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What did Google learn from letting AI write its code?

Remember 2019? AI was cool, but most devs didn't see how it would help them. Fast forward to today, and we're ALL using AI-powered tools to write code faster. Google's internal tools are no exception – they're evolving rapidly, and we're seeing some serious productivity gains.

What is going on here?

Google's been quietly supercharging their internal software development tools with AI. Think code completion, automated code reviews, build failure prediction – the works.

What does this mean?

Google started with code completion, then moved on to resolving code review comments and adapting pasted code, with even more ambitious features in the pipeline.

Collectively, there’s a 37% acceptance rate on AI code suggestions, with AI-assisted code making up a whopping 50% of characters written! During their internal builds, Google found that the most impactful tools are the ones that feel natural to use. Features that require extra effort to trigger just don't get used.

Google also found that high-quality data from their engineers' activities is crucial for improving their AI models. And, not surprisingly, they've learned that optimizing the entire process, from identifying opportunities for AI assistance to implementing the suggestions, is key to maximizing impact.

Why should I care?

There’s no stopping AI in software development now. Devs at Google are already using AI to help with code reviews, adapt pasted code, and predict build failures.

Because this is the future of coding, AI-powered tools are becoming indispensable, and the sooner you embrace them, the sooner you can reap the benefits.

Stay ahead of the curve, and let AI handle the heavy lifting so you can focus on what you do best – creating amazing software.

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