Dhanji Prasanna: How Goose agents save Block 10 hours a week

1. 0:00 – 5:26

   Introduction to Dhanji

   1. LRLenny Rachitsky0:00

      There's a lot of talk about productivity gains through AI. There's this camp of people that are like, "It's overhype. Nothing's working. Nobody is actually adopting this at scale."

   2. DPDhanji R. Prasanna0:07

      We see a significant amount of gain. We find engineering teams that are very, very AI forward are reporting about 8 to 10 hours saved per week.

   3. LRLenny Rachitsky0:16

      Whenever I hear a stat like this, I think an important element is this is the worst it will ever be. This is now the baseline.

   4. DPDhanji R. Prasanna0:22

      The truth is the value is changing every day, so you need to ride that wave along with it.

   5. LRLenny Rachitsky0:27

      There's a story I heard you share on a different podcast where there's an engineer who has Goose watch him.

   6. DPDhanji R. Prasanna0:31

      He'll be talking to a colleague on Slack or an email and they'll be discussing some feature that they think is useful to implement. And a few hours later he'll find that Goose has already tried to build that feature and opened a PR for it on Git.

   7. LRLenny Rachitsky0:43

      What level of engineer is most benefiting from these tools?

   8. DPDhanji R. Prasanna0:47

      What's been surprising and really amazing, the non-technical people using AI agents and programming tools to build things. The people that are able to embrace it to optimize for their particular workday and their particular set of tasks are really showing the most impact from these tools.

   9. LRLenny Rachitsky1:07

      How do you think things will look in a couple years in terms of how engineers work that's different from today?

   10. DPDhanji R. Prasanna1:12

       All these LLMs are sitting idle overnight and on weekends while humans aren't there. Like, there's no need for that. They should be working all the time. They should be trying to build in anticipation of what we want.

   11. LRLenny Rachitsky1:24

       What's maybe the most counterintuitive lesson you've learned about building products or building teams?

   12. DPDhanji R. Prasanna1:29

       A lot of engineers think that code quality is important to building a successful product. The two have nothing to do with each other.

   13. LRLenny Rachitsky1:37

       Today my guest is Dhanji Prasanna. Dhanji is Chief Technology Officer at Block where he oversees a team of over 3,500 people. With Dhanji's leadership, Block has become one of the most AI native large companies in the world and has basically achieved what many eng and product leaders are trying to achieve within their companies. In our conversation we chat about their internal open source agent called Goose that, by their measure, is saving employees on average 8 to 10 hours a week of work time, and that number is going up; how AI is specifically making their teams more productive, and the teams that are benefiting most. Interestingly, it's not the engineering team. What it took to shift the culture to be very AI oriented; the very boring change they made internally that boosted productivity even more than any AI tool; also lessons from building Google Wave and Google+ and Cash App and so much more. This episode is for anyone curious to see what a highly AI forward technology-driven large company looks like and can act like. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get a year free of 16 incredible products, including Devin, Replit, Lovable, Bolt, N8n, Linear, Superhuman, Descript, WhisperFlow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatBRD and Mobbing. Head on over to lennysnewsletter.com and click Product Pass. With that, I bring you Dhanji Prasanna after a short word from our sponsors. This episode is brought to you by Sinch, the customer communications cloud. Here's the thing about digital customer communications. Whether you're sending marketing campaigns, verification codes or account alerts, you need them to reach users reliably. That's where Sinch comes in. Over 150,000 businesses including eight of the top 10 largest tech companies globally use Sinch's API to build messaging, email and calling into their products. And there's something big happening in messaging that product teams need to know about: rich communication services, or RCS. Think of RCS as SMS 2.0. Instead of getting texts from a random number, your users will see a verified company name and logo without needing to download anything new. It's a more secure and branded experience. Plus you get features like interactive carousels and suggestive replies. And here's why this matters: U.S. carriers are starting to adopt RCS. Sinch is already helping major brands send RCS messages around the world, and they're helping Lenny's podcast listeners get registered first before the rush hits the U.S. market. Learn more and get started at sinch.com/lenny. That's S-I-N-C-H.com/lenny. This episode is brought to you by Figma, makers of Figma Make. When I was a PM at Airbnb, I still remember when Figma came out and how much it improved how we operated as a team. Suddenly, I could involve my whole team in the design process, get feedback on design concepts really quickly. And it just made the whole product development process so much more fun. But Figma never felt like it was for me. It was great for giving feedback and designs, but as a builder, I wanted to make stuff. That's why Figma built Figma Make. With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers. Figma Make is a different kind of bytecoding tool. Because it's all in Figma, you can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds. Stop spending so much time telling people about your product vision, and instead show it to them. Make code-backed prototypes and apps fast with Figma Make. Check it out at figma.com/lenny.

2. 5:26 – 7:33

   The AI manifesto: convincing Jack Dorsey

   1. LRLenny Rachitsky5:28

      (instrumental music) Dhanji, thank you so much for being here and welcome to the podcast.

   2. DPDhanji R. Prasanna5:34

      Thank you, Lenny. Uh, it's a great pleasure to be here.

   3. LRLenny Rachitsky5:37

      I want to start with a letter that I hear you wrote to Jack Dorsey to convince him that he and that Block needed to take AI a lot more seriously. I think you called it your AI manifesto. And it seems like it really worked. We're going to talk a lot about changes that came as a result of that. So let me just ask, what did you, what did you say in this letter and what happened right after you sent that letter to him?

   4. DPDhanji R. Prasanna6:00

      So about two and a half years ago or so, uh, Jack...... really felt like things needed to change. I think he had a sense that the industry was going in a different direction. So, he got about 40 of the company's top executives into a room on a weekly basis, and they all used to sort of talk everything through that was going on. And he added me to that group. Uh, so at some point, I observed that we were talking about lots of deep things, lots of relevant things, but no one was really paying attention to AI. And so, that's when I wrote that letter. And to be honest, it's, I think, taken on a life (laughs) of its own. But there wasn't much to the letter other than, "I think we should do this. I think we should do it centrally, and it's important for us to, uh, be ahead of the game and be an AI-native company, 'cause that's where the industry is heading."

   5. LRLenny Rachitsky6:55

      Let me just say, it's important to note, you were not CTO at this point. You were just, like, a senior engineer kind of person.

   6. DPDhanji R. Prasanna6:59

      No, yeah. I was, uh, just, in fact, I was part-time at the time. 'Cause I had just-

   7. LRLenny Rachitsky7:04

      Okay.

   8. DPDhanji R. Prasanna7:04

      ... had a kid and I was, you know, coming back in, and I was helping out one of the engineering teams. Uh, and then Jack came over to Sydney and spent two days with me. And, you know, both of us like long walks, so we, we walked all around Sydney, and talked it through up and down. And then, um, yeah, he, he offered me the job, and I thought it was a great opportunity, once in a lifetime, so I took it.

   9. LRLenny Rachitsky7:30

      It's like a, "Be careful what you're good at," sort of situation. (laughs)

3. 7:33 – 12:05

   Transforming into a more AI-native company

   1. LRLenny Rachitsky7:33

   2. DPDhanji R. Prasanna7:33

      (laughs)

   3. LRLenny Rachitsky7:34

      Okay. So what, what were some of the bigger changes that you made after Jack is on board and Block execs are on board of, "Cool, this is completely right. We need to go much bigger and think much more deeply about how AI is changing how we build and how we should build."? What were some of the bigger changes that you made from a perspective of other companies listening to this, trying to think about what they should be doing?

   4. DPDhanji R. Prasanna7:53

      At the start, my main focus was to get Block to think like a technology company. And it... For a long time, we had had a little bit of, uh, I'm gonna call it identity drift, maybe. We were talking, uh, uh, about ourselves as a financial services company, some people called us fintech, all of this stuff. But when I started working at what was then known as Square, we were always thought of as a technology company, just like Google or Facebook or any of the others. And so, I wanted to get us back to that. And so, the first thing I did was to try and institute a number of programs that focused on that. So, everything from getting the top ICs in the company together, uh, to talk to each other, to starting a whole bunch of special projects. So we got about two to five engineers per project. There were about eight or nine different projects. And we reinstituted the company-wide hack week. And so, all of this just kind of created a little bit of a spark of like, "Hey, we're building technology again. We're trying to push the frontier again." And that's how it started, and then there were a whole number of steps after that where we went from a GM structure to a functional org structure, which was, I think, the key to making our transformation into being more of an AI-native company.

   5. LRLenny Rachitsky9:19

      Okay. Talk more about that. What does that mean? What does that look like? Why is that so important?

   6. DPDhanji R. Prasanna9:23

      Absolutely. So when we were in our sort of mature phase, so when Square was working quite well, it w- was a very large business, and then we had started Cash App and that also followed suit, we had spun them out almost as a, uh, what we call a GM structure. So they were effectively run as a portfolio of independent companies, and they had their own CEOs who all reported to Jack, and it was still one single executive team. But they had separate engineering practices, they had separate design teams, they, they were kind of separate in almost every way except for some shared resources, like our foundational resources, like legal and some platforms and things like that. So, I think that that was very useful for us for the stage of company that we were in. But when you really want to go deep in technology, when you really want to connect with these things that are sort of industry-changing events that are happening, you need a singular focus. And, uh, we s- we changed the organization. So, all engineers report into one single team now, all designers report in one single team, and there's single head of engineering, single head of design, et cetera. And so, that was the big transformation that we made. And that meant we could really drive forward AI, we could drive forward platform, and just technical depth generally.

   7. LRLenny Rachitsky10:51

      For companies that are struggling with this potentially or trying to figure out how to do this, two things I'm hearing here is, uh, start to see yourself as a technology company. It doesn't n- necessarily apply to every company, but seems like an important element is like, "We're building technology. We're not a financial company, we're not a real estate company, we're not a

   8. NANarrator11:07

      Yeah.

   9. LRLenny Rachitsky11:07

      ... breach company, we're technology company."

   10. DPDhanji R. Prasanna11:09

       Yeah.

   11. LRLenny Rachitsky11:09

       And then two is organize the team such that, say, engineers report up to an engineering leader versus a GM who maybe doesn't understand engineering as well or doesn't take it as seriously as they should.

   12. DPDhanji R. Prasanna11:19

       Yeah. I think that's, that's pretty much what we did. And you know, not to lean too heavily on this, but this is what Jobs did when he came back to Apple as well.

   13. LRLenny Rachitsky11:28

       Mm-hmm.

   14. DPDhanji R. Prasanna11:28

       He reorganized Apple to be functional. And it wasn't like we were following a playbook. We, we discovered this as we were investigating what it's gonna take to make these teams more tech-focused and to bring our DNA back to, uh, back to our roots, which wa- really was putting engineering and design first, which is what technology first means to me. So yeah, I would say to companies, you know, find your DNA and like really try to optimize for what that is in a very simple and clear way.

   15. LRLenny Rachitsky12:00

       Okay. So you made a bunch of changes, you had this manifesto, everyone's on board, you made a bunch of changes, functional

4. 12:05 – 15:24

   How engineering teams work differently today

   1. LRLenny Rachitsky12:05

      technology first. Comparing the way that your, say, engineering team works today versus two or three years ago...What is most different?

   2. DPDhanji R. Prasanna12:13

      Not everyone was on board of, (laughs) I'll tell you that.

   3. LRLenny Rachitsky12:16

      Hmm.

   4. DPDhanji R. Prasanna12:16

      It was quite a painful transformation. I think that one of the things that I learned the most throughout this process is that, uh, Conway's Law can be really, really powerful, so it's the law that basically says, you know, you ship your org structure, so what you're organized as in terms of teams, in terms of collaborating groups and, and your operating model matters a lot to what you build. And so, I think that that was essentially the biggest change, is we had a lot of momentum in each of these silos, be it Cash App, be it Afterpay, be it Square or even Tidal, our music streaming service, and no one was really talking to each other, no one was really aligned on technical strategy, on what we even wanted to be five years from now as a collective team. And so all those things are different now. Uh, I'm not saying it's perfect, there's still a long road ahead of us, but we at least speak the same language, we're all, uh, have access to the same tools, we share the same policies, so like a certain level of senior engineer means the same thing across the whole company, uh, people can move from one team to another, s- into an area of need. All of these things are, are very different, but to sum it up, I would say we're technically focused and we're focused on advancing technical excellence as a goal, and that just really wasn't that true two to three years ago. And there were other things we were optimizing for then.

   5. LRLenny Rachitsky13:58

      Maybe going one level deeper in terms of how people actually work day to day, so if you're looking at an engineering team, say, uh, the average engineering team and maybe also, like, the top most optimal engineering team, how is the way they work today different from a couple of years ago?

   6. DPDhanji R. Prasanna14:12

      In the small, certain teams that are very, very AI native, sort of teams that are building AI first everywhere, are working much differently than before 'cause they're using vibe code tools and they're essentially building without writing lines of code by hand, uh, and that just wasn't true two to three years ago. I don't think it was true anywhere in the world. So that's dramatically different. In teams that are still working with very heavy legacy code bases, it's less true, but they're also encountering these sort of background AI processes, so we have these tools that run 24/7 or run in the CI pli- pipeline and they're analyzing vulnerabilities, they're looking at even bugs filed on tickets and trying to build patches while engineers are asleep, so they come in and, the next day and, and look at it. So it... I would say there are a number of ways in which they're different, but different teams have, um, adapted in different ways depending on how close

5. 15:24 – 20:18

   Goose: Block’s open-source AI agent

   1. DPDhanji R. Prasanna15:24

      they are to the tools.

   2. LRLenny Rachitsky15:25

      Okay, so let me lean into that AI piece...

   3. DPDhanji R. Prasanna15:27

      Yeah.

   4. LRLenny Rachitsky15:27

      ... which is, I think, where you guys are most ahead of a lot of other companies. You guys built your own, uh, agent, I think is what, is how you describe Goose? So there's a lot of talk about productivity gains through AI. There's this camp of people that are like, "You don't understand how much productivity there is to gain from AI. It's the future. This is the way it's all gonna work. We're all accelerating 10X."

   5. DPDhanji R. Prasanna15:47

      Yeah.

   6. LRLenny Rachitsky15:47

      There's also this camp of people that are like, "It's so over-hyped, nothing's working, people talk about it, all these pilots are failing, nobody's actually adopting this at scale." I feel like you're probably in that first camp. What sort of gains have you seen practically from AI tools on your teams?

   7. DPDhanji R. Prasanna16:03

      Our number one priority is to automate Block, which means getting AI and getting, uh, AI forms of automation through our entire company, and we feel that that's just at the beginning of where the utility is with all these large language models, and I think we're gonna continue to see that improve. But even now, we find engineering teams that are very, very AI forward that are using Goose every day are reporting about eight to 10 hours saved per week, uh, and this is self-reported, and then we also have a number of check metrics to try and validate that, so we look at PRs, we look at throughput of features, we look at a whole bunch of things and we have our data scientists come up with a complicated formula that tries to distill it all into something meaningful. And, uh, we feel across the whole company, we're probably trending towards 20 to 25% of manual hours saved, uh, and I think that's just the start, uh, of all of this. I do feel that the more AI native companies are doing a better job of realizing this, so companies that started just with AI, startups mostly. But there is some truth to this notion that AI isn't a panacea, and it's growing as well, right, in capability, so you need to ride that wave along with it, and I think a lot of the companies aren't realizing this. They're like, "Well, where is the value?" And the, the truth is, the value is changing every day, and so you need to be adaptable and look at what the value is today and plan for what the value will be tomorrow and then slowly expand to the areas where it's more- most efficacious. Like, I'll give you an example. One area in which we find that it's really good is for non-technical teams to be able to build little software tools for themselves, so this has been one of the most surprising and energizing uses of Goose within Block.... is we'll have our enterprise risk management team build a whole system for self-servicing, uh, enterprise risk. And this is compressing, like, weeks of work into hours. And, or, ordinarily, they would be waiting for an internal apps team or something to go and build that, and they would put that on their Q2 roadmap, and everyone (laughs) would be, uh, twiddling their thumbs until it all clicked into place. But now you can just go and do it. And so a lot of these kinds of use cases, we're seeing an enormous amount of, um, productivity gain in. The other area which I'm really excited about is we have this other tool called Gosling, which is a Goose for mobile, effectively, so it operates your Android, uh, OS at, at a native level using the accessibility API, and we use that for automating UI tests. So before, you would have to hire an army of contractors or Q- uh, QAs who would go and click through every screen, but now we can just bake those into, uh, automated tests and then give you, like, a report at the end. So we're seeing a lot of advantages in those types of areas, but where you have a lot of depth and a lot of, like, really strong people come together is where AI, I think, still, uh, underperforms humans, and that's something that's probably gonna get better over time, but it's also something where we should lean into as humans. So when you have some very senior engineers and they're thinking about things like architecture and design and race conditions, orchestration, things like this, that's still an area where AI isn't quite there. And so I think the companies that aren't feeling the success in AI are trying to just throw these tools at their giant code bases and hoping good things will happen, and that's not how it's playing out. Eventually, I do think it'll get there, but, uh, right now, we're still in the early utility

6. 20:18 – 21:38

   Measuring AI productivity gains across teams

   1. DPDhanji R. Prasanna20:18

      phase.

   2. LRLenny Rachitsky20:18

      Holy moly, there's so much there in what you just shared (laughs) . There's like five things I wanna follow up on.

   3. DPDhanji R. Prasanna20:23

      (laughs)

   4. LRLenny Rachitsky20:23

      Okay, so one is this metric you kind of alluded to, which is how you measure the impact of AI on your teams. So it was manual hours of, of humans ma- human manual hours saved. Is that how you describe it?

   5. DPDhanji R. Prasanna20:35

      Correct. Yeah.

   6. LRLenny Rachitsky20:36

      Okay.

   7. DPDhanji R. Prasanna20:36

      So that-

   8. LRLenny Rachitsky20:36

      And it's roughly a, a fourth of an engineer's time currently is being saved by AI tooling.

   9. DPDhanji R. Prasanna20:43

      That metric is across all teams. So that would be-

   10. LRLenny Rachitsky20:46

       Oh, wow.

   11. DPDhanji R. Prasanna20:46

       ... like, our support teams, our legal teams, our risk teams, all of them together.

   12. LRLenny Rachitsky20:51

       Wow.

   13. DPDhanji R. Prasanna20:52

       And then on the engineering side, it's very variable, because like I said before, it matters how big and how complex the code base is And so if you're building a totally new green fields code base or you're building an a- app for a new platform, then we s- we're seeing those pretty aggressive gains, but in, you know, very complex code bases that already exist, those gains are not quite there yet.

   14. LRLenny Rachitsky21:18

       That's amazing. And whenever I hear a stat like this, I think an important element that people need to think about is this is the worst it will ever be.

   15. DPDhanji R. Prasanna21:27

       (laughs) Yeah.

   16. LRLenny Rachitsky21:27

       This is the lowest.

   17. DPDhanji R. Prasanna21:28

       Stupid.

   18. LRLenny Rachitsky21:28

       This is now the baseline, right?

   19. DPDhanji R. Prasanna21:30

       Yeah.

   20. LRLenny Rachitsky21:31

       And so, uh, and so it may not sound that, may not sound that crazy yet, but it's gonna get crazy.

7. 21:38 – 32:15

   What Goose is and how it works

   1. LRLenny Rachitsky21:38

      Okay, the other thing that you talked about is Goose. You haven't explained what Goose is. This is a huge deal. Explain what Goose is and how important this has become to you guys.

   2. DPDhanji R. Prasanna21:49

      So Goose is a general purpose AI agent. Uh, so it... You can think of it as a desktop tool or a, uh, a program that you can download and install on your computer And then it has a UI, you can talk to it just like a chatbot, and you can say anything from, "Hey, Goose, organize my photos by category," and it has the ability to look within your photos and, you know, if there are a lot of trees, it'll organize them as nature photos, and if there are a lot of people, it'll organize them as portraiture, all of this sort of stuff, to writing software for you. So it can do all of these tasks And the way we've been able to do this is through something called a model context protocol, which... or the MCP, which a lot of your listeners might have heard And this is something that Anthropic came up with that we were a very early contributor to And the model context protocol is very simply just a set of formalized wrappers around existing tools or existing capabilities So if you have tools that you use in the enterprise, be it Salesforce, or be it Snowflake, or SQL, any, any of these things, you can wrap them in the MCP and then that... it exposes them to your LLM to be able to manipulate So until that point, the, uh, LLMs were not really able to do much other than chat, but Goose gives these brains arms and legs to go out and act in our digital world, and, and that's where we find it's had most impact And it's built on this fairly open protocol that anyone can implement. There have been an explosion of MCPs. Goose is entirely open source, by the way, so any of you can download it and extend it, write your own MCPs Uh, and that's, that's been our, uh, our core successes through Goose

   3. LRLenny Rachitsky23:54

      Okay, so essentially, like, Claude code with a UI desktop app sort of thing built on top of, uh, Claude and OpenAI, ChatGPT, and a bunch of open source models. Is that right?

   4. DPDhanji R. Prasanna24:06

      Yeah, it can use any model. So we have a pluggable provider system And you can either bring your own API keys and use...... the Claude family of models or OpenAI's family of models. Or you can use open source models, and you can download them and, uh, use them directly or via Ollama and other ... There are several tools that help you do that. But essentially, it's taking the capability of these models to generate text and to interpret text, and applying them to real world situations. So, one example that I really like is, you can ask Goose to go and build you a marketing report, and it has MCPs to connect to Snowflake and Tableau and Looker. So it'll write SQL to pull out data from there. It'll do some analysis in, uh, CSV, so it can write Python code on your desktop to do all that. It will generate some graphs using some JavaScript charting library that it knows about. And then finally, it'll put this all into a PDF or a Google Doc or whatever, and it can even email it for you or upload it somewhere. And it's doing all of this on its own, by the way, it's n- no one's sitting here telling it that. You're just saying, "Hey, I want this report. I want this emailed here. I want these pretty charts." Um, and it's orchestrating across all these systems.

   5. LRLenny Rachitsky25:31

      So essentially, at Block, instead of using Claude or ChatGPT directly or even Cursor and all these apps, they use Goose.

   6. DPDhanji R. Prasanna25:40

      Yeah. We allow our engineers and our general employee population to use any tools that they want. Goose is the one that's most well integrated into all of our systems because it's built on the MCP, and it's so easy to create an MCP for an existing system. So for example, if you're using a issue tracking tool and you want some AI automation added to it, before Goose, our teams would have to wait for the vendor to build that AI capability in there, or maybe there's some way in which OpenAI or Anthropic or Google would provide that general purpose capability where we could plug those in. But with Goose, that's no longer necessary. With a few lines of code that an MCP represents, all these systems are orchestratable with AI basically overnight, and Goose can write its own MCPs, so it's pretty, uh, bootstrappable as well.

   7. LRLenny Rachitsky26:43

      And this is open source, and basically, you've spent all this time building this thing. Any other company can now implement it and, and build on all the work you've done.

   8. DPDhanji R. Prasanna26:51

      Yeah. And we have a lot of companies using Goose pretty actively.

   9. LRLenny Rachitsky26:55

      Mm-hmm.

   10. DPDhanji R. Prasanna26:56

       Uh, I don't want to name too many names, but from our competitors to our sort of close partners, a lot of them are using Goose pretty regularly on their teams. I know Databricks talks about it a lot, but they're ... You know, everyone you can, you can think of in this mid-tech tier-

   11. LRLenny Rachitsky27:13

       All right.

   12. DPDhanji R. Prasanna27:14

       ... is using Goose in some form.

   13. LRLenny Rachitsky27:15

       That's insane. This feels like it could have been a massive business of its own, like, uh-

   14. DPDhanji R. Prasanna27:18

       (laughs)

   15. LRLenny Rachitsky27:19

       ... some of the fastest growing companies in the world, basically this is their product, and you've built it and given it away.

   16. DPDhanji R. Prasanna27:23

       Yeah. We believe in the power of open source, and you know, our, our core ... One of our core missions is to increase openness, and that means contributing to open protocols and contributing to open source, and ... You know, as a tech company, we're built on a lot of open source software. I think pretty much every tech company is, whether you're talking about Linux or Java or MySQL or any of these essential components. And so, we feel like we have a strong imperative to give back. We want to build things that not only are good for us and our customers, but that outlast Block and outgrow Block. That's certainly a core value for us and has been from the beginning, uh, even long before this whole AI phase. So yeah, Goose follows in that proud tradition, and yeah, we're very excited that it's had the success it's had.

   17. LRLenny Rachitsky28:18

       What's the story with the name Goose, by the way? Can't help but ask.

   18. DPDhanji R. Prasanna28:20

       (laughs) Goose is a Top Gun reference.

   19. LRLenny Rachitsky28:23

       Okay.

   20. DPDhanji R. Prasanna28:24

       Um, so- (laughs)

   21. LRLenny Rachitsky28:25

       Got it, got it, got it, got it.

   22. DPDhanji R. Prasanna28:25

       ... uh, our engineer that came up with it, uh, he also looks exactly like Goose, so it's kind of crazy if you put them side to side. He's gonna be really embarrassed with my sharing this, but, um, that's the reason why I call it Goose, and then we went le- lent into the whole bird theme after that.

   23. LRLenny Rachitsky28:41

       That's incredible. There's a story I heard you share on a different podcast where there's an engineer who takes this to the extreme and has Goose watch him. Talk, talk about that. Share that story.

   24. DPDhanji R. Prasanna28:50

       Yeah, absolutely. So he is very, very AI focused, and he's trying to extract all these crazy ideas from Goose. And Goose can do all of the things that I described through specific interactions with tools, but it can also just watch your screen. So like, it understands how to process images and process, uh, the things that it's looking at through screenshots. And so he built this system where it's essentially just watching everything he does, all the time, and he'll be talking to a colleague on Slack or in email, and they'll be discussing some feature that they think is useful to implement. And then a few hours later, he'll find that Goose has already tried to build that feature and opened a PR for it on, on Git, and, uh, and all sorts of other wacky things like that. So it'll, it'll try to nudge him out of a workflow if he's running over on a meeting and he's late for something else. Uh, it, it sort of comes up with these creative things that he didn't program or he didn't write prompts for, but that it thinks will help him improve his productivity or improve his workday. So yeah, it's pretty crazy. You have to have the stomach for it, (laughs) to, to be that, um, level of tied in to your working tools. But it kind of shows you what's possible with tools like this.

   25. LRLenny Rachitsky30:13

       Clearly, this is where things are going once this gets good enough. I love this guy is just trying it. So it's basically watching him work and anticipating what he should be doing, and does the work for him as a first draft so that he's like, "Oh, the PR is already done on this thing we were just talking about at this meeting."That's incredible.

   26. DPDhanji R. Prasanna30:31

       Yeah, exactly. Yeah.

   27. LRLenny Rachitsky30:32

       How, uh, how good is it? Like where is it at if you had to go zero to 100 of like, okay, it's- it's gonna... All you have to do is now think and talk, and it'll just do your job.

   28. DPDhanji R. Prasanna30:40

       (laughs) Yeah, so voice is the other big part of it.

   29. LRLenny Rachitsky30:43

       Mm-hmm.

   30. DPDhanji R. Prasanna30:44

       So, it- it has voice processing capability, so it's always listening to what he's saying as well and- and trying to interpret that. I would say that this is mostly an experiment, you know, given that he- he's on our core Goose team and he contributes to Goose, so he has a day job. This is a kind of thing on the side that he was developing. So, once this evolves into more- more of a native feature of Goose itself or other tools that we use in enterprise, I think it can have a lot of legs. But it's already pretty good. I mean, it's probably cutting down enormous amounts of busy work that he has to do. So for example, one thing he'll do is he'll say, "Oh, I have a meeting conflict, I can't make it that time," or, "I have to go pick up my kid." And Goose will automatically reschedule that meeting without him ever-
8. 32:15 – 37:42

   The future of AI in engineering and productivity

   1. DPDhanji R. Prasanna32:15

      who- who did the same thing.

   2. LRLenny Rachitsky32:16

      So, let me pull on that thread a little bit. You're- you're kind of seeing a glimpse of where things are going, you're very, uh, ahead of the curve in a lot of ways at Block. Where... How do you think things will look in a couple years in terms of how engineers work, how product teams work, that's different from today?

   3. DPDhanji R. Prasanna32:32

      I think a lot of it is dependent on the improvement of LLM performance. But I can tell you the way I'm trying to change how I work and how I'm trying to change our immediate team's way of working. So I think vibe coding has been an interesting, exciting thing, which is you talk to a chatbot essentially and it goes and builds software for you. But I think this is highly limiting. It's very ping pong. Like you do something, you wait for three or four minutes and it comes back with something sort of half-baked and you have to nudge it and guide it and massage it to get where it needs to be. I think that we're gonna see much more autonomy. So, we're wer- working on a couple of experiments with Goose, with the next version of Goose, where we're really trying to push it to work not just for two or three or five minutes at a time. Our aver- our median session length is five minutes and on average seven. But we're trying to push it to hours. You know, we're trying to say, "Hey, all these LLMs are- are sitting idle overnight and on weekends while humans aren't there. Like, there's no need for that. They should be working all the time, they should be trying to build in anticipation of what we want, if we go back to, uh, the earlier part of the conversation." But also I think that they should be able to build in ways that were never possible before. Before we, as humans, we had limited resources, limited bandwidth and a lot of coordination overhead. So, we would have to choose the best path to try in an experiment and I don't think we need that anymore. We need instead to be able to describe multiple different experiments in a great amount of detail and then maybe we go to sleep and then in the morning, all those experiments are built and we can sort of throw away five or six of them. So one of the things that I do regularly, so I write code every day, but one of the things that I do regularly is just throw away huge, huge amounts of code. And it's kind of hard for me 'cause I'm never, I've never done that before. I mean, obviously engineers love deleting code but this is different. This is, you build a whole new system or a whole new feature and you're like, "Ah, it doesn't feel exactly right. I'm just gonna delete and start a- start over." So I think you're gonna see a lot more of that way of working and I think that you're gonna see instead of us, for example, refactoring a- an app to have a different UI or to evolve into its new version, we're just gonna rewrite that app from scratch. And one of the things I'm really pushing our teams to think about is what would our world look like if every single release we RM-RF, like deleted the entire app and rebuilt it from scratch? And so we can't really do that today, but I think this shows you some of the direction of what's possible and where these tools are taking us.

   4. LRLenny Rachitsky35:42

      What's interesting about that is that there's kind of this, uh, common rule in software engineering and just product, don't ever just rewrite... Don't try to rewrite your thing 'cause you're gonna forget all of the small improvements and tweaks and bug fixes people have made over the years and you think it's gonna be this simple, straightforward thing and it ends up being now it's like a year or more of just getting it back to where it was. And it's so interesting that AI now can make that possible and what you're saying is that's actually maybe the way you should be working next time.

   5. DPDhanji R. Prasanna36:09

      I think so and I think that the trick is getting the AI to respect all of those incremental-

   6. LRLenny Rachitsky36:16

      Right, right, exactly.

   7. DPDhanji R. Prasanna36:17

      ... improvements and yeah, and sort of like bake those in as, um-

   8. LRLenny Rachitsky36:21

      Yeah, just-

   9. DPDhanji R. Prasanna36:21

      ... as a part of the specification, if you will. Yeah.

   10. LRLenny Rachitsky36:24

       Also, the point you made about this agent just kind of, you give it a bunch of ideas, it builds them overnight and then you could see what... I imagine it- it goes even further up the stack and comes up with the ideas and then starts building them and then you're like, "Okay. Oh, that was a great idea and now I can see it immediately in the same- same workflow."

   11. DPDhanji R. Prasanna36:39

       Yeah. That's- that's true. I was actually literally trying what you're saying just, uh, last week.And so I have this new version of Goose that we're working on, and I was asking it to come up with ideas to improve itself and implement it overnight. And- (laughs)

   12. LRLenny Rachitsky36:55

       (laughs) There you go. It's just a-

   13. DPDhanji R. Prasanna36:56

       ... sometimes it kind of goes-

   14. LRLenny Rachitsky36:56

       ... it's a paperclip problem. (laughs)

   15. DPDhanji R. Prasanna36:58

       (laughs) Yeah. Sometimes it kind of goes off, off the script entirely, and, uh, you have to sort of pull it back a bit. So I think we're, we're not quite at that era where it's completely self-improving and, um, completely autonomous. But I do think we're in a, in a transition phase where we can give it that nudge and say, "Hey, here is my, like, wish list of 10 things that I wish you could do. Go and figure out the best way to do them." And it's successful, I would say, on like 60% of those things, if the features are well-enough described. And it struggles on the remaining 40 where you have to kind of intervene and,

9. 37:42 – 40:10

   The importance of human taste

   1. DPDhanji R. Prasanna37:42

      and massage it. Yeah.

   2. LRLenny Rachitsky37:43

      Oh, man. I'm just imagining this future where you give it the goal of drive revenue and growth.

   3. DPDhanji R. Prasanna37:47

      (laughs)

   4. LRLenny Rachitsky37:47

      And then it's just like, "Okay, everyone's fired. Here's pay- here's your paychecks."

   5. DPDhanji R. Prasanna37:52

      (laughs)

   6. LRLenny Rachitsky37:52

      Uh, "I'll take it from here." (laughs) It's just the world is-

   7. DPDhanji R. Prasanna37:55

      I don't think we're going to be there. I, I, I-

   8. LRLenny Rachitsky37:56

      No.

   9. DPDhanji R. Prasanna37:56

      ... do think we're gonna need a lot of human taste to anchor these AIs so they don't go off script, to be honest. And that's really where, um, our, our design lead and our design teams are pushing us to think. And, and that's a differentiator that I think will push us beyond this era of AI slop that everyone's talking about. So yeah. It's very much like anchoring it into a thing that matters to people and a thing that's tasteful and useful and has value.

   10. LRLenny Rachitsky38:30

       To make that even more concrete, is, is there an example of something maybe AI was trying to do or a team was trying to pitch where you had to just like, no, this is where humans are gonna step in and keep things, keep things on track?

   11. DPDhanji R. Prasanna38:43

       I'd say it was more around things like process automation or... You know, it's a lot of times I'll get this sort of request where a team will say, "We need to buy this new tool from this vendor because our current tool isn't doing X, Y, and Z." And another team will say, "No, no, we can just use Goose to build an app that will, you know, do the same thing for us in half the time or less." And then as a human, you're sitting there thinking, "Is any of this necessary? Like, if we just change the process, do we even need to think about building tools?" And this is the thing that AI isn't good at. It's not a- able to have this sort of portfolio judgment or judgment across a global sense of what's important and what matters. So a lot of times I tell teams, "Just question, like, the base assumption," particularly our InfoSec teams, 'cause they're, they'll twist themselves into knots sometimes trying to secure something, and you'll be like, "Well, just ask the team that's building it to do it differently or to not build that at all if it doesn't matter, and then you won't have to increase your surface area of securing it, securing it." So I think those are the areas where, uh, it's better for a human to use judgment, and AI has

10. 40:10 – 44:08

    Building vs. buying software

    1. DPDhanji R. Prasanna40:10

       not done a great job.

    2. LRLenny Rachitsky40:11

       You make this point about building your own software, your own tools instead of buying stuff. This is a big question with AI. Is it gonna replace all these SaaS apps to Salesforce over? How... Is there a sense of just either how much money you guys have maybe saved building your own stuff? Or have you built a newfound respect for the existing SaaS software that everyone's using and, and c- pays lots of money for?

    3. DPDhanji R. Prasanna40:31

       I think there's a trap in getting away from your core purpose as a company. And our core purpose is economic empowerment, so getting customers or merchants or artists the ability to make a sale or pay their rent or upload their latest creation to Tidal. And I think that anything that serves that purpose, we should encourage and we should invest in. But if we're just purely looking at dollars versus dollars, then that's like pulling us off that purpose. Like, the savings and costs that there might be in replacing a vendor tool by something you build in-house is probably not worth it in the mental bandwidth that you've lost and the amount of the team's sort of technical focus that's being taken away. So yeah. I would say it's just keep coming back to the thing that matters to you as a company, and then the rest is... you know, will follow from that.

    4. LRLenny Rachitsky41:38

       Yeah. I think people forget just how much maintenance it takes to keep something you've built. Like, okay-

    5. DPDhanji R. Prasanna41:42

       Yeah.

    6. LRLenny Rachitsky41:42

       ... cool, we built it in a weekend. And now it's years-

    7. DPDhanji R. Prasanna41:45

       Yeah. (laughs)

    8. LRLenny Rachitsky41:45

       ... of endless maintenance and requests and support. And, and also, to your point, it's... Like, it feels like it comes back to the, uh, always motto of just focus on your core competencies and then buy everything else.

    9. DPDhanji R. Prasanna41:57

       Yeah. It's the classic 80/20 problem. And we have that enough with our, with the apps that we build for our customers.

    10. LRLenny Rachitsky42:05

        (laughs)

    11. DPDhanji R. Prasanna42:05

        You know? Like, we'll build some great experiments that, that really resonate, and then we have to spend a lot of time ironing out the long tail of problems. So in Cash Card, for example, we, we built the entire functionality of Cash Card, I would say, pretty much in a weekend or maybe a week of sort of integration and work. And then it took a really long time to iron out all these edge cases where, you know, someone would tip twice the value of the bill, and then it would completely break, uh, something in the back end. Or, you know, people would use it as a gas station, and they have a different way of, um, billing your card. So yeah. It's very much that. And...And to your point, I would go, always come back to like, what is the reason we're doing this? Why does it matter to us and to our customers? And if it doesn't clearly satisfy that, um, I would just push it off as a not interesting thing.

    12. LRLenny Rachitsky43:04

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11. 44:08 – 53:45

    How AI is changing hiring and team structure

    1. LRLenny Rachitsky44:08

       this episode. One of the biggest parts of the conversation around AI is head, is hiring, jobs, things like that. So there I have two kind of, this two-part question. One is just how has the rise of all these AI tools, this increased productivity impacted the way you plan headcounts and, uh, hire? And then what do you look for that's different in people you're hiring now that AI is such a big part of the way you guys work?

    2. DPDhanji R. Prasanna44:34

       I don't think that things have progressed far enough that it's really impacted in a fundamental way how you would, how many people you would need to sort of build an app of the scale of Cash App, for example. I think what's changed for us is much different and it has nothing to do with AI. It's that what we talked about earlier is moving from our GM structure to a fractional structure. And in our GM structure, our incentives were always to think of engineering headcount as a commodity, and so we would just add more engineers if we wanted to build more features and the classic, um, mythical man, person, month trap or whatever it's called. And I think that moving to a functional structure completely changes that, and you're like, well, we can leverage common platforms, common modules. We can bring in experts from across the company to advise us on how better to do this. And so those kinds of things I think have made it much different in how we hire, and we no longer see engineers as a commodity to, to just sort of add 100 people to go and build, you know, the next, uh, product in Cash App. But on the AI side, we're very much looking for people that are embracing these tools and that are eager to try and learn from it. We're not looking for people who are amazing AI practitioners on the get-go. I think we have those people and we're interested in those people if they're, ever want to work with us. But I'm much more keen on looking for that college grad who just really is eager to learn about these tools and like open to it, or even the veteran who has embraced these tools and figured it out. And that's kind of where we're optimizing for, uh, who we look for rather than, rather than sort of a specific set of skills.

    3. LRLenny Rachitsky46:44

       So essentially the biggest change is just looking for people that are embracing AI, not being like, no, I'm, I'm a, I don't need this stuff. I'm an, I'm an amazing engineer. I don't need to use Cursor or Goose or all these things.

    4. DPDhanji R. Prasanna46:55

       Yeah. A learning mindset is how I would put it. This is something that Jack, our CEO, talks about a lot, um, is he wants us to be a learning first company.

    5. LRLenny Rachitsky47:05

       That's-

    6. DPDhanji R. Prasanna47:05

       So everything we do, every experiment that we ship, what can we learn from it and did we feel that we gave it our best shot? And I think that that's more important to him than even sort of coming up with the right business, uh, answer every time.

    7. LRLenny Rachitsky47:25

       What about when you're interviewing? Are you encouraging engineers to use AI tools as they're doing exercises? How does that, how did that change over the past year or two?

    8. DPDhanji R. Prasanna47:33

       Yeah. We're starting to do that now. So traditionally we would just use, um, like CoderPad or something like that to whiteboard through a problem or, and even like program it, uh, in pseudocode or near pseudocode. But now we're, we're looking at can you use VibeCode to build something? Can you, how are you, how comfortable are you with these tools or how are you thinking about evolving, uh, with them as well? But it's early days yet. I would say that it's not clear to me that necessarily how someone knows how to use, you know, be it Goose or Cursor or any of these other tools matters that much to whether they're a good engineer. I still think the things that we interviewed for in the past, a critical mindset, the ability to really understand deeply the technical nature of a problem is still much more important than, um, whether you're a fully AI native pro- programmer or not.

    9. LRLenny Rachitsky48:36

       Another question that I've always been thinking about a lot of people wonder is, uh, what level of engineer is most benefiting from these tools? You could argue it's the junior engineers, now they could just get all this work done. You could argue it's senior engineers 'cause they know so much more about how things work and now they could just...... orchestrate thousands of agents doing their bidding. What have you seen in terms of which level is benefiting most?

    10. DPDhanji R. Prasanna48:56

        Yeah, so two answers to that. One is, you're definitely right that the more senior and the more junior they are, the more comfortable or the more eager they are to adopt these AI tools. And, and I think that's for a variety of reasons including some of them that you named. Like, I think the senior people really understand in great depth how everything works, and so they're almost relieved that this tool exists that can go and do all these things that they've done a million times before and couldn't be bothered. And then, the junior people are like, uh, my niece and nephew on a BlackBerry or something. They're just blitzing through thing, uh, not BlackBerry in the early days, in iPhones now, they're blitzing through, uh, a text message when I'm still sort of seek and destroying, uh, (laughs) through my keyboard. Shows you how old I am. So, I think there's that, but I think the non-technical people using AI agents and programming tools to build things is really what's been surprising and really amazing, and I think that speaks to how these roles are gonna evolve in the future. The lines are gonna be blurred between whether you're in legal or in risk or in engineering and design even. And so I think that the people that are able to embrace it, to optimize for their particular workday and their particular set of tasks are really who are showing, showing the most, um, most, uh, impact in, from these tools.

    11. LRLenny Rachitsky50:36

        It's interesting. No one talks about that element of engineering productivity, which is the reduction of asks from all the other parts of the company to build random one-off things. That feels like a huge productivity gain for engineers.

    12. DPDhanji R. Prasanna50:47

        It is massive. Although I think that it's a little bit like the analogy of if you build a bigger highway, you'll just get more cars on the road. So I think the fact that everyone's building software means that there's more software to be built, more coordination to happen, and everyone's more eager to ship things faster and, and, um, with greater results. And so we're just seeing an overall uptick in velocity and the ask for more features, if that makes sense.

    13. LRLenny Rachitsky51:21

        Mm-hmm. (laughs)

    14. DPDhanji R. Prasanna51:22

        Yeah.

    15. LRLenny Rachitsky51:22

        Absolutely, and it-

    16. DPDhanji R. Prasanna51:24

        Yeah.

    17. LRLenny Rachitsky51:24

        ... connects to your point about you're not slowing hiring. What I'm hearing is just headcount hiring, uh, desires for more engineers, more product people is not slowing at all. You're basically, it's as if AI wasn't really there.

    18. DPDhanji R. Prasanna51:37

        We're being more thoughtful about it. So like-

    19. LRLenny Rachitsky51:39

        Mm-hmm.

    20. DPDhanji R. Prasanna51:39

        ... I said, we were looking at it as a commodity in the GM era, and now that we're functional, it's much less about how many engineers we need as a function of the number of features we have in Square Cash App. And in the functional org structure, we think of it much more as what are the areas of optimization, where can we build depth, and what really accelerates our priorities through things like modularization, reuse and, uh, going deep into platforms.

    21. LRLenny Rachitsky52:12

        I love this hot take of, uh, if you're trying to f- be more productive, forget AI. Just reorg into a functional structure. (laughs)

    22. DPDhanji R. Prasanna52:18

        (laughs) It's not wrong in some ways, but here's another really interesting example where we are trying to improve our build times and we're using, we're using Goose and a lot of other tools to help us with this too, and they've done remarkable things. So we have this really cool tool that analyzes our test suites and selects the right test to run for changes that were made. So we've cut down basically 50% of test runs this way, which is pretty great and, like, we're not warming the planet as much with all these unnecessary CPU cycles being wasted on tests. But then things like offloading tests to the cloud or simply just deleting tests that don't make sense anymore probably save you two to three times that. So there is still a portfolio approach that you need to take, for lack of a better term. It's like that example I told you earlier about should we buy a vendor tool or should we build this in-house? It's like, well do we even need to do this process at all? So in some ways structure matters more than the efficacy of the tools you have.

    23. LRLenny Rachitsky53:34

        Wise words. Makes me think about Elan has this whole process, try to optimize stuff and one of the steps is like, do we even need this thing before we start o- optimizing it and automating it.

12. 53:45 – 55:13

    The importance of using AI tools yourself before deploying them

    1. LRLenny Rachitsky53:46

       Before I zoom out and ask about just general lessons that you've learned over the course of your career, is there anything else that you think might be really valuable or useful to folks that are trying to lean in further into AI or just help their teams think a little bit more forward-thinking?

    2. DPDhanji R. Prasanna54:04

       I would say really try and use these tools yourself. So the way in which I think we've been able to drive most of the adoption is Jack uses Goose, I use Goose, our executive team all have used Goose, um, and use it regularly and use other, uh, other AI programming tools and assistance as well. And we do it every single day, and so we learn a lot about how our own workflow can change, and that's gonna tell you so much more about how you're gonna change your organization's workflow than if you're reading a bunch of think pieces on LinkedIn or Harvard Business Review or whatever it is and then trying to get your teams to follow suit. So I think we do this with everything. It's...... like, feel it. Like, use the product yourself, feel it, understand its strengths and weaknesses and its ergonomics, and then figure out how to apply it to your teams.

    3. LRLenny Rachitsky55:06

       Something I have found helpful in doing that, which I completely agree with, which is like, stop reading about it, stop listening to us talking about it, just, like, build some stuff.

    4. DPDhanji R. Prasanna55:12

       (laughs)

13. 55:13 – 58:01

    How Goose helped solve a personal problem with receipts

    1. DPDhanji R. Prasanna55:13

    2. LRLenny Rachitsky55:13

       But the-

    3. DPDhanji R. Prasanna55:13

       Yeah.

    4. LRLenny Rachitsky55:14

       ... the, the thing that I found really helpful there is have a specific task or problem you want to solve for yourself, because that really motivates you and makes it very real.

    5. DPDhanji R. Prasanna55:21

       Yeah.

    6. LRLenny Rachitsky55:21

       For example, just the other day, I was trying to pull images out of a Google Doc. You know, like, Google Doc, it's like ... I think of it as Hotel California. You put images in there, but there's no way to get 'em back out (laughs) unless you-

    7. DPDhanji R. Prasanna55:32

       Right. (laughs)

    8. LRLenny Rachitsky55:32

       ... do some crazy stuff. So, I just went to Lovable and, like, built an app where I can give you a Google Doc URL and let me download the images really easily, and bam.

    9. DPDhanji R. Prasanna55:40

       Yeah.

    10. LRLenny Rachitsky55:40

        Perfect.

    11. DPDhanji R. Prasanna55:41

        Yeah, great example. I did something like this, uh, a couple months ago as well, where my son has a whole bunch of therapies. He, he has additional needs. And so I was trying to gather all ... the receipts for all these therapies, and, um, share them with my wife, and she, she will, like, claim it from our insurer. And I was really struggling to do this, 'cause they, they're in various forms or screenshots, in some cases, they're PDFs or whatever. So, I asked Goose to do this, and it was all sitting on my laptop. And Goose figured out that it could put all of these receipts into, um, my Apple Notes app into a single note. It converted it to HTML, so it would sync seamlessly to my phone, and then I could email it or share it with her from there. And that's just something I just never would have thought of. And it did this using AppleScript, so it just controlled my computer for me in the background. And yeah, so these are, like, surprising ways in which these tools help us. And the more you use them to solve real problems, to your point, the more you understand what their strengths are. And where to, where you can deploy them.

    12. LRLenny Rachitsky56:51

        I love this example. How di- ... So did you just go to Goose and be like, "Here's the problem I have. How would I, how would you solve it?"

    13. DPDhanji R. Prasanna56:56

        Yeah, pretty much. I said, "I have all these receipts. They're in Google Drive." (laughs) And so we have similar origin problem there. And I need to get them into a single form, and I need to, like, collate the totals and do all this. So, it tried a few approaches first. It tried to download them and it tried to read them using a PDF reader and this and that. And then the thing about Goose that I think a lot of the other AI agents learned from us as well is if it tries a few things and fails, it'll back up and it'll try a different route, and it'll just keep going until it makes some pro- uh, some progress. And that's what it did. And then it, it picked AppleScript as a way to do it, because it had the MCP extension to control my computer. And this is the same thing that our, um, our engineer we were talking about the other day uses to watch his screen and things like that. But this was a very focused problem, and it, and it managed to do that. So, yeah, it's- it's surprising what these tools can do, and allowing them the kind of flexibility to do that is a big part of learning

14. 58:01 – 59:57

    What makes Goose unique

    1. DPDhanji R. Prasanna58:01

       how to use them.

    2. LRLenny Rachitsky58:02

       That's cool. Uh, I love the ... By the way, can you run Goose la- like, as a regular person, can you just download Goose and use that instead of

    3. NANarrator58:07

       (instrumental music)

    4. DPDhanji R. Prasanna58:08

       Yeah, absolutely.

    5. LRLenny Rachitsky58:09

       ... Cloud or whatever?

    6. DPDhanji R. Prasanna58:09

       Yeah, yeah. You can just download it from-

    7. LRLenny Rachitsky58:11

       Yeah.

    8. DPDhanji R. Prasanna58:11

       ... uh, our, our URL. We, uh, we can share it in the show notes for you. Um, and, yeah, you can install it. It, it comes for Mac and Windows and Linux. I believe it's an Electron app, so it'll work on all of them. It also has a command line, so for people who are more comfortable, uh, using that, we have that, um, UI as well.

    9. LRLenny Rachitsky58:32

       Wow, you really are competing with these massive foundational model companies building ...

    10. DPDhanji R. Prasanna58:36

        (laughs)

    11. LRLenny Rachitsky58:36

        Is ... What's the simplest way to compare Goose to something else? Is it, like ... Is Cloud Code the cl- simplest comparison, or something else?

    12. DPDhanji R. Prasanna58:43

        I- I think it's a bit different than Cloud Code, because at its core, Goose is a platform that implements MCPs. And so-

    13. LRLenny Rachitsky58:51

        Mm-hmm.

    14. DPDhanji R. Prasanna58:52

        ... MCPs give it this dynamically extensible nature, so it can do all of these things for you, whether it's automating things like we were talking about with Google Docs and Notes and things like that, or it can do straight-up programming tasks for you using other MCPs. Like, so it can index code and do it that way. So, it's really more of, like, an extensible platform. So, I would say it sits somewhere between your classic AI assistant, where you just ask it, you know, "What's the weather today? Can you calculate how many months it's been since this date?" Or whatever it is. To, um, the more focused cursors and Cloud Codes of the world.

    15. LRLenny Rachitsky59:39

        Basically, it's everything combined. Totally. And free.

    16. DPDhanji R. Prasanna59:42

        (laughs)

    17. LRLenny Rachitsky59:43

        Uh, y- you pay for the LM tokens, but, uh, but, yeah.

    18. DPDhanji R. Prasanna59:46

        Yeah.

    19. LRLenny Rachitsky59:46

        There's no, like, subscription.

    20. DPDhanji R. Prasanna59:47

        Well, you can use open source models which-

    21. LRLenny Rachitsky59:49

        Oh, you c-

    22. DPDhanji R. Prasanna59:49

        ... um-

    23. LRLenny Rachitsky59:49

        Oh my god.

    24. DPDhanji R. Prasanna59:50

        ... yeah, which help.

    25. LRLenny Rachitsky59:50

        This is crazy. What a cool team to be on, building Goose at, at Block.

    26. DPDhanji R. Prasanna59:54

        (laughs)

    27. LRLenny Rachitsky59:54

        Man must having- must be having so much fun.

15. 59:57 – 1:01:49

    What Dhanji wishes he knew before becoming CTO

    1. LRLenny Rachitsky59:57

       Oh, man. Okay, let me zoom out a little bit. So, you've been CTO of m- like, again, LinkedIn right now for just about two years. What's something that you wish you knew before you stepped in this role? If you could go back couple years and just whisper a few tips and tricks or lessons in- into your ear, what would they be?

    2. DPDhanji R. Prasanna1:00:13

       I think maybe two different things. One is just the power of Conway's Law, like we talked about before. It's, like, how difficult it is to change outcomes without changing the structure of relationships between people in an organization. And I think I always kind of knew that at some level, but really appreciating it in a visceral way is big. And the other thing that I really learned the hard way maybe is you only hear about it when things are going wrong. So, when things are going well, you kind of have this eerie silence, and you're like, "Well, am I doing the right things here? Am I focusing on the right problems?"Uh, so having a bit of judgment, having a bit of time to step back and look at things holistically, those are things that you really need to make time for and, and, uh, do on a regular basis, which I wish I had known when I took up the role.

    3. LRLenny Rachitsky1:01:15

       Looking back at your time at Block... I keep trying to was- I almost say Square 'cause I'm so used to that over the year, but I know Block is, is the name of the broader company and Square is just one. Is that... Just so people understand, Square is one business unit, one product within Block.

    4. DPDhanji R. Prasanna1:01:28

       Correct, yeah. We have-

    5. LRLenny Rachitsky1:01:29

       Okay, cool.

    6. DPDhanji R. Prasanna1:01:30

       ... uh, Square, Afterpay, Cash App, and Tidal are, are, uh, four major brands, and then we also have Bitkey and Proto that are focused on Bitcoin for us and they, uh... we ship hardware, uh, in those two brands.

    7. LRLenny Rachitsky1:01:44

       Okay, great. I think the... Some people are like, "What are you even... What are you guys talking about?" Okay, cool. So

16. 1:01:49 – 1:04:56

    Counterintuitive lessons in product development

    1. LRLenny Rachitsky1:01:49

       reflecting back on your time at Block, what's maybe the most counterintuitive lesson you've learned about building products or building teams that goes against what most people believe, say common startup wisdom?

    2. DPDhanji R. Prasanna1:02:02

       I think code quality is one. Like, being an engineer, I learned this kind of very early on and it, it keeps coming true over and over and over again. A lot of engineers think that code quality is important to building a successful product. The two have nothing to do with each other. My favorite example is YouTube. Uh, I was working at Google around the time YouTube was acquired, and I just remember there was this whole wash of angst about how horrible the YouTube code base is and how terrible their architecture is, and they're storing videos as blobs in MySQL (laughs) and whatnot. And, you know, you could argue that YouTube is the most successful product at Google by a long way, right? Like, maybe more successful than many of their others combined. And so, it really has very little to do with how well it was architected. 'Cause the flip side of that, Google Video, which is this product that I don't know if people remember, it existed before YouTube. It supported more formats. It supported higher resolution. You could upload, you know, hour-long videos. YouTube had none of this. It just had the, like, one or two-minute quick video thing. And it's far and away, uh, blown away its competition, and so I think just keeping that front and center is, why are we building these tools or these apps or these products? They're for people to solve a specific problem. So in our case, it's for a Square merchant to make a sale, to sell coffee to you or to sell something they've b- made, and that's really what's important. It's not really important, uh, how well our Android platform performs, uh, unless it's serving that need. And so I think that's been a really hard one for me over my career, and I continually encounter engineers who think, "We need to refactor, we need to do this in a better way, we need..." and then I'm like, "No, this... all this code could be thrown away tomorrow, so just focus on what we're trying to build and whom we're trying to build for."

    3. LRLenny Rachitsky1:04:19

       That is an incredible insight and lesson. This YouTube story is so fun, uh, (laughs) and such a good example. (laughs) You were saying they were storing the video, like, uh, content in a, in this MySQL se- like, row and column as a blob theta tape.

    4. DPDhanji R. Prasanna1:04:34

       Yeah, this is, this is what, uh... I mean, I didn't actually look at the code, so I g- I couldn't verify it, but this, this was the sort of common wisdom, and then they had a, an entirely Python stack that was incredibly slow compared to the state-of-the-art sort of C++ and Java servers that we had hyper-optimized at Google back in

17. 1:04:56 – 1:08:07

    Why controlled chaos can be good for engineering teams

    1. DPDhanji R. Prasanna1:04:56

       those days.

    2. LRLenny Rachitsky1:04:57

       That is hilarious. Makes me think about also companies... Like, when you look inside a company, if you work at a company, you're just like, "This is just pure chaos. No one knows what's going on. This is just, uh, about to all fall apart." And if... That's basically what it's like at every successful hypergrowth company.

    3. DPDhanji R. Prasanna1:05:14

       Yeah, there's some truth to that, for sure. Yeah. (laughs)

    4. LRLenny Rachitsky1:05:17

       And so I think, again, it's just there's so much more that is more important to the success of a business, and it's what you said is, are you solving a real problem for people? Can you get it in their hands? Can you continue solving real problems for them? It's not about the quality of the code. It's not how well you operate internally.

    5. DPDhanji R. Prasanna1:05:32

       Absolutely. Uh, I think on Cash App, we had that as well. So, uh, in the early days of Cash App, I was head of engineering whe- from when we were about 10 engineers to, you know, 200 plus, and took us to about 10 plus or, um, 20 million users, thereabouts. And there was a very similar thing there. It's like, from the outside, it looked like everything was really chaotic. It's like people would build random experiments and ship them and it just didn't look like we were following strict policies on things like software lifecycle and stuff like that, and it was kind of true. And my philosophy was always, we have all these brilliant engineers and I'm going to do more harm than good by trying to harness them into very strict sort of blinkered areas. If they want to spin their wheels building something that is a complete waste of time for a little bit, but at the same time, if they're delivering these amazing things on the flip side, then I'll almost allow that. Like, I'll, I'll be okay with that. And, um, you know, it's a fine balance 'cause engineers can really go off in, (laughs) into rabbit holes if you let them. But yeah, there's a certain amount of creativity that chaos breeds, and you have to know how to build controlled chaos in some ways. So you have to create a foundation that isn't...... you know, uh, liable to rupture, like you have major reliability problems or something like that, or you're gonna lose money, in our case. And so as long as those things are bedded down, and you allow your engineers to have the freedom to experiment and iterate and do the things that energizes them, like that's the ideal.

    6. LRLenny Rachitsky1:07:26

       Uh, uh, speaking of cr- controlled chaos, your, uh, one of your titles during your time at Block, we- at- at- I guess this was while you were actually at Square, was mad scientist (laughs) for four, four and a half years.

    7. DPDhanji R. Prasanna1:07:37

       Yeah. (laughs) Yeah, that was, uh, that was a time when I was working part time, mostly because I had very young kids with lots of additional needs. And I was a consultant on various different projects, and I was trying to help sort of some va- wacky things get off the ground. And, uh, yeah, I just, I'm really grateful to Block that they afforded me the freedom to have that role in my career

18. 1:08:07 – 1:13:36

    Core leadership lessons

    1. DPDhanji R. Prasanna1:08:07

       as well.

    2. LRLenny Rachitsky1:08:08

       Maybe one more question before I take us to, uh, to fail corner, which I'll explain. So you've shared a few lessons of things you've learned over the course of your career. Are there any other just let's say core leadership lessons that you've learned that have- you think have been important to you being successful at the work that you've done?

    3. DPDhanji R. Prasanna1:08:25

       I think start small with everything. Like if you try to boil the ocean to make a cup of tea, I don't know who said that, but it's a really, uh, a useful phrase that I keep coming back to, uh, you'll n- you'll never get there. So if you're making a cup of tea, just make the cup of tea. You don't need to boil all the water that there is.

    4. LRLenny Rachitsky1:08:47

       That sounds like really, uh, not delicious tea, ocean-

    5. DPDhanji R. Prasanna1:08:49

       (laughs)

    6. LRLenny Rachitsky1:08:49

       ... ocean water.

    7. DPDhanji R. Prasanna1:08:51

       Uh, yeah. (laughs) I think, uh, there's another one of like, um, I think Carl Sagan said, uh, if you wanna make an apple pie from scratch, you have to first invent the universe. So it's like (laughs) narrow your scope to the thing that's in front of you and that's achievable. And so that, that I think is really important. And that's one of our core tenets and, uh, always has been, even when we were just Square in the early days, start small.

    8. LRLenny Rachitsky1:09:19

       Is there an example that, that maybe worked really well or maybe didn't work?

    9. DPDhanji R. Prasanna1:09:23

       Yeah, I mean, Goose started small. It was just an engineer working on their own time, trying to build something that was useful and that satisfied a thesis that they had. So, uh, Brad, our creator of Goose, um, believed very early on, I think long before we heard the buzzword going around that agents would be how we unlock value from LLMs. And he built a proof concept and he shared it with a bunch of people, he shared it with Databricks and Anthropic, got them excited, and you know, learned a lot from them. And so it just sort of built momentum from there. And even internally, it was, um, quite a, uh, quite a similar thing. Cash App itself was like that, and Cash App started more or less as a hack week sort of idea, and grew into a bigger and bigger and bigger thing. So a lot of our projects start with these small experiments that we try to then build on top of. Um, we became the very first com- company that was a public company to launch a Bitcoin product. And that was, again, a hack week idea that, um, actually Jack and me and another engineer worked on. Uh-

    10. LRLenny Rachitsky1:10:40

        That was the hackathon team, you and-

    11. DPDhanji R. Prasanna1:10:42

        That w-

    12. LRLenny Rachitsky1:10:42

        ... Jack Dorsey and an engineer?

    13. DPDhanji R. Prasanna1:10:44

        Yeah. (laughs) Yeah, it was the three of us.

    14. LRLenny Rachitsky1:10:45

        (laughs) Unreal.

    15. DPDhanji R. Prasanna1:10:46

        And, um, yeah, and, and it was great. It was, we, we went and bought a cup of coffee at Blue Bottle, and it was bought using Bitcoin over Cash Card. And I'll tell you those, in, in hindsight, probably the most expensive cup of coffee I've ever bought. (laughs)

    16. LRLenny Rachitsky1:11:01

        I was gonna ask, what was Bitcoin at like, like 20, 20,000?

    17. DPDhanji R. Prasanna1:11:03

        Given, I think it was like 6,000 or 7,000 back then. Yeah. (laughs)

    18. LRLenny Rachitsky1:11:07

        Oh, no. It's like 120,000 now. Great.

    19. DPDhanji R. Prasanna1:11:10

        Yeah. (laughs)

    20. LRLenny Rachitsky1:11:11

        Inflation.

    21. DPDhanji R. Prasanna1:11:11

        Um, but yeah, it's an example of how, you know, you, you get to, a working useful product to people if you focus on a small thing first and then build.

    22. LRLenny Rachitsky1:11:22

        And just to double down on this, does this count too, okay, we have a big idea, we're just gonna put a bunch of resources on it and go big immediately.

    23. DPDhanji R. Prasanna1:11:29

        Yeah, absolutely. And I've been part of teams like that too. So, uh, in my career, I worked at Google on this product called Google Wave, which was trying to be everything to everyone. And you know, we were 70, 80 engineers building this thing before it even really had any users outside Google. And so I think that's an example of something that started big, tried to go big on day one, and probably lacked some of that meeting the earth, um, where, where reality lies and, and, and adapting accordingly.

    24. LRLenny Rachitsky1:12:08

        I remember Google Wave, uh, absolutely. It was beautiful, a lot of hype. I don't remember what it was for specifically.

    25. DPDhanji R. Prasanna1:12:13

        (laughs)

    26. LRLenny Rachitsky1:12:13

        But it, it looked really nice.

    27. DPDhanji R. Prasanna1:12:15

        Yeah, I mean, a lot of learnings from that one for me. Yeah.

    28. LRLenny Rachitsky1:12:19

        What else? Any other big lessons?

    29. DPDhanji R. Prasanna1:12:21

        Those two are the big ones, but I would also say like question base assumptions on everything. You know, sometimes we get into traps where we are, as professionals, hyperfocused on what we're building that day, that week, that month, and we don't stop to think, should we even build this at all or what's the purpose of building this? Could we build something completely different that would matter more to our core reason for being? So I would say, yeah, question the sort of base assumptions. It's somewhat of a cliche, but you really need to remind yourself to apply it over and over and over again.

    30. LRLenny Rachitsky1:13:03

        I had a colleague of yours on the podcast back in the day, Ayo.... who worked with you on-
19. 1:13:36 – 1:15:50

    Failure corner

    1. DPDhanji R. Prasanna1:13:36

       with him on... and you on that one, yeah.

    2. LRLenny Rachitsky1:13:38

       Okay. I'm gonna take us to a, a recurring segment on the podcast I call Fail Corner. You already shared one example (laughs) of a product that failed that you worked on.

    3. DPDhanji R. Prasanna1:13:46

       Mm-hmm.

    4. LRLenny Rachitsky1:13:46

       I'm curious if there's another. And the question is just, what's a product you worked on that did not work out? 'Cause people listening to this have all... well, hear all these amazing successful people come on the podcast, share all these stories of success, endless success, but they don't hear the stories of when things don't work out. And so, this... question is just what's a product you worked on that didn't work out, and what did that teach you?

    5. DPDhanji R. Prasanna1:14:08

       It's a very valuable point. I mean, my career has basically been a string of failed product on top of failed product. (laughs) And I think that, yeah, the Google Wave example's there. I worked for a hot minute on Google Plus, which was another epic failure.

    6. LRLenny Rachitsky1:14:23

       Good one.

    7. DPDhanji R. Prasanna1:14:23

       I worked at this social networking startup called Secret, which, um, you know, burned hot, uh, for a bright minute and then blew up. And then there was, uh, an email startup that we did, and that was, again, very promising. Uh, and then it... that sort of fizzled, so the, the co-founder of Canva and I worked on that one. So there's been a whole string of failures. But at each point, I think I learned something, and I learned that, you know, I need to never make that class of failures or errors again. And so, Cash App was probably, like, the big success for me that... a product that I worked on that was very early on and grew to be, um, this sort of giant business and, and product that people love. And so, yeah, tha- that's been my career, is essentially taking the learnings from all these failures, getting some humility out of it in the process too, coming into things, um, willing to listen to other people's points of view, critical points of view, and not just kinda thinking that I have all the answers.

    8. LRLenny Rachitsky1:15:36

       Yeah. And I, I bet all these products that fail had really beautiful code, a lot of really good architecture decisions were made. (laughs)

    9. DPDhanji R. Prasanna1:15:43

       (laughs) Some of them. Some of them were awful in every way. (laughs)

    10. LRLenny Rachitsky1:15:47

        (laughs) So many reasons for it to fail.

20. 1:15:50 – 1:24:20

    Lightning round and final thoughts

    1. LRLenny Rachitsky1:15:50

    2. DPDhanji R. Prasanna1:15:50

       (laughs)

    3. LRLenny Rachitsky1:15:50

       Incredible. Uh, Dhanji, is there anything else that you wanted to share or, I don't know, double down on before we get to our very exciting lightning round?

    4. DPDhanji R. Prasanna1:15:59

       Uh, I would say, you know, I think that we're in this era of a lot of change, and people are scared or reticent or uncertain about where things are going. And I think that... look at the things that matter to you. L- uh, you know, for us, it's open source, open protocols, improving access for everyone. You know, I've been very lucky in my career to only work on products that are either free or almost free to anyone, you know, or they have a free tier and then you kind of pay for some premium services, and... that are usable by everyone. So, like, anyone can become a Square seller. You know, I, I remember even in the early days, people used it to pay each other as a, a peer-to-peer money transfer system, and that's why we built Cash App. And that was really successful on the back of that. So I think it's really look at the things that are important to you, um, and optimize for them. It's not really that important that the technology trends are going in a certain way, because technology is here to serve us, and if we have an important reason for being and an important purpose, then we can make that technology serve us. And that's much more important than, uh, being deep with the technology or being at the forefront of every trend.

    5. LRLenny Rachitsky1:17:27

       I have such great advice when there's so much to pay attention to and so much happening. So stressful to feel like I'm just not aware of all the things. I'm not as good as all these people I'm seeing on social media about what's happening-

    6. DPDhanji R. Prasanna1:17:39

       Yeah.

    7. LRLenny Rachitsky1:17:39

       ... with AI. I'm just, like, so behind. Uh, what I'm hearing from you is just, like, what is actually important to you? And just do that. Don't feel like you need to be the best at everything that's happening in... uh, on top of all the latest AI news.

    8. DPDhanji R. Prasanna1:17:51

       Yeah, exactly. And like, if it's not meaningful and fun, then you shouldn't-

    9. LRLenny Rachitsky1:17:56

       Mm-hmm.

    10. DPDhanji R. Prasanna1:17:56

        ... be doing it probably.

    11. LRLenny Rachitsky1:17:58

        (laughs) With that, Dhanji, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

    12. DPDhanji R. Prasanna1:18:03

        Okay, shoot.

    13. LRLenny Rachitsky1:18:04

        Uh, I see so many books behind you, so I love this first question, I'm excited to see (laughs) what you pick. What are two or three books that you find yourself recommending most to other people?

    14. DPDhanji R. Prasanna1:18:12

        Yeah, so I very, uh, much of the opinion that you shouldn't read books that are about, like, your daily work or your professional life. I, I read fiction, I read the classics, I re- I read poetry, philosophy, history. These are the books I really enjoy, and I think it expands your mind and gives you creative ideas and helps you question things about the human condition. And that's much more valuable than, like, some self-help book on... or some get good at being an engineering manager book. So, uh, yeah, having said that, The Master and Margarita by Mikhail Bulgakov is one that I really love. It's a masterpiece of Russian literature. And then, um, I've always been drawn to Tennyson's poetry, and I find that, um, on... in the times when I'm most uncertain and... or grieving-... uh, Tennyson's poetry has always kind of resonated with me and, and, and helped me find a center.

    15. LRLenny Rachitsky1:19:16

        Wow.

    16. DPDhanji R. Prasanna1:19:17

        Those two, yeah.

    17. LRLenny Rachitsky1:19:18

        Never heard these recommendations before.

    18. DPDhanji R. Prasanna1:19:20

        (laughs)

    19. LRLenny Rachitsky1:19:20

        I'm really excited to check these out. Very cool for a CTO of, of a big tech company.

    20. DPDhanji R. Prasanna1:19:24

        (laughs)

    21. LRLenny Rachitsky1:19:26

        What is a favorite recent movie or TV show you've really enjoyed?

    22. DPDhanji R. Prasanna1:19:30

        Alien Earth, I think is pretty awesome. It's by Noah Hawley, who did the Fargo TV series. And so it's a kind of, you know, it's someone with, like, all of these incredible skills and high art filmmaking, who's doing, like, a pulp sci-fi show, and it just looks stunning and it feels stunning, and it captures all of that essential alien, uh, pulpiness that makes it so interesting and fun. So, I really like that. And I'm also watching Slow Horses, which I think is one of the better shows on TV.

    23. LRLenny Rachitsky1:20:04

        Love Slow Horses. The new season's out. Uh, I think the fifth episode just dropped the day we're recording this, so, um, I love that show. Alien Earth, also just watched it. It's so creepy and just, like, all these slimy, gooey, little creatures just crawling around. It's (laughs) disturbing.

    24. DPDhanji R. Prasanna1:20:18

        Yeah, I just love the aesthetic. And they captured something essential about, like, the original Alien. And yeah, and they do it... Like, every scene in Alien Earth feels like you're watching a painting or something or, you know, someone's reading a novel to you. It's, like, really unfolds very thoughtfully.

    25. LRLenny Rachitsky1:20:36

        I've never watched any Alien content in my life-

    26. DPDhanji R. Prasanna1:20:39

        Okay. (laughs)

    27. LRLenny Rachitsky1:20:39

        ... and I really enjoyed Alien Earth. I will say the ending, I was just, like... Like, it felt like it kind of slowed down a bit. I'm just like, all right, I guess I see where it's going now. But it was really fun to watch. Okay, next question. Do you have a favorite product you've really enjoyed? Sorry, a favorite product you've recently discovered that you really enjoy? It could be an app, could be a gadget, could be some kitchen thing.

    28. DPDhanji R. Prasanna1:20:58

        Uh, well, I'm a gamer. I love playing games. So for me, it's the Steam Deck, the Steam Deck OLED, which is their latest version. It's like this gorgeous piece of hardware that lets you play, you know, the best games out there, but it's totally extensible and customizable. And in this era where, like, we're constantly told by big tech companies that we need to lock everything down, we need to lock down the user experience and customizability in order to have things work for people, I think Valve showed that's totally unnecessary and totally wrong. And you can build... You know, the Steam Deck, you can install competing app stores. You can install Windows on it. You can treat it like a computer, write programs, which I have done, um, to run on it. Uh, so yeah, I think it's an incredible thing, and it looks beautiful and it works great. So, big fan.

    29. LRLenny Rachitsky1:21:50

        Do you have a favorite life motto that you find yourself coming back to often in work or in life?

    30. DPDhanji R. Prasanna1:21:55

        If you're not waking up in the morning feeling energized about what you're going to do that day in your professional life, then change something. Like, quit, if that's what it comes down to, or find a new way of doing what you're doing. You know, just don't accept what's meted out to you. Yeah, so that's, that's how I've tried to do things. And sometimes it works, sometimes not, but yeah, it's a good thing to ask yourself.

Episode duration: 1:26:41