Few people are ever trained in interviewing future hires. You’re handed someone’s resume and tossed into a meeting with the candidate. The hiring manager might tell you what topics they want you to cover.

So you think back to the questions people asked you and ask versions of those. What are your strengths and weaknesses? Tell me about your greatest accomplishment? How do you prioritize?

But these don’t really tell you anything about the candidate. They don’t give you great signal about their relative skill in doing the job. What if their greatest accomplishment has nothing to do with building products? And even if it does, what are you going to learn from them describing it?

When you ask them how they do something, you’ll get an idealized version of what they would do, instead of a realistic description of how they really work.

The best way to improve your questions is to define your intent up front. Identify the traits and skills you’re looking for, and think of a way the candidate can demonstrate that skill in an interview. This usually involves asking them to describe real-world situations that used the skill.

For all your questions, write down what you are trying to learn by asking it. Think through what a strong answer would be and how you’d distinguish from a weak answer.

During the interview, probe their answers to find out what they specifically did. Product managers accomplish almost all their work through the team, so it can be hard to get them to say what they actually did. Getting this is crucial.

To get you started, here are some examples of questions I use in actual product management interviews.

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I use AI as a strategic thinking partner. Not to make decisions for me—that would be abdicating leadership. I use it to expose my own assumptions and clarify my thinking.

The Dialogue Approach

The process: I frame a business challenge. The AI asks questions and makes assumptions. I clarify what I meant. This back-and-forth exposes assumptions I didn’t know I was making.

Last month, I was struggling with a product roadmap decision. I thought I had considered all angles, but through this dialogue, I discovered I was unconsciously prioritizing technical elegance over customer adoption speed. This wasn’t a deliberate choice—it was an unexamined assumption the conversation helped surface.

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How do you distribute your strategy across your organization? A common approach is some form of "goals flow downhill". A leader tells their direct reports what needs to be done. Those reports each figure out how they’ll contribute to the strategy and create goals for their department or team. Then those department goals get passed down to the next rung of the ladder, and sub-goals are created and further passed down, until everyone knows what they’re working on.

The idea is to be able to trace the actions of an organization all the way from the bottom up to the corporate strategy. And to visualize how the strategy cascades into execution throughout the company.

Some OKR guides make this cascade explicit, requiring every one of your KRs to be an Objective for one of your subordinate teams.

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There’s no one-size-fits-all method for prioritizing product work. How you prioritize depends on the state of the company, what problems you’re solving, how you typically solve problems, what your customers expect, and dozens of other factors.

But still, a common question asked of product managers is, "How do you prioritize?"

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A team is running late. Their launch was supposed to be 6 weeks ago. They missed it. They’re still one feature short. What now?

First, dig out of the hole. Then make sure this never happens again.

The Truth About Deadlines

Your date and scope? They’re made up. Even customer commitments in contracts—someone invented those details. You already blew the deadline. Your new date is "ASAP."

That scope? It started vague. It grew as you planned. Now it’s a monster.

Ship dates and feature lists are proxies for value. Nobody cares if you hit the deadline with a useless product. But value is hard to define. It takes time to measure. So people pick dates and features instead. Those feel controllable.

They’re not.

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Venture Capital firms are about to see a shift brought on by the confluence of giant funds and AI. These two forces are going to break the way the venture model works for most firms.

VCs succeed by seeing lots of early stage companies, identifying the ones that have a high chance of being good, and betting that a few will be great. The biggest, most well-known firms get to see a lot of companies. Every founder wants the name recognition of a top-tier investor. These investors have companies coming to them and don’t need to hunt for deals.

Smaller venture firms thrive by building a niche and efficiently hunting within it. These smaller VCs have a contrarian viewpoint that forms their investment thesis. This unique way of looking at the world provides a lens that helps them see potentially-good companies that others miss. They’re able to pick winners from a pool that no one else is targeting.

This is about to change. The giant mega-funds have enough capital that they can A/B test investment theories. They can deploy cash to find out which contrarian viewpoints lead to high returns. But they haven’t, because of two constraints. They can’t create enough viewpoints and they can’t analyze companies against each thesis. But AI changes that.

With AI, a venture firm can generate unlimited investment theses and analyze massive numbers of companies to find matches. If that firm also has a virtually unlimited treasure chest, they can build a niche discovery machine.

It will get harder for smaller VCs to find and exploit these niches. The smaller firm’s advantage was their creativity. But in this case, that creativity is moot if someone can create all of the things and see what works.

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