Nvidia now produces three times as much code as before AI — specialized version of Cursor is being used by over 30,000 Nvidia engineers internally

Source: Tom’s Hardware

Image credit: Cursor

Nvidia’s internal code commits have tripled since it mobilized 100 % of its engineers with AI‑assisted programming tools. Cursor, an IDE made by Anysphere, now enables over 30,000 developers at the company to generate code with AI (source).

“Cursor is used in pretty much all product areas and in all aspects of software development. Teams are using Cursor for writing code, code reviews, generating test cases, and QA. Our full SDLC is accelerated by Cursor. We have built a lot of custom rules in Cursor to fully automate entire workflows. That has unlocked Cursor’s true potential.” — Wei Luio, VP of Engineering at Nvidia

Image credit: Getty Images

Benefits and Use Cases

• Debugging: Cursor excels at finding rare, persistent bugs and can deploy agents to resolve them swiftly.
• Git Flow Automation: Custom rules pull context from tickets and documentation, allowing Cursor to handle bug fixes and generate proper tests for validation.
• Onboarding: New hires and trainees can get up to speed quickly, using Cursor as a guiding hand with extensive knowledge.
• Complex Code Understanding: According to Luio, Cursor shines at grasping the complexity of long‑running, sprawling databases that would otherwise challenge a human developer.
• Human‑Centric Tasks: More experienced developers can focus on challenges that require human ingenuity, while Cursor handles mundane, repetitive tasks.

“Before Cursor, Nvidia had other AI coding tools, both internally built and from external vendors. But after adopting Cursor we really started seeing significant increases in development velocity.” — Wei Luio

Impact on Development Velocity

• Three‑fold increase in code commits across the engineering organization.
• Bug rates have stayed flat despite the surge in coding volume, indicating that quality has not been compromised.
• Critical components such as GPU drivers—used by gamers and professionals alike—are now partially generated by AI, accelerating delivery without sacrificing reliability.

These improvements align with Nvidia’s broader AI strategy, which has already leveraged generative AI for years (e.g., DLSS running on a supercomputer).