Venkatesh Dhongadi

When we launched Jules, it was an experiment to learn how asynchronous coding agents can help developers ship faster. Seeing what you built taught us so much about where agentic development is headed, directly shaping several products across @Google. We’re working on bringing our learnings from Jules into @antigravity so you get the best of both worlds: autonomous remote background task execution and an agent-first development environment. As we focus on this unified experience, Jules will be winding down. Whether you used Jules to crush bugs, write tests, or launch features— thank you for shaping the future of agentic coding! 💙🦑

If AI makes you 10x faster at coding, why is your team not shipping 10x faster? Amdahl's law has the answer... The law says the overall speedup is capped by the part you did not speed up. With `p` as the fraction of the work you accelerate and `s` as the speedup on that fraction, speedup is computed as attached. Now plug in some numbers. Say writing code is 30% of the time from idea to production, and AI makes that part 10x faster. The overall speedup is 1 / (0.7 + 0.03), which is about 1.37x. Even if coding is half of the cycle, you get about 1.8x. These numbers are illustrative, so use your own, but the shape stays the same. The rest of the cycle is still there: - Figuring out what to build - Aligning product, design, and other teams. - Code reviews, which are now more than ever - CI and CD takes the same wall time as before. - Debugging and incidents, where code that nobody on the team wrote line by line takes longer to understand. In fact, it can get worse than Amdahl predicts. Faster code generation increases the load on every stage after it, so the review queue grows and the bottleneck shifts. A team that writes 10x more code with the same review capacity is just building a bigger queue, unfortunately So if you want the team to actually ship faster, measure the whole path from idea to production and find where work waits. Then apply AI there too: review, test generation, flaky test triage, deploy safety checks. Otherwise, you have optimized the part of the system that was never the constraint. Speeding up the typing was always the easy part :)

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8 years to Oct 18 🫠

Gotta wake up at 5AM for a match 🏏 The only way I’ll probably get out is a run out. I’m itching to go out & smash! Night night! 😈

We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.