Fireside Product Management

Full disclosure before we start: I’ve advised Vectorial for a while since they were in the Berkeley Skydeck program and I’m a small investor, which is exactly why I tried to play this one straight. When I sat down in person with cofounders Taran Singh and Rahul Garg, with their third cofounder Jasmine Kaur joining us later, I wanted to understand two things. The first was what it actually looks like to build a company from scratch in 2026, when every startup is an AI startup and finding a defensible lane is harder than it has ever been. The second was a question I suspect many of you are quietly asking during annual planning season: if you need to build things customers love, is there anything in between a six week UXR study and typing “what would my users think of these ten ideas?” into a chatbot?

The founders

Taran has spent roughly a decade modeling human behavior, starting in defense research where he predicted how large population segments would act, then moving through a research collaboration with Coursera at Carnegie Mellon on how learners from different backgrounds learn, and later joining a small research group that tried to predict career decisions from enormous volumes of resume data. What stuck with him across all of it was a slightly uncomfortable finding, which is that people are more predictable than we like to believe, both individually and collectively. As a visiting research fellow at Berkeley he helped build a behavior model called Sapiens, which the team says is about 87% accurate at predicting human behavior, and that became the seed of Vectorial.

Rahul came at the same problem from the builder’s side. He started in logistics operations managing around 150 people on the ground, then built more than ten products from zero across seven industries, from hyperlocal delivery to travel to real estate marketplaces, and he describes the common thread as an obsession with user psychology that owes a lot to Thinking, Fast and Slow. Jasmine brings deep consumer health experience, most recently leading AI initiatives at Pfizer, where she sat on the buyer’s side of the very decision Vectorial now asks enterprises to make.

Why not just ask Claude?

This was the first thing I pushed on, because it’s what every skeptical PM will ask. Taran’s answer was the most interesting idea of the conversation: frontier models like Claude, Gemini and ChatGPT are trained and tuned to solve rational problems in code, science, law and medicine, since that’s where the near term money is, while human behavior is fundamentally about the irrational side of people. By his account, models asked to role play a customer have plateaued at roughly 51 to 56% accuracy in predicting behavior over the past two years, and he pointed to academic work from CMU and Berkeley suggesting that tuning for benchmarks may be making them worse at it. Vectorial claims Sapiens is about 40% more accurate because it learns from data annotated specifically for behavior, including the unstated needs and opinions people rarely articulate.

I found this framing genuinely clarifying. You wouldn’t hire your CMO out of an MIT math PhD program, and while that might be a fine place to find a CTO, your CPO, your CMO and arguably your CEO need to understand how humans actually work, including the messy motivations and feelings that shape how people use a product and why. If an LLM treats irrational, emotional behavior as noise to minimize on the way to a correct answer, then it is structurally the wrong tool for predicting customers, for whom that noise is the signal. As Taran put it, there are only so many ways to be logical and endless ways to be irrational, and your particular irrationality differs from mine.

That said, I’d treat these figures as the company’s own claims rather than settled fact, since “accuracy at predicting behavior” can mean very different things depending on the task, the baseline and who designs the benchmark. If you evaluate any simulation vendor, ask exactly what was predicted, against what ground truth, and on whose data.

What simulation actually is

The word “simulation” gets used loosely, so it’s worth being precise about what Vectorial means by it. There are two parts, the first being a model of your target customers built at the level of individuals, often thousands of them, and the second being scenarios you run against that modeled audience, whether a landing page, an onboarding flow, a campaign concept or a feature idea. Because each person is modeled individually, you can see how a specific segment reacts and then roll that up into how the collective responds, which is what Taran means when he describes simulation as exploring every plausible scenario rather than returning a single averaged opinion.

The data comes from two places. The team starts with public communities like Reddit threads, Facebook groups, Quora and Twitter, where people talk candidly about their lives, and then fills gaps with AI moderated interviews of real people recruited through curated panels (they claim reach into more than 30 million users) to capture the unstated context that public posts miss. The goal is what they call holistic behavioral coverage, a whole person picture spanning life stage, socioeconomic background, personality and other purchasing decisions, rather than narrow answers about your product alone.

Jasmine drew the sharpest distinction of the day, one I think every enterprise PM should internalize, which is that behavior simulation is not customer insight. Mining your existing data summarizes what customers have already said and done, whereas modeling a person means predicting how they’ll react to something new. Her healthcare example stayed with me, because pharma companies sit on terabytes of clinical and claims data, yet two clinically similar patients are not behaviorally similar, which helps explain why adherence to many treatments sits below 30%. That gap is also her core argument for buying rather than building in house, since most companies’ data is too narrow to model the whole person.

Finding their lane

As someone who has been through two founder journeys, what impressed me most was how deliberately the team has narrowed its focus. Other companies sell simulation for broad market research and high level strategy, while Vectorial has chosen to specialize in user research, the execution layer after a bet has been approved, where the questions are about what shape the product should take and how a feature will land. The logic is frequency, since customers reportedly run about 400 simulations a month, roughly 20 times the research volume they managed before, and a tool used daily becomes a habit in a way a quarterly study never does. Early traction is concentrated in healthcare and gaming, an unusual pairing given how differently those industries weigh the consequences of getting it wrong.

Two use cases surprised even the founders. PMs love simulated focus groups in which personas debate each other, surfacing disagreements they didn’t know existed, and a growing number of customers want to test AI agents against simulated humans who behave messily, adding the noise and wandering intent that a rational LLM acting as test user or judge would never produce. In one case, acting on those agent simulations reportedly led to a 25% increase in engagement.

Where I think it’s also still early

Several open questions deserve honest airing, and the first is trust. Vectorial runs a calibration period of about six weeks in which it uses a client’s past studies and live interviews to tune the audience, then runs blind studies and shows the overlap, and it exposes a “proof of work” view tracing each simulated opinion back to anonymized profiles and inferred traits. That transparency is welcome, but the team also argues that matching past human studies isn’t the true north star, because those studies carry their own small sample, incentive and interviewer biases. I agree in principle, yet it creates an awkward position in which agreement validates the tool while disagreement can be blamed on the original study, so the real proof has to come from shipped outcomes, which are slow to measure and hard to attribute.

The second question is the data itself, since people who post on Reddit and in Facebook groups are not a random sample of anyone, and I’d want to understand how well the modeling corrects for who is overrepresented online and who stays quiet. To address this, Vectorial measures the behavioral coverage that public data provides and uses AI-moderated interviews to complete user behavioral profiles and maintain diversity.

The third is the business pitch. Rahul and Taran suggest customers fund Vectorial from their existing UXR budget and get roughly ten times the simulations, or split the budget and turn the dial as confidence grows. That’s a smart commercial framing for a startup, though companies are currently running traditional research alongside Vectorial and often draw funding from Marketing as well, given that user acquisition and growth frequently live there. Still, I’d be wary of any org that hollows out its research team entirely, because a great researcher’s value lies not only in collecting data but in the empathy and judgment they cultivate in the people around them.

What this means for PMs

My own view is that as AI automates the analytical, communication and execution parts of the job, product sense, taste, deep customer insight and empathy become the last bastion of PM uniqueness. The hardest part of the role, picking next year’s three big bets out of eight that all sound credible, looks a lot like venture capital, where you need to be right a lot and the numbers can only guide you so far. Often the winning bet scores a 94 while a compelling alternative scores an 88, and that six point gap is frequently undetectable by AI and, frankly, by many humans.

If tools like Vectorial prove out, I don’t think they replace that judgment so much as give PMs far more reps with something that behaves like their customers, sharpening the intuition that makes the pick. In effect, this acts as an innovation enabler, creating a highly efficient system for PMs to test their boldest, riskiest ideas before committing market budget. The founders’ motto captures the stakes well, since when building has become ten times faster, building the right thing matters even more, and shipping more slop gets you nowhere. Whether simulation turns out to be the missing middle between real research and a chatbot’s guess is still an open question, but it’s one I’d rather test than dismiss.

If you want to kick the tires, Vectorial is launching a self serve product wait list, and enterprise teams can reach out directly to start calibration.

I sat down recently with Chris Berta, an executive search partner, to talk about a question he has been chewing on for two and a half years: if AI takes away entry-level jobs, where do people learn their craft, and how do they ever become leaders?

It is the kind of question that sounds abstract until you realize nobody has a good answer. Chris has been asking people leaders for years. No one does.

His starting point is the old 70-20-10 rule of thumb: roughly 70% of what you learn comes from doing the work, not reading about it. If the doing gets automated, the learning has nowhere to happen. His proposal is a more deliberate apprenticeship model. Map out a project, define the expected outcome, do the work, then run a real post-mortem where everyone talks honestly about what went wrong and what they learned. Not the glossed-over kind. The real kind.

I love the line of thought, and my first reaction was to wonder whether companies will actually pay for it. It reminded me of living in Switzerland, where apprenticeship is a deep part of the culture. I asked my barber there how it worked, and she told me the government pays the apprentice while she trains them. Free labor, basically. I asked why she was not doing it. “It just slows me down,” she said.

That is the whole problem in one sentence. Training an apprentice slows you down, and in a market where everyone is racing to ship faster with AI, nobody wants to be the one who slows down. You would expect the associate product management and rotational PM programs to be growing and getting more intentional right now. They are not.

Chris is honest that he does not know the solution either, but he named the deeper incentive problem. It reminded me of healthcare: private insurers do not invest much in preventative care because the average person stays on a policy for about three years, so the payoff lands on the next insurer. Companies face the same math with training. Why invest two years in someone who will take the skills to a competitor? The Swiss answer is that the government decided it is in the country’s interest to train the next generation and subsidized it accordingly. Whether the U.S. is in a policy-making mood right now is, shall we say, an open question.

We also talked about what the hiring market looks like from Chris’s seat. The pendulum has swung to employers. Internal recruiters are drowning in volume, and since every resume is now ChatGPT-polished to the same job description, candidates all look identical. A friend at Amazon told Chris he does not even look at careers-page applicants anymore. He works his network and asks the referrer real questions. That is where Chris thinks executive search gets more valuable, not less: judgment about who fits, not keyword matching. He told me about a placement at Bloom Energy where the candidate’s title did not fit the role at all, but the projects she had run were exactly what the role needed. “It wasn’t a keyword match,” she told him afterward. “It was a goal match.”

On org design, Chris is seeing real compression. Middle management layers are being knocked out. At Microsoft and Oracle the cuts skewed senior, partly because it is a bigger cost save. The role I am hiring for right now would have been a people manager a few years ago. Today it is a very senior IC. Chris is seeing the same pattern, plus new shapes emerging: CROs combining sales and marketing, and someone senior owning AI implementation at more and more companies.

His phrase for the current moment is “people plus AI.” You cannot hand the whole thing to the model. You need the human in the loop with oversight and editorial vision, or, as he put it, everything just looks like everyone else’s slop.

We are planning to reconnect in six to twelve months and see how it is shaking out. My bet is we are still mid-sort, and the companies that figure out the talent pipeline question first will have a real edge.

I met Albino Sanchez in the bleachers at a high school JV football game. While our sons battled it out on the field for Palo Alto High School, we found ourselves deep in conversation about something far removed from touchdowns and tackles: why some product leaders thrive while others crash and burn in seemingly similar companies.

Albino doesn’t fit the typical Silicon Valley mold. Born and raised in Mexico City, he spent his early career as a strategy consultant helping large companies implement frameworks like Balanced Scorecard and OKRs. But unlike most consultants who move on to the next engagement, Albino couldn’t stop thinking about his former clients. Some organizations flourished with these frameworks. Others abandoned them within months. The strategic tools were identical. The execution was completely different.

What he discovered would fundamentally change how I think about my own career moves—and it should change how you think about yours too.

The Pattern That Changes Everything

After years of looking back at his consulting clients, Albino noticed something remarkable: “Those organizations that were really thriving with these frameworks and really growing, they had a special type of leader. And that leader was usually a people-centered leader, a leader that was humble, that was a servant leader, and that this leader cared about their people, listened to them, and really wanted collaboration.”

This wasn’t just about nice leadership. It was about creating what he calls “the atmosphere for people to thrive.”

The insight hit him hard enough that he completely pivoted his career. He became an executive coach, spending the last 15 years working with leaders to shape healthier, more productive cultures. He moved his family from Mexico City to Palo Alto four years ago and recently founded Aha! Impact, a company focused on helping organizations achieve the right culture so both the business and employees can thrive.

But here’s what matters for you as a PM: Albino’s journey revealed something most of us learn the hard way. Culture doesn’t just influence whether a strategy succeeds. Culture IS the strategy.

Why “Culture Eats Strategy for Breakfast” Isn’t Just a Poster on the Wall

You’ve probably seen this quote attributed to Peter Drucker plastered on every startup’s office wall. But do you actually believe it?

Albino puts it this way: “We need to have the right environment so people can thrive and then implement and then be successful in business.” Without that environment, even the most brilliant product strategy becomes a document that sits in a Google Drive folder, gathering digital dust.

The Culture Paradox: Why Google, Amazon, Meta, and Microsoft All Win Differently

During our conversation, I pushed Albino on something that had been bothering me. If culture is so critical, how do companies with wildly different cultures all succeed? Amazon’s frugality and bias for action looks nothing like Google’s innovative freedom and psychological safety. Microsoft’s collaborative enterprise focus differs dramatically from Meta’s move-fast-and-break-things mentality.

His answer surprised me.

While different cultures can succeed, Albino sees clear patterns in what works today: “Innovation is one of them. We need to have nowadays with so many changes with AI, technology, globalization, communications. We need to be innovative. We need to be adaptive. We need to embrace change as something that’s part of our day to day.”

The successful organizations aren’t choosing between being people-centered OR innovative OR efficiency-driven. They’re becoming all three simultaneously. The old archetypes (pick your culture and stick with it) no longer apply in our rapidly evolving landscape.

But here’s the critical insight for PMs: You need to understand which cultural attributes matter most to you personally. Because while multiple cultures can succeed, not every culture will allow YOU to succeed.

The Real Reason You’re Miserable at Work

Albino shared something that hits close to home for many experienced PM’s: “People join organizations because of the company and they leave the organization most likely because of the boss.”

This tracks with every conversation I’ve had as an executive coach. The PMs who come to me aren’t struggling with their OKRs or roadmaps. They’re struggling with leadership dynamics, unclear values, and cultural misalignment.

Think about your own career. When you’ve been most energized, most productive, most creative. Was it because of the company mission statement? Or was it because you had a leader who created space for you to do your best work?

When you’ve been most miserable, was it really about the compensation or the commute? Or was it about a leader who micromanaged, who didn’t value collaboration, who created an atmosphere of fear rather than trust?

Culture doesn’t just make work more pleasant. It fundamentally determines whether you can bring your best self to the job.

The Leadership Styles That Shape Product Cultures

Here’s where Albino’s work gets really practical. He identifies four primary leadership archetypes that shape organizational culture, and understanding these can help you decode any company you’re considering:

1. The Controlling Leader This leader centralizes decision-making, micromanages execution, and views team members as resources rather than collaborators. They might get short-term results, but they create cultures where PMs become order-takers rather than strategic partners. Innovation dies because risk-taking gets punished.

2. The Competitive Leader Everything is a zero-sum game. Teams compete internally for resources, recognition, and rewards. This can drive individual performance but often at the expense of collaboration. For PMs, this means product launches succeed but platform thinking fails. You win your battle but lose the war.

3. The Collaborative Leader This is Albino’s people-centered leader. They invest in relationships, foster psychological safety, and view success as collective rather than individual. In product organizations, this looks like cross-functional partnerships that actually work, user research that influences decisions, and retrospectives that drive real improvement.

4. The Creative Leader These leaders embrace experimentation, tolerate failure, and push for innovation. They create cultures where PMs can propose bold ideas without fear. But without enough structure, these cultures can become chaotic.

The best leaders, and the best cultures, combine elements of all four, calibrated to the organization’s specific needs. As a PM evaluating a new role, you need to assess not just the stated values but the actual leadership style you’ll experience day-to-day.

The Questions You’re Not Asking in Interviews

Most PMs treat interviews as one-way evaluations. The company assesses you; you try to impress them. Albino argues this is backwards.

“This is a two-way assessment,” he told me. “You are also interviewing them.”

I know what you’re thinking: “Tom, that’s easy to say when you have options. When you’re desperate for a job, you can’t afford to be picky.”

I get it. But here’s the truth Albino helped me see: accepting a role at a company with cultural misalignment doesn’t solve your job search problem. It delays your job search problem by six months while making you miserable.

Your objective isn’t to get as many offers as possible. Your objective is to get offers from places where you’ll thrive.

So what questions should you actually ask?

On Work-Life Integration: “How do you manage team collaboration across different locations and time zones?”

These aren’t just logistics questions. They reveal whether the company trusts employees or requires surveillance. They show whether leadership believes productivity comes from presence or output.

On Decision-Making: “Tell me about a recent product decision where you had significant disagreement among stakeholders. How did you resolve it?”

This behavioral question (turned around on the company) reveals their true decision-making process. Do they rely on data, authority, consensus, or customer feedback? Do they value PM input or just expect execution?

On Failure and Learning: “Describe a recent product launch that didn’t meet expectations. What happened, and how did the team respond?”

The answer tells you everything about psychological safety. Do they blame individuals or examine systems? Do they learn from failures or hide them?

On Growth and Development: “How do PMs typically grow in their careers here? Can you share specific examples of PMs who’ve advanced and what enabled their growth?”

This reveals whether the culture actually invests in development or just talks about it in the handbook.

But here’s Albino’s most important advice: “It’s very important that you are authentic, you are yourself. Don’t try to make an act there. It’s very common to do that just to cover the expectations of the potential employer. But you know what? Try to get rid of that fear and try to be yourself.”

This is counterintuitive in a competitive job market. Every instinct tells you to mold yourself to what they want. But cultural misalignment has costs. Stress. Burnout. Short tenure. Another job search in six months.

Better to be yourself, assess fit honestly, and find a place where you can actually thrive.

How AI Is Changing Culture Assessment

Here’s where Albino’s work gets really interesting for those of us in tech. He’s building an AI-powered tool to help companies assess cultural fit during hiring.

Traditional culture fit assessment is notoriously unreliable. It often means “do I want to get a beer with this person,” which perpetuates homogeneity and bias. Or it gets delegated to a single interviewer who may not accurately represent the actual culture.

Albino’s approach is different. His tool analyzes the organization’s stated values, actual behaviors, and cultural attributes. Then it evaluates candidates against these dimensions through structured assessment.

“It’s going to analyze your organization, what are the values, and depending on your stage, your size, your location, what type of company you are, it’s going to analyze all this information and it’s going to recommend which are the key cultural factors or cultural behaviors that you need to assess when you interview a candidate,” he explained.

The tool is currently in beta testing, launching in January. But the concept matters even if you never use it: Culture fit should be systematic, not subjective. It should be measured, not assumed.

For PMs, this has implications beyond hiring. If companies can systematically assess culture, you can systematically evaluate it too. The questions you ask, the observations you make, the research you do before accepting an offer—these aren’t nice-to-haves. They’re essential.

The Framework: How to Evaluate Culture Before You Accept the Offer

Based on Albino’s expertise and my own painful lessons, here’s a practical framework for assessing culture fit:

Step 1: Define Your Non-Negotiables

Before you start interviewing, get clear on what cultural attributes you need to thrive. Not what sounds good in theory, but what you’ve actually needed in roles where you’ve done your best work.

For me, that includes:

* Collaborative decision-making where PM insights influence strategy

* Data-informed but not data-servant culture that values research

* Psychological safety to propose bold ideas and learn from failures

* Work-life integration that respects boundaries

Your list will be different. Maybe you thrive in competitive environments. Maybe you need more structure. Maybe remote work is essential. Be honest with yourself.

Step 2: Research Before You Apply

Don’t just apply to every open PM role. Albino recommends something smarter: “Make a list of those companies that you have learned about a little bit about their culture. Maybe you have a friend that worked at a company and they told you that it was an amazing place to work. So make a list of those companies and ask people about their companies they work for.”

Use LinkedIn to find people who’ve worked at target companies. Look for patterns in how long people stay. Read Glassdoor reviews not for specific complaints but for themes. Check whether executives walk the talk on platforms like Twitter or in company blog posts.

This front-loaded research saves you from wasting time in processes with companies where you’ll never fit.

Step 3: Interview Your Interviewers

During the interview process, systematically assess culture through:

* How they respond to your questions (defensive vs. open)

* Whether they can articulate values with specific examples

* How they talk about past failures and learning

* Whether individual contributors speak freely or defer to managers

* How they describe decision-making processes

* What they emphasize in describing the role (impact vs. tasks)

Step 4: Talk to Your Future Boss

Albino is adamant about this: “What’s really important is to get to talk to the hiring manager. Usually if you get to the final stages you get to talk, but if they are not planning on doing that, that’s critical because people join organizations because of the company and they leave the organization most likely because of the boss.”

Don’t accept an offer without substantive conversation with your direct manager. If the company won’t arrange it, that tells you something about the culture. I’d argue talking to the skip level is also really important if available.

Step 5: Trust Your Gut, But Verify

Pay attention to how you feel during the process. Are you energized or drained? Do you find yourself trying to be someone you’re not? Do the people you meet seem genuinely engaged or going through the motions?

But don’t rely only on feelings. Look for concrete evidence. Ask for examples. Request to speak with current team members. If they’re not willing to arrange it, that’s a red flag.

Choosing Culture Over Brand

One of Albino’s most powerful points challenges the default Silicon Valley career path: “You need to be intentional. You need to be really clear on what you want in your next job and not just go for the brand, not just go for the open position. Look for the environment, the leadership, and ask people that have worked there.”

This is hard advice to follow. The brand matters. The comp matters. The resume line matters.

But I’ve watched too many talented PMs burn out, get fired, or quietly quit because they optimized for the wrong variables. They went for the FAANG or unicorn prestige without assessing whether they could actually thrive there. They took the higher offer without asking about the leadership style. They joined the hot startup without understanding the culture they were stepping into.

The intentional career path looks different:

* Define success for yourself (not what TechCrunch or your parents think success looks like)

* Identify companies whose cultures align with your needs

* Pursue those companies specifically, even if they don’t have posted openings

* Assess fit rigorously during the interview process

* Choose the role where you can do your best work, even if it’s not the highest offer

This approach requires confidence. It requires clarity. It requires believing that your best work in the right culture is worth more than mediocre work in a prestigious culture.

What This Means for Your Next Career Move

If you’re currently employed and happy, use this framework to understand WHY you’re happy. What cultural attributes are enabling your success? How can you protect and expand them?

If you’re currently employed and miserable, stop trying to fix yourself. The problem might not be you, it might be cultural misalignment. Start researching cultures where your strengths would be assets, not liabilities.

If you’re searching for your next role, resist the temptation to spray and pray. Be intentional. Research culture. Ask hard questions. Be authentic in the process. The goal isn’t to get the most offers. The goal is to get the right offer.

And if you’re a hiring manager or product leader, recognize that culture isn’t something HR handles. Culture is shaped by your leadership every single day. The questions you ask, the behaviors you model, the decisions you make—these create the environment where your team either thrives or survives.

The Future of Culture and Product Management

Albino’s work on AI-powered culture assessment points to something bigger: culture is becoming quantifiable. We’re moving from vague values statements to measured behaviors. From gut-feel assessments to systematic evaluation.

For PMs, this is good news. It means you can make more informed decisions. It means companies can be more honest about their cultures instead of pretending to be something they’re not. It means better matches, longer tenure, and more impact.

But it also means you need to get serious about understanding culture. It’s no longer enough to read the values page on the careers site and hope for the best.

You need to research. You need to ask questions. You need to assess fit as rigorously as the company assesses your product skills.

Your Next Steps

Here’s what I’m taking away from my conversation with Albino, and what I recommend you do too:

This Week:

* Write down the cultural attributes of every job you’ve had where you thrived

* Identify patterns: what conditions enable your best work?

* Make a list of companies you’ve heard have cultures aligned with your needs

This Month:

* Reach out to three people who work at companies on your list

* Ask them specific questions about leadership, decision-making, and day-to-day culture

* Update your interview preparation to include questions that assess culture

This Quarter:

* If you’re searching, be more selective about where you apply

* If you’re employed, have an honest conversation with your manager about cultural alignment

* If you’re a leader, audit your own behaviors—are you creating the culture you claim to value?

Culture isn’t soft. Culture isn’t secondary. Culture is the environment where your product skills either flourish or wither.

Choose wisely.

If you’re navigating a career transition or want to develop a more intentional approach to your product leadership journey, I offer 1:1 executive, career, and product coaching. Learn more at tomleungcoaching.com.

And if you’re interested in being a beta tester for Albino’s culture fit assessment tool, reach out to him at [email protected] or visit ahaimpact.com. He’s looking for a few more organizations to participate in January testing at a significantly discounted rate.

OK. Let’s ship greatness.

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they’d all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They’re all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.

What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they’re better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.

If you’re an early or mid-career PM in Silicon Valley, this matters to you. Because here’s the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn’t whether to use these tools. The question is whether you’re using the right ones most effectively.

So let me walk you through exactly what I did, what I learned, and what you should do differently.

The Setup: A Real-World Test Case

Here’s how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”

So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.

For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that’s complex enough to stress-test these tools but straightforward enough that I could iterate quickly.

But here’s the critical part that too many PMs get wrong when they start using AI for product work: I didn’t just throw a single sentence at these tools and expect magic.

The “Back of the Napkin” Approach: Why You Still Need to Think

“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we’re gonna do this more, a little old-school AI approach where we’re gonna do some original human thinking.”

This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.

Your job as a PM isn’t to become obsolete. It’s to become more effective. And that means doing the strategic thinking work that AI cannot do for you.

So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here’s what I included:

Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”

Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”

This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.

Problem to Solve: What’s broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”

Key Elements: The feature set and approach.

Success Metrics: How we’d measure success.

Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that’s exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That’s all it took to create a foundation that would dramatically improve what came out of the AI tools.

Round One: Generating the Full PRD

With my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.

ChatGPT: The Reliable Generalist

ChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.

The document it produced checked all the boxes. It had the sections you’d expect. The writing was clear. But when I read it, I couldn’t shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.

Here’s what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.

But here’s what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.

The problem with generic output isn’t that it’s wrong, it’s that it’s invisible. When you’re trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company’s actual strategy. ChatGPT’s output felt like it was written by someone who’d read a lot of PRDs but never actually shipped a product.

One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren’t wrong, but they’re lazy. They don’t show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude’s output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”

Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn’t need to be exceptional. Think: internal updates, status reports, routine communications. Don’t rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.

Gemini: Better Than Expected

Google’s Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.

What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.

Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren’t in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.

But here’s where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren’t terrible, but they weren’t compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer’s brain.

For a PRD that you’re going to use internally with a team that already understands the context, Gemini’s output would work well. The text quality is strong enough, and if you’re in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini’s output directly into Google Docs and continue iterating there.

But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It’s good, but not great. It’s the solid B+ student: reliably competent but rarely exceptional.

Actionable Insight: Gemini is a strong choice if you’re working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It’s particularly good if you’re working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don’t expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.

Grok: Not Ready for Prime Time

Let’s just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.

“I don’t have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.

Actionable Insight: Skip Grok for product documentation work right now. Maybe it’ll improve, but as of my testing, it’s simply not competitive with the other options. It felt like 1-2 years behind the others.

ChatPRD: The Specialized Tool

Now this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.

The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”

The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.

Actionable Insight: If you’re a technical lead without a dedicated PM, or you’re a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It’s particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.

Claude: The Clear Winner

But the standout performer, and I’m ranking these, was Claude.

“I think we know that for now, I’m gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.

When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.

“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.

The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.

Actionable Insight: For any PRD that matters, meaning anything you’ll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it’s worth using Claude even if you primarily use another tool for other tasks.

Final Rankings: The Definitive Hierarchy

After testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here’s my final ranking:

* Claude - Best overall quality, most compelling mockups, strongest strategic thinking

* ChatPRD - Best for structured PRD creation, feels most “human”

* Gemini - Solid all-around performance, good Google integration

* ChatGPT - Reliable but generic, lacks differentiation

* Grok - Not competitive for this use case

“I’d probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.

The Deeper Lesson: Garbage In, Garbage Out (Still Applies)

But here’s what matters more than which tool wins: the realization that hit me partway through this experiment.

“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I’m sure the output would have been better. But as you can see we didn’t really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”

And we still got pretty good results.

This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what’s possible.

No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.

So the dual strategy is:

* Use the best tool available (currently Claude for PRDs)

* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.

Real-World Workflows: How to Actually Use This in Your Day-to-Day PM Work

Theory is great. Here’s how to incorporate these insights into your actual product management workflows.

The Weekly Sprint Planning Workflow

Every PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.

Here’s an example workflow:

Monday morning (30 minutes):

* Review upcoming priorities and open your rough notes/outline in Google Docs

* Open Claude and paste your outline with this prompt:

“I’m preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”

Monday afternoon (20 minutes):

* Review Claude’s output critically

* Identify gaps, unclear requirements, or missing context

* Follow up with targeted prompts:

“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”

Tuesday morning (15 minutes):

* Generate mockups for any UI-heavy stories:

“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”

* Even if the HTML doesn’t work perfectly, it gives your designers a starting point

Before sprint planning (10 minutes):

* Ask Claude to anticipate engineering questions:

“Review these user stories as if you’re a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”

* This preparation makes you look thoughtful and helps the meeting run smoothly

Total time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.

The Stakeholder Alignment Workflow

Getting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.

Here’s how:

Step 1: Map your stakeholders (10 minutes)

Create a quick table in a doc:

Stakeholder | Primary Concern | Decision Criteria | Likely Objections

Step 2: Generate stakeholder-specific communications (20 minutes)

For each key stakeholder, ask Claude:

“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”

Then you’ll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.

Step 3: Synthesize feedback (15 minutes)

After gathering stakeholder input, ask Claude to help you synthesize:

“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”

This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.

The Quarterly Planning Workflow

Quarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.

Six weeks before planning:

* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)

* Don’t wait until the last minute

Four weeks before planning:

Dump everything into Claude with this structure:

“I’m creating our Q2 roadmap. Context:

* Business objectives: [paste from leadership]

* Customer feedback themes: [paste synthesis]

* Technical capabilities/constraints: [paste from engineering]

* Competitive landscape: [paste analysis]

* Current product gaps: [paste from your analysis]

Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:

* Strategic rationale (how it connects to business objectives)

* Key initiatives (2-3 major features/projects)

* Success metrics

* Resource requirements (rough estimate)

* Risks and mitigations

* Customer segments addressed”

This gives you a strategic framework to react to rather than starting from a blank page.

Three weeks before planning:

Iterate on the most promising themes:

“Deep dive on Theme 3. Generate:

* Detailed initiative breakdown

* Dependencies on platform/infrastructure

* Phasing options (MVP vs full build)

* Go-to-market considerations

* Data requirements

* Open questions requiring research”

Two weeks before planning:

Pressure-test your thinking:

“Play devil’s advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”

This adversarial prompting forces you to strengthen weak points before your leadership reviews it.

One week before planning:

Generate your presentation:

“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”

Then add your company-specific context, visual brand, and personal voice.

The Customer Research Workflow

AI can’t replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.

Before customer interviews:

“I’m interviewing customers about [topic]. Generate:

* 10 open-ended questions that avoid leading the witness

* 5 follow-up questions for each main question

* Common cognitive biases I should watch for

* A framework for categorizing responses”

This prep work helps you conduct better interviews.

After interviews:

“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:

* Recurring themes and patterns

* Surprising insights that contradict our assumptions

* Segments with different needs

* Implied needs customers didn’t articulate directly

* Recommended next steps for validation”

AI is excellent at pattern-matching across qualitative data at scale.

The Crisis Management Workflow

Something broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.

Immediate response (5 minutes):

“Critical incident. Details: [brief description]. Generate:

* Incident classification (Sev 1-4)

* Immediate stakeholders to notify

* Draft customer communication (honest, apologetic, specific about what happened and what we’re doing)

* Draft internal communication for leadership

* Key questions to ask engineering during investigation”

Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.

Post-incident (30 minutes):

“Write a post-mortem based on this incident timeline: [paste timeline]. Include:

* What happened (technical details)

* Root cause analysis

* Impact quantification (users affected, revenue impact, time to resolution)

* What went well in our response

* What could have been better

* Specific action items with owners and deadlines

* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”

This gives you a strong first draft to refine with your team.

Common Pitfalls: What Not to Do with AI in Product Management

Now let’s talk about the mistakes I see PMs making with AI tools.

Pitfall #1: Treating AI Output as Final

The biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.

The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don’t hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.

The fix: Always ask yourself:

* Does this reflect my actual strategic thinking, or generic best practices?

* Would my CEO/engineering lead/biggest customer find this compelling and specific?

* Are there company-specific details, customer insights, or technical constraints that only I know?

* Does this sound like me, or like a robot?

Add those elements. That’s where your value as a PM comes through.

Pitfall #2: Using AI as a Crutch Instead of a Tool

Some PMs use AI because they don’t want to think deeply about the product. They’re looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.

This never works. AI can help you think more systematically, but it can’t replace thinking.

If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What’s our actual competitive advantage?” “Why would customers switch from the incumbent?”), you’re using it wrong.

The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you’re still the one choosing.

Pitfall #3: Not Iterating

Getting mediocre AI output and just accepting it is a waste of the technology’s potential.

The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what’s weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.

Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.

The fix: Budget time for iteration. Don’t try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.

Pitfall #4: Ignoring the Political and Human Context

AI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you’re working with.

They don’t know that your VP of Engineering is burned out and skeptical of any new initiatives. They don’t know that your CEO has a personal obsession with a specific competitor. They don’t know that your lead designer is sensitive about not being included early enough in the process.

If you use AI-generated communications without layering in this human context, you’ll create perfectly worded documents that land badly because they miss the subtext.

The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.

Pitfall #5: Over-Relying on a Single Tool

Different AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.

If you only ever use one tool, you’re missing opportunities to leverage different strengths for different tasks.

The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.

Pitfall #6: Not Fact-Checking AI Output

AI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren’t true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.

The fix: Fact-check everything, especially:

* Statistics and market data

* Competitive feature claims

* Technical capabilities and limitations

* Regulatory and compliance requirements

If the AI cites a number or makes a factual claim, verify it independently before including it in your document.

The Meta-Skill: Prompt Engineering for PMs

Let’s zoom out and talk about the underlying skill that makes all of this work: prompt engineering.

This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there’s a steep learning curve, prompt engineering is something you can get good at quickly.

Principle 1: Provide Context Before Instructions

Bad prompt:

“Write a PRD for an AI tutor”

Good prompt:

“I’m a PM at an edtech company with 2M users, primarily high school students. We’re exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.

Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”

The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.

Principle 2: Specify Format and Constraints

Bad prompt:

“Generate success metrics”

Good prompt:

“Generate 5-7 success metrics for this feature. Include a mix of:

* Leading indicators (early signals of success)

* Lagging indicators (definitive success measures)

* User behavior metrics

* Business impact metrics

For each metric, specify: name, definition, target value, measurement method, and why it matters.”

The structure you provide shapes the structure you get back.

Principle 3: Ask for Multiple Options

Bad prompt:

“What should our Q2 priorities be?”

Good prompt:

“Generate 3 different strategic approaches for Q2:

* Option A: Focus on user acquisition

* Option B: Focus on engagement and retention

* Option C: Focus on monetization

For each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”

Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.

Principle 4: Specify Audience and Tone

Bad prompt:

“Summarize this PRD”

Good prompt:

“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”

The audience and tone specification ensures the output will actually work for your intended use.

Principle 5: Use Iterative Refinement

Don’t try to get perfect output in one prompt. Instead:

First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”

Each iteration improves the output incrementally.

Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.

Strategy 1: The Structured Outline Approach

Don’t go from zero to full PRD in one prompt. Instead:

* Start with strategic thinking - Spend 10-15 minutes outlining why you’re building this, who it’s for, and what problem it solves

* Get specific - Don’t say “users,” say “high school students in the middle 80% of academic performance”

* Include constraints - Budget, timeline, technical limitations, competitive landscape

* Dump your outline into the AI - Now ask it to expand into a full PRD

* Iterate section by section - Don’t try to perfect everything at once

This is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.

Strategy 2: The Comparative Analysis Pattern

One technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.

For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”

This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.

Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.

Strategy 3: The Iterative Refinement Loop

Don’t treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.

After getting the initial PRD, I could have asked follow-up questions like:

* “What’s missing from this PRD?”

* “How would you strengthen the success metrics section?”

* “Generate 3 alternative approaches to the core feature set”

Each iteration improves the output and, more importantly, forces me to think more deeply about the product.

What This Means for Your Career

If you’re an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”

Absolutely not. But your role is evolving, and understanding that evolution is critical.

The PMs who will thrive in the AI era are those who:

* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility

* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones

* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy

* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably produces

Think of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.

The Real-World Application: What to Do Monday Morning

Let’s get tactical. Here’s exactly how to apply these insights to your actual product work:

For Your Next PRD:

* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice

* Open Claude (or ChatPRD if you want more structure)

* Copy your outline with this prompt:

“I’m a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”

* Review the output critically - Look for generic statements, missing details, or strategic misalignments

* Iterate on specific sections:

“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”

* Generate supporting materials:

“Create a visual mockup of the core user flow showing the key interaction points.”

* Synthesize the best elements - Don’t just copy-paste the AI output. Use it as raw material that you shape into your final document

For Stakeholder Communication:

When you need to pitch something to leadership or engineering:

* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)

* Compare them for:

* Clarity and conciseness

* Strategic framing

* Compelling value proposition

* Addressing likely objections

* Blend the best elements into your final version

* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.

For Feature Prioritization:

AI tools can help you think through trade-offs more systematically:

“I’m deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”

This doesn’t replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn’t thought of.

The Uncomfortable Truth About AI and Product Management

Let me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.

Less valuable:

* Writing boilerplate documentation

* Creating standard frameworks and templates

* Generating routine status updates

* Synthesizing information from existing sources

More valuable:

* Strategic product vision and roadmapping

* Deep customer empathy and insight generation

* Cross-functional leadership and influence

* Critical evaluation of options and trade-offs

* Creative problem-solving for novel situations

If your PM role primarily involves the first category of tasks, you should be concerned. But if you’re focused on the second category while leveraging AI for the first, you’re going to be exponentially more effective than your peers who resist these tools.

The PMs I see succeeding aren’t those who can write the best PRD manually. They’re those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.

Advanced Techniques: Beyond Basic PRD Generation

Once you’ve mastered the basics, here are some advanced applications I’ve found valuable:

Competitive Analysis at Scale

“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”

Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.

Scenario Planning

“We’re considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”

This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.

User Story Generation

After your PRD is solid:

“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].’ Include acceptance criteria for each story. Organize them into epics by functional area.”

This can save your engineering team hours of grooming meetings.

The Tools Will Keep Evolving. Your Process Shouldn’t

Here’s something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.

But the core principles won’t change:

* Do strategic thinking before touching AI

* Use the best tool available for your specific task

* Iterate and refine rather than accepting first outputs

* Blend AI capabilities with human judgment

* Focus your time on the uniquely human aspects of product management

The specific tools matter less than your process for using them effectively.

A Final Experiment: The Skeptical VP Test

I want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.

Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”

This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who’s brilliant, who’s seen a thousand product ideas fail, and who can spot b******t from a mile away?

The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.

But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.

The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.

Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.

Questions to Consider: A Framework for Your Own Experiments

As I wrapped up my Loom, I posed some questions to the audience that I’ll pose to you:

“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”

These aren’t rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.

For early-career PMs: I’d recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.

For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.

For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.

The Path Forward: Continuous Experimentation

My experiment with these five AI tools took 45 minutes. But I’m not done experimenting.

The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.

Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.

That’s why I created this Loom and why I’m writing this post. Not because I have all the answers, but because I’m figuring it out in real-time and want to share the journey.

A Personal Note on Coaching and Consulting

If this kind of practical advice resonates with you, I’m happy to work with you directly.

Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that’s leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.

I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.

The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.

Reach out through tomleungcoaching.com if you’re interested in working together.

OK. Enough pontificating. Let’s ship greatness.

Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.

Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.

To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:

• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples.

This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.

Table of Contents

* What AI Cannot Do and Why PM Judgment Still Matters

* The New AI Literacy: What PMs Must Know by 2026

* Why Building AI Products Speeds Up Some Cycles and Slows Down Others

* Whether the PM, Eng, UX Trifecta Still Stands

* The Biggest Risks AI Introduces Into Product Development

* Actionable Advice for Early and Mid Career PMs

* My Takeaways and What Really Matters Going Forward

* Closing Thoughts and Coaching Practice

1. What AI Cannot Do and Why PM Judgment Still Matters

We opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.

Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”

This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.

Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”

There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.

Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.

Why judgment becomes even more important in an AI world

David, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”

This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.

Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”

This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.

AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.

Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.

2. The New AI Literacy: What PMs Must Know by 2026

I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.

Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.

Skill 1: Understanding context engineering

David laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”

Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.

Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.

Skill 2: Evals, evals, evals

Rami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”

He is right.

• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.

AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.

Lauren said her PMs write evals side by side with engineering. That is where the world is going.

Skill 3: Knowing when to trust AI output and when to override it

Todd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”

This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.

A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.

Skill 4: Understanding the physics of model changes

This one surprised many people, but it was a recurring point.

Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”

PMs must understand:

• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned model

This is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.

Skill 5: How to construct AI powered prototypes in hours, not weeks

It now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.

But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.

3. Why Building AI Products Speeds Up Some Cycles and Slows Down Others

This part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.

Fast: Prototyping and concept validation

Lauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.

“You can think bigger because the cost of trying things is much lower,” she said.

For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.

Slow: Productionizing AI features

The surprising part is that shipping the V1 of an AI feature is slower than most expect.

Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”

Why. Because:

• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.

Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”

This should be printed on a poster in every AI startup office.

Very Slow: Iterating on AI powered features

Another counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.

David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”

Why is iteration so difficult.

Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.

Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.

4. The PM, Eng, UX Trifecta in the AI Era

I asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.

The trifecta is not going anywhere

Rami put it simply: “We still need experts in all three domains to raise the bar.”

Joe added: “AI makes it possible for PMs to do more technical work. But it does not replace engineering. Same for design.”

AI blurs the edges of the roles, but it does not collapse them. In fact, each role becomes more valuable because the work becomes more abstract.

• PMs focus on judgment, sequencing, evaluation, and customer centric problem framing• Engineers focus on agents, systems, architecture, guardrails, latency, and reliability• Designers focus on dynamic UX, non deterministic UX patterns, and new affordances for AI outputs

What does change

AI makes the PM-Eng relationship more intense. The backbone of AI features is a combination of model orchestration, evaluation, prompting, and context curation. PMs must be tighter than ever with engineering to design these systems.

David noted that his teams focus more on individual talents. Some PMs are great at context engineering. Some designers excel at polishing AI generated layouts. Some engineers are brilliant at prompt chaining. AI reveals strengths quickly.

The trifecta remains. The skill distribution within it evolves.

5. The Biggest Risks AI Introduces Into Product Development

When we asked what scares PMs most about AI, the conversation became blunt and honest.

Risk 1: Loss of user trust

Lauren warned: “If people keep shipping low quality AI features, user trust in AI erodes. And then your good AI product suffers from the skepticism.”

This is very real. Many early AI features across industries are low quality, gimmicky, or unreliable. Users quickly learn to distrust these experiences.

Which means PMs must resist the pressure to ship before the feature is ready.

Risk 2: Skill atrophy

Todd shared a story that hit home for many PMs. “Junior folks just want to plug in the prompt and take whatever the AI gives them. That is a recipe for having no job later.”

PMs who outsource their thinking to AI will lose their judgment. Judgment cannot be regained easily.

This is the silent career killer.

Risk 3: Safety hazards in sensitive domains

David was direct: “If we have one unsafe output, we have to shut the feature off. We cannot afford even small mistakes.”

In healthcare, finance, education, and legal industries, the tolerance for error is near zero. AI must be monitored relentlessly. Human in the loop systems are mandatory. The cycles are slower but the stakes are higher.

Risk 4: The high bar for AI compared to humans

Joe said something I have thought about for years: “AI is held to a much higher standard than human decision making. Humans make mistakes constantly, but we forgive them. AI makes one mistake and it is unacceptable.”

This slows adoption in certain industries and creates unrealistic expectations.

Risk 5: Model deprecation and instability

Rami described a real problem AI PMs face: “Models get deprecated faster than they get replaced. The next model is not always GA. Outputs change. Prompts break.”

This creates product instability that PMs must anticipate and design around.

Risk 6: Differentiation becomes hard

I shared this perspective because I see so many early stage startups struggle with it.

If your whole product is a wrapper around an LLM, competitors will copy you in a week. The real differentiation will not come from using AI. It will come from how deeply you understand the customer, how you integrate AI with proprietary data, and how you create durable workflows.

6. Actionable Advice for Early and Mid Career PMs

This was one of my favorite parts of the panel because the advice was humble, practical, and immediately useful.

A. Develop deep user empathy. This will become your biggest differentiator.

Lauren said it clearly: “Maintain your empathy. Understand the pain your user really has.”

AI makes execution cheap. It makes insight valuable.

If you can articulate user pain precisely.If you can differentiate surface friction from underlying need.If you can see around corners.If you can prototype solutions and test them in hours.If you can connect dots between what AI can do and what users need.

You will thrive.

Tactical steps:

• Sit in on customer support calls every week.• Watch 10 user sessions for every feature you own.• Talk to customers until patterns emerge.• Ask “why” five times in every conversation.• Maintain a user pain log and update it constantly.

B. Become great at context engineering

This will matter as much as SQL mattered ten years ago.

Action steps:

• Practice writing prompts with structured context blocks.• Build a library of prompts that work for your product.• Study how adding, removing, or reordering context changes output.• Learn RAG patterns.• Learn when structured data beats embeddings.• Learn when smaller local models outperform big ones.

C. Learn eval frameworks

This is non negotiable.

You need to know:

• Precision vs recall tradeoffs• How to build golden datasets• How to design scenario based evals for UX• How to test for hallucination• How to monitor drift• How to set quality thresholds• How to build dashboards that reflect real world input distributions

You do not need to write the code.You do need to define the eval strategy.

D. Strengthen your product sense

You cannot outsource product taste.

Todd said it best: “Imagine asking AI to generate 20 percent growth for you. It will not tell you what great looks like.”

To strengthen your product sense:

• Review the best products weekly.• Take screenshots of great UX patterns.• Map user flows from apps you admire.• Break products down into primitives.• Ask yourself why a product decision works.• Predict what great would look like before you design it.

The PMs who thrive will be the ones who can recognize magic when they see it.

E. Stay curious

Rami’s closing advice was simple and perfect: “Stay curious. Keep learning. It never gets old.”

AI changes monthly. The PM who is excited by new ideas will outperform the PM who clings to old patterns.

Practical habits:

• Read one AI research paper summary each week.• Follow evaluation and model updates from major vendors.• Build at least one small AI prototype a month.• Join AI PM communities.• Teach juniors what you learn. Nothing accelerates mastery faster.

F. Embrace velocity and side projects

Todd said that some of his biggest career breakthroughs came from solving problems on the side.

This is more true now than ever.

If you have an idea, you can build an MVP over a weekend. If it solves a real problem, someone will notice.

G. Stay close to engineering

Not because you need to code, but because AI features require tighter PM engineering collaboration.

Learn enough to be dangerous:

• How embeddings work• How vector stores behave• What latency tradeoffs exist• How agents chain tasks• How model versioning works• How context limits shape UX• Why some prompts blow up API costs

If you can speak this language, you will earn trust and accelerate cycles.

H. Understand the business deeply

Joe’s advice was timeless: “Know who pays you and how much they pay. Solve real problems and know the business model.”

PMs who understand unit economics, COGS, pricing, and funnel dynamics will stand out.

7. Tom’s Takeaways and What Really Matters Going Forward

I ended the recording by sharing what I personally believe after moderating this discussion and working closely with a variety of AI teams over the past 2 years.

Judgment becomes the most valuable PM skill

As AI gets better at analysis, synthesis, and execution, your value shifts to:

• Choosing the right problem• Sequencing decisions• Making 55 45 calls• Understanding user pain• Making tradeoffs• Deciding when good is good enough• Defining success• Communicating vision• Influencing the org

Agents can write specs.LLMs can produce strategies.But only humans can choose the right one and commit.

Learning speed becomes a competitive advantage

I said this on the panel and I believe it more every month.

Because of AI, you now have:

• Infinite coaches• Infinite mentors• Infinite experts• Infinite documentation• Infinite learning loops

A PM who learns slowly will not survive the next decade.

Curiosity, empathy, and velocity will separate great from good

Many panelists said versions of this. The common pattern was:

• Understand users deeply• Combine multiple tools creatively• Move quickly• Learn constantly

The future rewards generalists with taste, speed, and emotional intelligence.

Differentiation requires going beyond wrapper apps

This is one of my biggest concerns for early stage founders. If your entire product is a wrapper around a model, you are vulnerable.

Durable value will come from:

• Proprietary data• Proprietary workflows• Deep domain insight• Organizational trust• Distribution advantage• Safety and reliability• Integration with existing systems

AI is a component, not a moat.

8. Closing Thoughts

Hosting this panel made me more optimistic about the future of product management. Not because AI will not change the job. It already has. But because the fundamental craft remains alive.

Product management has always been about understanding people, making decisions with incomplete information, telling compelling stories, and guiding teams through ambiguity and being right often.

AI accelerates the craft. It amplifies the best PMs and exposes the weak ones. It rewards curiosity, empathy, velocity, and judgment.

If you want tailored support on your PM career, leadership journey, or executive path, I offer 1 on 1 career, executive, and product coaching at tomleungcoaching.com.

OK team. Let’s ship greatness.

The Interview That Sparked This Essay

Joe Corkery and I worked together at Google years ago, and he has since gone on to build a venture-backed company tackling a real and systemic problem in healthcare communication.

This essay is my attempt to synthesize that conversation. It is written for early and mid career PMs in Silicon Valley who want to get sharper at product judgment, market discovery, customer validation, and knowing the difference between encouragement and signal. If you feel like you have ever shipped something, presented it to customers, and then heard polite nodding instead of movement and urgency, this is for you.

Joe’s Unusual Career Arc

Joe’s background is not typical for a founder. He is a software engineer. And a physician. And someone who has led business development in the pharmaceutical industry. That multidisciplinary profile allowed him to see something that many insiders miss: healthcare is full of problems that everyone acknowledges, yet very few organizations are structurally capable of solving.

When Joe joined Google Cloud in 2014, he helped start the healthcare and life sciences product org. Yet the timing was difficult. As he put it:

“The world wasn’t ready or Google wasn’t ready to do healthcare.”

So instead of building healthcare products right away, he spent two years working on security, compliance, and privacy. That detour will matter later, because it set the foundation for everything he is now doing at Jaide.

Years later, he left Google to build a healthcare company focused initially on guided healthcare search, particularly for women’s health. The idea resonated emotionally. Every customer interview validated the need. Investors said it was important. Healthcare organizations nodded enthusiastically.

And yet, there was no traction.

This created a familiar and emotionally challenging founder dilemma:

* When everyone is encouraging you

* But no one will pay you or adopt early

* How do you know if you are early, unlucky, or wrong?

This is the question at the heart of product strategy.

False Positives: Why Encouragement Is Not Feedback

If you have worked as a PM or founder for more than a few weeks, you have encountered positive feedback that turned out to be meaningless. People love your idea. Executives praise your clarity. Customers tell you they would definitely use it. Friends offer supportive high-fives.

But then nothing moves.

As Joe put it:

“Everyone wanted to be supportive. But that makes it hard to know whether you’re actually on the right path.”

This is not because people are dishonest. It is because people are kind, polite, and socially conditioned to encourage enthusiasm. In Silicon Valley especially, we celebrate ambition. We praise risk-taking. We cheer for the founder-in-the-garage mythology. If someone tells you that your idea is flawed, they fear they are crushing your passion.

So even when we explicitly ask for brutal honesty, people soften their answers.

This is the false positive trap.

And if you misread encouragement as traction, you can waste months or even years.

The Small Framing Change That Changes Everything

Joe eventually realized that the problem was not the idea itself. The problem was how he was asking for feedback.

When you present your idea as the idea, people naturally react supportively:

* “That’s really interesting.”

* “I could see that being useful.”

* “This is definitely needed.”

But when you instead present two competing ideas and ask someone to help you choose, you change the psychology of the conversation entirely.

Joe explained it this way:

“When we said, ‘We are building this. What do you think?’ people wanted to be encouraging. But when we asked, ‘We are choosing between these two products. Which one should we build?’ it gave them permission to actually critique.”

This shift is subtle, but powerful. Suddenly:

* People contrast.

* Their reasoning surfaces.

* Their hesitation becomes visible.

* Their priorities emerge with clarity.

By asking someone to choose between two ideas, you activate their decision-making brain instead of their supportive brain.

It is no different from usability testing. If you show someone a screen and ask what they think, they are polite. If you give them a task and ask them to complete it, their actual friction appears immediately.

In product discovery, friction is truth.

How This Applies to PMs, Not Just Founders

You may be thinking: this is interesting for entrepreneurs, but I work inside a company. I have stakeholders, OKRs, a roadmap, and a backlog that already feels too full.

This technique is actually more relevant for PMs inside companies than for founders.

Inside organizations, political encouragement is even more pervasive:

* Leaders say they want innovation, but are risk averse.

* Cross-functional partners smile in meetings, but quietly maintain objections.

* Engineers nod when you present the roadmap, but may not believe in it.

* Customers say they like your idea, but do not prioritize adoption.

One of the most powerful tools you can use as a PM is explicitly framing your product decisions as explicit choices, rather than proposals seeking validation. For example:

Instead of saying:“We are planning to build a new onboarding flow. Here is the design. Thoughts?”

Say:“We are deciding between optimizing retention or acquisition next quarter. If we choose retention, the main lever is onboarding friction. Here are two possible approaches. Which outcome matters more to the business right now?”

In the second framing:

* The business goal is visible.

* The tradeoff is unavoidable.

* The decision owner is clear.

* The conversation becomes real.

This is how PMs build credibility and influence: not through slides or persuasion, but through framing decisions clearly.

Jaide’s Pivot: From Health Search to AI Translation

The result of Joe’s reframed feedback approach was unambiguous.

Across dozens of conversations with healthcare executives and hospital leaders, one pattern emerged consistently:

Translation was the urgent, budget-backed, economically meaningful problem.

As Joe put it, after talking to more than 40 healthcare decision-makers:

“Every single person told us to build the translation product. Not mostly. Not many. Every single one.”

This kind of clarity is rare in product strategy. When you get it, you do not ignore it. You move.

Jaide Health shifted its core focus to solving a very real, very measurable, and very painful problem in healthcare: the language gap affecting millions of patients.

More than 25 million patients in the United States do not speak English well enough to communicate with clinicians. This leads to measurable harm:

* Longer hospital stays

* Increased readmission rates

* Higher medical error rates

* Lower comprehension of discharge instructions

The status quo for translation relies on human interpreters who are expensive, limited, slow to schedule, and often unavailable after hours or in rare languages. Many clinicians, due to lack of resources, simply use Google Translate privately on their phones. They know this is not secure or compliant, but they feel like they have no better option.

So Jaide built a platform that integrates compliance, healthcare-specific terminology, workflow embedding, custom glossaries, discharge summaries, and real-time accessibility.

This is not simply “healthcare plus GPT”. It is targeted, workflow-integrated, risk-aware operational excellence.

Product managers should study this pattern closely.

The winning strategy was not inventing a new problem. It was solving a painful problem that everyone already agreed mattered.

The Core PM Lesson: Focus on Problems With Urgent Budgets Behind Them

A question I often ask PMs I coach:

Who loses sleep if this problem is not solved?

If the answer is:

* “Not sure”

* “Eventually the business will feel it”

* “It would improve the experience”

* “It could move a KPI if adoption increases”

Then you do not have a real problem yet.

Real product opportunities have:

* A user who is blocked from achieving something meaningful

* A measurable cost or consequence of inaction

* An internal champion with authority to push change

* An adjacent workflow that your product can attach to immediately

* A budget owner who is willing to pay now, not later

Healthcare translation checks every box. That is why Joe now has institutional adoption and a business with meaningful traction behind it.

Why PMs Struggle With This in Practice

If the lesson seems obvious, why do so many PMs fall into the encouragement trap?

The reason is emotional more than analytical.

It is uncomfortable to confront the possibility that your idea, feature, roadmap, strategy, or deck is not compelling enough yet. It is easier to seek validation than truth.

In my first startup, we kept our product in closed beta for months longer than we should have. We told ourselves we were refining the UX, improving onboarding, solidifying architecture. The real reason, which I only admitted years later, was that I was afraid the product was not good enough. I delayed reality to protect my ego.

In product work, speed of invalidation is as important as speed of iteration.

If something is not working, you need to know as quickly as possible. The faster you learn, the more shots you get. The best PMs do not fall in love with their solutions. They fall in love with the moments of clarity that allow them to change direction quickly.

Actionable Advice for Early and Mid Career PMs

Below are specific behaviors and habits you can put into practice immediately.

1. Always test product concepts as choices, not presentations

Instead of asking:“What do you think of this idea?”

Ask:“We are deciding between these two approaches. Which one is more important for you right now and why?”

This forces prioritization, not politeness.

2. Never ship a feature without observing real usage inside the workflow

A feature that exists but is not used does not exist.

Sit next to users. Watch screen behavior. Listen to their muttering. Ask where they hesitate. And most importantly, observe what they do after they close your product.

That is where the real friction lives.

3. Always ask: What is the cost of not solving this?

If there is no real cost of inaction, the feature will not drive adoption.

Impact must be felt, not imagined.

4. Look for users with strong emotional urgency, not polite agreement

When someone says:“This would be helpful.”

That is death.

When someone says:“I need this and I need it now.”

That is life.

Find urgency. Design around urgency. Ignore politeness.

5. Know the business model of your customer better than they do

This is where many PMs plateau.

If you want to be taken seriously by executives, you must understand:

* How your customer makes money

* What costs they must manage

* Which levers influence financial outcomes

When PMs learn to speak in revenue, cost, and risk instead of features, priorities, and backlog, their influence changes instantly.

The Broader Strategic Question: What Happens When Foundational Models Improve?

During our conversation, I asked Joe whether the rapid improvement of GPT-like translation will eventually make specialized healthcare translation unnecessary.

His answer was pragmatic:

“Our goal is to ride the wave. The best technology alone does not win. The integrated solution that solves the real problem wins.”

This is another crucial product lesson:

* Foundational models are table stakes.

* Differentiation comes from workflow integration, specialization, compliance, and trust.

* Adoption is driven by reducing operational friction.

In other words:

In AI-first product strategy, the model is the engine. The workflow is the vehicle. The customer problem is the road.

The Future of Product Work: Judgment Over Output

The world is changing. Tools are accelerating. Capabilities are compounding. But the core skill of product leadership remains the same:

Can you tell the difference between signal and noise, urgency and politeness, truth and encouragement?

That is judgment.

Product management will increasingly become less about writing PRDs or pushing execution and more about identifying the real problem worth solving, framing tradeoffs clearly, and navigating ambiguity with confidence and clarity.

The PMs who will thrive in the coming decade are those who learn how to ask better questions.

Closing

This conversation with Joe reminded me that most of the time, product failure is not the result of a bad idea. It is the result of insufficient clarity. The clarity does not come from thinking harder. It comes from testing real choices, with real users, in real workflows, and asking questions that force truth rather than encouragement.

If this resonates and you want help sharpening your product judgment, improving your influence with executives, developing clarity in your roadmap, or navigating career transitions, I work 1:1 with a small number of PMs, founders, and product executives.

You can learn more at tomleungcoaching.com.

OK. Enough pontificating. Let’s ship greatness.

I didn’t plan to make a video today. I’d just wrapped a client call, remembered that OpenAI had released Atlas, and decided to record a quick unboxing for my Fireside PM community.

I’d heard mixed things—some people raving about it, others underwhelmed—but I made a deliberate choice not to read any reviews beforehand. I wanted to go in blind, the way an actual user would.

Within 30 minutes, I had my verdict: Atlas earns a C+.

It’s ambitious, it’s fast, and it hints at a radical new way to experience the web. But it also stumbles in ways that remind you just how fragile early AI products can be—especially when ambition outpaces usability.

This post isn’t a teardown or a fan letter. It’s a field report from someone who’s built and shipped dozens of products, from scrappy startups to billion-user platforms. My goal here is simple: unpack what Atlas gets wrong, acknowledge what it gets right, and pull out lessons every PM and product team can use.

The Unboxing Experience

When I first launched Atlas, I got the usual macOS security warning. I’m not docking points for that—this is an MVP, and once it hits the Mac App Store, those prompts will fade into the background.

There was an onboarding window outlining the main features, but I barely glanced at it. I was eager to jump in and see the product in action. That’s not a unique flaw—it’s how most real users behave. We skip the instructions and go straight to testing the limits.

That’s why the best onboarding happens in motion, not before use. There were some suggested prompts which I ignored but I would’ve loved contextual fly-outs or light tooltips appearing as I explored past the first 30 seconds of my experience:

* “Try asking Atlas to summarize this page.”

* “Highlight text to discuss it.”

* “Atlas can compare this to other sources—want to see how?”

Small, progressive cues like these are what turn exploration into mastery.

The initial onboarding screen wasn’t wrong—it was just misplaced. It taught before I cared. And that’s a universal PM lesson: meet users where their curiosity is, not where your product tour is.

When Atlas Stumbled

Atlas’s biggest issue isn’t accuracy or latency—it’s identity.

It doesn’t yet know what it wants to be. On one hand, it acts like a browser with ChatGPT built in. On the other, it markets itself as an intelligent agent that can browse for you. Right now, it does neither convincingly.

When I tried simple commands like “Summarize this page” or “Open the next link and tell me what it says,” the experience broke down. Sometimes it responded correctly; other times, it ignored the context entirely.

The deeper issue isn’t technical—it’s architectural. Atlas hasn’t yet resolved the question of who’s driving. Is the user steering and Atlas assisting, or is Atlas steering and the user supervising?

That uncertainty creates friction. It’s like co-piloting with someone who keeps grabbing the wheel mid-turn.

Then there’s the missing piece that could make Atlas truly special: action loops.

The UI makes it feel like Atlas should be able to take action—click, save, organize—but it rarely does. You can ask it to summarize, but you can’t yet say “add this to my notes” or “book this flight.” Those are the natural next steps in the agentic journey, and until they arrive, Atlas feels like a chat interface masquerading as a browser.

This isn’t a criticism of the vision—it’s a question of sequencing. The team is building for the agentic future before the product earns the right to claim that mantle. Until it can act, Atlas is mostly a neat wrapper around ChatGPT that doesn’t justify replacing Chrome, Safari, or Edge.

Where Atlas Shines

Despite the friction, there were moments where I saw real promise.

When Atlas got it right, it was magical. I’d open a 3,000-word article, ask for a summary, and seconds later have a coherent, tone-aware digest. Having that capability integrated directly into the browsing experience—no copy-paste, no tab-switching—is an elegant idea.

You can tell the team understands restraint. The UI is clean and minimal, the chat panel is thoughtfully integrated, and the speed is impressive. It feels engineered by people who care about quality.

The challenge is that all of this could, in theory, exist as a plugin. The browser leap feels premature. Building a full browser is one of the hardest product decisions a company can make—it’s expensive, high-friction, and carries a huge switching cost for users.

The most generous interpretation is that OpenAI went full browser to enable agentic workflows—where Atlas doesn’t just summarize, but acts on behalf of the user. That would justify the architecture. But until that capability arrives, the browser feels like infrastructure waiting for a reason to exist.

Atlas today is a scaffolding for the future, not a product for the present.

Lessons for Product Managers

Even so, Atlas offers a rich set of takeaways for PMs building ambitious products.

1. Don’t Confuse Vision with MVP

You earn the right to ship big ideas by nailing the small ones. Atlas’s long-term vision is compelling, but the MVP doesn’t yet prove why it needed to exist. Start with one unforgettable use case before scaling breadth.

2. Earn Every Switch Cost

Changing browsers is one of the highest-friction user behaviors in software. Unless your product delivers something 10x better, start as an extension, not a replacement.

3. Design for Real Behavior, Not Ideal Behavior

Most users skip onboarding. Expect it. Plan for it. Guide them in context instead of relying on their patience.

4. Choose a Metaphor and Commit

Atlas tries to be both browser and assistant. Pick one. If you’re an assistant, drive. If you’re a browser, stay out of the way. Users shouldn’t have to guess who’s in control.

5. Autonomy Without Agency Frustrates Users

It’s worse for an AI to understand what you want but refuse to act than to not understand at all. Until Atlas can take meaningful action, it’s not an agent—it’s a spectator.

6. Sequence Ambition Behind Value

The product is building for a world that doesn’t exist yet. Ambition is great, but the order of operations matters. Earn adoption today while building for tomorrow.

Advice for the Atlas Team

If I were advising the Atlas PM and design teams directly, I’d focus on five things:

* Clarify the core identity. Decide if you’re an AI browser with ChatGPT or a ChatGPT agent that uses a browser. Everything else flows from that choice.

* Earn the right to replace Chrome. Give users one undeniably magical use case that justifies the switch—research synthesis, comparison mode, or task execution.

* Fix the metaphor collision. Make it obvious who’s in control: human or AI. Even a “manual vs. autopilot” toggle would add clarity.

* Build action loops. Move from summarization to completion. The browser of the future won’t just explain—it will execute.

* Sequence ambition. Agentic work is the destination, but the current version needs to win users on everyday value first.

None of this is out of reach. The bones are good. What’s missing is coherence.

Closing Reflection

Atlas is a fascinating case study in what happens when world-class technology meets premature positioning. It’s not bad—it’s unfinished.

A C+ isn’t an insult. It’s a reminder that potential and product-market fit are two different things. Atlas is the kind of product that might, in a few releases, feel indispensable. But right now, it’s a prototype wearing the clothes of a platform.

For every PM watching this unfold, the lesson is universal: don’t get seduced by your own roadmap. Ambition must be earned, one user journey at a time.

That’s how trust is built—and in AI, trust is everything.

If you or your team are wrestling with similar challenges—whether it’s clarifying your product vision, sequencing your roadmap, or improving PM leadership—I offer both 1:1 executive and career coaching at tomleungcoaching.com and expert product management consulting and fractional CPO services through my firm, Palo Alto Foundry.

OK. Enough pontificating. Let’s ship greatness.

Introduction

One of the great joys of hosting my Fireside PM podcast is the opportunity to reconnect with people I’ve known for years and go deep into the mechanics of business building. Recently, I sat down with Jason Stoffer, partner at Maveron Capital, a venture firm with a laser focus on consumer companies. Jason and I go way back to my Seattle days, so this was both a reunion and an education. Our conversation turned into a masterclass on scaling consumer businesses, the art of finding moats, and the brutal realities of marketplaces.

But beyond the case studies, what stood out were the actionable insights PMs can apply right now. If you’re an early or mid-career product manager in Silicon Valley, there are playbooks here you can borrow—not in theory, but in practice.

Jason summed up his approach to analyzing companies like this: “So many founders can get caught in the moment that sometimes it’s best when we’re looking at a new investment to talk about if things go right, what can happen. What would an S-1 or public filing look like? What would the company look like at a big M&A event? And then you work backwards.” That mindset—begin with the end in mind—is as powerful for a product manager shipping features as it is for a VC evaluating billion-dollar bets.

In this post, I’ll share:

* The key lessons from Jason’s breakdown of Quince and StubHub

* How these lessons apply directly to your PM career

* Tactical moves you can make to future-proof your trajectory

* Reflections on what surprised me most in this conversation

And along the way, I’ll highlight specific frameworks and examples you can put into action this week.

Part 1: Quince and the Power of Supply Chain Innovation

When Jason first explained Quince’s model, I’ll admit I was skeptical. On its face, it sounds like yet another DTC apparel play. Sell cheap cashmere sweaters online? Compete with incumbents like Theory and Away? It didn’t sound differentiated.

Jason disagreed. “Most people know Shein, and Shein was kind of working direct with factories. Quince’s innovation was asking, what do factories in Asia have during certain times of the year? They have excess capacity. Those are the same factories who are making a Theory shirt or an Away bag. Quince went to those factories and said, hey, make product for us, you hold the inventory, we’ll guarantee we’ll sell it.”

That’s not a design tweak—it’s a supply chain disruption. Costco built an empire on this principle. TJX did the same. Walmart before them. If you can structurally rewire how goods get to consumers, you’ve got the foundation for a massive business.

Lesson for PMs: Sometimes the real innovation isn’t visible in the interface. It’s hidden in the plumbing. As PMs, we often obsess over UI polish, onboarding flows, or feature prioritization. But step back and ask: what’s the equivalent of supply chain disruption in your domain? It might be a new data pipeline, a pricing model, or even a workflow that cuts out three layers of manual steps for your users. Those invisible shifts can unlock outsized value.

Jason gave the example of Quince’s $50 cashmere sweater. “Anyone in retail knows that if you’re selling at a 12% gross margin and it’s apparel with returns, you’re making no money on that. What is it? It’s an alternative method of customer acquisition. You hook them with the sweater and sell them everything else.” In other words, they turned a P&L liability into a marketing hack.

Actionable move for PMs: Identify your “$50 sweater.” What’s the feature you can offer that might look unprofitable or inconvenient in isolation, but serves as an on-ramp to deeper engagement? Maybe it’s a generous free tier in SaaS, or an intentionally unscalable white-glove onboarding process. Don’t dismiss those just because they don’t scale on day one.

Part 2: Moats, Marketing, and Hero SKUs

Jason emphasized that great retailers pair supply chain execution with marketing innovation. Costco has rotisserie chickens and $2 hot dogs. Quince has $50 cashmere sweaters. These “hero SKUs” create shareable moments and lasting brand associations.

“You’re pairing supply chain innovation with marketing innovation, and it’s super effective,” Jason explained.

Lesson for PMs: Don’t just think about your feature set—think about your hero feature. What’s the one thing that makes users say, “You have to try this product”? Too often, PM roadmaps are a laundry list of incremental improvements. Instead, design at least one feature that can carry your brand in conversations, tweets, and TikToks. Think about Figma’s multiplayer cursors or Slack’s playful onboarding. These are features that double as marketing.

Part 3: StubHub and the Economics of Trust

After Quince, Jason shifted to a very different case study: StubHub. Here, the lesson wasn’t about supply chain but about moats built on trust, liquidity, and cash flow mechanics.

“Customers will pay for certainty even if they hate you,” Jason said. Think about that. StubHub’s fees are infamous. Buyers grumble, sellers grumble. And yet, if you need a Taylor Swift ticket and want to be sure it’s legit, you go to StubHub. That reliability is the moat.

Lesson for PMs: Trust is an underrated product feature. In consumer software, this might mean uptime and reliability. In enterprise SaaS, it might mean compliance and security certifications. In AI, it could mean interpretability and guardrails. Don’t underestimate how much people will endure friction if they can be sure you’ll deliver.

Jason also pointed out StubHub’s cash flow hack: “StubHub gets money from buyers up front and then pays the sellers later. That’s a beautiful business model. If you create a cash flow cycle where you’re getting the money first and delivering later, you raise a lot less equity and get diluted less.”

This is a reminder that product decisions can have financial implications. As PMs, you may not directly set billing cycles, but you can influence monetization models, free trial design, or even refund policies—all of which affect working capital.

Actionable move for PMs: Partner with finance. Ask them: what product levers could improve cash conversion cycles? Could prepayment discounts, annual billing, or usage-based pricing reduce working capital strain? Thinking beyond the feature spec makes you more valuable to your company—and accelerates your own career.

Part 4: Five Takeaways from StubHub

Jason listed five lessons from StubHub:

* Trust is a moat – Even if users complain, reliability keeps them loyal.

* Liquidity is a moat – Scale compounds, especially in marketplaces.

* Cash flow mechanics matter – Payment terms can determine survival.

* Tooling locks in supply – Seller-facing tools create stickiness.

* Scale itself compounds – Once you’re ahead, momentum carries you.

Part 5: What Surprised Me Most

As I listened back to this conversation, two surprises stood out.

First, the sheer size of value retail. Jason noted that TJX is worth $157 billion. Burlington, $22 billion. Costco, $418 billion. These aren’t sexy tech names, but they are empires. It made me rethink my assumptions about what “boring” industries can teach us.

Second, Jason’s humility about being wrong. “Reddit might be one,” he admitted when I asked about his biggest misses. “I had no idea that LLMs would use their data in a way that would make it incredibly important. I was dead wrong. I said sit on the sidelines.” That candor is refreshing—and a reminder that even seasoned investors get it wrong. The key is to keep learning.

Lesson for PMs: Admit your misses. Write them down. Share them. Don’t hide them. Your credibility grows when you own your blind spots and show how you’ve adjusted.

Closing Thoughts

Talking with Jason felt like being back in business school—but with sharper edges. These aren’t abstract frameworks. They’re battle-tested strategies from companies that scaled to billions. As PMs, our job isn’t just to ship features. It’s to build businesses. That requires thinking about supply chains, trust, cash flow, and marketing moats.

If you found this helpful and want to go deeper, check out Jason’s Substack, Ringing the Bell, where he publishes his case studies. And if you want to level up your own career trajectory, I offer 1:1 executive, career, and product coaching at tomleungcoaching.com.

Shape the Future of PMAnd if you haven’t yet, I’d love your input on my Future of Product Management survey. It only takes about 5 minutes, and by filling it out you’ll get early access to the results plus an invitation to a live readout with a panel of top product leaders. The survey explores how AI, team structures, and skill sets are reshaping the PM role for 2026 and beyond.OK. Let’s ship greatness.

When I sit down with product leaders who’ve spent decades shaping how Silicon Valley builds products, I’m always struck by how their career arcs echo the very lessons they now teach. Michael Margolis is no exception.

Michael started his career as an anthropologist, stumbled into educational software in the late 90s, helped scale Gmail during its formative years, and eventually became one of the first design researchers at Google Ventures (GV). For fifteen years, he sat at the intersection of startups and product discovery, helping founders learn faster, save years of wasted effort, and—sometimes—kill their darlings before they drained all the fuel.

In our conversation, Michael didn’t just share war stories. He laid out a concrete, repeatable framework for product teams—whether you’re a PM at a FAANG company or a fresh hire at a Series A startup—on how to cut through noise, get to the truth, and accelerate learning cycles.

This post is my attempt to capture those lessons. If you’re an early to mid-career PM in Silicon Valley trying to sharpen your craft, this is for you.

From Anthropology to Gmail: The Value of Unorthodox Beginnings

Michael’s path to Google wasn’t a linear “go to Stanford CS, join a startup, IPO” narrative. Instead, he started in anthropology and educational software, producing floppy-disk learning titles at The Learning Company and Electronic Arts. That detour turned out to be foundational.

“Studying anthropology was my introduction to usability and ethnography,” Michael told me. “It gave me a lens to look at people’s behaviors not just as data points but as cultural patterns.”

For PMs, the lesson is clear: don’t discount the odd chapters of your own career. That sales job, that nonprofit internship, or that side hustle in teaching can become your secret weapon later. Michael carried those anthropology muscles into Gmail, where understanding human behavior at scale was just as critical as writing code.

Actionable Advice for PMs:

* Audit your own “non-linear” career experiences. What hidden skills—interviewing, pattern-recognition, narrative-building—could you bring into product work?

* When hiring, don’t filter only for straight-line resumes. The best PMs often bring unexpected perspectives.

The Google Years: Scaling Research at Hyper-speed

Michael joined Gmail in 2006, when it was still young but maturing fast. He quickly noticed how different the rhythm was compared to the slow, expensive ethnographic studies he had done for consulting clients like Walmart.com.

“At Walmart,” he explained, “I had to compress these big, long expensive projects into something faster. Gmail demanded that same speed, but at enormous scale.”

At Google, the prime “clients” for his research were often designers. The questions he answered were things like: How do we attract Outlook users? How do we make the interface intuitive enough for mass adoption?

This difference matters for PMs: in big companies, research questions often start downstream—how to refine, polish, or optimize. In startups, questions live upstream: What should we build at all? Knowing where you sit in that spectrum changes the kind of research (and product bets) you should prioritize.

Jumping to Google Ventures: Bringing UXR Into VC

In 2010, Michael made a bold move: leaving the mothership to become one of the very first design researchers embedded inside a venture capital firm. GV was trying to differentiate itself by not just writing checks but also offering operational help—design, hiring, PR.

“I got lucky,” he recalled. “GV had already hired Braden Kowitz as their design partner, and Braden said, ‘I need a researcher.’ That was my break.”

Working with founders was a shock. They didn’t act like Google PMs. “It was like they were playing by a different set of rules. They’d say, ‘Here’s where we’re going. You can help me, or get out of my way.’”

That forced Michael to reinvent how he showed value. Instead of writing reports that might sit unread, he had to deliver insights in real-time, in ways founders couldn’t ignore.

The Watch Party Method: Stop Writing Reports

Here’s where the gold nuggets come in. Michael realized traditional reports weren’t cutting it. Instead, he invented what he calls “watch parties.”

“I don’t do the research study unless the whole team watches,” he said. “I compress it into a day—five interviews with bullseye customers, the whole team in a virtual backroom. By the end, they’ve seen it all, they’re debriefing themselves, and alignment happens automatically. I haven’t written a report in years.”

Think about that. No 30-page decks. No long hand-offs. Just visceral, shared observation.

Actionable Advice for PMs:

* Next time you run a user test, insist that at least your core team attends live. Skip the sanitized recap slides.

* At the end of a session, have the team summarize their top three takeaways. When they say it, it sticks.

Bullseye Customers: Getting Uncomfortably Specific

One of Michael’s most powerful contributions is the bullseye customer exercise.

“A bullseye customer,” he explained, “is the very specific subset of your target market who is most likely to adopt your product first. The key is to define not just inclusion criteria but also exclusion criteria.”

Founders (and PMs) often resist narrowing. They want to believe their TAM is huge. But Michael’s method forces rigor. He described grilling teams until they admit things like: Actually, if this person doesn’t work from home, they probably won’t care. Or if they’ve never paid for a premium tool, they won’t convert.

Example: Imagine you’re building a new coffee subscription. Your bullseye might be: Remote tech workers in San Francisco, ages 25-35, who already spend $50+ per month on specialty coffee, and who like experimenting with new roasters. If your product doesn’t delight them, it won’t magically resonate with “all coffee drinkers.”

Actionable Advice for PMs:

* Write down both inclusion and exclusion criteria for your bullseye.

* Add triggers: life events that make adoption more likely (e.g., new job, new diagnosis, move to a new city).

* Recruit five people who fit it exactly. If they’re lukewarm, rethink your product.

Why Five Interviews Is Enough

Michael swears by the number five.

“After three interviews, you’re not sure if it’s a pattern,” he said. “By five, you hit data saturation. Everyone sees the signal. Any more and the team is begging you to stop so they can make changes.”

For PMs under pressure, this is liberating. You don’t need 100 customer calls. You need five of the right customers, observed by the right team members, in a compressed timeframe.

Multiple Prototypes: Don’t Ask Customers to Imagine

Another Margolis rule: never show just one prototype.

“If you show one, the team gets too attached, and the customer can only react. With three, I can say: compare and contrast. What do you love? What do you hate? I collect the Lego pieces and assemble the next iteration.”

Sometimes those prototypes aren’t even original mockups—they’re competitor landing pages. As Michael joked: “Have you tested your competitor’s prototypes? No? Then you’ve left something out.”

Actionable Advice for PMs:

* When exploring value props, mock up three different landing pages. Don’t ask “Which do you prefer?” Instead ask: “Which elements matter most, and why?”

* Treat mild praise as a “no.” Only visceral excitement counts as signal.

Founders, Stubbornness, and the Henry Ford Trap

I pressed Michael on what happens when founders dismiss customer feedback by invoking Henry Ford’s famous line about “faster horses.”

He smiled. “The beauty of bullseye customers is it forces accountability. If you told me these people are your dream users, and they shrug, then you can’t hand-wave it away. Either change your customer definition or your product.”

This is a crucial lesson for PMs who work with visionary leaders. Conviction is necessary, but unchecked conviction can sink a product. Anchoring on bullseye customers creates a shared contract that keeps both egos and hypotheses grounded.

Bright Spots > Exit Interviews

When teams ask him to interview churned customers, Michael often refuses.

“There are a bazillion reasons people don’t use something,” he said. “It’s inefficient. Instead, I go find the bright spots—the power users who love it. I want to know why they’re on fire, and then go find more people like them.”

This “bright spot” focus helps PMs avoid premature pivots. Instead of chasing every no, double down on the yeses until you understand the common thread.

Case Study: Refrigerated Medications and Zipline

To illustrate, Michael shared a project with Zipline, the drone-delivery company. They wanted to deliver specialty medications. The core question: was speed or timing more important?

Through interviews, the bright spot insight emerged: refrigeration was the killer constraint. Patients didn’t care about “fastest possible” delivery in the abstract. They cared about not leaving refrigerated drugs on their porch.

That nuance completely changed the product and infrastructure design.

For PMs, the takeaway is that sometimes the decisive factor isn’t the flashy benefit you advertise (“we’re the fastest!”) but a practical detail you only uncover through careful listening.

AI and the Future of Research

We couldn’t avoid the AI question. Has it changed his process?

“I worry about how AI is creating distance between teams and customers,” Michael admitted. “If my bot talks to your bot and spits out a report, you miss the nuance. The power of research is in the stories, the details, the visceral reactions.”

That said, he does use AI for quick prototype copywriting and summaries. But he insists on live team observation for the real work.

For PMs, the advice is to use AI as an accelerant, not a replacement. Let it write the rough draft of your landing page copy, but don’t outsource customer empathy to a transcript.

What PMs Should Do Differently Tomorrow

Let’s distill Michael’s 15 years of wisdom into actionable steps you can implement this week:

* Define your bullseye. Write down exact inclusion, exclusion, and trigger criteria.

* Recruit five. Stop at five, but make them exact matches.

* Run a watch party. Get your designer, engineer, and PM peers in the virtual backroom. No observers, no insights.

* Prototype in threes. Landing pages are cheap. Competitor screenshots are free.

* Look for visceral reactions. Anything less than “Wait, can I get this now?” is a polite no.

* Study the bright spots. Find your power users and figure out what makes them glow.

* Compress cycles. The whole exercise—recruit, test, learn—should take days, not months.

Quotes Worth Remembering

To make these lessons stick, here are five quotes from Michael that every PM should tape to their desk:

* “I don’t do the research unless the whole team watches.”

* “A bullseye customer is the very specific subset of your target market most likely to adopt first.”

* “After five interviews, you hit data saturation. Everyone sees the pattern.”

* “If you show one prototype, the team gets too attached. With three, you collect the Lego pieces.”

* “Mild encouragement is a polite no. Only visceral excitement counts as yes.”

My Takeaways as a Coach and PM

Talking to Michael reinforced something I’ve seen in my own career: product failure often comes not from bad execution, but from weak learning cycles. Teams don’t test the right people, don’t synthesize together, and don’t act quickly on what they learn.

Michael’s methods aren’t magic—they’re discipline. They compress time, sharpen focus, and force alignment. Whether you’re building the next Gmail or the next startup idea in a Palo Alto garage, these principles apply.

If you’re an early to mid-career PM, start by practicing on a small scale. Don’t wait for your manager to bless a massive UXR budget. Run a five-person watch party with your next prototype. You’ll be surprised at how quickly the fog lifts.

Closing

If this resonated and you’re looking for deeper guidance, I also work 1:1 with PMs and executives on career, product, and leadership challenges. You can learn more at tomleungcoaching.com.

And if you haven’t yet, I’d love your input on my Future of Product Management survey. It only takes about 5 minutes, and by filling it out you’ll get early access to the results plus an invitation to a live readout with a panel of top product leaders. The survey explores how AI, team structures, and skill sets are reshaping the PM role for 2026 and beyond.

Let’s ship greatness.