How to Reorg After AI Changes Everything | Block's Owen Jennings on the a16z Show

1. 0:00 – 6:06

   Introduction

   1. OJOwen Jennings0:00

      The biggest moat is gonna be which companies understand something that's super hard for other people to understand. And if your answer to that is, "I don't know," then you maybe could get vibe coded away.

   2. DHDavid Haber0:10

      Block was one of the first to make a pretty drastic decision in cutting forty percent of the workforce. What led up to that decision?

   3. OJOwen Jennings0:17

      There's been this correlation between the number of folks at a company and the output from the company for decades and decades. I think that basically broke, and what we're seeing is that one or two engineers who is on the tools is able to be ten, 20, 100X more productive. Over time, it's, like, pretty obvious that these systems are just gonna be so much better than, like, having 1,000 humans who are doing that work. I, I do believe that fundamentally, for a given product or for a given roadmap, you're gonna need fewer engineers, fewer designers, fewer PMs. I think that's, like, very, very clear.

   4. DHDavid Haber0:49

      So you show up on Monday, forty percent of the company's gone. What's the most meaningful difference in how you're operating?

   5. OJOwen Jennings0:53

      I think the biggest thing is-

   6. DHDavid Haber0:58

      What does it actually look like for a large public company to restructure itself around AI? Owen Jennings is the business lead at Block, where he oversees product, operations, and customer support across Square, Cash App, and Afterpay. Before this role, he was the CEO of, uh, of Cash App during its critical scaling period. And recently, uh, Block executed a roughly forty percent reduction in force, and they've been pretty candid about AI being a critical component of that decision. Owen has gone through the AI transformation at scale across product lines and business units, and so we're gonna dig into the, that decision around the RIF, how Block has adapted, the current and future state of the business. So thank you so much, Owen. Welcome to the stage. [audience applauding]

   7. OJOwen Jennings1:46

      Thanks, man. Awesome. [laughs] Um, so you know, Jonathan, I think, did a, an amazing job kinda setting the stage, you know, for this conversation, uh, you know, talking about how important it is to be founder-led. Uh, you know, Block was one of the first to make a pretty drastic decision in, in cutting forty percent of the workforce. Um, maybe walk us through kind of what led up to that decision and how you thought about it. Sure. I, I think, I would pro- It probably starts two or three years ago. I think one thing about Jack [clears throat] is I, I find Jack to be generally right and generally early, uh, sometimes very early. Um, and I think that's flowed through Twitter, Square, Cash App, Bitcoin, et cetera. And so we were pretty early on the agentic development side. We actually launched Goose, which was the first agent harness, at least that I know of, um, in early 2024, and that started to augment how we approached software development, uh, how we thought about internal tooling. And I would say that over the, over that period, '24 and '25, it was, like, pretty meaningful progress. Um, and then late November, first week of December, it was just-- there was a binary change. You basically have Opus 4.6. You have, uh, Codex 5.3, and essentially you get this shift where I think the, the, the tools and the foundational models were pretty good at writing code, especially for new ventures and kind of, like, green space. Um, it became clear almost overnight, maybe in a couple of weeks, that now they're incredibly capable working with existing complex code bases. Um, and so there was a massive paradigm shift where, at least from my perspective, th- there's, there's been this correlation between the number of folks at a company and the output from the company [lips smack] uh, for, you know, decades and decades. I think that basically broke the first week of December, and what we were seeing is that one or two engineers or a, a designer and an engineer who is on the tools, quote unquote, a-as we say, is able to be 10, 20, 100X more productive. And so that's really what led us to make the, the decision a few weeks ago. We spent Q1 discussing, like, what does this mean? Fundamentally, what does this mean in terms of how we're gonna build products, how we're gonna build software for customers, and then also, um, how we're gonna run a company? What is it gonna mean to actually run a company? And we spent Q1 as an executive team, uh, with Jack, um, working through that, uh, and ultimately that's what led us to this place where, where we, we did a reduction in force that was, you know, slightly greater than, than forty percent. And that wasn't even, uh, you know, to the, to the conversation we were just having. The tools were flowing through really meaningfully on the development side, and so the cuts were way larger on the development side. If you think of something as outbound sales or account management, um, the cuts were, you know, fairly de minimis.

   8. DHDavid Haber4:47

      Yeah.

   9. OJOwen Jennings4:47

      Um, and so that was really what we were reacting to.

   10. DHDavid Haber4:51

       C-Can I push you a bit on this a, a little bit? I mean, A-Alex, when he kind of introduced the, you know, the conference, uh, just, you know, an hour ago, talked about the ZIRP period. Uh, you know, h-how much of the RIF was sort of overhang from 2021 kind of over-hiring versus AI and, and kind of, like, the product, actual productivity gains gonna be in the business?

   11. OJOwen Jennings5:10

       Like, uh, if you look at where we were from a, from a gross profit per full-time employee basis from, like, 2019 through 2024, we were basically, like, right in the middle of the pack with all of the, um, [lips smack] uh, with all the competitors. Um, if you look at last year, I think we were kind of, I don't know, second quintile or something like that. I think it's basically, like, NVIDIA and Meta that are ahead of us. Um, and then when you look at the composition of what we did, if you thought it was, like, cruft and bloat and so on and so forth, then, like, this RIF would've accrued to the operational teams and the, like, like, that sort of stuff. These were really, really meaningful cuts on the development side.

   12. DHDavid Haber5:46

       Mm.

   13. OJOwen Jennings5:46

       You don't make really, really significant cuts on the development side if you're not seeing a technology and a tool that's just fundamentally changed how we build. I mean, we're, we're, like, we're not writing code by hand anymore.

   14. DHDavid Haber5:58

       Mm.

   15. OJOwen Jennings5:58

       That's over. That's done. Um, and so, uh, so anyway, everyone has their narrative. Um, uh, uh, it, it's largely not true.

2. 6:06 – 9:08

   How the RIF Actually Executed

   1. DHDavid Haber6:07

      Um, so maybe just walk through, like tactically, how did you actually execute, you know, this, this transition, you know, culturally, you know, operationally in the business?

   2. OJOwen Jennings6:15

      So I think-- So we're, um... The, the, the nice part about this RIF, uh, relative to some other, you know, things that have happened at Block or at other companies is we were coming from a position of strength on a, on a profitability and operating income side. And so sometimes when it's really financially motivated, you know, the CFO or the CEO says, "Okay, we need to do a sixteen percent RIF in order to like hit this, hit this target," and, um, that wasn't the case at all. We said, "What should the org look like given how these AI tools are flowing through now and what we expect to happen in the, in the coming months and quarters?" We had some core principles. Um, the first one was reliability. When you do something this size, worst case scenario is you have an outage or you go down. So that's like P zero zero, not acceptable at all. Obviously, you know, things have been great over the past several weeks, which is fantastic. Second is building trust with customers and, um, compliance and navigating the regulatory environment. We all operate in a super complex, nuanced regulatory environment. That's a non-negotiable. We have to make sure that we're, that we're doing, we're doing right there. For instance, like we, we basically did not touch our, our compliance team and our compliance technology team. Even if the tools are there, it's like let's not take any risks. And then third was let's continue to drive durable growth. So there's things that are on the roadmap that we already know that we're building. We need to continue to do that. We know that it might be a squad of three people instead of a feature team of fourteen who's building that. We wanna make sure we're continuing to build those features and that we're continuing to make longer term bets. And then we built up the org from scratch, and in some areas, like, um, the regulatory council team or the SDR/BDR team, the org looked pretty similar to how it looked in January. Um, on the development side, it looks completely, completely different. Um, and then, you know, from a, from an execution perspective, um, you know, we thought very deliberately. Obviously, I've been at the company twelve years. A, a number of folks who we parted ways with are friends and colleagues for, for, you know, more than a decade. Um, we were in a position we were able to be generous in terms of, you know, the, the severance packages that we gave. We didn't cut people's technology access instantly, which can suck. Uh, we chose to have an all-hands with everybody at the company, so Jack and the executive team were, um, you know, looking each other in the eyes and explaining this decision and explaining the, the drivers behind it. And, um, I, I think that that-- It was on a Thursday. I think like the Friday, Saturday, Sunday, there's a lot of shock, uh, dealing with ambiguity. Um, and then what we've been doing is, uh, we massively reduced the number of meetings we have, probably like seventy or eighty percent, so I now have time to like build and work, and it's not back-to-back meetings. We're also meeting with the company every week, so we have like a one- or two-hour all hands with Jack every, every Monday. And it just feels like we're, we're smaller, we're leaner, we have fewer layers, we have larger spans, and it's, it's been back to building.

3. 9:08 – 12:57

   The Most Meaningful Difference in How We're Operating

   1. DHDavid Haber9:08

      So you show up on Monday, forty percent of the, of the company's gone. Like what-- H-how is-- What's the most meaningful difference in how you're operating? I don't know. Maybe it's in the EPD org or elsewhere.

   2. OJOwen Jennings9:17

      Um, I think that there's a, there's a, there's a few different components to this. I think the biggest thing is it-- So o-one concern that I have with like how some of these org changes might flow through the tech industry is that, and it, and it gets back to the, to the founder-led point. If you're not founder-led and you don't have the, the ability to be bold, then you're gonna probably take a more incremental approach. And so the way that that's gonna feel is like you do a fifteen percent RIF, and it's like, "Oh, it's fine." And then you do another fifteen percent RIF, and then culturally that's just like devastating for your team because there's always this like pending RIF looming, looming over your, over your shoulder. Um, this was obviously a decision to go in a different direction. I think one of the benefits that we got from this is like we were already seeing a pr- a very meaningful increase in AI tool usage, especially on the development side. This is just a massive forcing function. Like if we're building, um... Okay, we're, we're building Money Bot, and we wanna roll Money Bot out to fifty percent, and there used to be a team of fifteen people working on it, and now there's a team of four people plus two thousand dollars on the tokens. That-- This is like un-unlimited access to tokens, and you can use fast mode on Claude Code. Um, so now you have four people plus the tools. It's like, okay, well, you need to have eight instances of Goose up, and you need to shift your workflow from sequentially working through a PR, submitting it, getting a review, making the change, to I have fourteen agents who are building PRs on my behalf right now, and I'm gonna context switch between all of those. And it's not just, uh, on the software development side. It's for PMs too. It's for growth marketers too. The biggest shift, uh, uh, myself included, I, I have, you know, countless agents running right now that I have to go, I have to go check on. Uh, it's, it's not, um, it's less of a linear workflow, and it's more of like in the background there's ten or twenty agents who are doing a whole bunch of stuff, and then I have to check in on the work and nudge it and change it and what have you. And then I can commit it to GitHub, and I can, I can get the markdown file. We can put it in the source of truth, and we can move on.

   3. DHDavid Haber11:16

      Yep. So we, we have a lot of, you know, public companies in the audience. We have a lot of founder-led businesses in the audience. Do you expect other companies to kind of follow a similar path? And, and, and I guess what conditions need to be in place for that to be successful?

   4. OJOwen Jennings11:29

      I don't, I don't, I don't necessarily wanna... Like I, I talked at the beginning about, um, the groundwork that happened in '23, '24, and '25. Like we built this agent substrate Goose, and then we built a lot of tooling at the company on top of it. We have a agentic operating system, internal only, called G2, where anyone can automate any deterministic workflow. So anyway, there, there, I think there's work to do to, to be successful. I would expect many companies are doing that work. So some of them are incredibly, um, far ahead than, than others. Um, and so I, I, I don't know what to expect. What I will say is like t-to the extent that... I, I do believe that fundamentally for like a given product or for a given roadmap, you're gonna need fewer engineersFewer designers, fewer PMs. I think that's, like, very, very clear based-- A-after, like, December. [sniffs] Um, that doesn't necessarily mean that there's gonna be fewer engineers, designers, and PMs in the world. Um, it's like the classic Jevons paradox thing where I, I think that there's probably now just a superset of things that, that can be built. Um, so I don't know. A g- a, a, you know, a given tech company might be, might be way smaller, but there might be fifty or a hundred more tech companies. Or you're gonna start getting this development working in, in sectors and, and areas where that hasn't historically been the case. [lip smack] Um, but I, I'm not here to, to predict the future. I'm focused on Block.

4. 12:57 – 17:09

   AI Infrastructure Build Across the Org

   1. DHDavid Haber12:58

      Uh, fair. You, you talked a bit about kind of the-- some of the AI infrastructure you build. Maybe you can g-go into a bit more depth, uh, you know, both in how it's impacting the kinda technology org. I'm also curious about, you know, how you're using AI in, in other parts of the business. You oversee ops, customer support.

   2. OJOwen Jennings13:12

      Yeah. Um, so I got asked at a investor conference, uh, last week, like, "How is AI affe- like, flowing through Block?" And to me that was like asking, um, "How are computers flowing through Block?" Uh, [chuckles] like, it, it's, it's a, uh, fundamental inbuilt thing that has changed o-o-in, like, a binary way over the past eighteen months, and then feels like it changed all over again in the past four months. Um, so I'll break it down into internal and then external and how we're thinking about our products, what we're putting in customers' hands. And then I can talk a little bit about the, the future and where we think things are going. So on the internal side, th- I think the biggest difference is the shape of the, of the org. So we used to have kind of like a classic hierarchical, uh, structure. It, it was functional, um, which was great, but it was, like, fairly standard if you, like, averaged through a bunch of medium-sized tech companies. Um, and so you would have kinda eight server engineers, four client engineers, a PM, a designer, and you would work linearly through your roadmap. [sniffs] Now we have, um, small squads, so squads of, like, one to six people, [lip smack] um, so meaning-meaningfully smaller than the other teams would be. And we have way more flexibility and, and fluidity, where a given squad can work a few cycles on this product, get it live, and then a cycle on this other product, [lip smack] um, which is different than how things worked a year or two ago, where it's like, "I'm on the banking team. I'm gonna be on the banking team forever." We also have way fewer layers. So on the development side, I think we probably cut our layers by, I don't know, fifty or sixty percent. Like, on the product side, I only have, I think two layers, maybe three layers in a, in a couple of places. And so information is flowing, um, way more freely. I think that then in terms of how we actually build on the development side, things have changed. I think everyone's probably seen, you know, every, every CEO out there is going on Twitter and showing their, like, green dot on, on, uh, on GitHub. Um, but that's real. Like, all, all of our designers are, are shipping PRs. All of our product managers are shipping PRs. That's not that interesting anymore. I think more interesting is that we have, uh, internal tools that are similar to Claude Code, but they're, like, more plugged into our infrastructure. So we have a tool called Builder Bot. Builder Bot is just autonomously m-merging PRs and actually, like, building features to a hundred percent. We've had some fairly complex features that are built to a hundred percent. More often than not, it's building them to, like, eighty-five or ninety percent, and then a human who, who has a lot of context and understands does, like, the final, the final ten percent. So that feels really, really different. The ability to go from, um, [lip smack] to go from idea to, like, this is in the hands of a hundred thousand or a million customers has been compressed massively since, uh, since December. Outside of development, I would say most of what we're seeing is, like, anytime there's a deterministic workflow, we're, we're able to automate that. And so generally at a at-scale tech company, you have individuals who are working queues. Um, a lot of that is just being completely automated away. Like, from a customer support perspective, this is not new, but, you know, our chatbots and, and AI phone support and, and whatnot are automating a, a majority of inquiries that we get. And then it gets into, like, um, product operations and risk operations and compliance operations. Any sort of decisioning, like, generally, um, generally the, the, the models and the agents are gonna do a better job than humans. Right now, I think it's critical that we have a human in the loop. Uh, that's, like, the key kind of buzzword, uh, when you talk to, talk to partners and regulators and, and what have you. Um, but over time, it's, like, pretty obvious that these systems are just gonna be so much better than, like, having a thousand humans who are, who are doing that work. [sniffs] So that's on the internal side. [lip smack] Um, on the, on the product side, I

5. 17:09 – 20:00

   The Shape of the Business: Square, Cash App, Afterpay

   1. OJOwen Jennings17:09

      think that w-

   2. DHDavid Haber17:10

      May- and maybe just catch people up on kinda the shape of the business. Obviously, you have Square, you have Cash App. You, you made a big acquisition in Afterpay.

   3. OJOwen Jennings17:16

      Sure.

   4. DHDavid Haber17:16

      What do those businesses look like, and then, yeah, how are they kinda changing with, with AI?

   5. OJOwen Jennings17:20

      S-sure. So, um, so we used to operate in a business unit structure. So Square used to be kind of its own business unit with its own CEO. Cash App was its own business unit with its own CEO. Um, that wasn't leading to the right outcome. So about eighteen months ago, we functionalized the company, just meaning that all of engineering rolls up to our head of engineering, all of design to our head of design, all of product to me. So we have a financial platform team that spans the entirety of Block. We have a business platform team that's doing a lot of this automation that spans the, the entirety of, of Block. And then increasingly, we're building features and products that actually connect the Square side, the Cash App side, and the Afterpay side. And so naturally, you're, you're building technology and you're building infrastructure that is not, um, brand specific, and that's actually, like, kind of central to our, our overall strategy and, and, and overall thesis. Um, [lip smack] But yeah, I mean Ca- Cash App went from-- When I joined Cash App in twenty sixteen, uh, we had just, just started to, to figure out how to monetize and had our first dollars of gross profit, and now I think Cash App's probably like s- I don't know, si- sixty-ish percent of like overall gross profit at the, at the company. So overall been, been growing at a healthy clip over the past decade. Um, but, uh, Cash App and Afterpay have definitely been growing, um, m- more quickly. But increasingly we're trying to think about things from an ecosystem perspective, and, and that's maybe where like Goose as a platform comes in, which is we bui- we built Goose internally. The way to think about Goose is, um, it's a nod to, uh, Top Gun or whatever, the co-pilot thing. But way to think about Goose is it's a, it's a agent harness, and it's model agnostic. So I can run Goose on an Anthropic model, on a, on a, on a OpenAI model, on an open source model. There's probably like a hundred and twenty models that we have, and depending on what I'm trying to do, I'll kind of swap out the, swap out the models. And then that was useful for a human to use, but we've built like the agentic layer on top, and so now a lot of the automations at, at Block are actually routing through the Goose agent harness. And, um, we've been able to leverage this across the products that we're building. So Money Bot, which we like to think of as like a CFO in your pocket, but it's essentially like a proactive, um, uh, a proactive, uh, chatbot that can take actions on your behalf within Cash App, that's built on top of Goose. Manager Bot, which is roughly a similar thing on the Square side, that's built on top of Goose. So it's a lot of this foundational work on agentic systems and then like the, the triggers and the underlying data and events that you need to power them that's working across the, uh, the entirety of the, of the company.

6. 20:00 – 23:23

   From Static UI to Generative UI

   1. OJOwen Jennings20:00

      So on the, on the product side, um, I think the, the, the biggest shift has really been like we're going from a world where, uh, for the past ten or fifteen years, everyone's used to a static UI, a rigid UI. You tap through the UI. Everyone has the same-- Everyone's Uber or Lyft or Cash App or whatever looks the same. That's gonna fundamentally change in the next like six months. Um, generative, generative UI is, is, is here. We're seeing it with Money Bot. We're seeing it with Manager Bot as the models get better.

   2. DHDavid Haber20:28

      What i- what is that gonna look like kind of in practice? I'm curious.

   3. OJOwen Jennings20:30

      I think, I mean, in the simplest terms, it's like your Cash App should look really different from mine, and the reason why it's like, okay, well, I get my paycheck into Cash App, and I'm super into Bitcoin. Let's say like you don't, and you use Afterpay all the time. Great. When we open up our apps, that should be totally different. That-- You could probably achieve that just through personalization. That's not that interesting. What we're actually seeing, and Anthropic had some releases this week that are, that are incredible. What we're actually seeing is like I can go into Money Bot and say, "How have I been spending my money?" And it'll show me a bunch of charts and, uh, and visual- visualizations where it is actually like on the fly generate- generating that visualization. It's not actually in the code itself. So that's really cool. It's also potentially a nightmare from like a QA perspective, and so we need to figure out how you're gonna QA all of these like non-deterministic outputs for, for tens of millions of customers. But, um, a great example on the, on the Square side is with Manager Bot, maybe charts aren't that impressive to you, but with Manager Bot, let's say you're a, you're a, a-- you own a, a multi-location quick serve restaurant. You say like, "Hey, can you build me an app where I can, uh, manage scheduling for these two locations and like automatically fire off texts via, you know, WhatsApp or, or Signal or whatever to my, um, to my employees?" It's actually gonna like create that app for you, and the, the way that that app looks and feels is not in the source code of the actual application that we push to the, to the App Store. And so I think it's, um, it gives folks way more control. It's way more personalized and, uh, and ultimately, I think it'll lead to higher engagement. Um, I think it'll lead to better product discovery and re- and really, I think the key thi-- I, I don't think that if we ask customers to, to like prompt these tools themselves, they're gonna necessarily know the right prompts and come up with the right answers. So we've invested massively on the proactive intelligence side, where what we've found, especially as it relates to money, is like we need to be prompting our customers with things that we think make sense for them, and that's where we're creating a lot of the, the value.

   4. DHDavid Haber22:32

      So I, I mean, I think we're all incredibly bullish on, on kind of the impact of AI, you know, in, kind of in the way that all these businesses run and the products you can create. How does that flow back to your stock price? You know, the, the, the business is-- The stock has been r- roughly flat for, I don't know, six or seven years.

   5. OJOwen Jennings22:48

      Thanks for reminding me.

   6. DHDavid Haber22:50

      But [laughs] but the bi- [laughs] the business has grown a lot. You know, to your point, the gross profit per employee has grown, you know, massively. Like, how do you sort of reconcile the, that, that dimension?

   7. OJOwen Jennings22:59

      Yeah, I think, um... So, uh, so I think, you know, markets are, markets are cyclical, and there's all sorts of things that are happening. I remember, uh, in twenty twenty-one when our stock price was like, I don't know, two hundred and sixty bucks, and I was like, "That was a little bit irrational." Um, you can take a, a kind of longer term mature view and say, you know, markets are voting machines in the near term, but they're weighing machines in the long term, just like focus on building.

7. 23:23 – 27:08

   Defensibility in the AI Era

   1. DHDavid Haber23:23

      You know, Dave and Jonathan earlier talked a bit about kind of defensibility. H- how do you think about your own moats at Square? I mean, at, at Block, excuse me. You, you know, you talked a bit about the ecosystem. You guys obviously have, you know, regulatory infrastructure. Um, you know, how do you think about, you know, the, that, the business overall in that context?

   2. OJOwen Jennings23:40

      Yeah. I think in the, I think in the near term and the medium term, um, there's a bunch of, there's a bunch of moats that exist for, for Block, and, and we can talk about the industry more broadly. I think, I think distribution and network effects are, are one of them. I, I agree on the, the Citrini piece and, uh, and DoorDash. I don't think anyone's vibe coding DoorDash in the next, uh, couple of weeks here. Uh, I like to say like any of us can, can create a peer-to-peer app in probably a week. Uh, no one's gonna vibe code, you know, fifty or sixty million monthly actives who are actually using that. So I think that that's true. Uh, I think, um-You know, licenses and, and regulatory posture, um, uh, it, it definitely exists. I think hardware right now, it's like harder to imagine how some of the AI tools flow through to the, to the hardware side. Like, you can't vibe code a, a piece of Square hardware. Um, but I, I think longer term, if we continue... Like, if we look at the rate of the change and, and the change in the change, I think longer term, the key thing that's gonna make, uh, a company defensible is, um, the extent to which the company understands something that is pretty hard for other companies to understand. And so we're increasingly building toward a world and talking about Block as an intelligent system itself.

   3. DHDavid Haber25:01

      Mm.

   4. OJOwen Jennings25:02

      So basic- Like, the, the, the, the, the way that I see this going, if we can-- if you extrapolate forward the past several months, is that ultimately a company is sitting on top of some sort of signal, some sort of like rich data and, and, and deep insight. Um, for us, it's like how sellers and buyers participate in the economy. Um, and, and most companies, I think, have this thing that they understand deeply. And then the question is gonna be: How quickly can you iterate to improve that understanding over time? And so we're building world models internally and externally of like understanding who our customers are, but then also understanding how Block operates. Like, you can imagine, you can imagine for any company, just like a markdown file-

   5. DHDavid Haber25:50

      Mm

   6. OJOwen Jennings25:50

      ... of like who you are, and then you need the feedback loop with two things. You need the feedback loop with the signal, which is like what do you, what do you deeply understand that's hard for others to understand? And then you need a tool like Builder Bot or Claude Code or what have you, and then you can just iterate through that loop over and over and again. It's like, "This is, this is what I'm seeing. This is what's happening. Great. This is our markdown file for, for Block. These are our values. This is the metrics we're trying to optimize for. Um, this is what we care about. This is what we don't care about." And then you have agentic systems, so you can just build stuff. And right now, you basically have-- you've taken that-- W- Humans used to do that, and it used to take a couple months to build a feature. Um, now it takes maybe a week or two, and there's still humans involved. Pretty clear that in the future you'll be able to run that loop, like, I don't know, hundreds, thousands of times a day, and maybe there's some humans involved, maybe not. Maybe the humans are more like editors. And so I think the, the biggest moat is gonna be like which companies understand something that's super hard for other people to understand. And if your answer to that is, is, um, "I don't know," then, uh, then you maybe could get vibe coded away.

   7. DHDavid Haber26:58

      This has been an amazing conversation. Thank you, uh, thank you so much for, for joining us.

   8. OJOwen Jennings27:02

      Appreciate it. Thanks so much.

   9. DHDavid Haber27:03

      Awesome. [audience applauding]

Episode duration: 27:16