CHAPTERS
- 0:00 – 1:28
Why PM homework is getting harder in the AI era (and what companies want now)
The conversation opens with how take-home submissions that once passed are now screened out because companies are trying to detect shallow, AI-generated work. The core expectation is that candidates demonstrate unique experience, sound thinking, and real product judgment rather than polished “AI slop.”
- •A past deck that succeeded would likely fail today due to stricter screening
- •Companies are pushing candidates to show thinking, not AI-generated text
- •Aakash emphasizes demonstrating unique experience and expertise, not prompting skill
- •Sumant describes using a “could your younger cousin make this?” test to detect generic work
- 1:28 – 3:32
Why companies assign take-homes: filtering, skill validation, and AI-driven tightening
Sumant explains that take-homes help companies handle high applicant volume by filtering before committing interview time. He also notes that AI has changed the game, pushing companies to tighten expectations and look for real reasoning and rigor.
- •Take-homes reduce load by filtering candidates before interviews
- •Assignments test technical capability, problem-solving, and presentation quality
- •Companies use take-homes to spot typical pitfalls they’d otherwise have to train
- •AI has raised the bar by making it easy to generate content, so evaluation standards are stricter
- 3:32 – 8:17
Which companies tend to use take-homes (and where they show up in the loop)
Sumant contrasts smaller or AI-native companies that often use take-homes with larger established companies that usually avoid them, especially for senior hiring. He then maps where homework can appear: pre-screen, mid-loop deep dive, or final presentation to a panel.
- •Smaller teams and AI-native companies are more likely to use take-homes to assess real AI skills
- •Larger established companies often avoid take-homes, with early-career exceptions
- •Homework can be a pre-interview filter, a mid-loop deep dive, or a final panel presentation
- •Senior candidates may be questioned on strategic decisions and AI economics in panel settings
- 8:17 – 9:53
The five common types of PM homework (including the newer AI prototyping focus)
Sumant lays out five broad categories of assignments PM candidates commonly receive. He highlights AI prototyping as an increasingly common expectation for builder PM roles.
- •PRD assignments test assumptions, composure, and ambiguity-handling (with AI-specific assumptions)
- •Strategy/competitive benchmarking can lead to build vs. buy vs. integrate recommendations
- •Roadmap and prioritization exercises are common take-home formats
- •Case studies emphasize user empathy, judgment, and product taste
- •AI prototyping is a newer, growing category—especially for builder PM roles
- 9:53 – 13:09
Using AI as a thought partner: structured prompts, iterative critique, and human-in-the-loop
They discuss how candidates can use LLMs without outsourcing the thinking. Sumant describes breaking work into steps, running recursive iterations with critique/red-teaming, and keeping a human in the loop to prevent hallucinations and maintain direction.
- •Good AI use starts with decomposing the task and providing context step-by-step
- •Recursive iterations with critique improve output quality more than one-shot generation
- •Red-teaming/sub-agents can be used to check steps and provide feedback
- •A human-in-the-loop is essential to prevent hallucinations and keep work grounded
- •Aakash frames the goal as AI being a thought partner, not a writer
- 13:09 – 18:49
Good example #1: Taking an LLM incident triage PoC to production (systems thinking and controls)
Sumant walks through a strong submission where the candidate clearly separates proof-of-concept from production requirements and designs for real-world constraints. The submission shows maturity by placing rules, thresholds, and human oversight above the model rather than delegating everything to AI.
- •The assignment: propose how to take an incident triage PoC into production (architecture, compliance, rollout, metrics)
- •Candidate contrasted PoC vs. production component-by-component (e.g., static files vs. real-time ingestion)
- •AI is positioned for suggestions while hard rules, thresholds, and human approval govern decisions
- •Transparency about mocks/untested components is paired with a clear future vision
- •Value propositions and compliance mapping are explained with clarity and specificity
- 18:49 – 20:24
The missing 20%: eval methodology and feature economics (what separates ‘good’ from ‘elite’)
Sumant explains that most candidates stop short of what truly de-risks an AI product: evaluation methodology and economics. He argues this is where many otherwise-strong submissions fail, and why the best ones stand out.
- •Many candidates cite accuracy/precision without explaining eval methodology
- •Economics and cost models are often missing even in strong-looking decks
- •Sumant describes this as the “last 20%” most candidates don’t reach
- •Aakash reinforces tying AI features to evals and business impact
- 20:24 – 26:36
Good example #2: Unifying five data products by user needs, primary research, and trade-offs
A second strong example shows a candidate unifying a portfolio of products by segmenting based on how users actually use them. The candidate uses domain experience, outreach to contacts, and layered research, then articulates rationale, trade-offs, and opportunity cost.
- •The assignment: unify five data products and propose new insights using sample tables and structures
- •Candidate classified products by user needs (reporting, analytics/exploration, and tech/data dump use cases)
- •Domain experience and contacts enabled real primary research to validate how users work
- •Secondary research was used to corroborate primary signals rather than replace them
- •Trade-offs and opportunity cost were explicitly discussed, which Sumant calls rare and valuable
- 26:36 – 29:58
Should you do every take-home? Choosing assignments where you can truly differentiate
They discuss the meta-strategy of selecting which homework assignments to accept. Sumant argues for focusing on cases where you can realistically compete and bring an advantage, then going all-in on those instead of spreading effort thin.
- •Sumant describes choosing assignments where he had the best chance to succeed
- •Opportunity cost matters when others may have stronger fit, experience, or access
- •Aakash frames this as a “homework selection meta game” based on differentiation potential
- •If you accept an assignment, Sumant emphasizes committing fully rather than doing half-measures
- 29:58 – 32:34
Constraints and formatting: why insight beats polish (and how links can extend depth)
They reinforce that strong submissions prioritize credible, data-backed insights and clarity over fancy formatting. They also discuss working within page limits, using links as optional depth without violating constraints, and recognizing that companies may use AI to screen submissions.
- •Quality of insight matters more than formatting, and companies may use AI for initial screening
- •Claims should be backed by real data and clear rationale
- •Follow constraints (like page limits) because the constraint itself is part of the test
- •Links can provide optional supporting detail while keeping the core submission within limits
- •Keep deliverables readable for both humans and automated review processes
- 32:34 – 36:23
Bad example #1: The ‘MVP’ that tried to do everything (and risky monetization without trade-offs)
Sumant critiques an MVP proposal that includes too many scattered features and fails to test a single core idea. He also flags a monetization idea—selling anonymized user data—as risky, especially without acknowledging downsides and trade-offs.
- •An MVP slide with too many features signals weak prioritization and lack of focus
- •Recruiters interpret “everything makes it in” as a major red flag
- •A monetization idea involving selling anonymized user data is presented as problematic to include casually
- •Sumant notes older standards may have let this pass, but current screening is stricter
- •Aakash suggests risky proposals must include explicit risks and considerations if mentioned at all
- 36:23 – 39:35
Bad example #2: AI features with no eval plan, no economics, and overconfident timelines
They review another weak deck proposing AI-powered features without specifying data, accuracy targets, cost per request, success metrics, or an evaluation plan. Sumant highlights that omitting evals while committing to a timeline creates an ungrounded, ‘blind’ plan.
- •Competitive analysis deck proposes AI features but lacks specs: data inputs, targets, metrics, and costs
- •No evaluation plan is included despite describing complex implementation elements
- •Sumant argues missing evals makes the plan unsafe and not credible
- •He reiterates the recurring gap: evals and economics are consistently overlooked
- •Aakash notes you can’t just sprinkle AI into a homework assignment without rigor
- 39:35 – 44:29
How to split limited hours: problem understanding, structured execution, then readable formatting
Sumant outlines a time allocation approach: invest early effort in understanding the problem and doing research, then build a structured response with assumptions and trade-offs, and only then polish formatting for readability. He also cautions against asking for deadline extensions now that recruiters assume AI can accelerate drafting.
- •Spend early hours deeply understanding the prompt and problem before solutioning
- •Use research (including AI) to explore nuances rather than to generate generic output
- •Next, build an outline with assumptions, open questions, prioritization, and trade-offs
- •Only after content is strong, polish formatting for readability (avoid overcomplication)
- •Avoid requesting more time; complete on time because AI availability changes expectations
- 44:29 – 49:28
When to build a prototype (and why going beyond the ask can backfire)
They address whether candidates should prototype for every assignment. Sumant says prototyping fits when the role or company expects it, but doing extra work not requested can signal poor prioritization or inability to follow instructions—unless kept as a thin slice that supports the requested deliverable.
- •Prototype when the role is builder-focused, the prompt asks for it, or the company’s product is agentic
- •Building an unrequested app can signal inability to follow instructions
- •Extra work can be viewed as wasted effort and added cost, not extra credit
- •If prototyping, keep it a thin slice that supports the core response
- •Stay focused on what’s being evaluated in that specific round (including eval considerations)
- 49:28 – 1:00:08
The biggest homework mistakes—and the three-part system to consistently succeed
Sumant lists recurring pitfalls: sloppy early-slide errors, answering a different question than asked, obvious AI-isms, misuse or absence of numbers, and skipping preparation for defending decisions. They close with a three-part system: understand the problem fast, evaluate and iterate with strong reviewers, and prioritize content (including evals and economics) over flashy formatting.
- •Common mistakes include typos/misalignment early in the deck and misreading the prompt
- •Candidates sometimes deliver benchmarking when the ask is to make a decision and recommendation
- •Obvious AI-isms and generic language can undermine credibility
- •Numbers should support storytelling—missing or misplaced numbers both hurt
- •Skipping defense prep is costly; expect to be grilled on decisions and reasoning
- •System: rush to understand the problem, iterate with AI/self/experienced reviewers, and focus on value plus evals/economics while keeping formatting readable
