OpenAI announced that its agentic coding tool Codex is now being rolled out company-wide at NVIDIA to around 30,000 engineers.
In a post on X, the company announced, “We partnered closely with their team to deliver cloud-managed admin controls and US-only processing with fail-safes.”
Several engineers from NVIDIA also revealed that the latest version of the tool—Codex with the GPT-5.3-codex model—has enhanced their workflows.
Dennis Hannusch, an engineer at NVIDIA, wrote on X, “I’ve gotten used to complex workflows and context management, but Codex just does what I ask. I keep expecting quality to drop deep into a session, but it doesn’t.”
Similarly, Benjamin Klieger, another engineer from NVDIA, wrote on X, “The engineers I know here are big Codex power users.”
“With 5.3, I’m particularly impressed with context management and token efficiency, probably the two most important advances for agents right now,” he said, adding that the tool has made his day-to-day work much more ‘motivating and thrilling’.
NVIDIA’s announcement about deploying OpenAI’s Codex came just days after Cursor, the AI-powered coding platform, said its tool is also being used inside the company.
Cursor, in a blog post, revealed that 30,000 users actively engage with Cursor in NVIDIA. This has led to a threefold increase in committed code and improved onboarding times for junior developers.
Cursor also revealed that NVIDIA set an ‘engineering mandate’ last year to leverage Cursor to embed AI across every phase of the software development life cycle (SDLC) and eliminate manual bottlenecks in code generation, testing, reviews, and debugging.
“Beyond code generation, NVIDIA customised Cursor for its engineering workflows, extending the impact of AI models from individual productivity enhancements to the automation of core production workflows across the SDLC,” said Cursor.
Wei Luo, VP of Engineering at NVIDIA, said, “Each of NVIDIA’s product lines has a complex codebase that is evolving quickly. It’s very hard for developers to stay on top of these changes and understand the entirety of the codebase.”
“This is where Cursor really shines.”
Luo added that before Cursor, NVIDIA had other AI coding tools from both internal builds and external vendors. “After adopting Cursor, is when we really started seeing significant increases in development velocity.”
Teams at NVIDIA are also using Cursor to automate entire workflows. One engineering team at the company is using custom rules to automate the Git flow—branch creation, code commits, CI debugging and issue tracking.
Another team, for instance, is said to use Cursor to fix bugs via an automated workflow that pulls context from tickets and documentation via MCP servers, then implements bug fixes and runs tests for validation.
“Our full SDLC is accelerated by Cursor,” said Luo.
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