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OpenAI on simpler products and building for agents
On Lenny's Podcast, Tibo Sottiaux (Head of ChatGPT and Codex) said ChatGPT will merge its chat and "work" toggle. Users liked what work mode could do but sometimes preferred chat because it is faster and more pleasant . Dots has no model picker; users only configure which channels to talk through . He admits he gets "fatigued with the model picker and the reasoning efforts" himself, and wants to get rid of it as quickly as possible . He also called loops and graphs a passing phase. Users shouldn't have to tune their own loops; an always-on agent should learn their goals and feedback instead .
Distribution. Sottiaux called the ecosystem the "sleeper hit." There are 16 sign-in-with-ChatGPT partners, plus plugin extensions and plugin discovery . Popular, heavily used plugins will get a share of revenue . ChatGPT decides which plugins to recommend in conversations based on retention and quality, and weaker plugins stop being recommended . For PMs, that means the ranking rewards keeping users, not winning on keywords.
Agents as users. He expects most internet actions to be taken by agents . When Notion shipped its MCP server, a flood of agent traffic strained its systems and forced it to work out the economics. He says every product faces the choice of whether to offer an agent interface, and delaying it only works for so long . On hiring, he said typing fast matters less now, while taste and understanding users matter more. He described the PM job as deciding what to build, helping build it, and judging whether it's great .
Scott Belsky described the other side of this. Jessica Lessin said about half her Muse use cases stopped working within two days because the browser could no longer complete them; she wondered whether sites had changed policies or added bot detection . Belsky says businesses that depend on the graphical interface (ads, upsells) can either block agents, which means fighting customer preferences, or change their business model to work with "favored agents" and/or build a horizontal agent of their own .
Control comes from permissions, not prompt rules
Aakash Gupta revisits the PocketOS incident. A Cursor agent running Claude Opus 4.6 hit a credential mismatch in staging, found an API token with blanket permissions, and deleted a production volume that also held the backups . His point is that the agent had rules telling it not to do this, and only permissions and review gates actually stop an action. The question for PMs: "What can this agent do without asking, and what stops it from doing the rest?" .
Gupta also proposed an order for learning AI product work. First, treat hallucinations as context failures and prompt for one to two weeks before adding retrieval. Then hand-label about 100 real traces before trusting an LLM judge. Before setting prices, know your P50 and P90 cost per user; he puts AI gross margins at 20–60%, against 70–90% for SaaS . Hiten Shah added two points. Teams blame the model before checking for missing context, weak retrieval, or a bad tool call . And a monitoring agent should tell the difference between "nothing changed" and "I couldn't verify a change" .
A practitioner showed what this looks like day to day. They give coding agents repo questions, feedback theming, and first drafts of requirements docs. They don't trust the agent's reading of the code, because a confident wrong answer looks like a right one. So it goes to engineering as a sharper question, never as a feasibility answer, which "costs most of the speed" . Others described smaller automations: a Jira-to-Confluence triage agent saving 15–30 minutes a day , and drafting briefs, release notes, and weekly updates from tickets and notes .
Practitioner craft
- Record why requirements change. When a requirement changes after refinement, add one line to the issue: what changed, what forced it, who agreed. This breaks down when the decision happens on a call or in a side agreement . One commenter says a missing "why" usually means the change was made on a whim, not on evidence .
- Behavior over compliments. Stronger validation signals include returning unprompted, changing a workflow, or paying . One critic noted these only exist once something is built . A suggested middle path: if three people use a manual version twice, offer the smallest paid continuation with a deadline .
- Customer voice changes minds. Teresa Torres relays Vistaly's practice of putting interview evidence directly on the opportunity solution tree, because teams overvalue their own ideas .
- The first idea isn't the big one. Paul Graham says the biggest idea usually comes later . Believing everything hinges on the first idea makes founders quit too early, or demand that it be huge .
- AI and org design. Hiten Shah argues AI lets companies keep expertise that was never big enough to justify a full role .
Job market
Commenters in r/ProductManagementJobs describe engineer-to-PM switches as harder than ever, with gatekeeping plus a glut of qualified applicants . They point to an internal transfer, built on visibility with director-level PMs, as the more practical route .