Inside OpenAI’s Race to Catch Up to Claude Code

Katy Shi, a researcher who works on Codex’s behavior at OpenAI, says that while some folks describe its default personality as “dry bread,” many have come to appreciate its less sycophantic style. “A lot of engineering work is about being able to take critical feedback without interpreting it as mean,” Shi says.

Several major enterprises have signed on to use Codex too. “The fact that ChatGPT is synonymous with AI gives us a massive advantage in the B2B market,” says Fidji Simo, OpenAI’s CEO of applications. “Companies want to use technologies their workers are already familiar with.” OpenAI’s strategy to sell Codex is largely based on packaging it in with ChatGPT and other OpenAI products, Simo said.

Cisco’s president and chief product officer, Jeetu Patel, says he has told employees not to worry about the cost of using Codex, because they’ll need to be comfortable with the tool. When employees ask if “they’re going to lose their job because they’re using these tools,” Patel says, “what we have to tell our people is no, but I guarantee you’ll lose your job if you don’t use them, because you won’t be relevant. So you’re going to be out.”

Today, the panic around AI coding agents has spread far beyond Silicon Valley. The Wall Street Journal credited Claude Code with causing a $1 trillion tech stock sell-off last month, as investors feared that software would soon become entirely obsolete. Weeks later, IBM’s stock had its worst day in 25 years after Anthropic announced that Claude Code could be used to modernize legacy systems that run COBOL, common on IBM machines. OpenAI has worked tirelessly to make its AI coding agent part of the societal conversation, spending millions of dollars on a Super Bowl commercial about Codex, rather than ChatGPT.

At the Mission Bay temple, no one needs to be pitched on Codex. Many OpenAI engineers I spoke with said they rarely type out code at all anymore. They just spend their days speaking to Codex. And sometimes they get together and do it in congregation.

At headquarters, I sat in on a Codex hackathon—about 100 engineers crowded into a large room. Everyone had four hours to build the best demo with Codex. A senior OpenAI leader stood at the front of the room, twisting away from the laptop in his hands and speaking team names into a microphone. Team representatives nervously walked to a podium and gave short speeches about their AI projects through shaky voices. Winners received Patagonia backpacks.

Many of the projects were both created with Codex and designed to help engineers use Codex better. One group built a tool that summarizes Slack messages into weekly reports. Another group built an AI-generated Wikipedia-style guide to internal OpenAI services. Many of these demonstrations would have taken days or weeks to spin up previously, but now they can be done in an afternoon.

On my way out the door, I ran into Kevin Weil, the former Instagram executive who is now heading OpenAI for Science, the company’s new unit building AI products for researchers. He told me Codex was working on some projects for him overnight, and he would check on them in the morning. That’s become regular practice for Weil, and hundreds of other employees. One of OpenAI’s goals for 2026 is to develop an automated intern that does research on (what else?) AI.