How AI is reshaping Wall Street

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2:17Maithra Raghu:I think actually as the AIs get better, paradoxically, and the thing no one's talking about, is that we'll need more and more people in the loop so that we can make sure that they all have the right frameworks for judgment and all of that. It's kind of a big mental and emotional shift to move from research to entrepreneurship, to leave big tech, go start from scratch. What was that transition like? I always say the motivation for doing a company is really, really important. I think it has to be quite pure in some ways. I think you have to leave not caring too much about, you know, know, the exact outcome or where it goes.

2:56If you could wave a magic wand and have a personal agent that can do anything for you, what would you want it to do?

3:03Maithra Raghu:Oh my goodness, probably clone myself, you know, be me in all of these places.

3:14Maitre Rago is the co-founder and CEO of Samaya AI. They're building AI systems for the financial sector. Think hedge funds and investment firms like Morgan Stanley. Their AI agents tackle complex analysis that financial professionals do every day, which raises some big questions, even if you don't work in finance. What happens when an AI agent can do your work? And when billions of dollars are on the line, how do we ensure that these AI systems are accurate? Plus, how close are we really to seeing personal agents at scale? Today, we're digging into those questions and a lot more. So let's get into it.

3:54I'm Rana Elkaloubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

4:10Hi, Maitra. Welcome to Pioneers of AI. I'm so excited to have you on the show. So excited to be here, Rana. So first things first, we both went to Cambridge. I guess you did your undergraduate there in mathematics at Trinity College. That's right. How is that experience?

4:28Maithra Raghu:How is that experience? Extremely intense is the truth of it. Trinity attracts all these folks who have competed in these international math competitions, these math olympiads, which are now in the news again because AI is now making progress on these math competitions. So that's been an especially exciting thing for me, seeing parts of my past and parts of my present come together in this way. But my story of getting into Trinity is just, I was really into math around high school. Did you grow up in the U.S.? You grew up all around, right? I grew up all around. The bulk of my life was split between the U.S.

5:10Maithra Raghu:and the U.K. So when I was in London, I discovered these math competitions pretty early on, got really excited about these Olympiads, the reasoning and the complexity, the abstraction involved in them, the problem solving. And if you compete internationally in an Olympiad, that just takes you straight to Trinity College in Cambridge, that just pulls everybody across Europe who's competed in these Olympiads. And some people go on to pursue, you know, mathematics research of various kinds. But of course, many people end up in London working in financial services and especially, you know, in hedge funds, lots of different types of investment firms.

5:51So before you started Samaya AI, you were a researcher at Google Brain. And I am just kind of curious about your experience there. And also, what was it like to do research in industry versus research in academia?

6:06Maithra Raghu:You know, I started collaborating, working with folks at Google Brain all the way back in 2015. And to put that in perspective for where the AI space is right now, this was before OpenAI even existed. So there was Google Brain. Facebook had just started the Facebook AI research effort with Jan LeCun joining them. And that was basically it. One exciting piece about being in industry at that time and maybe a difference to academia is even back then you were seeing the formation of these slightly larger teams that were going after specific capabilities or specific infrastructure or specific foundations for AI.

6:45Maithra Raghu:That was maybe a little bit different to the academic approach. So that's what really pulled me into Google Brain. I mean, there are all these people, they have all of this secret know-how, sort of all these intuitions about how to build and train some of these systems. And now that I mention it, maybe that's one thing that's actually stayed the same between back then and now, where working with this group of people and getting these insights that are not easily written down anywhere, but it's just lived experience is something that sort of stayed through the same in the AI field. I actually think people don't talk about that, right?

7:20And it's almost like a bit of an oxymoron that in this age of AI where we're thinking about automation and everything's kind of, right, like productivity focus and automation focus, there is still a bit of intuition. How do you train these models? Talk about that a little bit more, actually.

7:39Maithra Raghu:Yeah, I think people are falsely underestimating how much of the human perspectives and human inputs are required to really enable our glorious AI future, you know? And it's going to come up in all kinds of different ways. One thing that's been top of mind for me recently, as I've seen some of these AI agents develop, is that pretty soon we're going to have AI agents running through the night. You know, in some sense, nighttime might not be dead time in the same way that it is right now. And you can imagine that, you know, you as a human are going to do a handoff process with some AI agents that are going to go do things for you and then come back to you the next morning being like, hey, here's all of the things I found, all of that.

8:22Maithra Raghu:Actually, this future that we're heading towards is not that different to the experience of being a machine learning researcher over even the past decade. Because as a machine learning researcher, you wouldn't want to leave those GPUs not being used overnight. So you'd be setting stuff up for your models to go train. And And then the next day, you'd look at it, look at the results that you're going to get. And then now, that's just going to be more accessible to everybody. So we don't have to think about the models anymore. People are going to have these agents, and then these agents are going to go do things for them overnight.

9:00Maithra Raghu:But coming back to your question, for those agents to do well, just like with the machine learning researcher, you had to make sure that that handoff had to be really, really good. You had to go in there, like get all those details right, make sure that the model was well set to train. Similarly, people are going to have to make sure their agents are well enabled to succeed. And that's something that we're really, really underestimating. So in a world of AI agents, I don't think you have that productivity, that positive impact, unless you also have people guiding them on how to succeed. Yeah, I remember those days.

9:35A, you don't want to like run the training kind of, you know, the training iteration while during the day, because then you're just sitting kind of idle, right? Yes. So overnight runs where we're super smart. But to your point about setting it up for success, you don't want to like go home, you know, be asleep. And then, you know, an hour in the code runs into something and it just stalls or stops, right?

10:00Maithra Raghu:Yes, exactly. It's going to be similar for the agents if they have this amount of time. Yes, we're going to see the AIs get better at decision making, get better at planning, reasoning, all of those things that we're seeing. But putting that within the right framework, the right context for that decision making, that's still going to be heavily driven by humans. And I think actually as the AIs get better, paradoxically, and the thing no one's talking about, is that we'll need more and more humans, more and more people in the loop so that we can make sure that they all have the right frameworks for judgment and all of that.

10:36Maithra Raghu:There's going to be an element of that that's not easily scalable and there is going to be a heavy human touch in those pieces. And, yeah, that's really like the future that I see us heading to. Hold that thought because I want to come back to it. I think it's a very important one and a very relevant one. And I think it's top of mind for a lot of people. But you left Google Brain to co-found Samaya AI. And as I think of my own experience, kind of leaving research and starting a company, it's kind of a big mental and emotional shift to move from research to entrepreneurship, to leave big tech, go start from scratch.

11:12What was that transition like for you? And also, what was the impetus for the transition?

11:17Maithra Raghu:Yeah, so I always say the motivation for doing a company is really, really important. I think it has to be quite pure in some ways. I think you have to leave not caring too much about, you know, the exact outcome or where it goes, but driven by this desire to bring something to the world and hopefully be driven by that positive impact that you see in bringing that that thing to life. So that was definitely the driving force for me. You know, I'd had 10 years as an AI researcher at that point. I had this beautiful body of work. I'd been fortunate to collaborate with a number of the leading figures in the field, some of whom went on to become some of Samaya's first angel investors.

11:59Maithra Raghu:and it was a very satisfying period. And I think the thing that really drove me was exactly that desire to go zero to one, to bring something to life that didn't exist yet. And what I saw is over this past decade of AI research, AI had not really been ready to be used in the real world the way it is today. And people had tried in various ways, but it was either too brittle or you had to make it very, very focused on one specific thing or it was better within a larger ecosystem of some kind. It wasn't in a place where it could really stand on its own. And finally, with the emergence of large language models, which we saw early.

12:43Maithra Raghu:So I think the world saw it in late 2022 when ChatGPT came out and everyone started paying attention. But sitting where I was in Google Brain, you started seeing the pieces come together in 2019, 2020 or so. So I think from that point on, I could see the potential of finally having this AI that was general purpose enough, that was generalizable enough that you could put it out into the real world and have it drive impact for people. Very cool. And then what's behind the name? Oh, so Samaya actually means time or moment in time. So I really like the name. It has a history in Sanskrit, which is ancient language in India.

13:24Maithra Raghu:and I'm Indian. And the, I mean, most of all, the goal with starting Samaya, that impact that we wanted to see in the world is giving people back that their time and then, you know, driving them to that moment in time, which is sort of that moment of insight as they go about their day-to-day, which is in our case, you know, a lot of these investment decision-making use cases. in a minute why chat gpt won't cut it when it comes to making big investment decisions plus we go behind the scenes of samaya and see what it takes to build an ai system that can model real world messiness

14:21When it comes to AI, we spend a lot of time talking about what it takes to move models from research into reality. Enter LTX2 from Lightrix. It's an open-source audio-video foundation model built for synchronized sound and video, native 4K output, expressive motion, and precise multimodal control, all running on consumer GPUs. With over 3 million downloads on Hugging Face, LTX2 gives you full model weights, training frameworks, and evaluation tools to build real production workflows. Don't settle for a first draft. Get the complete creative engine to reach your idea's full potential. Try LTX2 today at ltx.io slash model.

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16:55so samaya has garnered a lot of attention so i'll just name some of your awesome investors um that includes former google ceo eric schmidt jan la coon mark cuban who we've had on the show um you also announced recently a new investment from nventures that's nvidia's venture arm as well as databricks ventures so yeah congratulations that's a huge accomplishment um how did that all

17:21Maithra Raghu:come about. Okay. Wow. So the Univine theme between the investors was our clear North Star of seeing both these AI capabilities, but then our vision on how we would take that and really translate that into meaningful value in the investment decision-making space, how that influenced what we wanted to focus on in terms of our AI work and the use cases we were able to support was something that I think that was very inspiring for investors. So from the inception of the company, we saw both the development of these fundamental AI capabilities, but we also saw the work it would take to take some of those capabilities and translate those into practice.

18:05Maithra Raghu:You see all these benchmarks, but these benchmarks are often quite stylized evaluation settings, and progress on benchmarks does not translate to progress in the real world. Can you give us an example? Because I think, again, the average person is looking at how AI is passing all these benchmarks and not realizing that oftentimes it does not translate to the messy real world. Let me give you an example just from Samaya. So Samaya, we work on investment decision making. And there are some benchmarks out there to try and test out the capabilities of AI in the financial domain. And what these benchmarks look like is the following.

18:45Maithra Raghu:there's a very specific question that has a very specific numerical answer. And you can look at that question and the numerical answer the AI gives you and decide whether it's right or wrong. That's the benchmark. That is not at all representative of the real world. So in the real world, what it looks like is somebody is asking some very complicated, ill-formed question. There are many variations for what right would look like. Some things are more right than others. And so it's not about just like a, you know, a specific number, yes or no. So what we did, and this is a project that we call Criteria Eval, is we actually created an evaluation rubric that you would use to evaluate different types of answers and then grade on sort of how many different things did they touch in the rubric.

19:36Maithra Raghu:So that's one example of the messiness seeping in. And there are many, many more examples like that. Can you give us an example of what this looks like at one of your customers or users? Take the financial market. It's a market because people have opposite views of what might happen. Now, imagine two users of something like ChatGPT and two users give ChatGPT a relatively similar prompt. then ChatGPT is going to give these two users a relatively similar response. But what we need to do is we need to be able to personalize the AI so much that they're able to give opposite responses to different people based on the context and frameworks and views that the people are providing to the AIs.

20:26What's an example of an instruction? Are you asking the agent to go do research on a specific investment or are you asking it to actually go execute on the, like how agentic, what does agentic mean in this context?

20:41Maithra Raghu:I'd say it's adjacent to the decision-making. It's not doing the end like execution all by itself just yet. And I think having people in the loop is still very important. So, I mean, one example is on maybe evaluating a thesis. So, you know, one of our users, an investor might come in, they might have a particular thesis, a view of the world, of the market. That thesis might also be tied into, you know, specific like firm-wide context frameworks, as well as their exact portfolio. And so they might want the agent to go do research on their thesis, understand the implications for their portfolio, be able to, you know, cross-reference that with like the broader frameworks, firm-wide context, and, you know, come back to them with, you know, kind of key takeaways, like things that, you know, they should change a position on, potentially like new opportunities that they could pursue that's in line with everything they've shared.

21:43Maithra Raghu:Yeah. You know, the analogy kind of I'm creating in my mind is in the same way that Vibe Coding democratized access to building apps and websites and whatnot, are you in a way democratizing access to financial information? So, you know, or databases or manipulating and visualizing data? So, yeah, so Vibe coding, I'd say it didn't democratize access to code bases. It democratized access to coding itself. And so you could say that. So similarly, I wouldn't say Samaya democratizes access to information necessarily. I mean, we certainly make it easier. We put it all together. So like within within a firm's context, putting it all together.

22:28Maithra Raghu:But I think some of that reasoning and analysis on that information does become more accessible. And this has also been like a kind of a guiding piece for us, which is we really want to see AI that up levels humans, that like lets us do things that, you know, that are innovative. So it's not just, you know, just like, oh, this is like a productivity game, but you can approach things completely differently. And so that's what we're seeing a little bit, too. So now that people can put all of these like content sources, this data in that wasn't even possible before, maybe you're putting in some structured data with like broader views of the market, plus like very specialized like views, then then that becomes something innovative.

23:08Maithra Raghu:That becomes something you couldn't do before. Yeah. Yeah. I love that point of view. Now, one of the considerations when you're making investments is also like the geopolitics of the world, right? So how do you incorporate this into your models? That's actually one place where I think Samaya has been especially valuable to all of our users and our clients. Because like I said before, when you look at financial services, often you have this choice between zooming out and zooming in. But that's still not perfect for the decision making. So it's a mechanism that we as humans have put together just because we need some way to navigate what we have to do.