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Tulsee Doshi on what AI product managers actually do
On Lenny's Podcast, Google's Gemini product lead argued that when anyone can build easily, building something great is what sets you apart. Taste, meaning pulling signal from noise and finding the insight that unlocks an experience, is now the PM's core value . Fast building also creates chaos, so the PM who drives consensus and brings structure matters more .
The practical points:
- Hiring. She wants PMs who use models every day, hold opinions, stress-test capabilities, dig into data and can build an eval. She also wants adaptability, because "a road map you wrote two months ago, you're going to be looking at it again and rewriting it" .
- Start evals small. Five prompts, or a mini-set of about 20, can show differences between two models. Act as the rater yourself to work out what "good" means before you scale up . Evaluate end to end: judge quality, production filters and serving all change what users experience. In audio, for example, serving affects how natural the voice sounds .
- Check that your metric represents your users. Google's skin-tone evals once used the six-tone Fitzpatrick scale, which was built for healthcare and skewed light. Switching to Professor Monk's 10-tone scale changed how products were evaluated and fed into improvements in the Pixel camera and image generation .
- Model output is unpredictable. Set capability goals, but the job is also to look at what comes out, find where the model shines unexpectedly, and turn that into something for users . The model's behavior is UX too. Her team turns user feedback into a behavior spec, then into rubrics, evaluation data and reward modeling .
- The 60/20/20 rule. About 60% of what you build should work now. Another 20% should work well enough, with capabilities you expect to arrive soon. The last 20% is an ambitious target where you see "glimmers of light" . Retest regularly: an older, rigid setup can hold back what a newer model can do .
- AI-written docs. Use them for data, such as post-launch usage and feedback roundups. The insight should come from the PM . Her broader point is that the PM no longer just writes requirements for others to flesh out. The job now carries "some heft," whatever format that takes .
Fewer handoffs between understanding a problem and building the fix
A widely shared thread forecasts that the PM/engineer distinction will start to disappear. "PMs who can't build and engineers who don't understand customers will both struggle." Customer understanding becomes more valuable because "building 10 features is becoming easier than figuring out which 1 actually matters" . Hiten Shah pointed to Monaco, where one of the best sellers now leads agentic deployment and the best GTM people help build the agents . He says that after working with coding agents since February, he questions how much of a software org chart exists only because "the person closest to the problem couldn't build it" .
Shah also shared his team's setup, which looks a lot like a PM operating model:
- One lead agent per project keeps the plan and records decisions. It delegates parallel tasks to other agents .
- Anything that needs human judgment goes into a single queue, and risky work needs explicit approval .
Validating an MVP that already exists
A founder with a launched MVP and no users drew many replies. The most useful one splits the problem into two questions. First, do people actually have the problem? Ask them to walk through the last time it happened. Second, does this version deliver value? Watch them try a task without coaching, then check a week later whether they came back or went back to their old workaround . Another founder says his first 12 users came from DMs. He offered manual setup to frustrated people he found complaining on Reddit, and charged from day one, even $5: "If nobody will pay $5, that's your answer" . Others said the signals to look for are an existing workaround or money already spent on the problem .
Entry-level PM roles
In r/ProductManagement, one commenter said entry-level PM jobs have gone "from rare and super competitive to non-existent." He blamed overhiring in 2021–23 and AI agents doing entry-level PM work . Another says junior BA roles, a common path into PM, are also becoming less common because of AI . Both are personal views, not hiring data.
Also
Ryan Hoover says founders should introduce their company with "We are…" instead of "We're trying to…" .
- For AI-model PMs, setting capability goals is necessary but not enough: model behavior is partly unpredictable, so inspect what actually emerges, find where it shines, and turn those strengths into user experiences.
- As building gets easier, PMs add value through taste and discernment—separating signal from noise and creating alignment. For AI-focused roles, look for hands-on model use, personal judgment, stress-testing, data fluency, and adaptability; revisit plans as the field changes.
- Start model evaluations small: use as few as five prompts or a roughly 20-prompt set, personally judge outputs, and test tasks you care about. Evaluate end to end, since judge quality, mitigations, and serving can change what users experience.
- Revisit whether evaluation measures represent the users and outcome you care about: Google moved from the six-tone Fitzpatrick scale—which was designed for healthcare and skewed light—to Professor Monk’s research-based 10-tone scale, then applied it to work on skin-tone representation in Pixel camera and image generation.
- The 60/20/20 approach to AI product bets is to make about 60% work now, another 20% work well enough with capabilities plausibly near, and reserve 20% for an ambitious target where there are early signs of potential; build for today while preparing to grow with the models.
- Retest products against improved models: a rigid system built around an older model can force-fit or hamper a newer one.
- Use AI-generated documents where it can efficiently gather data and feedback, while keeping insight human-led; the PM role also moves beyond writing requirements to adding substantive work such as prototypes, data analysis, and evaluations.
- Validate demand before building: talk with people in the target group before coding and ask how they currently handle the problem, rather than pitching a solution. Look for existing workarounds and ask whether they have searched or spent money to solve it; an observed problem alone does not establish that enough people need a solution.
- For an MVP already built, separate whether users experience the problem from whether this version delivers value: ask users to recount the last occurrence and what they did before showing the MVP, observe them attempting a relevant task without coaching, then follow up a week later to see whether they returned or resumed their old workaround.
- One commenter described replying helpfully to relevant subreddit complaints, then DMing five frustrated people with an offer to set up the product manually; three agreed. The same commenter recommends charging from day one—even $5—as a willingness-to-pay signal, while another suggests giving a demo or beta away to gather feedback.
- As building gets easier, PMs add value through taste—separating signal from noise and finding the insight that unlocks a good experience—and by bringing clarity and consensus so speed does not create chaos. AI PMs should use and stress-test models themselves, inspect data, build evals, and keep revisiting plans as the market changes rapidly.
- For model products, set capability goals but expect outcomes to be partly unpredictable; PMs should identify where a model unexpectedly excels and turn that into user value. Model behavior is also UX: PMs can turn user feedback into behavior specifications and rubrics, then work with researchers to connect them to evaluation and model-training data.
- The 60/20/20 approach balances a product that works now with capabilities that work well enough or may arrive soon, plus more aspirational ideas grounded in early signs of potential. Keep the product adaptable: retest existing structures as models improve, since old harnesses may constrain what a newer model can do.
- Start model evaluations small: a handful of prompts or roughly 20 examples can expose differences between models and help clarify what “good” means before scaling. Evaluate the end-to-end user experience, including the judge, production mitigations and filters, and serving; for audio, serving can affect voice naturalness. As an inclusivity example, the team found the six-tone Fitzpatrick scale was healthcare-oriented and skewed light, then incorporated Professor Monk’s 10-tone scale into evaluations and used it to improve Pixel camera and image-generation results.
- AI-generated documents are most useful for gathering current data and feedback; PMs should contribute the insight. More broadly, the PM role now calls for work beyond writing requirements, such as prototyping, analyzing data, and adding substance to the chosen format.
- In virtual-cell research, mining public records increased the dataset from 36 million to over 500 million cells, but immune cells—especially T cells—dominated. The speaker argued for AI-guided experiments that generate data for specific research questions rather than simply scaling data upfront.
- For patient-specific cancer analysis, clinicians retain the final care decision; the team said findings need to be delivered in a communicable format because clinicians have limited time and capacity to run their own data infrastructure, and reported that clinicians welcomed this support.
- A team presented few-shot EVO2 prompting as useful when task-specific labeled data for fine-tuning is scarce. For cell-specific enhancer design, they used an RL loop with a sequence-to-function reward model; a lab test found a designed enhancer active and specific in mouse brain.
For a B2B speech-data venture, the founder described consented, PII-redacted conversational recordings and professional-actor studio recordings, distinguishing the offering from translation or synthetic-speech platforms; a commenter pressed for clarity on the customer problem it solves versus existing platforms. The founder reported 3–4 inbound inquiries in one month and one small deal closed by a hired salesperson; replies recommended revisiting the leads with a short sample and consent/licensing terms, and pursuing founder-led outreach or paid pilots to establish buyer demand before seeking a sales cofounder.
Commenters distinguish shareholders from those making layoff decisions: executives typically decide, sometimes with board approval or pressure, rather than shareholders directly. One commenter’s simplified model is that expense cuts can lift short-term profit by reducing R&D investment and quality, while the stock response depends on expectations about medium-term effects—a product-investment tradeoff, not a measured outcome.
Lenny Rachitsky’s episode with Tulsee Doshi, Google’s Head of Product for frontier AI models including Gemini, Nano Banana, Genie, and Veo, explores why PMs are becoming more valuable and a shift in the role from writing requirements toward creating work with “heft.” It also covers Google’s full-stack frontier-AI strategy and how to write a first eval.
Monaco is already testing a more end-to-end product model: one of its best sellers leads agentic deployment, and its best GTM people help build agents, bringing firsthand customer understanding into the software. Hiten Shah says working with coding agents has made him question handoffs between people who understand a problem and those who build the solution: some improve the product, while others exist only because the person closest to the problem cannot build. The linked forecast is that AI will blur PM and engineering roles and make customer understanding—and choosing which feature matters—more valuable as building features gets easier.
Organize coding agents like a managed project: give each project a lead agent to maintain the plan, remember decisions, delegate tasks and track blockers; let it run task-specific agents in parallel that bring questions and report back. Have agents show screenshots, live pages, options and changes, then route work needing human judgment into one queue for feedback or approval; keep feedback open until addressed, test changes, get a second opinion on merges and approve risky work.
- Several commenters characterize product management as a mid-level or leadership role rather than an entry-level starting point, recommending that aspiring PMs first build work experience and expertise in a sector. Suggested routes include an APM role or junior business analysis, though one commenter says junior BA roles are becoming less common with AI.
- Commenters describe entry-level PM hiring as extremely constrained, attributing it to post-2021–23 overhiring and AI’s ability to handle entry-level PM work; this is anecdotal discussion, not hiring data. One mentor adds that prospects vary by market, résumé narrative, and professional connections.
Hiten Shah raises a friend’s hypothesis that helping people build and use AI agents and bots could be a large services business. He says the idea made him curious and asks what people would pay for help with, framing it as an open demand-discovery question rather than validated demand.
Patrick Collison says AI’s positive case is hard to envision: benefits such as curing diseases and material abundance are only ingredients, and the larger social equilibrium—including people’s roles and resource allocation—remains unclear. He also asks how public support will change as people understand AI’s medium-term stakes and what vision would excite them, making the articulation of a desirable future a strategic question for AI builders.
Hiten Shah says AI has led him to work on products in software categories he had never expected to enter, suggesting AI is changing which categories he considers viable for product development .
AI vision and positioning: Patrick Collison argues that benefits such as curing diseases and material abundance do not yet explain what a desirable AI-driven society would look like; he asks how work, human contribution, resource allocation, scarcity, and human control would change, especially given the pace of progress and limited time for incremental correction. He wonders how public support for AI may evolve as people consider these medium-term questions, and what vision of AI would excite them.
A senior business analyst with six years’ experience in MNCs and banking reported few callbacks after applying for PM and senior BA roles since January; a commenter suggested that lacking prior PM experience may make PM interviews less likely and recommended LinkedIn cold outreach for senior BA roles instead of common job portals.
Ryan Hoover advises founders to describe their company with the direct “We are…” rather than the tentative “We’re trying to…” when introducing it, a concise positioning and messaging tip for product teams.
