Invest Like the Best with Patrick O'Shaughnessy - Jack Kokko - Building AlphaSense - [Invest Like the Best, EP.404]

Starting point is 00:00:02 Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. Invest Like the Best is part of the Colossus family of podcasts, and you can access all our podcasts, including edited transcripts, show notes, and other resources to keep learning at join colossus.com. Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of positive sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

Starting point is 00:00:45 Clients of positive sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. My guest today is Jack Coco. Jack is the CEO and founder of Alpha Sense, where I and Positive Sum are investors, an AI-powered search engine for market intelligence. He shares how AlphaSense began by aggregating fragmented financial data sources and evolved with the advent of large language models to completely change the research experience for investors and others. He speaks to their recent acquisition of Teegis earlier this year, reshaping the business and further supporting their expansion to serve all types of companies instead of exclusively investment firms. Jack has been navigating the AI revolution from its earliest days and you can feel his excitement when he talks about the future.

Starting point is 00:01:31 We discussed building an agile platform, the importance of managing cultural integration. balancing AI capabilities with user trust and the frontier for this technology. Please enjoy my conversation with Jack Coco. So Jack, you are in a very unique position, having been one of the few entrepreneurs that was effectively building an AI product many years ago before everyone was talking about AI and using data and search and these tools probably for longer than just about anybody. So I think you're uniquely positioned to tell us what. you've learned about applying the technology to build a great product today in late 2024.

Starting point is 00:02:10 And maybe that's the perfect place to begin, which is just like a state of the union from you on what kinds of things AI enables for a product builder trying to serve a customer. What it unlocks? I'm also going to ask about what the limitations are today. But since you've been doing it for so long, just give us that felt experience. And then we'll talk about the future. Well, maybe I'll start from the very beginning of what we are building with the prior generations of AI and machine learning. We wanted to build a semantic search engine that would understand financial and business content, read it line by line, almost like a human analyst would, and understand all these billions of data points, millions of documents, and connect the dots between them, then map that to a user's search query and deliver the right data points and right insights to them. And this was really hard to do because all this content was siloed in thousands of sites.

Starting point is 00:03:08 silos and sort of paywall data sources. It wasn't like the internet where Google and others had billions of web pages with links between them. And those links would tell you about the authority of those pages and had billions of consumers clicking on pages that also told you what's good content, what's not so good content, and what's most relevant to a particular query. None of this existed in these fragmented separate databases that we needed to go and access and then semantically index and help users search. And this was a really hard problem. Even Google and others in that space had tried to build enterprise search and had retreated from that market because it's so hard to do this without those benefits that you have in the public web. So we used earlier generations of machine learning to categorize information and semantically try to understand the context of words and sentences and then organize that information so we could then map that to each user query.

Starting point is 00:04:02 Later on, you got the simpler earlier language models like Google's birth and where level of understanding expanded. You were no longer categorizing things or looking at small local contexts. You started to understand longer sentences and passages better and really be more accurate about understanding topics and KPIs and what is a company saying? Is it bullish? It's a bearish. All of this kept on improving the categorization, the linkages between conversations. But the true big breakthrough, of course, was with large language models and generative models where now you had massive context and you could have a model that reads even up to your whole book,

Starting point is 00:04:45 whatever you give it to it. Obviously, different models of different capabilities. But the contextual understanding was incredible. And the model now had world knowledge. And it was able to make its own conclusions statistically, but still, you know, apparently to us humans in a pretty smart way. So that now enabled you to do a couple of things that wasn't possible before. You could take a natural language query from a user and actually understand it. Take a sentence and really, really solid, good precise understanding of what the user is looking for,

Starting point is 00:05:16 which now tells the machine a lot more about what the true intent is that you're looking for in a way that couldn't be expressed in those keywords. And then similarly, we had a much greater understanding of all the underlying text, because the system understood the context at the really broad level. So it took the capabilities another huge leap forward, enabled even better mapping of what a user wants and finding the right contextual insights from these vast volumes of information. So it's a really exciting space to be in. And making sense of information has just become so much more easy and powerful with LLMs now.

Starting point is 00:05:59 that's evolving so rapidly that the answer changes in terms of what's possible almost every month or two. Just for context for those listening, just give us a general sense of the sort of data sets, document sets that AlphaSense had pre-ChatGPT, pre-LLMs, pre-GPT models. What were the key things that your users came to you to find information within those data sets? It was basically everything that an investment analyst would use in their research process or a corporate strategy or competitive intelligence professional or finance professional would use in their research about their competitive landscape, launching new products and things like that. So it was basically company information company filings, earnings call and other conference transcripts.

Starting point is 00:06:44 Anything a company would publish mandated by the SEC, companies put a lot of information out there, so collecting all that, and then collecting everything that the sell side say. So you've got a vast number of banks and their research departments, producing a lot of high-quality research, but that's in the past been fragmented. So bringing all that together and making it available based on who's entitled to what. So kind of figuring all that out and bringing that to users. Back in those days, that was the dataset. Mostly information that you might get access to from financial data terminal that you

Starting point is 00:07:17 might be using as an investment analyst or as a corporate typically would have less access to some of these more private proprietary sources like the bank research, but still those were the types of content. And since then, we've obviously added a lot more really insightful proprietary content on top of that. Yeah. And so if I think about oversimplifying, I'm sure, but conceptually, the early version of the product is all this relevant data that is in disparate places, disparate silos, and the best possible search tool to get me the pieces of information I want based on what I'm asking for as quickly as possible. Right. It was information that's hiding in plain sight. How do you find the needle in a haystack? Everybody has.

Starting point is 00:07:57 had the haystack more or less, but it was just a lot of work, so much work that you often didn't have the time to invest in it. So you had to guess and hope for the best that you had covered enough. And you were always fearing that you might be missing something because it was really so inefficient to come through this information. So this allows me to ask a question I'm so interested in, which is, okay, so you had been doing this. You had been using machine learning models and I'm sure investing tons of brain power and dollars and people into an amazing fast search experience. Take me back to the day that chat GPT came out or GPT3 came out or whatever the relevant milestone was for you all, how that then changed your world? What changed in the product?

Starting point is 00:08:38 How did you take in that new capability? Did it displace a lot of the advantage that you had built up? Do you feel as though these general models make it easier for other people to do, let's say, great semantic search in their document database for enterprise search? How did your world change and how did you react to that first technology and then the subsequent ones that have come out? So again, it was going from these smaller language models with more predictive or language understanding capabilities, but without generative capabilities. And so the large generative models gave a lot more capability, a lot more general purpose abilities that you could deploy. For us, that was just an incredible playground and opportunity to go and just apply these brains in a box as I like to think about them. in lots of different ways. There are really such general purpose tools that we just spent the first

Starting point is 00:09:28 few months thinking about the first very obvious things to build, like summarization of documents and summarization of insights across an industry and so forth, like letting users get access to cross-company insights in a way that would have been too tedious in the past and suddenly became very doable and figuring out how to control these LLMs and how to control Bill Godrails to prevent hallucination and retain some of the capabilities of our search engine where some of the beauty was really in the user experience of being able to really see the individual sentences and snippets of text where the insights came from and quickly validate and get to the source context and read the underlying document really know where

Starting point is 00:10:13 all the pieces of information came from figuring out how do we deploy this in a way that retains all that greatness when this new world where you're now able to use allums to do it in a smarter way. Now, did that give the rest of the market new tools? Yes. I think that it's in some ways given deceptively interesting tools where you even see, I mentioned earlier, how even Google had an enterprise search product that they'd be pulled out of the market because it wasn't feasible. Now you see some large corporations and large financial firms tinkering around their IT teams have been given a budget by a CEO to say, hey, go figure out something to do with Gen AI. And the most obvious thing that seems to always come up is that, hey, we've got all this

Starting point is 00:10:58 internal content, hiding in all kinds of disparate places. And we've done all this work to produce all these insights and memos and so forth. But when we need it, we don't find it. So can we go solve this? And suddenly, Gen AI gave some tools to those IT teams that allowed them to try to do and create some very interesting demos that then got funding often. And so people started to try to build these things that even Google had backed away from doing the past. So yes, it certainly gave capabilities that weren't there before. But as people started calling this rag, retrieval augmented generation, and started to look at all the components of creating a robust software system, the reality has still been that, okay, you've now made some new leaps, but you also have

Starting point is 00:11:43 some new difficulties, the leaps that this technology enables. We feel like we're a that has spent the last decade and some years on top to really get really good at this. And so we made those leaps quickly. And you've got the large frontier model companies offering their solutions. And those are advancing quickly and staying on top of this technology and developing something great that users have now come to expect by using something like a JAT GPT. It's actually now become, in some ways, even harder because of the really fast pace of development. Just when you think you've built something great, some new great model,

Starting point is 00:12:20 advances in its capabilities, and that's available to consumers. And now those consumers expect that you have that also in your professional solution. And so I feel like that actually gives us even more advantage as a startup that can move quickly and deploy these capabilities and has a really smart search engine that feeds the Gen AI on top that is doing now the conversation with the user on top of this really powerful search engine that semantically kind of finds all the right information so that it can feed it to the Gen. to summarize. So I feel like this actually has been a massive accelerator for our roadmap and created a ton of even higher demand from the market because we've gotten closer to the original

Starting point is 00:13:01 vision we had of a machine you could talk to and get answers from. I always thought it was much further away. There was when you could actually deliver that. And now we're suddenly able to, I could pick up my phone and pick up the office and ask a question, any business question, get the answer. And I thought that was decades away. Realistically. but here we are, and that's possible. It's a wonderland for a company like us. What have you learned so far from user behavior? Everyone can sort of imagine all these cool ideas, and then you put them in front of people, and maybe you never know exactly what they're going to use. Is there anything that surprised you about what they use or don't use that's newly possible because of these models,

Starting point is 00:13:39 whether that be synthesis or summarization or condensing or just better search? What have you learned about the revealed preference of your user applying this technology? Mostly, it's been fairly expected. It's just making these better connections, summarizing across companies, industries, having a better natural language conversation with the user instead of keyword-based semantics. Maybe what has surprised me is more on what you see on the consumer side where people use a chat GPT or perplexity or Gemini, etc. and they, in their daily lives, in my mind, trust these LLMs too much. And I find myself doing that too.

Starting point is 00:14:27 You don't have infinite time. And if you don't have citations, you just have a credible-looking LLM-based answer that you can read and feels true. And let me run with that and check against my own recollection. And if it makes sense, it's too much work. They don't make it easy to go and validate it, even if that was possible, whatever the alum pulls from its vast memory, it doesn't even know where it came from. That consumer world, there is a lot of trust in that. And we see what surprises me a little bit

Starting point is 00:14:55 is that we occasionally see and hear about professionals using those same tools and defaulting to trusting them in my mind way too much. Most people don't, but that's been a bit of a surprise where we've always worked with serious researchers that do deep dive work. And our function is to take them to the right content, but they want to really read it and get the full context and see where it came from. What's the analyst that wrote it and really get to the bottom of something before trusting it, before making seven figure bigger bets based on this information. But maybe the biggest surprise has been that you also do see professional users fairly often trusting some of these public alums too much. If you think about the things that differentiate and just

Starting point is 00:15:39 the general categories of the tools and the data, two simple things. You mentioned that you've migrated a lot towards proprietary content that just you have. You were an early aggregator of proprietary content, sell-side research that you could get directly from lots of banks, but not aggregated in one place. How much do you think the separation between the winners and the losers, and let's call it enterprise financial search, will be determined by tooling versus data? Because you see like perplexity. Now you can search over financial documents and perplexity, ICC documents or whatever. I'm curious how you think the battle. shakes out between providers trying to solve this problem for the customer and how much of it

Starting point is 00:16:19 is tools versus how much of it is more and more useful data? That is a great question. I think for us, the fundamental problem has always been about fragmentation. I experienced this as an investment banking analyst where I first came out with the understanding of the problem that needed to be solved in this industry, where I was logging into dozens of different systems, and asking the library to go to even more places to try to gather information. So the fragmentation was always a huge pain point. Back then, it was so fragmented that you were keyword searching a single PDF, one keyword at a time, and doing that across lots of PDFs

Starting point is 00:16:59 and trying to aggregate this information. Even with more aggregation, it's a dissatisfying solution unless you have something where you can ask one question from one system. So in that sense, it is both a technology, battle of having a really great solution layer on top, the smartest LLMs applied in the smartest way and sequenced in the right way that you can create a trustworthy answer, but also then the user experience that helps users still get comfortable and trust, but verify and validate and get to the context and read. But the underlying content, you want it to be all in one

Starting point is 00:17:36 place. You don't want to go and do multiple searches in different systems and learn the intricacies of each one if you could do everything in one place. So our strategy from day one has been like, hey, we'd love to be one place where one search and done. You can really expect that if you didn't find it in our platform, it doesn't exist. And we often hear clients telling us that how they think about it, that it gives them the comfort that they didn't have to go to 20 different systems to get that comfort. They could go into one system. Yeah, I'm curious also, if you think about planning a business like yours for technology landscape that just seems to be explosively evolving. How do you do that? If you don't know what GPT6 is going to be like or something like

Starting point is 00:18:20 that, how do you do effective planning not knowing what the technology itself will allow on the product tooling side? Well, you have to try to, in my mind, organize your team and the org structure to be agile because you just have to expect the unexpected. It's going to happen and it's going to happen very frequently. So you have to be able to react to new developments and you don't know what they're going to be. Nobody knows, really. This LLM developers, even the frontier model developers, don't know what the next breakthrough might be when you cross some scaling threshold or new algorithmic development. For us, the solution has been to first try to create a system that is pretty agnostic, that can use any LLM that's out there and use different systems to

Starting point is 00:19:06 accelerate their performance or build the methods where you can very quickly pivot and change what model you use for, what task, if there's suddenly some model is better at multimodal or better at a bigger context window, is better at doing those things much faster and so forth. So you can actually, you've got less latency, more time to get more intelligence into the system. And so whatever those things are, you want your systems to be more modular so you can orchestrate all this in a much more flexible and agile way. And then have a more agile organization that tracks what's going on and it's on top of those new developments. And even when they just come out as research papers, you are reading everything that comes from credible sources and try to stay on top of what's a real development, what actually could be deployed today, what's not quite there yet, but what's giving us some hints about what might come next. So I find myself spending a much larger percentage of my time now and just product development,

Starting point is 00:20:02 reading everything myself. I guess I get more of the filtered information from the team that reads everything and then one out of ten things that really is impactful gets on my screen and many others do the same, different levels of deep dive and screening of that information. But you've got to be on top of it all the time and willing and able to pivot. I know you can't tell me exactly how. investors will work in five years that's different than today because the future is hard to predict. But I'm sure you can say a little bit about what might be possible for them that isn't possible

Starting point is 00:20:36 today. It's a sneaky way of asking just what is most exciting to you from a product perspective of what you will enable your customers to do five years from now because of all this. Put your dreamer hat on. What do you hope you're able to do for customers five years from now that you can't do today? We're trying to take all the friction and barriers out of the way and try to make the cost of acquiring information and insights really, really low. So you could suddenly scale it and do a lot more work, do a lot more diligence, and have it be organized for you and done for you by machines so that the things that you used to take weeks, that you don't have the intern around to say go research this for three weeks,

Starting point is 00:21:19 but if it takes three minutes because the machine can organize that information and ask the same five questions from 500 documents and say this is the most important set of documents you really should read and maybe some tentative conclusions that you could make. Hard to really see how soon and how far that goes, but I like to think of it as the computer systems and the large language models within them doing a lot of the work and acting as a large team of superhuman analysts that get smarter and smarter. And maybe It's a large group of interns that now turns through a lot of the work that would have been in the past, required a long time and didn't get done. Now it can get done. So the level of diligence,

Starting point is 00:22:03 level of research gets quickly better. And then the systems get smarter and they can start to draw some tentative conclusions and you can start to evaluate out of these five that the machine generated, these other five scenarios, now you can start to play with them as an investor. Yeah, it's so exciting to imagine that you might be able to say, build me a industry primer on X, Y, Z, and have it be done in two to three minutes. That's pretty exciting. Right. Those kinds of things are very much doable today. But then what do you do with that? And maybe you let the machine take alternative views at the industry and try to simulate where could that go. And it's not going to be able to tell you where it will go, but it can tell

Starting point is 00:22:39 you different paths of where it might go. What data sets are most exciting to you? One of the things about the Alpha Cent story that's clear, we were a big investor in TIGIS, and obviously you acquired TIGIS. That's an incredibly valuable data set. I have questions about M&A and the role it plays in building businesses in a little bit. First, how do you know you're onto a dataset that is interesting, different, useful? I'm sure you've looked at every data set imaginable. What are the features of data sets that make them valuable? To me, it's the net addition to the information volume that's out there already in terms of impact of those insights. Are they overlapping with what's already out there?

Starting point is 00:23:21 Are they totally new perspectives on something? And you rarely get that new perspective. The expert interview content is definitely what I see as the most exciting one because it provides a whole new lens into what's going with companies and industries and private markets where those markets have been lacking transparency, have been in the dark in terms of information you could get on those companies in the public markets, which are becoming a smaller and smaller part of the total picture. You've got SCC telling companies exactly what information they need to be putting out at what frequency,

Starting point is 00:23:56 and you've got the sell side writing research on those companies. So you've got pretty good information out there on public companies, but even that is filter. The companies try to do their best, to show their best sides of their business and get their best story out, positive story. So interviewing those experts, getting by-side interviews with investment analysts that are about to invest in a company, interviewing an expert at a company, former executive, customer, partner, a competitor,

Starting point is 00:24:22 and really asking them the most important questions about the business model, the competitive environment, the company demand environment and so forth, and getting real answers from real people that don't have any skin in the game to alter that viewpoint. That is incredibly valuable and even adds a ton of value to public companies because you didn't have this impartial additional set of perspectives from people in the trenches, the operators. But then now you look at the same new capability for a new source of insight for private companies where there's almost nothing out there because there is no regulatory need to put out information. So this now fills a much larger vacuum. Private markets are becoming so much more important, but there still isn't anything out there that you can systematically get to other than expert interviews.

Starting point is 00:25:10 And so that's what got me so excited about that space. And I'm still thinking this is the most exciting area. of got new content and new insight about companies and industries out there. So we're really doubling down on this in our business. With the TIGAS acquisition relatively recent, talk about the role that M&A can play in building a business towards a specific vision and just the lessons you've learned doing MNA. Not that many people get to do lots of large-scale M&A.

Starting point is 00:25:38 What have you learned about doing it well? What mistakes have you made? They've taught you lessons. I'm just curious about this part of the business story. It's actually funny in that I started my career working on MNA as an analyst in investment banking, and I thought I knew a thing or two about it, but when we were looking at our first acquisition a few years ago, I was still wondering, okay, what is this going to be about? What do I need to know? I couldn't find any rulebook or instruction manual to it. It's shocking how little there is really

Starting point is 00:26:07 out there. Some podcasts, some of your guests have been really smart. Brad Jacobs, and you've got people that have done a lot of MNA and you try to read it, but you have to collect it here and there. In an MBA program, you don't learn this. How do you go and acquire a company and then integrate it and really leverage it in your business? So you have to learn it yourself. So it was a little daunting at first, but still preceding TIGERS. It was kind of taking us a step in that same direction of an expert interview library. Despite those concerns, the strategic logic was so incredible that it felt like a drop everything kind of thing. So it's a, okay, we're going to have to go and just do this, learn this, and figure out

Starting point is 00:26:41 how to do it. And then you feel like, okay, we got this one done. And And it actually went really well, and the strategic logic made all the sense in the world. And investors are really excited about the outcome. And, okay, this is actually, yes, you have landmines along the way, but if you're careful, maybe you can avoid them even without having gotten a course on this. So, you know, then we did a couple of more and then got the really fortunate, incredible opportunity with Tegas earlier this year where we had been the number two in that space, but a distant number two where Tegas had really innovated and created this incredible business.

Starting point is 00:27:14 model and created sort of great scale that we were far behind. That was the bigger bite and figuring out with our board, now of investors, could we get something like this done? Getting close to a billion dollars for a startup company worth a few billion itself, can we really raise a lot of cash very quickly in a process that requires you to move quickly? One learning was that it was worth patiently building an investor base that has deep pockets where you didn't need the capital, but when you knew it, it was there. And suddenly everybody was able to pitch in and say, I was shocked and excited about how quickly people just were willing to go back it because they said, yeah, this makes perfect sense. If you have strategic logic and two products that really belong

Starting point is 00:28:01 together and our one plus one is going to be four or five, then, yeah, absolutely. People are going to very quickly without doing much extra research, they knew both products and Yeah, these two belong together. And so that was a big learning as well, that even something much, much bigger, investors buy into great strategic logic. Certainly not into doing acquisitions for the sake of acquisitions. I would never go for that either. Just trying to buy scale. But the strategic logic of building something really valuable that was super exciting.

Starting point is 00:28:32 And it was excited to get the investors behind it so quickly as well. It felt like you can suddenly play in a bigger league when you have the capital behind you. knowing that that class, Brad Jacobs and a few others have left breadcrumbs all over the place for how to do this well, but knowing the class doesn't exist, having now done it several times and how to be accretive, what would be your syllabus for a class that you would teach entrepreneurs that want to do M&A, how would you guide them? Here's how to think about this. Beyond, obviously, make sure that you're buying has the strategic alignment and there's some strategic insight. This helps you accomplish your mission faster or better. what other components of that playbook would you write if you had to write it? The thing that I found the least on is, of course, you've got some textbooks on deal negotiations,

Starting point is 00:29:18 structuring and all that stuff, but I don't think that's where the hard pieces are. It's more, okay, once you put two organizations together, how do you figure out how the cultures are compatible or what work you need to do to make sure that people quickly trust each other? And always when you try to imagine yourself on the other side being acquired, what do they need to hear day one to feel good that, hey, this new company that has just appeared in our lives, everything changed. Can we feel confident in what they're planning to do here and their intentions and really actually intent on growing this and doubling down what we're doing? and how do you get them really excited about the future when suddenly they're fearing all the natural fears that come with an acquisition? So those softer factors and identify all the great talent and what they're capable of. There isn't any system that I'm aware of for that. It's still a very

Starting point is 00:30:13 manual work. So far, what works? Is it over communication early on? What is it? What are the elements that you did do that did work in that direction? I think it's really important to try to answer. like what's in the minds of people on the other side, then trying to address their concerns very quickly. I'm developing my own playbook here after a few acquisitions, trying to show up there day one with answers to the questions that people really care about. Trying to be honest that we don't know everything, but we're really excited about your business and we're really excited about your team and all your talent here. And we are a lot bigger. And today we're going to be even bigger. and together, we're going to be even bigger and we're going to grow faster.

Starting point is 00:30:56 And that's going to create so many career opportunities for everybody. We can't tell you exactly what they're going to be for all of you, but they're going to be great because we are a high-growth company. And here's what's exciting about that. So trying to think about how do you calm people's fears that when they read about M&A, people naturally have in their head. So try to enslave those and address those, but also then try to figure out how do you make this new story that suddenly appeared for them, an exciting story where they think that,

Starting point is 00:31:24 If I ride this wave, this can be pretty awesome. Maybe there's a public company one day where I was able to make a big impact and this is the story I tell my grandchildren. What have you learned about selling a tool to investors? Investor specifically as a buyer. I'm curious about corporates too. Maybe it's the same question. What is the key to selling something to them as a buying group? It's definitely an interesting buying group.

Starting point is 00:31:49 This was where we started, even though I had figured out the need for. from the perspective of an investment banker, from my kind of own experience, figured that it was really going to be hard to sell to these big banks. They were conservative. How do you even figure out who can buy for whom in these big departments? But small hedge funds, we figured out, were actually really good customers.

Starting point is 00:32:13 If you can add value, people have the ability to spend money because they are working really hard. And there's this intense pressure to find some advantage and edge. And if you can give it to them and if you can prove that edge, they'll be happy to pay for it. You need to continue to prove that you have that edge and advantage for them. But if you do, then now you've got some great adoption in a market pretty quickly. What surprised me about them, though, was they often, especially in the early days and years, they often wanted to keep it as

Starting point is 00:32:45 their own secret weapon. We thought that, hey, now the word will spread and everybody will know. Not a high word of mouth group. No, no, exactly. And it might be a good. be some psychology around it as well. Like if you're going to be an investment analyst, you might not be tweeting things as much, although that happens Fairmont today, but it felt like they weren't helping us a lot in marketing it, just like a corporate buyer would. But still a great, really agile, aggressive, ambitious small businesses. If you think of hedge funds that way, then they were a great customer base to start with. And then from there, you could take the jump to corporations that had more established buying processes.

Starting point is 00:33:22 Yeah, what is the biggest difference in selling to corporations versus investors? Just the normal procurement process is slower or the value proposition. I'm curious if the value proposition framing had to be different. It was hard to define the people when you're selling in a large corporation, often really hard to figure out what's the title of a person that is doing research that we're trying to make or advocate for some million dollar or a billion dollar decision acquisition and product launch, et cetera, like the variety. of titles is so broad that it was hard to find those people. You often had to get some

Starting point is 00:33:57 Sherpa inside of a company to show you around and tell you who would be your next buyers. Because they were so large, they were harder to navigate. So it's taken many years and we still continue to find new personas. And we started from Investorations and investors took us to corporate strategy and financial planning and competitive intelligence and then corporate development, you know, acquisitions, then strategic marketing and product development, product marketing, sales even, sales engineering. There was just these new roles and each one of those had many titles depending on the company you were selling to. And you had to find this nooks and crannies globally inside those organizations to find the people. So that was definitely very different from

Starting point is 00:34:41 a hedge fund where you can walk into a floor and find most people there. Why do you care about this business? Like what is it in your own experience? the customer, the cocktail of things that go into a business, what is the why for you beneath? Obviously, you have a customer, you do something for them. That's the mission. But if I asked you why five times, you personally, why did you start this thing? Why do you want to continue to run it? Why are you ambitious to make it much larger than it is? There's a great question for any entrepreneur. Why are you going to keep on doing this for a long period of time? For me, the why started to stare at me. In my first,

Starting point is 00:35:19 startup that I was doing during my studying electrical engineering and finance and I figured I'd put tech and finance together and go work at a startup stock exchange in Brussels during my studies called East Dak. It was trying to build the European NASDAQ. I had this affinity to the idea that, hey, you could build a startup and you could take it public and really grow it fast and find all those resources through public market investors. I'd seen my dad building a biotech company in Finland where I grew up and it was really hard. It was very hard to find investors and get funding in a way that in the US, you've got much more developed capital markets. So ESAC was trying to build that in Europe and say, here's a path for European companies to go public. And I jumped in there and was helping

Starting point is 00:36:03 to raise money, working with the CEO to call all these big investment banks and see, would you invest in this and we'll create this new ecosystem? And I was just fascinated by all this companies that go public and they become more professional and all the disclosures that they put out. And it was like happy, excited reading prospectus documents. I think very few people, certainly very few tech people would be excited about that. So maybe there was something about that. And the other thing I was really excited about was building a startup. And so it wasn't really the right time for Europe didn't have enough exciting tech companies to sustain that European NASDAQ. So I ended up going into investment banking and then finding my way to Silicon Valley, working with tech companies

Starting point is 00:36:44 and investment banking and just continue to be super excited about how those companies now in this most powerful tech ecosystem in the world were starting from humble beginnings and very quickly zooming out to be very important businesses and how finance was able to help them and how you had this developed capital market system where information was out there and was pre-produced and researched. So I felt like, hey, there's something here that I could do to really add value. I felt how really in my bones as an analyst, I still remember walking into client board meetings and fearing how the information I was able to acquire in the two days that I had time to research their business, I'm just going to be exposed.

Starting point is 00:37:27 The CEO of that company or the board is going to see this analyst, it just doesn't know what they need to do. And that was because the process was so manual and inefficient of acquiring information. So for me, it's still this source for inspiration because I know there are millions of people out there in finance and business that are still struggling with the inefficiency of it. And I feel like if I can pull my passion here and our technology and our capabilities together in the best possible way to help the whole business and investing world to be more efficient in acquiring information, making better decisions, deploying capital better, than I've made

Starting point is 00:38:03 a contribution. Sort of like Steve Jobs talks about wanting to put a dent in a universe. You want to find something that you can uniquely do that nobody else was trying to do. somehow it was being ignored and feels like this is the one thing where my nerdy interest in financial disclosures gives me an edge, perhaps. There was a chart floating around last couple of days showing since I can't remember what year the technology businesses and the market caps shown by the size of the bubble in the U.S. And then in Europe.

Starting point is 00:38:31 It's what you would imagine. It looks like. It's this massive dominance by the U.S. versus Europe. A lot of people have written about this. What do you think is the reason why this is the case? What are the missing conditions in Europe, which has a history of many incredible businesses, at least in the last 20, 25 years, been unable to produce the sorts of technology businesses that have thrived in the U.S.?

Starting point is 00:38:54 So firstly, I would say now Europe is producing some. Certainly not anywhere near the rate of the U.S. And often they get acquired by U.S. companies, so they don't fly as far and high. it's just really hard to get going from a European entrepreneur's perspective where it's harder to raise money. If you want to find capital to scale, you probably have to go to the U.S. At least you have to go to London, but you probably have to go to U.S. You have a harder time-finding experience, founders, co-founders and employees. There's a bit of a different culture.

Starting point is 00:39:30 American culture is more friendly to working around the clock, and there is always the same one in Europe. you've got more risk aversion. It's sometimes just hard to find people that are willing to join an early stage startup. They'd rather work for some, let's say, developers working for a software consultancy, which close my mind. But that is often that people fear the risk in startups much more. Where in the U.S. people admire and get excited about building something new and are fine with the risk.

Starting point is 00:39:59 And entrepreneurs are more fine with the risk. Failure is considered just a step in the way to success, whereas in Europe it might be a terminal state. Yeah, right. It's a big chicken and egg problem. The flywheel has been getting going and it's producing some successes, but it's hard when it's a chicken and egg problem. Having the entrepreneurs, the ecosystem, the investors and everything feeding itself,

Starting point is 00:40:22 it takes a long time to build. And so our attempt with Eastak is way, way too early. I don't know if that might succeed better today, but still it might be early. If you think on the entire history of the business so far, what do you think the largest mistake that you've made is? And looking back on whatever that is, do you take any specific lesson from it? I'm just always fascinated to learn from people's mistakes. The thing as an entrepreneur, as a CEO, scaling a company, you always think that I've done something wrong. I should have scaled faster. And now that it all feels so obvious that, of course,

Starting point is 00:40:57 this is successful. Like, no, everybody look at this thing that you really killed it 10 years ago. Yeah, see, if you had believed in it more, if you had invested more, earlier, then I looked at myself, why didn't I convince them of this bigger story earlier? So maybe that's the one mistake of failure where he wasn't confident enough to tell them that this will be amazingly successful. Don't ask me for more milestones. Let's just go. If you think about, I ask you to really zoom in to a moment.

Starting point is 00:41:24 What do you think the defining moment so far of Alpha Sense has been? Maybe the one moment is breaking into the corporate market. There was always question. of if you're only selling to financial services, then how big is that addressable market? And nobody had really done this before building a financial services, financial research platform, and taking it to the corporate market. Everybody talked about it and said, well, wouldn't that be great? And here's our corporate story.

Starting point is 00:41:52 Everybody had that story, but nobody had really proven it. And so when we managed to prove that, actually, we had a search engine for professionals, business professionals, not just financial professionals, that breakthrough and proving that addressable market was 10 times bigger, covering really every type of business. I think that was the most defining moment where we could say, okay, we're no longer a niche business. We're actually a horizontal solution that has massive opportunity for scale. Can you tell me that story? How did you do it? What were the key ingredients? We had the assumption that somehow we'd have to change the product, but we'd just try to sell it first

Starting point is 00:42:30 to corporate users that we were just coming into contact with. Investor relations was already a function that was dealing with investors. And we learned that, okay, they needed to be smart about what the investors were looking at and researching when they were evaluating whether to buy their stock or not. And they wanted to get up to speed on that, get smarter about that, and even try to replicate the analysis that the by side was doing and tell a better story, smarter story, understand a competitive landscape in the way that an investor would look at it.

Starting point is 00:43:01 We found that we could do, we expand quickly, we could get them to talk to their peers across firms and the word of mouth really spread quickly and suddenly we started to get large numbers of investor relations departments to jump on board. And we doubled down on that and said, let's hire more people to sell into corporates and really try to make it big and it worked. A good beachhead. Yeah, exactly. And then saying, okay, this works.

Starting point is 00:43:26 We didn't have to change the product. Let's just go. It's a scale. Is there any peer person, founder, CEO or company, that you watch most closely or learn the most from? I watch the foundational model developers with fascination, just how quickly they've been able to get adopted. And frankly, I was having a hard time believing

Starting point is 00:43:51 that in a business that seemed to be commoditizing with LLMs being open-sourced. And from a user perspective, you can suddenly get this incredible product that a couple of months ago was 100 times more expensive. if somebody really got it to be in the market for pennies. And still, they just keep on really aggressively growing and finding ways to scale faster than anybody has ever scaled. So what's that with fashionation for sure? I think a lot of people probably do.

Starting point is 00:44:19 Is there any part of the models themselves that you're most excited about getting better? Certainly reasoning capabilities are valuable. Right now, we're building that in our own system, building the planning to ask a research question. from the system, like you'd ask an analyst, and let's say, what is the market landscape for semi-glutides? And the system breaks it down and says, well, that means I need to understand the market size and the players and growth rates, etc. You know, kind of all those kinds of things. But maybe the LLMs can get really smart at reasoning and do this type of work natively. That would be exciting. Or being able to really expand the context window while still keep

Starting point is 00:45:04 keeping speed high and cost low and being able to do multimodal analysis, video, audio, just process things really fast and accurately, which already is possible, but you've got to do everything in a way that it's sufficiently accurate that you natively rely on the model as opposed to plugging together multiple layers of verification and validation checking. Right now you have to build a lot of guardrails around LLMs and have a sequence of those models where one is interpreting what work needs to be done and planning the work and then doing the work and then another one is checking the work and another one is really cleaning up for a user and so it's a big sequence. And if you had a really smart model, you could maybe do

Starting point is 00:45:48 many of those things all at once. Of course, that's what people are trying to build with, they call a GI, a smarter system that can just think for itself. But right now you plug these things together and create a sequence and create a system orchestrate. And you can do those things pretty well, but more intelligence is better. As a user of Tegis and AlphaSense and an investor, my conclusion from so much interaction with these tools is, if you're an investor or an analyst, you better not hang your hat on the ability to just go find information and synthesize it. These tools are making it better. That's a good thing in the net because it just makes the market more efficient, probably, and allocates capital better, the exact thing you said your underlying

Starting point is 00:46:26 Y is. So I think it's so cool what you're building and how you're building it, and I appreciate you're doing this with me. Every time I do an interview like this, I ask the same traditional closing question. What is the kindest thing that anyone's ever done for you? First thing that comes to mind is a nerdy teenager really excited about electronics and growing up in a middle class family in northern Finland. And somehow my parents allowed me to just go wild in diving into electronics and ordering components from around the world. And I felt that that was kind of them to let me just pursue my nerdy passion. and I felt like I got a lot out of that.

Starting point is 00:47:04 And so it feels like they thought that maybe that nerdy passion was worth backing. So that comes to mind. Jack, thank you so much for your time. Thank you. If you enjoy this episode, check out join colossus.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning. You can also sign up for our newsletter, Colossus Weekly, where we condense episodes to the big ideas, quotations, and more, as well as share the best content we. find on the internet every week.

Invest Like the Best with Patrick O'Shaughnessy - Jack Kokko - Building AlphaSense - [Invest Like the Best, EP.404]

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