Humanity’s Last PRD
Latest drop: Gemini 4 Argon · 13 Oct
Tasks / Leadership

Hire a PM

Can the model design a hiring process that finds the right PM, and make the call on real candidates from the evidence?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 18 graded outputs by 9 models. 61% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    The output provides a complete loop, scorecard, and decision plan in the requested form, under 900 words, usable by Amara with light edits.
    GPT-6.1 Sol · API · A loop for the first growth PM
  2. Tests what the last hire failed at100% pass
    The loop includes an influence simulation with the Head of Sales and a scorecard dimension on influence without authority, directly testing what the last hire failed at.
    GPT-6.1 Sol · API · A loop for the first growth PM
  3. Finds which rounds line up with performance100% pass
    It compares each round's scores with 12-month ratings, finding behavioral lines up and take-home, presentation, VP Engineering do not.
    GPT-6.1 Sol · API · What our interviews predict

Where it slips

  1. Uses the supplied evidence correctly56% pass
    The take-home correlation is stated as -0.57, but the data yields approximately -0.33; this fact about the current situation is incorrect.
    GPT-6 Astra · ChatGPT · What our interviews predict
  2. Avoids unsupported claims60% pass
    The claim 'A growth PM in a senior role is likely to be exactly that kind of person' is presented as fact without evidence and is not labelled as an assumption.
    Opus 5.5 · Claude · A loop for the first growth PM
  3. Defines good for this role first63% pass
    The memo does not explicitly name the competencies that matter most for this PM role and level, nor define what strong and weak look like before designing the interviews.
    Sonnet 5.5 · API · What our interviews predict

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Judges on evidence, not presence

    Does the output judge candidates, or design the process to judge them, on specific evidence of results they drove and how they worked with their team, rather than impressions such as presence, confidence or polish?

    Passes when Asks for or weighs concrete evidence: the metric a candidate moved, the part they played, how they shared credit, what they learned from a failure. Impressions are treated as weak signals.

    Product and growth leadersOn Lenny’s Podcast

  2. Defines good for this role first

    Does the output set out what good looks like for this specific role (the competencies that matter most at this level, and what strong and weak look like) before judging candidates or designing interviews?

    Passes when Names the competencies weighted for this role and level, with strong and weak signals, and the judgement or the process follows from them.

    Petra WilleLenny’s Podcast

  3. Keeps each judgement independent

    Does the output keep each interviewer's judgement independent (written down before the group discusses it), and guard against the loudest voice or the first speaker deciding?

    Passes when Requires or relies on written, independent evaluations before discussion, and discounts views that changed under group pressure.

    Product and growth leadersOn Lenny’s Podcast

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · and 3 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're helping Amara Osei, VP Product at Copperline, hire our first Growth PM (a senior role). She's shared her draft interview loop and asked you to redesign it. Write the loop you'd run (each round: who runs it, what it tests, how long it takes), the scorecard (what strong and weak look like for each thing we're testing), and how we'll make the decision at the end. No more than 900 words. What we know is below.

What the model was given6 items: About Copperline, The role, Amara's draft loop, The last two PM hires, From the recruiter, Who can interview
About CopperlineInvoicing and payments software for small accountancy firms. 60 people. Trial to paid conversion is 9%.
The roleOwns trial conversion and expansion revenue. Works with three engineers and a designer, reports to Amara, and has to ship experiments every week. Needs Sales and Marketing to change how they work, without managing them.
Amara's draft loop1. Recruiter screen. 2. Take-home: 'design a growth strategy for Copperline', a 10-page deck, five days to complete. 3. Presentation to a panel of six. 4. Culture fit with the CEO: 'would I grab a beer with them?' 5. Debrief: everyone discusses, then votes by show of hands. There's no scorecard.
The last two PM hiresOne left after five months: 'couldn't get Sales to change anything'. The other is doing well. Both sets of interview notes say mainly 'great presence' and 'very smart'.
From the recruiterOf the last 40 candidates given the take-home, 14 withdrew, saying they didn't have time. Nine of the 14 had young children or demanding current roles.
Who can interviewAmara, the Head of Engineering, a senior designer, the Head of Sales, a data analyst and the CEO. Each can give at most two hours to this hire. Amara wants an offer out within three weeks of the first interview.
What a strong answer doesThe answer key the graders mark against

A loop built on a scorecard written first: for a senior growth PM, experimentation and analytics, execution, and influence without authority (weighted up, since the last hire failed on it), with strong and weak signals for each. Rounds test the real job: a live diagnosis of Copperline's own trial funnel instead of a five-day generic deck (which loses candidates, mostly parents and people in demanding roles), a behavioural round on past experiments and metrics actually moved, and an influence round run by the Head of Sales. Replaces the 'grab a beer' culture fit with a structured values or collaboration conversation. Each interviewer writes their evaluation before the debrief, and the decision is made against the scorecard, not a show of hands. Fits each interviewer's two hours and the three-week target, and adds reference checks.

Critical failures (cap the score at 40)
  • Keeps a culture-fit test based on whether the interviewer would like a beer with the candidate
Task

Mid-level · v1.1

The outputs

9 setups answered this brief.

GPT-6.1 SolAPI98Usable with a quick editOpus 5.5Claude94Usable with a quick editGPT-6 AstraChatGPT92Usable with a quick editSonnet 5.5API90Usable with a quick editGPT-6 LunaAPI87Usable with a quick editGemini 3.8 FlashAPI87Usable with a quick editGemini 4 ArgonAPI85Usable with a quick editHaiku 5.5API83Usable with a quick editGemini 3.5 Flash-LiteGemini81Usable with a quick edit
Gemini 3.8 FlashAPI

Usable with a quick edit9 of 12 checks passed

The output is a complete, actionable plan that a product manager could implement with minimal editing, though adding a note on uncertainty would strengthen it.

Output

Here is the redesigned loop, scorecard, and decision framework tailored to Copperline’s constraints and failure modes.

---

1. The Redesigned Loop

We replace the 10-page take-home (which filters out working parents and busy seniors) with a Live Working Session using a 1-page data brief sent 24 hours prior (max 30 minutes prep). We eliminate the 6-person panel and beer test in favor of targeted, structured interviews.

Total interviewer commitment: Amara (105 min), Data Analyst (75 min), Head of Sales (75 min), Head of Eng (75 min), Designer (75 min), CEO (45 min). All under the 2-hour cap.

RoundInterviewer(s)Focus & FormatDuration
:---:---:---:---
0. Recruiter ScreenRecruiterComp, logistics, baseline communication, role alignment.30 min
1. HM ScreenAmara (VP Product)Growth philosophy, track record of weekly experiment cadences, why Copperline.30 min
2. Live Growth JamAmara & Data AnalystSimulated Work: 1-page brief on Copperline’s 9% trial conversion. Candidate leads a 30-min problem-solving session to generate, prioritize, and design 3 testable hypotheses.45 min
3. Influence & Stakeholder AlignmentHead of SalesPast Behavioral: Changing sales/marketing behavior without authority, handling resistance, aligning self-serve vs. assisted motions.45 min
4. Pod Execution & VelocityHead of Eng & Senior DesignerCollaboration: How they run a 4-person growth pod, maintain weekly shipping velocity, make scoping trade-offs, and treat engineering/design as thought partners.45 min
5. Values & AmbitionCEOStructured interview assessing ownership, comfort with 60-person ambiguity, and customer obsession for small accounting firms.30 min
6. Async DebriefAmara (Lead)Written-first calibration and final decision.30 min

Pacing: Rounds 2–5 can be scheduled across two consecutive days. Total timeline from screen to offer: 10–14 business days.

---

2. The Scorecard

Interviewers evaluate against specific evidence, not “smartness” or “presence.”

#### A. Influence Without Authority (Crucial copper-plate failure mode) Evaluated by: Head of Sales, Amara * Strong: Uses shared metrics to align incentives; brings Sales qualitative feedback into experiment design; creates lightweight workflows that save reps time; shows empathy for quota-carrying teams; demonstrates wins where an adversarial relationship was turned into a partnership. * Weak: Relies on executive mandate (“Amara said so”); views Sales as an execution arm; blames others when processes fail (“Sales refused to update the CRM”); uses heavy-handed documentation instead of relationship-building.

#### B. Experiment Velocity & Growth Rigor (9% trial-to-paid target) Evaluated by: Amara, Data Analyst * Strong: Understands the mechanics of PLG onboarding for B2B accounting workflows; defines clear leading indicators; comfortable shipping imperfect MVPs to learn fast; ruthlessly prioritizes based on impact vs. effort; embraces invalidated tests as learning. * Weak: Proposes massive, multi-month redesigns instead of iterative tests; lacks mathematical intuition around sample sizes and statistical significance; fixates on vanity top-of-funnel metrics instead of revenue and conversion.

#### C. Cross-Pod Execution & Scoping Evaluated by: Head of Engineering, Senior Designer * Strong: Involves design and engineering early in hypothesis generation; defines crisp acceptance criteria; scopes experiments down to hours/days, not weeks; protects pod morale by celebrating learning over pure "wins." * Weak: Hands fully formed PRDs over the fence; treats engineers as ticket-takers; expects pixel-perfection on throwaway split tests; cannot articulate trade-offs between tech debt and learning speed.

#### D. Customer Domain Empathy & Ownership Evaluated by: CEO * Strong: Shows genuine curiosity about the mundane operational pains of small accountants; low ego, high accountability; self-directed; articulates clear reasons for joining an early-stage B2B SaaS business. * Weak: Generic answers suited to consumer tech; dismissive of non-glamorous industries; expects corporate infrastructure and large support teams.

---

3. Decision-Making Framework

To prevent the groupthink, bias, and anchoring seen in show-of-hands voting:

1. Independent Scorecard Submission: Within 2 hours of their interview, every interviewer must submit their score (Strong Hire, Hire, Lean No Hire, Strong No Hire) and written evidence mapped to their specific competencies. Scores are locked; no one can view others' ratings until their own is submitted. 2. The 30-Minute Debrief: * Amara chairs the meeting. * If any interviewer marks Lean No Hire or Strong No Hire, the discussion starts entirely on those specific concerns. * The Head of Sales holds an effective veto on stakeholder influence. If the Head of Sales says, "This person will alienate my team," the candidate is disqualified. * "Culture fit" is explicitly redefined: We do not hire people we want to grab a beer with; we hire people who elevate our operating standard and live our core values. 3. Single Decision-Maker: This is not a democracy. Amara owns the final decision and accountability for the hire. If the scorecard is clean and Sales approves, Amara makes the call immediately.

This process directly solves candidate drop-off, screens for Copperline's exact historical failure mode, and enables an offer inside two weeks.

Grades and run details

Decision model 88 · LLM judge 85
Decision model checks
  • passUses the supplied evidence correctly20%
  • failAddresses the actual decision58%
  • passRespects explicit constraints27%
  • passIdentifies material uncertainty13%
  • partialAvoids unsupported claims55%
  • passProduces the required deliverable88%
  • passTests what the last hire failed at100%
  • passFixes the take-home's cost to candidates97%
  • passFits the people and the time72%
  • passJudges on evidence, not presence95%
  • passDefines good for this role first80%
  • passKeeps each judgement independent100%
Run
Run
#1
API response time
29 s
Submitted
5 Oct 2026
Gemini 4 ArgonAPI

Usable with a quick edit9 of 12 checks passed

The output is a complete, actionable loop with clear evidence-based scorecard and decision process. A PM could adopt it with minimal edits, even though it omits explicit uncertainty acknowledgment.

Output

Here is the redesigned interview loop for Copperline’s first Growth PM.

We’ve removed the 10-page take-home—which was costing us 35% of our pipeline, disproportionately filtering out parents and busy top performers—and replaced the six-person panel, which burns interviewer hours and selects for "presence" over substance. We’ve also replaced the CEO’s "beer test" and the hand-raise vote to eliminate groupthink and bias.

This new loop fits your 3-week timeline, requires zero unpaid homework, and keeps every interviewer well under their 2-hour cap per candidate.

1. The Interview Loop

Stage 1: Recruiter Screen (30 mins) * Interviewer: Recruiter * What it tests: Role alignment, compensation, B2B SaaS growth experience, and baseline appetite for high-velocity experimentation.

Stage 2: Hiring Manager Screen (45 mins) * Interviewer: Amara (VP Product) * What it tests: Past Impact & Strategic Thinking. Deep dive into a past funnel or expansion initiative they owned end-to-end. Focuses on how they diagnose bottlenecks and drive commercial outcomes.

Stage 3: Live Growth Jam (60 mins) * Interviewers: Data Analyst & Senior Designer (plus 15 mins prep) * What it tests: Experimentation Velocity & Squad Collaboration. No prep required. We share a mock snapshot of Copperline’s 9% trial-to-paid funnel during the call. The candidate works live with the Analyst and Designer to diagnose drop-offs, form hypotheses, and scope two scrappy experiments to ship next week.

Stage 4: Go-To-Market Alignment (45 mins) * Interviewer: Head of Sales * What it tests: Cross-Functional Influence. Directly addresses why our last PM left. Behavioral questions and a live roleplay on getting Sales and Marketing to change workflows without direct authority.

Stage 5: Delivery & Ambition (60 mins — two 30-min back-to-backs) * Part A: Head of Engineering (30 mins). Tests Pragmatic Execution: balancing tech debt with weekly experiment shipping, and how they work with engineers. * Part B: CEO (30 mins). Tests Commercial Empathy & Ownership: passion for small accountancy firms, comfort with startup ambiguity, and executive alignment.

Time commitment per finalist: Amara (1.25 hrs), Analyst/Designer (1.5 hrs), Sales (1.25 hrs), Eng/CEO (1 hr)—including the 30-minute debrief.

---

2. The Scorecard

Interviewers will grade only their assigned competencies on a 1–4 scale (1 = Strong No, 2 = No, 3 = Yes, 4 = Strong Yes), citing specific evidence—never "great presence" or "very smart."

1. Experimentation Velocity & Rigor (Tested in: Live Jam, Eng) * Strong: Comfortably forms hypotheses from imperfect data. ruthlessly scopes MVPs down to ship within a week. Focuses on learning velocity over perfection. * Weak: Wants months of research before shipping. Designs massive features instead of tests. Confuses shipping features with moving metrics.

2. Cross-Functional Influence (Tested in: GTM Alignment, HM Screen) * Strong: Speaks the language of Sales (quota, pipeline, commission) and Marketing. Co-creates solutions, anticipates resistance, and uses data and quick wins to earn trust. Has clear examples of changing GTM behavior. * Weak: Expects Sales/Marketing to comply "because the data says so." Complains about stubborn stakeholders or escalates to the VP immediately when blocked.

3. Funnel & Expansion Strategy (Tested in: HM Screen, Live Jam) * Strong: Connects micro-experiments to macro revenue goals. Clearly articulates levers for moving a 9% conversion rate and driving expansion in a B2B workflow tool. * Weak: Throws random growth hacks at the wall. Focuses purely on top-of-funnel acquisition or B2C viral loops that don’t apply to accountancy firms.

4. Squad Collaboration & Execution (Tested in: Live Jam, Eng) * Strong: Treats Design, Data, and Eng as problem-solving partners, not ticket-takers. Welcomes pushback during the Live Jam and adapts their ideas. * Weak: Dictates exact UI solutions to the designer; ignores technical constraints raised by engineering; talks over teammates.

5. Commercial & Customer Empathy (Tested in: CEO, HM Screen) * Strong: Genuinely curious about how small accountancy firms work, make money, and experience friction with invoicing/payments. High ownership mentality. * Weak: Views users solely as rows in a database. Uninterested in the unglamorous realities of accounting software.

---

3. Making the Decision

  1. Blind Submission: Interviewers must submit written scorecards in our ATS within 24 hours of their interview. No one can see others’ feedback until their own is locked in.
  2. Pre-Read (5 mins): At the start of the 30-minute debrief, everyone silently reads all submitted scorecards.
  3. Evidence-Based Discussion (20 mins): No show of hands. We discuss only areas of calibration discrepancy (e.g., Sales scored Influence a 2; Amara scored it a 4) or missing evidence. If someone says a candidate is "smart," the moderator (Amara or Recruiter) will ask: "What specific behavior showed that, and how does it map to the scorecard?"
  4. The Call (5 mins): If a candidate scores below a 3 on Cross-Functional Influence or Experimentation Velocity, they are an automatic pass. Otherwise, Amara—as the hiring manager—weighs the evidence and makes the final hiring decision. Single-threaded ownership, no voting.

Grades and run details

Decision model 75 · LLM judge 92
Decision model checks
  • failUses the supplied evidence correctly56%
  • failAddresses the actual decision31%
  • partialRespects explicit constraints19%
  • passIdentifies material uncertainty35%
  • partialAvoids unsupported claims62%
  • passProduces the required deliverable72%
  • passTests what the last hire failed at100%
  • passFixes the take-home's cost to candidates98%
  • passFits the people and the time52%
  • passJudges on evidence, not presence100%
  • passDefines good for this role first85%
  • passKeeps each judgement independent100%
Run
Run
#1
API response time
15 s
Submitted
13 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 2

Uses the supplied evidence correctlyRightMixed
Gemini 3.8 Flash · API

All statements about the current situation are taken directly from the supplied context.

Gemini 4 Argon · API

All factual claims about the current situation are directly supported by the brief or supplied context.

Avoids unsupported claimsWrongRight
Gemini 3.8 Flash · API

The output presents forecasts like 'This process directly solves candidate drop-off' as established fact without labelling them as hypotheses.

Gemini 4 Argon · API

Interpretations like 'disproportionately filtering out parents and busy top performers' are directly anchored in the recruiter's withdrawal data and not presented as speculation.

All mixed 2

Addresses the actual decisionMixedMixed
Gemini 3.8 Flash · API

The output commits to a specific redesigned loop, scorecard, and decision framework, which is the answer requested.

Gemini 4 Argon · API

The output commits to a specific redesigned loop, scorecard, and decision process, framed for Amara, and states clear conditions that would lead to a pass (score below 3 on key competencies) and the final hiring call.

Identifies material uncertaintyMixedMixed
Gemini 3.8 Flash · API

The output does not name any unknowns that could change the loop design or decision process, nor does it say how they would be resolved.

Gemini 4 Argon · API

The output does not name any unknowns that could alter the decision or describe how they would be resolved; it presents the process as definitive without acknowledging external uncertainties.

All got right 8

Respects explicit constraintsRightRight
Gemini 3.8 Flash · API

The output is under 900 words, addresses the VP Product, and respects all stated constraints.

Gemini 4 Argon · API

The loop respects the 900-word limit, keeps each interviewer under 2 hours, fits the 3-week timeline, and provides the required loop, scorecard, and decision method.

Produces the required deliverableRightRight
Gemini 3.8 Flash · API

The output provides a complete loop, scorecard, and decision framework that a product manager could act on with light edits.

Gemini 4 Argon · API

The output includes a complete redesigned loop, a scorecard with strong/weak signals, and a clear decision-making procedure, all within the word limit and usable by a product manager.

Tests what the last hire failed atRightRight
Gemini 3.8 Flash · API

A round with the Head of Sales specifically tests influencing teams without authority, with strong and weak signals defined.

Gemini 4 Argon · API

A dedicated round with the Head of Sales tests cross-functional influence, directly addressing the reason the last PM left, with strong and weak signals defined.

Fixes the take-home's cost to candidatesRightRight
Gemini 3.8 Flash · API

The five-day take-home is replaced with a live, bounded exercise, and the withdrawal data is cited to justify the change.

Gemini 4 Argon · API

The five-day take-home is replaced with a live 60-minute exercise on Copperline's real funnel, and the withdrawal data (35%, parents/busy candidates) is explicitly cited.

Fits the people and the timeRightRight
Gemini 3.8 Flash · API

Each interviewer's time is under two hours, and the total timeline of 10-14 business days fits the three-week target.

Gemini 4 Argon · API

Stated interviewer times (Amara 1.25h, Analyst/Designer 1.5h, Sales 1.25h, Eng/CEO 1h) all fall under the 2-hour cap, and the structure supports a three-week offer timeline.

Judges on evidence, not presenceRightRight
Gemini 3.8 Flash · API

The scorecard and process require concrete evidence of results and collaboration, not impressions like presence or smartness.

Gemini 4 Argon · API

The process explicitly forbids vague impressions, requires specific evidence in scorecards, and defines behaviors (moved metrics, handled pushback) as the basis for ratings.

Defines good for this role firstRightRight
Gemini 3.8 Flash · API

The scorecard defines the weighted competencies for this role with strong and weak signals before the decision framework.

Gemini 4 Argon · API

The scorecard defines the key competencies for this senior growth PM role with strong/weak signals, and the process automatically fails candidates below 3 on the weighted competencies.

Keeps each judgement independentRightRight
Gemini 3.8 Flash · API

Interviewers must submit written evaluations independently before any group discussion, preventing groupthink.

Gemini 4 Argon · API

Interviewers must submit written scorecards independently before seeing others' feedback, and discussion is limited to calibration discrepancies, not group opinion.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolAPI91.7100.02None
2Sonnet 5.5API85.492.32None
3GPT-6 AstraChatGPT89.680.82None
4Opus 5.5Claude89.680.82None
5GPT-6 LunaAPI87.576.92None
6Haiku 5.5API79.273.12None
7Gemini 4 ArgonAPI70.880.82None
8Gemini 3.8 FlashAPI75.065.42None
9Gemini 3.5 Flash-LiteGemini66.753.82None

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Task score against relative cost for each setup. The table below has the same figures.50607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6.1 Sol · API: task score 96, 10.0× the cheapestSonnet 5.5 · API: task score 89, 36× the cheapestGPT-6 Astra · ChatGPT: task score 84, 53× the cheapestOpus 5.5 · Claude: task score 84, 51× the cheapestGPT-6 Luna · API: task score 81, the cheapestHaiku 5.5 · API: task score 75, 1.7× the cheapestGemini 4 Argon · API: task score 75, 8.1× the cheapestGemini 3.8 Flash · API: task score 69, 6.2× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 60, 1.7× the cheapestGPT-6.1 Sol · APISonnet 5.5 · APIGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeGPT-6 Luna · APIHaiku 5.5 · APIGemini 4 Argon · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and cost

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

The PM job

Hiring product managers.

Why it matters

A bad PM hire costs a team a year. Most loops reward polish and presence, and the debrief goes to whoever speaks first.

What good looks like

  • Defines what good looks like for this role before judging anyone
  • Judges on evidence of results and how they work with a team
  • Tests the real job, not a rehearsed framework
  • Keeps each interviewer's judgement independent

Deliberately not measured

  • Employment law
  • Compensation benchmarking
Capability tested

Hiring judgement

The failure we’re looking for

Hiring for presence and polish over evidence

Grading

Decision model and LLM judge, calibrated against a blind PM review