speaker 1 [00:00:00-00:00:10]: The biggest moat is going to 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.
speaker 2 [00:00:10-00:00:17]: Block was one of the first to make a pretty drastic decision in cutting 百 分 之 4 0 of the workforce. What led up to that decision?
speaker 1 [00:00:17-00:00:48]: 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 were seeing is that one or two engineers who is on the tools is able to be 1 0 2 0 1 0 0 x more productive over time. It's like pretty obvious that these systems are just going to be so much better than like having 1 0 0 0 humans who are doing that work。 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.
speaker 2 [00:00:48-00:01:46]: very clear. so you show up on monday, forty percent of the company's gone. what's the most meaningful difference in how you're operating? i think the biggest thing is, 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 Cash App during its critical scaling period. And recently, Block executed a roughly 百 分 之 4 0 reduction in force, and they have 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 going to dig into the that decision around the riff, how block is adapted the current and future state of the business. So thank you so much, owen, welcome to the stage. 谢谢。
speaker 2 [00:01:49-00:02:13]: Awesome，so you know Jonathan，I think did an amazing job kind of setting the stage，you know，for this conversation，you know，talking about how important it is to be founder-led，you know，block was one of the first to make a pretty drastic decision in cutting 百 分 之 4 0 of the workforce，maybe walk us through kind of what led up to that decision。 And how you thought about it.
speaker 1 [00:02:14-00:03:20]: sure. I think I would probably start 2 or 3 years ago. I think one thing about Jack is I find Jack to be generally right and generally early, sometimes very early. And I think that's flowed through Twitter, Square Cash App, Bitcoin, etc. 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 twenty twenty four. and that started to augment how we approached software development how we thought about internal tooling and i would say that over the over that period twenty four and twenty five it was like pretty meaningful progress. UM, AND thenEN lateATE NO NovemberBER, FIRST weekEE OF DE December, IT WAS JUST THERE WAS A BIN binaryARY changeGE. YOU B basicallyICALLY HAVE OPUS 4 six, YOU HAVE CodeDEX FIVE TH, AND ES essentiallyENALLY you GET THIS shift WH I thinkINK THE THE toolsLS AND THE foundationalATION MOD models W PRETTY GOOD AT WRING CODE, ESPECIALLY FOR NEW ventUR AND KIND OF LIKE GREEN SPACE.
speaker 1 [00:03:20-00:04:22]: UM IT became C clearAR. Almost overnight, maybe in a couple of weeks, that now they're incredibly capable working with existing complex code bases. And so there was a. massive paradigm shift where at least from my perspective, there's there's been this correlation between the number of folks at a company and the output from the company 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 designer and an engineer who was on the tools, quote unquote, as we say, is able to be ten, twenty, one hundred x more productive. and so that's really what led us to make the the decision a few weeks ago. we spent q one discussing like what does this mean? Fundamentally, what does this mean in terms of how we're going to build products, how we're going to build software for customers and then also how we're going to run a company. What is it going to mean to actually run a company? And we spent q1 as an executive team with jack working through that.
speaker 1 [00:04:23-00:04:50]: 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 40% and that wasn't even, 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，the cuts were you know fairly de minimis。 And so that was really what we were reacting to。
speaker 2 [00:04:51-00:05:10]: Can I push you a bit on this a little bit？ I mean Alex，when he kind of introduced the you know the conference just you know an hour ago，talked about。 Desert period, you know? How much of the riff was sort of overhang from 2021, kind of overhiring versus ai and kind of like the product actual productivity gains is going to be in the business.
speaker 1 [00:05:10-00:06:05]: Like if you look at where we were from a from a gross profit per full-time employee basis from like 2019 through 2024, we're basically like right in the middle of the pack with all of the with all the competitors. 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. And then when you look at the composition of what we did, if you thought it was like craft and bloat and so on and so forth, then like this riff would have accrued to the operational teams and like that sort of stuff. It's really, really meaningful cuts on the development side. 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 like, we're not writing code by hand anymore. That's over. That's done. And so anyway, everyone has their narrative. It's largely not true.
speaker 2 [00:06:07-00:06:15]: UmSo maybe just walk throughR like tactically, how did you actually execute you know this this transition, you know culturally, you know,PERally in the business?
speaker 1 [00:06:15-00:07:16]: So I think so we were um the nice part about this riff relative to some other you know things that have happened at Block or at other companies is we're coming from a position of strength on ah on a profitability and operating income side. and so sometimes when it's really financially motivated. You know，the CFO or the CEO says，ok，we need to do a 16% riff in order to like hit this hit this target，and 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 coming months and quarters, we had some core principles. 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 P00 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. 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 doing right there.
speaker 1 [00:07:16-00:08:18]: For instance, like we basically did not touch 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. Might be a squad of three people and instead of a feature team of 14, who's building that we want to 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 the regulatory council team or the sdr bdr team, the org looked pretty similar to how it looked in january on the development side. It looks completely completely different. And then you know, from an execution perspective, you know, we thought very deliberately. Obviously, I've been in the company 12 years. A number of folks who we parted ways with are friends and colleagues for you know more than a decade. We were in a position where we were able to be generous in terms of you know the severance packages that we gave.
speaker 1 [00:08:18-00:09:08]: We didn't cut people's technology access instantly, which can suck. We chose to. Having all hands with everybody at the company. So jack and the executive team were you know, looking each other in the eyes and explaining this decision and explaining the drivers behind it. And i think that that it was on a thursday. I think like the friday, saturday, sunday, there's a lot of shock dealing with ambiguity. And then what we've been doing is. Massively reduced the number of meetings. We have probably like 70 or 80%. 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. 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.
speaker 2 [00:09:08-00:09:16]: So you show up on monday, 40% of the company's gone. Like, 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.
speaker 1 [00:09:17-00:10:17]: Um, I think that there's a there's a there's a few different components to this. I think the biggest thing is so one concern that I have with like how some of these org changes might flow through the tech industry is that and 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 going to probably take a more incremental approach. And so the way that that's going to feel is like you do a 15% riff and it's like, oh, it's fine. And then you do another 15% riff and then culturally, that's just like devastating for your team because there's always this like pending riff looming looming over your over your shoulder. 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 very meaningful increase in ai tool usage, especially on the development side. This is just a massive forcing function. Like if we're building okay, we're building money bought and we want to roll money bought out to 50% and there used to be a team of 15 people working on it.
speaker 1 [00:10:17-00:11:17]: And now there's a team of four people plus. 2000 dollars on the tokens that this is like unlimited access to tokens and you can use fast mode on cloud code. 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 14 agents who are building prs on my behalf right now and i'm going to context switch between all of those. AND it's not JUST AH on the SO softwareW development sideIDE, it'sS for pms too, it's for GRTH marketers too. THE biggestG shift, MY IN includedUDED, I have, you KNOW, countlessLESS agents runningUN right now that I HAVE to go, I have to go checkEC on. IT it's not... UM it's less of a linearAR workflowK, and it's more of like in the backgroundRO, there's T or TWY AGENTS who are DO a whole bunchUNCH of stuff, and then I have to checkK in on the work and NUDudge it and changeGE it and WHAT have you, and then I can COMM it to gub, and I CAN I can get the MARKdown fileI. we can put it in the sourceOURCE of truthUT, and we can move on. yeah.
speaker 2 [00:11:17-00:11:29]: So we have a lot of 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 I guess what conditions need to be in place for that to be successful?
speaker 1 [00:11:29-00:12:28]: I don't necessarily want to... Like I talked at the beginning about the ground work 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, i think there's work to do to be successful. I would expect many companies are doing that works. Some of them are incredibly. Um, far ahead than others. Um, and so I don't know what to expect. What I will say is like to the extent that I do believe that fundamentally for like a given product or for a given roadmap, you're going to need fewer engineers, fewer designers, fewer PMS. I think that's like very, very clear based after like December. Um, that doesn't necessarily mean that there's going to be fewer.
speaker 1 [00:12:28-00:12:56]: Engineers, designers and PMs in the world. It's like the classic Jevons paradox thing where I think that there's probably now just a superset of things that can be built. So I don't know, you know, a given tech company might be way smaller, but there might be fifty or one hundred more tech companies, or you're going to start getting this development working in sectors and areas where that hasn't historically been the case. um but i'm not here to to predict the future. i'm focused on block.
speaker 2 [00:12:58-00:13:11]: A fair, you talked a bit about kind of some of the ai infrastructure build, maybe you can go in a bit more depth, you know, both in how it's impacting the kind of technology org. I'm also curious about, you know, how are you using ai and in other parts of the business? You oversee ops customer support.
speaker 1 [00:13:12-00:14:17]: YEAH, UM SO I GOT askedK AT AH IN investorESTOR CONFENCE LAST WEEK, LIKE HOW IS AI LIKE FLING THROUGH BLOCK? AND TO ME though IT'S LIKE askingKING, UM HOW ARE COM computersTERS FLING THROUGH BL? IT' IT'S a FAMENT INBUT THING THAT HAS CHANG IN LIKE A binaryARY WAY OVER THE pastAST 18EN MONTH, AND THEN FES LIKE IT CHANGED ALL OVER again IN THE pastAST FOUR MONTH. 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 future and where we think things are going. So on the internal side, i think the biggest difference is the shape of the of the org. So we used to have kind of like a classic hierarchical structure. It was functional. Um, which was great, but it was like fairly standard if you like average through a bunch of medium-sized tech companies. And so, you would have kind of eight server engineers for client engineers, a pm, a designer, and you would work linearly through your roadmap.
speaker 1 [00:14:17-00:15:19]: Now, we have small squads. So squads of like one to six people. 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. Which is different than how things worked a year or two ago where it's like i'm on the banking team. I'm going to 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, 50 or 60% like on the product side. I only have. I think 2 layers, maybe 3 layers in a couple places, and so information is flowing way more freely. I think that then in terms of how we actually build on the development side, things have changed. I think everyone is probably seen, you know, every CEO out there is going on Twitter and showing their like green dot on GitHub, but that is real. Like all of our designers are shipping PRs, all of our product managers are shipping PRs.
speaker 1 [00:15:19-00:16:18]: That's not that interesting anymore. I think more interesting is that we have internal tools that are similar to Claude code, but they're like more plugged into our infrastructure. So we have a tool called Builderbot. Builderbot is just autonomously merging PRs and actually like building features to 100%. We've had some fairly complex features that are built to 100%, more often than not, it's building them to like 85 or 90%. And then a human who has a lot of context and understands does like the final 10, so that feels really, really different. The ability to go from idea to like this is in the hands of 100000 or a million customers has been compressed massively since December. Outside of development, I would say most of what we're seeing is like anytime there's a deterministic workflow, we're able to automate that. And so. Generally at a at scale tech company, you have individuals who are working queues.
speaker 1 [00:16:20-00:17:10]: 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 AI phone support and whatnot are automating a majority of inquiries that we get. And then it gets into like product operations and risk operations and compliance operations and any sort of decisioning like generally. Generally，the models and the agents are going to do a better job than humans right now。 I think it's critical that we have a human in the loop，that's like the key kind of buzzword when you talk to partners and regulators and what have you。 But over time，it's like pretty obvious that these systems are just going to be so much better than like having a thousand humans who are doing that work。 so that's on the internal side. um on the on the product side, i think that.
speaker 2 [00:17:10-00:17:20]: and maybe just catch people up on kind of the shape of the business. obviously you have square, you have cash app, you you made a big acquisition after pay. sure. what do those businesses look like? and then yeah, how are they kind of changing with sure?
speaker 1 [00:17:20-00:18:22]: 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 18 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 entirety 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 building technology and you're building infrastructure that is not brand-specific. and that's actually kind of central to our overall strategy and overall thesis. But yeah, I mean, Cash App went from when I joined Cash App in 2016, we had just started to figure out how to monetize and had our first dollars of gross profit.
speaker 1 [00:18:22-00:19:34]: And now I think Cash App's probably like I don't know 60-ish percent of like overall gross profit at the company. So overall been growing at a healthy clip over the past decade. But Cash App and AfterPay have definitely been growing more quickly. But increasingly, we're trying to think about things from an ecosystem perspective. And that's maybe where like Goose as a platform comes in, which is we built Goose internally. The way to think about Goose is it's a nod to Top Gun or whatever, the copilot thing. But way to think about Goose is it's an agent harness and it's model agnostic. So I can run Goose on an Anthropic model, on an OpenAI model, on an open source model. There's probably like 120 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 we've been able to leverage this across the products that we're building，so money bot，which we'd like to think of as like a cfo in your pocket，but it's essentially like a proactive.
speaker 1 [00:19:34-00:20:28]: UM,A proactiveTI chat botOT THAT can takeAKE actionsS ON YOUR BE behalfFIN C APP, THAT' builtILT on top of goose MAN bot, which is roughlyOUGH a SIM similar thingING on the S squareARE SIDE, THAT' builtILT on top OF goose. SO IT's a lot of THIS foundational workK on AG AGIC SY systems, AND TH like THE triggersGG and THE underlying D data and events that you need to POW themM THAT' WORK across THE THE EN entirety OF THE OF THE CO company. So on the product side, I think that the biggest shift has really been like we're going from a world where for the past 10 or 15 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, it looks the same. That's going to fundamentally change in the next like six months. Generative UI is here. We're seeing it with Money Bot, we're seeing it with Manager Bot, as the models get better.
speaker 2 [00:20:28-00:20:30]: What is that going to look like kind of in practice? I'm curious.
speaker 1 [00:20:30-00:21:31]: 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, an anthropic had some releases this week that are that are incredible. we're actually seeing is like, i can go into moneybot and say, how have i been spending my money? and it'll show me a bunch of charts and and visualizations. where it is actually like on the fly generating 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 going to 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 ah you're ah you own ah a multi-location quick serve restaurant.
speaker 1 [00:21:31-00:22:31]: you say like hey q, you build me an app where i can. manage scheduling for these two locations and like automatically fire off text via you know what's app or signal or whatever to my to my employees. It's actually going to 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 it gives folks way more control, it's way more personalized. and uh, and ultimately, i think it'll lead to higher engagement. i think it'll lead to better product discovery. and, and really, i think the key thing, i don't think that if we ask customers to, to like prompt these tools themselves, they're going to 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.
speaker 2 [00:22:32-00:22:58]: So i mean, i think we're all incredibly bullish on kind of the impact of ai, you know, in kind of in the way that all these businesses run in the products, you can create how does that flow back to your stock price? You know, the business is the stock has been roughly flat for, i don't know, six or seven years, but the mining me, but the business has grown a lot, you know, to your point, the gross profit per employee is grown in a massively. Like, how do you sort of reconcile the that dimension?
speaker 1 [00:22:59-00:23:23]: I think um so so i think you know markets are markets are cyclical and there's all sorts of things that are happening. I remember ah 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 ah 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 folks on building.
speaker 2 [00:23:23-00:23:39]: you know david and jonathan earlier talked a bit about kind of defensibility. How do you think about your own moats at square? I mean, at blog, excuse me, you know, you talked a bit about the ecosystem. You guys obviously have, you know, regulatory infrastructure. You know, how do you think about the business overall in that context?
speaker 1 [00:23:39-00:24:39]: Yeah, i think in the i think in the near term and the medium term, there's a bunch of. There's a bunch of moats that exist for block, and we can talk about the industry more broadly. I think distribution and network effects are one of them. I agree on the Sittrini piece and DoorDash. I don't think anyone's vibe coding DoorDash in the next couple of weeks here. I like to say, like any of us can create a peer-to-peer app in probably a week. No one's going to vibe code you know 50 or 60 million monthly actives who are actually using that. so i think that that's true. i think um you know licenses and and regulatory posture um definitely exist. 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 piece of square hardware. um but 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.
speaker 1 [00:24:40-00:25:35]: That's going to make a company defensible is 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. So basically like 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 deep insight for us. It's like how sellers and buyers participate in the economy. And most companies, I think, have this thing that they understand deeply. And then the question is going to be how quickly can you iterate to improve that understanding over time?
speaker 1 [00:25:35-00:26:32]: 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. OU canG for any CO companyANY, JUST like a markdown fileILE OF like who you are, AND then you needED the feedbackBACK loopOO with two thingsINGS. YOU needED the FE feedbackBACK loopP with the S signal, whichICH is like, WHAT DO you... WHAT do you deeplyEEPLYDERST? THAT's HARD FOR OTH to UN understandST. AND thenEN you needED a toolOO like builderIL-bot or CLD codeDE or WHAT have you AND thenEN you CAN JUST iterateATE throughROUGH that loop over and over IT againIN. IT'sS like, THIS IS this is what I' seeingING, THIS is what'S H happeningENING. GREAT, THIS is OUR MARKdown fileILE FOR FOR block. TH are OUR VALUES. THIS IS the M metricsIC we' tryingYING to optimizeMIE FOR. THIS is what we careE ABOUT, this is WHAT we don'tT careE ABOUT. AND thenEN you HAVE AG AGIC SY systemsEMS, YOU can just buildILD stuffUFF. And right now you've basically you've taken that humans used to do that, and it used to take a couple months to build a feature. Now it takes maybe a week or two, and there's still humans involved.
speaker 1 [00:26:33-00:26:58]: 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 going to be like which companies understand something that's super hard for other people to understand. And if your answer to that is is. i don't know then ah then you maybe could get vibe coded away.
speaker 2 [00:26:58-00:27:03]: this has been an amazing conversation. thank you ah thank you so much for for joining us. appreciate it. thanks so much. awesome.