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Aakash GuptaAakash Gupta

How To ACE AI Product Sense Interviews (OpenAI PM Mock Interview)

There are ZERO videos about AI Product Sense interviews on YouTube... until now.

Aakash GuptahostDr. Bartguest
Oct 29, 202552mWatch on YouTube ↗

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At a glance

WHAT IT’S REALLY ABOUT

Ace AI product sense interviews with framework, prioritization, and feedback loops

  1. The mock interview centers on growing ChatGPT image-creation weekly active users from 175M to 350M in three months with only three engineers, forcing ruthless scoping and prioritization.
  2. Aakash builds a custom framework live—mission context, user segmentation, user problems, solutions, prioritization/metrics, and safety—demonstrating structured thinking under interview pressure.
  3. He argues the biggest growth must come from low-tech-literacy users and focuses on discoverability, onboarding, and “loading/thinking” experience as key conversion levers.
  4. Competitive comparisons (e.g., Midjourney quality/styles, “NanoBanana” editing UX, Synthesia-like avatars) are used to quickly surface capability gaps and solution directions.
  5. The debrief emphasizes interviewer alignment: check in midstream, adapt the framework to prompts/curveballs, and subtly “sell” your unique fit with relevant past experience and scale credibility.

IDEAS WORTH REMEMBERING

5 ideas

Build a bespoke framework for the specific prompt, not a template.

Aakash explicitly takes time to create a problem-shaped structure (mission → users → problems → solutions → prioritization/metrics → safety), which signals strong product sense and prevents rambling.

Anchor growth strategy in user segmentation and a believable source of new users.

He reasons that doubling image WAUs won’t come from “AI power users” alone and shifts focus toward low-tech-literacy users, aligning solutions toward discoverability and reduced friction.

Treat UX friction (especially waiting/loading) as a first-class growth lever.

He claims most time is spent in the “thinking/loading” state and argues that improving clarity, progress indicators, and delight can materially improve activation and repeat usage.

Use competitor analysis to identify gaps, then translate gaps into testable features.

Midjourney informs quality/style aspirations, NanoBanana informs editing/selection workflows, and Synthesia highlights realism/likeness gaps—each mapped back into a problem/solution list.

Prioritize by impact vs. effort explicitly tied to constraints.

With only three engineers and three months, he favors fast UI/discoverability wins and targeted editors (infographic/thumbnail/meme) over heavier bets like a standalone app or deep personalization.

WORDS WORTH SAVING

5 quotes

This is a 45-minute case interview where they will give you a specific problem, and you as a product manager need to, in 45 minutes, speed run through the product management process.

— Aakash Gupta

By now I'd be expecting you to be more in a prioritization mode and figuring out what could be done in three months.

— Dr. Bart

It's not really about the features, it's about the journey in those questions.

— Dr. Bart

You have 45 minutes to give the person on the other side a tour of your thinking process.

— Dr. Bart

Do not come in and just use the same framework for every single question, but create a unique framework.

— Aakash Gupta

Read the available transcript
AI Product Sense interview format and expectationsLive framework creation and narrative clarityUser segmentation and growth math (800M ChatGPT WAU → 20% image users)Discoverability, onboarding, and loading/thinking UXCompetitor gap analysis (Midjourney, NanoBanana, Synthesia)Prioritization under constraints (3 engineers, 3 months)Safety, copyright, and policy tradeoffs (Ghibli example)

SPEAKERS

  • Aakash Gupta

    host

    Product management creator and coach who hosts mock interviews and PM content on the Aakash Gupta channel.

  • Dr. Bart

    guest

    Industry expert who conducts the mock AI product sense interview and provides hiring-style feedback.

High quality AI-generated summary created from speaker-labeled transcript.

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