The New Application Layer - Malte Ubl, CTO Vercel

Channel: aiDotEngineer

Published at: 2026-04-20

YouTube video id: XKup1pj-34M

Source: https://www.youtube.com/watch?v=XKup1pj-34M

[music]
>> Our first speaker draws on over 25 years
of software engineering experience.
From his time at Google and now Vercel,
he will explore what it means to build
infrastructure and applications in a
world where agents are both the builders
and users of software. Please join me in
welcoming to the stage the CTO of
Vercel, Malte Ubl.
>> [music]
[applause]
>> Good morning, everyone. This is awesome.
I'm so glad to be here.
Welcome to the first ever AI Engineer
conference in Europe. Um my name is
Malte and I'm the CTO of Vercel.
Now, this is not, you know, usually I
give technical talks, but I thought
because I'm apparently going first that
I need to give a proper keynote. But I
did want to feature what I call my vibe
coding uh stack. Uh I've been hacking on
a thing called chat SDK, which is a way
to hook your agents to whatever like
Slack, Telegram, WhatsApp chat app you
you like. And I've been working on just
bash, which is a bash interpreter
written in TypeScript that gives you
something like a sandbox with zip
nanosecond startup time um for your
agents cuz they love bash.
All right.
One thing I want to mention is that
the reason why I'm so excited to be here
is that I used to run a little
conference in Berlin called JSConfEU.
And I feel like once in my life I had
completely impeccable timing because it
was the summer of 2019 and we decided
after 10 years it was enough and we went
out with a bang.
And the reason why this was such great
timing was that there would just
wouldn't have been a conference one year
later because of COVID.
But also when we decided that we would,
you know, step away, we were hoping that
someone else would take the reins.
And again, that did not happen because
of COVID.
So it's now been more than half a decade
and I'm very excited that things are
finally starting up again.
But it was also clear that it wasn't
going to be like a web development
conference that would really bring the
tech community in Europe back together
in 2026, right?
In many ways I think AI engineering is
the legitimate successor to web
development as a really mainstream
discipline of engineering
that will shape the next decade of
software development as, you know,
software engineering itself faces an
unprecedented disruption.
So you're definitely in the right place
today.
And it's more important than ever to
come together as a community and reflect
on both our professional as software
engineers and AI engineers.
And that's because we're facing a
disruption of both how we build, which
is with AI,
and what we build, which is AI and
agents.
And of course disruption can sometimes
lead to anxiety.
In fact, I really actually very often
get asked, "Hey Malte,
is there still a place for engineers in
the future?
And what about that next generation of
engineers?"
And I couldn't be more convinced that
the answer is yes.
I often give this example of like
envision me doing a TikTok video.
They mentioned in the intro that I have
25 years of experience, which is
actually substantial under uh statement.
And so I would not be good at the TikTok
video. I should not be recording TikTok
videos because I didn't grow up with
this, right? And in a very similar way,
the next generation of junior engineers
are just going to be so much better at
this discipline because they get molded
in the AI world just like all of you
are.
But it's not only the kids that are
going to be all right. We'll all be fine
and this is why.
One of our main thesis is that agents
are a new kind of software.
Because there was always all this stuff
we wanted to automate,
but not all of it was economically
viable to do with traditional software.
But it is with agents.
And what that means is there will be
just so much more software in the
future.
Indulge me with a Venn diagram.
Um
maybe the circle should be better
because the circle represents all
software that should exist. Imagine all
software that should exist.
The problem was that we couldn't
write all of it because it was too
expensive using traditional methods.
Like you can envision like all these
things where like I have all these if
statements, you have all this like
knowledge about the the business, like
you have to figure it all out, you have
to hardcode into the application. So
much of this software you just would
never write because it would be
obviously
uh too expensive.
But now with agents,
that part of software becomes
economically viable. I can build it now
um with not that much much effort,
right? And that means that with AI
agents,
essentially all the software, maybe not
all of it. We'll find more in the
future, but like that circle gets filled
out, right? All of this stuff that
should be automatable is automatable.
There's going to be so much more
software out there.
And
in a similar line, more and more
companies, when they ask that question
whether they should buy some software,
like a SaaS product, or make some
software themselves, they're answering
that with the make side, right?
Over in Silicon Valley where I live
today,
we are talking a lot about the SaaS
copocalypse. I think that's what it's
called, right? People like make their
own stuff and they don't buy the SaaS
software anymore.
I actually think the SaaS companies will
be all right, you know. Don't worry
about them.
But as
engineers ourselves,
more companies making more software
again leads to us having more work, even
if it's faster.
And in fact, the way I've been kind of
framing this for a while is that we are
speed running what's really an
experiment in economics of how elastic
the software market is.
The thesis being that the cheaper it is
to make software,
the more software we're going to make,
right?
And as a consequence, what's actually
happening is the demand for software
engineers is going up.
Now, we don't know like, you know, like
there's going to be an S-curve, you
know.
But there's no signs of of of us
reaching the S-curve. In fact, because
we're getting better at agents, etc.,
there's so much leeway in the future. Um
I think we'll be all right.
So
as AI engineers, it's our job to build
that next application layer.
And of course, what that actually means
is building agents, right?
I want to spend some time talking to
talking about archetypes of agents that
I'm seeing actually being built today,
actually being effective, actually
something you can do today without, you
know, having to make major changes.
I think we're all a little bit drunk on
the coding agents because they're so
great, right? They work so well and it
seems so obvious that you can translate
that to all other domains. And and
sometimes these things don't go so well,
right?
But the thing is that we don't really
have to be doing the most advanced
agents you could possibly imagine.
There's just so much low-hanging fruit
to be be be done where you can really
really help companies save them millions
or billions of dollars without actually,
you know, making these massive changes
to processes that in practice will
always take a long time, fail often,
etc.
So this is what I'm actually seeing in
the wild.
The first part, when you think about
what agents you can think build, is
people think, "Well, agents, that rings
a bell. I have team of support agents.
Maybe I can automate part of that,
right?"
And that's also where kind of the first
generation of what we call
agent as a service. You can make that
acronym in in your head.
Um
startups are uh shipping, right? Like,
you know, the the CRMs and Decagons of
this world. Um but more generally, I
think it's worth asking yourself,
in your business,
is there a job where it would be quite
transformative if if that, you know, we
went from a 9-to-5 thing cuz, you know,
people need to sleep, and I can actually
do it 24/7 because agents don't need to
sleep, right?
And I think there's there's many places
for that.
The next one is probably actually even
more important. Um I call them call it
compressed research.
Because every business has a certain
type of business process that in a very
abstract fashion has the following
shape.
There's some business event
and you have to do some research
and then you make a human decision,
right?
And you can just build an agent that
does the research phase automatically.
And that's all you do. That's all you
ship, right? And the important part why
this is like such an easy thing to ship
is because the process is still the
same. There's still that business event,
there's now the research,
and there's a human decision.
The research goes faster and, you know,
maybe it goes from something that took a
human 30 minutes,
now they can do the same thing in 5
minutes. And if you run that process
100,000 times a year, you just save the
company a whole lot of money, but you
didn't increase the risk profile and you
didn't have to change the process.
At Vercel, we actually have like at
least two agents of this shape.
When you go to vercel.com and you hit
the contact sales button,
that message actually goes to an agent,
right? And I hear about 75% of the time
that agent says, "Well, actually, they
just wanted support." And hand it over
to the to the support team. But then in
the other case, it will go, "Oh, that's
interesting. Um let me check out their
LinkedIn. Let me Google the company. Let
me figure out the how large they are.
Let me route it to the the person,
right?" And then there's a human
eventually taking a look at it makes
sense, but
that obviously was something that took
maybe a person 15 minutes before, and
now they don't have to do it anymore.
And another example is exactly the same
process. If you sent us a abuse report,
again, there's an agent taking a look.
Is that website abusive?
What What should we do, right? Still
obviously the decision in the end should
be done by by the actual professional,
but they don't have to like do all this
research themselves anymore.
Next is what I think is probably the
most magical thing you can do in any
company today,
which is to surface information that
already exists.
It's extremely common
that there's information somewhere in
the company, right? But for all intents
and purposes, you cannot practically use
it.
Take for example, everyone you you all
engineers you have issue trackers,
right? So,
is it up to date?
Probably not all the time, right?
Could it be up to date? Like it does the
information exist? Did you slack it? Did
you have a granola recording that
technically contains the information
that could update your issue tracker?
Yes, right? Like probably yes. And so
you can,
you know, build an agent that does this
for your company, right? Whenever you
have like a manager saying, well, give
me a last list of updates, right? Why
don't they already have that updates,
right? Why doesn't an agent have already
kind of done that research already? Um
so again, this just makes takes
advantage of existing information, which
is so powerful.
And finally, for the last big category,
um
there's a magical question that you can
do to figure out
agents you should build in your company,
which is to ask folks, what do you hate
most about your job?
And I actually have a case study about
this in Ed for sale. So, we actually did
build our own in-house support agent,
and it has what's called a 90%
deflection rate. So, 90% of the time it
just helps the person in real time
rather than going down somewhere else.
And what happened?
The job satisfaction rate on our support
team exploded.
Why?
Because they no longer have to do the
boring stuff, right? Oh, my credit card
got rejected, blah blah blah, right? Now
they get to actually go and figure out
each actually interesting cases,
actually help people who really need
help rather than doing all the toil,
right?
So,
that's like I think eliminating boring
work is a very noble mission that we
should all kind of strive to do for the
companies that we work for.
Cool.
So, clearly that new application layer
are agents,
but we also have to shift uh we have to
consider another shift that the software
itself is going to be used by agents
now, right?
And
you know, I work in software
development, developer tools, etc. And I
think we're kind of ahead of the game
here speech running that transformation.
Um
what I will share though is that, you
know, on our own web properties,
humans are actually now in the minority.
So, in the last 7 days, and we have not
shared this before, over 60% of page
views on vercel.com were AI agents.
In a similar way, we're seeing the
way you use our platform going from
people clicking around in the dashboard
to
uh usage shifting to our APIs and CLIs.
So, whenever I know, you know, I have
someone proposing a feature to me and
they show me like a UI,
I'm like, guys, what's the CLI? Like how
do you do how do I automate this? How do
how does an agent use this? You know,
you know, UI is now something that's so
cheap.
The other thing that we're observing is
that kind of the relationship changes
between software development software
developers and infrastructure, right? If
I didn't write the code myself,
I also don't have maybe a strong
feelings about how that stuff runs in
production, right? And so for company
like us, it's really important that we
shift how we deploy infrastructure to a
model where
most of the software was written by
agent, and you know, has to just run.
And people are like expected to run just
like they prompted the agent to do the
work.
And finally,
and nobody here obviously is surprised
about this,
the applications themselves are, you
know, they're agent now.
And that requires us to have different
infrastructure available, right?
Everyone's now shipping sandboxes. I
think it's almost a meme.
Um I was mentioning earlier that I
created this thing called just bash, and
I'm really interested in kind of this
innovation of how you can give an agent
a computer maybe without giving them a
computer. There's lots of interesting
stuff there in the market. Um and I'm
I'm sure this conference is going to
have lots of stuff there as well.
And then also more broadly, again, it
was mentioned been here for for a while,
like we're we're like marching head on
into a security nightmare. It almost
feels like a little bit like 1999, where
really everything can be hacked, right?
And we just didn't know how to make
something secure. Um
I think we'll have a rude awakening,
but what that really means is that we
have to be open-minded for for how to
change things. Uh I will give one
example. Um I think almost all currently
popular agent harnesses have
fundamentally the wrong architecture.
And that is that they combine where the
harness runs with where the code that it
runs
uh that it generates runs, right? Um as
of actually yesterday, I I did see that
Anthropic
disagrees with that thesis cuz they they
uh on their new agent product, they do
have that separation, and it's really
really key.
And that's really just also a point that
that these are all solvable problems,
but my main message today is that we are
still in the very early innings, and we
have to be prepared to be open-minded
about like paradigm shift happening in
the future, right? We just had the
paradigm shift of
agents being kind of these like very
general sandbox using things. In the
future, we will see more of those
paradigm shifts.
Cool. Um last point I wanted to make is
that
this new application layer that we're
building can thrive independent of the
models, right? Cuz sometimes model X is
better, sometimes model Y is better,
but
we are as AI engineers building a stable
layer on top.
And one of the very interesting
consequences is that
we don't have to work at an model lab to
drive AI innovation.
In fact, and I think this is almost like
a narrative violation,
Europe is the leader in AI engineering
innovation, right?
Um
our own AI SDK, which Vercel makes, it
has now over 10 million dollars a week
and is led by Last Gammel, who lives in
Berlin, right? Um he's working on this.
Then there's obviously Pi, the coding
agent, uh made in Austria. You'll be
hearing from Mario about it tomorrow.
And of course,
probably some of you have heard of it,
there's a little thing called Open Claw,
um and Peter will be on stage here in an
hour.
And so it appears to me that Europe,
against all odds, is taking actually a
leadership role in AI engineering.
But we also have to be realistic, right?
Like Europe isn't going to play a major
role on the model side.
But I don't think it needs to.
In fact, I do see kind of two big
futures ahead of us.
One is where the big model labs win.
In that world, AI will stay very
expensive.
All the value of
all that cool agent tech will accrue to
that company,
and we won't really be AI engineers
anymore, right? We'll be like
forward deployed engineers who whoever
the winner is, if it's OpenAI,
Anthropic, or Google.
But I don't think that's very likely,
and I think what's actually going on is
that the opposite is happening.
The model companies are commoditizing.
Cloud is amazing, Codex is amazing,
Google will catch up.
And importantly,
I'll give them props now because I think
Google's playing an amazing role here
because they have the cheapest
infrastructure on the on the cost side,
and so in that commoditized world, they
will always decide to make it cheaper,
right? And that will keep the price for
what where it should be, which is very
low, and that's the outcome that we
want, right?
Because in that world, we the AI
engineers are the powerful ones.
Our agents are the one that actually
create the business value,
and it's the application layer where the
real innovation happens, right?
This is where Open Claw is invented, and
that's where the next paradigm of AI
engineering is discovered.
And that's really all I wanted to leave
you with today. Thank you very much.
>> [applause]
[music]