Over Half of Google's Production Code Is Now AI-Generated

Fifty percent.

Not prototypes. Not experiments.

Production code that ships.

The threshold

At Google, more than half of the code checked into production each week is generated by AI. This is code that passes review, gets accepted, and does not get rolled back.

"Each week, over 50% of the code that gets checked in, and through code review, is accepted, isn't rolled back, is generated by AI."

Paige Bailey, Office Hours S01E15

This is not a demo. This is not a projection. This is the current state of one of the world's largest engineering organizations.

The tools driving this: AI Studio, Gemini CLI, and Jules. Engineers are not evaluating whether to use AI. They are using it to get their work done.

What this means for fast-moving teams

If you are still running pilots or debating adoption policies, the competitive landscape has shifted underneath you.

Google has the resources to build custom tooling, train internal models, and run extensive evaluations. But the workflows that enable this level of AI contribution are not proprietary magic. They are built on patterns any team can adopt: agentic iteration, code review integration, and clear approval boundaries.

The barrier is no longer "does AI code actually work?" The barrier is "do we have a workflow that lets AI contribute code that survives review?"

The workflow that survives review

The 50% number is not about raw generation. It is about code that passes the same review gates as human-written code.

That means:

• The AI contribution fits the codebase style and patterns
• The PR addresses a real issue or ticket
• The code compiles, passes tests, and does not introduce regressions
• A human reviewer can verify and accept it

Execution context is the difference. Agents that can run commands, see outputs, and iterate produce code that survives review. Agents that generate suggestions in isolation produce code that needs fixing.

The tradeoff

Adopting agentic workflows is not free. There is upfront investment in:

• Defining approval boundaries (what can the agent run without asking?)
• Integrating with your existing review workflow
• Setting up a development environment where the agent can run and verify code
• Helping engineers shift from writing code to managing agents (clear outcomes, evaluate output, give feedback)
• Building trust through small wins before scaling

The shift is less about learning new tools and more about thinking like a manager: outcomes-oriented, clear in what you want, and focused on evaluating output and giving feedback.

The teams that have crossed the threshold did not adopt AI everywhere at once. They started with narrow, high-frequency tasks: boilerplate generation, test scaffolding, refactoring patterns. They built confidence in the workflow before expanding scope.

Why this matters for your team

If your competitors are shipping with 50% AI-generated code, they're moving twice as fast. Start with one engineer, one repo, one workflow, and scale from there.

How Roo Code delivers review-ready code

Roo Code gives the agent execution context. It proposes a diff, runs your tests, sees the actual output, and iterates until the code passes. The agent doesn't hand you suggestions to fix. It fixes them itself. Without this, agents confidently claim success while producing a blank screen.

For teams looking to cross the 50% threshold, the workflow matters more than the model. Roo Code provides the agentic iteration layer that turns AI suggestions into merged PRs.

Traditional vs. agentic AI coding workflows

The first step

Pick a low-risk task: test generation, docs, or boilerplate. Let the agent contribute PRs for a week. After that, check what merged, what needed fixes, and how much time it saved.

You don't need Google's infrastructure. You need one passing PR from an agent. Start there.