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Hey, everyone. I'm Ishan, and I work on anti-gravity at Google DeepMind.

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Today I'm going to talk about the future of building products,

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what judgment looks like,

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and some lessons that we've learned

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from shipping antigravity at Google.

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So, antigravity is a tool to get work done with agents,

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and we're trying to make the best interface

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for developers and knowledge workers

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to do high-intelligence tasks.

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The software engineering we used to know

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has completely changed.

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We can do things now

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that we wouldn't have even dreamed of a year ago.

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A sentence can turn into an operating system,

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and my voice can create a AAA game.

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So what's left for us?

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The tools we use have changed.

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We used to use the code editor.

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It's been around for decades.

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But now, every year, if not more often than that,

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there's a new paradigm for how to do our work.

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It redefines what our work even looks like.

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And on the side of building products,

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That's changed as well.

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We used to spend a lot of time up front,

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figuring out the right thing to make,

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because building was expensive.

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It took a lot of time, effort, and resources,

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so we ideated more and invested more in that phase.

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This is completely flipped,

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where now you should still understand the problem

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you're trying to solve and how to go about it,

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but if you want to make something, it can exist just like that.

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You can try it, feel it, get feedback, and understand the nuances that you might not have

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realized about it before, and use that to inform how to move forward.

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This is exciting, but it's also a little bit deceptive.

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It's easier than ever to just turn our brains off and blindly accept what's made for us.

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So if anything can be made by anyone, then it's more important than ever to know what

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what the right thing to even make is.

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Everyone knows their own definition for what taste or judgment looks like.

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And I think that's OK.

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That's good.

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Because we all work in different ways, and we're all working towards different things.

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But by the end of this talk, I'm hoping that I can help you form a little bit of a clearer

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idea on what judgment in building products looks like to you.

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I've gone from the scale of working at startups to now at Google DeepMind, where the scale

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is, it feels pretty incredible.

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We're making frontier models, and on the product side,

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we're able to use this internal frontier

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to build the interfaces that will use the next steps

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everyone will see in intelligence.

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We're very close to the research,

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and we can experiment and iterate really fast

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and ship to over 100,000 people inside Google.

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These people at Google are using antigravity internally

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in really ambitious and unique ways.

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And this creates a really cool feedback loop where we can continue building and do awesome things.

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I want to walk you through three big lessons that we've learned from this cycle.

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So I think we've all seen this first pattern of the tools we use scaling up with intelligence.

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We used to build products in a pretty relatively straightforward way.

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We had to identify a problem, understand it deeply, and make something that people want with a clear target.

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Now that target is moving.

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The ceiling is going up for what's even possible to make.

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And with that, the possible value that you can create

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in the products that you build is changing as well.

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So I'm always coming back to a few thoughts whenever I'm

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working of what we can achieve now

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and what's just past the edge of what we can achieve.

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And we've seen this look like two main ways

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building at DeepMind.

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As our models got better earlier in the year, they got better at communicating with and

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orchestrating each other, we created systems for teamwork to do more ambitious work.

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We stepped up in intelligence to let users do more.

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At the time, it was awesome.

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You could create operating systems and really cool things we've never seen before, but it

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was also pretty prescriptive because the models needed it at the time.

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More recently, this has looked like our version of agent swarms.

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You can actually try this now in anti-gravity.

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But this has been creating a ton of value inside Google for us.

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People can be more ambitious, there's more agents working together, and we're able to

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relax the constraints to achieve more.

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You can bet that with the next models coming out, we'll see even more cool patterns and

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things we didn't expect.

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But I think we all already kind of knew this.

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We've been seeing this for the past few years.

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All the tools we're using are just adding more and more new, cool ways to scale up with

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intelligence.

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But the second way I've seen evolving with intelligence look

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is a little bit trickier, because being ambitious does not

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always mean adding more.

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Let's go back in time a little bit and walk together

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through a decision in the product.

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The way we used to use AI tools looked very much

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like a structured turn-based format, like this.

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I would ask for some small scoped change,

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for example, editing a few files.

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The model would do it, and then we'd

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move on to the next thing, step by step.

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It made sense at the time to be able to cue instructions

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so the next things I wanted could be delivered to the model

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one at a time.

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They might not have been smart enough to get everything at once,

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but it made sense to give it limited context progressively

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it could get the work done bit by bit.

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Fast forward to now.

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There's a lot more going on.

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Long running agents, asynchronous tasks,

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external triggers, and everything else we're doing.

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Things are looking more complicated,

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and the agents are also smarter.

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So maybe the decision we made in the past

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might no longer be correct.

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Maybe a better pattern looks like treating the agent more

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as an entity that can make better decisions,

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and giving it user intent immediately, just like this.

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Maybe it'll choose to orchestrate to a bunch of worker agents,

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or do the task after we're done the current task at hand.

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It's smarter, so it can decide for itself.

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The decision we made in the past can easily become obsolete as intelligence grows, and you need to challenge your prior assumptions.

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If we separate the timeline of what we can achieve into three clear parts, if you build too far in the past, it'll be obsolete.

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If you build too far for the future, it could be visionary but impractical.

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It's important for us to ideate quickly and work towards building in the right time window

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create the most value.

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Challenge your past assumptions and rebuild your foundations to create good systems that

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scale with intelligence.

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With these foundations, we've seen another really awesome pattern evolve or come out

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at Google where we see the value in creating a platform for ambition.

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So, something like this might sound familiar.

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I used to use design tools in a very manual way.

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I'd create frames, build user interfaces manually,

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and create things by hand.

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And the tool, if we look at how it changed from before to now,

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it looks pretty similar, but the way I use it has completely flipped.

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Maybe now agents are creating hundreds of visualizations

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for me to go over very broadly before I go deeper into something.

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building software now doesn't always look like giving the user value in a very clear and

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deterministic way users can create their own leverage and make room for their own ambition

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let me walk you through how this has looked for us in our product there's a lot of room for

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extensibility people can build specialized tools that work for them and this can often end up

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leading back into the product.

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I'll show you some examples.

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Personal software can come up where I can make a dashboard

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where agents will scan across feedback channels and bug

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fixes to show me all this information in a way that just

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I like.

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Maybe it'll look like software for my team, where designers

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can create the tool that works just for them, where they can

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collaborate visually on demos.

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This can also branch out even more.

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The science team at Google is doing some awesome things

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with their science plugins.

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We're creating this platform for ambition

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for a wide range of users to create their own value

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and create their own leverage.

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And this can actually feed back into the product

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you're making at its core.

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The agent swarm example I mentioned earlier

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actually started as a plugin through our extensibility system.

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It's created a ton of value internally for us at Google.

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And by giving the users surface area

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to explore and create value for themselves,

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we were able to learn from it and iterate in the product

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to make the core better.

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Let your users be ambitious

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and watch it feedback into your product

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to create more value for everyone.

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The last principle I want to go over

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kind of applies to all of this.

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There's a lot of great talks at this conference

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about software factories.

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You know, it's really exciting.

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We can give our agents ways to build more,

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verify more reliably, and ship faster.

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It's exciting.

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But if all the parts of what software engineering used to look like get abstracted away,

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then how do we define what the remaining value in our human judgment is?

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I'll argue that human judgment is more important than ever.

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If I ask an agent to, say, make this slide better,

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maybe it'll add some visualizations and a bunch of dashboards and cool colors and everything.

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Maybe it'll rewrite the whole slideshow in Rust.

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So it'll solve every problem, but does every problem need to be solved?

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Take, for instance, what the agent manager looked like around a year ago when Antigravity

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first launched.

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If we want to make it better to an agent, that might look like adding a lot more detail.

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Ship everything.

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Add more detail into every single step the agent is taking and give the user knobs for

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everything.

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It might be better in some ways.

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But it's easy to blindly accept the first thing that we're given.

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What does better even mean to us?

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If we're trying to make a better agent-manager interface,

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maybe that looks a little bit different.

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Maybe we need to ship fast,

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but understand at each step of the iteration process

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what better actually means to us and what the next direction is.

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Let's start here.

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Better might mean simplifying the interface,

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only showing the user exactly what the latest thing the agent

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is doing semantically.

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So they're not shown a wall of text and tool calls.

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That might not actually matter.

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Maybe it looks like simplifying the whole interface

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to give the user less cognitive load

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so that they can multitask easier

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and be able to achieve more in a different way.

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So this kind of brings us closer to what antigravity looks like today.

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All these steps along the process have brought us closer to what good means.

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And we're making a lot of improvements fast, so I'm excited for you all to see it.

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Now's the time to try many things.

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Be ambitious.

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It's more exciting than ever, and it'll actually get more exciting as the models get better.

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Try many directions, but take a moment to think. Use your human judgment. Just because

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you're finished creating something first, which can be done in an instant, it doesn't

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actually mean you're done. Think about what you're actually trying to achieve and how

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the thing you're making feels. Iterate quickly, identify many directions you can go in and

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try them, but arrive at the right thing and go deep into that.

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So make time to think, make room to think, and use your human judgment.

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To summarize, some of the lessons we've learned involve scaling up with intelligence, which

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doesn't always mean adding more.

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It could mean rebuilding your foundations.

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Take these foundations and enable your users to be ambitious in new ways you might not

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have expected before.

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And at the end of all this, when we're shipping faster and faster, make room to think and protect

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the human side of software engineering.

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At Google, this has led to some awesome things by creating a platform for ambition.

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Improvements to YouTube, better video decoders, and memory optimizations.

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As for DeepMind and anti-gravity, it's a more exciting time than ever, with new models coming

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out.

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And on the product side, we're also moving quicker than ever.

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These are just some of the future launches we've announced in the last two weeks.

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And we're continuing to use the principles we've covered today to work on the future

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of interfaces for higher intelligence.

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I'm excited for you all to see it, and I'll leave you with this.

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This new age of software engineering is actually worth celebrating.

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Agents might be doing more and more, but it's also more important than ever to solve real

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problems and use good product thinking.

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Protect your judgment and build things that people want.

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Thank you.
