At a glance
WHAT IT’S REALLY ABOUT
How PMs can crush take-home assignments without AI slop
- The episode explains why interview homework is increasingly used to filter large candidate pools and assess skills like problem-solving, technical thinking, and presentation before investing interview time.
- It distinguishes which companies tend to give take-homes (often smaller or AI-native firms) versus which often do not (typically larger established companies), with some exceptions for early-career programs.
- It breaks PM homework into five major types and emphasizes using AI as a thought partner with iterative checks, not as an end-to-end writing engine that produces detectable “AI-isms.”
- Through real good/bad submission examples, it argues that systems thinking, constraints, trade-offs, and evidence-backed insights matter more than formatting, and that eval plans plus economics often separate “good” from “elite.”
- It outlines common candidate mistakes (ignoring the prompt, weak MVP scope, missing numbers where they matter, skipping risk/defense prep) and ends with a three-part system: understand the problem fast, evaluate/review repeatedly, and prioritize content and the “two E’s” (evals/economics).
IDEAS WORTH REMEMBERING
5 ideasHomework now tests thinking and judgment more than “nice slides.”
Both speakers argue that with AI making it easy to generate polished-looking deliverables, companies are increasingly screening for independent thinking, judgment, and real product maturity—not generic AI-generated content.
There are five recurring categories of PM take-home assignments.
Sumant Malhotra describes five common buckets: PRD, strategy/competitive benchmarking, roadmap/prioritization, case studies focused on product sense, and AI prototyping—especially common for builder PM roles.
The best AI homework shows controlled AI usage (rules, thresholds, and humans), not end-to-end automation.
In the strong LLM incident-triage example, the submission explicitly scoped what changes from PoC to production and emphasized rules above the model plus human-in-the-loop decision-making, rather than “let the AI do everything.”
Evals and economics are the differentiators most candidates omit.
Sumant Malhotra repeatedly highlights that many candidates get “80% of the way” but miss “the 20%”: evaluation methodology and feature economics/cost considerations, which often decide whether a strong idea gets shortlisted.
Depth comes from user understanding, evidence-backed insights, and explicit trade-offs.
In the data-products unification example, the candidate classified products by how different user groups use them, backed claims with data provided, stated trade-offs and opportunity cost, and used both primary insights (industry contacts) and secondary research.
WORDS WORTH SAVING
5 quotesThe push is towards making the candidate think rather than just providing AI slop.
— Sumant Malhotra
You need to demonstrate your unique experience and expertise, not how well you can prompt AI.
— Aakash Gupta
Most candidates land just 80% of the way. That 20% is, uh, the evals and the economics behind it, and that's not something any LLM can help you do.
— Sumant Malhotra
That's basically extra cost to the company, not extra credit.
— Sumant Malhotra
Following the constraint is also part of the test.
— Sumant Malhotra
High quality AI-generated summary created from speaker-labeled transcript.
