Sundar Pichai on AI Agents

First of all, great to be here. Thanks for doing it, Matt. And I love your content. And I appreciate all of you joining as well.

Going to be a fundamental part of how we work because having used them, you know, if you're living, maybe today the people who are on the frontier of how agents work are developers, right? 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 etc. But over the last few months, developers are actually doing agentic workflows, right? The developers on the frontier are actually deploying agents, orchestrating agents. You saw the demo in anti-gravity 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, they're genuinely adding value in a way 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, but I do expect agents to be a core part of how we use the web, but that doesn't mean it'll take away from people using the web for a lot of reasons. You're entertaining yourself, you're trying to do something meaningful at times, and it depends on your shopping, what you're shopping for, if it's your weekly groceries versus you're trying to buy your loved one a gift. I think it'll allow humans to use the internet in ways that gives them joy and purpose and not always be forced to deal with filling these 18 fields to renew a DMV license. So that's how agents will separate it out. 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. How do we make sure that we are putting the trust in the right agents and the agents are deciding correctly what information we should have? You are doing a version of that if you use Gmail and there's a spam filter working on your behalf filtering spam. In some ways you're trusting an agent. 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 because we've done the work over time to demonstrate to people both with data and with how we have operated it that it's fundamentally safe and it's freeing you up to enjoy the ride. I think it all depends on the value you deliver. 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 surfaces like Gmail, calendar, etc. 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. So we are getting their feedback, improving the product as we give those capabilities.

Look, we've been seeing, be very clear, we've deeply cared about cyber. We've been working on frontier technologies for a while. Google pioneered many important security frontiers like zero trust and so on. We've 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 deploying agentic workflows. Our internal security teams use agentic workflows to help detect vulnerabilities and then work to patch them. 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 capturing that moment in time. They put out a model which was really well built for that particular task and it was frontier there. But what we are excited about is part of the reason we are sharing maybe one of the undermentioned announcements today at iOS code mener. So codemener is a product which we use internally and which we're building to share externally. It not only helps you identify the vulnerabilities, generate patches, test and verify that they work and deploy them.

That's right. And in real time, we completed our recent acquisition of Viz, so Viz is state-of-the-art in being able to do this real time monitoring of vulnerabilities etc. So I think the combination of what we have with Viz and codemener, we are using it internally to stay at the frontier. I think it's an important moment for the industry. I have to say I'm heartened by the cross industry collaboration going on at this moment. One of the examples I would call out today, be it SynthID or watermarking, companies coming together around cyber. That is so important for this industry with this technology. So I'm encouraged by those trends as well.

I think 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 okay to put it out. But in the security world, there's a well-established practice. Google has done Project Zero for a long time. 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 wide. 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.

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 model 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? Look, 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 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, and they're awesome. I think the recent release of Gemma 4 was a great release. 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 right. You've seen our CapEx dollars. So you're putting a lot of R&D dollars to generate those incremental frontier models. You're discovering new techniques as part of doing those models. So we all have to be mindful of that. But I think we are also committed to making sure there's an open source ecosystem which is able to develop. So we take a balanced approach there, and I think we'll continue to take that balanced approach. That's how I see it.

Look, first of all, we not only do open source, part of the reason we invest so much in flash and flash models is so that we are giving a range of options. And those models are workhorses too. But your question on open source, it's not that we don't. We've been moving the open source frontier too. There's a lot of very very good open source models, particularly from China, which startups are adopting too.

So I think it depends, right? 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 or takes a break. So it's tough to predict 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. There have been a number of open source ecosystems that have been really successful over many decades in technology. But I think the upfront cost of making 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. They have been putting out incredible open-source models. If you can put yourself in the shoes of another enterprise CEO, 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?

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.

That's how I think about it.

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. So I think you have to be dynamic enough that you have to be able to adapt because the model frontier and the model ecosystems are changing pretty sharply. So that's the way I would think about it now. It's too early to predict if this is a real concern or not.

Look, 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. 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. We've had a lot of success with the strategy. 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.

Look, I think first of all there's a responsibility that comes with this technology. I think we all need to be careful to avoid this race condition at all costs. 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.

Look, 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 we had done a little bit more. So we're living in one of those moments in time. Costs are going up too. So for a given budget you may be getting less compute than you had previously planned for, your 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. Some of it is like 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 anti-gravity and say, 'How are you exactly running it at 800 tokens per second? 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. We are constantly thinking as far ahead as possible and we are making trade-offs like every other company right now.

Absolutely.

And hence the emphasis on something like 3.5 flash even more.

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 a bottleneck. That's the whole definition of bottlenecks. I think at various times, 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're all the bottleneck.