How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna

Transcript from https://podmenti.com/t/29c46f7fdd9419ec

There's a lot of talk about productivity gains through AI. There's this camp of people are like so overhyped, nothing's working, nobody's actually adopting this at scale. When you see a significant amount of games, we find engineering teams that are very, very AI forward are recording about eight to ten hours save.
Per week. 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. The truth is the value is changing every day. So you need to ride that wave along with it.
There's a story I heard you share on a different podcast where there's an engineer who has Goose watch him. 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. Now a few hours later he'll find that Goose has already tried to build that feature and open a PR for it on Git. What level of engineer is most benefiting from these tools?
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.
How do you think things will look in a couple years in terms of how engineers work? That's different from today. 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. What's maybe the most counterintuitive lesson you've learned?
About building products or building teams. A lot of engineers think that code quality is important to building a successful product. The two have nothing to do with each other.
Today my guest is Donji Prasana.
Donji's chief technology officer at Block, where he oversees a team of over thirty five hundred people.
With Donji's leadership, Block has become one of the most AI native large companies in the world.
And it's basically achieved what many Enj 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 eight to ten hours a week of work time, and that number is going up.
How AI 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 Plus 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 sixteen incredible products.
Including Devin, Replit, Lovable, Bolt, N8N, Linear, Superhuman, Descript, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chapier, DN, Mobbin.
Head on over to Lenny's newsletter.com and click product pass.
With that
I bring you
Danji Prasana.
After a short word from our sponsors.
This episode is brought to you by Cinch, 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 cinch comes in.
Over one hundred and fifty thousand businesses, including eight of the top ten largest tech companies globally,
Use Cinch'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 two point oh.
Instead of getting text from a random number, your users will see your 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 suggested replies.
And here's why this matters.
US carriers are starting to adopt RCS.
Cinch 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 US market. Lear more and get started at cinch.com slash Lenny. That's s-inc dot com.
Slash 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.
Give 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 fulctional prototype or app that anyone can iterate on and validate with customers.
Bigma Make is a different kind of vibe coding 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 dot com slash Lenny.

Donji, thank you so much for being here and welcome to the podcast.
Thank you, Lenny. Uh it's a great pleasure to be here.
I wanna 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 call it your AI manifesto.
And it seems like it really worked. We're gonna 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?
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 forty 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
I think taking on a life 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.
Let me just say it's important to know you were not CTO at this point. You were just like a senior engineer. No. Yeah. I was uh just in fact I was part time
at the time.'Cause I had just 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.
Okay so what
What were the 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.
Or 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.
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
I'm gonna call it identity drift, maybe. We were
Talking 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
Two
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 had reinstituted the company wide hack week.
And so all of this just kinda 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.
Okay, talk more about that. What does that mean? What does that look like?
Why is that so important?
Absolutely. So
When we were in our
sort of mature phase. So when Square was
working quite well with 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 are put 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 depths generally.
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 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 breach company, we're
Technology.
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. 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. 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.
to bring our DNA back to
uh back to our roots which was really was putting engineering and design first.
Which is what technology first means to me.
So yeah, I would say the company
You know, find your DNA and like really try to optimize for what that is in a very simple and clear way.
Okay, so you made a bunch of changes. You had this manifesto, everyone's on board, you made a bunch of changes, functional technology first.
Comparing the way that your say engineering team works today.
versus two or three years ago.
What is most different?
Not everyone was on board, I'll I'll tell you that. 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 your
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 after pay.
Be it square or
even title 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 in into an area of need.
All of these things are are very different.
But to sum it up, I would say
What
technically focused and we're focused on
advancing technical excellence as a goal.
And not just
really wasn't that true.
Two to three years ago.
I mean, there were other things you were optimizing for.
Then
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 was the way they work today different from a couple of years ago?
In
The small
certain teams that are very, very AI native, sort teams that are building
AI first everywhere.
are working much differently.
Then
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 or 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
twenty four seven or running the CI pi 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 they're
a number of ways in which they're different, but different teams have um adapted
in different ways depending on how close they are to the tools.
Okay, so let me lean into that AI piece.
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 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.
There's also this camp of people are like so overhyped, nothing's working, people talk about it.
All these pilots are failing, nobody's actually adopting this at scale.
Feel like you're probably in that first camp.
What sort of thing is?

practically from AI tools on your teams.
our number one priority is to automate block.
Which means getting AI and getting
Uh
AI forms of automation throughout 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 you're using Deuce every day.
A reporting about eight to ten 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 twenty to twenty five percent.
Of manual hours saved.
Uh, and I think that's just the start of 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 this. They're like, Well, where's 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 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 O acid. Okay.
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
underperforms humans
And not 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 phase.
Holy moly. There's so much there in what you just shared. Like five things I want to follow up on. Okay. So one is this metric you kind of alluded to, which is how you measure
the impact of AI and your teams. So it was
Manual hours of
Um
Humans.
Ma human manual hours saved. Is that how you describe it?
Yeah.
And is that roughly a a fourth.
of an engineer's time currently is being
Saved by AI tooling.
That metric is across all teams, so that would be
Like our support teams are
legal teams, our risk teams, all of them together.
Wow. 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 Greenfields code base or you're building a n app for a new platform,
then we s we're seeing those pretty aggressive
Gains but
In
you know, very complex codebases that already exist, those gains are not quite there yet.
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 they will ever be.
This is the lowest.
This is now the baseline, right?
And so
Uh
And so it may not sound that may not sound that crazy yet, but
It's gonna get crazy.
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.
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 chat bot.
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 fill out of people, it'll organize them as portraiture.
All 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 M C P 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
sequel, 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 LMs 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've been an explosion of MCPs. Goose is entirely open source, by the way, so any of you can download it and
extended.
Write your own MCPs.
Uh and that's that's been our uh
occur successes through Goose.
Okay. So this essentially like claud code with a UI desktop app sort of thing built on top of
Uh.
Claude and OpenAI, chat GPT and a bunch of open source models. Is that right?
Yeah, it can use any model. So we have a pluggable provider system.
And
You can
Either bring your own API keys and use uh
The Claude family of models or open AI's family of models.
Or you can use open source models and you can download them and
uh use them directly or via Olama 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 in
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 and a 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 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.
So essentially
Add block.
Instead of using
Claude or Chat GPD directly, or even Cursor and all these apps that use Goose.
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 the
few lines of code that an M C P 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.
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.
Yeah, and we have a lot of companies using Goose pretty actively.
I don't wanna 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
Data Bricks talks about it a lot, but they're
You know, everyone you can you can think of in this
Mid tech.
Tier is using goose and something. That's insane. This feels like it could have been a massive business of its own. Like uh some of the fastest growing companies in the world, basically, this is their product, and you've built it and given away.
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 wanna 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.
What's the story with the name Goose, by the way, I can't help but ask.
Goose is a top gun reference. Okay. Um so our engineer that came up with it.
Uh he also looks exactly like Goose, so it's kinda 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.
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 of that story.
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 an 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 open a PR for it on on Git.
And uh and all sorts of other
wacky things like that. So it'll
It would 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 work day.
So yeah, it's pretty crazy. You have to have the stomach for it 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.
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. Exactly. How uh how good is it? Like where's it at if you had to go of zero to hundred 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.
Yeah, so voice is the other big part of it. So 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 the 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
Sort of
Sitting in front of his calendar and clicking through ten times.
Yeah, so these are things that I think we were waiting for.
the calendar vendor to build as features into calendar.
But we don't need to do that anymore because AI is able to orchestrate this for us. This isn't that guy that had like four jobs at four different startups that was able to paralyze all his work in the No, it's not. He's he's uh
Uh, he's someone that I work with for a long time and uh he's been at Block for a long time and he's
He just loves experimenting.
And he embodies that culture of experimentation just like um
Our creator of Deuce.
Uh who who did the same thing.
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 of years in terms of how engineers work?
how product teams work that's different from today.
I think a lot of it is dependent on
The improvement of LM 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 chat bot 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 w 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 average our median session length is
Five minutes and on average seven.
But we're trying to push it to ours. You know, we're trying to say
Hey, all these LMs 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 kinda 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 whole new feature and you're like
Uh that 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 R M minus R F like deleted the entire app and rebuilt it from scratch.
And so we can't really do that today, but
I think these shows you some of the direction of what's possible and where these tools are taking us.
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 because you're gonna
forget all of the small improvements and tweaks in
bug fixes people have made over the years, and you think it's gonna be this simple, straightforward thing, 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
But you're saying is that's actually maybe the way you should be working.
I think so. And I think that
The trick is getting the AI to respect all of those incremental improvement.
Yeah, and sort of like bake those in as um
Yeah. Yeah.
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 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, now I can see it immediately in the same
Same workflows. 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 paper clip problem.
Yeah.
Sometimes it kinda 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's my like wish list of ten 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
Sixty percent of those things if the features are
Well enough described.
And it struggles on the remaining forty, where you have to kinda intervene and
And massage it. Yeah.
Oh man, I'm just imagining this future where you give it the goal of drive revenue and growth and then it's just like
Okay. Everyone's fired. Here's pay here's your paychecks. Uh I'll take it from here. It's just I don't think we're gonna be there. I I 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 slob 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.
To make that even more concrete, is is there an example of something maybe AI was trying to do?
Or team was trying to pitch where
You had to just like know this is where humans are gonna step in and
Keep things keep things on track.
I'd say it was more around
things like process automation
Or you know, 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, we can just use Goose to build an app that will
you know, do the same thing for us in half the time or bless.
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 infosect teams because they are
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 the you won't have to increase your surface area of security.
Securing it.
So I think those are the areas where
uh it's better for a human to use judgment and AI has not done a great job.
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 of Salesforce over
H 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
Saa software that everyone's using and and can pays lots of money for.
I think there's a trap.
And
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.

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.
It's 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.
Yeah. I think people forget just how much maintenance it takes to keep something you've built. Like okay, cool, we built it in a weekend, and now it's years 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.
Yeah, it's the classic eighty twenty problem and we have that enough with our
With the apps that we build for our customers. 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.
This episode is brought to you by Persona, the verified identity platform helping organizations onboard users, fight fraud, and build trust.
We talk a lot on this podcast about the amazing advances in AI, but this can be a double edged sword.
For every wow moment, there are fraudsters using the same tech to wreak havoc, laundering money, taking over employee identities, and impersonating businesses.
Persona helps combat these threats with automated user
business and employee verification.
Whether you're looking to catch candidate fraud, meet age restrictions, or keep your platform safe.
Persona helps you verify users in a way that's tailored to your specific needs.
Best of all, Persona makes it easy to know who you're dealing with without adding friction for good users.
This is why leading platforms like Etsy, LinkedIn, Square, and Lyft trust Persona to secure their platform.
Persona is also offering my listeners 500 free services per month for one full year.
Just head tip with
Persona dot com slash Lenny to get started.
That's with Pristona dot com slash Lenny.
Thanks again to Persona for sponsoring 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 higher.
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.
I don't think that things have
progress 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 our functional 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
A hundred 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
embrace these tools and figure 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.
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 a I'm an amazing engineer. I don't need to use cursor or goose or all these things.
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.
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.
What about when you're interviewing, are you encouraging engineers to use AI tools as their
Doing exercises, how does that how did that change over the past year or two?
Yeah, we're starting to do that now. So
Traditionally we would just use um
Like coder pad or something like that to whiteboards or 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 vibe code 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.
Another question that I've always been thinking about, a lot of people wonder is
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 because 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? Yeah, so two answer 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 things um not Blackberry in the early days and iPhones now, they're blitzing through
uh a text message when I'm still sort of
Seek and destroying uh 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
optimized for their particular workday and their particular set of tasks are really
Where should I?
Showing the most um
Most uh impact in from these tools.
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.
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. Yeah. Absolutely.
And it 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.
We're being more thoughtful about it. So like I said
We were looking at 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 or Cache App.
And in the functional org structure
We think of it much more.
As
What are the areas of optimization?
Where can we build depths?
And what really accelerates our priorities.
through things like modularization, reuse, and uh going deep into platforms.
I love this hot take of uh
If you're trying to f be more productive, forget AI, just reorg into a functional structure.
Yeah.
It's not
wrong in some way. So here's another
Really interesting example.
where we're trying to improve our build times and we're using
You were 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 analyz our test suites.
And selects
the right test to run for changes that were made.
So we cut down basically fifty percent 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 tasks.
But then things like offloading
Test 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.
Wise words.
Makes me think about Elon has this whole process for out optimized stuff and one of the steps is like do we even need this thing before we
Start o optimizing and automating it.
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.
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 are you 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
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.
Something I 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. The th the the thing that I found really helpful there is have a
specific task or problem you wanna solve for yourself.
'Cause that really motivates you and makes it very real.
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 his Hotel California.
You put images in there, but there's no way to get'em back out unless you do some crazy stuff.
So it just went to Lovable Mike.
build an app where I can give you a Google Doc URL and let me download the images really easily and bam.
Yeah. Perfect.
Yeah, great example. I did something like this uh a couple of 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 because they they're in various forms. There are 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
And my Apple Notes app.
Into a single note.
It converted it to HTML so it would sink 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 Apple Script. 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.
I love this example. How did you so did you just go to Goose and be like, Here's the problem I have, how would how would you solve it?
Yeah, pretty much. I said
I have all these receipts there in Google Drive. 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 learn 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 prog uh some progress.
And that's what it did. And then it
Yeah.
Picked Apple Script 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 how to use them.
That's cool. Uh I love the by the way, can you run Goose Lo like as a regular person? Can you just download Goose and use that instead of
Yeah, absolutely.
Yeah, yeah. You can just download it from uh our our URL. 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
Where people are more comfortable.
uh using that.
We have that.
um UI as well.
Wow, you really are competing with these massive foundational model companies building
Is what's the simplest way to compare goose to something else? Is it like
The squad code, the cliff simplest comparison, or something else?
I I think it's a bit different than Cloud Code because
At its core, Goose is a
platform that implements MCPs.
And so 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 sit 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 clod codes of the world.
Basically it's everything combined.
Holy and free. Uh uh y you pay for the L M tokens, but uh
But yeah.
Yeah. Well, these open source models which Oh my God.
Yeah.
What a cool team to be on, building goose that's at block.
They must have been having so much fun.
Oh man.
Okay.
Let me zoom out a little bit. So you've been CTO of LinkedIn LinkedIn right now for just about two years.
What's something that you wish he knew?
before you stepped in this role, if you could go back a couple of years and just whisper if you
Tips and tricks or lessons in and see your year. What would they be?
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 kinda knew that at some level, but really appreciating it in a visceral way is big.
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.
Looking back at your time at
Block. I keep trying to s 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's just one is that just so people understand Square's one
business unit one product within block.
Correct. Yeah. We have uh Square, Afterpay, Cash App and Title are our
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.
Okay, great. I think that some people are like, What are you even what are you guys talking about? Okay, cool. So
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 start up wisdom.
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 their storing
Videos as blobs in MySQL 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 a product that I don't know if people remember.
It existed
Before you two.
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 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.
That is an incredible insight and less and this YouTube story is so fun.
Uh and such a good example. You're saying they were storing the video
Like uh
content in a in a s MySQL set like row and column as a blob.
Yeah, this is this is what uh
I mean I didn't actually look at the code, so I 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 plus plus and Java servers that
We had hyper optimized at Google back in those days.
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 about to all fall apart.
And if that's basically what it's like at every successful hypergrowth company.
Yeah, there's some truth to that for sure. Yeah.
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 selling real problem for people? Can you get 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.
Absolutely.
I I think on Cash App we had that as well, so
Uh
In the early days of Cash App.
I was head of engineering w from when we were about ten engineers to
Yeah, two hundred plus and
Took us to about ten
Less or um
twenty 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 life cycle and stuff like that. And it was kinda true.
And my philosophy
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 bl blinkered areas.
If they wanna 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 and
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 way. 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
Uh uh speaking of cre control chaos, you're uh
What are your titles during your
Time it's
block w at at I guess this was why you were actually at Square was mad scientist for four four and a half years.
Yeah.
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 v wacky things get off the ground and
Uh yeah, I I just I'm really grateful to Block that they afforded me the freedom to
have that role in my career as well.
Maybe one more question before we 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 a
you think are have been important to you being successful at
The work that you've done.
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
Yeah, 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.
That sounds like really uh not delicious tea. Ocean ocean water.

Uh yeah.
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
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 always has been, even when we were Joss Square in the early days, start small.
Is there an example that
That maybe worked really well. Or maybe didn't work.
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 Deuce. Um
believed very early on, I think long before we heard the buzzword going around that
Agents would be how we unlock value from LMs.
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
Saying
CashApp itself was like that. I mean 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 co
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 that was the hackathon team, you and Dorsey and an engineer. Yeah, it was the three of us.
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. What was Bitcoin at? I think it was
Six or seven thousand back then. Yeah. Oh no. It's like a hundred twenty thousand now.
Great. Yeah.
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 in a build.
And just to double down on this, this is counter to
Okay, we have a big idea. We're just gonna put a bunch of resources on it and go big immediately.
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 seventy, eighty 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.
I remember Google Wave, uh absolutely it was beautiful.
Lot of hype. I don't remember what it was for specifically, but it was it looked really nice.
Yeah, I mean a lot of learnings from that one for me. Yeah.
What else? Any other big lessons?
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 hyper focused 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.
I had a colleague of yours on the podcast back in the day, Io.
Work with you on Cash App.
Yeah. Uh he's a friend of mine. He's uh amazing. He's the metaphor along those lines of just like
I forget exactly what it was, but it was just like get to the bare metal of the thing that you're working on. Just like touch the thing that you're building.
And go to the
the base of it to really understand what's going on in
I imagine that was really important with building Cash App and Cash Card.
Yeah, I owe one of the best product.
people I've ever worked with and um, you know, one of my closest friends actually. So
Uh absolutely with him on and you're on that one.
Yeah.
Okay, I'm gonna take us to a a recurring segment on the podcast like I'll Fail Corner.
You already shared one example of a product that failed that you worked on. 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
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 when things don't work out.
And so the question is just what's a product you work on that didn't work out and what did that teach you?
It's a very valuable point.
I mean, my career has basically been a string of failed product on top of failed product.
And
I think that yeah, the Google Wave example is there. I work for Hot Minute on Google Plus, which was another epic failure. Good one. I worked at this social networking startup called Secret, which um
You know, burned hot and 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 at 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, that 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. Yeah.
And I I bet all these products that fail had really beautiful code, a lot of really good architecture decisions were made.
Some of them. Some of them were awful in every way.
So many reasons for it to fail.
Incredible.
Uh, Donj, 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.
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?
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 kinda pay for some premium services.
and that are usable by everyone. So like anyone can become a square seller
Yeah, I I remember even in the early days people used it to
pay each other as a uh 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's here to serve us.
And if we have an important reason for being and an important purpose
then we can make it 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.
Uh 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 to the 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
On top of all the latest AI news.
Yeah, exactly. And like if
It's not meaningful and fun, then
You shouldn't

With that.
Donji, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?
Okay, she
Uh, I see so many books behind you, so I love this first question. I'm excited to see what you pick. What are two or three books that you find yourself recommending most to other people?
Yeah, so I'm 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 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 in
Engineering manager book.
So uh yeah, having said that
The Master and Margarita by
Mikhail Bulgakov is one that I really love.
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 as always.
kind of resonated with me and and and helped me find a center.
Wow. Yeah.
Never heard these recommendations before. I'm really excited to check these out.
Very cool for a CTO of of a big tech company.
Yeah.
What is a favorite recent movie or TV show you've really enjoyed?
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 in high art film making.
is 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.
Love slow horses, the new season's out. Uh
Like the fifth episode just dropped the day we're recording this, so um
Well that show.
Million Earth also just watched it.
So creepy and just like all these slimy gooey little creatures just crawling around.
I just love the aesthetic and the
Captured something essential about like the original alien.
And yeah, and they do it the every
seen in Alien Earth feels like
You're watching a painting or something or
you know, you're someone's reading a novel to you, it's like really unfolds very thoughtfully.
I've never watched any Alien content in the in my life and I really enjoyed Alien Earth.
I will say the ending, I was just 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.
Your favorite product you really enjoyed?
Sorry, your favorite product you recently discovered.
That you really enjoy could be
An app, could be a gadget, could be some kitchen thing.
Uh well, I'm a gamer. I love playing games, so for me it's the Steam Deck.
The Steam Deck to 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
Totally unnecessary and totally wrong and you can build, you know, the 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
Yeah.

Do you have a favorite life motto that you find yourself coming back to often in work or in life?
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.
I I really love this advice. It's really hard to do that.
For a lot of people, is there anything that has helped you get over that fear of just like, oh man, I'm gonna quit this thing. I don't know where I'm gonna go next.
The main thing is
Telling yourself
That
A year from now you're gonna look back on what looks like
A monumental problem.
a life changing thing and you're gonna be like, Oh, that was so trivial.
You know, like
A lot of times we get into these
Traps where we're
overthinking something or really nervous about making a change.
But in hindsight, those
don't seem that big but and
you know, all the time that's passed since and all the events that have happened.
Teach you that there's more to the world and
You know, it's never too late to do something useful or never too late to do something that's for yourself and improving yourself.
So yeah, I think just kinda
Remembering that like
Things are not as big or bleak or decisive as they seem.
In the moment is
Always important.
Final question.
So you were a mad scientist at Square for many years.
Do you have a another favorite mad scientist from pop culture?
Or real life.
That's an interesting one. I think the image that always
comes to my mind is uh Doc Brown from Back to the Future.
I feel like he's the canonical mad scientist of
My generation anyway.
Uh, but there've been a lot in like video games and stuff too, but
He was the one that was like
I'm just gonna do this crazy thing because I must have this burning desire, need to do it. Whether I want to or not, I must build this time machine.
And then he spends the entire movie
Um, trying to fix the problems that it creates.
But yeah, that he's always been uh
Like a really fun
character for me.
Yeah what I think about I think about Pinky.
I don't know.
Yeah. Oh man
Ganji, this was awesome. You were wonderful. Thank you so much for being here.
Two final questions before we actually wrap up.
Where can folks find you online if they want to reach out, learn more about say goose or anything else going on in a block?
And how can listeners be useful to you?
I mean check out our GitHub pages uh for Goose.
And all of the other open source projects we have at Block, so
There's a lot that's useful there.
Um we do a lot on Android open source as well, so check
Check that stuff out.
You can always find me on LinkedIn, so feel free to connect. Um
I'm very happy to be
Contacted.
And I would say the way
People can be useful is
You know, again, going back to this era we're in of a lot of change and uncertainty
I think people that demand more
of their companies
of their employers, of their teams.
You know, demand something better. Like at Block, we always ask, can we default to making this open source?
Can we build this for
Пил дар на час.
Or our customers can everyone benefit.
And I think that's particularly important in this era of AI where
Everyone's kind of locking themselves in walled gardens and
Trying to capture parts of the platform that are emerging.
So yeah, just demand more of of people, you know, like the the internet was created as a
Promise.
for open sharing of information.
Uh to the benefit of all and
I think that AI should realize that for us, and so
Yeah, just demand that of people.
A really beautiful way to end it. Donji, thank you so much for being here.
Thank you, Lenny. I really appreciated it. Thank you.
I appreciate you.
Hi everyone.
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.
Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast.
You can find all past episodes or learn more about the show.
at Lenny's podcast dot com.
See you in the next episode.