Google Gemini 产品负责人 Tulsee Doshi 谈 AI 时代产品经理的七个心得

她的团队按“对模型有自己观点”来招人：要求 PM 每天实测模型、有个人用途作为判断标准、能动手做 eval。评估新模型时，她建议选自己关心的任务，且选模型“已有生命迹象”但未达完美的场景，这样能看出不同模型的处理差异。她认为模型训练像“斯内普的魔药课”，配方可控但总有意外，产品团队的价值在于发现模型未被规划的长处。

PM 角色的重心也从写需求文档转向产出“有分量”的东西：原型、数据分析或可直接上线的 MVP。她提醒做 eval 不必等大规模搭建，从 5 个提示词、自己当打分员起步即可。

My biggest takeaways from Head of Product for @Gemini and Google's other frontier AI models—Tulsee D...

My biggest takeaways from Head of Product for @Gemini and Google's other frontier AI models—Tulsee Doshi ( @tulseedoshi ): 1. Great PMs are more in demand than ever. “When you can build anything, building something great actually is now where most of the value is,” Tulsee said. The scarce skill is now identifying which of the thousands of possible things to build is the one worth building. Alongside that is putting that judgment into practice: aligning people and converting speed-induced chaos into a coherent direction. 2. The PM role has shifted from writing requirements to creating something with “heft.” The PM who hands a requirements doc to an engineer and calls it done is not going to do well in this new environment. The new baseline is going one step further: actually producing something of substance, be it a prototype, a thorough data analysis, or even a ready-to-ship MVP. 3. Tulsee hires PMs who have opinions about models. She wants PMs who play with models every day, have their own use cases for judging whether a model is good, and stress-test capabilities themselves. They’re also technical enough to roll up their sleeves, dig into data, and build an eval. And they’re adaptive: “The space is changing every two weeks. A roadmap you wrote two months ago, you’re gonna be looking at it again and rewriting it.” 4. To understand a new model, test it on work you care about. When a new model comes out, some people try 3D animations, others creative writing or hard financial analysis. Tulsee’s advice for evaluating new models is to pick tasks where models “are already showing signs of life” but aren’t perfect by your own definition. That leaves room to see different models handle the problem differently, and it’s where you’ll notice how the models improve. 5. Model development is less like engineering and more like alchemy. “Model training is kind of like Snape’s potions class,” she says. “You can put all the ingredients in and be thoughtful about what you expect, but you also have to expect that there will be a little bit of magic.” Her team’s job is to identify where the model shines (often in ways no one planned) and to turn that into something great for users. 6. Tulsee’s 60-20-20 rule is a practical way to build ahead of the models. Your AI product roadmap should look like this: roughly 60% of what you build should work today, another 20% should be close—“not perfect, but you can see how the models might get here in the very near future”—and the final 20% should show glimmers of something magical that the model can’t quite do yet. The exact numbers are flexible, but the idea is to be ready to double down on something that just became possible. 7. Your first eval can be five prompts. When tackling evals, the most common mistake people make is waiting until you can build something large-scale before even starting. “Start with even just five prompts,” she says. “Start simple. Start with even just yourself as the rater.” A set of even 20 prompts that creates a signal between model A and model B is “so powerful.” Getting to a small, honest set of examples forces you to articulate what “good” looks like. Get clear on a set of good examples first, then scale up. 8. Burnout comes from chaos and grunt work more than from long hours. Tulsee admits everyone on her team is “putting in all of the hours.” But in her experience, burnout comes from churn and chaos, and work that feels like a waste of time. Work you’re excited about is exhausting but brings more joy. That’s why she hopes AI tools can reduce burnout by removing the grunt work, so people spend more time thinking and being creative. My tech workforce survey found the same thing: more hours doesn’t necessarily equal burnout. 9. Google’s full-stack AI strategy gives it an advantage few competitors can match. From custom TPU hardware to products used by over a billion people each, Google operates across the entire value chain. Tulsee describes Google’s goal as building models across the “Pareto curve of cost to quality”—not just the frontier LLM but models that serve a search user, a software engineer, and a cyber defender. Frontier competition matters to Google, and Tulsee is direct about Google’s intention to win the SOTA model race. Lenny Rachitsky @lennysan Why PMs are suddenly valuable again with Tulsee Doshi ( @tulseedoshi )—Head of Product for @Google ’s frontier AI models, including @Gemini , Nano Banana, Genie, Veo, and more. We discuss: 🔸 The rising value of great PMs 🔸 Google’s full-stack frontier AI strategy 🔸 Why model development is like Snape’s potions class 🔸 Why the PM role has shifted from writing requirements to creating something with “heft 🔸 How to write your first eval Listen n youtu.be/qGf2Yvan4Wg NUUSsB 🔗 View Quoted Tweet📊 2 ⚡

This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Gemini, Google, 提示词工程, 产品经理.

Source attribution: curated from Lenny Rachitsky (@lennysan).