𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. That is what I told myself when I first started working on AI products. Turns out, I had no idea what an AI PM really did. After spending years watching world-class AI PMs, building AI products at scale, making 100s of bad decisions, I have a much better definition of the role. But, sadly, even today, most PMs trying to work on AI are in the same boat: confused and clueless about "what does an AI PM actually do." Here's the simplest mental model to think of an AI PM's role: An AI PM is responsible for finding answers to these 7 questions. 1. What problem should we solve to maximize impact? 2. Does this need AI? 3. Do we have the right data? 4. How do we turn data into something useful? 5. How will users experience it? 6. How do we know it works before launch? 7. How do we keep making it better? Let's understand in detail: 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘀𝗼𝗹𝘃𝗲 The problem must be specific, validated with real users, and solution-agnostic. If you get this wrong, the model does not matter. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗔𝗜 This is the most important question an AI PM asks. And the answer is usually no. Saying no to AI when the situation does not call for it is not a failure. It is the job. 𝗗𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 "We have data" is not a strategy. An explicit data plan that includes what data we need, what we have, and what is missing is the right strategy. AI is only as good as the data behind it. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘂𝘀𝗲𝗳𝘂𝗹 Simple prompt, ML model, RAG, or agents. Each has a different use case, cost profile, and failure modes. The PM who skips this hands those decisions to engineering. 𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝘂𝘀𝗲𝗿𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗶𝘁 Users need trust, control, and recovery. Design for when the AI fails, not only for when it works. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵 There is no binary pass/fail. Build an eval framework. Define good, bad, and edge cases. Ship only when the product clears your threshold. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗳𝘁𝗲𝗿 𝗹𝗮𝘂𝗻𝗰𝗵 AI products degrade in production if you stop watching them. Sample live conversations. Add new failure modes to your test set. Never stop monitoring. -- Want more details? I just published a full document covering everything I know about AI Product Management--the role, mental models, the mistakes I made and much more. Linked in the comments. Free. No email required, no paywall. I will keep it updated as the field evolves.

🚨 BREAKING: The man who built Tesla's AI brain just released the architecture for mass-producing executive decisions. Karpathy built a $50M executive team for $0.02 per decision. Yet most CEOs are still paying for one opinion. It's called LLM Council. And it makes asking one AI look like asking one intern for strategy advice. Your question goes to 4 of the world's smartest AIs at once. Each answers independently. Then they review each other's answers, anonymously. They rank them. They call out weak reasoning. Then a Chairman AI delivers one final verdict. I fed it 6 months of revenue, offer suite, and pipeline data. In one session, the Chairman delivered: → A GTM motion chasing buyers who never close (and the ICP that does) → A positioning gap letting cheaper competitors win our deals → A competitor using AI in a way we hadn't considered → The one process costing us 30 hours/week (fixable in a day) → The hire we thought we needed (we didn't) It didn't give me 4 opinions. It gave me one verdict. ICP rebuilt. Positioning fixed. Hiring plan killed. This isn't a chatbot. It's a shadow board that never plays politics. And unlike a $400K strategist, it never defends its own recommendations. I packaged the full implementation: ✅ The Multi-Model Consensus Prompt Pack ✅ The 4-AI Peer Review Template ✅ The Chairman Synthesis Framework ✅ The Plug-and-Play Config (ready to run) Four minds. One decision. Zero politics. Want it? 1. Comment "COUNCIL" below 2. Connect with me (so I can send it) 3. Like this post P.S. Repost for priority access