CHAPTERS
Why AI product design interviews are uniquely hard (and lucrative)
Aakash sets the stakes: AI product design interviews are becoming the toughest PM screen in 2026 across top AI labs and mainstream software companies. He explains why AI design requires different muscles than traditional product design and tees up a first-of-its-kind AI product design mock interview.
The 5 AI product design question archetypes interviewers use
Before the mock begins, Aakash outlines five common categories of AI product design questions and notes where candidates most often fail. The video focuses on the “new product design” variant using a famous recurring OpenAI-style prompt.
Mock prompt: design an AI product to communicate with pets
Bart, acting as interviewer, gives the core design challenge and invites Aakash to lead. This establishes an intentionally ambiguous space to test clarification, scoping, and prioritization.
Clarifying constraints and defining the success metric (AGI)
Aakash asks targeted clarifying questions and uncovers an unusual success metric: progress toward AGI, not revenue or engagement alone. This reframes the rest of the interview around mission alignment.
A reusable interview framework: users → problems → solutions → design → AGI + risks
Aakash proposes a clear end-to-end structure to avoid rambling and to ensure a real product design output. The framework becomes the backbone for the remainder of the mock.
User and market segmentation: buyers vs pet types
Aakash segments the market along two axes: who pays/uses (buyers) and which animals are in scope. He argues for starting with humans (buyers) while keeping pet type choice practical for MVP.
Choosing the target buyer: owners dealing with pet issues
Aakash selects ‘pet owners with issues’ as the initial target due to strong motivation, willingness to pay, and measurable outcomes. This narrows problem discovery to high-signal, high-urgency scenarios.
Problem brainstorming and prioritization: the “interpretation + facts” foundation
Aakash rapidly enumerates core pains and then organizes them into a pyramid, emphasizing foundational needs: understanding why behaviors happen and capturing what happened. The interviewer pushes to incorporate the AGI metric into prioritization.
Solution ideation: seven concepts from software to hardware to ‘magic moment’
Aakash proposes a diverse solution set spanning wearables, multimodal apps, edge devices, coaching, nutrition, matching, and a conversational simulation. He explicitly distinguishes true ‘translation’ from AI speaking on a pet’s behalf.
Prioritizing with a scoring table (impact, feasibility, differentiation, engagement)
Aakash evaluates each concept with a lightweight rubric to justify why one path wins. The interviewer suggests including AGI in the rubric; Aakash instead treats AGI as downstream of building impactful, scalable capability.
Final product choice: behavior coach + conversation mode, software-first
Aakash combines the top concept with the ‘magic moment’ feature to create a cohesive product: real-time behavior coaching plus a simulated pet conversation mode. He emphasizes software-first rollout leveraging multimodal understanding and existing ChatGPT scale.
Core product design: onboarding, passive monitoring, active coaching, and unlockable conversation
Aakash outlines the main user flows and how data collection builds a pet-specific baseline over time. The design includes onboarding with pet profiling, passive summaries, active real-time coaching, and a gated conversation feature after sufficient observation.
Key design decisions + modern prototyping approach (Lovable prompting)
Aakash calls out core product principles (voice-first, avoid anthropomorphizing, progress tracking, hardware-light) and demonstrates how he’d translate requirements into a prompt for an AI prototyping tool. The focus is on specifying states, deep links, and clickable blocks rather than pixel-perfect UI in the interview.
AGI alignment, risks/mitigations, and storytelling finish (plus evaluation + cohort pitch)
Aakash ties the product back to AGI by framing it as automated, general, intelligence applied to non-human behavior understanding at scale. He covers major risks (bad advice, hallucinations/anthropomorphizing, privacy, limited pet coverage), ends with a narrative example, and Bart explains why the answer scored highly before both promote the cohort.
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