CHAPTERS
- 0:00 – 1:27
Why interview homework is getting harder in the AI era
Sumant explains that what used to pass in past cycles may not survive today, because companies are tightening expectations as AI makes it easy to generate generic-looking submissions. Aakash frames the core bar as demonstrating unique experience and insight rather than polished AI-generated content.
- •Sumant says the hiring push is toward making candidates think, not submit “AI slop.”
- •Aakash emphasizes showing unique experience/expertise rather than prompting ability.
- •Sumant highlights systems thinking (governance, audit trails, phased rollout) as a differentiator.
- •Aakash notes insight quality matters more than formatting.
- •Sumant flags that many candidates miss the “last 20%”: evals and economics.
- 1:27 – 3:28
What interview homework is for, and what it tests
Aakash asks why PMs should care about homework, and Sumant explains how companies use it to filter candidates amid high volume. He outlines what these assignments reveal—problem-solving, technical capability, communication, and common pitfalls—especially now that AI can inflate output quality superficially.
- •Sumant says take-homes help companies filter candidates before investing interview time.
- •Homework can assess technical capability, problem-solving, presentation, and pitfalls companies don’t want to train.
- •Sumant says AI-driven “conjuring” is forcing companies to raise standards and emphasize reasoning.
- •Sumant uses a litmus test: if a much less experienced person could make similar slides, redo the work.
- •Aakash reiterates the goal is differentiated judgment, not AI-writing polish.
- 3:28 – 8:13
Which companies tend to assign take-homes (and when in the process)
Sumant contrasts companies more likely to give homework with those that generally avoid it, and explains where homework appears in interview loops. Aakash adds his own experience with how homework shows up for junior and senior roles, which Sumant agrees is representative.
- •Sumant lists smaller or AI-native companies as more likely to assign take-homes due to limited interviewer bandwidth.
- •He says some larger established companies typically don’t use take-homes, with possible exceptions like early-career programs.
- •AI-native companies use homework to distinguish “AI-enabled PMs” from those with deeper functional AI product knowledge.
- •Homework can be used as pre-interview screening, a mid-loop deep dive, or a final panel presentation.
- •Sumant says senior candidates may be grilled on strategic decisions and AI economics in panel formats.
- 8:13 – 9:46
The five common types of PM homework assignments
Sumant lays out five broad buckets of homework assignments PM candidates should expect. He notes AI prototyping is increasingly common for builder PM roles and will likely remain prominent.
- •PRD creation from an ambiguous prompt, with emphasis on assumptions and clarifying questions.
- •Strategy and competitive benchmarking with build/buy/integrate recommendations.
- •Roadmap and prioritization exercises.
- •General product case studies focused on user empathy, judgment, and taste.
- •AI prototyping as an emerging and highly sought skill for builder PM roles.
- 9:46 – 12:35
Using AI effectively: structured prompts, iteration, and a human-in-the-loop
Aakash asks what prompts and workflows look like in practice, and Sumant describes a staged approach rather than one-shot generation. They align on treating AI as a thought partner, with multiple iterations, reviewers/agents, and human checks to prevent drift and hallucinations.
- •Sumant says one-shot “write a PRD” prompts produce low-value output; candidates should break work into phases.
- •He describes using a structured, context-rich prompt and then iterating recursively with evaluations and feedback loops.
- •Sumant recommends a constant human-in-the-loop to catch hallucinations and steer content.
- •Aakash observes AI detection/watermarking trends and argues for AI as a thought partner, not a writer.
- •Both emphasize process and thinking, not AI-produced prose.
- 12:35 – 18:45
Good example #1: taking an incident-triage LLM PoC to production
Sumant walks through a strong real submission where the candidate had to propose how to move an incident triage PoC to production. The candidate differentiates PoC vs production scope, adds rules above the model, and prioritizes governance and safety over “AI does everything.”
- •The assignment: review an incident triage PoC and propose production architecture, compliance, rollout, and success metrics.
- •Candidate contrasts PoC inputs (e.g., static files) vs production needs (real-time ingestion via APIs).
- •Sumant praises not choosing the “AI end-to-end” shortcut; AI is limited to suggestions under hard rules.
- •Human-in-the-loop decisioning is kept as the final authority, signaling maturity.
- •Candidate is transparent about what’s mocked vs real while still outlining a future vision.
- 18:45 – 20:19
The missing 20%: eval methodology and feature economics
Sumant explains what separates good from elite: explicit eval plans and economics/cost models. Aakash summarizes the takeaway as connecting AI work to evals and to business impact rather than only claiming accuracy.
- •Sumant says many submissions cite high-level accuracy without explaining eval methodology.
- •He says cost models and “AI economics” are often absent, causing otherwise strong work to miss the bar.
- •Sumant reiterates most candidates reach 80%, but fail on the remaining 20%: evals and economics.
- •Aakash frames a simple rule: for AI features, discuss evals; for any feature, tie to business impact.
- •They reinforce that depth and rigor beat polish.
- 20:19 – 26:32
Good example #2: unifying five data products through user-based segmentation
Sumant describes a second strong submission: unifying five existing data products and proposing new insights. The candidate uses domain knowledge and primary research to segment products by how different user groups use them, and he explicitly discusses rationales and trade-offs.
- •The assignment: unify a portfolio of five data products and propose new data insights based on provided tables.
- •Candidate uses research (including competitor research and conversations) to understand use cases and users.
- •He segments products by user needs (reporting templates, analytics exploration, raw data dumps) rather than pricing tiers.
- •Sumant says the approach anticipates future AI-native evolution and scaling into the same buckets.
- •Candidate documents rationales, trade-offs, and opportunity cost—rare but highly valued.
- 26:32 – 28:36
When to accept homework (and how domain experience changes your odds)
Aakash questions whether candidates should take every homework assignment, given time costs and the advantage of domain knowledge. Sumant shares his personal approach: selectively choosing assignments where he has the best chance to differentiate, and going all-in when committing.
- •Sumant says candidates should pick assignments where they have the strongest chance of getting through.
- •He argues time has opportunity cost, especially if others will outcompete via better fit or better access to insights/contacts.
- •Aakash frames it as a “homework selection meta game” based on whether you can bring real alpha.
- •Sumant cautions that if you choose to do one, don’t do it halfway—commit fully.
- •This discussion ties differentiation directly to experience and research leverage.
- 28:36 – 32:30
Backing claims with real data and respecting constraints
Sumant highlights why the strong submissions worked: claims grounded in provided data, clear risk mapping, and minimal reliance on AI buzzwords. He also tees up the importance of constraints—sticking to requested length and scope—before transitioning into bad examples.
- •Sumant says strong work backs every claim and insight with real data, not surface-level generalities.
- •He mentions mapping stakeholders and allocating risks as a transferable skill to AI autonomy and risk tiers.
- •He notes the best AIPM homework may barely talk about models; it focuses on ideas, risks, and value.
- •Sumant warns candidates often exceed slide/page limits to show more, but constraints are part of the test.
- •Aakash shares a tactic: use links within the constrained doc to optional deeper materials.
- 32:30 – 36:20
Bad example #1: an MVP that tries to do everything (plus risky monetization)
Sumant critiques a real submission where an “MVP” includes too many scattered features and misses the core hypothesis of an MVP. He also flags a monetization idea—selling anonymized user data—as a major red flag when presented without careful risk framing.
- •The prompt: create an MVP for an app to revolutionize the in-person live music experience.
- •Sumant says the feature list is unfocused and doesn’t test a single core idea, undermining the MVP concept.
- •He notes that excessive detail doesn’t compensate for lack of prioritization and tough choices.
- •Sumant calls out selling anonymized user data as a problematic thing to propose in a case submission.
- •He adds that standards have tightened; what once passed may now be rejected quickly, especially with AI screening.
- 36:20 – 39:32
Bad example #2: AI features with no evals, no economics, and unrealistic timelines
Sumant reviews another poor submission: competitive analysis followed by AI feature proposals that lack specification depth. He emphasizes that omitting eval plans, accuracy targets, cost per request, and success metrics turns AI into empty marketing rather than a buildable plan.
- •The assignment: competitive analysis for a tech platform and feature proposals to beat competitors.
- •Candidate proposes AI-powered features and a roadmap but omits data needs, accuracy targets, and eval plans.
- •Sumant flags missing cost per request and unclear feature economics and success metrics.
- •He critiques claiming delivery under six months while listing complex AI work without including evaluation in the plan.
- •Sumant reiterates the “two E’s” many miss: evals and economics.
- 39:32 – 44:25
How to split your hours: problem understanding, execution, then readable formatting
Aakash asks for a repeatable approach given limited time, and Sumant proposes a simple allocation. He prioritizes deep understanding and research first, then structuring the response and trade-offs, and only then polishing formatting for readability and screening.
- •Sumant recommends spending early hours understanding the prompt and nuances before solutioning.
- •He encourages using AI during research, but keeping focus on core PM problem-solving.
- •Next phase: build a clear structure—assumptions, open questions, outline, prioritization, and trade-offs.
- •He cautions against rushing to solutions; recommend modest tone and flexible framing rather than overconfidence.
- •Final phase: formatting for readability (including for AI screeners), avoiding overcomplicated visuals; also avoid asking for extensions due to AI availability.
- 44:25 – 49:24
Prototypes and page limits: when extras help vs when they backfire
Aakash asks whether candidates should prototype for every assignment and how strictly to follow length constraints. Sumant argues prototypes should match the prompt and role expectations, and that constraints are part of what’s being evaluated—use links for depth without violating limits.
- •Sumant says prototyping makes sense when the role is builder PM, the prompt asks for it, or the company’s product is agentic.
- •Building an app when not asked can signal inability to follow instructions and poor prioritization.
- •If prototyping, he recommends a thin slice that supplements ideas and includes evaluation—not a sprawling build.
- •Sumant insists page limits are non-negotiable; concision under constraints is part of the test.
- •Aakash supports using links strategically to show deeper work while keeping the submitted doc within limits.
- 49:24 – 55:45
Biggest mistakes in PM homework: AI-isms, weak storytelling, and skipping defense prep
Sumant lists recurring pitfalls he sees even among strong PMs, from avoidable errors to misreading the prompt. He emphasizes that candidates must prepare to defend every decision because the homework often becomes interview material.
- •Simple quality issues (typos, misalignment) on early slides create a strong negative first impression.
- •Candidates often answer the prompt they wish they got, misinterpreting the task and skipping required judgment calls.
- •Sumant describes “AI-isms” that reveal AI writing (generic hype language and recognizable stylistic patterns).
- •He warns against missing key numbers or adding numbers in the wrong places; storytelling requires well-placed evidence.
- •Sumant says many skip defense prep; Aakash suggests using AI to generate likely critiques and questions, and finishing early to practice defense.
- 55:45 – 1:00:22
A 3-part system for succeeding on PM take-homes + closing and sponsor message
Sumant summarizes a concise system: quickly understand the problem, iterate and get rigorous review, and focus on content and evaluation rigor over flashy formatting. They close with where to find Sumant, followed by Aakash’s sponsor/program message.
- •Sumant’s system: rush to understand the problem, not to ship AI-generated filler.
- •Evaluate and review repeatedly—self/AI review first, then feedback from experienced evaluators.
- •Focus on content and value; keep formatting readable but avoid fancy templates and buzzword stuffing.
- •Sumant reiterates not forgetting evals, metrics, and economics for AI product roles.
- •Closing includes where to find Sumant, then Aakash’s sponsor/program promotion.
