--- id: "zPLc3jjHbnU" title: "3Blue1Brown Talks Machine Learning with Jane Street: Culture and Research" channel: "Jane Street" host: [] guests: [] date: "2026-08-21" updated: "2026-10-15" duration: "27:39" duration_seconds: 1659 source_type: "youtube" youtube: "https://www.youtube.com/watch?v=zPLc3jjHbnU" canonical: "https://pod-exp.pages.dev/en/zPLc3jjHbnU/" transcript_jsonl: "https://pod-exp.pages.dev/en/zPLc3jjHbnU/transcript.jsonl" language: "en" translation: false timestamp_precision: "block" topics: ["Artificial intelligence","AI research","Quantitative investing","Career development"] series: ["Jane Street"] --- # 3Blue1Brown Talks Machine Learning with Jane Street: Culture and Research > English transcript prepared from captions; audio accuracy has not been verified. `>>` marks a speaker change where captions do not name the speaker. Timestamps are block-level (start of the timestamped block), not per sentence, unless a paragraph carries its own [hh:mm:ss]. > Prepared from English captions; manual or automatic track origin is unknown. Title, summary and chapter labels are editorial additions. Audio accuracy has not been verified. **Summary:** In a conversation with Jane Street employees, the speakers describe a culture built around open discussion, low-ego debate, and informal collaboration across teams. They explain how that culture supports shared responsibility, including a real-time auction for compute resources, while larger projects still use formal structure. The discussion then turns to Jane Street’s market-making role, the growing importance and research demands of machine learning, and changes in trading methods over two decades. The speakers reflect on why colleagues stay, how people move between work and study, and how the firm interviews for adaptable generalists. They close with personal paths into the firm and examples of mathematics, programming, and physics as practical or recreational interests. ## [0:00] A Low-Ego, Collaborative Culture · YouTube Okay, one of the things I I want to do, thank you for taking the time, is just try to get a feel for what the Jane Street culture even is and also just technically what do you actually do? What are the salient features, right? What comes out as like you want to distill a sense of what Jane Street culture actually is as it maybe differs from other places? What pops to mind? \>\> Chain Street is really weird, but in like the best possible way. Okay. Like truly when people are um having conversations like the goal isn't to like have ego or like to prove a point or kind of show people how smart you are or something that'd be very exhausting. It's really approaching like a topic that's kind of interesting uh and trying to arrive at a tr at the truth by kind of like talking through things. And um I think people are very just like receptive to things that they didn't know before. And um I don't know, it's like pretty cool if you can like approach any conversation and expect to have your view change um at the end of it. \>\> Is there a time that I guess any of you like had your mind changed in one of these interactions or vice versa where like you're having an interaction and you like changed someone's mind that felt very low ego uh that like exemplifies what you're talking about here. \>\> I feel like it would more it would be like more anomalous to find a time when that didn't happen. Like I think like a lot of meetings like you know you know people like come in with some some thoughts and often we'll ask people to like share their thoughts before they hear everyone else's just to like see what everyone is starting with and talk about it and then maybe Dan will like share what our like after thoughts are based on hearing everyone's and usually they've converged. It's like very it's like very nice to see actually. It's like wow. I think it's like a pretty unique um way of conversing. Um, and I feel like I know that it's unique from just like going back and talking to people from college and they're like, "Wow, the way that you talk is really strange." \>\> Like the way you talk about interactions here or you've like adopted a certain basian habit that like \>\> Well, there's like a certain Beijian habit. There's also like judisisms. You know, people will say things like, "Oh, that's not unreasonable." Um, I don't know. I think there's just like phrases that you just hear a lot here and it's just absolutely wild outside of Jane's chain. My impression is that we have much more of like there's kind of just one company. \>\> Um, and it doesn't matter so much like which group you're in or something. So, people talk across groups a lot without going through uh like their manager talks to somebody else's manager kind of things to set it up. You just kind of walk over to another group and you say, "I was thinking about something. Does anybody have a minute to spitball on it?" Like it seems like something that you guys would know something about. And you know so it's lots of uh very organic \>\> that seems hugely important but it also seems very hard to scale right you know if I try to picture why is it that other organizations evolve kind of hierarchically and it is you're going through manager manage manager is that and maybe I'm naive here it just seems like chaos otherwise \>\> is a little bit \>\> okay okay and so is there kind of an embracing the chaos of that because of the the benefits of crossexpertise communication \>\> yeah a little bit but I mean there are like you know people have managers, right? And there are like kind of team leads or project leads or you know who to ask about certain kinds of topics. Um so there is some structure and like organization to it. But I think that that kicks in where you know if you're going to start like a month-long project with multiple people know okay let's be organized about it right. Um, but if it's just kind of like you're going to walk over to try to come up with some ideas or something, that's at the like a small enough scope where just the chaos seems like the better way to do it, I guess. \>\> Gotcha. Gotcha. Um, man, I have many more questions there, but \>\> just to add on to that, one thing that's like emblematic of this chaos is uh we run like a real-time auction for our compute. Uh it's like our compute cluster is available for everybody and um you know like you're tasked with the um you know task of realistically thinking about um how to value kind of like this job um that you're running versus all the other things and um there's not really any guard rails. \>\> That's so interesting. So whereas like other company large compute there's a whole like bureaucratic like shifting around chips kind of game to decide like what allocation they get. It's just straight up auction on and like anyone can kind of bid for the compute time. And it's funny, I almost would have said that that's a case of organization. We've got an auction system that somebody implemented. \>\> I mean, I I do think there are like advantages cuz I think like people like really feel collectively responsible like in a good way for like everything everyone is doing like I feel like if I, you know, leave my desk or, you know, have some job running and then it's like doing something horrible and bumping into errors, like it's not on me to be like checking every like one minute. Like I do feel like I do feel more responsible for my jobs than everyone else's, but like someone else might see it and like you know be like, "Oh, I noticed that there's an error. Let me just like pause it for you until you come back." And I think that's really nice and people feel like that sense like we're like one, you know, one team even outside the local team I'm on. \>\> Yeah, that's so fascinating. \>\> Yeah. I mean you you'll kind of like the people who are actually I don't know running these jobs um like people have access to kind of the jobs that are running and if you kind of see something that's a little off um you can just go and talk to them and say hey I see that you know like you're running this job and this MFU is just terrible like what's going on um and you can have a conversation and a lot of like things can be sorted out without it like going up the ranks or something and um I think it just allows things to run much faster. \>\> I think it's globally readr too, right? If like you can just nuke somebody else's job anytime. \>\> Really? \>\> Yeah. There there was an instance where a unwitting researcher uh ran a command in a Jupyter notebook um uh of adjusting the uh bids on his job. Uh he accidentally ran the command without any arguments which meant that he uh changed the bids of every single job running on the hive. \>\> Um \>\> and he had to do that. \>\> Yeah, that was not great. I think we fixed that. But like \>\> it it is kind of like a you know we'll build a system as it kind of makes sense and uh kind of change it organically as it fits rather than coming up with like a structure um necessarily that's restricted from the getgo. ## [5:50] Explaining Jane Street’s Market-Making Business · YouTube \>\> I'm actually curious how you describe to outsiders what Jane Street does cuz you've got the quantitative finance just like handwave sort of description. Then you've got a level of specificity that it's like trade secrets and kind of obviously can't talk about. But there's a lot of room in between for actually clarifying for you know people outside of finance like me. What what do you do? What is the actual uh organization pointing towards? \>\> Yeah. I guess there was a phrase that I think Chander came up with um which is kind of a mouthful of uh J Street being a uh generalist financial services liquidity provider. I will be honest that has so far not added clarity \>\> when we have like college students in the office I give a lot of intro talks and one thing I say a lot is like you know I think one of Jane Street's core businesses that was really really really our core business you know many years ago but I think still drives like our philosophy around trading is being a market making firm okay which is you know what I what I tell folks is like a grocery store for stocks um not literally but you know I feel like you know grocery stores are pretty magical like if you had to like go you know buy all your groceries and you had to like go to an farm to buy your apples and go to a dairy farm to buy, you know, milk, you'd be pretty sad. And the grocery store has to think about how to price things fairly, right? They can like charge like, you know, $10 for an apple if they want. No one is going to buy their apples. And I think that that's kind of like a pretty similar principle. like you know we do do the business of like kind of pricing ETFs relative to stocks and kind of like you know if they trade out of line but I think a different way to like conceptualize that is that like you know if I am like you know sitting in the United States and I want to like invest in you know some companies in like China or I want to invest in some companies in you know some foreign market the best way that I can do that is by buying an ETF. So that's I mean that's one category of answering to like what Jane Street does which I'm sure doesn't scratch the surface like on just like the trading half. But a lot of the tone that I also hear much more recently is more it's a lot more similar to if I'm talking like an AI lab. Yeah. I think um maybe the gap between AI lab and grocery stores metaphors is like a little bit hard to maybe one thing that would help there is um if you think about with a grocery store they need to know to have a lot of jacko'lantern pumpkins for \>\> you know um Halloween and and these kinds of things right so there's a lot of like anticipating supply and demand and then you can kind of see how that's a prediction problem that could be somewhat complicated right um so that that is kind of where you the machine learning and stuff like that kind of comes in. You want to have sort of the inventory to provide liquidity to you know what's upcoming and what people will want. Um this tracks with I think a conception because you know in theory if there's value to be added from these instruments like ETFs just as an example you could have like a management firm around it that like is deliberately trying to keep keep things in line but then if it's in a market driven way it's some other participant in the market that does that. Is there anything that's misunderstood? Like if if you see in in so far as there's I don't know discussion in popular press or just like uh interviews that you have with candidates coming in. Are there misconceptions where someone thought about it one way and you realize oh actually that's just that just doesn't match with what you see on the inside? ## [8:56] Machine Learning Research and Common Misconceptions · YouTube \>\> I think there's a misconception not just about the scale of machine learning that we're doing but the novelty of it. I think it's like kind of easy to imagine that mostly what we're doing is we're taking some like models off the shelf and maybe we like bought a bunch of GPUs and we're like running it and that like basically doesn't work at all um in in financial domains. I think the problems that we're working on are much much much noisier. And so we've had to do there's a lot more like really I think it surprised me even a lot more like basic science that we're doing um like you know where we're really trying to understand fundamentals and working on the frontier of what is known you know in the research community at large. M \>\> it's not that surprising that the misconceptions exist just because um things have ramped up really really quickly um over the like last couple years. \>\> Um but yeah, I don't know. It's just been kind of an incredible ride. \>\> So you felt a meaningful shift just in the last couple years for like what the what the actual work feels like \>\> honestly just kind of like the amount of success that we've seen with um some of the ML efforts. it kind of feels like we are just kind of like at the beginning of the scurve and um but it's also just like a very meaningful input to our trading. Um and I would say it's like you know the single largest driver of um the systematic trading that we have. \>\> Yeah, I think there's a lot of misconceptions but they're kind of like different coming from different directions. you know, people at other trading firms or like banks or stuff like that will generally assume it's going to be more similar to these other institutions than it actually is. Um, so often if we hire somebody from one of these places, they'll be surprised that it's maybe a little bit more like tech or academia in some ways or some something kind of like that um compared to what they were used to. people see that we have like a lot of traders and then like a research group that's meaningfully smaller. I'd assume that means like the majority of our trading is being done by people manually entering orders into like a a Bloomberg or something. But like mostly this is historical accident in naming and like the the role that we call trader is like in many ways largely a research one. Um and like the firm is way more systematic than I think people tend to assume. \>\> Right. Yeah. Pop quiz. Which the people here appear in the corporate directory as a researcher or a trader, right? Like \>\> I'm not sure. \>\> Oh, is it like it's just genuinely you like thinking of your coworker, you're like I'm actually not sure what their title is. \>\> Yeah, there are sort of like you know teams and some kind of structure to this even though not everyone's exactly sure which team they're on or you spend some time on one and some time on the other, that kind of thing. Um but yeah, broadly there's kind of like specialization at the level of um people who mostly code but understand, you know, the trading and stuff pretty well. \>\> Then there's people who uh code but mostly with other coders as their customers, right? And so they're like a little bit a step further removed from the trading and stuff like that. \>\> This is like internal tools. Yeah, internal tools for like managing all of our um I guess branches, you know, um and who's code reviewed what and is it ready to be released and various kinds of infrastructure for um dealing with all the market data. It turns out there's actually a lot of data um pabytes. So, um that's a big task. Um and then there's kind of like a a loose um spectrum between sort of like traders and researchers with a lot of in between \>\> sort of having to do with how much you think about like particular securities. um what is happening today in particular trading systems as opposed you know on the trading end as opposed to a little bit more like you know launch off some compute job to do some machine learning stuff to train some model or build a tool for doing that you know that would eventually like feed its way into these trading systems on kind of like a longer time cycle. ## [12:44] How Trading Work Changed Over Two Decades · YouTube \>\> Gotcha. Now, this is maybe while I'm talking to you, Craig, like you've been around for 20 years in that time. How are things different uh compared to then? And I have some suspicions for what the answer might be. But as you look over the last 20 years or so, like what is the difference? And let's maybe start like technically and then I'll ask culturally like technically for what the actual work looks like back then compared to now. \>\> Yeah, it's it's very very different. I mean the the work um when I started you felt pretty fancy pants if it was like oh we've actually logged data you know and we can now like train a linear model on a compute cluster you know like wow uh and so that was kind of considered you know the more advanced stuff we were doing as opposed to um in a way a lot of it back then was stuff that you could just kind of look at two securities and if you knew how they work and thought about it a bit you would be like Oh, that's out of line. We should trade them to be in line. \>\> Um, and so there were literally uh trading systems that uh would run in an Excel spreadsheet, you know, to like do these kinds of things and you could just look at the trades and be like, "Oh, yeah, that was a good trade." You didn't need any like statistics or things like that, you know. \>\> Um, and so the challenges for that kind of stuff were totally different. Um, at this point, uh, you know, most of the modeling type of work really is machine learning kind of stuff. um neural nets increasingly recently you know GBTs and stuff also just all kinds of things and we hit many you know points in between so I would say like every five years or so really the techniques have changed like quite a lot \>\> and yet kind of strangely it feels um similar as far as like kind of the culture and stuff like that like a lot of the original people are still around. ## [14:35] Why People Stay and Work Across Teams · YouTube Why is it that people stick around so long, right? Like what what is it that makes people stay that seems to not happen at other places? \>\> I think like earlier you had asked like about like you know what makes Jane Street Jane Street and I think one thing that I've really felt is like \>\> the people I like there are lots of workplace setting that are super nice, they're super friendly where like your colleagues are super friendly to you. Um I think J Street is also one of those. But I think more than that like a lot of the my colleagues like especially even new ones I've met and on teams I move to recently are like my real life friends. Um, I think that like people help each other out and collaborate, not just cuz they're like nice people and they're friendly, which is, you know, really great, but because like they feel like really invested in your success. Like, you know, when you're in college and you like had a roommate or a friend and like they wanted you to succeed on the promise set, like almost maybe sometimes more than you did. And I think that like I really feel that from a lot of the people around me that they are like as invested in my success as I am. Um, and they really feel that like for me as a person and not just cuz like they're a really nice person or that's like kind of a norm at the company. I think it has become a norm at the company for people to help each other out. But I think that also means just like everything feels like kind of like really low friction. It feels really like fun to go to go to work and like oh I'm going to go to work. I'm going to be with my friends. I'm going to work on really cool problems. Like if you know things are really tough and challenging and high friction, especially as they can be sometimes when you are moving into new areas, there's going to be a lot of support and people are going to be like with me doing it. And I think that that really I don't know for me that's that that that makes a big difference. I think for a lot of people I talk to it seems like a really important input. I mean, from a personal point, like I feel like it's not like I came in came into Jere thinking, oh, like I'm going to work here for 10 years. Um, it's kind of been like an everyday reevaluation of like, do I feel like I'm learning a lot here? Am I having fun? Like, um, you know, are the conversations that I'm having, like the amount of agency that I have, like is it, you know, better um versus like, um, comparable other opportunities? And I don't know, I just kind of feel like like the pace at which I feel like I've learned new things um has kind of like increased over the years, which is kind of um shocking. And I feel like that's like a pretty nice feeling to have. ## [16:33] Career Paths, Leave, and Returning to Jane Street · YouTube \>\> What one of the things I was a little bit curious to ask was some of the story around joining Jane Street and I might start actually Nitio with you. Are you able to tell me like what were you doing before you joined Jane Street and kind of what was the trajectory to come in? Yeah, like I think I personally had like a I don't know maybe a normal trajectory for a person here, but it felt like pretty unique. Like I really thought I wanted to be a mathematician. I was like really like set and my family and my friends and they're like, you know, this like math thing is super great. We really love if you're going to be a mathematician like maybe you should like try like once like working at like a job that's like not teaching math just like once like see how you like it. You never know. And I applied for an internship and I got it um at J3 and I came and I like really really loved it. I was like super surprised. Then I came back the next summer and then I came back full-time. \>\> Okay. That's so that the retention there kind of speaks volumes. \>\> But your path was even less inertial than that, right? Because you kind of went back to academia and then came. \>\> Yeah. So after I came, I worked at James Street for a couple years and then I did go on leave and get my PhD and then come back and that was like a super lovely experience. \>\> Do you like how normal is that? You you took a little bit of a leave to do the PhD. Is that a little bit of an exception or are there other Jane Streeters that have a similar um leave for PhD return? \>\> I think I'm one of the very few people who did the particular thing that I did, but I think there are like lots of people at Jane Street who've had their own unique particular experiences and I think I don't know. I feel like people at J3 have been super flexible about making you know people's individual circumstances possible. I I think I'm one of the very few people who like went on like pseudo leave for a few years to get a PhD in the middle of my career. ## [18:04] Interviewing for Adaptability and Generalist Skills · YouTube What was the what was the interview process like? Uh \>\> the interview process was like super fun. Uh I was asked like a mix of like probability questions and like kind of more like miscellaneous problem solving questions. I think the phone interviews were really fun. When I came to the on-site interviews, I think it was like very different than what I was expecting. I think I was kind of expecting to do like more like even more advanced math and then I think on interviews was like playing a bunch of games. Um which I found, you know, very fun but also in the moment very stressful. I think I was like not really prepared. I didn't have a lot of friends who had gone through the interview process. I think it was like I don't know what people call like type two fun like in hindsight like wow that was such an interesting interview process in the moment I don't think I would have told you though. Um I I really remember that I had like a lot of interviews scheduled during my day and my final interview was with with Chander and we were like throwing chips at a wall. Um and uh during the interview midway through two recruiters ran in and said Nath's going to miss her flight. Nia's going to miss her flight and then I was like shephering onto an airplane and I was like wow I really must have failed the interviews. Um, but it seemed to go okay for me. \>\> Is there anything when you're like interviewing people that you feel has changed? Uh, kind of the the things that you were looking for, the ways that you would interview, you know, 5 years ago versus now? \>\> The bulk of our strategy has always been to hire people who are just like smart and hardworking and reasonable and adaptable and like to learn new things and then you can just do the new thing. like now for sure like machine learning skills or um a plus but I think mostly it hasn't changed a super ton just because we're mostly looking for these very general kind of skills. So we deliberately try to like kind of have like math puzzles but but not like something where if you took the right class you would have the tool and then it's easy but more just something to talk about and think through that's kind of maybe a little bit novel to them or something like that \>\> which is not easy to do right like I think it's very easy to like hire the person who's smart and generalist and like can adapt to a new thing but way easier said than done. \>\> Yeah and I think also it relates to your previous question about like turnover and how much people tend to stay here. It wouldn't be worth your time if the median time until somebody left was in three years to spend a year retooling them on ML. You'd rather just go and like hire somebody, right? Um but if you're pretty sure that most of these people are going to be here for a long time, then it like makes more sense to invest in that kind of thing. \>\> Yeah. A really tricky thing about interviewing um is especially interviewing for a job where you don't come in with the full skill set needed to do the job is that you're basically trying to evaluate like what will a person's like skills and output look like a year from now or 5 years from now. Um and yeah, this is really hard to do in like an hour or multiple 1 hour sessions or something like that. like how do you how do you tell if you can like you know train a person over the course of months to be highly effective at something? Um and there's no there's no punch line here. I don't have like oh yeah we did it we did it but this one no we didn't. Oh we like we like try we try heristics. like try to, you know, the strategy of like find people who can like learn to do a bunch of things and basically find like generalists who are really adaptable has worked really well. And I think it's also one of the ways that we've managed to adapt to like new demands in the job quickly because like if someone was good at learning and applying new skills 5 years ago, even if the skill set that we, you know, are demanding from them has changed quite a bit now, they're probably still pretty good at that. M m \>\> and it makes sense that that would kind of line up with the kind of people who just want to learn that that seems to be like what what makes the career a little bit more fulfilling. Look, uh kind of same question for you on the origin stories like where were you before Jane Street? What was the trajectory into it? What was the interview process like? \>\> Yeah, so I I also came in through a relatively like typical pipeline. I guess I applied to the the research internship for summer of uh 2018. At the time I was studying math and computer science, but it was always kind of like I don't know. My general assumption was like computer science is the one you do to like get a job and then math is like fun and interesting, but like I don't know who uses that in like \>\> was that the motive for like joining CS with it was like kind of a vocational one. \>\> Not exactly. Like I also did just enjoy CS for its own sake, but I had kind of imagined in my mind like okay like the jobs I'm considering are the jobs that will like use the CS part of my background. like if I get to do any math that's like a fun bonus but like not really a criteria I can set. Um but like I I guess even at the time it was kind of clear in my mind that there were roughly two fields that cared about like both these things and one of them was like broadly machine learning and data science and the other was like whatever they were doing over in finance. Um wasn't really sure at the time. My expectation was like I don't know there's like surely there's a bunch of people like sitting around with their galaxy brain models that like you know explain everything um or at least explain everything in like this very specific domain. Um I guess after after spending a summer there I realized there was like in many it's more similar to data science elsewhere that I give it credit for like I was actually kind of shocked at how well a lot of the other stuff that I've been studying kind of transferred or like or ideas I learned in college classes that seemed pretty abstract and like theoretical actually found like pretty concrete applications in just like analyzing the modeling we were doing and things like that. So that was that was a really interesting surprise. I was really kind of glad to to learn that ## [23:28] Applying Statistical Theory to Practical Modeling · YouTube \>\> examples are always hard, but does an example pop into mind of that abstract way of thinking that you as the young math and CS student didn't think would pop in and suddenly it did? \>\> Sure. So, one thing that I had studied just like the semester before in a in a stats class I was taking was like the theory of M estimators, which is this idea um from a guy named Huber in like the 1900s. And it seemed like I don't know the kind of thing where the theoretical math uh researchers like come up with something that is like asmtoically correct only if you're working at like galactic scale or something and like I don't know there are a lot of cases like this but uh it turns out this is actually just like a pretty concretely useful thing when you're trying to deal with like malformed data or things like that. like it's it just sometimes just is is better like it just using these this kind of odd class of like robust estimators. So this was like what I think I worked on during the internship actually like trying to apply this theory to a concrete like modeling problem. ## [24:26] An Early Career, OCaml, and Informal Physics Talks · YouTube \>\> Oh that's great. That's great. Now Craig I feel like you and I have crossed paths a number of times in the past and so this is not just a cookie cutter question. It's actually sincere. I I don't know how did you come to Jane Street? Um what was that? I think this might be a slightly different answer from \>\> Yeah, it is a little bit different because the company was pretty small and like people sort of joke that I was raised by wolves or something like that because you know it seems like you know uh most people these days have come from some decent school and like you know uh they knew each other from math camp or or you know something like that. For me it was pretty um random. In fact I think or you could call this very not random in a way. I only applied to one job ever basically. I just I saw a posting on like the O'Camel mailing list and I had made a resume just kind of for practice but mostly I was planning to start my PhD and then you know I just thought well I'll send it in you know and the process was very different back then like I don't think I would get hired now. um they only did like one phone interview, you know, and then I I came in in person because free trip to New York, why not, you know, and then I just ended up liking the people I met and stuff like that and so I took the job and that was in like 2005 and here I am. \>\> Let me back up a little bit. So you're you're following O camel a little bit. Are you able to just describe one what that is and then uh like why were you into it? Why are you like looking at the jobs postings on this kind of obscure language? \>\> Oh yeah, I mean just nerd broadly, right? As a baseline. So whenever using it for like what what are the person \>\> personal projects you know like uh I want to like graph a fractal or something or like you know when you just kind of like use a computer to do fun things I would by default use a camel you know I made a little uh a bot you know if you made that if you know that board game like \>\> oh yeah \>\> just to kind of see how you make bots for games you know um so it was just stuff like that nothing serious you know \>\> what do y'all do for fun \>\> I would call your your physics lessons in the office hanging out like like e to the power of graphs or something. What is all this stuff? \>\> Okay. Yeah. Every so often I give like an ad hoc like math or physics talk in a in a side room. Uh \>\> what was the most recent ad hoc analog talk? \>\> Uh something about system mechanics and like we were talking about some partition functions and then we ended up on the topic of like how you can expiate anything basically uh as long as you uh you know expand your mind enough. \>\> It was flaming diagrams wasn't it? \>\> Um yeah. So we ended up so that then we started talking about how fineman diagrams kind of relate to partition functions and statistical mechanics and like they're kind of they're very very closely linked to things. Um but but now you get into things like yeah e to the power of graphs and it starts getting a little a little odd. \>\> Yeah. Yeah. No, that's just such classic like three blue and brown clickbait on like e to the power of a graph or something. \>\> Yeah, you should do that. \>\> Yeah. No, it would be fun. That actually would be fun. I mean honestly that was extremely enlightening. I think just I've I've chatted with Craig. I've crossed your path before, Nitia. We've talked before, but even then, I feel like there's a lot of um question marks I have about Jane Street. I think this helped clarify things. I think it'll also help just make it a lot more real for whoever might be watching this. So, let me just say thank you so much for taking the time. I really enjoyed this.