Factory's Matan Grinberg: The Coming ‘Dark Factory’ Where Software Builds Itself

1. 0:00 – 1:37

   Introduction

   1. MGMatan Grinberg0:00

      Bezos at Amazon, it's customer obsession. But in our mind, that's an input metric. Like, you don't wanna measure input metrics. It doesn't matter if you're customer obsessed. Like, you could be customer obsessed and they file a restraining order against you-

   2. PGPat Grady0:11

      [laughs]

   3. MGMatan Grinberg0:11

      ... 'cause they don't like what it is that you're doing. Like, our job is to build something so good that our customers themselves become obsessed with us. That is our job. It's like, you know, the analogy is if you're a coach of a basketball team, you don't wanna tell your players before they come out there like, "Hey guys, make sure to sweat." It's like, what? Like, no. Like, score points.

   4. PGPat Grady0:29

      [laughs]

   5. MGMatan Grinberg0:29

      Like, we need to score points. And in doing so, yeah, you're probably gonna sweat. And I think similarly, to create obsessed customers, you probably need to be really obsessed yourself with the customers, but the output is what matters. [upbeat music]

   6. PGPat Grady0:58

      We're here in the studio with Matan from Factory. This is our second time with Matan.

   7. MGMatan Grinberg1:03

      Yes, indeed. Thanks for having me.

   8. PGPat Grady1:04

      You're in the small and elite group of second-time Training Data attendees, so thank you.

   9. MGMatan Grinberg1:09

      Oh, yeah.

   10. PGPat Grady1:09

       Matan is the co-founder and CEO of Factory, which makes droids, which are autonomous agents for the art of software development.

   11. MGMatan Grinberg1:17

       Yes, indeed.

   12. PGPat Grady1:18

       And Matan, we're gonna jump right in because I think you guys are a little bit of a dark horse candidate in this world of software development. It is a market that has absolutely taken off. There are folks like Claude Code and Cognition and others who, who have a lead, but you guys are coming up strong. Talk about the competitive dynamics and what makes Factory special.

   13. MGMatan Grinberg1:36

       It's been a

2. 1:37 – 4:26

   Enterprise Lock In Fears

   1. MGMatan Grinberg1:37

      wild ride. We started Factory th- three and a half years ago now, so in April of 2023, when the world and the enterprise in particular was barely ready for GitHub Copilot, let alone fully autonomous agents. And so, um, I think the first two years, it was kind of our journey in the desert, is, is how I like to refer to it, because we were focused on fully autonomous agents, but engineers weren't ready, procurement teams at the enterprise weren't ready. And so I think retrospectively, we really, like, honed our craft and learned a lot about how to build for developers in the enterprise. But, um, you know, it, it took a lot of time to actually come around to when they were ready to receive it. And so we're kind of now emerging much more, and some of these other players like Anthropic or OpenAI, who have a ton of distribution, um, are going in and, you know, bringing their incredible tools like Claude Code or Codex. The thing that enterprises are really caring about that we have learned through those two years is they do not want anyone to kind of be their single point of failure. They do not want anyone to kind of control their fate. And so something that really matters is model independence. Everyone learned from cloud, where, you know, back in the cloud days, it was like AWS or, or, or Azure being like, "Hey, you know, come on in, sign this three-year contract. It's gonna be so cheap. We're gonna subsidize it. It'll be great." And then a couple years later when it came time to renewal, they would 10X the, the, the contract.

   2. PGPat Grady3:08

      Ha ha-

   3. MGMatan Grinberg3:09

      I mean-

   4. PGPat Grady3:09

      ... David Gravity, we got you now.

   5. MGMatan Grinberg3:10

      Yeah, we got you. What are you gonna do, a two-year migration to go to someone else? Like, no way. Everyone has scars from that now, and so everyone knows, look, Claude Code is fantastic. Uh, Codex from OpenAI is fantastic. We cannot put our fate in any one of these model providers' hands. Also, like, you just look at the risk profiles of the model labs versus the cloud providers. What's the last piece of drama that came out of a, one of the cloud providers versus like the model labs? It seems like there's kind of always some sort of chaos of, you know, internal fighting or getting in spats with the government or, you know, any other entities. And so, uh, if you're gonna, you know, build this very important part of your business, you wanna make sure that you're robust to any of these changes. And that's something that we've learned over those kind of initial two years is, like, developers really care about things being modular. They wanna know that they can customize it to what they want. They wanna know that if there's a new model that comes out that's faster or cheaper or more performant, they can kind of hot swap it in. And that's, I think, one of the biggest reasons why a lot of the largest enterprises are taking the momentum that they had from a Codex or a Claude Code and then are carrying that into Factory because they get that performance from these fantastic models, but they do it without the vendor lock-in that, you know, the model labs

3. 4:26 – 5:24

   No Lock In Promise

   1. MGMatan Grinberg4:26

      directly provide.

   2. PGPat Grady4:26

      Well, and if I'm the enterprise, I'm gonna be like, "Wait a minute. Am I now just getting locked into Factory?" What's the answer to that?

   3. MGMatan Grinberg4:31

      So the good-- It, it's a really good question 'cause that is something that you might think of, like, "Okay, wait, so we're just switching the, the lock-in point?" All of the modularity that we build is such that if at some point you wanted to say, "Hey, you know what? Factory's not staying at the frontier anymore," whether it's, like, the automations that you build or the skills registry that we help you create, the work that we've done stays in your code base, and any of the automations that we've created, the artifacts also live in your code base. In other words, there aren't really things that we're saying, like, our tribal knowledge about your org that we're keeping on our side and not giving to you. Um, and that's part of the relationship that we have with customers is, like, we similarly want to make sure we're providing the best experience possible. If we help you arbitrage between different models to get cost optimization, we're giving you that optimization. We're not taking that away from you. Um, and I think that's a really important part of the trust that we're building with, with these

4. 5:24 – 7:24

   Two Years Early

   1. MGMatan Grinberg5:24

      enterprises.

   2. PGPat Grady5:25

      You and I were talking probably a couple months ago at this point, and I was trying to give you credit for having the right vision for this market two, three years ago, and you responded with something along the lines of, "Thank you, but being two, three, two or three years early is the same as being wrong."

   3. MGMatan Grinberg5:40

      Yes.

   4. PGPat Grady5:41

      Um, which I thought was a wonderful response in so many ways. Can you talk about, like, that, those two years in the desert? How did it feel to have this vision that turned out to be right that nobody appreciated for a year or two? Can you just talk about, like, that journey and what it has done to the DNA of, of your company?

   5. MGMatan Grinberg5:58

      Yeah, I mean, in the moment it's really, really difficult because, you know, uh- I hadn't had a job before. I dropped out of my PhD to start this company and, you know, over the course of those two years convinced, you know, 20 of the smartest people that I've ever met to quit what it was that they were doing and, you know, join Factory and join us on this mission. Um, and these are people with families, these are people with kids who are, like, dedicating years of their lives to this problem and going, you know, customer after customer and they, like, they weren't ready for agents. They didn't get it. Also, the models weren't as performant, but I think a lot of it was behavioral. And I mean, even just a fun anecdote of, like, giving developers an NPS survey. If you ever are giving a developer an NPS survey, they do not like [laughs] whatever it is that you're giving it to them.

   6. PGPat Grady6:47

      [laughs]

   7. MGMatan Grinberg6:47

      Because, like, developers, they vote with their feet.

   8. PGPat Grady6:49

      Yeah.

   9. MGMatan Grinberg6:49

      They are very clear what they like and what they don't like, and if you're like, "Hmm, I wonder if they like it," they definitely don't.

   10. PGPat Grady6:54

       [laughs]

   11. MGMatan Grinberg6:55

       Um, and uh, but during that time, I think there were, there was a lot that we were learning. There was a lot that I myself was learn- I'd never had a job before. Enterprise sales is not something that comes obvious to, to a physicist. Um, but at the end of the day, it doesn't, it doesn't matter. There's no, you don't get any, you know, bonus points for being early because, like, who, who cares? Like, there's no consolation prize. It's either you do the thing or you don't do the thing, and that's all that matters.

5. 7:24 – 13:49

   Refunding Revenue

   1. MGMatan Grinberg7:24

      And for the team, it was really tough. There were points where, uh, we ended up getting good at enterprise sales, but the product still wasn't good.

   2. PGPat Grady7:31

      Mm.

   3. MGMatan Grinberg7:31

      And that's a very tricky position to be in because we ended up, you know, getting to a point where we were, like, just under 2 million in revenue and the product was not good. And there was a point in time where we realized this. 'Cause if you're really good at sales, you can sign contracts. That's like, you can definitely do that. But if you're doing that and the developers don't like your product, it's like a ticking time bomb because it's eventually they're gonna churn and it's gonna be really, really bad. We realized this and we proactively gave all of those customers their money back, and I remember that, uh, was one of the most difficult decisions to make because not only is there a, uh, you know, a group of, you know, 20 people who are getting ridiculous offers from all the labs. They have these huge, you know, financial incentives to go elsewhere. There are all these other companies that are doing well, and they decided to do this, and then we're gonna say, "Oh yeah, hey, by the way, that, you know, little bit of revenue we managed to get, we're actually gonna give it back 'cause we don't think the product is making their developers happy." We also had to tell-

   4. PGPat Grady8:28

      Why did you make that decision?

   5. MGMatan Grinberg8:30

      You know, we sold them on a good vision and convinced them that, you know, this is the, the right team to work with and that we were gonna deliver the solution for them. But we realized that the way that we had sold them on it and the product that we were delivering was not up to snuff in a way that I don't think it would hold true to one of our operating principles, and one of our operating principles that I really like is create obsessed customers.

   6. PGPat Grady8:54

      Yeah.

   7. MGMatan Grinberg8:54

      This kind of like flips over Bezos's thing, where Bezos at Amazon, it's customer obsession. But in our mind, that's an input metric.

   8. PGPat Grady9:01

      Mm.

   9. MGMatan Grinberg9:02

      And like input metrics are r- Like you don't want to measure input metrics. It doesn't matter if you're customer obsessed. Like you could be customer obsessed and they file a restraining order against you-

   10. PGPat Grady9:10

       [laughs]

   11. MGMatan Grinberg9:10

       ... 'cause they don't like what it is that you're doing. Like our job is to build something so good that our customers themselves become obsessed with us. That is our job. It's like, you know, the analogy is if you're a coach of a basketball team, you don't want to tell your players before they come out there like, "Hey, guys, make sure to sweat." It's like, what? Like no, like score points.

   12. PGPat Grady9:29

       [laughs]

   13. MGMatan Grinberg9:29

       Like we need to score points. And in doing so, yeah, you're probably gonna sweat. And I think similarly to create obsessed customers, you probably need to be really obsessed yourself with the customers, but the output is what matters. And I think that it, coming back to this, the product that we were delivering was not creating obsessed customers. And we wanted to make sure, like this was a group of the smartest people I've ever met. We were getting there. Like we were getting a lot of intuition. Things were starting to come together internally. Like we could see internally we were starting to become a lot more agent native in how we were doing things, and the product was kind of scratching that itch. But we were kind of ahead of our customers, and we wanted to maintain trust with our customers so that when it does hit, we can come back to them and say, "Hey, guys, this is the real deal, I promise." And to build that credibility, we had to say, "Hey, look, you know, even though you were maybe happy to continue, we're gonna give you this back and say three months from now, I think it'll be ready. Give us some time and I promise we will knock your socks off."

   14. PGPat Grady10:27

       How did your customers react when you had that conversation?

   15. MGMatan Grinberg10:30

       Some of them were like, "Oh, great." [laughs] Like, "Sounds good." 'Cause I think it wasn't something that they, you know, were obsessed with. Some of them were a little bit confused. Um, but I think gen- generally it's, especially enterprises, they're not used to these things. A lot of times enterprise budget, once it's gone, it's gone and no one really cares.

   16. PGPat Grady10:45

       Yeah. [laughs]

   17. MGMatan Grinberg10:46

       And so some of them didn't even know if they had a mechanism by which to take back the money. [laughs]

   18. PGPat Grady10:50

       [laughs]

   19. MGMatan Grinberg10:50

       Um, but you know, it's a difficult thing to tell also like investors who believe in you. Like, you know, I remember having the conversation with Sean. Um, I think Sean obviously he's stayed really close with the company, so he was very like on the same page. But it's kind of a scary thing to be like, "Hey, by the way, you know, remember all those updates?" And you're saying, "Hey, look, the, you know, revenue's going up. It's about to go down to zero." Um, it was a scary thing. Uh, and I think it was kind of a leap of faith of like we see the signal internally early of like this is the direction we need to go. We need to kind of pivot the approach on the product. But I remember that all hands where we told the whole team, it was like, oh my God. That was like one of the worst months of my life. Like I was just... 'Cause no, like n- not everyone was gonna say like, "What the hell is this? What's going on?" But it's kind of the looks on their faces where they kind of go a little bit pale and they're like, "Oh boy," like, "Is this just the early signs and we're about to sink completely?" Um-

   20. PGPat Grady11:43

       How'd you keep the team together through that?

   21. MGMatan Grinberg11:46

       I think honestly, the only reason the team stayed together is we were so ruthless about hiring early on, where it was like people that are genuinely really, really obsessed with the mission, which our mission is to bring autonomy to software engineering. And like really, really caring about that, making sure everyone, um, was also like very clear feedback loops as to like- This, the fate is in our hands. It's not like this is like, oh, something that I go do. It's like we all have a part to play in, you know, making this work, and I think embracing how much it sucked was also, I think, something that was very valuable.

   22. PGPat Grady12:22

       Just being honest about it.

   23. MGMatan Grinberg12:23

       Just being super honest about like, "Yeah, this sucks." Like, "Yo, look, look at those competitors, their revenue's going up like crazy." Like, "This is not good." Like, "We are in a very bad position." Like, "We just had to give back all of our revenue." Like, "We need to really get our shit together." Um, and in the moment, I think retrospectively, those are the moments where really the deepest bonds are made. Like, if you talk to people who are like athletes or even like academics or whatever, whenever you're in the like stressful period, whether it's like cramming before finals or, you know, in intense like... You know, we have some, some rowers on our team, and I think that's an example we always go to. Like-

   24. PGPat Grady12:58

       That's a pure pain-

   25. MGMatan Grinberg12:59

       ... that sport sucks

   26. PGPat Grady12:59

       ... pure pain sport. [laughs]

   27. MGMatan Grinberg13:00

       It's pain. It's literally just there is one number that quantifies your performance. It's just what is your time on your 2K or your time in that. Um, but like embracing that is what creates those enduring bonds such that afterwards, like we know what it's like to be at rock bottom. We know what it's like to lose. We know what it's like... I mean, when we first started the company, our valuation was five million. Like a lot of our competitors, a lot of the companies out there these days, they don't know what it's like to not be a unicorn. That's like manifestly, that is what they are day one. Whereas like we have been there kind of in those dark moments, and not a single person left.

   28. PGPat Grady13:35

       Yeah.

   29. MGMatan Grinberg13:36

       That makes us so resilient and so strong that, you know, going forward, things are going a lot better now. But there are going to be really bad times, but we have that resiliency in our DNA that I'm not sure some of these other companies do.

   30. SHSonya Huang13:48

       I love

6. 13:49 – 16:44

   Droid CLI Breakthrough

   1. SHSonya Huang13:49

      that. Um, so talk us, talk to us about what changed, and I'm curious your comment from earlier that the model's getting better is not the most important thing that happens.

   2. MGMatan Grinberg13:57

      Yeah.

   3. SHSonya Huang13:57

      'Cause at least in my mind-

   4. MGMatan Grinberg13:58

      Yes

   5. SHSonya Huang13:58

      ... the model getting better is the most important thing that happens.

   6. MGMatan Grinberg14:00

      Yes.

   7. SHSonya Huang14:00

      So just help me understand.

   8. MGMatan Grinberg14:01

      Yeah, so, so a couple things. So one is the interaction pattern that we were building for before was too ambitious. Like to your point, we were right in that what we were building for was fully autonomous agents, but it was two years too early, which makes it wrong. And fully autonomous agents require a complete change in behavior from the developer, and we were trying to do that out of the box before they were even using tools like Copilot.

   9. SHSonya Huang14:23

      Mm.

   10. MGMatan Grinberg14:23

       And it was just too much of a leap. It was too much of a step function jump. So it's an important day. I, September 26th, 2025, was when we first put out, um, basically the, the Droid CLI. And the Droid CLI met developers where they were in a manner that previously these fully autonomous agents did not. Um, and also its performance was like completely state-of-the-art, and it was model-agnostic, so it could use every model that was out there. September 26 was also two years after we initially started, so the world had gotten much more used to using things like autocomplete. Like by, by late 2025, most engineers were using an autocomplete tool, and many were starting to, um, at the time, use like a chat interface to ask an agent to go do changes like wholesale, so like the more agentic interaction. However, what we see is that like if you go back now and use, in this like agentic interaction, some of these older models, they're still good. So the biggest thing that changed was developers and, and in particular in the enterprise, like being open-minded to this new way of working. In particular, you know, developers, they've established their workflows over the last 30 years. They can be stubborn. A lot of them were like, "No, no, no," like, "My craft could never be done by an, you know, an AI tool." So a lot of it was just like understanding how to work with these tools and having the willingness to go in and try, and also the intuition about what are the guardrails that you need to provide in order for it to succeed.

   11. SHSonya Huang15:49

       Yeah.

   12. MGMatan Grinberg15:50

       Um, and so I think it was a combination of both of these things. The model's getting better, so you need to do less in the way of providing guardrails, but also developers lowering their guard and being like, "Okay, you know what? Let me go try and do these things. It's gonna go do things I don't like." And then also, there's a certain degree to which when Andrej Karpathy tweets about something, then every engineer suddenly is like, "Okay, you know, maybe this is true." And Andrej started to tweet about these agentic work. Early on, he wasn't as open to it, and then him being more open to it genuinely just changed some people's minds. Um, which is funny, but that's some of the things that go into behavior changes, like you hear it from people you trust. You start seeing it, you know, from people within your organization who are maybe a little bit more agent native, but that's, that's kind of these things together is what, what changed that.

   13. SHSonya Huang16:36

       And now we're all gonna be on Slack. [laughs]

   14. MGMatan Grinberg16:38

       We might, we might be on Sl- we might be pushing the limits of Slack-

   15. SHSonya Huang16:41

       Yeah

   16. MGMatan Grinberg16:41

       ... which I think is gonna be another interesting thing.

   17. SHSonya Huang16:43

       That's cool.

   18. MGMatan Grinberg16:43

       But

7. 16:44 – 26:42

   Harness Frontier Tactics

   1. MGMatan Grinberg16:44

      yeah.

   2. SHSonya Huang16:44

      That's cool. Okay, so September 2025, you launched the Droid CLI. You said frontier performance SOTA. What does that mean for you?

   3. MGMatan Grinberg16:51

      There's like the benchmarks, which have a very short half-life. Like anytime there's a good benchmark, it gets benchmaxed-

   4. SHSonya Huang16:56

      Yep

   5. MGMatan Grinberg16:56

      ... within like three to six months.

   6. SHSonya Huang16:58

      Yeah.

   7. MGMatan Grinberg16:59

      At the time, I think the one that we kind of championed when we launched and kind of it ended up becoming a pretty good benchmark was Terminal Bench. So prior to that, the one that was kind of leading was SWE-bench, which was, um, kind of took some open source projects and some examples of issues that were then solved. The problem with that was it was very focused on like Python and like scripting or like individual file changes, whereas Terminal Bench was more, one, it was in the terminal setting, so it was things like scheduling runs and things that were not just like changing the code file, but general software development tasks. And that was something that we ended up, you know, having really frontier performance on. Now it's like benchmaxed to the extreme to where it's like, I think, you know, models that come out now are like 90% on it.

   8. SHSonya Huang17:46

      Yeah.

   9. MGMatan Grinberg17:46

      And I think, um, there's a very short time horizon from putting out a good benchmark to then it being kind of in the training data.

   10. SHSonya Huang17:53

       What goes into building a great, and is it a, is it a great harness? And it seems like there's almost a lot of FUD in the ecosystem of, "My harness is better than your harness," and just-

   11. MGMatan Grinberg18:00

       Yeah, yeah

   12. SHSonya Huang18:00

       ... you know, "You need to own the model to have a good harness," or actually you're, you have a better harness if you don't own the model. Like-

   13. MGMatan Grinberg18:05

       Yeah, yeah

   14. SHSonya Huang18:05

       ... what's your mental model for, for-

   15. MGMatan Grinberg18:07

       Yeah

   16. SHSonya Huang18:07

       ... you know, benchmark maxing aside-

   17. MGMatan Grinberg18:10

       Yes

   18. SHSonya Huang18:10

       ... what keeps you at the frontier?

   19. MGMatan Grinberg18:11

       Yeah, so a couple, so some general things that matter are, um, the way you do caching. So, you know, cache tokens end up being like a tenth as expensive, and so one big piece of performance for a given harness is what, what is your, like, rate of, of token caching? Um, another example would be how do you perform while in compression or compaction? So typically when you're dealing with a, a long session, you're gonna exceed the context limit of the model itself, and so the harness will do some sort of, you know, summarization, compression, compaction, whatever you wanna call it. And the way that you perform during that compaction is a big determining factor of how good your harness is. Um, and tests that they do for that are like, you know, they call it needle in the haystack, where you have some long thread and maybe there's one piece of information that's really important. How often will your harness preserve-

   20. SHSonya Huang19:00

       Yeah

   21. MGMatan Grinberg19:00

       ... that through compaction? Other examples are, like, tool use or how does it use the environment to validate whatever work that it's doing? Um, these are things that you can kind of have individual metrics on and that we kind of have our own internal benchmarks to measure how do the out of, out of the box agents do versus how does Factory perform? I think one thing that, um, naively everyone believed initially was if you train the model and you build the harness, you're gonna make them better together.

   22. SHSonya Huang19:26

       Yeah.

   23. MGMatan Grinberg19:27

       And much to the chagrin of many of my friends at OpenAI and Anthropic, this is not true. If you build a harness that supports different models, that harness will be better.

   24. SHSonya Huang19:38

       What's the... Like, my intuition would be model harness co-design makes you better.

   25. MGMatan Grinberg19:41

       Yes.

   26. SHSonya Huang19:42

       What's the intuition for why it's actually not?

   27. MGMatan Grinberg19:44

       It's very analogous to the idea maybe like, I don't know, 10 years ago of if you were to be like, "Hey, I wanna train my personal AI," back in like ear- like ML days before, like, GPT-3, "I wanna train my personal AI. I'm gonna give it all of my data because I want it to know me." Turns out the answer was train it on the whole internet and it'll be so much better for you than if it were just trained on your data. So there's a sort of analog that emerges where it's what data is to a model, models are to a harness, where the more models you expose to a harness, you avoid overfitting that harness to the nuances of that model in particular. And there are certain intricacies about different models that you can learn from and then improve different models' performance in your own harness. And this was why, for example, we kind of stopped doing it because Terminal Bench got so, uh, benchmarked, but initially when, like, every new Opus or GPT model would come out, it would perform better on Terminal Bench in Droid than it would in Claude Code or Codex. Um, which is why, like, and this is something that, you know, I think was somewhat frustrating to... 'Cause ide- like from a lab perspective, you ideally want it so that it's better together because then that means you have to use their harness and you can't use a different one. But I think the reality is it, it, it's, uh, you know, having that multi-model harness ends up getting kind of frontier on, on all of those aspects.

   28. PGPat Grady21:05

       Is there a good, like, example or illustration of that? Conceptually it makes sense. Is there, like, an easy way to illustrate it?

   29. MGMatan Grinberg21:11

       Maybe, maybe a good example of it is, like, if you're familiar with the different behaviors of, uh, Opus and GPT 5.6 right now.

   30. SHSonya Huang21:19

       I am. He's on it.
8. 26:42 – 27:32

   Natural Language Routing

   1. MGMatan Grinberg26:42

      This part of the org, they're just vibe coding. They can use Gemini Flash. This part of the org, they're doing COBOL. We fine-tuned this great model to work on COBOL. Let's route to that when we're working on that part of the code base. Maybe this other part, we really care about reliability, so let's generate the code with OpenAI, test it with Anthropic, review it with, like, Gemini," things like that. And we can actually take in your routing procedure instructions in natural language. So you could even say things like it's not purely deterministic. It can even be like, "Hey, you know, Pat, I don't know, like, I don't know what he's doing," like t- typically-

   2. PGPat Grady27:16

      Give Pat Gemini Flash

   3. MGMatan Grinberg27:16

      ... give him Flash.

   4. PGPat Grady27:17

      [laughs]

   5. MGMatan Grinberg27:17

      Like, I don't know. Or like [laughs] or you know, I think we really need to, uh, avoid h- having them use open models because, you know, whatever reason, we don't like the way open models perform here. Um, and we'll do internal benchmarking to know which models are better at which of these

9. 27:32 – 29:26

   Open Models Catch Up

   1. MGMatan Grinberg27:32

      tasks.

   2. PGPat Grady27:32

      How close are the open models at this point? Which one's the best?

   3. MGMatan Grinberg27:35

      GLM 5.2 is incredible. Um, it's at the point where internally we have no token limits for our engineers, and like half of our tokens are open to open models.

   4. PGPat Grady27:45

      Wow.

   5. MGMatan Grinberg27:45

      Yeah. 'Cause they're just faster and they're cheaper. They're just as performant. And I think the thing that everyone gets wrong is everyone is comparing, like, GLM 5.2 to the latest model, like Opus 4.8 or GPT 5.6, but really they should be compared to Opus 4.7 or GPT 5.5. Um-

   6. PGPat Grady28:04

      Why?

   7. MGMatan Grinberg28:05

      Because generally the open models come later and they're, they're kind of a generation behind, and that's kind of the, the, the frontier models will be frontier. The question is, are the open models getting as good as, like, frontier minus one? And the answer is unequivocally yes, which I think is a really, really interesting outcome. It's great for consumers, and by consumers I don't mean, like, individuals, I mean the consumers of the APIs. Because if you're a, you know, a business that is doing in arch- like software engineering, your job is at a very high level to solve problems. And if we can allow you to solve those problems faster and with cheaper models that are just as performant, that means you can solve more problems. Like, that is a good thing. And it is a very good world where there is not, like, a monopoly on intelligence, but instead kind of a, a, a garden of intelligence that you can pick and choose, um, you know, when you'd like. S- something that we joke about is, like, you know, on this intelligence allocation thing, um, if you're, if you're trying to get a, a tutor for your daughter in algebra, you can probably find someone cheaper than Albert Einstein to be that tutor. Now, it might be that she eventually goes and becomes, like, a leading, you know, physicist or something, in which case, yeah, maybe let's, let's get Albert Einstein in there. But most likely you can get, you know, a high school student or something like that. Um, and it's probably much more cost-effective for you as well to do so. So...

10. 29:26 – 31:02

    Token Share Forecasts

    1. PGPat Grady29:26

       Since you guys do the model routing, like if you look at the, you know, if there's a pie chart that shows the complexion of models being used by your customer base today, what did it look like a few months ago? What does it look like today? What do you think it'll look like in a year?

    2. MGMatan Grinberg29:38

       Yeah. I will caveat this with saying that right now enterprises haven't gone too opinionated yet into the routing procedures.

    3. PGPat Grady29:46

       Okay.

    4. MGMatan Grinberg29:46

       This is something that will happen over the next six to 12 months, but right now they're just going from no router to router.

    5. PGPat Grady29:51

       I see.

    6. MGMatan Grinberg29:51

       That's kind of the first change. Then it's gonna be, like, the exact nature of the, of the routing. At the beginning of the year, it was less than 1% of tokens went to open models. In the first quarter, it became a single-digit percent. It has now crossed into being a double-digit percent of tokens. Um, now percent of tokens is not always the same as percent of cost 'cause the open tokens are cheaper. Um, but it is, uh, it's pretty crazy to see the, the growth there.

    7. PGPat Grady30:17

       Yeah.

    8. MGMatan Grinberg30:17

       What's your forecast? [sighs] My sense is that we will asymptote towards vast majority being open just because it provides you more optionality and it's cheaper, um- But that doesn't mean they're gonna be... Like, that's of token share, not necessarily of leverage share, 'cause maybe there are 1% of tokens that are incredibly, incredibly valuable, um, and are, like, very key decision-making, and then the rest are more, like, implementation tokens or kind of, uh, lower stakes, if you will. I don't think there's gonna be a world in which, like, it's ever gonna be 100%.

    9. SHSonya Huang30:52

       Yep.

    10. MGMatan Grinberg30:53

        I think the frontier of intelligence will inherently always be valuable for every business j- just 'cause the stakes are gonna get higher-

    11. SHSonya Huang30:59

        Yeah

    12. MGMatan Grinberg30:59

        ... and the kind of intricacy with which you think is gonna be more important.

11. 31:02 – 32:36

    Automating Low Leverage Work

    1. MGMatan Grinberg31:02

       But we'll be better at offloading certain tasks. And this is like you can loosely think of this, uh, already with the way orgs are structured, where, you know, in general, engineering leaders are more tenured engineers who in theory have, like, more wisdom and each kind of minute of their brain power is higher leverage, in theory. Um, and even, you know, you can also imagine, like, consider a human engineer and try mapping over the course of their day, like, how much brain power-

    2. SHSonya Huang31:30

       Yeah

    3. MGMatan Grinberg31:30

       ... they're using. And like, you know, it's probably gonna be really low for a lot of it, but then there are gonna be some moments where they're, like, going pretty high, like they're deeply concentrating and thinking about some, you know, systems design problem or whatever. All of those low leverage moments we wanna automate away, and, like, we wanna... Like, those, like, very high leverage moments sometimes, like, you know, we're referring to them as, like, the eureka moments or the moments where they're, like, doing something that's very high leverage. What if those aren't just moments, but what if those are, like, hours at a time? Because you don't have to deal with all the other stuff. And I think that's kind of the, the way to think about intelligence allocation is if you're an engineer and you're writing docs, that is such a low leverage use of your time. Like, you've become an expert in your craft, and you used to spend hours writing docs. Like, I remem- it was actually valuable. Like, I remember Stripe had so much alpha for just having incredible docs.

    4. SHSonya Huang32:21

       Mm.

    5. MGMatan Grinberg32:21

       But imagine all the other stuff those incredible engineers could do if it wasn't writing documentation. Like, we should live in a world where everyone can have docs as good as Stripe, and that is, like, strictly beneficial for everyone. And then the question is, okay, what do those really smart engineers do with their time once they don't

12. 32:36 – 35:56

    Pricing Beyond Tokens

    1. MGMatan Grinberg32:36

       have to do that?

    2. SHSonya Huang32:37

       Maybe this is a good time to talk about business model, given that, you know, especially with the rise of open-weight models, the cost differential-

    3. MGMatan Grinberg32:44

       Mm

    4. SHSonya Huang32:44

       ... um, I imagine that means very different things for your, for your cost structure, but very similar value delivered to customers. How do you think about, uh, business model and pricing?

    5. MGMatan Grinberg32:53

       Yeah. This is more what our customers want and need as opposed to what we want and need. So for example, I think right now usage-based is clearly the way to go. We wanna be aligned with, like, what they are doing and what we are doing. I think seat-based doesn't make sense, at least for what we are doing. My sense is that eventually we will change to outcome-based. Now, I don't think the enterprise is ready for that, and we've learned our lesson from those first two years. We are not going to impose things, right?

    6. SHSonya Huang33:18

       [laughs]

    7. MGMatan Grinberg33:18

       Um, but my suspicion is that, you know, in the 2030s things will probably look more like outcome-based.

    8. SHSonya Huang33:25

       Yeah.

    9. MGMatan Grinberg33:26

       Um-

    10. PGPat Grady33:26

        What does outcome-based mean for your market? What would be the definition of an outcome?

    11. MGMatan Grinberg33:29

        Yeah. So maybe here's a way to put it. So right now we char- we, we are usage-based. Like, the more tokens you use, you know, the more you pay, the more we get. Um, now, since we are model independent, we kind of, with our router, we are kind of pointing a token cannon at either OpenAI, Anthropic, AWS, GCP, you know, any one of these people. Uh, to a certain degree, this is like a really dumbed down version of a marketplace, where right now there is a, a buy- the buy side is an engineer who wants a task done, and then you have the model providers who are saying, like, either in benchmarks right now, they're like, "We perform at this cost and this performance, and then we determine who we go to for that given task."

    12. PGPat Grady34:15

        Yeah.

    13. MGMatan Grinberg34:15

        There's a world in which, you know, if it's so important to get these tokens, they might kind of unof- like, bid in a certain way of saying like, "Look, here is our cost for this task. We will get this task done at this cost no matter what." But they're pricing it such that, you know, they hope that they can make a margin there.

    14. PGPat Grady34:33

        Yeah.

    15. MGMatan Grinberg34:33

        If they price it wrong, they're at a negative margin. If they price it right and win the bid, then they get the positive margin. And the way you determine if the task was successful is by some validation loops, 'cause no one is using these tools anymore where it's just like, "Write me code." Great. Thank you. It's generally, "Write me code, and here's how I know it was done well."

    16. SHSonya Huang34:51

        Mm-hmm.

    17. MGMatan Grinberg34:51

        And similarly, if you are, like, a model lab and you are given, "Here's a task. Here's the validation criteria," you'll be able to say roughly how much you think you would be willing to pay to get those tokens. Uh, so and, you know, you wanna have some, some margin on that. And then in that world, that's basically, that's a way to, that you kind of dynamically shift from usage-based to outcome-based. Um, I think that there are so many questions with this, and this is very much forward-looking. Um, but I think there's a lot of questions about how do you subdivide tasks. You know, divvying that up I think is something that's not obvious.

    18. PGPat Grady35:22

        Yeah.

    19. MGMatan Grinberg35:22

        But as these tools get better, doing things like that actually become way easier.

    20. PGPat Grady35:25

        Yeah. That's fascinating.

    21. MGMatan Grinberg35:26

        Um, yeah.

    22. PGPat Grady35:27

        Yeah, if you can scope a task and then create a competitive marketplace, that'd be a fascinating version of the future.

    23. MGMatan Grinberg35:32

        Yes. And as a user, it then creates an incentive to be very thorough in your validation criteria.

    24. PGPat Grady35:36

        Yeah.

    25. MGMatan Grinberg35:36

        'Cause like, you know there are stories of, like, you know, you ask an agent to, like, fix my code and it deletes your code.

    26. PGPat Grady35:41

        Yeah. [laughs]

    27. MGMatan Grinberg35:42

        It's like, you know, the solution is just get rid of it all together.

    28. SHSonya Huang35:44

        It's like that Silicon Valley episode.

    29. PGPat Grady35:45

        No bugs.

    30. SHSonya Huang35:46

        It's crazy how prescient it was.
13. 35:56 – 43:43

    Software Factory Vision

    1. PGPat Grady35:56

       Good reference.

    2. SHSonya Huang35:57

       Um, maybe zooming out a little bit, you named the company Factory. Actually, you named it Droid before, before Factory.

    3. MGMatan Grinberg36:02

       That's right.

    4. SHSonya Huang36:03

       But you named it Factory before this concept took off, and now it feels like everybody wants to build a software factory. Where do you think we are today in, in terms of the building of software factories, and how close are we to the ultimate vision of a software factory?

    5. MGMatan Grinberg36:16

       Yeah. Everyone has a software factory, whether they know it or not. It's just a very inefficient one. So it's kind of like it feels like, you know, pre-industrialization where, like, you know, people were manually, like, you know, sewing things together or, like, woodworking or whatever it might be. And These things are very inefficient. Like right now, if you go to an organization that has more than 10,000 people and you were to ask about the process by which they decide and release a feature, there is like hundreds or maybe thousands of people in that process, and most likely they couldn't even draw it for you. Like, there's very low likelihood that they would know what, what that process looks like. Um, that is not because they think that is the right way of doing things. That is just kind of the nature of building large software as it is kind of today. But with these systems, so much tribal knowledge can be codified. So much of this stuff that typically would require, "Oh, we need to ask this guru who's been here for 30 years who has the wisdom. Oh, we then need this approval and that approval. Oh, and I forgot, there was some doc that said we always have to do this checklist." Um, and it relies so much on kind of human behavior and, like, redundancy. So much of that can be automated and refocused on, like, what actually moves the needle for our business. And I think this move towards software factories is a move towards how do we figure out what are the actual inputs that determine what features we need to build, and that might be inputs from the customers, inputs from the market, inputs from like, you know, product leaders at the company. And let's be very clear, these are the signals, the inputs that we are taking in here. Okay, great. We have those signals. Then what is the process by which we build this? Um, and really, like, mapping out the, like, assembly lines of how you are building software is really important because then you get to close the loop and say, "Did this actually deliver outcome for our business?" Talking before about the tokenomics, if you're that CIO and you're faced with that question of where do you put every incremental token, really the question two years from now is gonna become where do you put every incremental dollar? And so you're gonna have to be, be asked, do you put that incremental dollar towards headcount or towards tokens? And if tokens, to where in the org? And these are things that you can only really know when you have these kind of feedback loops that give you examples of like, "Hey, by the way, we made those decisions based on this data, and it did not matter at all. We added these new features and no one cared. It didn't create more retention, it didn't create more usage," or whatever metrics that business is looking to optimize. And the only way to do this is, like, you need kind of more rigor and more process. It almost feels like, like 10 years from now, we're gonna look back at this previous era of software, and it's gonna feel like businesses in, like, ancient times where they didn't do accounting. It is like-

    6. PGPat Grady38:58

       It's gonna be like, it's gonna be like marketing in the day of Mad Men, right?

    7. MGMatan Grinberg39:01

       Yes.

    8. PGPat Grady39:01

       Where it's, like, all creative and you have no idea what's actually working.

    9. MGMatan Grinberg39:04

       It makes no s- like it's like, "Oh yeah, let's ship that feature. Oh, I think it went well. Like, yeah, we had-- I got some metrics on that."

    10. PGPat Grady39:10

        Yeah.

    11. MGMatan Grinberg39:10

        It's like, no. If you guys read the, the blog post that Jack Dorsey put out-

    12. PGPat Grady39:15

        Yeah

    13. MGMatan Grinberg39:15

        ... about how every company is like an AGI.

    14. PGPat Grady39:17

        Yeah.

    15. MGMatan Grinberg39:18

        There's also this degree to which if your company is an AGI, you wanna optimize the weights.

    16. PGPat Grady39:23

        Yeah.

    17. MGMatan Grinberg39:23

        You wanna figure out what nodes are doing what things, which are load-bearing, which are not, which need more tokens, where do you need more nodes. And in order to do-- like, you don't train a model by vibes. I mean, okay, actually you kind of do.

    18. PGPat Grady39:36

        [laughs]

    19. MGMatan Grinberg39:36

        But you don't, I guess, more importantly, you don't do backprop in a model by vibes.

    20. PGPat Grady39:40

        Yeah.

    21. MGMatan Grinberg39:40

        Like, you are running those actual, like, calculations, and you are seeing when we change this node, what happens. Now, you might be making bets on how to change the model by vibes, but you, like, you're-- it's pretty, like, mathematical in what you are doing. Meanwhile, at companies, you know, people are determining token budgets just by shooting from the hip. People are laying people off by shooting from the hip and just being like, "Oh yeah," like 20,000. There is no way there is science to laying off 20,000 people. That is just like, "Here's a chunk, and let's just see what happens." Instead, I think in these organizations, the way they can do things is much more mathematical of like, this part of the business matters a lot and does better if we give it more tokens. It doesn't actually matter if we give it more humans, so let's give them more tokens. There might be other parts of the business where actually giving them more tokens doesn't matter, but more people matter, because if we build more relationships with our customers and deeper relationships with our customers, that matters. But these are things that we're gonna need, like, quantitative insight on, and you need a software factory to do that. Otherwise, you're just, like, shooting from the hip and just guessing, which won't work as well.

    22. SHSonya Huang40:44

        In the limit, how much do you think people will spend on tokens versus on engineering headcount?

    23. MGMatan Grinberg40:50

        It'll depend on the business.

    24. SHSonya Huang40:52

        Mm.

    25. MGMatan Grinberg40:52

        I think every business will have a balance, and it just depends on, like... look, they're just gonna be d- like an easy example is generally salespeople, they probably don't need that many tokens if they're good salespeople. 'Cause generally the, where they provide the most alpha is, like, when they're in the seat face-to-face with their customers, talking about the customer's problems, understanding, you know, how they build software, in our case, and how we can make that, you know, more efficient, more productive. They can use tokens a little bit of like, oh, whatever, generate them some, you know, AI debrief, take some notes, like help them with the follow-up. But like, that's so minimal, the number of tokens, it basically doesn't matter. Like, if you add more tokens to the sales team, it probably won't change their output. If you add more humans to the sales team, it probably will. Meanwhile, engineering teams are pretty different, where engineering teams, generally, it seems like the m- you want people to own an outcome end to end, but then if you give them more tokens, they can produce a lot more. And so it seems like there-- and then there's a lot of kind of places in between of like operations, finance, marketing. These are places where are neither here nor there, where I think they're, they're somewhere in between and it kinda depends on your business. But I think every business is going to have to ask, like, "What is our core competency?" Something that we see a lot in the market, or we used to see, and now they finally kind of hit reality. What we used to see is, "Oh, like we're gonna build our own, like, software development agents." And we're like, "Okay, like you're a, like a consumer, uh, like logistics company. Like, are you sure you wanna do that?" They're like, "Yeah, yeah. We're, this is a core, we have to do this." And it's like, okay. And then six months later it's like, "Wait, actually, this is not a core competency for our business. We don't wanna hire, you know, AI engineers to be doing this. Our core competency is, you know, consumer logistic. That's what we wanna focus on." And I think this is an opportunity for every business to Double down on their core competency and what matters for them, and then procure externally whatever it is that doesn't matter for them.

    26. SHSonya Huang42:40

        Yeah.

    27. MGMatan Grinberg42:41

        Like a trivial example of this is like, I don't know, in the days of the early internet, you probably had to be a programmer to build a website. And like websites generally help if you're a pizza shop 'cause you wanna have, you know, people come to your pizza shop, they wanna be able to or- like whatever. At that time, would you say it was a core competency of like a pizza shop to have engineers? Like certainly not. Like that is kind of a byproduct of like a brief moment in time, but then there were companies out there that help you build a website. You don't need to be technical, and then this is why we live in a world where like most pizza shops don't have an engineering department, which I think is probably a good thing. Um, and I think similarly, a lot of businesses have dealt with the reality of if you want to do X, Y, Z other thing, you have to bring in people of this type of role. But I think that's been like something you had to do, not because it's a core competency of the business. And allowing businesses to focus and double down on the things that they are best at, I think is gonna be good for the consumers of their business. And so I think we're just gonna see like a lot, like ruthless refocusing on what actually matters, um, which is gonna be cool to

14. 43:43 – 46:13

    AI Transformation Playbook

    1. MGMatan Grinberg43:43

       see.

    2. PGPat Grady43:43

       Well, on that, so, you know, every company kinda has to go through this process of reinvention. You know, 10 or 20 years ago, people talked about digital transformation, and I don't know if anybody's given it a buzzword now, but AI transformation, something of that sort. Um, couple years ago, you ran into a bunch of organizations that just weren't ready to deal with autonomous agents. Things you've seen your customers start to change, and so the question is, when you look at your customers as they kinda go up this maturity curve and sort of reinvent themselves for the future, um, any good like tricks or techniques that you've seen them use to repot themselves a bit?

    3. MGMatan Grinberg44:19

       Yeah, I mean, I think, um, surprisingly, like the companies that have been doing like company-wide hackathons really end up doing well. It seems like relatively trivial, but like just setting aside a day for everyone in the workforce is just like build shit with AI. It really sets the tone and sets the pace.

    4. PGPat Grady44:39

       Sonya's giving me a look 'cause [laughs] she's-

    5. SHSonya Huang44:41

       I, I tried to force him to build several coding agents.

    6. PGPat Grady44:43

       [laughs]

    7. SHSonya Huang44:44

       It, it didn't go so well.

    8. MGMatan Grinberg44:45

       We'll work on it. We'll do it after this one.

    9. PGPat Grady44:46

       I, you know, I-- we gave it a great effort.

    10. SHSonya Huang44:48

        [laughs]

    11. MGMatan Grinberg44:49

        But that's it. Like it literally just setting aside the time to like do it, and like even if it fails miserably, like it's fine. And also like the orgs that are okay with failing.

    12. PGPat Grady44:57

        Yeah.

    13. MGMatan Grinberg44:57

        Like it feel like, feels like there are some who are like, "We need to do it exactly right. We need to make the right decision from day one. No er-"

    14. PGPat Grady45:04

        Yeah.

    15. MGMatan Grinberg45:04

        Like, you're gonna make mistakes. Everyone is going to. And the orgs who are kind of leaning into it and embracing it to a certain degree, I think are succeeding. Like one of our largest customers is EY. EY is not necessarily known to be like at the absolute frontier of AI, but I think for them, they were just like, "Look, this matters. We were kind of-- There have been other transit, transformations that we relate to. We're not gonna be late to this. Like we're just gonna go in. We might mess up," but like obviously respecting like secur- the things that you're not allowed to mess up-

    16. PGPat Grady45:32

        Sure

    17. MGMatan Grinberg45:32

        ... you know, put those aside. But like let's go and get our engineers to mess around and build this stuff and see where it breaks and understand what they like and what they don't like. Um, I think that really matters a lot in the ones that we're seeing succeed, and also the ones who are like pretty bold in reinventing the processes that they've put in place.

    18. PGPat Grady45:51

        Mm.

    19. MGMatan Grinberg45:52

        And just saying like, "Hey, it's a, there's no sacred cows. Like let's, let's put this aside, try something out. If it doesn't work, we'll put that sacred cow right back." Um, and I think that's, that's been kind of a determining factor there. Um, and when it comes from within. If it comes from the board, probably not gonna go well.

    20. PGPat Grady46:06

        Yeah.

    21. MGMatan Grinberg46:07

        If it comes from within like the tech team or the, the ICs or the leadership, that's when we see it go better.

    22. SHSonya Huang46:12

        Hmm.

15. 46:13 – 51:30

    Async Agents And Optimism

    1. SHSonya Huang46:13

       Do you have any predictions for the most important changes that are gonna happen in your space over the next, call it, 12 months?

    2. MGMatan Grinberg46:19

       A lot of AI consumption's going up like crazy, and everyone's super, super excited because their re- revenue's going wild. Like a lot of this is synchronous usage. In other words, like if everyone woke up sick tomorrow, like a lot of Claude Code usage would be zero 'cause it's all just, "Hey, Claude Code," or, "Hey, Codex," or, "Hey, Droid," right? I think in 12 to 24 months, like 90% of tokens will be asynchronous tokens.

    3. PGPat Grady46:43

       Mm.

    4. MGMatan Grinberg46:43

       So these are gonna be, you know, droids on their own autonomously being like, "Hey, here's some signal that I found from a customer. Let's go fix it," or, "Let's go create a first pass solution to this." And I think that is gonna be where the real like agent native stuff begins 'cause right now we're still kind of in like co-pilot mode. Like if you're going to an agent and say, "Hey, go do this for me," it is more agentic because it's not gonna come back and ask you a ton, but it's still like you are kicking it off. Like yeah, if you guys have ever been to Tesla's factories, which was one of the sources of inspiration for the name, is like it's just robotic arms everywhere going and doing stuff.

    5. PGPat Grady47:15

       [laughs]

    6. MGMatan Grinberg47:16

       Like it's not like there are people there like going and, you know, attaching the widget to the thing. Um, and this idea of like a dark factory where like the lights are off and things are just happening, that is where software development's going. That's kind of where the, the name came from is like, you know, Elon was always talking about the factory is the machine that builds the machine.

    7. PGPat Grady47:32

       Yeah.

    8. MGMatan Grinberg47:32

       And that's been something that we took to heart. Um, and I guess also that combined with his whole thing about how you're destined to become the opposite of your name.

    9. PGPat Grady47:41

       Hmm.

    10. MGMatan Grinberg47:42

        Um, and in our case, you know, factory becomes artisanal, which is kind of a good, uh, a good flip there, so.

    11. PGPat Grady47:48

        What's your most optimistic version of the future, both for Factory and for the world at large?

    12. MGMatan Grinberg47:53

        So I think short term, there's gonna be a lot of turbulence because I think a lot of companies have misallocated resources pretty poorly. There's been a lot of bloat, um, and I think the correction that's gonna happen there is gonna be really painful for a lot of people, and I think that's something that I think every AI CEO should really bear much more responsibility than they currently are for. Um, and also figuring out ways to like address and kind of ameliorate in some way because this is something that's gonna be very painful for a lot of people. Now, I al- I have optimism that we can actually address that faster than we think. We just need to start now in terms of addressing that. Now, the longer term and why I think this is a good thing is- And why I don't believe at all like, you know, the BS that people are saying of, "Oh, engineers are going away." Generally, there is a huge number of problems in the world. A large subset of those problems can be solved with software. A small subset of those problems are currently being solved with software. And so in the short term, this means that, okay, first there's a given problem that was over-allocated engineering resources, so okay, we need to reallocate those. Reallocate those is a very kind of cold way of saying some people are gonna lose their jobs. But I think the, the thing that's gonna happen in the longer term is we need engineers. Engineers are some of the best systems thinkers and the best problem solvers, and there are so many problems that can be solved with software that are not being solved with software. And so that means that we are going to take those engineers and have them go and solve problems that previously were not being solved. That is such a net good for the world. Because again, there are so many of these problems that we are not solving, and also there's so many problems that we are maybe solving but with really shitty software. And like this is gonna enable people to solve it with incredible software. And, you know, the vision for Factory is that we are kind of the, the factory that allows them to go and build this incredible software to solve these different problems. And these problems range from like things that are trivial to, you know, like government software typically is not very good.

    13. SHSonya Huang49:51

        Yeah.

    14. MGMatan Grinberg49:51

        Whether it's like DMV or like IRS web, like all that stuff is generally a pretty poor experience. Um, we don't need to live like that. Like we can live in, we can live in a world where all software is really fantastic. Um, but also things like, you know, pharmaceutical research. Like so much that goes into solving diseases is not just like a biology problem. A lot of it requires the best software engineers in the world, and previously those problems haven't allocated the right dollars to attract the best engineers. But now because of what's happening, I think we will be much more closely allocated to like these are the biggest problems, let's get the best minds and the best problem solvers to solve that. Um, I think it's kind of our job as an industry to do that relocation, reallocation as quickly as possible. So it's not 10 years, but maybe like six months or a year.

    15. SHSonya Huang50:40

        Wonderful. Matan, I think the clarity and consistency of your vision over time has just always been very inspiring, and then just seeing how much you've grown as a leader and how much Factory has grown as a company, even since the last time we did this Training Data episode, it's truly a- awe-inspiring. So thank you for, for joining us again to share what you're up to.

    16. MGMatan Grinberg50:57

        I appreciate it a lot. Thank you.

    17. SHSonya Huang50:59

        Thank you. [outro music]

Episode duration: 51:31