From IDE to impact: AI measurement and governance

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Leveling the playing field with tech giants

AI tools like GitHub Copilot, Cursor, and Windsurf are fundamentally reshaping software development. But for engineering leaders, they raise urgent and complex questions:

• How much of our code is AI-generated?
• Where is AI being used, and by whom?
• Which models perform best for each type of coding task?
• Is it increasing velocity and quality—or introducing risk and rework?
• How do we prove its business value to executive stakeholders?
• Can we measure this at scale, while preserving privacy and trust?

Tech giants are already answering these questions:

• Satya Nadella: up to 30% of Microsoft’s code is now AI-generated
• Sundar Pichai: over 25% of Google’s new code is now AI-generated
• Mark Zuckerberg: expects 50% of Meta’s code to be AI-authored within a year

These benchmarks are influencing board-level conversations across industries.  But they’re only possible because these tech giants have entire groups dedicated to building the internal platforms that empower developers and give executives end-to-end SDLC instrumentation.

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Most enterprises can’t justify that level of investment—so a growing number of companies are turning to Faros AI for a faster, more scalable path to AI measurement and governance.

Why building an AI measurement and governance solution internally isn't the right path

Outside of Microsoft, Google, and Meta, building this internally is usually the wrong bet for enterprises, due to:

• Slower time to insight—years instead of weeks
• Ongoing maintenance costs—that only grow over time
• Talent misallocation—critical engineers working on plumbing instead of innovation
• Opportunity cost—delaying your GenAI strategy while competitors surge ahead

The Faros AI approach to AI measurement and governance

Faros AI is an engineering data platform that delivers a complete, data-driven view of the software development lifecycle—from inner-loop code creation to delivery and operations.

While our IDE extension is one powerful component, Faros AI connects signals across the entire toolchain—Git, task management, CI/CD, incidents, org charts—to create a unified, contextualized picture of engineering activity.

Instrumentation solution overview

So how does this all work? Here's an overview of the Faros AI code attribution archictecture.

1. IDE-Level Instrumentation

• Faros AI plugins capture fine-grained edit events directly in developers’ IDEs (VSCode-based IDEs and JetBrains). 
• These events are attributed to branches, files, and eventually PRs.

2. Classification & Signal Processing

• Data is transmitted securely to a Faros AI backend. 
• Heuristics and models classify code as human-authored or AI-generated.
• Faros AI leverages APIs (e.g., Copilot) to improve accuracy.

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3. Multi-Source Correlation

• Faros AI connects IDE activity with signals from Git, task management, CI/CD, incidents, and org charts.
• This enables GenAI insights to be viewed alongside broader engineering context — e.g., bugs, rework, or velocity changes after AI-generated code.

4. Visualization

• % of AI-generated code per repo, developer, or team
• Trends over time
• Language and team-level adoption patterns
• and much more!

5. Governance & Orchestration

• Faros AI enables real-time governance by allowing enterprises to annotate AI-driven code, enforce policies, and introduce new checks based on usage context.

The Bottom Line

“What gets measured gets improved.”

The companies that will lead in the AI era aren’t just the ones using AI—they’re the ones measuring it and executing on this transformation with data.

Faros AI gives AI leaders the power to maximize AI’s potential with data-led strategies—without the cost, complexity, or distraction of building it in-house. Contact us today to learn more.