a16z Podcast - What Happens When a Public Company Goes All In on AI

Episode Date: April 1, 2026

David Haber speaks with Owen Jennings, executive officer and business lead at Block, about how the company rebuilt itself around AI agents, small squads, and internal tools like Goose and Builder Bot after restructuring more than 40% of its workforce. They discuss what it took to execute a major restructuring, how teams of three are now doing what teams of 14 used to, and how Block is shipping AI-native products like Money Bot and Manager Bot that generate custom interfaces on the fly for tens of millions of users.   Resources: Follow Owen Jennings on X: https://twitter.com/owenbjennings Follow David Haber on X: https://twitter.com/dhaber   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. 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 were seeing is that one or two engineers who was on the tools, is able to be 10, 20, 100x more productive.

Over time, it's pretty obvious that these systems are just going to be so much better than having 1,000 humans who are doing that work. I do believe that fundamentally, for a given product or for a given roadmap, you're going to need fewer engineers, fewer designers, fewer PMs. I think it'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 have 14 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 MoneyBot and ManagerBot 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 Haper 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 40% 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 going to dig into 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. Thanks, Beth.

So Jonathan, I think, did an amazing job kind of setting the stage 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, 24 and 25, it was like pretty meaningful progress. And then late November, first week of December, there was a binary change. You basically have Opus 4-6, you have Codex-5-3, 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 code bases. 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 10, 20, 100 X more productive.

And so that's really what led us to make the decision a few weeks ago, we spent Q1 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 40%. 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 RIF was sort of overhang from 2021 kind of overhiring versus AI and kind of like actual productivity gain is going to be 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. I think it's basically like invidia 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 like that sort of stuff. through 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 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're 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 16% 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 to size, worst case scenario is you have an outage you go down. So that's like P00, 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, we basically did not touch our compliance team and our compliance technology team, even if the tools

are there. 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 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 different and then from an execution perspective we thought very deliberately. Obviously, I've been in the company 12 years, a number of folks who we

parted ways with, our friends and colleagues, for more than a decade. We were in a position 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, Sunday, there's a lot of shock dealing with ambiguity. And then what we've been doing is we've massively reduced the number of meetings we have, probably like 70 or 80 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 Monday. 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 percent of the company's gone. Like, what's the most meaningful difference in how you're operating? I don't know. Maybe. 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 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, 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 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 MoneyBot and we want to roll MoneyBot out to 50%, and there used to be a team of 15 people working on it, and now there's a team of four people plus $2,000 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 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 on the software development side. It's for PMs, too, is 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 10 or 20 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 get the markdown file. We could put it in the source of truth and we can move on. 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? I don't necessarily want to like I talked to 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 an 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 work. Some of them are incredibly far ahead. others.

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 after like December. That doesn't necessarily mean that there's going to be fewer engineers, designers, and PMs in the world. It's like the classic Jevins paradox thing where I think that there's probably now just a super set of things that that can be

built. So I don't know, a given tech company might be way smaller, but there might be 50 or 100 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. But I'm not here to predict the future. I'm focused on block. Fair. You talked a bit about some of the AI infrastructure you 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 in other parts of the business? You oversee ops, customer support. Yeah.

So I got asked at an investor conference last week, like, how is AI, like, flowing through block? And to me, though, it's, like, asking, how are computers flowing through block? it's a fundamental inbuilt thing that has changed in like a binary way over the past 18 months and then feels like it changed all over again in the past four months. 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, which was great, but it was like fairly standard if you like averaged through a bunch of medium-sized tech companies. And so you would have kind of eight server engineers, four client engineers, a PM, a designer, and you would work linearly through your roadmap. Now we have small squads, so squads of like one to six people. So meaning meaningfully smaller than the other teams would be. And we have way more flexibility 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, two layers. maybe three layers in a couple of 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's probably seen, you know, every CEO out there is going on Twitter

and showing their like green dot on GitHub. But that's real. Like all of our designers 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, internal tools that are similar to Claude code, but they're more plugged into our infrastructure.

So we have a tool called BuilderBot. BuilderBot is just autonomously merging PRs and actually 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 to go from idea to like this is in the hands of 100,000 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 cues. A lot of that is just being completely automated away. Like from a customer support perspective, this is not new, but 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 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. On the product side, I think that... And maybe just cash people up on kind of the shape of the business. Obviously, you have Squared, you have cash app, you made a big acquisition and afterpay. What do those businesses look like? And then, yeah, how are they kind of changing with?

Sure. 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. 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 connects 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, like, kind of central

to our overall strategy and overall thesis. But, yeah, I mean, Cash App went from, when I joined CashUp in 2016, we had just started to figure out, how to monetize and had our first dollars of gross profit. And now I think cash apps 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 after pay 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 a Top Gun or whatever, the co-pilot thing. But the 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 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 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 MoneyBot, which we'd like to think of as like a CFO in your pocket, but it's essentially like a proactive chat bot 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 triggers and the underlying data and events that you need to power them that's working across the entirety of the of the 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 looks the same, that's going to fundamentally change in the next six months. Generative UI is here. We're seeing it with MoneyBot. We're seeing it with ManagerBot as the models get better. What is that going to look like 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. You could probably achieve that just through personalization. That's not that interesting. Well, we're actually seeing an Anthropic had some releases this week that are incredible. We're actually seeing as 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 visualizations where it is actually like on the fly 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 non-deterministic outputs for tens of millions of customers. But a great example 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 own a multi-location quick serve restaurant. You say like, hey, can you build me an app where I can manage scheduling for these two locations

and like automatically fire off text via, you know, WhatsApp or signal or whatever to my, to my employees, it's actually going to, like, create that app for you. And the way that that app looks and feels is not in the source code of the actual application that we push to the app store. And so I think it's, it gives folks way more control. It's way more personalized. And ultimately, I think it'll lead to higher engagement. I think it'll lead to better product discovery. 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 value. So I mean, I think we're all incredibly bullish on kind of the impact of AI, you know, 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 business is, the stock has been roughly flat for, I don't know, six or seven years. Thanks for reminding me. But 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 that dimension? Yeah, I think, so, so I think, you know, markets are cyclical and there's all sorts of things that are happening. I remember in 2021 when our stock price was like, I don't know, 260 bucks. And I was like, that was a little bit irrational. You can take 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. You know, David and Jonathan earlier talked a bit about kind of defensibility.

How do you think about your own moats at square, 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? Yeah, I think in the near-time. term and the medium term. 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 the Citrini 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 50 or 60 million monthly actives who are actually using that. So I think that that's true. I think you know, licenses and regulatory posture definitely exists. I think hardware right now, it's like harder to imagine how some of the

AI tools flow through to the hardware side. Like you can't vibe code a piece of square hardware. But I think longer term, if we continue if we look at the rate of the change and the change and the change, I think longer term, the key thing

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 way that I see this going, 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 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? 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 a 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 builderbot or clawed code or what have you. And then you can just iterate through that loop over and over it again. It's like this is what I'm seeing. This is what's happening. Great. This is our markdown file for block.

These are our values. This is the metrics we're trying to optimize for. This is what we care about. This is what we don't care about. And then you have agentic systems. You can just build stuff. And right now you basically have 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. 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 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, I don't know,

then you maybe could get vibe coded away. This has been an amazing conversation. Thank you so much for joining us. Appreciate it. Thanks so much. Awesome. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.com.

Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments,

please see A16Z.com forward slash disclosures.