1 Apr 2026 · 27m · Owen Jennings, David Haber · 23 quoted facts
Owen Jennings of Block argues that AI coding agents broke the long-standing link between headcount and output. A few people working with agents can now do what whole teams once did. That is why Block cut more than 40% of its staff, mostly on the development side, and reorganized into small squads. He says the company is no longer writing code by hand.
The tension is between speed and safety. Block moved boldly, but kept humans in the loop and left compliance, regulatory and reliability work largely untouched. The company is also building products whose screens are generated on the fly, which makes testing hard. Jennings says the lasting moat will belong to companies that understand something rivals cannot, and that keep improving that understanding.
Jennings says new models such as Opus 4.6 and Codex 5.3 suddenly worked well on existing, complex codebases, not just new projects. Block spent the first quarter of 2026 working through what that meant before restructuring.
Jennings says the cuts were large on the development side but 'de minimis' in outbound sales and account management. Compliance teams were left alone. He uses this to argue that the layoffs were not general bloat trimming.
Staff now run many agents in the background, such as fourteen at once building pull requests, and check in on them. Builder Bot, Block's internal tool, sometimes builds features fully and usually gets them to 85-90% before a human finishes.
Both are built on Goose, Block's model-agnostic agent harness. Charts and even whole small apps are created live rather than coded into the app, which gives customers more control but makes quality testing harder.
Jennings says distribution, licenses and hardware protect Block today. Over time, he says, the moat is a deep, hard-to-copy understanding of a domain, such as how buyers and sellers use money, plus a fast loop that keeps improving it.
Owen Jennings says AI coding tools improved sharply in late 2025, so one or two engineers using them could be ten to a hundred times more productive. He says Block restructured because of that shift, not because of a profit target.
Jennings rejects the over-hiring story. He says Block's gross profit per employee was mid-pack from 2019 to 2024 and that the biggest cuts were in development, not operations.
Goose is Block's internal 'agent harness', software that lets an AI model carry out tasks. It works with many models, including Anthropic, OpenAI and open-source ones, and many Block automations and products such as Money Bot run on it.
It means the app's screens are created by AI for each person rather than being fixed in the code. Jennings says Money Bot can draw spending charts on request, and Manager Bot can build a scheduling app for a restaurant owner.
He says a given product will need fewer engineers, designers and product managers. He points to the Jevons paradox, where cheaper building leads to more things being built, so there may be more tech companies, but he says he won't predict the outcome.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
In early 2026, Block restructured more than 40% of its company and reorganized around small squads of one to six people working with AI agents.
“In early 2026, they restructured more than 40% of the company and rebuilt around small squads of one to six people, working alongside AI agents.”
Owen Jennings says Block launched its Goose agent harness in early 2024 and believes it was the first such harness.
“We actually launched Goose, which was the first agent harness, at least that I know of, in early 2024.”
Owen Jennings says Block's cuts in outbound sales and account management were minimal compared with the cuts on the development side.
“If you think of something as outbound sales or account management, the cuts were fairly de minimis, and so that was really what we were reacting to.”
Owen Jennings says Block's gross profit per full-time employee from 2019 through 2024 was in the middle of the pack among competitors.
“If you look at where we were 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 competitors.”
Owen Jennings says Block did not cut its compliance team or its compliance technology team in the restructuring, to avoid regulatory risk.
“we basically did not touch our compliance team and our compliance technology team, even if the tools are there, it's let's not take any risks.”
Owen Jennings says Block gave generous severance packages and did not cut departing employees' technology access immediately.
“We were in a position where we were able to be generous in terms of the severance packages that we gave. We didn't cut people's technology access instantly, which can suck.”
Owen Jennings says Block reduced its number of meetings by roughly 70 to 80% after the restructuring.
“we massively reduced the number of meetings we have, probably like seventy or 80%.”
Block has an internal-only agentic operating system called G2 that lets employees automate any deterministic workflow.
“We have a agentic operating system internal only called G2, where anyone can automate any deterministic workflow.”
Owen Jennings estimates that Block cut its management layers on the development side by about 50 to 60%.
“So on the development side, I think we probably cut our layers by, I don't know, fifty or 60%.”
Owen Jennings says Builder Bot usually builds features to about 85 to 90% completion, and a human with context finishes the rest.
“More often than not, it's building them to, like, eighty-five or 90%. And then a human who who has a lot of context and understands, does, like, the final, the final ten.”
About eighteen months before this conversation, Block moved from separate business units to a functional structure with engineering, design and product each reporting to a single head.
“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.”
Owen Jennings says that when he joined Cash App in 2016, it had only just started monetizing and earning its first gross profit.
“when I joined Cash App in 2016, uh, we had just just started to to figure out how to monetize and had our first dollars of gross profit.”
Owen Jennings estimates that Cash App now accounts for roughly 60% of Block's overall gross profit.
“now I think Cash App's probably like, s, I don't know, s sixty-ish percent of, like, overall gross profit at the at the company.”
Owen Jennings says Block's Goose harness can run on roughly 120 different models, including Anthropic, OpenAI and open-source models.
“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.”
Money Bot is a proactive chatbot inside Cash App that can take actions on a user's behalf, and Block describes it as a CFO in your pocket.
“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 a proactive, uh, chatbot that can take actions on your behalf within Cash App.”
Manager Bot is a Square-side counterpart to Money Bot and is built on top of Block's Goose agent harness.
“Manager Bot, which is roughly a similar thing on the Square side, that's built on top of Goose.”
Owen Jennings says the look and feel of apps that Manager Bot generates for a merchant is not part of the source code Block ships to the app store.
“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.”
Owen Jennings says generated interfaces create a quality-assurance challenge because their non-deterministic outputs reach tens of millions of customers.
“we need to figure out how you're gonna QA all of these, like, non-deterministic outputs for for tens of millions of customers.”
Owen Jennings says Block's chatbots and AI phone support automate a majority of its customer support inquiries.
“our chatbots and and AI phone support and and whatnot are automating a a majority of inquiries that we get.”
Owen Jennings invokes the Jevons paradox to argue that cheaper software building may lead to more things being built, even if each company needs fewer staff.
“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.”
Owen Jennings recalls that Block's stock price was around $260 in 2021 and says he considered that level somewhat irrational.
“I remember, uh, in 2021, when our stock price was, like, I don't know, 260 bucks, and I was like, that was a little bit irrational.”
Owen Jennings argues that a peer-to-peer payments app is easy to build, but the tens of millions of monthly active users that Block's products have cannot be vibe coded.
“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 60 million monthly actives who are actually using that.”
Owen Jennings says building a feature at Block has gone from taking a couple of months to roughly a week or two with agent tools.
“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.”
Jennings explains how Block's early agent work met a sudden jump in model quality. That jump pushed the company toward a major restructuring.
“And then late November, first week of December, there was a binary change.”
Pressed on whether the cuts were a correction for over-hiring, Jennings points to productivity data and the engineering-heavy shape of the cuts.
“we're not writing code by hand anymore. That's over, that's done.”
Block put reliability, customer trust and compliance first. It also chose generous severance and company-wide all-hands meetings.
“worst case scenario is you have an outage or you go down.”
Jennings says a single bold move avoids the lingering fear of repeated layoffs, and that smaller teams force a new way of working.
“I have fourteen agents who are building PRs on my behalf right now”
The org became squads of one to six people with fewer management layers. Builder Bot now builds much of the product, with humans finishing the last stretch.
“More often than not, it's building them to, like, eighty-five or 90%.”
Block's model-agnostic agent platform powers both internal automation and customer-facing assistants across Cash App and Square.
“it's a it's a agent harness, and it's model agnostic.”
Jennings says apps will stop looking identical for everyone. Screens and mini-apps will be generated on the fly, with new testing challenges.
“It's not actually in the code itself, so that's really cool.”
Jennings answers the flat-stock question with a long-term view. He then argues that deep, hard-to-copy understanding is the lasting moat.
“which companies understand something that's super hard for other people to understand.”
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.
Block was one of the first to make a pretty drastic decision in cutting 40% of the workforce. What led up to that decision?
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, twenty, a hundred x more productive. 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 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, very clear.
So you show up on Monday, 40% of the company's gone. What's the most meaningful difference in how you're operating?
I think the biggest thing is
For most of the history of software, building faster meant hiring more people. The relationship was so consistent, it became a law of the industry. Headcount equals output. Block, the parent company of Square, Cash App, and Afterpay, decided to test what happens when that equation breaks. In early 2026, they restructured more than 40% of the company and rebuilt around small squads of one to six people, working alongside AI agents. Teams that once had fourteen engineers now run with three.
Their internal tool, Builder Bot, autonomously ships features to production. Designers and PMs write code, and the company is building products like Money Bot and Manager Bot that generate custom interfaces on the fly for tens of millions of users. This is what reorganizing a public company around AI actually looks like from the inside. a16z general partner, David Haber, speaks with Owen Jennings, executive officer and business lead at Block.
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 forty 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 that decision around the riff, how Block has adapted, the current and future state of the business.
So thank you so much, Owen. Welcome to the stage.
Thanks, boss.
So Jonathan, I think, did a amazing job kinda setting the stage, you know, for this conversation, talking about how important it is to be founder-led. Block was one of the first to make a pretty drastic decision in cutting 40% of the workforce. Maybe walk us through what led up to that decision and how you thought about it.
Sure, I think it probably starts two or three 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, 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, in early 2024. And that started to augment how we approached software development, how we thought about internal tooling.
And I would say that over that period, twenty four and twenty five, it was like pretty meaningful progress. And then late November, first week of December, there was a binary change. You basically have Opus four six, you have Codex five three, and essentially you get this shift where I think the tools and the foundational models were pretty good at writing code, especially for new ventures and kind of like green space. It became clear almost overnight, maybe in a couple of weeks, that now they're incredibly capable working with existing complex codebases.
And so there was a massive paradigm shift where, at least from my perspective, 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 the first week of December. And what we were seeing is that one or two engineers, or a designer and an engineer who is on the tools, quote unquote, as we say, is able to be ten, twenty, a hundred x more productive.
And so that's really what led us to make the decision a few weeks ago. We spent q one discussing 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, and ultimately that's what led us to this place where we did a reduction in force that was slightly greater than forty, and that wasn't even 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 fairly de minimis, and so that was really what we were reacting to.
can I push you a bit on this? A little bit? I mean, Alex, when he kind of introduced the conference just an hour ago, talked about dessert period. How much of the riff was sort of overhang from 2021, kind of overhiring versus AI and kind of, like, actual productivity gains, kind of in the business?
If you look at where we were 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 competitors. If you look at last year, I think we were kind of, I don't know, second quintile or something like that. But 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 cruft and bloat and so on, and so forth, then, like, this riff would have accrued to the operational teams and the like, like that sort of stuff.
These were 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 not writing code by hand anymore. That's over, that's done. So, anyway, everyone has their narrative. It's largely not true.
so maybe just walk through, like, tactically, how did you actually execute this transition culturally, operationally in the business?
The nice part about this riff, relative to some other things that have happened at Block or at other companies, is we were coming from a position of strength on a profitability and operating income side. And so sometimes when it's really financially motivated, the CFO or the CEO says, Okay, we need to do a sixteen riff in order to 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 P zero zero, not acceptable at all. Obviously, 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 doing right there.
For instance, like, we basically did not touch our compliance team and our compliance technology team, even if the tools are there, it's 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 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 different. And then, from an execution perspective, we thought very deliberately. Obviously, I've been at the company twelve years, a number of folks who we parted ways with are friends and colleagues for more than a decade.
We were in a position where we were able to be generous in terms of the severance packages that we gave. We didn't cut people's technology access instantly, which can suck. We chose to have an all-hands with everybody at the company. So Jack and the executive team were looking each other in the eyes and explaining this decision and explaining the drivers behind it. And 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 we massively reduced the number of meetings we have, probably like seventy 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 Monday. And it just feels like we're smaller, we're leaner, we have fewer layers, we have larger spans, and it's been back to building.
So you show up on Monday, 40% of the company's gone, like, what's the most meaningful difference in how you're operating? I don't know, maybe it's in the EPD org or elsewhere.
I think that there's a few different components to this. I think the biggest thing is one concern that I have with, like, how some of these org changes might flow through the tech industry is that. And it gets back to the founder-led point. If you're not founder-led and you don't have 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 riff and it's, oh, it's fine, and then you do another fifteen riff.
And then culturally, that's just like devastating for your team, cause there's always this, like, pending riff looming 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 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 Money Bot and we wanna roll Money Bot out to fifty, and there used to be a team of fifteen people working on it, and now there's a team of four people.
Plus two zero zero zero on the tokens, this is like unlimited access to tokens, and you can use fast mode on Claude 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 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 on the software development side, it's for PMS too, it's for growth marketers too. The biggest shift, myself included, I have countless agents running right now that I have to go check on. 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.
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?
I, I, I don't, I don't necessarily want to. Like I I talked at the beginning about, um, the groundwork that happened in twenty three, twenty four, and twenty five. 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 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, 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 engineers, fewer designers, fewer PMs. I think that's like, very, very clear based a after, like, December. 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, 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. Um, but I I'm not here to to predict the future.
I'm focused on block.
Uh, fair, you you talked a bit about kind of the some of the AI infrastructure build. Maybe you can go in a bit more depth, uh, 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 in in other parts of the business? You oversee ops, customer support.
Yeah, um, so I got asked at a investor conference, uh, last week, like, how is AI eff- like flowing through block? And to me, that was like asking, um, how are computers flowing through block? Uh, like, it's it's a, uh, fundamental, inbuilt thing that has changed on on 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, the 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.
Now we have, um, 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. 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 60%. Like on the product side, I only have, I think, two layers, maybe three layers in a in a couple 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 100%. We've had some fairly complex features that are built to 100%.
More often than not, it's building them to, like, eighty-five or 90%. And then a human who who has a lot of context and understands, does, like, the final, the final ten. So that feels really, really different. The ability to go from, um, to go from idea to like this is in the hands of 100,000 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. So that's on the internal side, um, on the on the product side, I think that what?
And may and maybe just cash 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. What do those businesses look like? And then, yeah, how are they kind of changing with with you?
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 outcomes. 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 connects 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, but yeah, I mean, Ca- Cash App went from when I joined Cash App in 2016, 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, s 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 bu- we built Goose internally. The way to think about Goose is, um, it's a nod to, uh, Top Gun or whatever, the copilot thing, but the 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 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, 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 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. So, on the on the product side, um, I think that 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, it 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.
What is what is that going to look like, kind of in practice? I'm curious.
I think, I mean, in 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, 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, uh, 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 thing 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.
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, uh, the stock has been r- roughly flat for, I don't know, six or seven years. Thanks for reminding But the me. but but the bit 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?
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 2021, when our stock price was, like, I don't know, 260 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 folks on building.
I, you know, Dave and Jonathan earlier talked a bit about kind of defensibility. Uh, 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?
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 60 million monthly actives who are actually using that. So I think that that's true. Uh, I think, um, f- you know, r- 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. So basic 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 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 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 who can just build stuff. And right now, you basically have you've taken that f- 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.
This has been an amazing conversation. Thank you. Uh, thank you so much for for joining us.
Appreciate it. Thanks so much. Awesome.
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