How Google’s dev tools manager makes AI coding work
As Google’s project manager for developer tools, Ryan Salva has a front-row seat to the ways AI tools are changing coding. Formerly of Github and Microsoft, he’s now responsible for tools like Gemini CLI and Gemini Code Assist, nudging developers into the new world of agentic programming.
His team released new third-party research on Tuesday showing how developers actually use AI tools – and how much progress is left to make. I sat down with Salva to talk about the report and his personal experience with AI coding tools.
This interview was edited for length and clarity.
Every year, Google does a survey of developer trends – but this year’s report really focuses on AI tools, and specifically how agentic developers are willing to get in their approach to programming. Was there anything in the research that surprised you?
One of the really interesting findings was the median date when developers started using AI tools. They found it was April 2024, which corresponds fairly neatly to Claude 3 coming out and Gemini 2.5 coming out. This is really the dawn of the reasoning or thinking models, and around that same time, we got much better at tool-calling.
For coding tasks, you really need to be able to leverage external information in order to problem solve, so it may need to grep, it may need to compile the code. If the code compiles it may want to run that unit test, and that integration test. I think that tool-calling really is the important piece that gave models the ability to self-correct as they move along.
How are you using AI coding tools personally?
Most of my coding these days is for hobby projects, and I spend most of my time using command line-based tools. So that includes Gemini CLI. Then there’s a little bit of Claude Code, little bit of Codex in there. And you don’t ever really use a terminal-based tool by itself, so I’m really heterogeneous around the IDEs that I use. I use Zed. I use VS code. I use Cursor. I use Windsurf, all of them, because I’m interested in just seeing how the world works and how the industry is evolving.
On the professional side, product managers tend to live in documents, so the first thing is using AI to help me write the specification and requirements docs.
I’m curious how that works. You’re using Gemini CLI to build Gemini CLI, but I would imagine it doesn’t just run itself.
A development task will usually start as an issue, maybe it’s a GitHub issue that someone’s dropped with a bug. Often, if I’m really being honest, it’s a fairly under-specified issue. So I’ll use Gemini CLI in order to create a more robust requirement doc in Markdown. That will usually create probably about 100 lines of fairly technical, but also outcome-driven specification. Then I will use Gemini CLI to write the code based on that specification and the general preferences in the team documents.
Leave a Comment
Your email address will not be published. Required fields are marked *