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    Ambition as AI’s current bottleneck

    A post summarizing OpenAI product lead Tara Seshan’s interview on Lenny’s Podcast highlights her operating rules: build for models two to three months out and ship prototypes instead of documents.

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    TLDR

    A user’s recap of OpenAI’s Tara Seshan on Lenny’s Podcast emphasizes using AI to expand what people can attempt, not just automate routine tasks. In that account, agents do the “rowing” while humans choose the direction, and Seshan expects that steering role to keep moving up a level. The recap also identifies collaboration as her next challenge, noting that people at OpenAI were sharing screenshots of their Codex threads in Slack.

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    Combined views

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    first seen 18d ago
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    20 comments
    155 saves
    13 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @gokulrAmbition is the Bottleneck now @tarstarr (Tara Seshan) (@OpenAI , product lead for Codex and ChatGPT Work), interviewed by @lennysan (Lenny Rachitsky), Lenny's Podcast The easy work is now trivially easy and the hard work is easy, so what separates people and companies is the ambition behind what they are willing to attempt. Seshan runs Codex and ChatGPT Work at OpenAI. Her operating rules follow from that: build for models 2 to 3 months out, ship prototypes instead of documents, and treat raising other people's ambition as part of the job. 1. Steering Over Rowing. Agents do the rowing and people steer, and the steering keeps moving up a level. It used to be a line of code, then pressing tab, then a goal, and Seshan expects it to keep climbing. Picking the direction stays human, and she describes it as a positive determinism about what you want the world to look like rather than a readout from data. Her next problem is multiplayer: people at OpenAI were sending each other screenshots of their Codex threads in Slack, which is a poor way to work with agents together. 2. The Ambition Bottleneck. "Not only are we able to be more ambitious, we almost need to be more ambitious." The people Seshan sees getting the most out of AI use it to widen the set of things they can do at all, beyond automating rote tasks. The old unicorn hire was the product thinker who could also engineer and design, because that person removed the translation layers between functions, and everyone has that now. The constraint moved from what you can execute to what you can imagine, and expanding your own thinking is the hard part. 3. Raising Other People's Ambition. Seshan cites Tyler Cowen: people underrate walking up to someone and asking whether they could do the more ambitious version, or do it faster, or do it at 10x the scale. She treats that as a large part of the PM job, so when someone proposes a timeline or a v1 scope, the response is to ask whether the ceiling is higher. Her evidence is Patrick Collison's list of projects executed at unreasonable speed, all of which happened before these tools existed. If those were possible then, the count should be climbing fast now. 4. The Two To Three Month Rule. "You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong." Seshan builds for capability 2 to 3 months out, which only works if product stays tied to what research has on its roadmap. The discipline is putting model capability at the center and getting your own product constructs out of the model's way. She quotes Kevin Weil's line that this is the worst the models will ever be, and says it is absurd that it keeps being true. 5. Empirical Over Academic. At Stripe, payments rewarded rigor: you could reason through a competitor's next move, and failing to do that showed up as carelessness. Seshan found AI markets too emergent for that, so being prolific beats being theoretical, and the switch felt jarring enough that she wondered whether she was skipping her due diligence. What replaced the long reasoning doc is sharpening one hypothesis to a point, testing it, and feeding the result back in. She borrows Shishir Mehrotra's term for it, the eigenquestion: the one thing that determines whether the product works. 6. Founders, Plural. OpenAI is founders led rather than founder led, with very little top-down direction and almost no distance between a product lead and the market. Seshan expected a treasure trove of secret strategy, the way a new hire at Stripe gets handed the payments bible, and there was none. Every view about how the world should work becomes public product or public messaging quickly. She credits the Codex turnaround to that structure: people who noticed something should be better went and built it without asking. 7. Three Questions That Run Product. OpenAI operates on three internal questions. "Is this maximally accelerated?" came from Nick Turley and covers speed. "Are you mainlining it yet?" is the one Seshan and Andrew Ambrosino ask their team, and it means using the product all day every day to do your actual job. The third asks whether the team is being as ambitious as possible, which covers scope. 8. Knowledge Work Isn't Code. Coding is output-verifiable: run the tests and you can trust the answer. Seshan says knowledge work breaks that, because you cannot look at the finished deck, see 90%, and believe it without inspecting the process, the inputs, and the reasoning. So the product has to show in-progress work, citations, and chain of thought, and take the user along to the answer. It also puts the thread format itself in question, since threads were built for coding. 9. Writing As Thinking. Seshan splits writing at work in two and treats the halves oppositely. Writing as reporting covers status updates and launch plans, and she hands all of it to the model. Writing as thinking covers the brief that argues for a product or a strategy, and she never automates it: "I start myself and I end myself," using AI in the middle only to pull data or push back on her ideas. The same rule governs her meetings, where she prepares for the total time everyone will collectively spend in the room. 10. Mocks, Not Docs. A long document stopped being proof that you thought about something, because anyone can now generate a long document that proves the opposite. Seshan still writes hundreds of docs, and she writes them for herself. What she circulates is a prototype people can try, or better, an A/B result with a recommendation attached. She calls this the biggest personal change of the era. 11. The 70 Percent Doc. Take a document to 70% and let the people whose buy-in you need carry it to 100%. Seshan got the rule from an old manager and still runs it. A perfectly polished idea repels new ideas, which bounce off it, while something with rough edges invites people to work on it with you. At Stripe she used this to open new product areas, shopping a brief around and letting each person attack it before taking it to the next. 12. Product Marketing Fit. Seshan spent time at Sutter Hill to learn whether product market fit is luck or a playbook, and came away convinced it is a playbook. What surprised her most was how badly she had underrated product marketing fit, having treated PMM as glue between functions. Mike Speiser tests the narrative before the product exists: pitch 100 people, refine the positioning until the story lands, then commit to a product shape. Done well, that work can decide whether the company succeeds.

    1 Source

    @gokulrAmbition is the Bottleneck now @tarstarr (Tara Seshan) (@OpenAI , product lead for Codex and ChatGPT Work), interviewed by @lennysan (Lenny Rachitsky), Lenny's Podcast The easy work is now trivially easy and the hard work is easy, so what separates people and companies is the ambition behind what they are willing to attempt. Seshan runs Codex and ChatGPT Work at OpenAI. Her operating rules follow from that: build for models 2 to 3 months out, ship prototypes instead of documents, and treat raising other people's ambition as part of the job. 1. Steering Over Rowing. Agents do the rowing and people steer, and the steering keeps moving up a level. It used to be a line of code, then pressing tab, then a goal, and Seshan expects it to keep climbing. Picking the direction stays human, and she describes it as a positive determinism about what you want the world to look like rather than a readout from data. Her next problem is multiplayer: people at OpenAI were sending each other screenshots of their Codex threads in Slack, which is a poor way to work with agents together. 2. The Ambition Bottleneck. "Not only are we able to be more ambitious, we almost need to be more ambitious." The people Seshan sees getting the most out of AI use it to widen the set of things they can do at all, beyond automating rote tasks. The old unicorn hire was the product thinker who could also engineer and design, because that person removed the translation layers between functions, and everyone has that now. The constraint moved from what you can execute to what you can imagine, and expanding your own thinking is the hard part. 3. Raising Other People's Ambition. Seshan cites Tyler Cowen: people underrate walking up to someone and asking whether they could do the more ambitious version, or do it faster, or do it at 10x the scale. She treats that as a large part of the PM job, so when someone proposes a timeline or a v1 scope, the response is to ask whether the ceiling is higher. Her evidence is Patrick Collison's list of projects executed at unreasonable speed, all of which happened before these tools existed. If those were possible then, the count should be climbing fast now. 4. The Two To Three Month Rule. "You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong." Seshan builds for capability 2 to 3 months out, which only works if product stays tied to what research has on its roadmap. The discipline is putting model capability at the center and getting your own product constructs out of the model's way. She quotes Kevin Weil's line that this is the worst the models will ever be, and says it is absurd that it keeps being true. 5. Empirical Over Academic. At Stripe, payments rewarded rigor: you could reason through a competitor's next move, and failing to do that showed up as carelessness. Seshan found AI markets too emergent for that, so being prolific beats being theoretical, and the switch felt jarring enough that she wondered whether she was skipping her due diligence. What replaced the long reasoning doc is sharpening one hypothesis to a point, testing it, and feeding the result back in. She borrows Shishir Mehrotra's term for it, the eigenquestion: the one thing that determines whether the product works. 6. Founders, Plural. OpenAI is founders led rather than founder led, with very little top-down direction and almost no distance between a product lead and the market. Seshan expected a treasure trove of secret strategy, the way a new hire at Stripe gets handed the payments bible, and there was none. Every view about how the world should work becomes public product or public messaging quickly. She credits the Codex turnaround to that structure: people who noticed something should be better went and built it without asking. 7. Three Questions That Run Product. OpenAI operates on three internal questions. "Is this maximally accelerated?" came from Nick Turley and covers speed. "Are you mainlining it yet?" is the one Seshan and Andrew Ambrosino ask their team, and it means using the product all day every day to do your actual job. The third asks whether the team is being as ambitious as possible, which covers scope. 8. Knowledge Work Isn't Code. Coding is output-verifiable: run the tests and you can trust the answer. Seshan says knowledge work breaks that, because you cannot look at the finished deck, see 90%, and believe it without inspecting the process, the inputs, and the reasoning. So the product has to show in-progress work, citations, and chain of thought, and take the user along to the answer. It also puts the thread format itself in question, since threads were built for coding. 9. Writing As Thinking. Seshan splits writing at work in two and treats the halves oppositely. Writing as reporting covers status updates and launch plans, and she hands all of it to the model. Writing as thinking covers the brief that argues for a product or a strategy, and she never automates it: "I start myself and I end myself," using AI in the middle only to pull data or push back on her ideas. The same rule governs her meetings, where she prepares for the total time everyone will collectively spend in the room. 10. Mocks, Not Docs. A long document stopped being proof that you thought about something, because anyone can now generate a long document that proves the opposite. Seshan still writes hundreds of docs, and she writes them for herself. What she circulates is a prototype people can try, or better, an A/B result with a recommendation attached. She calls this the biggest personal change of the era. 11. The 70 Percent Doc. Take a document to 70% and let the people whose buy-in you need carry it to 100%. Seshan got the rule from an old manager and still runs it. A perfectly polished idea repels new ideas, which bounce off it, while something with rough edges invites people to work on it with you. At Stripe she used this to open new product areas, shopping a brief around and letting each person attack it before taking it to the next. 12. Product Marketing Fit. Seshan spent time at Sutter Hill to learn whether product market fit is luck or a playbook, and came away convinced it is a playbook. What surprised her most was how badly she had underrated product marketing fit, having treated PMM as glue between functions. Mike Speiser tests the narrative before the product exists: pitch 100 people, refine the positioning until the story lands, then commit to a product shape. Done well, that work can decide whether the company succeeds.