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.
- •AI product design prompts (e.g., “design the next ChatGPT”) differ from classic product design
- •These roles span compensation tiers but share similar AI design evaluation patterns
- •Goal of the episode: show an end-to-end AI product design mock with “what interviewers want”
- •Introduces co-instructor Bart and the Land PM Job cohort context
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.
- •Five types: product improvement, new product design, platform/API, constraint-based, UX/UI for AI
- •New product design is the most common failure mode in their coaching experience
- •OpenAI repeatedly uses a specific “new AI product” style question
- •Sets expectation that a good answer needs structure + AI-specific reasoning
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.
- •Prompt: “Design a new AI product that will help communicate with pets”
- •Interviewer leaves scope open (pet type, integration, product form)
- •Candidate is encouraged to ask follow-ups but ultimately drive to a concrete design
- •Ambiguity is part of the evaluation
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.
- •Clarifies: pet types can be chosen freely; standalone vs integrated is candidate choice
- •Key constraint: OpenAI mission—progress toward AGI is the primary metric
- •Acknowledges “white space” and requests time to structure thinking
- •Sets up how to map product decisions back to mission
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.
- •Start with user segments, then deeply enumerate problems
- •Brainstorm multiple solutions, then prioritize explicitly
- •Design the selected solution: core flows + key decisions
- •Close with progress toward AGI and risk/mitigation thinking
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.
- •Buyer segments: new owners, behavioral-issue owners, aging/sick pet owners, trainers, multi-pet households, pet considerers, ‘health-conscious’ owners
- •Pet types: focus on dogs/cats as the majority market vs exotic/farm animals
- •Interviewer challenges whether problems should come first; Aakash defends user-first approach
- •Sets up later prioritization by picking a primary buyer segment
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.
- •Behavioral issues create clear pain and urgency (training, anxiety, aggression, separation)
- •Target segment already spends on vets/trainers/behaviorists
- •Measurable outcomes enable product learning loops (behavior change)
- •Emotional bond can drive retention and ongoing engagement
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.
- •Problems: unclear causes of behavior, expensive/inaccessible help, conflicting advice, missed moments, diet-behavior uncertainty, pet fit concerns
- •Prioritizes foundational: (1) why the pet is doing something, (5) catching behavior when it happens
- •Frames “facts first” before advice and higher-level guidance
- •Adds AGI rationale: improving non-human understanding/empathy as a stepping stone
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.
- •Smart collar with biosensors + notifications/‘pet voice’ explanations
- •PetGPT Vision app using phone camera + multimodal body-language analysis
- •Two-way edge hub for continuous sensing + stimuli playback (sounds/colors)
- •Real-time behavior coach observing owner+pet with live voice guidance + replay
- •AI dietician coach correlating nutrition and behavior; potential delivery integration
- •Pet match advisor for choosing the right pet + environment modifications
- •Conversation simulator as an emotional/engagement ‘wow’ moment (not literal dog-to-words translation)
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.
- •Criteria: user impact, technical feasibility, differentiation, engagement potential
- •Real-time behavior coach scores highest overall; conversation simulator is second
- •Hardware solutions score high on impact but lower on feasibility/iteration speed
- •One-and-done products (pet match) score poorly on engagement despite feasibility
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.
- •Chosen direction: real-time behavioral coach as core; conversation simulator as retention/differentiation layer
- •Solves prioritized problems: “why” + “catch it when it happens”
- •Software-first for faster iteration and broad reach vs hardware dependency
- •Mentions leveraging ChatGPT’s large user base and phasing in add-ons (e.g., dietician as subset)
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.
- •Onboarding: pet profile (species/breed/age/issues), video of ‘normal state,’ natural-language issues list
- •Passive mode: camera/audio during key times; build baseline model over 7–14 days; daily 30-second summary
- •Active coaching: user initiates during incidents/training; real-time voice guidance; post-session replay + annotations
- •Conversation mode: unlock after ~2 weeks; voice interface; strict grounding to observed behavior to reduce hallucinations
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.
- •Design choices: voice-first, careful anthropomorphization, progress visualization, software/hardware-light approach
- •Interview tactic: offer tool-based prototyping approach when time is short
- •Lovable prompt strategy: define product states (data-collection vs post-baseline), entry points, deep-linkable summaries
- •Specify interaction model (what’s clickable, notifications driving re-entry) then iterate from the generated layout
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.
- •AGI connection: ‘Automated + General + Intelligence’ applied to interpreting animal behavior via multimodal signals
- •Risks: incorrect advice (mitigate with disclaimers, confidence, escalation), anthropomorphization (interpretation not translation), privacy (on-device options, easy delete), uneven performance across pets (phased rollout)
- •Story: Sarah and rescue dog Max—quantified improvement and emotional ‘conversation’ moment
- •Evaluation: strong structure/visual narration, breadth of ideas, transparent prioritization, user-centric buyer focus
- •Wrap-up: Land PM Job cohort details + calls to subscribe/review and tool bundle promotion
