I can remember exactly when I began to think that OpenAI would lose the mandate of heaven—the seemingly insurmountable lead they had built in AI coding since ChatGPT originally took off. I was in their Mission Bay office, in the summer of 2025, the night before the launch of GPT-5.

There was a certain buzz to the building. The overhead lights burned like stage lights, and people seemed to walk the warm rusts and browns of its halls just a little faster than usual. Even the gravity felt heavier—as if the sheer size of the following day’s launch had drawn the rest of the world into its orbit.

This made what I had come to say a little awkward.

Every, the company I run, had begun to build a reputation for testing new AI models on real work and explaining what they could—and couldn’t—do. OpenAI had given us access to GPT-5 about a month before launch so we could help them understand the model’s strengths and weaknesses and publish our Vibe Check—a detailed review—on launch day.

Ten of us had spent a week in a house in upstate New York for a team retreat, eating and working together at one long wooden table and gathering on a couch in front of a big screen to test the long-awaited model. We were especially excited to try it for coding, where AI was advancing fastest. But while we liked it for everyday use, our reaction to it in Codex was muted.

Today, Codex is the centerpiece of OpenAI’s comeback—the product that has swallowed ChatGPT from the inside and become the foundation for everything the company is building next. But that summer it was still a scrappy effort to build an AI software engineer. A version launched in the cloud a few months earlier hadn’t found product-market fit, and the team was mid-pivot to a new version that ran on a developer’s computer in their terminal and in their code editor.

In Codex, GPT-5 was fast, and it seemed smart. One of our most technical engineers, Naveen Naidu, had used it to track down a mysterious freeze in his voice dictation app Monologue that had stumped him for days. But it also seemed to stop too early, as if it were overly wary of stepping outside the bounds of what it was allowed to do. It felt designed to be used as a pair programmer.

As a team, we were moving beyond step-by-step collaboration with AI models. Using Anthropic’s terminal agent, Claude Code, a group of us led by Kieran Klaassen had started handing entire tasks to Opus 4 and walking away until a finished feature—or sometimes an entire app—came back. With minimal help from an engineer, staff writer Katie Parrott built and deployed her own AI content management system in a few days. It had some bugs, but building it would previously have been out of reach.

The tapping of McCallum’s keys slowed. “Tell Alexander about this new engineering workflow you have,” I remember her saying without looking up from her laptop.

Embiricos swiveled his chair to focus fully on me.

“We don’t even really look at the code anymore,” I began. But I blushed as I said it. I felt ridiculous explaining agentic delegation to Embiricos, of all people—building an autonomous coding agent was his job. I also wasn’t sure, at the time, that our engineering approach would work beyond our tiny team.

A Slack ping lit up Embiricos’s screen and broke the sudden quiet of the conference room. He muted his computer and pushed its lid three-quarters shut—just enough to keep his Codex agent thinking.

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Leaving Mission Bay that night, I wondered if OpenAI was building the perfect tool for a dying workflow. Burned once by its failed cloud agent, the company looked like it was retreating to the code editor, where AI helped programmers work step by step. Meanwhile, Claude Code was taking off among people handing over entire tasks and walking away.