Ex-Citadel Quant and AI Researcher On Breaking In, Tech vs Finance Careers

In this episode, I talked to Nimit Sohoni, a Stanford PhD and AI Researcher at Cartesia who previously worked as a quant at Citadel. We discussed the differences between AI research and quant careers, including work-life balance and the value of a PhD in these fields. Nimit also shared what he's currently working on and offered advice for those looking to transition into AI research. 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 𝗹𝗶𝗻𝗸𝘀: • YouTube: https://youtu.be/_jECS37M3dQ • Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835 • Transcript: https://www.developing.dev/p/stanford-phd-ai-researcher-and-quant 𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀: 00:00:00 - Intro 00:00:45 - Do you need a PhD? 00:06:25 - Research taste and finding problems 00:09:04 - Why become a quant 00:12:01 - What quants do 00:14:53 - How quants and SWEs collaborate 00:16:29 - Quant vs tech culture 00:26:39 - Quant firm tier list 00:27:56 - Quant insider trading and perf culture 00:30:53 - Going back to AI research 00:35:08 - Who the top competitors are in voice AI 00:39:22 - AI startups vs big labs 00:42:08 - State space models vs transformers 00:49:33 - AI labs: research or product? 00:52:38 - Advice for SWEs who want to try AI research 00:56:48 - Advice for younger self 00:57:49 - Outro 𝗪𝗵𝗲𝗿𝗲 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗡𝗶𝗺𝗶𝘁: • Twitter/X: https://x.com/nimit_sohoni • LinkedIn: https://www.linkedin.com/in/nimit-sohoni-68998854/ • Cartesia: https://cartesia.ai/sonic 𝗪𝗵𝗲𝗿𝗲 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗥𝘆𝗮𝗻: • Newsletter: https://www.developing.dev/ • X/Twitter: https://x.com/ryanlpeterman • LinkedIn: https://www.linkedin.com/in/ryanlpeterman/ • Threads: https://www.threads.com/@ryanlpeterman • Instagram: https://www.instagram.com/ryanlpeterman • TikTok: https://www.tiktok.com/@ryanlpeterman

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

• 0:00Speaker 00

  90% of the battle in research is actually finding the right problems.

• 0:04Ryan Peterman?

  This is Nimitz Sohany, Stanford PhD, AI researcher, and previously a quant at Citadel. And I asked him about both AI research and quant careers. Do you have any advice for someone who wants to move into AI research?

• 0:18Speaker 00

  If you want to sort of switch from a SUI track to AI track, there's got to be something behind it, right?

• 0:23Ryan Peterman?

  We compared and contrasted these roles, which had some surprising insights.

• 0:27Speaker 00

  Yeah, I think quant actually probably has a better work-life balance than AI. Unlike in tech, one thing I was surprised by culturally is how tight-lipped people are in finance, even within a firm.

• 0:39Ryan Peterman?

  We also went deep into what he's actually working on now.

• 0:42Speaker 00

  The main challenge of transformers is that...

• 0:45Ryan Peterman?

  Here's the full episode.

• 0:51Ryan Peterman?

  When you think about the opportunities that are not available to you without a PhD, what comes to mind?

• 0:58Speaker 00

  Yeah, so I think, you know, there's not really too many opportunities that are, you know, actually unavailable to people without a PhD, but some of them just get a lot easier with PhDs. I mean, so academia is an obvious one that does require a PhD. That was never something I was super interested in for a lot of reasons. But I think some roles that are definitely a PhD opens a lot of doors to are kind of the two that I've had experience with. One is like, you know, sort of doing industry research in AI like I'm doing now. Or, you know, back in the day, there were a few different,

• 1:43Speaker 00

  like, industry research in computer science or mathematics was a little bit more diverse, but more and more people are converging towards AI. So I'll just say like AI research is one of them where having a PhD helps. Or, not that you, you know, a lot of people do AI research without a PhD, but you know, the type and shape of the role can look kind of different. And another one is quantitative finance. So again, a lot of people go into quant, you know, out of undergrad, but certainly having a PhD like opens you up to, you know, some sort of different opportunities. And it can be a lot easier to get your foot in the door there.

• 2:17Ryan Peterman?

  So if we think concretely, let's say I was going for an AI researcher role or something like that. Are you saying the PhD helps you in that first step of filtering? Or does it help somewhere else in the process in getting one of those roles?

• 2:32Speaker 00

  Yeah, so I think it's it's both. So certainly, it's a lot easier to get an interview if you've differentiated yourself from the pack in some way. You know, just applying for AI research role at a, you know, top firm can be can be difficult. You know, if you don't have, you know, whatever the, you know, right schools, quote unquote, on your resume, the right internships, the right connections, whatever. But it's certainly doable. And then I guess like, I think like more less transactionally, having a really like doing the PhD can like develop like a key critical skill set that can help you as as along your path towards becoming a great AI researcher.

• 3:16Speaker 00

  But of course, you know, there is an argument for being thrown into the fire as well and just like kind of learning on the job. And that's certainly an option that works for for many people. I think, you know, there are some things that are harder to do in industry than in academia. Like kind of the more exploratory first principles, like fundamental research without necessarily like a direct application. You know, industry definitely skews a lot heavier towards the applied side of things. But I think like having that fundamental background can be very valuable, depending on what kind of research you're targeting.

• 3:52Ryan Peterman?

  You mentioned the the type and the shape of the role could be different if you had a PhD versus not. Could you give an example of what you mean?

• 3:59Speaker 00

  If you're if you're working on more like engineering heavy stuff in AI, so you know, building, you know, training or evaluation infrastructure, you know, working on like, you know, data processing, things like that. Those are not things like a PhD is really necessary for at all. I'd say if you want to do more like sort of pie in the sky type stuff, like, you know, architecture, design, things like that. A PhD can be, you know, can be helpful there because, you know, you have more time to kind of explore directions that may not pay off in the short term.

• 4:36Speaker 00

  But again, there are examples of people being successful with or without a PhD in both domains. So I think if your only goal is to be an AI researcher and you're not super tied to the particular type of work you do, you just really want to get into the field, a PhD is definitely not necessary. But I think if you're still in the exploration phase of your career and you want to find a problem that really draws your interest, then a PhD can be a good way to do that.

• 5:15Ryan Peterman?

  You mentioned the PhD skill set or something that you develop when you get a PhD. What is that skill set?

• 5:23Speaker 00

  I would say like 90% of the battle in research is actually finding the right problems. So you have to find a problem that is interesting, it's meaningful, that people are actually going to care if you solve it. You have to sometimes convince people it's interesting because they might not have thought about it the same way. And then you have to execute. And you need to make sure it's a problem that's appropriately scoped, that is actually tractable for you to make progress on. So I think all of those things were not skills that I had developed. It was more, just like execution was my strength. And so definitely that was a big learning process for me during the PhD,

• 6:05Speaker 00

  is that sort of like research taste and problem selection. And this is something that, just like being immersed in the field really helps with. Once you've read enough papers, talked to enough people, you kind of get a sense of the patterns and trends that are going on in the field.

• 6:25Ryan Peterman?

  You mentioned the research taste and finding the right problems. If you could kind of condense what you learned in your PhD, is there maybe some top tips that kind of lead to you finding the right problems?

• 6:38Speaker 00

  I would say the main things that I find useful are just keeping abreast of the current literature, just reading as many papers as you can. And it doesn't have to be reading them like end to end, just like skimming abstracts, seeing what's going on, what are people thinking about? And yeah, I think the other thing is just working your way up. So initially, early in your career, you want to attack like, you know, very small sub-problems that are, you know, that you're reasonably likely to make progress on, right? So one example, you know, example of this can be like, you take a, you know, method and you try to extend it to some like, you know, special case or some, something like slightly different

• 7:23Speaker 00

  from the original application. And then as you, as you go on and as you mature as a researcher, you can start tackling like bigger and bigger problems. So, you know, not just like kind of extending previous work, but maybe coming up with like totally new ideas, things like that. So I think there is, you know, a gradual stage of maturation as a researcher. And I think, you know, some people do try to skip those steps. And I think that's generally, you know, inadvisable, I think.

• 7:48Ryan Peterman?

  You mentioned keeping abreast of the, the literature. What's the go-to spot for you to kind of get your feed of the hot research paper to read?

• 7:58Speaker 00

  Honestly, Twitter is one of the, probably the, the main way that I keep up with papers. I think if you follow enough good people on, on X, I guess your, your feed becomes like pretty curated to that. So that's usually the first way I find out about stuff. Obviously, you know, just like talking to people, you know, coworkers and so on. But yeah, I think X is my go-to. So I try to, I try to curate my feed in such a way that it's mostly, yeah, machine learning papers and pictures of cute animals.

• 8:30Ryan Peterman?

  Do you have like a good starting point for someone who's like, just wants to plug in?

• 8:35Speaker 00

  I pretty much like, you know, started with following people I knew from Stanford and, you know, elsewhere, you know, professors whose papers I'd read, like, you know, prominent people at big labs and so on. And then like anytime, you know, they tweet a paper, they like a paper or something like that. If it's, if it's interesting to me, I just click on that and I, you know, follow all the people, you know, tagged in or associated with that work. And that's, yeah, so I sort of grew my follow list organically via that.

• 9:03Ryan Peterman?

  I understand after your PhD, you became a quantitative researcher at Citadel. Where were you in your career and why did you decide to become a quant?

• 9:13Speaker 00

  Yeah, I joined Citadel Securities after graduation from my PhD. And so there, yeah, so basically I had actually interned there right before, the summer right before graduating. And I liked it a lot. The reason I decided to intern, just kind of, I wanted to see what else was out there. I had a few friends who had interned at Citadel Securities and enjoyed it, or at other quant firms. And I was just kind of curious, you know, by that point, I'd been working AI research for like four to five years. And yeah, like I said, I was generally, you know, when I entered my PhD, I was interested in careers in which I could apply my

• 9:58Speaker 00

  interests in mathematics and computation. And so I think the three major careers of that form at the time were, you know, machine learning research, which I already had experience with, you know, quantitative finance. And then the last one, maybe quantum computing, but you know, it was a much smaller sort of domain and one I had no experience with, although that one's also kind of blowing up these days. So yeah, quant finance, I was just, just kind of curious and you know, I'd heard good things. And so I decided to intern and I ended up liking it a lot. I think it was, you know, refreshing in some ways. You know, like I said, the PhD is a grind, you know, you can burn out at various points.

• 10:40Speaker 00

  And it was kind of a fresh, fresh set of problems, totally different environment.

• 10:46Ryan Peterman?

  Well, it's funny that you say that the grind of the PhD, you kind of took a break to become a quant at Citadel, because I've heard that the work culture is pretty intense at these finance companies. Is that the case?

• 11:00Speaker 00

  Yeah, so that is the reputation, but I think that definitely varies a lot based on the team you're on, the firm you're at.

• 11:09Speaker 00

  Yeah, I personally, I had a pretty great work-life balance, as funny as that might sound as a quant. You know, I think one reason is that, you know, traders typically will work, you know, trading hours or whatever locale they're in. You know, of course, there are markets all over the world, you know, APAC, you know, Europe and so on. But in the US, US traders are typically working, you know, around US trading hours. And, you know, of course, a little bit before and after just to prepare and stuff. And so I think that generally has a sort of trickle-down effect on the culture where, you know, most people are just kind of, you know, really clustered, working around trading hours and then don't take their work home all too much.

• 11:54Speaker 00

  So, you know, even though the work can be done at any time, I think it just, that is sort of how, how the office culture operates.

• 12:01Ryan Peterman?

  Yeah, when it comes to quantitative finance or quant work, how would you describe the work?

• 12:06Speaker 00

  It really, really depends a lot on both the, you know, team you're on, like the sector you focus on. You know, whether you're at a hedge fund or a market maker, whether you're like front office or back office quant.

• 12:21Speaker 00

  And, you know, of course, the company. And so, you know, some quants will spend all their time, just like on alpha generation. So, you know, generating new, you know, trading strategies and, you know, backtesting them and so on. And then putting them into practice and monetizing them. You know, I mean, well, some people will focus just on the alphas, some people will focus on the monetization. Or you can be, you know, like a risk quant, so you're basically not necessarily generating strategies at all, but just like, you know, trying to come up with metrics to capture risk and, you know, avoid that, or reduce risk without reducing, you know, cutting into profits. You might be, you might be doing like data analysis.

• 13:01Speaker 00

  So you might have like a ton of like historical like trade data and stuff and analyzing them in various ways and so on. So, yeah, I think.

• 13:12Speaker 00

  Yeah, like I said, hedge fund versus market making, they're actually very different problems. So I think the thing that unifies all of them is really having a strong math background.

• 13:25Ryan Peterman?

  Like in the day to day, let's say your project that you're currently working on, like what would that look like? What would the shape of that problem look like? And how does math concretely play a role?

• 13:36Speaker 00

  Depending on what sector you're in, there's a lot of different math that'll come into play. I mean, the backbone of finance is stochastic calculus. So I think that comes up almost everywhere. But then there are other things like numerics, numerical optimization, numerical interpolation, things like that. You know, machine learning, of course, is now more and more firms are like getting into, getting really deep into the deep learning space, even like establishing their own research, research arms that do like LLM type research and stuff like that. Yeah, numerical linear algebra.

• 14:14Speaker 00

  Yeah, so there's a lot of different math and it's actually very diverse in terms of what, you know, people are always trying to come up with ways to apply like different, different fields of math to quant. I think some of it is just for fun, kind of, you know, because quants are such a mathy, intellectual bunch. But there is actually a lot that, you know, underpins the entire field. So yeah, I mean, I think stochastic calculus is probably the most unifying part. Like that's kind of the, you know, finance 101 type math.

• 14:53Ryan Peterman?

  So you mentioned coding a lot as a quant. And I have had some friends who were SWEES at Citadel and these various companies. Understand the roles are quite different. How do quants and SWEES typically collaborate at these companies?

• 15:08Speaker 00

  Really depends a lot on the company. You know, some, some companies like, you know, Jane Street, for instance, like the number of people who are called quants is actually very small. And, uh, you know, traders themselves are quite technical and like implement a lot of stuff. Um, and then of course they have like software engineers as well. Whereas Citadel, I think is a more quant forward firms. I think, uh, you know, you know, a quants might be, uh, if not the largest like percentage of employees, like it might be like about equal in terms of the technical staff. Um, and so, yeah, I think, um, you know, there can be a lot of overlap in what a quant, uh, quantitative researcher and what a, you know, software engineer does. And also, you know, between a quant and a trader.

• 15:48Speaker 00

  So, um, yeah, it, it kind of just depends like at some firms, I think it's more divorced where quants are really doing, you know, the, the strategy work. Uh, and then it's like, kind of handed off to software engineers to implement. But at other firms, I think, uh, you, you might do a bit of both, uh, because, uh, you know, of course, like the, the person like, i- i- if, if they have the implementational skills, the person best posed to like actually implement something is the person who like understands all the, you know, reasons and, you know, uh, edge cases and things like that. And so, um, yeah, like I said, I, you know, I did a ton of coding, um, mostly in C++ also some Python.

• 16:28Ryan Peterman?

  If you were to compare and contrast finance and tech generally across these roles, uh, what, what comes to mind?

• 16:37Speaker 00

  So I think a lot of this skill set, first of all, is actually quite similar. Yeah, like I said, um, you know, math and, math and computer science were my main interests and I wanted a job that would leverage both of them. And I think that's been the case in quant and that's also been the case, um, in, um, you know, in AI research that I've done. Um, and so, you know, I, I knew nothing about finance when I, before I joined Citadel Securities, but, um, you know, I read a few textbooks recommend that were recommended by people and, um, you know, that was really all, all I needed. And, um, yeah, from there I just, you know, drew upon my sort of technical skills. And I think like AI research is, is a lot of the same way. I think. If you have really strong fundamentals, you can pick up, you know, pick up the rest.

• 17:22Speaker 00

  So, um, yeah, in terms of technical skills, I don't think it was, you know, really a rough transition. Either going, you know, going either way.

• 17:32Speaker 00

  I think, uh, you know, obviously the, you know, culture is different, uh, you know, SF versus New York, uh, those kinds of things. Yeah, work, work hours, I would say. Yeah, I think, uh, you know, quant actually probably has a better work-life balance than AI. You know, particularly because the, um, level of competition in AI right now. Like, uh, you know, it's, it's just a very competitive space. And so one of the ways you can gain a comparative advantage is just like by outworking your competition. And that kind of is, uh, you know, what happens, uh, in, in practice, a lot, a lot of places. I know a lot of people who are just like working around the clock.

• 18:08Ryan Peterman?

  I've heard insane stories about the comp structures at quantitative finance firms. Is that all true? Like, is it heavily bonus weighted? And, uh, I've also heard stuff about garden leave.

• 18:22Speaker 00

  So, yeah, in terms of comp, yeah, I think one thing is like, you know, there's not really standardized levels like there are in tech. You know, you, you can't just say, you know, it's not like someone is just like IC5 and you kind of know like what, you know, kind of pay bands they're, they, they, um, what, what they're making. Uh, it's, it's, uh, yeah, I think comp is really driven by a few things. Um, you know, how the company does that year, how your team does that year. If you are really on like the alpha side of things, like, you know, how you, how your particular strategies did that year. Um, and then, um, of course there's like, uh, other things that play into it, like seniority, both in terms of, you know, hierarchy.

• 19:00Speaker 00

  If you're at one of the firms that, you know, does have a kind of explicit hierarchy or in terms of like, uh, you know, just like tenure at the firm or years of experience, things like that. And so, um, yeah, I think, you know, Quant firms, I think, are more secretive and partly because of the relative lack of standardization. So it is kind of opaque in terms of like how those factors actually combine for your final comp. But yeah, I think it can be very bonus driven if you're really on the alpha side of things. And that attracts some people to that kind of thing where they really would just want like as they want to be as exposed as possible to, I guess, like the fruits of their labor. But it is, you know, the downside is can

• 19:42Speaker 00

  be much riskier business as well. So yeah, it's just more variable. But like if you're you know, more back office type thing, I think the comp is, you know, probably a little bit more deterministic if you're not, you know, directly tied to alpha generation.

• 19:56Ryan Peterman?

  Yeah, it's interesting. I mean, because we were talking about AI research versus quants and obviously being a quant is famous for earning a lot. If you have generated a lot of alpha, I hear compensation like easily in the millions for for a lot of these people. But at the same time, AI research also popped off too.

• 20:18Speaker 00

  You know, if you're the top 1% of either of these firms, you're going to do very well. Yeah, I mean, yeah, they're kind of crazy. Yeah, I mean, these, these things really do exist where people are making like NBA player salaries and stuff. I think, you know, for the for the median case, yeah, it's still, it's still very good. But I think, yeah, it's, it's not, not exactly that outlandish. Yeah, sorry. You also mentioned stuff about NDAs and stuff and garden leave. So, or sorry, non competes, I guess. So, yeah, I think, you know, so finance firms are very, you know, they're very serious about this sort of thing, you know, unlike in tech. You know, one thing I was surprised by, you know,

• 21:00Speaker 00

  culturally is how tight lipped people are in finance, even within a firm, you know, there is things that you can and cannot share across teams and or people might just want to be more secretive because, you know, they're protective of their alphas. And so if you like know kind of what they're doing, you can sort of re implement a similar thing and like. Capture, take over some of their alpha, right? Because it, what makes it alpha is that it's, you know, secret. If, if more, the more people know, who know about it, like the less profitable it's going to be for any individual. And so, um, yeah, I think it's, you know, quite secretive. Uh, you know, even, even the firms that are, you know, have a reputation for being more open are, are actually quite secretive. Versus in tech, you know, people talk about things all the time.

• 21:45Speaker 00

  And so it was a bit drawing for me, returning it to tech and like hearing like people, you know, talk about what they're doing in like a, you know, very open way. It was like, wow, like you're just going to tell me that for free. So yeah, a non-compete is like, yeah, probably the, you know, most notorious part of this is, yes, a lot of firms will, um, have a clause in your, you know, in the contract you sign at the beginning stating that you cannot work for a competitor for a period of time after you, um, after you leave the firm. Uh, and this period of time is typically decided by the company and when you, when you leave, but it can be, um, anywhere from, well, it can be zero, uh, up to like two years. Uh, I think I've heard like even up to three years for, for some places, but I think that's where I think the norm is. I, I would say the norm is like, you know, six months to two years.

• 22:31Speaker 00

  Um, and so, yeah, during this period, you're basically just paid to not work. Yeah, it's called garden leave because I guess, you know, you, you sit at home and garden or whatever. Um, and it's actually like, I mean, it's actually a quite interesting thing, you know, creates interesting incentives for some people because you are typically compensated quite well during this garden leave period. So, um, it's not necessarily a, you know, it's not necessarily a downside for some people. Um, and yeah, basically the idea is like, you know, um, you won't, you know, leak ideas to your competitors. And, you know, by the time your garden is leave is over, you know, if you have some special alphas or trading strategies, um, you know, two years down the line, they're probably not even relevant anymore. So it doesn't even matter.

• 23:14Ryan Peterman?

  You mentioned the secrecy within, uh, in quantitative finance and I see a natural incentive here to kind of be, uh, hostile, or I guess.

• 23:25Ryan Peterman?

  Competing within the firm because

• 23:28Speaker 00

  Yeah.

• 23:29Ryan Peterman?

  My alpha is my alpha. I'm not gonna help you. Did you ever feel that or see stories of that?

• 23:35Speaker 00

  Yeah, no, it's definitely a thing. You know, people are, yeah, I think a lot of people are reluctant or even forbidden to talk about any details, basically, of what they do. You know, some people, you know, some people, you know, won't even, don't even like say what sector, you know, they work on, you know, at least across companies and stuff like that. So, um, yeah, I think that's definitely a thing. You know, some firms are set up where it's like basically pods. So, you know, one pod is just responsible for basically all of their P and L and then the firm takes a cut. And so, you know, different pods might be working on, you know, very similar things unknowingly, right? But they're not sharing any of the information.

• 24:19Speaker 00

  And there is some logic behind this because the idea is you want to have uncorrelated, you know, uncorrelated returns. So if all the pods are like, you know, talking to each other, sharing ideas, you know, chances are they're gonna start doing very similar things. And then, you know, that exposes you to risk where, you know, what if the thing you're doing is actually wrong and, you know, you can wipe out not just one pod, but entire team of them. Whereas if people are working independently, then, you know, that's, that's not, that's less of a risk.

• 24:50Ryan Peterman?

  Earlier, you mentioned the top 1% of AI researchers and quants are gonna do extraordinarily well. I'm curious, what sets the top 1% of AI researchers and quants apart from the rest?

• 25:04Speaker 00

  There are a lot of things. I think there are different ways to get to that point as well. It can be raw technical skill. Like, some people are just really, really good at what they do, able to, you know, the prototypical like 10x engineer, that kind of thing. And they just have, you know, a better, a higher level of intuition and/or execution, speed, stuff like that. You know, of course, there's politics involved, like, you know, people who are better at playing the A political game can rise up in the ranks. I think in quant, one thing is that it is a bit, it's harder to game the system because there are kind of hard metrics that it's easier to evaluate how someone does, especially if you're alpha quant, it's quite clear, right?

• 25:48Speaker 00

  Like if you implement a strategy and you make the firm a ton of money, like that's obviously going to be recognized. I think in AI can be a little bit harder, but of course, I mean, the analog might be like you publish like a seminal paper, like you make a true breakthrough in the field, you make the models much better than, yeah, that sort of thing. So, yeah, I think it's, yeah, I guess it's probably similar to other domains, it's just a combination of skill and like, you know, playing the game. And I think being in the right place at the right time has a lot to do with it, both in quant in terms of seeing something before other people do and then like making, taking

• 26:32Speaker 00

  advantage, capitalizing on market trends and turning that into a profit. Or in AI, like having the right idea at the right time.

• 26:39Ryan Peterman?

  When it comes to quant firms, I'm kind of curious, there's all these tier lists out there, what are the top firms and why?

• 26:47Speaker 00

  Rentech, like we talked about, is one of the sort of mythical firms in the space. You can't really argue with their returns, the historical returns over like a 20 year, 30 year period, it's pretty insane. So, you know, Rentech is maybe, you know, the gold standard, depending on who you ask. Then there are other firms, like, you know, some of the slightly bigger ones, like, you know, Jane Street, Citadel, Jump Trading, Hudson River, I think those are generally very well regarded firms. And, you know, having that kind of thing on your resume can definitely be, you know, an asset to future quant roles and things like that. So, yeah, very good firms, I think, you know, great technical talent, great returns, obviously.

• 27:30Speaker 00

  And then there are some like elite, smaller ones, you know, similar to Rentech, like, smaller, more secretive, less well known, but still very, very excellent returns. So, yeah, TGS is one, it's in Southern California. Yeah, XTX is another one, one of the newer firms, I think RadX is another newer firm that's, yeah, in that boat, so, yeah.

• 27:56Ryan Peterman?

  Are there any stories you working in the space that you think might be interesting?

• 28:01Speaker 00

  You know, finance firms do not mess around. So you hear stories about like, you know, people are just doing, you know, dumb things like, you know, traders or quants having like an internal WhatsApp group where they, you know, talk about strategies and they're like, like, so as a quant, you have like trading restrictions. You have to get all trades pre-approved. And so of course, if you're, you know, someone who works in equities or something, you know, you probably are not going to be able to trade those tickers at all. But, you know, people try to get around it with their little WhatsApp groups or whatever, like telling their friends to, you know, buy these, buy these stocks or some things with the profits or whatever. If that happens and you get found out, you know, they're going to go after you. You'll,

• 28:43Speaker 00

  you'll get fired. Obviously there'll be lawsuits. You can, you can even go to jail. Yeah, there's like a few stories about this because it is, it is against the law. And so, um, yeah, I've heard, heard more stories about this. That's one of the things they tell you about in training, actually, is like, uh, yeah, do not do this. Similar with like non-competes, you know, people going to competitors or starting their own thing or something and like, uh, getting accused of taking strategies and stuff like that. Um, yeah, the, all these firms have like elite legal teams and yeah, just not something you want to mess with.

• 29:17Ryan Peterman?

  I've heard that in quantitative finance that it's kind of intense sometimes, or rather, people may get fired, uh, very often. Did you ever have, like, you just were working with someone that kind of just disappeared?

• 29:32Speaker 00

  Yeah. Yeah. I mean, yes, that, that does happen. Um, yeah, I think it's interesting in quant because, um, like I said, yeah, comp is a function of many things among, uh, among which is seniority. And so I think your job security can actually be kind of U-shaped because senior quants, like, you know, even if they're very good, they just get very expensive after a while because that's sort of what the, you know, market rate is for, for senior quants. And so, you know, even, even a good quant, you know, can stop being worth it after a while. Whereas like earlier career quants, you know, um, you can, uh, you know, they might be very good and also not command, you know, as high of a salary. So yeah, the job shape is degree, not, not usually,

• 30:14Speaker 00

  like, kind of like an inverse parabola almost. Um, and yeah, I mean, people certainly get fired. It's a, quant in general, I think, has a culture of, uh, you know, Well, one is like up or out and two is like, you know, just trimming the low performers. And again, I think this can become, you know, especially easy if you're like more like on the on the alpha side. Like if you're just not making money, like it can be pretty clear. But uh, in in general, like even for like, you know, engineers, um, yeah, I think there is this kind of culture. Yeah, traders, of course, it's, yeah, since they're, you know, making trades and stuff. Again, it's like very easy to monitor, so I think that can be even more more brutal.

• 30:53Ryan Peterman?

  Why did you leave Citadel to join Cartesian?

• 30:57Speaker 00

  When I joined Citadel, it was partly because I was just interested in learning about a new problem domain and like, um, you know, learning some learning some new stuff, uh, you know, learning about finance in general, I think was also like kind of interesting to me. And uh, yeah, I think, you know, I became more financially literate as a result of things and and then stuff like that. Like it was, it was, it was a great learning experience for me and I was kind of optimizing for like growth potential partially as well. But yeah, I mean, by that point, you know, I'd been at Citadel for, you know, a couple of years and was, um, yeah, I think, um, you know, like it's, it's sort of like you, you, your growth, uh, you know, at most places will kind of like accelerate for a while and then like sort of taper off. But I think there was still a lot more to to be learned had I decided to continue on that path.

• 31:41Speaker 00

  But I, um, you know, I saw what was going on in the field of AI, uh, you know, when I graduated, actually, it was right before, um, ChatGPT came out. And so I think a lot had changed even since I joined Citadel. Um, and I heard that, um, you know, the founders of Cartesian were starting this company. Uh, and for context, I knew all of them from my PhD at Stanford. They were actually all in Chris Ray's lab with me. Um, I knew Albert pretty well. Um, and so, um, yeah, I had, you know, tons of respect for them. They're, you know, great researchers. I worked, uh, you know, pretty closely with some of them. Um, you know, Albert was a good friend of mine, knew, knew, uh, knew the other guys. And so it just seemed like a great opportunity, uh, and a great time to get into, get back into the field of AI when things were sort of taking off.

• 32:23Speaker 00

  And I thought it would be great, um, in terms of, uh, you know, personal and technical growth. Also, the opportunity to join a small startup, um, was definitely something that was interested me. And, uh, kind of like shaped the company and the culture, uh, you know, as one of the earlier employees. And so, um, yeah, it was really, um, yeah, I think, uh, yeah, it was all about, all about growth, getting back into AI. And, um, and I think like, you know, um, There is like a definitely a different risk profile. I think when I graduated my PhD, I was kind of more like risk averse. You know, quant was like a, you know, stable, you know, lucrative opportunity that, you know, was the right choice for me that time.

• 33:06Speaker 00

  Now that I had sort of established myself a little bit, gotten some of that stability, I thought it was, you know, opportune time to take a risk.

• 33:13Ryan Peterman?

  Cartesia, I guess, if you could give us some context on the primary problem the company's solving and just like what the company's about?

• 33:21Speaker 00

  Uh, yeah, we are a voice AI company. You know, our current mission is to build sort of the next generation of voice AI and a platform for that. So what that means is we do, you know, our flagship product is text to speech. We, you know, we also have products around speech to text, you know, voice agents and yeah, stuff like that. And um, yeah, I think, you know, we believe voice AI is the future. You know, it's already, it's actually one of the fastest growing areas of AI. You know, people are using voice AI in, you know, many applications, you know, call centers being one of the predominant ones, but also a bunch of, a bunch of applications in entertainment, you know, a bunch of like, you know, companions, like a bunch, a bunch of different things. And so that's, that's

• 34:05Speaker 00

  kind of the, the product set we're building. And yeah, in terms of, you know, why, why do we choose voice? So I think, you know, voice is actually a very interesting testbed for a lot of research ideas that are, that we're exploring. So we're, you know, we also have like a sort of research arm of the company that focuses on kind of longer term research around, you know, around long context, around multimodality, things like continual learning and memory, you know, test time compute. And in general, like, you know, or, or, or, you know, sort of overall, like even higher, higher level goals to build real time, you know, systems that are truly intelligent and that you can like interact with and that

• 34:49Speaker 00

  can learn from experience. And so I think, you know, building these, you know, voice agents, you know, speech to speech models and so on is, you know, it requires you to kind of, you know, solve some of these problems for the, you know, sort of eventual, eventual idea of like a kind of like always on assistant, personal assistant.

• 35:08Ryan Peterman?

  When it comes to this voice AI space, who are the top competitors?

• 35:13Speaker 00

  So our main competitor is a company called Eleven Labs.

• 35:18Speaker 00

  They're, you know, another voice AI company, basically. And yeah, so they, I think, had about a 18th month head start on us. Yeah, I actually used to play with Eleven Labs, you know, long before Cartesian was ever a thing, just like, and make like, you know, fun videos and whatnot. And so, yeah, it's, you know, very, very similar company, I think. You know, where Cartesian stands out, I think, is, you know, we have sort of a focus on, you know, things like latency. So, you know, low latency is really important for a lot of voice AI applications, you know, for naturalness, you know, like the conversation we're having now. You know, you can't afford to have, you know, you know, a second pause in between like each, each,

• 36:05Speaker 00

  you know, each turn of the conversation, you know, that just really breaks the sort of illusion and, and immersion. And so, you know, latency is really important for, you know, a lot of our customers.

• 36:17Speaker 00

  Yeah, you know, we're continuing to try to push the boundary of sequence modeling and stuff to get, you know, better and better quality without compromising on latency. And then, you know, going into like more end-to-end systems as well. So right now, the way voice agents are typically implemented is you have a speech-to-text system that transcribes some text. Then you feed this into a language modeling backbone. And then you have a text-to-speech system that will take the text that is output by the language model and, you know, speak the result. But this has a lot of problems in terms of latency, again, in terms of naturalness, because it's kind of not an end-to-end system. So there's a lot of, you know, loss in between each of these components and so on. And so, yeah, that's, you know, one thing that we're trying to build towards.

• 37:03Speaker 00

  But even right now, I would say, you know, even if you just look at our text-to-speech products, I think, you know, we're definitely right up there as, you know, one of the leaders in the space. I think, yeah, you know, Eleven wins on some languages, we win on some. You know, I would say we have like better voice cloning, things like that. So, yeah, we're trying to become number one in everything.

• 37:26Speaker 00

  But yeah, I think, like I said, voice AI is a very fast growing space. And so a lot of people are jumping into the space, but I think the pie is very large.

• 37:34Ryan Peterman?

  What does it look like if Cartesian completely destroys 11 Labs?

• 37:39Speaker 00

  I think we already win in terms of things like latency, in terms of cost. I think if we can conclusively win in terms of quality, not just for a subset of tasks and not just for a subset of things, but there are many things that people care about for text to speech quality. There is just adhering to the transcript, so actually reading what is put in front of the model, which can be surprisingly hard, especially if you have different languages, especially if you know special characters, repetitions, whatever, all models struggle with this. But there's also naturalness, like, does it really sound like a person saying this? Or does it sound robotic? Of course, a lot of applications actually care about naturalness even more than just transcript fidelity.

• 38:23Speaker 00

  And then, of course, there are all the speed and things like that. And then there are features like voice cloning, accent localization, so taking my voice and making it have a different accent, things like that, controllability, speed, emotion, things like that. And so, yeah, like I said, I think we have better quality in some areas, maybe worse in some others. We'd like to get to number one in as many categories as possible, right? And so I think that's sort of the thing, right? Switching costs exist, even in AI, I think, depending on the size of the customer. Some customers are reluctant to switch over from one thing to the other.

• 39:07Speaker 00

  Obviously, startups can be more nimble, but when you're talking about enterprise scale, this matters. But if you conclusively show that you're better in every way, then I guess at some point it becomes hard to argue for not switching.

• 39:22Ryan Peterman?

  I imagine you could have worked at a big lab, OpenAI, Thropic, etc. What's the main difference in working in an AI startup versus one of these big AI labs?

• 39:32Speaker 00

  Big labs have obviously amazing resources. You know, they have all the compute in the world, you know, tons of researchers and so on. I think one thing is that like the, the, the flip side of that is that, um, I think big labs can sometimes be more averse to sort of out of the box ideas and a little bit more susceptible to group think or like, uh, sort of overarching trends in the field and like less willing to take a risk, uh, on, uh, something different. Um, and then, you know, that, that makes sense, right? Because, um, you know, with, uh, sort of these, you know, great resources, you know, that's, there's, there's a lot of cost, uh, to investigating new ideas that don't, that don't turn out well. Um, whereas as a startup, I think you're a bit more nimble.

• 40:14Speaker 00

  You're able to, you know, um, you're able to be a little, little bit more exploratory if you do it strategically, uh, and sort of challenge the orthodoxy, um, in, in that, in that way. And so that was one of the things, uh, like I mentioned, that drew me to Cartesian. Uh, you know, Albert has, um, you know, a lot of interesting ideas that I think don't necessarily go, um, go with the, like, the accepted grain, like, uh, you know, in, um, you know, around the time Mamba came out, you know, people were kind of like of the opinion, a lot, a lot of people were of the opinion that like, you know, sequence modeling was kind of a solved problem and all you need is scale. Like you just take the transformer recipe and you just scale it further and further. Um, yeah, I mean, Albert showed that, you know, that's not necessarily the case, right?

• 40:57Speaker 00

  With Mamba, um, that you can actually get real advantages, um, in terms of, in terms of things like, you know, efficiency, computational efficiency, but also like even in terms of just like raw quality, um, you know, state space models can be advantageous for a lot of classes of problems or like things like hybrid models where you take some state space model layers, some transformer layers, things like that. Um, you know, another more recent work that we put out at Cartesian was this idea of H nets where, uh, so yeah, for, for context, the way that text, uh, modeling is usually done is you take, um, you know, you take, you know, raw text, you know, a sequence of characters or, you know, UTF eight bytes or whatever, uh, and then you compress it or, you know, you represent it as, uh, these things called tokens, which are basically

• 41:41Speaker 00

  like little pieces of words or sub words. And then you run modeling over that. So it's like a two-stage pipeline. You know, we showed that if you actually just go from the raw, you know, raw characters and you kind of learn this tokenization, you learn how to like draw these boundaries in between groups of letters instead, you can actually get better performance. And so, yeah, that's the kind of thing, you know, I think challenging accepted ideas, like that's the kind of thing that appealed to me.

• 42:08Ryan Peterman?

  For context, you mentioned state space models versus transformer. Could you just give a quick primer, I guess?

• 42:14Speaker 00

  Without going into too much, I guess, technical detail, um, you know, basically, uh, the, the main challenge of transformers is that the, um, you know, the memory that they use at inference time grows linearly with the sequence length. Because what they do is like, you know, they will take each token and store, you know, a representation of it in what's called the KV cache, uh, you know, the key value cache. And so, um, as you, as your sequence grows longer and longer, like you're still storing all of this information in context in your memory. And so for very long sequences, you know, this can get prohibitive, both in terms of, you know, computational costs and in terms of memory. Uh, SSMs are different because, uh, instead of storing everything into, you know, everything

• 42:57Speaker 00

  like in this uncompressed way, uh, they take that information and they compress it. So the size of the state is, is fixed. And so as a result, like you're, um, you know, the cost of doing a certain step doesn't change, uh, with the length of the sequence and, uh, you know, the amount of information you have to keep in memory, um, does not grow with the sequence length. And, uh, so kind of an intuition, you know, our co-founder Albert Gu has a great blog on this, uh, is that SSMs are kind of like a brain. You know, the human brain also does not store an unbounded amount of context. Uh, you know, it takes in information and it processes it and it like, you know, keeps it in this fixed size state, which is, uh, you know, which is our brain. Of course, you can simulate having a, you know, um, unbounded state by, via use of

• 43:41Speaker 00

  external tools, like, you know, writing stuff down and so on. But the core, you know, the core primitive, uh, remains, uh, fixed. Uh, whereas transformers are more like a database where you can kind of recall anything in the context. And so I think both of these approaches are complementary, right? Um, and, um, yeah, so, you know, we're currently exploring, uh, kind of, you know, extensions of that, um, analogy. But yeah, I would say that's kind of the, the, um, you know, high level thing.

• 44:09Ryan Peterman?

  Is the sequence just the input? So the longer the prompt, the longer the sequence and therefore more memory consumption at inference?

• 44:16Speaker 00

  That's right. So the sequence is the prompt plus the response. So you know, as, as the model is generating the response, you know, the context, you know, the context includes what has been generated so far, right? So you can refer back to what you yourself have said and you'll figure out what is, you know, what is the next appropriate token to say. And so this can get obviously especially large for multi-turn conversations where now the context includes like everything that has been said in the entire conversation up to that point. And so, you know, beyond a point, you know, as I'm sure we've all had experience with, you know, if you're chatting with these language models, you know, it sort of ceases to be, you know, that useful maybe after, you know, 100, you know, tens or something of turns and, you know, it can be best to start a new conversation.

• 45:00Speaker 00

  But the challenge with that, and you know, of course, companies are doing things to try and sort of address or Band-Aid this, you know, for instance, like ChatGPT now, like say of some like global context in between conversations and stuff like that. But it doesn't really truly learn from, you know, from your, you know, personal proclivities and preferences and like the things you've asked in the past. Like there is some semblance of this, of course, but I wouldn't say that it's like, you know, truly personal yet in terms of like an actual agent that is like kind of learning and growing every day.

• 45:34Ryan Peterman?

  Yeah, you know, I've been using Claude Code a bunch and I noticed occasionally it does this thing, it says compacting or something like that. I imagine it's taking the multi-turn conversation. I don't know what it's doing, just maybe summarizing it and restoring it.

• 45:49Speaker 00

  Yep. Yeah, there are all, there are all sorts of different ways to kind of compress the KV cache, either sort of mechanistically or kind of doing like a, you know, textual summaries or things like that. There, yeah, this is a pretty active area of research as well.

• 46:04Ryan Peterman?

  You mentioned that the state space models, they have a compressed representation of the KV cache or, and so I'm curious, does that have a trade-off in terms of the quality of inference? Is it lossy?

• 46:19Speaker 00

  Yeah, so there are certainly trade-offs, you know, the

• 46:25Speaker 00

  Yeah, I think depending on the task, like so for very like recall-heavy or fact-based tasks, pure SSM models can lag transformers because like the ability of transformers to do this kind of exact in-context recall turns out to be very helpful for this kind of task. Whereas for other tasks that don't require this type of thing, SSMs can scale just as well or better as transformers, even for like a fixed parameter budget, let alone inference budget. You can kind of get the best of both worlds. A lot of people have shown this by doing a hybrid model. So you just basically interleave state-space model and transformer layers with some ratios.

• 47:09Speaker 00

  And so Nvidia has put out stuff like this, even the latest Quant models follow the strategy as well. So yeah, I think the cutting-edge, I would say, for text is probably in these hybrid models, at least in terms of like what's out there for open source. But the interesting thing is that for other modalities like audio, it actually makes a lot of sense to have this compression as an explicit inductive bias. So using SSMs for audio has proven very useful for us. We found that it actually improves performance. It's kind of almost a free lunch. You get improved quality and improved performance inference time.

• 47:51Speaker 00

  And the reason is that sort of like, if you think about what these models are doing, audio is, depending on how you represent it, is a very like, you know,

• 48:04Speaker 00

  there's very little information contained in any one time step or token, if you will, of audio. It's like a frame of, depending on what you're doing, like 10 milliseconds to 100 milliseconds. And so one frame to the next doesn't really vary that much. And so compressing these into sort of fixed-size state actually makes a lot of sense. As opposed to text, which is a much densely informational modality. One word to the next, there is actually a ton of information contained in each of those tokens. And so compression is less, it's kind of already pre-compressed if you're using a token-level representation.

• 48:47Speaker 00

  But yeah, even so, I think hybrid models, I would say hybrid models, I think, are the future in that regard.

• 48:55Ryan Peterman?

  I see. Okay, so it's because the modality itself has, I guess, redundancy in the data that means that this lossiness is actually an asset rather than a problem.

• 49:07Speaker 00

  Exactly. Yeah. So yeah, I think there's a lot of interplay between modality and architecture. It's definitely not something you cannot design your architecture independently of your data. And so yeah, kind of this like, you know, co-design and like thinking about, you know, modality, multimodality from a fundamental level. You know, this is one of, you know, the research problems that I mentioned that like kind of drives, drives a lot of the work we do here.

• 49:33Ryan Peterman?

  When you think about companies that focus on product versus research, what pattern do you think is most effective?

• 49:43Speaker 00

  So I think, you know, personally, and this is one of, you know, also one of the reasons I decided to join Cartesian, I think it is very important to have, have both. I think like, so there are, you know, several startups popping out recently that are really, you know, focused on core research and not, don't even necessarily have like an idea how to productionize it or turn that into, you know, a product or revenue stream. I think like, you know, I personally am fairly skeptical of this approach. I think, you know, for a few reasons. I think, first of all, you know, you know, big labs, you know, have tons of resources and also have, you know, large teams focused on this sort of

• 50:23Speaker 00

  thing. I think, yeah, I think like, you know, ultimately the goal of a company is to, is to make money, right? And so I think, you know, eventually, you know, if you are, if you are a company of this form, like you need to eventually deliver like, you know, massively outsized returns, you know, at some point. And so I think you're, you're taking a big risk where it can kind of be all or nothing type thing. I think the flip side of like a sort of product only company that's built on AI models that are built by other people, I think that is like risky in the sense that you don't have as much of a moat. So, you know. Like we saw this with, you know, the initial chat GPT or, you know, going from GPT three or GPT four, right?

• 51:09Speaker 00

  A lot of these wrapper companies kind of just got made obsolete by the fact that the base models improved so much that they could often just do what the wrapper was trying to do by themselves without very much scaffolding. And so it became kind of thing you can just build in-house rather than needing another company to, you know, post-process the output of these, outputs of these models. I think like being in the intersection is actually quite valuable for, you know, for many reasons. I think having a product, a real product that customers use is something that can drive the research. So you see firsthand the issues and you can use that to drive, you know, your next iteration of modeling.

• 51:52Speaker 00

  You know, try and fix these issues, not as a Band-Aid, but like, you know, from the ground up, right? Like from, at the model level itself. And so I think having control over the models is like very important when you're, when you're building an AI product. Which is not to say that like, you know, there's no room for any non-research company. I think it just like, it has to be in the, you know, right, right kind of, right kind of space. And so, yeah, I think Cartesa is a great blend of research and product. You know, we're very, I would say we're first and foremost a product company. But, you know, we want to build the best products we can. And we believe that that requires us to actually solve some of these fundamental research problems in order to do that.

• 52:38Ryan Peterman?

  I think there's a lot of people who want to get into AI research. I mean, I was just talking to a friend today who's a Swede and he's saying, I don't think software engineering is going to be around in, in years or something like that. So he's been investigating. And I'm curious, do you have any advice for someone who is technical and wants to move into AI research?

• 53:00Speaker 00

  My philosophy has always been to try and build up my technical skills as much as possible. I think if your fundamentals are good enough, like, you know, at some point, you know, the opportunities will just come to you rather than the other way around. And so I would say just focus on getting as good as you can at, you know, at coding, at, you know, AI, read tons of papers. Yeah, I think math skills and math intuition are really important. And so that's, that's what I've kind of been optimizing for, you know, ever since undergrad, when I realized what I wanted to do was at least, you know, some combination of math and computer science. And so I've always more focused on like kind of building up those fundamentals. And I think that is the way to get your foot in the door.

• 53:44Speaker 00

  I think like bigger companies, it can be a bit harder to pivot, you know, teams or what you work on. And so for, for someone like that, I think just switching like teams or companies can, can be like, you know, sort of the only path forward. I think you, you can kind of get siloed in a little bit. If you're at a bigger company sometimes, although I do think, you know, some companies are better about it. And, you know, I have seen people transition from SWE's to research and, and stuff like that. So I think, you know, this is one of the areas where getting a sort of qualification on your resume can be useful. Like getting a, you know, masters in AI at least, or something like that can help when, when you're looking to make a sort of lateral career change like that.

• 54:29Ryan Peterman?

  You're, you're saying there's kind of two common paths. One would be get more education and use that qualification to kind of pivot directly into AI research. Or go to a startup where you can kind of like mold yourself into an AI research role.

• 54:46Speaker 00

  That's kind of right, but I think even if you want to go to a startup, right? And you want to, but you want to sort of switch from a SWE track to AI track. Like, there's got to be some, there's got to be something behind it, right? Like, you have to have some evidence of a skill set, whether it's like sort of, you know, organically grown or from, you know, from, from schooling. But I think it can probably be a lot easier to get your foot in the door if you have some evidence of, of it on your resume.

• 55:15Ryan Peterman?

  So like, let's say you were. You hired at Cartesian and then that person comes to you and he's like, hey, I wanna do more AI research. In that case, is that something where it's like just flip the switch and next project is AI research project?

• 55:32Speaker 00

  This has actually happened in Cartesian itself. We have had people transition roles like that. So I think it is definitely easier at a startup, which is, it can be a bit more flexible just because everyone kind of knows everyone. And so you can get a sense of whether this might be an appropriate career change just by like kind of knowing the person for a while. And so, yeah, I mean, we've actually done, you know, people have done this in Cartesian with a lot of success.

• 56:02Ryan Peterman?

  Do you have a biggest regret when you look back on your whole career?

• 56:06Speaker 00

  I mean, I think I often overthink things and I think I have spent a lot of time regretting past decisions that turned out not to matter in the end. And I kind of regret the amount of time I spent regretting other things. So I try to learn from that now. I think like, don't sweat the small stuff. Minor setbacks happen and they happen, but I think, you know, there's a risk of putting too much stress on yourself and you're like beating yourself up and stuff like that. And those are just like not productive ways to spend your time and they don't make anyone feel good. And so I think, yeah, I try, I try not to regret stuff because I think it's just not, not a super good use of time.

• 56:48Ryan Peterman?

  If you had to go back in time and you could give yourself some advice when you're just entering the industry, what would you say?

• 56:56Speaker 00

  Focus on building the deep technical skills. Yeah, don't waste time with like sort of trifling stuff or spreading yourself too thin. Yeah, just like focus on what you want to focus on, I guess. Like basically, like the skills that you want to leverage in your day job, just like do those and get good at those. And that's, that's, that's where you should spend all your, all your time at work. And yeah.

• 57:23Ryan Peterman?

  You make it sound so simple. Maybe it is.

• 57:27Speaker 00

  It is. It is a simple. It's kind of a simple recipe that's very hard to follow, right? Like, it's very hard to maintain that discipline. Uh, it's kind of like, you know, um, you know, what's the secret to being healthier? It's, you know, exercising, eating right. And those are things that are just very, much easier said than done. Um, but yeah, I think that is, it is that simple.

• 57:48Ryan Peterman?

  Awesome. Cool. Well, yeah, thanks so much for your time, Amit. Thanks for listening to the podcast. I don't sell anything or do sponsorships, but if you want to help out with the podcast, you can support by engaging with the content on YouTube or on Spotify. If you want to drop a review, that'll be super helpful. And if there's any guests that you want to bring on to, please let me know. I feel like sourcing very senior ICs. There's no well-studied list out there on Google that I can just search this up. So if there's someone in your org or at your company who you really look up to and you want to hear their career story, let me know and I'll reach out to them.