OpenAI

“building and shipping an internal beta of a software product with 0 lines of manually-written code”

OpenAIPublished by the organization itself (post), no named author

“roughly 1,500 pull requests have been opened and merged with a small team of just three engineers driving Codex.”

OpenAIPublished by the organization itself (post), no named author

“This translates to an average throughput of 3.5 PRs per engineer per day, and surprisingly the throughput has increased as the team has grown to now seven engineers.”

OpenAIPublished by the organization itself (post), no named author

“engineers using it ship about 70% more merged pull requests”

OpenAIPublished by the organization itself (post), no named author

“Escalate to a human only when judgment is required”

OpenAIPublished by the organization itself (post), no named author

“They pull review feedback, respond inline, push updates, and often squash and merge their own pull requests.”

OpenAIPublished by the organization itself (post), no named author

“Codex reviews every internal pull request.”

OpenAIPublished by the organization itself (post), no named author

“We're talking about roughly a 10x increase in load on some systems. At most companies, that kind of growth might happen over two or three years. At OpenAI, we see it in about six months.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader

“We're now seeing agentic code reviews that look at code changes through a series of different lenses. In the past, it would have been impractical for a cloud infrastructure engineer and a security engineer to review every single code change. With agents, that becomes possible.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader

“The big theme of the past months has been that everything is now a coding agent. Whether the visible code is your output or not, agents write your artifacts.”

Andrew AmbrosinoLead, Desktop · Leader

“We are building an agent that "handholds" a change all the way to production — whether it's a code change or a change behind a feature flag. It observes the relevant monitoring graphs, but can also build its own dashboard to monitor important signals. More of our code changes are going to production with this kind of agent monitoring.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader

“For a long time, we had a 'capability overhang': the models were capable but the products didn't fully bring that out. Now, we're seeing an awareness gap. Some people have figured out they can use Codex to monitor Slack, update Airtable, or create onboarding materials. But many others still use it for one task and then discover more uses from teammates via word-of-mouth.”

Akshay NathanEngineering Lead, Productivity team · Leader

“The number one thing that is changing is that people are starting to use threads for much longer, and this longer usage has been a breakthrough. It's surprising to see the sheer length of time that people spend on a thread – even days! They often set a goal and then have the model crank.”

Andrew AmbrosinoLead, Desktop · Leader