So we can exactly, as you said, train a model that knows exactly what source to go for, what tool to use to execute a given task. And so we think we're going to be able to reduce

• 31:34Speaker 01

  cost by up to 40x while delivering much higher quality than even frontier models can. And to be clear, that doesn't mean we won't use frontier models in AlphaSense. Like, we think they're fantastic for the things they're really good at, like writing code, doing deep data analysis, producing high-quality artifacts like slide decks or Excel memos. And so those two systems or models will work together like two separate agents. One to coordinate the actual code generation or synthesis of data and one that is an ultra-effective researcher. Those systems will work together.

• 32:09David Weisburd?

  I wanted to actually go there. Marc Benioff has explained the future of software as headless. So you're not necessarily going to Salesforce and making prompts. Salesforce essentially does an ADI through what's called an MCP. Is AlphaSense looking in that direction and is that where you see the future of applications?

• 32:28Speaker 01

  There's no question that the market is going to move in a world where agents work with other agents. And they're going to do that through different protocols. MCP is one. And so when we build a world-class agent like SuperAnalyst, we expect it to work inside, you know, the AlphaSense desktop where we've got an agent workspace that's purpose-built to make working with SuperAnalyst really great and a delight. All the history and the work that you produce with SuperAnalyst is there. All the skills to tune it are sort of in that desktop workspace experience, but also that SuperAnalyst is your teammate. It should work wherever you work. And so today, it already works in PowerPoint and Excel, but it'll also be available through API and MCP to integrate into

• 33:11Speaker 01

  other systems, applications and workflows that, you know, you're engaging with as an individual user or more even on a broader sense, working with, you know, IT teams, technology teams inside these firms to use AlphaSense as an agentic kind of automation infrastructure for market intelligence to build many different applications and automate many different workflows on top of.

• 33:33David Weisburd?

  In many ways, it's meeting where the customer is. Today, they're used to prompting and they're used to being in the application. In the future, at least in theory, the next generation of knowledge workers will be trained on AI-native processes.

• 33:47Speaker 01

  There's no question. And as you, agents allow you to go more from sort of question answering to truly delegating work, SuperAnalyst will be working 24/7, like the analyst on your team that never sleeps. It's going to deliver work where you work, to your phone, to your email, to your Slack and Teams, just like a remote teammate would do in a much more human, interactive way.

• 34:14David Weisburd?

  At AlphaSense, you guys are growing at a crazy rate. You've grown over half a billion in revenue. But certainly with any business, there are always things that keep you up at night, especially given where the world is today. What keeps you up at night?

• 34:32Speaker 01

  We're incredibly focused on delivering decision-grade answers and outputs. And so where we focus is ensuring that, you know, when someone gets an output from AlphaSense late on a Friday night, they can close their laptop and go home and spend their weekend without worrying if that data came from a source they can't trust, if there's a hallucinated metric or piece of financial data. That's what we think about day and night. More tactically, in the sort of chatbot, like generative search era, verification was a pretty

• 35:12Speaker 01

  simple problem for us to solve because we had, you know, all the data integrated in AlphaSense. And so everything was attributed to the right source, cited, and you could click in and read the original sources. As these systems get more and more sophisticated, they're not just generating a report where it's easy to click through the citations. They're generating like

• 35:34Speaker 01

  much more visualized, abstracted recommendations and suggestions and charts and tables. And so thinking a lot about how to reduce the friction on verification and maintain that trust is, you know, what we think about every day.

• 35:51David Weisburd?

  Going back to the beginning of the career, as I mentioned, you were at IBM Watson. A lot of your peers are now working at companies like OpenAI, Anthropic, and really building the future. If you give yourself one piece of timeless advice back then that would have helped accelerate your career or helped you avoid costly mistakes, what would that be?

• 36:08Speaker 01

  Everyone working on AI has a lot of scar tissue from underestimating the pace at which these models would improve. And all of us are, you know, grappling every day with how much do we build the scaffolding around the models based on how they operate today to get them to sort of a quality that makes a product usable today versus where the model's going to be in six months or a year. And we are now incredibly unsentimental about deleting scaffolding and moving towards the future. And going back five years, you know, the advice I would give myself is, you know, the advice

• 36:49Speaker 01

  that Jack and Raj, our founders, have given all of us, which is, you know, just look to the future, move as quickly as possible. Don't be sentimental. Continue innovating. We're incredibly excited about where that's going to lead us.

• 37:01David Weisburd?

  More than ever, it's embracing this Buddhist philosophy of the beginner's mind. You always have to wake up as a beginner every single day because everything is changing so much beneath you. You mentioned the scaffolding. What's an example of scaffolding that has changed within AlphaSense?

• 37:15Speaker 01

  With every generation of these models, to make sure the products that we were launching met the demands of professional users, that they could trust them, that we weren't putting something out that would deliver a hallucination that would, you know, impact an investment decision or something like that they would make. And so like we started our first generative AI features back in, excuse me, 2023. We focused on just document summarization. Could we summarize an earnings call effectively, things like that? And then we've marched towards, you know, a generative search solution that could find and synthesize answers from across all the content in AlphaSense to workflow agents that allow you to automate complex workflows to now

• 37:56Speaker 01

  SuperAnalyst where you can delegate work. And so in each of those evolutions, we've essentially deleted a lot of the scaffolding required to, in 2023, get an LLM at that time to be able to summarize an earnings call near 100% accuracy. There was a lot of work involved in making that happen. Today, that's not necessary.

• 38:22Speaker 01

  You know, these models can do that much more effectively. But now, similarly, if you're delegating your entire earnings process to SuperAnalyst, there's an enormous amount of work to make that effective, reduce hallucination risks, increase accuracy, manage tokens efficiently. So that's sort of where we're focused today.

• 38:43David Weisburd?

  And this is almost a cultural competitive advantage. You mentioned Jack and Raj talk about what I would call the beginner's mind, always having to reinvent yourself. A lot of people think that's a technological issue, but that's actually a cultural issue.

• 38:56Speaker 01

  I think that's right. There are investments that we've made that will be durable through the life of the company. The content data sets that we've aggregated, the search data that we've built, millions of search queries that allow us to train models on how to find the right context for the right job. These are durable data sets that will continue to power the company. Looking forward to the ways in which people will engage with a product like AlphaSense. You always have to be looking forward and willing to innovate and disrupt yourself.

• 39:31David Weisburd?

  Well, Chris, I've been a happy AlphaSense customer. Once in a while, my companies do actually make it to an IPO. I use it to see if I should buy or sell more. And thanks so much for sitting down and thanks for making time.

• 39:44Speaker 01

  Thanks, David. It was fun.