Block's significant workforce reduction was driven by a strategic pivot towards AI, enabling drastically increased productivity and a fundamental restructuring of how the company builds products and operates.
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The biggest moat is going to be which
companies understand something that's
super hard for other people to
understand. And if your answer to that
is I don't know, then you maybe could
get vibe coded away.
>> Block was one of the first to make a
pretty drastic decision in cutting 40%
of the workforce. What led up to that decision?
decision?
>> There's been this correlation between
the number of folks at a company and the
output from the company for decades and
decades. I think that basically broke
and what we were seeing is that one or
two engineers who was on the tools is
able to be 10 20 100x more productive
over time. It's like pretty obvious that
these systems are just going to be so
much better than like having a thousand
humans who are doing that work. I I do
believe that fundamentally for a given
product or for a given road map, you're
going to need fewer engineers, fewer
designers, fewer PMs. I think that's
like very very clear.
>> So you show up on Monday, 40% of the
company's gone. What's the most
meaningful difference in how you're operating?
operating?
what does it actually look like for a
large public company to restructure
itself around AI? Owen Jennings is the
business lead at Block where he oversees
product operations and customer support
across Square, Cash App, and Afterpay.
Before this role, he was the CEO of uh
of Cash App during its critical scaling
period. And recently uh block executed a
roughly 40% reduction in force and
they've been pretty candid about AI
being a critical component of that
decision. Owen has gone through the AI
transformation at scale across product
lines and business units and so we're
going to dig into the that decision
around the riff how block has adapted
the current and future state of the
business. So thank you so much Owen.
Awesome.
Um, so you know, Jonathan, I think did
an amazing job kind of setting the
stage, you know, for this conversation.
Uh, you know, talking about how
important it is to be founderled.
Uh, you know, Block was one of the first
to make a pretty drastic decision in
cutting 40% of the workforce. Um, maybe
walk us through kind of what led up to
that decision and how you thought about
it. Sure. I I think I would pro it
probably starts two or three years ago.
I think one thing about Jack is I I find
Jack to be generally right and generally
early. Uh sometimes very early. Um and I
think that's flowed through Twitter,
Square, Cash App, Bitcoin, etc. And so
we were pretty early on the agentic
development side. We actually launched
Goose, which was the first agent
harness, at least that I know of, um, in
early 2024. And that started to augment
how we approached software development,
uh, how we thought about internal
tooling. And I would say that over the
over that period 24 and 25, it was like
pretty meaningful progress. Um, and then
late November, first week of December,
it was just there was a binary change.
you basically have Opus 46, you have uh
codeex 53 and essentially you get this
shift where I think the the the tools
and the foundational models were pretty
good at writing code especially for new
ventures and kind of like green space.
Um it became clear almost overnight
maybe in a couple of weeks that now
they're incredibly capable working with
existing complex code bases. Um and so
there was a
massive paradigm shift where at least
from my perspective there's there's been
this correlation between the number of
folks at a company and the output from
the company uh for you know decades and
decades. I think that basically broke
the first week of December and what we
were seeing is that one or two engineers
or a designer and an engineer who was on
the tools quote unquote as we say is
able to be 10 20 100x more productive.
And so that's really what led us to make
the the decision a few weeks ago. We
spent Q1 discussing like what does this
mean fundamentally? What does this mean
in terms of how we're going to build
products, how we're going to build
software for customers, and then also um
how we're going to run a company. What
is it going to mean to actually run a
company? And we spent Q1 as an executive
team uh with Jack um working through
that. Uh and ultimately that's what led
us to this place where where we we did a
reduction in force that was you know
slightly greater than than 40%. And that
wasn't even uh you know to the to the
conversation we were just having the
tools were flowing through really
meaningfully on the development side and
so the cuts were way larger on the
development side. If you think of
something as outbound sales or account
management um the cuts were you know
fairly dimminimous. Um and so that was
really what we were reacting to. C can I
push you a bit on this a little bit? I
mean, Alex when he kind of introduced
the, you know, the conference uh just,
you know, an hour ago talked about the
zer period. Uh, you know, how much of
the riff was sort of overhang from 2021
kind of overhiring versus AI and and
kind of like the product actual
productivity gain is going to be in the
business? Like if you look at where we
were from a from a gross profit per
full-time employee basis from like 2019
through 2024, we're basically like right
in the middle of the pack with all of
the um uh with all the competitors. Um
if you look at last year, I think we
were kind of I don't know second
quintile or something like that. I think
it's basically like Nvidia and Meta that
are ahead of us. Um, and then when you
look at the composition of what we did,
if you thought it was like croft and
bloat and so on and so forth, then like
this riff would have acred to the
operational teams and like like that
sort of stuff. Those were really really
meaningful cuts on the development side.
You don't make really really significant
cuts on the development side if you're
not seeing a technology and a tool
that's just fundamentally changed how we
build. I mean, we're we're like we're
not writing code by hand anymore. That's
over. That's done. Um and so so anyway,
everyone has their narrative. Um it's
largely not true.
>> Um so maybe just walk through like
tactically how did you actually execute
you know this this transition you know
culturally you know operationally in the business.
business.
>> So I think so we were um the the the
nice part about this riff uh relative to
some other you know things that have
happened at block or at other companies
is we were coming from a position of
strength on a on a profitability and
operating income side. And so sometimes
when it's really financially motivated,
you know, the CFO or the CEO says,
"Okay, we need to do a 16% riff in order
to like hit this hit this target." And
um that wasn't the case at all. We said,
"What should the org look like given how
these AI tools are flowing through now
and what we expect to happen in the in
the coming months and quarters." We had
some core principles. Um the first one
was reliability. When you do something
this size, worst case scenario is you
have an outage or you go down. So that's
like P 00 not acceptable at all.
Obviously, you know, things have been
great over the past several weeks, which
is fantastic. Second is building trust
with customers and um compliance and
navigating the regulatory environment.
We all operate in a super complex
nuanced regulatory environment. That's a
non-negotiable. We have to make sure
that we're that we're doing doing right
there. For instance, like we we
basically did not touch our our
compliance team and our compliance
technology team. Even if the tools are
there, it's like let's not take any
risks. And then third was let's continue
to drive durable growth. So there's
things that are on the road map that we
already know that we're building. We
need to continue to do that. We know
that it might be a squad of three people
instead of a feature team of 14 who's
building that. We want to make sure
we're continuing to build those features
and that we're continuing to make
longerterm bets. And then we built up
the org from scratch. And in some areas
like um the regulatory council team or
the SDRBDR team, the org looked pretty
similar to how it looked in January. Um
on the development side, it looks
completely completely different. Um and
then you know from a from an execution
perspective um you know we thought very
deliberately obviously I've been in the
company 12 years. A number of folks who
we parted ways with our friends and
colleagues for for you know more than a
decade. um we were in a position where
we're able to be generous in terms of
you know the the severance packages that
we gave. We didn't cut people's
technology access instantly which can
suck. Uh we chose to have an all hands
with everybody at the company. So Jack
and the executive team were um you know
looking each other in the eyes and
explaining this decision and explaining
the the drivers behind it. And um I I
think that that it was on a Thursday. I
think like the Friday, Saturday, Sunday
there's a lot of shock uh dealing with
ambiguity. Um and then what we've been
doing is uh we massively reduced the
number of meetings we have probably like
70 or 80%. So I now have time to like
build and work and it's not backto-back
meetings. We're also meeting with the
company every week. So we have like a
one or two hour all hands with Jack
every every Monday. It just feels like
we're we're smaller, we're leaner, we
have fewer layers, we have larger spans,
and it's it's been back to building.
>> So, you show up on Monday, 40% of of the
company's gone. Like, what how is what's
the most meaningful difference in how
you're operating? I don't know, maybe
it's in the EPD or elsewhere.
>> Um, I think that there's a there's a
there's a few different components to
this. I think the biggest thing is so
one concern that I have with like how
some of these org changes might flow
through the tech industry is that and
and it gets back to the to the founder
point. If you're not founder le and you
don't have the the ability to be bold,
then you're going to probably take a
more incremental approach. And so the
way that that's going to feel is like
you do a 15% riff and it's like, oh,
it's fine. and then you do another 15%
riff and then culturally that's just
like devastating for your team because
there's always this like pending riff
looming looming over your over your
shoulder. Um this was obviously a
decision to go in a different direction.
I think one of the benefits that we got
from this is like we were already seeing
a a very meaningful increase in AI tool
usage especially on the development
side. This is just a massive forcing
function. Like if we're building um
okay, we're building Moneybot and we
want to roll Moneybot out to 50% and
there used to be a team of 15 people
working on it and now there's a team of
four people plus $2,000 on the tokens.
That's this is like un unlimited access
to tokens and you can use fast mode on
cloud code. Um so now you have four
people plus the tools. It's like okay
well you need to have eight instances of
goose up and you need to shift your
workflow from sequentially working
through a PR submitting it getting a
review making the change to I have 14
agents who are building PRs on my behalf
right now and I'm going to context
switch between all of those and it's not
just uh on the software development side
it's for PMs too it's for growth
marketers too the biggest shift myself
included I I have you know countless
agents running right now that I have to
I have to go check on. Uh it's it's not
um it's less of a linear workflow and
it's more of like in the background
there's 10 or 20 agents who are doing a
whole bunch of stuff and then I have to
check in on the work and nudge it and
change it or what have you and then I
can commit it to GitHub and I can I can
get the markdown file. We can put it in
the source of truth and we can move on.
>> Yep. So we have a lot of you know public
companies in the audience. We have a lot
of founder businesses in the audience.
Do you expect other companies to kind of
follow a similar path and and I guess
what conditions need to be in place for
that to be successful?
>> I I I don't I don't necessarily want to
like I I talked at the beginning about
um the ground work that happened in 23
24 and 25 like we built this agent
substrate goose and then we built a lot
of tooling at the company on top of it.
We have an agentic operating system
internal only called G2 where anyone can
automate any deterministic workflow. So
anyway, I think there's work to do to to
be successful. I would expect many
companies are doing that work. Some of
them are incredibly um far ahead than
than others. Um and so I I I don't know
what to expect. What I will say is like
to the extent that I I do believe that
fundamentally for like a given product
or for a given road map, you're going to
need fewer engineers, fewer designers,
fewer PMs. I think that's like very very
clear based after like December. Um that
doesn't necessarily mean that there's
going to be fewer engineers, designers,
and PMs in the world. Um, it's like the
classic Jevans paradox thing where I I
think that there's probably now just a
superset of things that that can be
built. Um, so I don't know, you know, a
given tech company might be might be way
smaller, but there might be 50 or 100
more tech companies or you're going to
start getting this development working
in in sectors and and areas where that
hasn't historically been the case. Um,
but I I'm not here to to predict the
future. I'm focused on block.
>> Uh, fair. you you talked a bit about
kind of the some of the AI
infrastructure you build. Maybe you can
get go in a bit more depth uh you know
both in how it's impacting the kind of
technology or I'm also curious about you
know how are you using AI in other parts
of the business you oversee ops customer support.
support.
>> Yeah. Um so I got asked at a investor
conference uh last week like how is AI
like flowing through block and to me
that it's like asking um how are
computers flowing through block? uh like
it's it's a uh fundamental in-built
thing that has changed on in like a
binary way over the past 18 months and
then feels like it changed all over
again in the past four months. Um so
I'll break it down into
internal and then external and how we're
thinking about our products, what we're
putting in customers hands and then I
can talk a little bit about the the
future and where we think things are going.
going.
So on the internal side, I think the
biggest difference is the shape of the
of the org. So we used to have kind of
like a classic hierarchical uh
structure. It it was functional. Um
which was great, but it was like fairly
standard if you like averaged through a
bunch of medium-sized tech companies. Um
and so you would have kind of eight
server engineers, four client engineers,
a PM, a designer, and you would work
linearly through your road map. Now we
have um small squads. So squads of like
one to six people. Um so meaning
meaningfully smaller than the other
teams would be. And we have way more
flexibility and and fluidity where a
given squad can work a few cycles on
this product, get it live and then a
cycle on this other product. Um which is
different than how things worked a year
or two ago where it's like I'm on the
banking team. I'm going to be on the
banking team forever. We also have way
fewer layers. So on the development
side, I think we probably cut our layers
by I don't know 50 or 60%. Like on the
product side, I only have I think two
layers, maybe three layers in a in a
couple places and so information is
flowing um way more freely. I think that
then in terms of how we actually build
on the development side, things have
changed. I think everyone's probably
seen, you know, every every CEO out
there is going on Twitter and showing
their like green dot on on uh on GitHub.
Um, but that's real. Like all of our
designers are are shipping PRs. All of
our product managers are shipping PRs.
That's not that interesting anymore. I
think more interesting is that we have
uh internal tools that are similar to
clawed code, but they're like more
plugged into our infrastructure. So we
have a tool called Builderbot.
Builderbot is just autonomously merging
PRs and actually like building features
to 100%. We've had some fairly complex
features that are built to 100%. More
often than not, it's building them to
like 85 or 90%. and then a human who who
has a lot of context and understands
does like the final the final 10%. So
that feels really really different. The
ability to go from um to go from idea to
like this is in the hands of 100,000 or
a million customers has been compressed
massively since uh since December.
Outside of development, I would say most
of what we're seeing is like anytime
there's a deterministic workflow, we're
we're able to automate that. And so
generally at a at scale tech company you
have individuals who are working cues.
Um a lot of that is just being
completely automated away like from a
customer support perspective. This is
not new but you know our chat bots and
and AI phone support and and whatnot are
automating a a majority of inquiries
that we get. And then it gets into like
um product operations and risk
operations and compliance operations and
any sort of decisioning like generally
um generally the the the models and the
agents are going to do a better job than
humans. Right now I think it's critical
that we have a human in the loop. Uh
that's like the key kind of buzzword uh
when you talk to talk to partners and
regulators and and what have you. Um,
but over time it's like pretty obvious
that these systems are just going to be
so much better than like having a
thousand humans who are who are doing
that work. So that's on the internal
side. Um, on the on the product side, I
think that
>> and maybe just catch people up on kind
of the shape of the business. Obviously
you have Square, you have Cash App, you
you made a big acquisition, Afterpay. >> Sure.
>> Sure.
>> What do those businesses look like and
then yeah, how are they kind of changing with
with
>> Sure. So um, so we used to operate in a
business unit structure. So Square used
to be kind of its own business unit with
its own CEO. Cash App was its own
business unit with its own CEO. Um that
wasn't leading to the right outcome. So
about 18 months ago, we functionalized
the company. Just meaning that all of
engineering rolls up to our head of
engineering, all of design to our head
of design, all of product to me. So we
have a financial platform team that
spans the entirety of block. We have a
business platform team that's doing a
lot of this automation that spans the
the entirety of of block. And then
increasingly we're building features and
products that actually connect the
Square side, the Cash App side, and the
Afterpay side. And so naturally you
you're building technology and you're
building infrastructure that is not um
brand specific. And that's actually like
kind of central to our our overall
strategy and and and overall thesis. Um,
but yeah, I mean, CA Cash App went from
when I joined Cash App in 2016, uh, we
had just just started to to figure out
how to monetize and had our first
dollars of gross profit. And now I think
Cash App's probably like I don't know
60ish% of like overall gross profit at
the at the company. So overall been been
growing at a healthy clip over the past
decade. Um but uh Cash App and Afterpay
have definitely been growing um more
quickly, but increasingly we're trying
to think about things from an ecosystem
perspective and and that's maybe where
like goose as a platform comes in which
is we bu we built goose internally. The
way to think about goose is um it's a
nod to a top gun or whatever the
co-pilot thing but the way to think
about goose is it's a it's an agent
harness and it's model agnostic. So I
can run goose on an anthropic model, on
a on a on an open AI model, on an open-
source model. There's probably like 120
models that we have. And depending on
what I'm trying to do, I'll kind of swap
out the swap out the models. And then
that was useful for a human to use, but
we've built like the agentic layer on
top. And so now a lot of the automations
at at block are actually routing through
the goose agent harness. And um we've
been able to leverage this across the
products that we're building. So,
Moneybot, which we'd like to think of as
like a CFO in your pocket, but it's
essentially like a proactive um a
proactive uh chatbot that can take
actions on your behalf within Cash App.
That's built on top of Goose. Manager
bot, which is roughly a similar thing on
the Square side, that's built on top of
Goose. So, it's a lot of this
foundational work on agentic systems and
then like the the triggers and the
underlying data and events that you need
to power them that's working across the
uh the entirety of the of the company.
So, on the on the product side, um I
think that the the biggest shift has
really been like we're going from a
world where for the past 10 or 15 years,
everyone's used to a static UI, a rigid
UI. you tap through the UI, everyone has
the same, everyone's Uber or Lyft or
Cash App or whatever looks the same.
That's going to fundamentally change in
the next like six months. Um, generative
generative UI is is is here. We're
seeing it with Moneybot. We're seeing it
with manager bot as the models get better.
better.
>> What what is that going to look like
kind of in practice? I'm curious.
>> I think I mean in simplest terms, it's
like your Cash App should look really
different from mine. And the reason why
it's like, okay, well, I get my paycheck
into Cash App and I'm super into
Bitcoin. Let's say like you don't and
you use Afterpay all the time. Great.
When we open up our apps, that should be
totally different. That you could
probably achieve that just through
personalization. That's not that
interesting. What we're actually seeing
and Anthropic had some releases this
week that are that are incredible. We're
actually seeing is like I can go into
Moneybot and say, "How have I been
spending my money?" And it'll show me a
bunch of charts and uh and visual
visualizations where it is actually like
on the fly generating generating that
visualization. It's not actually in the
code itself. So that's really cool. It's
also potentially a nightmare from like a
QA perspective. And so we need to figure
out how you're going to QA all these
like non-deterministic outputs for for
tens of millions of customers. But um a
great example on the on the Square side
is with ManagerBot. Maybe charts aren't
that impressive to you, but with Manager
Bot, let's say you're a you're a uh you
own a multilocation quicks serve
restaurant. you say like, "Hey, can you
build me an app where I can uh manage
scheduling for these two locations and
like automatically fire off text via,
you know, WhatsApp or or Signal or
whatever to my um to my employees. It's
actually going to like create that app
for you. And the the way that that app
looks and feels is not in the source
code of the actual application that we
push to the to the app store." And so I
think it's um it gives folks way more
control. It's way more
uh ultimately I think it'll lead to
higher engagement. Um I think it'll lead
to better product and and really I think
the key thing I I don't think that if we
ask customers to to like prompt these
tools themselves, they're going to
necessarily know the right prompts and
come up with the right answers. So,
we've invested massively on the
proactive intelligence side where what
we've found, especially as it relates to
money is like we need to be prompting
our customers with things that we think
make sense for them and that's where
we're creating a lot of the the value.
>> So, I mean, I think we're all incredibly
bullish on on kind of the impact of AI,
you know, in kind of in the way that all
these businesses run and the products
you can create. How does that flow back
to your stock price? you know the the
business is the stock has been roughly
flat for I don't know six or seven
years. interesting sort of reminding me
>> but the b the business has grown a lot
you know to your point the gross profit
per employee has grown you know
massively like how do you sort of
reconcile the that that dimension
>> yeah I think um so so I think you know
markets are markets are cyclical and
there's all sorts of things that are
happening I remember uh in 2021 when our
stock price was like I don't know 260
bucks and I was like that was a little
bit irrational um you can take a a kind
of longer term mature view and say you
So markets are voting machines in the
near term, but they're weighing machines
in the long term. Just like focus on building,
building,
>> you know, David and Jonathan earlier
talked a bit about kind of
defensibility. How do you think about
your own moes at Square? I mean, at
Block, excuse me. You, you know, you
talked a bit about the ecosystem. You
guys obviously have, you know,
regulatory infrastructure. Um, you know,
how do you think about, you know, that
the business overall in that context?
Yeah, I think in the I think in the near
term and the medium term, um there's a
bunch of there's a bunch of modes that
exist for for block and and we can talk
about the industry more broadly. I think
I think distribution and network effects
are are one of them. I I agree on the
the catrini piece and uh and Door Dash.
I don't think anyone's vibe coding Door
Dash in the next couple of weeks here.
Uh I like to say like any of us can can
create a peer-to-peer app in probably a
week. uh no one's going to vibe code you
know 50 or 60 million monthly activives
who are actually using that. So I think
that that's true. Uh I think um you know
licenses and and regulatory posture um
definitely exists. I think hardware
right now it's like harder to imagine
how some of the AI tools flow through to
the to the hardware side. Like you can't
vibe code a piece of square hardware. Um
but I I think longer term if we continue
like if we look at the rate of the
change and and the change in the change
I think longer term the key thing that's
going to make uh a company defensible is
um the extent to which the company
understands something that is pretty
hard for other companies to understand.
And so we're increasingly building
toward a world and talking about block
as an intelligent system itself.
>> So basic like the the the the way that I
see this going if we can if you
extrapolate forward the past several
months is that ultimately a company is
sitting on top of some sort of signal,
some sort of like rich data and and and
deep insight. Um for us it's like how
sellers and buyers participate in the
economy. Um and and most companies I
think have this thing that they
understand deeply. And then the question
is going to be how quickly can you
iterate to improve that understanding
over time. And so we're building world
models internally and externally of like
understanding who our customers are but
then also understanding how block
operates. It's like you can imagine you
can imagine for any company just like a
markdown file of like who you are and
then you need the feedback loop with two
things. You need a feedback loop with
the signal which is like what do you
what do you deeply understand that's
hard for others to understand and then
you need a tool like builderbot or
clawed code or what have you. And then
you can just iterate through that loop
over and over and again. It's like this
is this is what I'm seeing. This is
what's happening. Great. This is our
markdown file for for block. These are
our values. this is the metrics we're
trying to optimize for. Um, this is what
we care about. This is what we don't
care about. And then you have a gentic
system, so you can just build stuff. And
right now, you basically you've taken
that humans used to do that and it used
to take a couple months to build a
feature. Um, now it takes maybe a week
or two and there's still humans
involved. Pretty clear that in the
future you'll be able to run that loop
like I don't know hundreds, thousands of
times a day and maybe there's some
humans involved. Maybe not. Maybe the
humans are more like editors. And so I
think the the biggest moat is going to
be like which companies understand
something that's super hard for other
people to understand. And if your answer
to that is is um I don't know, then uh
then you maybe could get vibe coded away.
away.
>> This has been an amazing conversation.
Thank you. Uh thank you so much for for
joining us.
>> Appreciate it. Thanks so much. >> Awesome.
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