My biggest takeaways from OpenAI's Codex lead @ajambrosino: 1. Product work has inverted. The old product process was built around the assumption that building things is expensive, so de-risk everything up front with specs, research, and prototypes. That assumption is gone. The hard work has shifted from “Should we build this?” to “Of all the prototyped attempts at this idea, what's the best idea, what should we fold together, and what do we go all-in on?” 2. Your role is now defined by the average of what you spend time on. Deginers write code, engineers do design, PMs ship. So what are you? You're now defined not by your title but by how you spend your time. If you averaged out everything you do in a week, where do most of those dots land? That’s your role. 3. Codex PMs use a "zone defense" strategy to stay on top of everything. With ideas flying at them from every direction, top-down annual planning doesn't work, so they spread their team out to cover the whole company. If two product people are working too closely, without any gaps, that's a bad sign. They space out PMs across the org for full coverage, and backfill gaps with product-minded engineers. 4. What is AI so bad at design? For two reasons: one practical, and one structural. Practically, design is harder to grade than code, and labs prioritize coding because it accelerates AI research. Structurally, good design requires novelty and culture—a model that outputs the @Linear website every time isn’t showing taste—and there’s a visual-to-code abstraction layer models can’t yet bridge. The practical reasons will likely be solved; some deeper challenges around novelty, culture, and abstraction may persist. 5. The original Codex Web release was “too AGI-pilled for the moment.” The first public Codex release was built on too ambitious a premise: give the model a task, and it comes back with the task finished. The problem was that the models at the time weren’t good enough to deliver on that promise reliably. Claude Code launched locally, asked questions, and sat with the user—a much better fit for where model capability actually was. Andrew thinks about that this constantly: are we building for where the models are, or for where we wish they were? 6. Andrew is confident that the Codex app launched in February 2026 would have failed if it had shipped in November 2025. The product was identical—the models were not. The lesson he learned was to keep prototypes that aren’t ready yet, and revisit them with each new model generation. Resist the temptation to kill a feature just because the experience isn't perfect. “It might not be ready yet” is very different from “it’s a bad feature.” 7. Taste isn’t just about aesthetics—it’s deciding what to build when you can build anything. Andrew points to a tweet arguing that people overemphasize taste’s aesthetic side (the example: Paul Graham has great taste and wears cargo shorts). Real taste blends aesthetics with systems thinking: knowing the direction, the theme, and how to present an idea. Ask “If we can build anything, what should this be?”—which he says is now the most important decision to make, in every field. 8. The design process isn’t dead. Yes, the formal design process as taught in design schools is finished. What remains is the meta-awareness of where in the product development process you actually are. The danger Andrew sees is the fully polished prototype that looks production-ready before anyone has done the research, and a roomful of people who assume it’s further along than it is. “That’s the design process now,” he says, “multiplayer exploration that looks like a finished product.” 9. “PRDs are dead” is also completely wrong. Because implementation has become cheap across every format, it’s tempting for non-engineers to jump straight to prototypes and for engineers to write long documents—when neither is the right tool. Andrew’s rule: if you’re trying to establish product clarity around a vague area, it’s probably a document; if you’re stress-testing an interaction pattern, it’s a prototype. The medium used to carry an implicit signal about where you were in the process, and now it doesn’t. 10. Most careers are longer than any one moment of failure. Andrew’s current success at OpenAI is, in his telling, 10 to 15 years of accumulation: skill set, passion, and market timing finally lining up at once.
Andrew Ambrosino (@ajambrosino) leads the team behind the Codex desktop app at @OpenAI. Codex usage has 6x'd since February, reaching over 5M weekly active users, and nearly 100% of OpenAI's employees use the Codex app regularly (and not just the engineers). Andrew's personal mission is to build "the best desktop app that has ever existed, full stop." If you've used the Codex app lately, you know he's not far off from that goal. In our in-depth conversation, we discuss: 🔸 The "zone defense" model of how PMs at OpenAI operate 🔸 Why AI is so bad at design 🔸 Why Andrew thinks the Codex app would have flopped if they'd shipped it in November instead of February (same product—only the model changed) 🔸 What “taste” really means as a professional skill 🔸 How Andrew uses Codex to run his workflows 🔸 His vision for Codex + ChatGPT Listen now 👇 https://t.co/rVoohRbCiu
