"I'm still, you know, a humanist, right? I still just believe that we have this little spark that no machine is gonna come and take that away anytime soon. And so I just fundamentally believe that freeing up time for humans to be creative and innovative has got to be better for all of us."
What can ancient languages and epic poems teach us about building resilient tech and future-proof teams?
Host Andrew Zigler welcomes Matt Greenwood, Chief Innovation Officer of Two Sigma, for a conversation that uniquely blends their shared background in classical languages with over two decades of Matt’s experience at the forefront of financial technology. Matt unveils how this classical training has profoundly shaped his approach to building enduring innovation, fostering a resilient company culture, and leading with empathy.
Matt explains his vision for building and evolving innovation via platforms, alongside his strategies for effective goal-setting and fostering deep conceptual understanding in his team. He offers invaluable lessons on the importance of forgiving the decisions of our past selves, adapting processes for evolving needs, and thoughtfully engaging with AI to augment human creativity. This conversation is a masterclass in strategic foresight, cultivating a constant hunger for learning, and building an adaptable organization ready for whatever the future holds.
Transcript
(Disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)
[00:00:00] Andrew Zigler: And welcome to Dev Interrupted. I'm your host, Andrew Zigler.
[00:00:10] Ben Lloyd Pearson: and I'm your host, Ben Lloyd Pearson.
[00:00:12] Andrew Zigler: Oh, Ben, we were just chatting about how much has happened this week.
[00:00:15] Ben Lloyd Pearson: Yeah, it's just for at least now, this will be known as the week where everything AI was announced,
[00:00:20] Andrew Zigler: I suppose.
[00:00:21] Ben Lloyd Pearson: Yeah, a lot of big events. some incumbents, some, some new newer players to the market. let's just start with the event that I think had the biggest news this week, and that is the Google IO event.
[00:00:33] Ben Lloyd Pearson: So tell our audience a little bit about what happened there.
[00:00:36] Andrew Zigler: Yeah, so there was the very recent Google IO event where Google released a lot of new updates around products and features. And to no surprise, AI was heavily featured on the bill of new things that come out from our friends at Google. We're gonna break it down maybe into some of the different categories of things that came out of Google io because what I loved about their approach to this is understanding that AI and its implications are impacting a lot of different industries and fields.
[00:00:59] Andrew Zigler: [00:01:00] So there was something for everyone at Google ai. Some of the stuff that stood out to me, was around the new headset that came out I thought was really fascinating, that even had onboard access to Gemini. And it had a really cool demo about translating a conversation in real time.
[00:01:16] Andrew Zigler: Not the first time we've seen, um, you know, glasses and accessories
[00:01:20] Ben Lloyd Pearson: Yeah.
[00:01:20] Andrew Zigler: Google, but, uh, it was actually really cool to see that, um, on
[00:01:24] Ben Lloyd Pearson: Yeah. Also, not the first time we've seen this concept of flattening communication, like human communication using ai, so that's pretty neat too.
[00:01:32] Andrew Zigler: What stood out to you?
[00:01:33] Ben Lloyd Pearson: there's quite a few announcements. I mean, you know, obviously they, they announced their own agentic coding assistant called Jules. I haven't really had time to look into it, but obviously everyone that wants to be in this game has gotta have some sort of agentic coding assistant,
[00:01:47] Ben Lloyd Pearson: today.
[00:01:48] Ben Lloyd Pearson: but I, I mean, this event touched on a lot of professions even outside of software development. the thing that's really been going viral is their new VO three, ai, model for generating videos. We're really entering a new [00:02:00] era a video, based on what I've seen so far. and then there's also like an an AI video editor, which was pretty crazy.
[00:02:06] Andrew Zigler: Yes, flow, the flow AI video editor was very cool, allowing creatives to give their, video generation a lot more direction and
[00:02:14] Ben Lloyd Pearson: yeah.
[00:02:14] Andrew Zigler: input, and it helps them level up their workflows. It goes back to actually what we talked about several episodes back, Ben, about, you know, there needs to be tooling that allows creatives to iterate and the
[00:02:23] Ben Lloyd Pearson: Exactly.
[00:02:24] Andrew Zigler: can.
[00:02:25] Andrew Zigler: And, and, and this is Google really hitting home on that. I'm excited to see what folks make with it.
[00:02:30] Ben Lloyd Pearson: Yeah, exactly. You know, creatives are feeling disrupted by ai. it's really nice to finally see a focus on tools that help them versus like, just replace their capabilities. you know, it, it's Google who knows where these products are actually gonna go. They may abandon them in, in a year or two years or something.
[00:02:47] Ben Lloyd Pearson: but I think the real. Point to take away from this is that incumbency is still a very powerful force in software technology. you know, in fact, to the point where I think even companies that are being disrupted by ai, like you think [00:03:00] about how much search like Google search has been disrupted by AI because they have so much data, so much.
[00:03:06] Ben Lloyd Pearson: Powerful network effect. They actually have quite a bit of capability to not just survive in this world, but also thrive. because they have the data to give these models context. They can train data on this stuff. we've been seeing going around social, comments about how companies like Stack Overflow are losing a lot of their traffic.
[00:03:27] Ben Lloyd Pearson: but the reality is they aren't losing a lot. They aren't really losing their value. Like there's still a lot of value in the data set that Stack Overflow has from decades at this point, or more than a decade of being this centralized knowledge source. For developers, and that is extremely useful for training AI models.
[00:03:44] Ben Lloyd Pearson: So, yeah, it's, it's, you know, everyone's focused on all the hot startups that are emerging in ai, but the incumbents might be a little bit slower to get around to it, but I think Google's really showing us that once they get there, it can be pretty powerful, particularly considering just how much Google stumbled [00:04:00] in the early days of ai.
[00:04:01] Ben Lloyd Pearson: Likethis is a very different experience this time just based on what I'm seeing.
[00:04:06] Andrew Zigler: There's definitely a lot at play and a lot in motion. And a, a really cool thing that we're actually gonna get an understanding of is, I sat down recently with one of the developer relations engineers at Google DeepMind gave us a glimpse at how these kinds of products are built and shipped and educated to folks at scale. so I'm really excited for that upcoming guest because, it's tied to all these great things that came from Google io.
[00:04:27] Ben Lloyd Pearson: Yeah.
[00:04:28] Andrew Zigler: explore some of the new toys from them. So, moving on though to, there's been, uh, a, a lot more than just Google io happening in the last week.
[00:04:36] Ben Lloyd Pearson: Yeah.
[00:04:36] Andrew Zigler: Uh, what else? really caught your eye.
[00:04:38] Ben Lloyd Pearson: Yeah, I mean, Microsoft is also running their annual event, it's called Build, tons and tons of product announcements. I think the biggest one that, that I'm paying attention to, but I think most people are paying attention to, is I. GitHubcopilot coming out with their own agentic coating capability, um, effectively bringing it to parody with Cursor and Windsurf and now Google's [00:05:00] Jules, I guess, I'm really withholding judgment on this one for now.
[00:05:04] Ben Lloyd Pearson: we will have to see how it actually gets rolled out with teams, but, what were you seeing from this event?
[00:05:09] Andrew Zigler: Yeah, so an interesting point that you made there about it. coming more into parity with Cursor and Windsurf, really what I see is this is a tool that goes head to head with that Jules agentic coding assistant that we saw from Google. And the reason for that is these workflows and these tools, they work in the cloud, they work on platforms and services, and it's being able to trigger and use GitHub copilot to solve issues and merge prs and write,
[00:05:33] Andrew Zigler: brand new code all on the GitHub platform instead of on your local IDE. And that's kind of the same thing that Jules is doing operating in, virtual machines on your Google Cloud platform. So you're kind of seeing this level of orchestration in the cloud of these age agentic tools. I'm really intrigued to see how it evolves and how teams are able to actually use it, because this is us arriving at that opportunity that we kind of all talk about all the time of like,
[00:05:58] Andrew Zigler: How much time could you save if you had a [00:06:00] whole bunch of developers working around the clock on your code at all times? Right. This is, these large incumbents attempt to explore that market for the first time.
[00:06:10] Ben Lloyd Pearson: Yeah, I, I said I'm withholding judgment, but I definitely believe this is a step in the right direction for copilot and it's the direction that everyone is moving. I do wanna call out 'cause there was a particularly interesting situation around this that I. kind of looks bad for Microsoft, but I also want to just call out some behavior that I think, is unwarranted, but one, so one of the teams.
[00:06:30] Ben Lloyd Pearson: Yeah. So one of the teams that, works on the .NET runtime decided, like after this event, it looks to me like they just decided, Hey, we want to try out this new capability that our parent company or the company we worked for released. And, they deployed it onto their repo. And, you know, this is an open source repo, so anyone can go and, and take a look at what it was doing.
[00:06:52] Ben Lloyd Pearson: But I mean, it, it failed just like hilariously bad. to the point where it, it's almost like a perfect example of [00:07:00] how like unstructured AI adoption can just like, kind of blow up in your face. but you know, this repo has like 70 some checks that run on it, which, I mean, that's pretty crazy to me, part of this could be like they could optimize some of the CI services that they have running.
[00:07:16] Ben Lloyd Pearson: but regardless, all of the checks failed for these prs that copilot generated. And in the comment thread you can see the developers like trying to coax it into like addressing the fact that every single one of the checks failed. And it makes changes very diligently, but none of them actually resolve like any of the failing checks, and they just continued to fail.
[00:07:38] Ben Lloyd Pearson: So it's kind this hilarious story, but what wasn't so funny is this actually went viral and a lot of attention got drawn to it. There's even some people that showed up into the pull request comment section to like just like, denigrate the development team and make them feel bad for doing this.
[00:07:54] Ben Lloyd Pearson: Like that's not cool. especially when they're like open source developers, but these are probably just developers that wanted to [00:08:00] try out some cool new technology. And yeah, it didn't go the way they expected it to, but sometimes that's just life. And then you adjust and you try to do it better next time and not make the same mistakes.
[00:08:10] Ben Lloyd Pearson: But if you're somebody who's out there, like going out into an open source repo and. Telling a developer that,they're bad because they made certain decisions like you are the jerk, not the developer.
[00:08:22] Andrew Zigler: Yeah, it's a good thing to call out and I completely agree with you, Ben, and there's a certain level of empathy that, I have for these developers that are in this position. And I think it's a position that many of us sometimes feel like we're in, you're under a lot of pressure, and you wanna try out new things and ultimately when you're working in open source, you're working on an open stage.
[00:08:39] Andrew Zigler: Where folks can come and contribute. Because that's the beauty of open source, is we can all build together, but we can only do that if we're collaborative and we're friendly and nice. it's okay to disagree or to have your opinions on new tools that folks are adopting, but to take that behavior into a pr, it doesn't look good on anybody.
[00:08:56] Andrew Zigler: ultimately they're just trying to, ship good software, just like the rest of [00:09:00] us.
[00:09:00] Ben Lloyd Pearson: Yeah. But if I'm not mistaken, isn't it possible that we have a guest from GitHub coming up that maybe can help shed some light on, on how things are going with copilot?
[00:09:08] Andrew Zigler: It is not only possible it is happening, dev Interrupted is sitting down, with a guest, from the GitHub team at Microsoft that's going to, uh, explore with us some of these synchronous, asynchronous emerging workflows folks are seeing with Agentic tools. We're going to, understand the impact of GitHub co-pilot.
[00:09:25] Andrew Zigler: So is it gonna be a really great episode. So we're covering Google io, we're covering Microsoft Build, we're covering everything in between. You don't wanna miss these upcoming chats. they're gonna be really insightful.
[00:09:35] Ben Lloyd Pearson: Let's talk about, a younger company that, maybe didn't make as big a waves this week, but still made some waves.
[00:09:41] Andrew Zigler: Oh wow. are you talking about,
[00:09:43] Ben Lloyd Pearson: I.
[00:09:43] Andrew Zigler: certain incumbent anthropic? 'cause I would certainly say that they're no small company. Maybe
[00:09:48] Ben Lloyd Pearson: Yeah,
[00:09:48] Andrew Zigler: a
[00:09:49] Ben Lloyd Pearson: that's true.
[00:09:49] Andrew Zigler: no Microsoft. But, uh, our friends at Anthropic also had a pretty awesome week on the demo and release front. while their splash wasn't a huge corporate event, because they're obviously not operating [00:10:00] on that same kind of scale.
[00:10:01] Andrew Zigler: They did have a small, invite application only, events called Code with Claude. That was a group of industry leaders, professionals, founders talking about, the new releases coming
[00:10:10] Ben Lloyd Pearson: Wow.
[00:10:11] Andrew Zigler: And among them is Claude Opus three, uh, Opus four, excuse me. Uh, which, emerges with new capabilities in the agent, uh, coding space, being one of the best code, writers by the benchmarks that they released at that event. and really this was a feature on how, Tools like Anthropics Claude are combining with other incumbents to, provide a better model. Right? You're, this is already GA on Amazon Bedrock. This is something that you can toggle and use with your GitHub copilot agentic workflows we just talked about. Um, they're providing that strong coding model, that other teams and other large enterprises are building on top of.
[00:10:47] Ben Lloyd Pearson: first of all, I love to see it like, I love Claude. Anthropic is doing some really great work with it. Like I use Claude almost weekly, almost daily at this point. Actually, I.I always love to just see these incremental improvements coming out because I [00:11:00] feel like every time this happens, my life gets a little bit better.
[00:11:04] Ben Lloyd Pearson: but I, I think what's really, what we should really take away from this and from all the stories we have this week, is that this stuff is very rapidly shifting. And I keep saying this to the point where I feel like I'm just being overly redundant, but it also feels like it's accelerating too, like the pace of change.
[00:11:21] Ben Lloyd Pearson: Within the AI space is getting faster, at a faster rate. and because of that, I think it's very risky right now to tie yourself too strongly to a single AI solution at this point. Like, you know, Andrew, I think you can attest to this, like, as a part of our team, like a lot of our processes, like we're keeping our, prompts very separate from the workflows, separate from the project management.
[00:11:46] Ben Lloyd Pearson: and that way wewe get a lot more granular control over every level of it. So when a new model comes out or when a new workflow capability opens up. It's really easy for us to experiment and to test new things and to [00:12:00] migrate to the latest and greatest. So, you know, I think really what our listeners need to be taking away from, and taking back to their engineering teams is that, now is the time to experiment with a lot of this.
[00:12:11] Ben Lloyd Pearson: And, you know, just don't get too tied into a single product or a single ecosystem right now, because the potential for, whatever you choose to be disrupted is so high, like. The thing that works for your problems today may not work in three months from now, or may not be the best solution three months from now.
[00:12:27] Ben Lloyd Pearson: So the more flexibility you have to adapt, the better off you're gonna be.
[00:12:32] Andrew Zigler: Absolutely.
[00:12:33] Ben Lloyd Pearson: Yeah. So, uh, before we go, you know, our producer Adam wanted, obligated us to have at least one story that's not about ai. So, uh, yeah, just real quick.
[00:12:43] Andrew Zigler: that we're gonna do our, our non-AI happy hour moment? Uh, here, here,
[00:12:47] Ben Lloyd Pearson: Yeah.
[00:12:47] Andrew Zigler: things out, just because everything that happened in the last week, gosh, so many huge AI related announcements.
[00:12:53] Ben Lloyd Pearson: Yeah, so long before AI ever existed, or at least AI as we think of it today, existed, lived this [00:13:00] programming language called Java, uh, which has been around for a long time. It was foundational to me learning computer science. it turned 30 recently, which is really cool. you know, we, we will share an, a really interesting article in the show notes for anyone who's like, just sick of all the AI and wants something that's not about it.
[00:13:18] Ben Lloyd Pearson: go learn about the history of, of Java. It's a great language, great to learn about.
[00:13:23] Andrew Zigler: Java's turning 30. Java was my first programming language. and some of the. Java's core identity is being able to write code that can be anywhere and all sorts of
[00:13:33] Ben Lloyd Pearson: Yeah.
[00:13:34] Andrew Zigler: And I think that's really appealing for developers who wanna have a really broad impact, right? Which we all wanna do.
[00:13:39] Andrew Zigler: We wanna ship software that's used by the world. so Java, you know, candle for me and as I kind of went towards that mission, really cool to see it, reach 30, I know it's gonna reach 40, 50, 60, and beyond because this is a language that's gonna be sticking around.
[00:13:53] Ben Lloyd Pearson: So Andrew, tell us about our guest today.
[00:13:55] Andrew Zigler: Oh yes. So in just a moment, we're bringing Matt Greenwood, the Chief Innovation [00:14:00] Officer at Two Sigma, and we're talking about ancient history and future potential and everything in between when it comes to innovating at scale.
[00:14:09] Andrew Zigler: So stick around.
[00:14:12] Ben Lloyd Pearson: Join us for a live 35 minute panel featuring past podcasts.
[00:14:15] Ben Lloyd Pearson: Guests from Adnan Ijaz from Amazon Q and Birgitta Bockeler from ThoughtWorks, alongside experts from LinearB. We'll explore how leading teams are going beyond copilot to experiment with a agentic ai. Measure real impact and drive meaningful DevEx gains registrants. Get the full recording plus early access to the DevEx Guide to AI driven software development, packed with tools, prompts, and insights from the 2025 AI developer survey. Reserve your spot today and stay ahead of the AI curve.
[00:14:50] Andrew Zigler: today's guest has spent over 20 years shaping one of the most innovative financial firms in the world. We're joined by Matt Greenwood, who's the Chief Innovation Officer at Two [00:15:00] Sigma and I've been doing this show for a little while now, but today's episode is rather special to me. Because it's not every day that I get someone on the pod who studied ancient history and classical languages like myself. Uh, so between Matt and my and me, we could probably cover like 3000 years in this conversation.
[00:15:18] Andrew Zigler: And we kind of will to an extent, uh, because today's conversation is all about innovation, culture, how you build it to last, uh, and what it can mean for you. But first. I want to have a little fun, Matt, like a true classical scholar. He gave me some homework. Uh, so kicking things off, I, I wanted to turn it over to you about your question you had for me about my classical background,
[00:15:39] Matt Greenwood: I, uh, you know, in doing my homework here, I also like to read the bio, understand who I'm talking to, and I noted that, uh, you study classics,
[00:15:46] Andrew Zigler: Yes.
[00:15:47] Matt Greenwood: and it's rare. I think I tell people I'm classically trained. I, maybe I should say I was classically beaten. That was a different time you went to school. are a little bit more austere. Um, and so it's rare that I get to see someone who's actually, you know, [00:16:00] done Greek and Latin and, and the classics. And so my question for you was, which of the, uh, Odyssey translations most resonated with you or you found most interesting?
[00:16:09] Andrew Zigler: I love this question because the Odyssey, like all classical texts, there's a million ways you can read them. There's a bunch of different translations for those that don't know, you know, the classical languages. They follow really strict, uh, rhyming schemes. And the meaning that you can gain out of every line is
[00:16:23] Andrew Zigler: so deep just based upon, uh, how long you hold even just a syllable. So what this means is when you take that ancient language and you turn it into something like English, you can lose a lot of nuance. So my personal favorite translation is probably Fagles translation, just because it's a little more fun to read. It's a little faster. And he does take some of those really weird Greek idioms, 'cause trust me, there's a lot of 'em and makes them something that you understand in English.
[00:16:48] Andrew Zigler: Um, but what do, what do you think, Matt? What, what, what tickles your fancy.
[00:16:51] Matt Greenwood: I have been, you know, I really love Wilson's translation. I, I
[00:16:57] Andrew Zigler: Oh yeah.
[00:16:58] Matt Greenwood: that, that, that was, you know, and, and, [00:17:00] and it's, it's relatively new, so, uh, I had to kind of
[00:17:02] Andrew Zigler: Yeah.
[00:17:02] Matt Greenwood: back to it, but I had a child who was studying, uh, classic civilization, so I got to read it again, and She does an amazing job of kind of bringing it current in ways that, are, are missing in, in kind of, you know, Lattimore or Pope or, you know, any, any of the, the other
[00:17:18] Andrew Zigler: Yeah.
[00:17:18] Matt Greenwood: I, I think she's kind of my number one favorite.
[00:17:21] Andrew Zigler: Lattimore's great. But, but you're right. Wilson is probably the most approachable text. I think that was when I first read the Odyssey in school, like before I went to college. I think it was, um, I think it was that translation because it is so approachable. That's great that you've had a chance to come back and revisit the classics literally with
[00:17:36] Andrew Zigler: someone else as they're learning. I think that's the best part about classics is passing it on, teaching someone else. and I, I wanna kind of kick off our conversation too at innovation by asking, uh, 'cause I don't have this opportunity every day, you know? How has your background in classics influenced your leadership approach?
[00:17:52] Matt Greenwood: So,
[00:17:53] Matt Greenwood: so, uh,
[00:17:53] Matt Greenwood: you know, I mean,
[00:17:55] Matt Greenwood: for the readers, we, or the listeners, we, we, I, sometimes we get these questions a little bit ahead of [00:18:00] time. Um, and for me, this was yesterday and I was like, I. Um, I really have no answer, but it must be huge. Right. You know, we are
[00:18:08] Andrew Zigler: Yeah.
[00:18:08] Matt Greenwood: by everything that we do. That is a, a kind of a fundamental cornerstone to my management approach.
[00:18:14] Matt Greenwood: This kind of bring your whole self to work that I've talked about before, and we talk in Two Sigma about a 360 degree view of the problem. And so, you know, knowing that that's my management, you know, approach and, and, and the philosophy that we, that we espouse and, and deeply buy into here. Reflecting on, on what you know as a child, 10 years of classical education did to me, uh, uh, really, you know, did make me think deeply last night about, well, did that do?
[00:18:41] Matt Greenwood: I bring, and I, I think. It's, it's the appreciation that life is not in the moment. You know, we are, we are on this kind of very long spectrum that goes back very, very far and, you know, hopefully goes into the future very, very far as well. And that in order to [00:19:00] be, uh, really kind of rounded, I think you wanna take as much as you can from as many places as you can.
[00:19:07] Matt Greenwood: And I think that classical education of kind of, of dabbling 'cause it, it really wasn't that long that I did it. But, you know, in, in Latin and in Greek and in ancient civilizations and, you know, I study other ancient civilizations by myself. The ability to kind of look at. How they are different, how they compare, and how that, you know, how you bring that forward into today and where you go with that. Uh, in the future, uh, clearly has influenced, uh, my style of leadership and management.
[00:19:34] Andrew Zigler: Thank you for sharing that. I, and for those of us, listen, those of you listening, you know, I know this is a tech podcast, you're probably like, where have I dropped into? Uh, but if you have not, if you have not explored anything in classics, I hope this conversation maybe inspires you to, uh, to, to learn a little bit about it.
[00:19:50] Andrew Zigler: It's a really cool field and there's so much you can apply in humanities every day in tech and working with people and teams. And on that note, um, I wanna dive [00:20:00] into solving innovation at scale and the kind of problems that, that you faced over, over your time. And you previously shared with me that the problems that you solve, they only get harder and more complex.
[00:20:11] Andrew Zigler: Uh, can you talk some about how those challenges have evolved over your career?
[00:20:16] Matt Greenwood: One of the real privileges that I think you get, uh, of being in a place for 20 plus years is that you get to be a major figure in that arc of time. And so, you know, when I talk about what we've done at Two Sigma, it's, it's really what we've done, me included from the very beginning.
[00:20:36] Matt Greenwood: I don't have to rely on, on stories that people tell me I was there, uh, uh, for everything that happened. And, and, and when we began here, you know, if we rewind to 2000, I, I joined Two Sigma in 2003. Most of what we have today just didn't exist. Right. You know, we had actual real computers that we had to wire up. we had a compute farm that was literally a whiteboard. You had to kind of write down when [00:21:00] you wanted to use the n computers where n was a very small number. Um, and so it was a fundamentally different technological world. People really had no understanding about what big data was. Uh, for, for many years, people would just look at me, curiously, what is it that you actually do? Um, and we, I had to phone, you know, uh, I started off at Two Sigma running data and phoning round vendors in 2003 and asking them, cajoling them to kind of give me access to their data, uh, was, was really, you know, fun, fascinating, interesting, and, and fundamentally unlike, uh, everything that we've done here. And so, you know. We built technology for the time and the time was not Google Cloud with, amazing, analysis tools and vendors that just will drop their data for you. It was, you know, the vendor would ship me a bunch of spark stations and we'd have to kind of. Yank out the hard drive and build tooling to do that. And [00:22:00] so, um, you know, what we've learned, or, or, or what I've inferred over the years is, is, is that innovation and, and, and platform building. And we're a platform company here, really about trying to understand how to bring that kind of, um, hockey curve for today. Right. That is to say, I have a bunch of problems that I need to solve today, I want the solution to those problems to be much, much faster.
[00:22:28] Matt Greenwood: Right? That's the, that's the kind of the, the curve in, in, in the hockey stick, right? That's great and you do that and it, it works and things are hard, you know, twice as fast or five times as fast, or a hundred times as fast as they used to be.
[00:22:40] Andrew Zigler: Mm-hmm.
[00:22:41] Matt Greenwood: Um, but people don't always hang around to figure out what happens after. Nothing doubles forever. So what we found inside Two Sigma is that actually that curve levels off. I like to call it an s curve. So we have some problems. we make, uh, solutions [00:23:00] for them, and that gives us this incredible lift where we're solving things much, much faster. But eventually that kind of flattens out, and that's because the, the, the, the platforms that you built are built for the problems that you had, you know, when you started the platform. Here's the beauty of a platform, is that literally you can use the physical metaphor. Once you've built a platform, you can stand on it and you can see. Further than you did. And you can see problems that you never had before. And now you've got new problems, new problems that were given to you because of the platform that you've built. And so now you're in a new situation, you have new technology, you have new understanding of the problems you're trying to solve. You have new problems that you wanna solve, and so you have to start all over again. And so I like to think about innovation as this kind of stacked S-curves where at each point you have the problems of today and you want to lift those and, and solve those, know orders of magnitude faster, but you know that coming around the corner are gonna be new problems for tomorrow, and that's really [00:24:00] what makes it so exciting even after 20 years.
[00:24:02] Andrew Zigler: I like the visual of climbing up and up and up on the curve and encountering new problems. And along the way it changes your entire viewpoint of, of the entire problem space and when you are working on problems for that long, and they'll a lot of cases, maybe the problem doesn't quite exist yet, or people aren't able to define the problem.
[00:24:19] Andrew Zigler: And so you're trying to define solutions around it. Over time, you get that common language, you get those common solutions. Everyone's on the same page. That s-curve flattens out and you find new problems. And when you go on this journey, you know, s-curve to s-curve, the platform to platform, how, uh, how do you, uh, build a culture that helps attract some of the, like the smartest people in the world who are gonna come and
[00:24:42] Andrew Zigler: stand on that platform with you, see all these problems and find the new heights. Uh, are there, are there ways that you approach that to, uh, kind of build that culture?
[00:24:51] Matt Greenwood: we, we
[00:24:51] Matt Greenwood: talk about what we call an epsilon and a Omega approach at Two Sigma.
[00:24:55] Matt Greenwood: Uh, which is to say, in order to, to solve these problems, you have [00:25:00] to have a well-defined but almost unapproachable goal. You have to know what does the far future look like? Where are you actually trying to go? But if you did it around trying to figure out how to build that future, you'll almost never get till, till tomorrow, right?
[00:25:15] Matt Greenwood: You'll get, you know, analysis paralysis we call it around
[00:25:18] Andrew Zigler: Yeah.
[00:25:19] Matt Greenwood: And so in order to do that, what we, in order to kind of counter that counterbalance that we have a notion of what we call epsilons, and then epsilon is a, a kind of the smallest step that you can take that drives you in the direction of the omega that you're seeking.
[00:25:36] Matt Greenwood: It's not MVP, right? An MVP is maybe the smallest, uh, thing that you can do You wanna make sure that the, the after you finish these epsilons that you are moving, you're gathering enough information to understand is this the right direction to be moving to? Are we getting towards this omega maybe. We change the Omega because we've learned things during the epsilon that, that, that, uh, uh, make us [00:26:00] rethink how the future is going to look. And so those are the things that we kind of constantly talk about those. That's the language that we've built here. think that's one other thing that I encourage people to, to take on, and that is I. To be a little forgiving. We, we have, uh, phenomenal people at Two Sigma. We are pri I'm, I'm privileged to hire people who are brighter than me every day of the week. And that. Genuinely is one of the, the, the, the joys that I get here is hiring people and thinking, wow, like I. I don't know if I would've made it if I came.
[00:26:28] Matt Greenwood: Now, these people are just stunning in their approach to everything, but one of the things that happens is, you know, you have a, you have a, company that's around 20 years. You have millions, maybe tens of millions of lines of code, hundreds of pieces of platform with some maybe questionable architectural decision. now, and what I constantly remind people is we should forgive our past selves. Let's assume that when we made those decisions 15 years ago, we made the best decisions that we [00:27:00] had at the time. Now, with the benefit of hindsight, they look kind of maybe dumb, but back then when you only had two computers or you didn't have enough storage, they might have been the best that we could do.
[00:27:11] Matt Greenwood: And so we have to look at the past, look at the what we've built and take that, you know, give it a little bit of forgiveness and understand that, you know, in the future, our future selves might be looking at us with the same way. So we wanna pay it forward with a little bit of forgiveness as well. So it's really important to kind of bring that humility and, and to, to the process because we are really trying to get to a better future for the company rather than prove a point to each other.
[00:27:36] Andrew Zigler: I, I like this idea of bringing people in to, to move along this platform journey or like the, the s-curve as we continue to talk about, and your past mistakes. They, they might look silly or the decisions that you made, they might not make. Perfect sense now, but I think that's a great lesson for everybody to be more forgiving for your past self.
[00:27:53] Andrew Zigler: You're operating in an environment where you have the best knowledge available to you at the time and you're making the best decision that you can at the time. [00:28:00] And like you mentioned, you know, when you innovate for a long arc of time, you accumulate some baggage or you accumulate architectural decisions that, uh, you have to go back and revisit.
[00:28:10] Andrew Zigler: But that's the benefit I think of that platforming approach is you get that vantage. You now have all the viewpoint you need to go back to those older decisions and refine them. And along the way, there's a lot of things that can slow you down, uh, in innovation. One of them is process, and we talked a bit about process initially, and you, you really called out like the danger of being beholden to process and how it slows you on this journey.
[00:28:34] Andrew Zigler: What does good process look like to you?
[00:28:36] Matt Greenwood: Yeah, so, so, uh, you know, at at Two Sigma, um, you know, we deal in forecasting, right? That's fundamentally what we do here. We try and forecast the markets and so we, we, we work a lot with our data as what we call time series, right? So, you know,
[00:28:49] Andrew Zigler: Mm.
[00:28:50] Matt Greenwood: changes through time and, and what we've learned is the developing software for time series, for kind of time series software. Uh, is is absolutely critical. And [00:29:00] you know, one of the things here that you have to be aware of is that there is another time series going on, and that is the time series of the company.
[00:29:06] Andrew Zigler: Yeah.
[00:29:06] Matt Greenwood: so all of these things that we're talking about processes and, and, and, and, and things like that, they exist. Both along the arc of time, but at also at every point in time. And so one of the things that's important to understand is that you need to continue to evolve and grow and change these processes as fits the company at the time that it's in. And one of the most amazing things about building a company like this with, with, you know, thousands of people in it, is that you can actually have multiple kinds of company in the same company at once, right? You can have one portion of the company that is, is kind of building a good, steady platform and looks like a thousand person company. And then you might have teams here and there that are trying to innovate very quickly and look more like startups. And you have to understand that even a single point in time process doesn't [00:30:00] work across the whole company. So you have these multiple dimensions and the key, you know, what I try and land with, with, with folks is try first to kind of think deeply and understand why we have a process in the first place.
[00:30:14] Andrew Zigler: Mm.
[00:30:14] Matt Greenwood: we have a process in the first place fundamentally to what I call lower the cognitive load of the organization. You know, sometimes people think that's a personal thing and they come to me and they go, Greenwood, you know. This isn't easier for me. And I'm like, no, no, no, no, it wasn't for you. This is for the company. It might be more complicated for you, but it's less complicated for the company or less cognitive overhead for the company. Um, and so what we need to do is we need to kind of investigate, does the process that I'm using here that I would immediately apply to this, right? So I have a process for, let's say, code release the main, uh, platform. Do I wanna use that same process? For this kind of fast team. Now ordinarily you might think, yeah, sure I have a process such as apply the [00:31:00] process, but actually these two parts of the organization exist at different points in their lifecycle, right?
[00:31:06] Andrew Zigler: Mm-hmm.
[00:31:07] Matt Greenwood: a sure code release pro process and apply that to, you know, four people trying to get great ideas quickly off the ground.
[00:31:16] Matt Greenwood: And so it's really incumbent upon everyone in the organization to say, okay. What processes do we have? What are good to apply and what should we change for the circumstances and the time and the part of the organization? And I think that that really is key. And if you don't do that, then you really can become beholden to your process.
[00:31:33] Matt Greenwood: It's very, sometimes very easy to give up in the face of a process. I'd love to help you, but the process says you have to fill out these 105. No, we, you know, we, we need to kind of constantly, and everyone in the organization I think has a responsibility to that process, right? I told, I told folks I was at lunch with the other day. Um, I'm relying on the fact that, you know, I, I, I'm not coding every day. I wish I was, maybe the company doesn't wish that, but I wish I was [00:32:00] coding every day. But it's important that, that, that I don't see what goes on, on the front lines, right? I'm not there. I'm not the leaves,
[00:32:08] Andrew Zigler: Yeah.
[00:32:08] Matt Greenwood: coding. you see something wrong, you have to say it 'cause I won't see it unless you put your hand up and you say, you know, this emperor may not be wearing any clothes. It's really important to encourage that kind of communication up and down the.
[00:32:22] Andrew Zigler: You know, going back to something you said earlier in, in our talk just now about even in the early days of big data and data, uh, a culture and, and kind of building that within orgs, you were, I used the word cajoling, you know, getting on the phone and talking with folks about their data practice, the, the stuff they had on hard drives.
[00:32:38] Andrew Zigler: And this is before the cloud where it was just easy to just like, they give you a URL, you had to go and get some hard drives and then you had to actually like yank the stuff out. So, um, this is really a, a cool thing to dive into, I think because you're talking about innovating on a new idea before there was tooling, before there was common language, before there was a real understanding.
[00:32:57] Andrew Zigler: That sounds very familiar to the [00:33:00] environment that we're in right now with things like AI and how people are trying to build business units within their orgs to go really fast. And you see these large traditional companies kind of just like giving free reign to smaller teams to just. Build, build, build.
[00:33:13] Andrew Zigler: Don't be slowed down by our process. We need you to find the new process. And I, I'm, I know there's lessons packed in there from your journey in early data, and I'm wondering how did you build kind of that, that understanding of a sustainable data practice before big data was even a thing. How did you socialize it?
[00:33:33] Matt Greenwood: Yeah, I, I, I think that, um. the journey at Two Sigma in many ways begins and ends with the people that we look for. Look, you know, we're, we're, we are not a big company even though we, you know, we have more than a thousand people. That's not big by comparison with other companies. And so we, we do have a luxury.
[00:33:52] Matt Greenwood: We are privilege to really be able to hire phenomenal people and it Two Sigma, almost everything [00:34:00] begins and ends with the people that we have here. Right. I, you know, I just believe fundamentally that if you hire great people and give them interesting ideas and then stand back a little bit, they'll do great things. But you have to also of explain to them a little bit about not what great looks like. I think everyone understands what great looks like, but you have to explain a little bit about you continue to make sure that you're doing great. Right. So one of the problems that you have, in any company that's successful is you tend to believe in yourself a little bit too much sometimes. And so it's, it's really critical and we, you know, we are a systematic. Uh, uh, company. And so we, we have tied ourselves to this scientific process, but really it's one of query and, and curiosity and, and asking those questions and then being really intentional about it.
[00:34:53] Matt Greenwood: I, I have this phrase that I use it Two Sigma, I call it turtles all the way down, right? The idea being that [00:35:00] you can come and give me a number, but I'll really only buy that number from you if I, if I have a belief that all the way down to the bottom, you understand where that number's coming from. what will happen is if, if there aren't turtles all the way down, you know, if you are, if you're drifting on nothing, everything falls apart. And so I really ask my people, you know, there, there are many different management techniques. One of them is five whys. And so, and, and two, so whats right? So the idea being that you ask why, why, why, why? And sometimes, you know, I sound like a little child, but you know, the idea is what you know. How you shown me that you've really thought this problem through and you are intentional about it and then pedal to the metal, let's go and get it done. But if you're just doing this because it's fun, or you know, big companies sometimes will just say, just let them loose and see what happens. The chances of you getting something good, uh, like maybe monkeys typing Shakespeare, right? So you have to kind of really think hard about [00:36:00] what is that process that you use to encourage innovation and at being intentional and really being able to explain how you get from A to Z.
[00:36:08] Andrew Zigler: It really what it boils down to is being aligned on impact. It sounds like ultimately you need to understand what you're doing. Turtles all the way down is a great way to describe it. Um, I, you know, the why, why, why, really making sure that you can fully understand their train of thought and why they are justifying their decisions.
[00:36:23] Andrew Zigler: And then, like you said, then once you have that alignment again on the impact, then you can go, uh, really fast. And I, I'm wondering too about how you're applying this nowadays. How does Two Sigma think about keeping up with the latest and greatest in this impact driven way, like specifically around like AI and LLM technology?
[00:36:42] Matt Greenwood: Yeah, so, so again, happily, we've been doing this for a long time.
[00:36:47] Andrew Zigler: Yeah.
[00:36:48] Matt Greenwood: We've been doing machine learning, we've been doing AI for literally decades. We have built a whole practice around how to understand data, how to understand data specifically as it as it changes [00:37:00] over time. And so all of that really has helped by providing a great foundation when we come to this new and incredibly exciting kind of gen ai, you know, maybe flowering is the wrong word.
[00:37:13] Matt Greenwood: I kind of, it's, it's more like mushrooms after summer rain. Than, Flowers. It's a little bit, you know, havoc ridden. Um, and so, you know, that that has definitely, uh, uh, you know, given us, comfort. We think we understand the questions that we should be asking, but really deploy, uh, you know, deploying AI effectively is first and foremost how you think you can use it, and that, that is non-trivial.
[00:37:41] Matt Greenwood: Right? You know,
[00:37:42] Andrew Zigler: Yeah.
[00:37:42] Matt Greenwood: we, we say, oh, we have to use it, right? That's true, Understanding how we have to use it is really key. And it's not about how we have to use it now. It's how, you know, where do we want to be in three to five years, right? There's no question about it. Gen AI is going [00:38:00] to, you know, be like see change, uh, across a, across engineering.
[00:38:05] Matt Greenwood: And so you wanna think about how do you benefit from that inflection. You know, what do you do with your people? How do you figure out? And I have, I have, again, like I, I keep, I keep saying this, I have great people. I have great people. I have people who graduated from the most amazing places in the world, and they say to me, I'm nervous about the future.
[00:38:25] Andrew Zigler: Mm-hmm.
[00:38:26] Matt Greenwood: my role as leadership, uh, in and management and innovation is to try and explain to them what a future looks like, where their role, their value will be even more than it is today. And you know, I, I understand that it, that it, that it can be concerning, but the future is incredibly bright if you figure out where you're going. And so first and foremost, you know, on top of that, that, that, that that we've built is thinking about how do we use it gen AI to, to automate. And we've been doing [00:39:00] automation again for 20 years. And I love automation, right? Because if I can automate something away, that means I can spend more of my time that I used to do that thing that I, that that may have been fun, but it was repetitive to actually think about things more interesting. And I'm still, you know, a humanist, right? I still just believe that we have this little spark that no, no one, no, no, no, no machine is gonna come and take away for anytime soon. And so I just fundamentally believe that freeing up time for humans to be creative and innovative has got to be better for all of us than worrying about whether they're gonna type a hundred lines of code tomorrow, or some GenAI will do it for.
[00:39:38] Andrew Zigler: So it sounds like a, a lot of the ways y'all are harnessing AI now is around things like automation to free up cognitive load, human process to work on deeper process.
[00:39:46] Matt Greenwood: There are many ways that we use AI today and, and, and we kind of tend to clump them together. And so the first, one of the first steps about trying to understand how we're gonna use this gen AI in the future is to try and understand [00:40:00] all the ways that we are even using it today.
[00:40:02] Matt Greenwood: Right? So,
[00:40:03] Andrew Zigler: Hmm.
[00:40:03] Matt Greenwood: I'm sure like most of your listeners, I, I have some subscription to, to some kind of, uh, gen AI something or other, and I use that in what I would call an advisory role, right? I ask it questions, it gives me answers. And that's it. That's an advisory role, and that's a great role for, uh, uh, gen ai.
[00:40:21] Matt Greenwood: And it lies, you know, half the time people like to say hallucinates I don't pull punches. It lies, it just plain out, flat out lies. Then there are, you know, what I would call, uh, a more Oracle role. I guess I'm getting classical again, right? Which is to say, uh, something that will accept or reject the outcome of a, of an advisory role, right?
[00:40:42] Matt Greenwood: So. You know, Hey, can you write this piece of code for me? Now I've got a piece of code. I wanna know, is this true? Like, does this do what I want? So I can pass that off to an Oracle and say, here's the code, here's the thing I wanted it to do. Can you just check that it does the thing it wants and just give me an answer and I'll accept the [00:41:00] answer.
[00:41:00] Matt Greenwood: That's kind of more of an, you know, an Oracle type role. And then we have, another role that we use AI for around, I would call an operational role. Can you create a code base, check it out, a git, make a branch, these kind of things where would be a bunch of, you know, pretty standard, uh, um, uh, commands that I would do you know, together make a a, a small piece of workflow. Tiny piece of workflow. And so, you know, when I, when I, when I was, uh, programming once upon a time, we used to have, people who looked after the build, right? We had, they call 'em build masters. And so that, that's a classic example of where you, you might, you know, use an operational, uh, you use AI for an operational role, and then there is the kind of, you know, maybe the top or the bottom, depending on which way we're drawing this ladder, the agentic role.
[00:41:47] Matt Greenwood: And, and, and that is someone who kind of coordinates all of these. So, so, so you might have, and you know, we've seen, I think there was a. Uh, an article I read the other day, I think it was my, I, some large company I won't name [00:42:00] who tried to create a company of agents. Right. So they had business analysts and,
[00:42:03] Andrew Zigler: Oh, I read this. Yeah, that was very interesting.
[00:42:07] Matt Greenwood: You could think about a, you know, an agentic role where you would have a bunch of advisory roles.
[00:42:12] Matt Greenwood: So these would be writing code and a, and a bunch of Oracles were saying whether the road would, the code would be correct and maybe operational role who's checking things in and out of git then, uh, an agent role, whose role it is, is to kind of give tasks out to these advisors, right? Maybe features.
[00:42:30] Matt Greenwood: Right? So, and now what does the human do in this case? Well, the human, in this case, the human is left to do what I call the most interesting thing, which is actually describe what they want of the day. Very few people. I know who writes software, writes software to write software. write software to express an idea, to create something. And we have better ways as human beings than writing in, you know, c or Python or even Pearl.
[00:42:58] Andrew Zigler: You know, when you talk with like the, the smart people [00:43:00] who work at Two Sigma and they come to you and express like, oh, you know, I'm, I'm nervous about the future, or like, things are changing so quickly.
[00:43:06] Andrew Zigler: is this part of like the mentality that, uh, within Two Sigma of how people are preparing for the future, is that like we need to understand. How to interact with this technology. And that might mean having very clear roles, like how are you helping them be future, uh, ready in that regard.
[00:43:25] Matt Greenwood: It's a great question. I, I, I think that, um, know, this notion of roles is, look, my formal training is as a mathematician. So I like crisp definitions that leave no room for doubt. And what we found over the years is that some of the ambiguities that we have amongst human beings. because people don't understand their role. And
[00:43:45] Andrew Zigler: Yeah.
[00:43:46] Matt Greenwood: there's a classic tool, a racy diagram where you write who's responsible, who's accountable, who's consulted, who's informed. Most problems that go wrong are because people are in a conversation and they don't understand which role they're taking in the conversation.
[00:43:59] Andrew Zigler: Yeah.
[00:43:59] Matt Greenwood: [00:44:00] so they. Believe they're being consulted or informed when in fact they're responsible. That's a bad one, right? Or they want
[00:44:06] Andrew Zigler: Yeah.
[00:44:06] Matt Greenwood: responsible, but actually you're just informing them. Then they get upset and so, you know, if we see this in humans, there's no reason to believe that. We wouldn't see this in computers as well.
[00:44:15] Matt Greenwood: That is to say, if we can help define what roles are and kind of keep them tight, then we might be able to get better juice. Now, that doesn't mean to say that we've covered that there are turtles all the way down, right? We may be missing a few. that's the, the, where we live in AI right now is terribly exciting. There's always new things going on, and so, so there may be more learnings to have, and we might look back and, and say, wow, those were, those were dumb decisions we made. But, know, at each point you wanna try and be as intentional and as crisp as you can. And when it comes to people, uh, you know, I, I, I, I don't know.
[00:44:51] Matt Greenwood: I, I think it's. It's complicated.
[00:44:54] Andrew Zigler: Yeah,
[00:44:54] Matt Greenwood: hand, I think it's incumbent upon every leader and every manager to, to understand that [00:45:00] today is different than, than even just a few years ago. Right. There, there is, there's a
[00:45:04] Andrew Zigler: I. Mm-hmm.
[00:45:23] Matt Greenwood: to kind of, you know, wrap cotton wool around every developer, right? We have to give them the tools that they can then work through those concerns themselves.
[00:45:33] Matt Greenwood: That's my job as a manager is to be able to give you enough information. That you can work through that concern that you have. And if I don't do that, then you're gonna have more concerns. Right? That's reasonable. And if I say, don't con don't, don't be concerned, then I'm actually depriving you of, of the ability to work through that.
[00:45:49] Matt Greenwood: And so, so it's really important and it's a little bit of a balancing drop here, right? It's really important that, that we, that we find the right path to be able to give people the tools they [00:46:00] need to work through the problems rather than keep them from thinking about those problems.
[00:46:04] Andrew Zigler: And has that problem itself ever changed through all of these hype cycles? Is that always the core problem to solve? Or is there maybe different nuances that you've noticed having been in a company across so many hype cycles in tech?
[00:46:18] Matt Greenwood: think this does because I, you know, I, I think unlike other hype cycles, not obvious. To people on the ground that the outcome of this is rapid growth in, in, in their opportunities in every other hype cycle. If you, if you kind of, I'm just kind of going through them in my head right now. You could see massive opportunities ahead, you know, internet, wow. Like websites unbelievable,
[00:46:46] Andrew Zigler: Whoa.
[00:46:47] Matt Greenwood: huge opportunities Here, it's not at all obvious that there are huge opportunities. And so that, that is fundamentally different about this, uh, this cycle. I, I, I'll [00:47:00] give you an example. Couple of years ago, my daughter, uh, was graduating high school. And, uh, we were talking about what she wants to do and you know, like me, she's kind of split brain, you know, history and math, something like that. And I said, you should just do computer science 'cause there is no, like, this is your time. This
[00:47:18] Andrew Zigler: Absolutely.
[00:47:19] Matt Greenwood: computer science. that was literally six months, five or six months before ChatGPT. And after that, so like, I think it came in November, right? Something like
[00:47:28] Andrew Zigler: Yeah.
[00:47:29] Matt Greenwood: the, the cycle hadn't finished yet. She's still applying for colleges. And I'm like, you know what, maybe philosophy is a good major. Because actually asking questions might be the right thing that you need now, and so people still don't understand.
[00:47:42] Matt Greenwood: But there is this concern that, that that opportunities might be narrowed and that is really different than than previous, uh, hype cycles.
[00:47:50] Andrew Zigler: Yeah, that's definitely a difficult one to navigate and even within your own organization over those hype cycles, you know, um, you, you've grown this engineering [00:48:00] organization, you've moved through s-curve to s-curve, to platform to platform, and you've found those new, those new heights, those new vantages that give you new opportunities and new challenges.
[00:48:08] Andrew Zigler: And along the way, you know, you've had to kind of take the temperature on your own engineering org. What they're doing, how they're adapting to your process. The one you've been explaining to us so clearly in our conversation, and it's clear that you lead with a lot of empathy with how you, you approach problems with, with your people.
[00:48:23] Andrew Zigler: And I'm wondering how do you, uh, what are the underrated signals you think of like a healthy, adaptable engineering organization?
[00:48:31] Matt Greenwood: That is a phenomenal question. Perhaps if someone had asked me that 15 years ago, we would be in a different place. Um, you know, I, I think this kind of goes back to my comments right at the beginning about, um, have being a full rounded organization,
[00:48:49] Andrew Zigler: Yeah.
[00:48:50] Matt Greenwood: you know, one of the, one of the key lessons that I, I talked to my initial manager, you know, my first time managers about, is understanding that everyone brings their whole self to work. [00:49:00] and, you know, there are many, many dimensions about what that means, right? It means, you know, if they have issues at home. They're gonna come to work, like whether you see them or not, they're gonna be there. And so, yes, you have to lead with empathy. You have to understand that you're seeing this person for a small amount of their life, and they have a whole life out there.
[00:49:16] Matt Greenwood: On the one hand, on the other hand, the more, and this is maybe gonna sound slightly Machiavellian, but the more of of them you can bring into the company, the longer you're gonna keep them, the more excited they're gonna be, the better it's gonna be for the company. And so, think about how you kind of engage, um, uh, you know, uh, more of, of the people who, who work, uh, with and for you. One of the drawbacks of having phenomenal people here, it's not drawback, that's the wrong word. One of the, um, potential, uh, concerns is that they don't have to come to work here. These people can literally get jobs anywhere. And so
[00:49:55] Andrew Zigler: Yeah.
[00:49:56] Matt Greenwood: very different so you have to, you have to make sure that it's exciting enough that [00:50:00] they wanna come back to work every single day. Um, and we, we've done that at Two Sigma by doing kind of notoriously geeky things, right? We are, we, we've talked about being the best place for nice geeks and, one of the things we did very early on was have a hacker lab. So we have a hacker lab, which is exactly what it sounds like. It's Full of old equipment, where we do things from the obvious soldering to kind of knitting and crocheting and baking. And We
[00:50:29] Andrew Zigler: call
[00:50:29] Matt Greenwood: it a gym for the mind, right? The idea being, you know, it's, it's clear I think to every manager in every company that if you have a gym, it's gonna be better for your employees. Why? Because they get to exercise and physical exercise is good and, you know, pumps the blood. The same thing is true for your mind.
[00:50:46] Matt Greenwood: And the more, the more you can encourage people to kind of use that whole brain that they have, the
[00:50:54] Andrew Zigler: more
[00:50:54] Matt Greenwood: chances that that, that more of that brain will be used to further, uh, the, the [00:51:00] aims of the company, which is ultimately what we all want.
[00:51:02] Andrew Zigler: What a great note to end that on. I think that's such a good takeaway for everybody and for me, this was like one of those episodes that makes you wanna study both the old and the new. We covered so much great territory here that just is like evergreen that you can really is relevant forever, which is why Matt and I have such an obsession with things like classics.
[00:51:20] Andrew Zigler: And you know, Matt, thank you for showing us that lasting innovation doesn't come from chasing things, but it, it comes from cultivating depth and challenge and curiosity in both yourself and your team. And before we wrap up here, you know, where can our audience go to learn more about Two Sigma and the work that you're doing there?
[00:51:37] Matt Greenwood: Certainly begin at the website, www.twosigma.com. And we have, uh, on the website we try and put blog posts that we make and, and, and kind of a, a window into, into what Two Sigma looks like inside.
[00:51:50] Andrew Zigler: I think people are gonna want to peer in that window 'cause we covered some really cool stuff and people are gonna want to go check it out. So we're gonna be sure to put that in our show notes and listeners, you know, if you, if you're, if you're curious [00:52:00] about, about what Matt's building, please go check it out.
[00:52:02] Andrew Zigler: I, I, I've, I've checked out extensively their website. They have some really cool videos actually that do a good job of kind of like visualizing how this data, um, is impacting our world and the kind of things that Two Sigma is doing with it. Very cool. And so we're gonna drop that in the show notes and.
[00:52:17] Andrew Zigler: Thank you listeners for joining us this far. If you made it all the way to the end, then you clearly liked this conversation and you should probably go pick up a copy of the Odyssey. Uh, be sure to subscribe and share the episode and check out our substack for Leadership Insights 'cause this is only half the story.
[00:52:34] Andrew Zigler: We're gonna be covering more of it there. And that's it for this week's Dev Interrupted. See you next time.
[00:52:39] Matt Greenwood: Thanks very much.