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MATT BERMAN: Welcome.

Welcome.

Welcome to-- thank you.

Google I/O 2026.

This is the dialogue stage.

My name is Matt Berman.

I'm the CEO of Forward Future.

And--

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--today, I am super excited to share a conversation

with the man who has been leading Google for the last 10

years.

Please help me welcome Sundar Pichai!

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SUNDAR PICHAI: Hi, guys.

How are you?

MATT BERMAN: All right.

Well, thanks for joining me.

SUNDAR PICHAI: A real pleasure to be here.

MATT BERMAN: Congratulations on all the announcements,

of course.

I kind of want to dive right into it.

I actually recently found out that you're

the first PM of Google Chrome.

SUNDAR PICHAI: Oh, yes.

MATT BERMAN: You have a unique insight, and probably

very strong opinions about the future of the internet.

And so that's where I want to start.

It seems like the internet is being transformed right

before our eyes.

Agents are being built. They're infiltrating the internet.

And I'm wondering, do you see the future

as agents being the entry point to the internet for most people?

SUNDAR PICHAI: First of all, great to be here.

Thanks for doing it, Matt, and love your show

and love your content.

And I appreciate all of you joining as well.

Look, I do think agents are going

to be a fundamental part of how we work, because having used

them, if you're living, maybe today,

the people who are on the frontier of how agents work

are developers.

And particularly with coding, two years ago, most developers

started using these tools.

They started giving them more and more auto-completions.

And you were accepting them, et cetera.

But over the last few months, developers

are actually doing agentic workflows,

the developers on the frontier.

And they are actually deploying agents, orchestrating agents.

You saw the demo in Antigravity for building an OS.

You are effectively in an agentic workflow.

So I think once you get the taste of using something

like that and the superpower that

comes with it, I think they're genuinely adding value,

in a way.

I think people will use them.

I think it's important we build it in a way

that users feel a sense of control and agency

and transparency when they use agents.

I think that's important.

That's an important foundation.

But I think, yes, I do expect agents

to be a core part of how we use the web.

But that doesn't mean it will take away from--

people use the web for a lot of reasons, right?

You're entertaining yourself.

You're trying to do something meaningful at times.

And, it depends on, if you're shopping,

what you're shopping for.

If it's your weekly groceries versus you're

trying to buy your loved one a gift, right?

And so I think it will allow humans

to use the internet in ways that gives them joy and purpose

and not always be forced to deal with,

I have to fill these 18 form fields to renew a DMV license.

So that's how agents will separate it out, I think.

MATT BERMAN: Yeah I mean, I'm particularly

excited to have agents actually do real world tasks.

You mentioned the DMV.

That use case is incredible.

But I also think about putting so much trust

into agents to really be the arbiter of our information diet.

And I'm wondering, how do we make sure that we are putting

the trust in the right agents?

And the agents are deciding correctly what information

that we should have?

SUNDAR PICHAI: We are already.

You are doing a version of that.

I don't know if you use Gmail.

And there's a spam filter working

on your behalf, filtering spam.

In some ways, you're trusting an agent.

It is an agent, not the way we think of agents today.

So I think we've always--

I look at it as, working on Waymo,

you have to make people trust sitting

on the backseat of a Waymo and let the car go.

That is an agent, in some ways.

But people are willing to trust.

But that's because we've done the work over time

to demonstrate to people, both with data,

with how we operated it, that it's fundamentally safe.

And it's freeing you up to enjoy the ride.

And so I think it all depends on the value you deliver.

That's why I think the building the agency and trust with users

is a shared journey.

And we have to get that part right.

Part of the reason in Gemini Spark, Gemini Spark

is actually very powerful under the hood.

But we are taking the deliberate, careful step

of making it work with your first party services,

like Gmail, Calendar, et cetera, before we

expose third party with MCP and full computer use and browser

use.

It can do all that.

But we want to make sure users are in control.

And they feel comfortable.

We are getting their feedback, improving the product

as we give those capabilities.

MATT BERMAN: Sundar, if I'm crossing the street--

you mentioned Waymo.

I actually trust walking in front of a Waymo

more so than I trust walking in front of a human driver.

I don't know if other people agree with that.

But it was almost an immediate trust that I had for Waymo.

So I'm hoping agents are the same way.

I'm old enough to remember the early days

of the internet, which was an absolute wild west.

But you got to raw information very quickly.

And I think part of something I'm concerned about

is that we're increasing the buffer between us

and the raw internet.

We saw it a little bit with the browser, a little bit with apps,

and now more so with agents.

How do we account for that?

SUNDAR PICHAI: I think, look, people

in our experience with Search and YouTube,

YouTube is a great example.

People have this sense of connection

with the creators they like and follow.

And so that's a big part of what they're looking for.

So I think people are in different mindsets.

I mean, there are times they want

to discover content on the web.

Like, shopping is delightful for a lot

of people in a lot of moments.

And so they're not trying to fully outsource that.

That's why I try to distinguish between what

feels like something which may be a chore at times

and what feels like something which is delightful.

Similarly, I think it could be in news.

It could be-- people have their trusted sources.

At least at Google through Search and YouTube

and through agents, we think there will always

be an incredible value from the ecosystem which

users want to connect to.

And the agents should be in the job of doing that.

But you are right.

There are times the agents are playing a role in the middle.

Sometimes it's very good because it

helps improve user satisfaction because users

are getting to what they want in a better way.

But there's a layer of abstraction, too.

And that's what you're talking about.

But I think it's always been true with technology a bit.

But I don't think, ultimately, at the same time,

the tools with which you can create

content are also exploding.

So people will also be creating more content.

So I think there will be a new balance which we will find.

But, yeah, it is an interesting moment of evolution.

MATT BERMAN: Yeah, I like that.

I think you're saying, there's definitely

going to be a strong place for agents

to do that curation on our behalf,

especially in the era of complete slop domination.

but also, that feeling of exploration can still be there.

SUNDAR PICHAI: Yes.

MATT BERMAN: OK.

SUNDAR PICHAI: Because it's a fundamental human need that

doesn't go away.

MATT BERMAN: Yeah, exactly.

OK.

So speaking of the Wild West, and I

know you're probably feeling this pretty strongly at Google.

There have been increasing cybersecurity cyber attacks.

The models are getting better at cyber.

Obviously, Google has been thinking about cybersecurity

for decades.

Are you seeing cyber attacks, especially AI-enhanced cyber

attacks, ramp up at Google?

SUNDAR PICHAI: Look, we've been seeing,

to be very clear, that we deeply care about cyber because we've

been working on frontier technologies for a while.

Google pioneered many important security frontiers

like zero trust and so on.

We have worked hard to keep the company at the frontier.

And also, we operate many products and platforms

around the world that touch billions of people.

We've been pretty aggressive in deploying agentic workflows.

Our internal security teams use agentic workflows

to help detect vulnerabilities.

And then how do you work to patch them?

And we have steadily seen over the last two years,

as the model capabilities have progressed,

we are able to detect more vulnerabilities.

And we've been working very hard to patch them.

I think Mythos was a point of inflection, of capturing

that moment in time.

They put out a model which was really well built

for that particular task.

And it was frontier there, right?

But what we are excited about, it

is part of the reason we are sharing

maybe one of the undermentioned announcements

today at I/O is CodeMender.

So CodeMender is a product which we use internally,

in which you're building to share externally.

It not only helps you identify the vulnerabilities,

generate patches, test and verify that they work

and deploy them.

MATT BERMAN: And it's running 24/7.

SUNDAR PICHAI: That's right, and real-time.

We completed our recent acquisition of Wiz.

So Wiz is state-of-the-art in being able to do this real-time

monitoring of vulnerabilities, et cetera.

So I think combination of what we have with Wiz and CodeMender,

I think we are using it internally

to stay at the frontier.

And I think it's an important moment for the industry.

I have to say, I'm heartened by the cross-industry collaboration

going on in this moment.

I think one of the examples I would call out today, be it

SynthID or watermarking, companies coming together,

companies coming together around cyber.

That is so important for this industry with this technology.

So I'm encouraged by those trends as well.

MATT BERMAN: So you mentioned Mythos.

So I want to talk about that for a moment.

Obviously, Anthropic decided not to release Mythos publicly, just

a handful of companies.

We have OpenAI releasing GPT 5.5 Cyber.

Which approach do you think is more appropriate?

Which is right for Google?

Is there some model that is just too

good and you're going to hold it back,

or is the more iterative deployment strategy

that OpenAI takes more aligned with what Google believes?

SUNDAR PICHAI: Look, I think it depends on where you feel.

It depends on the model capability.

If it is not fundamentally changing what's out there

already in terms of the state-of-the-art,

I think it's definitely OK to put it out.

But in the security world, there's

a well-established practice.

Google has done Project Zero for a long time, right?

And we have teams of people who find vulnerabilities.

Then we notify the vendor, give them

90 days to patch it before we acknowledge

the vulnerability in the wild.

And so there are well-established practices

in the security industry around how to do it.

So I think it makes sense to me, if you suddenly

have something which dramatically changes

the frontier, first of all, I think

it's important to work closely with the government on that.

And you approach it in a responsible way.

So I think that is consistent with how

the security industry works.

But I do think it's important to also make sure enough people get

access to it so they can patch their systems and so on.

And they go hand in hand.

So I think there's validity to that approach.

MATT BERMAN: So is there some threshold

by which you would say, past that point, we can't release it,

or is it more, let's look at what the landscape of model

capabilities are.

Let's look at how cyber is right now.

And let's make the determination on a model for model basis.

SUNDAR PICHAI: Yeah, that's what I would say.

The next one you're introducing, does it dramatically

change the frontier?

Is it a 1% to 2% improvement over the current

state-of-the-art or are you taking these 20% jumps?

That's where the judgment comes in.

And I think that's how I would change my approach.

MATT BERMAN: So on the topic of model strategy,

something near and dear to my heart is open source.

Basically, Google and NVIDIA are the only companies

with a real open source model strategy nowadays.

Google's model is smaller, meant to run on edge devices.

Why not release a large open source frontier model?

SUNDAR PICHAI: Look, first of all,

Google, we've been big fans of open source.

Google was built on a lot of open source systems.

We have worked on many big things which are open source.

I mean, I personally worked on Chromium and Android

and so on, Kubernetes.

And I can name many projects which

Google has contributed pretty strongly

to the world in open source.

In AI, we've been building Gemma models.

And we've been updating them year after year.

MATT BERMAN: They're awesome.

SUNDAR PICHAI: And I think the recent release of Gemma 4

was a great release.

And so we are pushing it.

I think all of us are trying to make

sure the frontier takes a lot of investment

to get the frontier done.

You've seen our CapEx dollars.

And so you're working.

You're putting a lot of R&D dollars

to generate those incremental frontier models.

And you're discovering new techniques

as part of doing those models.

So we all have to be mindful of that.

But I think we're also committed to making

sure there's an open source ecosystem which

is able to develop.

And so we take a balanced approach there.

And I think we'll continue to take that balanced approach.

That's how I see it.

MATT BERMAN: Yeah.

And by the way, I do love the Gemma models.

I run them locally at home.

They are fantastic.

So, definitely, thank you.

Obviously, a company of the size and the resources of Google,

if you're making that decision of, OK, large

closed source, large open source.

We can't do both.

Probably, most startups in the US

also will struggle with that decision.

What is your sense of the business model

for open source in America?

Is it viable right now?

SUNDAR PICHAI: Look, first of all, we not only do open source.

Part of the reason we invest so much

in Flash-Lite and Flash models is so that we

are giving a range of options.

And those models are workhorses, too, to support.

But your question on open source, it's not that we don't--

we've been moving to open source frontier, too.

There's a lot of very, very good open source models,

particularly from China, which startups are adopting, too.

MATT BERMAN: Yeah, we're going to talk about that.

SUNDAR PICHAI: So I think it depends.

You go through moments in technology

where the frontier moves so fast.

Maybe sometimes, the open source may not

be able to fully keep up with it.

But then there are moments where open source will take

leaps if the technology curve slows down and takes

a break, right?

So it's tough to predict it fully in the future.

But I expect there to be a demand for a strong open source

ecosystem.

And I think we will definitely play a part in it.

And I hope others do, too.

MATT BERMAN: There have been a number of open source ecosystems

that have been really successful over many decades in technology.

But I think just the upfront cost of baking a model

makes it extremely difficult, especially if you're putting out

the model.

And then all of a sudden, your competitors

are serving inference at a higher margin than you.

But I am hopeful.

I do love open source.

And we're going to talk about the workhorse

models in a moment.

But I want to talk about China.

And they have been putting out incredible open source models.

If you can put yourself in the shoes of another enterprise CEO.

And you're looking at the landscape of which AI model

to choose for your business.

And you're seeing DeepSeek at a fraction of the cost,

but still near the frontier.

Why wouldn't America adopt Chinese open source AI?

What's the argument to go with American AI?

SUNDAR PICHAI: So you're saying, why wouldn't you

just use the best open source models available?

MATT BERMAN: Yeah.

SUNDAR PICHAI: Look, I think at the end of the day, what

are companies trying to do?

They're trying to solve problems.

And they're trying to solve a problem with a solution.

So the question is, what are the solutions available?

Remember, they are designing-- let's

say you're doing something in customer service.

You want predictability.

You want reliability.

You want consistency.

And you want safety, security.

So companies are optimizing for a lot of factors.

So I think that gives a place for both open source models.

And there'll be providers who will take the open source models

and build that ecosystem around it, which makes a lot of sense.

There will be closed source models.

And it will be an open marketplace.

And people will have a lot of choice, I think.

I am more OK if it is open source with the right licenses.

It should matter less where it came from.

I think, over time, there are good ways

to inspect open source.

I'm not saying that's exactly true, particularly with how

the AI models are developed.

But with open source comes a community

which is responsible for it, cares about it.

So if something wrong is happening in that software,

it's not like it's going to go unnoticed.

So I think that creates a level of trust for people

to adopt that technology, I think.

So I worry less about, are we adopting open source models

from China and more that, are we doing enough in the US

to make sure we are staying at the frontier?

That's how I think about it.

MATT BERMAN: Now I know Google is big on co-design, full stack,

and just continuing on where the open source

model is coming from.

I've heard that argument that is, if it's open source,

it doesn't necessarily matter.

We're going to fine-tune it or customize it for our needs.

But ultimately, if we continue to build

on top of China's open source, there's

also an argument that they're going

to optimize their models for their own chips.

And then all of a sudden, we're built on another country's

technology.

Is that an incorrect argument?

SUNDAR PICHAI: Look, I think the fundamentals of AI,

the way people should be building use cases on top

is, because the models are changing so fast anyway,

you need to build it in a way in which you're able to evolve

the models underneath.

I think that has got to be the way you're working

through this moment, right?

And so I think you have to be dynamic enough that you have

to be able to adapt, because the model frontier,

the model ecosystems are changing pretty sharply.

And so that's the way I would think about it now.

And it's too early to predict if this is a real concern or not.

MATT BERMAN: Yeah.

OK, talking about model strategy,

one of my favorite things is watching the frontier labs

and seeing how their model strategies play out.

Anthropic and OpenAI seem almost exclusively

focused on the absolute frontier.

And Google has that.

But you guys also put a lot of emphasis

on what I'll call the workhorse class of models,

the Flash class of models.

Talk a little bit about why.

Why is that such a big part of Google's strategy?

SUNDAR PICHAI: Look, I mean, in our mission statement,

we have this thing to make technology universally

accessible and useful.

We've always deeply cared for, what

is the most important technology in our lifetimes,

that it diffuses as broadly as possible.

And we get really excited at driving efficiency and making

sure the best models can work in the fastest possible way,

cheaper, because we need to do it for Search,

because we have to give it to billions of people.

We want to put it in Gemini.

And so we want to give it to developers so that they

can do powerful things with it.

And we've had a lot of success with this strategy.

And I think 3.5 Flash, particularly,

I made this point during the keynote.

But I've heard anecdotally from a lot of CIOs

who are so concerned about how much their companies are blowing

through budgets.

MATT BERMAN: Yeah.

SUNDAR PICHAI: And you can feel it talking to them.

And I think the problem is going to get worse

as we go through the year.

And I think that's where I think the Flash model will really

shine, because particularly in the agentic workflow where you

need these things to be repeatedly used and used

a lot of times, I think it's so important to have a model which

is very capable, but is fast and efficient.

And even accounting for token use,

Flash is remarkably cost-efficient, right?

And so I'm really excited.

We are finding it internally.

We are using it as a blend of Pro and Flash, internally.

And I think most companies should learn to use it that way.

To be very clear, we're super committed to being

at the frontier on every category.

I'm excited for Pro which we are working on.

But I think Flash has a unique role

to play in this constrained, compute constrained time.

MATT BERMAN: Yeah, no, I agree completely, especially,

most companies are not solving math olympiad problems.

They're not at the absolute cutting edge of science.

They need real work done.

And that is truly where the Flash model shines.

And not everybody is token maxing or unconcerned

with the budget.

So I definitely appreciate that.

If you think about the future of AI,

is it just truly a race to self-improving AI?

Maybe just to play the devil's advocate for a second,

the Flash family of models are great now.

But ultimately, whoever reaches self-improving AI first wins.

And then nothing else matters.

Do you think about it like that?

SUNDAR PICHAI: Look, I think first of all,

there's a responsibility that comes with this technology.

And I think we all need to be careful to avoid this race

condition at all costs.

And I think we owe it to humanity

to make sure we deploy this technology responsibly.

You are right in your question that there

is this current moment where people feel

like the curve is so steep.

And where you are in the curve matters.

But just like in a few months ago when we launched 3.0,

people were like, oh, we are so in the frontier.

No one will ever be able to catch up.

And I think at the frontier labs, it's very dynamic.

The competition is fierce.

We all have our strengths and weaknesses.

We all also have different cadences

of our pre-training release cycles.

So the peaks don't exactly match.

So all this creates that perception gap

which shifts widely in four to six weeks.

But I think a few labs are really at the frontier.

And then there's a big gap.

And I think there are scenarios in which

things like recursive self-improvement come into play.

But I think if they come into play with that no difference

from the cyber moment, we all have to handle those moments far

more responsibly than today.

And so that goes hand in hand.

And so I think the more AI becomes advanced,

the more it's a societal conversation

versus a single company conversation.

MATT BERMAN: Yeah.

Yeah, well said.

So we're talking a lot about models.

But all of it is downstream from compute.

I'm always both impressed and in awe of Google's ability

to serve-- you're serving your own models inference on the API.

You're also powering your suite of products with Gemini

to literally billions of users.

You're also allowing your competitors

to use your inference.

You're also selling TPUs.

I've heard the reason for this is because--

I mean, Thomas told me.

He said, we planned really well, which makes a lot of sense.

You've been at this for 10-plus years.

I think I've also heard that Google's revenue is literally

constrained by compute.

So I wanted to give you the opportunity.

What is the state of compute at Google right now?

SUNDAR PICHAI: Look, all of us are--

I think we have made a set of bold right

decisions over the last few years to invest in compute

and scale it up aggressively.

But having said that, I don't think any of us sit in the chair

and say, we wish--

you look back and say, I wish I had done a little bit more.

So we're living in one of those moments in time.

And there are costs going up, too.

So for a given budget, you may be getting less compute

than you had previously planned for, memory, prices, what

have you.

So the costs are going up.

I think we plan.

We are able to plan long term for cloud

separately from our own internal needs.

And we do long range plans.

And we plan for it.

I think it's good.

And some of it is, when you're in Google Cloud

and you're supporting customers, your customers

may look at something and say, well, I want access to that.

They may look at the demo of 3.5 Flash on Antigravity

and say, how are you exactly running it

at 800 tokens per second?

And could we get access to that?

So you're also supporting customers

through those journeys.

And hence, you're meeting them in what they're asking for.

But it is not an easy balancing act.

And we are constantly thinking as far ahead as possible.

And we are making trade-offs like every other company

right now.

MATT BERMAN: So do you have, maybe an obvious question,

more demand than you have compute to serve it?

SUNDAR PICHAI: Absolutely.

MATT BERMAN: What is the scale of that?

SUNDAR PICHAI: And hence, the emphasis on something like 3.5

Flash even more.

And could we have done an even better Omni model?

Yes.

But how can we do Omni model which we can give

to as many people as possible?

So constantly, you're making trade-offs

like that, including on, do you build a very large model,

an ultra-sized model that will increase the capability

frontier?

But then who all can you give it to?

So constantly, all of us are making these trade-offs.

And sometimes you make a trade-off.

It looks like, well, maybe you've done that trade-off a bit

differently.

MATT BERMAN: Yeah.

So we only have a few seconds left.

One last question for you.

What is the main bottleneck for compute for Google right now?

Is it land?

Is it political will?

What is it?

SUNDAR PICHAI: I think the way it works is--

by definition, bottlenecks work this way.

If you think something is a bottleneck and you solve it,

something else becomes the bottleneck.

That's the whole definition of bottlenecks.

I think at various times, I think

you're having a few areas, your ability

to physically permit and construct data

centers, the power they need.

And then very quickly, you get into the core components

for these systems.

They are all the bottlenecks.

And it's like, you need all of that

to work together to get a square set of a chip you need.

And so I feel like there are a few parallel bottlenecks going

through.

And it almost doesn't matter.

There are a few of them which are bottlenecks.

And at various times, you may conclude, it is memory.

But then if everyone concludes it's memory,

tomorrow, you turn around and say, OK.

No, no, no, it's actually this.

But I think there are systemic bottlenecks across all layers

of the stack now.

MATT BERMAN: Whatever the bottleneck is in the moment,

that's the most compute you can have in that moment.

OK.

Everybody, please help me thank Sundar Pichai.

Thank you so much.

SUNDAR PICHAI: Thank you so much.

MATT BERMAN: Thank you.

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