Owen Jennings (2:53)

Thanks, Buff.

David Haper (2:55)

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.

Owen Jennings (3:13)

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 was on the tools, quote, unquote, as we say, is able to be 10, 20, 100x 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 company conversation. We were just having the tools are 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.

David Haper (5:32)

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 gain is going to be in the business.

Owen Jennings (5:48)

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 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 like, like that sort of st 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.

David Haper (6:36)

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

Owen Jennings (6:43)

The nice part about this RIF 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 16% RIF 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 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, like 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, 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 at the company 12 years. A number of folks who we parted ways with are 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, 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 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 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.

David Haper (9:10)

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.

Owen Jennings (9:18)

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, devastating for your team because there's always this like pending rift 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 working on 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. It's for growth market, 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 can put it in the source of truth and we can move on.

David Haper (10:59)

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?

Owen Jennings (11:12)

I don't necessarily want to. I talked at the beginning about the groundwork that happened in 23, 24 and 25. 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 than 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 a given product or for a given roadmap, you're going to need fewer engineers, fewer designers, fewer PMs. I think that's very, very clear after 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 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, 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 that hasn't historically been the case. But I'm not here to predict the future. I'm focused on block Fair.

David Haper (12:41)

You talked a bit about some of the AI infrastructure you build. Maybe you can go in a bit more depth both in how it's impacting the technology Org. I'm also curious about how are you using AI in other parts of the business? You oversee ops, customer support.

Owen Jennings (12:54)

Yeah. I got asked at an investor conference last week, how is AI flowing through block? And to me that's asking how are computers flowing through block? It's a fundamental inbuilt thing that has changed in 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 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 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, every CEO out there is going on Twitter and showing their like green dot on, on, on GitHub. But that's real. Like all of our designers are, are shipping PRs. All of our product managers are shipping PRs. That's 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 Builder Bot. Builder Bot 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 85 or 90%. And then a human who has a lot of context and understands does the final 10%. So that feels really, really different. The ability to go, 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 queues. A lot of that is just being completely automated away 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 product operations and risk operations and compliance operations, any sort of decisioning. 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 pretty obvious that these systems are just going to be so much better than having 1,000 humans who are doing that work. So that's on the internal side, on the product side, I think that and

David Haper (16:52)

maybe just catch people up on kind of the shape of the business. Obviously you have Square, you have Cash App, you made it a big acquisition. Afterpay, what do those businesses look like? And then. Yeah. How are they kind of changing with.

Owen Jennings (17:02)

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 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. And now I think Cash App's probably like, I don't know, 60ish percent of 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 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, a proactive 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 the triggers and the underlying data and events that you need to power them, that's working across the entirety of the company. So on the product side, I think that the biggest shift has really been 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 like six months. Generated generative UI is, is, is here. We're seeing it with moneybot, we're seeing it with Manager Bot. As the models get better, what is

David Haper (20:10)

that going to look like? Kind of in practice? I'm curious.

Owen Jennings (20:12)

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. What we're actually seeing and Anthropic had some releases this week 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 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 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 own a multi location quick serve restaurant. You say, hey, can you build me an app where I can Manage scheduling for these two locations and automatically fire off text via WhatsApp or Signal or whatever to my employees. It's actually going to 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. I think 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 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 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.

David Haper (22:14)

I think we're all incredibly bullish on 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 business is. The stock has been roughly flat for, I don't know, six or seven years.

Owen Jennings (22:30)

Thanks for reminding me.

David Haper (22:32)

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?

Owen Jennings (22:40)

Yeah, I think so. I think, you know, markets are. 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 markets are voting machines in the near term, but they're weighing machines in the long term. Just like focus on building.

David Haper (23:05)

Dave 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 talked a bit about the ecosystem. You guys obviously have regulatory infrastructure. How do you think about the business overall in that context?

Owen Jennings (23:21)

Yeah, I think in the near 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 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, 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 licenses and regulatory posture definitely exist. I think hardware right now it's harder to imagine how some of the AI tools flow through to the hardware side. 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 in 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, the, the, the, the way that I see this going, if we can, if you extrapolate forward the past several months, is that ultimately a company is sitting on top of some sort of signal, some sort of like rich data and, and, and deep insight. 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 understanding who our customers are, but then also understanding how Block operates. You can imagine, you can imagine for any company just like a markdown file of 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 deeply understand that's hard for others to understand. And then you need a tool like Builderbot or Claude Code or what have you and then you can just iterate through that loop over and over 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 who can just build stuff. And right now you 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. 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, 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 then you maybe could get vibe coded away.

David Haper (26:40)

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

Owen Jennings (26:44)

Appreciate it. Thanks so much.

David Haper (26:45)

Awesome.

Podcast Host (26:49)

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be to sure. 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 Podcasts, and Spotify. Follow us on X16Z and subscribe to our substack@a16z.substack.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 disclosures.

David Haper (27:45)

It.