Ideas are the new bottleneck @akshaynathan_, Core Product Engineering, @OpenAI, interviewed by @swyx and @Vibhu (@LatentSpacepod) Summary: A…

Ideas are the new bottleneck @akshaynathan_, Core Product Engineering, @OpenAI, interviewed by @swyx and @Vibhu (@LatentSpacepod) Summary: Akshay Nathan runs the productivity pillar at OpenAI, the team behind ChatGPT Work and Codex. He argues that once anyone can build, the scarce inputs become ideas and taste, and the old proxies for productivity stop telling you anything. His warning to managers is that AI makes activity almost free while progress still costs the same discipline it always did. 1. The motion trap. The trap is conflating motion with progress. Adding models and standing up dashboards is easy now, and Nathan says plenty of teams do exactly that and find nothing has changed. Motion got cheap because the tooling got good, while progress still requires being prescriptive and deliberate about what you are trying to achieve. If your team cannot say what progress looks like this quarter, better tooling only speeds up the drift. 2. Ideas and taste. The bottleneck is now ideas and taste. Anyone can build, so build capacity stopped being the constraint. What limits you is the number of ideas and the number of things you are carrying at any given moment. Nathan calls this the era of bottoms-up ambition, where the scarce input is someone worth listening to about what to make. 3. Grounded ideas. Ideas do not come from a vacuum. One of the hosts said the single automation he wants and still cannot get is "bring me new ideas," and that LLMs keep failing at it. Ideas come from talking to users, reacting to friction you saw, or building on a foundation you already laid. That is why generalists who close that loop keep their value even as the building gets automated. 4. At-bats. For managers, Nathan watches at-bats, both the quantity and the quality. The loop he means runs from generating an idea, to building it, to getting feedback, to reacting, to actually validating or invalidating the hypothesis, then on to the next one. He measures the team's ability to complete that circuit efficiently, not the artifacts produced along the way. It is also a culture measure, since running the loop many times takes humility and the motivation to stay in it. 5. Falling proxies. The old productivity proxies are coming apart. Commits, lines of code, pull requests, story points, and now tokens used to track whether a team would hit its goal. Nathan says that correlation is breaking, and thumbs up and thumbs down do not rescue it, because you cannot tell if the user is rating the content, the vibe, or whether it helped them. Someone has to invent the replacement, because measurement is how anyone judges whether this is working. 6. Pride as signal. ChatGPT Work exists because non-developers at OpenAI started using Codex. In internal research sessions, people from strategic finance and marketing were using it for their own work, and what stood out was how proud they were, as if they were not supposed to have it. Nathan read that pride as evidence the power was never developer-only. Watch for users who are smug about your product, because that beats a satisfaction score. 7. Show, don't tell. Teaching capability through articles and onboarding does not work. In the early enterprise days, Nathan asked customers with big AI budgets what discrete use case they wanted, and got wild variance back, because a box you can say anything to is both the magic and the reason nobody knows what to do with it. People find the next use case by watching someone do it. He says show-not-tell is still not cracked. 8. T-shaped generalists. Everyone becomes a generalist with a specialty. Nathan says he could never have produced a design before, and still lacks the visual taste, but he can now iterate on one with AI. The generalist range comes cheap, and the specialty is the thing you are interested in and keep going deeper on. That combination is what makes his ceiling feel close to limitless. 9. No boxes by role. Do not draw product boundaries around who someone is. Nathan says his own job changes every few months, and the lines keep blurring between writing code, writing strategy docs, planning events, doing marketing, and recording podcasts. So Codex and ChatGPT Work share one agent harness, with opinionated differences only in the interface and the sandbox defaults. Let users choose an experience without locking them inside it. 10. Sites over decks. Interactive websites are replacing slide decks as the canonical team artifact. OpenAI's corporate finance team used to build its monthly reports in decks and spreadsheets, and now builds them as Sites. PowerPoint and Excel stay flexible until you hit a wall, either a feature you do not know or one the product never had, and with a site you can ask for anything. The model slider in the ChatGPT Work launch was designed inside a site. 11. Retry what failed. Retry the capability that failed you 3 months ago. Nathan's example is performance reviews: 6 months ago the model's help was slop, and this cycle it beat him at pulling context on what people had done and surfacing wins he never saw, because the agent reads the code, the reviews, and Slack. He still refuses to present model-written text as a review of a person, so what he delegates is the search and not the judgment. Broadening your sense of what is possible is his biggest piece of advice. 12. Ambition over headcount. Asked whether AI makes his teams smaller, Nathan says the opposite happens. Individuals and small groups now finish what used to take more people, and the amount worth doing grew at least as fast. So teams get more ambitious rather than leaner. That is a choice worth making on purpose instead of defaulting to headcount savings.

Source: Swyx (X) | 2026-08-16

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