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?

Measures the modelTask type v1.1 · 3 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 19 graded outputs by 7 models. 79% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    Complete recommendation memo with evidence, risks, and actionable next steps, usable as is.
    GPT-6 Luna · API · Two finalists, one Group PM role
  2. Judges on evidence, not presence100% pass
    Judges on concrete evidence (metric moved, team credit, learning from failure) and treats impressions like presence as weak signals.
    GPT-6 Luna · API · Two finalists, one Group PM role
  3. 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

Where it slips

  1. Spots the interviewer pattern65% pass
    Does not notice that VP Engineering scores big-tech candidates higher and those hires were rated lower; only reports a negative correlation without the pattern.
    GPT-6 Luna · API · What our interviews predict
  2. Avoids unsupported claims70% 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. Identifies material uncertainty72% pass
    The output does not name unknowns that could change the hiring decision or the loop design, nor does it say how they would be resolved.
    Sonnet 5.5 · API · A loop for the first growth PM

How it’s graded

The checks come from what the best product leaders have said about doing this job well on Lenny’s Podcast. Each one names the guest it comes from: follow a name to the idea on the Lenny’s Podcast wiki.

  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.

  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.

  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.

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 put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're Director of Product at Medvia, and you're the hiring manager for a Group PM. The debrief was yesterday. Write your recommendation to Rosa Lindqvist, our VP Product, in no more than 1,000 words: who to hire (Dev, Joy, or neither), the evidence for it, the risks, and what you'd check before making the offer. The CEO wants a decision by Friday. The interview notes and everything else are below.

What the model was given6 items: The role and the scorecard (agreed before interviews began), Dev Malhotra: interview notes, Joy Adeyemi: interview notes, Scores (1 to 4, entered after the debrief discussion), References, Compensation
The role and the scorecard (agreed before interviews began)Group PM for patient payments, leading three PMs. Must set the strategy for patient payments, lift the online payment rate (now 41%), and lead PMs while working closely with Finance and Compliance. Weighting: execution and influence 35%, product sense 25%, analytics 20%, people leadership 20%.
Dev Malhotra: interview notesProduct sense (Head of Design): 'Brilliant. Redesigned our statement flow live, lots of ideas, very creative.' Notes show six feature ideas in the first five minutes, before asking who pays and why. Analytics (Data lead): 'Strong.' Notes: asked what metric he moved at his last job, he said 'we grew the payments platform massively'; pressed, 'the team shipped 40+ features'. No number. Leadership (VP Engineering): 'Confident, inspiring.' Notes: every story in the first person; said his last PM team 'struggled to keep up with me'. Presentation: 'the best deck we've ever seen'.
Joy Adeyemi: interview notesProduct sense (Head of Design): 'Solid, slower.' Notes: spent 15 minutes asking who pays and why they don't pay online, then proposed one change: text-to-pay for the 58% of patients who pay by phone, with its trade-offs. Analytics (Data lead): notes say she raised payment completion at her last company from 33% to 47% over 18 months, and walked through an experiment that failed and what it taught her. Leadership (VP Engineering): notes say she coached a struggling PM into a different role, promoted another, and named her engineers when describing wins. Debrief comments: 'Not much presence.' 'Not sure she'd command the room with the CFO.'
Scores (1 to 4, entered after the debrief discussion)Dev: 4, 4, 4, 4. Joy: 3, 3, 3, 3. The Head of Design spoke first in the debrief. The VP Engineering's own notebook, written before the debrief, says: 'Joy: 4, strongest leader we've seen.' They entered a 3 after the discussion.
ReferencesDev: two references, both people who report to him now. Rosa knows someone at his previous company, who says: 'great in front of execs; his team turned over a lot.' Joy: her former manager and a peer, both strongly positive. Nobody has asked either reference about working with a CFO.
CompensationBand $220k to $250k. Dev is asking for $245k, Joy for $230k.
What a strong answer doesThe answer key the graders mark against

Recommends Joy, or at minimum not Dev on this evidence. Weighs both against the agreed scorecard rather than presence or the deck: Joy gives a metric she moved (33% to 47%), learns from failure, credits her team and coaches PMs, which fits execution, analytics and people leadership; Dev couldn't name a metric he moved, frames everything as 'I', jumped to features before asking about users, and the back channel reports high team turnover, all red flags. Points out that the scores were entered after a discussion the Head of Design opened, and the VP Engineering moved Joy from 4 to 3, so they show anchoring, not independent judgement. Treats 'presence with the CFO' as the one real open question and proposes a targeted reference check on how Joy worked with Finance leaders before the offer. Suggests the next loop collect written scores before the debrief.

Critical failures (cap the score at 40)
  • Recommends hiring Dev on the strength of the debrief scores or the presentation, without addressing that he couldn't name a metric he moved
Task

Staff-level · v1.0 · synthetic · healthcare, fintech, hiring, staff-level

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Usable with a quick edit12 of 12 checks passed

The memo is complete, evidence-based, and directly usable by the VP Product with at most minor wording tweaks.

Output

To: Rosa Lindqvist, VP Product From: Director of Product Date: [Current Date] Subject: Recommendation: Group PM, Patient Payments

---

Recommendation

Hire Joy Adeyemi. Do not hire Dev Malhotra.

While the post-debrief scores show Dev at straight 4s and Joy at straight 3s, the underlying interview notes tell the opposite story. Dev embodies a classic “style over substance” trap that poses severe risks to our culture and business metrics. Joy is an evidence-driven, outcome-oriented product leader whose track record directly maps to our core objective: moving our online payment rate from 41%.

Before extending an offer by Friday, we need to complete one targeted reference check on Joy’s executive influence with Finance.

---

Debrief Context & Scorecard Calibration

The debrief scores were distorted by presentation polish and group dynamics: * Anchoring: The Head of Design spoke first, captivated by Dev’s live wireframing and presentation deck. * VP Engineering flip: Before the group discussion, the VP of Engineering wrote in his private notes: “Joy: 4, strongest leader we’ve seen.” He only downgraded her to a 3 after the room leaned into Dev’s charisma. * Scorecard alignment: Our mandate is lifting payment conversion, leading three PMs, and collaborating with Compliance and Finance. When evaluated against our weighted scorecard, Joy clearly outperforms Dev.

---

The Evidence: Why Joy

#### 1. Analytics (20%) & Product Sense (25%) * Joy delivers outcomes; Dev counts outputs. Joy grew payment completion from 33% to 47% over 18 months at her prior company—almost the exact challenge we face with our 41% baseline. She demonstrated authentic experimentation rigor by walking through a failed test and the specific learnings extracted. * Methodology vs. guesswork: In product sense, Dev generated six feature ideas in five minutes without asking a single question about our users. Joy spent the first 15 minutes diagnosing who pays and why they do not pay online. Only then did she offer a targeted solution: text-to-pay targeting the 58% of patients who pay by phone, accompanied by nuanced trade-offs. * In contrast, when pressed on metrics, Dev claimed he “grew the platform massively,” pointing solely to shipping “40+ features.” He showed zero metric accountability.

#### 2. People Leadership (20%) * Retention vs. Turnover: Joy demonstrated servant leadership—she credited her engineers, coached a struggling PM into a better-fitting role, and promoted another. * Dev spoke entirely in the first person (“I”) and remarked that his past PM team “struggled to keep up with me.” Critically, your backchannel reference confirmed: “great in front of execs; his team turned over a lot.” * Bringing Dev in to manage three PMs is an unacceptable retention risk. Joy will develop and retain our talent.

#### 3. Execution & Influence (35%) * Dev’s influence relies on performative deck-building. While impressive on the surface, feature-heavy roadmaps disconnected from metrics collapse when scrutinized by cross-functional peers. * Joy influences with data, structural diagnosis, and trade-offs. In a domain governed by Compliance and Finance, rigorous substance will earn far more durable trust than slick slides.

---

Key Risks & Mitigation

Risk 1: Joy’s perceived “lack of presence” with the CFO. * The concern: Debrief feedback questioned whether she can “command the room” with Finance. * The reality: Introversion or deliberate pacing is often misread as a lack of authority. CFOs in healthcare payments do not want flash; they want airtight unit economics, regulatory awareness, and defensible data. Joy’s structured, trade-off-led approach is well-suited to Finance, but we must verify this. * Mitigation: We will run a targeted reference check specifically addressing her relationship with senior finance stakeholders (detailed below).

Risk 2: Dev’s counter-risk (The Cost of the Alternative). * Hiring Dev risks churn across all three PM reports within 12 months, feature bloat that fails to budge our 41% rate, and friction with Compliance when rapid-fire feature shipping bypasses regulatory rigor.

---

What to Check Before Making the Offer

We can resolve remaining questions and finalize an offer before Friday’s deadline:

1. Targeted Reference Check (Finance/CFO Influence): Neither candidate had their references asked about working with Finance. Today, I will call Joy’s former manager and peer to ask: * “How did Joy manage pushback from Finance, Legal, or Compliance?” * “Describe a time Joy had to present a high-stakes proposal or trade-off to the CFO/executive team. How was it received?” 2. Optional 30-Minute Touchpoint with our CFO (Thursday): If the reference leaves any ambiguity, set up an informal 30-minute working chat between Joy and our CFO focused on a single payment reconciliation scenario. This will give our CFO direct input and dispel presence-based bias.

---

Compensation

  • Band: $220,000 – $250,000
  • Joy’s Ask: $230,000
  • Dev’s Ask: $245,000

Joy’s ask sits comfortably near the midpoint of our band, leaving room for merit increases while saving $15,000 in baseline compensation compared to Dev.

Next Steps

With your sign-off, I will conduct Joy’s targeted Finance reference check this afternoon, brief the VP of Engineering on our rationale, and have an offer letter ready for Joy at $230k by Thursday morning to meet the CEO’s Friday deadline.

Grades and run details

Decision model 96 · LLM judge 13 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly41%
  • passAddresses the actual decision93%
  • passRespects explicit constraints51%
  • passIdentifies material uncertainty89%
  • partialAvoids unsupported claims42%
  • passProduces the required deliverable98%
  • passWeighs against the agreed scorecard99%
  • passCatches the red flags in Dev's evidence100%
  • passSays what would change the call84%
  • passJudges on evidence, not presence96%
  • passDefines good for this role first64%
  • passKeeps each judgement independent76%
Run
Run
#1
API response time
22 s
Submitted
5 Oct 2026

Usable with a quick edit12 of 12 checks passed

The output is well-structured, evidence-based, and actionable with only minor wording tweaks needed; no re-checking of evidence or redoing of reasoning is required.

Output

To: Rosa Lindqvist, VP Product Subject: Group PM, Patient Payments — recommend Joy, subject to targeted checks by Friday

Rosa,

I recommend hiring Joy Adeyemi, contingent on validating her ability to set strategy and influence Finance and Compliance. The interview evidence is stronger for Joy on the work this role requires: improving payment outcomes, making customer-grounded product decisions, and developing PMs. Dev’s presentation strength is clear, but the evidence does not support his uniformly exceptional scores.

We should complete focused checks before Friday rather than let either the debrief’s consensus or the deadline substitute for evidence.

Why Joy

Our agreed scorecard—not presentation polish—should drive the decision.

DimensionWeightEvidence and assessment
------:---
Execution and influence35%Joy described raising payment completion from 33% to 47% over 18 months: a concrete outcome, with an example of learning from a failed experiment. Dev described platform growth without quantifying it, then cited 40+ features shipped—output, not demonstrated impact. Neither candidate has established CFO or Compliance influence. This remains the most consequential gap.
Product sense25%Joy investigated who pays and why patients do not pay online before proposing text-to-pay for the reported 58% who pay by phone, including trade-offs. That is stronger evidence of problem selection and prioritization than six ideas before understanding the payer. Dev demonstrated creativity, but creativity alone does not establish strategy.
Analytics20%Joy supplied a baseline, endpoint, timeframe, and failed-experiment learning. Dev supplied no outcome metric even when pressed. Joy’s result still needs verification of her contribution and how it was measured; her prior completion metric should not be assumed equivalent to our online payment rate.
People leadership20%Joy gave specific examples of coaching, promotion, and shared credit. Dev’s first-person accounts and comment that his PMs “struggled to keep up with me” raise questions about his ability to develop three PMs. The report of turnover warrants investigation, not acceptance as fact.

Why I would not rely on the recorded scores

The 4s for Dev and 3s for Joy were entered after the discussion, so they are not independent assessments. The VP Engineering’s contemporaneous note—“Joy: 4, strongest leader we’ve seen”—conflicts with the submitted 3. Combined with the Head of Design speaking first and enthusiasm about Dev’s deck, this creates a credible risk of anchoring and presentation halo.

“Not much presence” and “command the room” are not sufficiently specific hiring criteria. We need evidence of gaining alignment, handling disagreement, and changing consequential decisions—not a proxy based on communication style. This does not prove bias or invalidate every judgment; it means the scores need reconciliation with observed behavior.

I would ask each interviewer to independently reassess their dimension against the original rubric, cite evidence, and explain any change from their pre-debrief assessment. I would not manufacture replacement scores from these notes.

Risks in hiring Joy

  • Executive and cross-functional influence is unproven. Strong product judgment will not be enough if she cannot resolve disagreements with Finance and Compliance.
  • Group-level strategy remains under-tested. One well-reasoned product proposal does not establish an ability to set a portfolio strategy or allocate work across three PMs.
  • Her reported impact needs attribution. We need to understand what she owned, what other changes contributed, and whether the gains were durable.

Dev’s executive presentation skill is a genuine strength. However, selecting him now would require overlooking gaps in measurable outcomes and substantial unanswered leadership questions. His current direct-report references offer a limited perspective; the informal turnover report is also insufficiently substantiated.

Checks before an offer

1. Focused, structured working session with Finance and Compliance. Ask Joy to outline a patient-payments strategy from our 41% online payment baseline, identify missing data, prioritize opportunities, and explain how she would organize three PMs. Introduce a realistic conflict involving collections, patient experience, cost, and compliance constraints. Assess decision quality, listening, trade-off handling, and ability to reach a workable decision—not performance style. A CFO or senior Finance delegate and Compliance partner should score independently before discussing.

2. Targeted reference follow-ups, with Joy’s consent. Ask her former manager and peer for concrete examples of disagreement with a CFO or Finance leader, work with Compliance, strategy ownership, and developing PMs. Verify the 33%–47% result, her contribution, and measurement. Seek an additional relevant cross-functional reference if those references lack direct visibility.

3. Reconcile the interview record. Complete the independent score review and explicitly resolve the leadership-score discrepancy. Document remaining uncertainty rather than forcing consensus.

Friday decision: If these checks substantiate Joy’s strategy and influence capabilities, proceed with her offer. If they reveal a material gap—or cannot establish the essential evidence—I recommend neither, not defaulting to Dev.

Joy’s $230k request and Dev’s $245k request are both within the $220k–$250k band. Compensation should not decide this choice; set Joy’s offer based on scope and internal equity, not simply her lower ask.

Grades and run details

Decision model 100 · LLM judge 13 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly88%
  • passAddresses the actual decision98%
  • passRespects explicit constraints55%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims72%
  • passProduces the required deliverable94%
  • passWeighs against the agreed scorecard100%
  • passCatches the red flags in Dev's evidence100%
  • passSays what would change the call100%
  • passJudges on evidence, not presence100%
  • passDefines good for this role first81%
  • passKeeps each judgement independent88%
Run
Run
#1
Time to output
36 s
Submitted
5 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.

All got right 12

Uses the supplied evidence correctlyRightRight
Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

All factual claims about the current situation are taken directly from the supplied context without invention.

Addresses the actual decisionRightRight
Gemini 3.8 Flash · API

The output unambiguously recommends hiring Joy and not Dev, and says the decision is conditional on a targeted reference check on Joy's finance influence.

GPT-6 Astra · ChatGPT

The output commits to hiring Joy contingent on checks, and says it would recommend neither if checks reveal a material gap, giving a clear conditional answer.

Respects explicit constraintsRightRight
Gemini 3.8 Flash · API

The output is a recommendation memo to Rosa Lindqvist, well under 1,000 words, and respects the form and reader.

GPT-6 Astra · ChatGPT

The output is a recommendation to Rosa Lindqvist, well under 1,000 words, and respects the requested form and reader.

Identifies material uncertaintyRightRight
Gemini 3.8 Flash · API

It names the open question about Joy's influence with finance leaders and proposes a specific reference check and optional CFO touchpoint to resolve it.

GPT-6 Astra · ChatGPT

It names the unknowns (Joy's influence with Finance/Compliance, strategy scope, attribution) and specifies how to resolve them with a working session and reference checks, and what would change the call.

Avoids unsupported claimsRightRight
Gemini 3.8 Flash · API

The output's causal claims (e.g., scores distorted by presentation) are presented as reasoned conclusions from the evidence, not as unsupported facts.

GPT-6 Astra · ChatGPT

Interpretations like anchoring risk are clearly labelled as risks, and confident claims are backed by the evidence.

Produces the required deliverableRightRight
Gemini 3.8 Flash · API

The memo is complete, addressed to the VP Product, within the word limit, and actionable with a clear recommendation and next steps.

GPT-6 Astra · ChatGPT

The memo is complete with recommendation, evidence, risks, and pre-offer checks, and Rosa could act on it directly.

Weighs against the agreed scorecardRightRight
Gemini 3.8 Flash · API

The recommendation explicitly weighs both candidates against the agreed scorecard competencies and notes that presence and the deck are not on it.

GPT-6 Astra · ChatGPT

The recommendation explicitly weighs both candidates against the agreed scorecard dimensions and weightings, and notes that presence and the deck are not on it.

Catches the red flags in Dev's evidenceRightRight
Gemini 3.8 Flash · API

It names at least four red flags: no metric moved, first-person credit and team struggled to keep up, jumping to features before user questions, and back-channel turnover report.

GPT-6 Astra · ChatGPT

It names at least three red flags: no metric moved, first-person credit and team struggled to keep up, jumping to features, and the turnover report.

Says what would change the callRightRight
Gemini 3.8 Flash · API

It identifies the open question about Joy's influence with senior finance leaders and proposes a targeted reference check and optional CFO touchpoint, implying a negative result would change the decision.

GPT-6 Astra · ChatGPT

It identifies Joy's unproven influence with senior finance leaders as the open question and proposes a targeted reference check and a working session with Finance and Compliance, with a clear decision trigger.

Judges on evidence, not presenceRightRight
Gemini 3.8 Flash · API

The output judges on concrete evidence (metric moved, team credit, learning from failure) and treats presence as a weak signal to be verified.

GPT-6 Astra · ChatGPT

It judges on concrete evidence (metric moved, team credit, learning from failure) and treats impressions like presence as weak signals.

Defines good for this role firstRightRight
Gemini 3.8 Flash · API

It sets out the role's weighted competencies and uses them to define what good looks like before evaluating the candidates.

GPT-6 Astra · ChatGPT

It sets out the weighted competencies for the role and describes strong and weak signals for each before judging the candidates.

Keeps each judgement independentRightRight
Gemini 3.8 Flash · API

It uses the VP Engineering's independent pre-debrief note to discount group-influenced scores, effectively keeping that judgement independent.

GPT-6 Astra · ChatGPT

It requires written independent evaluations before discussion and discounts scores that changed under group pressure, citing the VP Engineering's notebook.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI94.4100.03None
2Sonnet 5.5withAPI95.896.22None
3Gemini 3.8 FlashwithAPI89.692.32None
4GPT-6 AstrawithChatGPT95.887.23None
5Opus 5.5withClaude91.787.23None
6GPT-6 LunawithAPI93.184.63None
7Gemini 3.5 Flash-LitewithGemini75.064.13None

About the task

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