Tasks / Strategy

Challenge an idea

Can the model find the strongest reason an idea may fail, backed by evidence?

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

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

From 16 graded outputs by 8 models. 44% were usable with at most a quick edit.

Reliably right

  1. Finds the load-bearing assumption100% pass
    The critique targets the assumption that agencies' growth is limited by a shortage of new leads, using referral, abandoned-outbound, and capacity evidence.
    GPT-6.1 Sol · API · An AI SDR for small agencies
  2. Uses the interviews faithfully100% pass
    All quotes are accurately taken from the transcripts and correctly attributed to A05, A04, and A08.
    GPT-6.1 Sol · API · An AI SDR for small agencies
  3. Finds the timeline that sinks it100% pass
    Cites 9-month build and concludes Q1 launch is unsupported.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper

Where it slips

  1. Avoids unsupported claims55% pass
    Presents the existence of local taxes and multi-state complexity as fact without support from the context.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper
  2. Uses the supplied evidence correctly55% pass
    Claims that local taxes and multi-state operations affect eligibility are not supported by the supplied context, which only mentions state-by-state tax rules.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper
  3. Rebuilds the market size81% pass
    It uses the $150 price for 10+ staff businesses instead of the higher UK revenue per customer for those segments, so the market size is understated and not correctly rebuilt.
    GPT-6 Luna · API · Five billion dollars, on paper

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. Finds the need behind the idea

    Does the critique identify the underlying need the idea is meant to serve, and offer at least one other way to meet that need, at a different cost, saying what would have to be dropped for it?

    Passes when States the need behind the proposal, and gives an alternative way to meet it with its cost and what it displaces.

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 · Tigers, not paper tigers · and 2 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 the VP Product at Halyard. Our CEO is taking the US launch plan below to the board next week and has asked you to challenge it hard first. Write a memo of no more than 1,000 words: what the plan gets wrong, what would sink it, what's less scary than it looks, and what you'd propose instead. The deck excerpt and our data are attached.

What the model was given4 items: about_halyard.md, ceo_board_deck_excerpt.md, uk_customers_by_size.csv, us_small_business_stats.md
about_halyard.md3 lines · Download
# About Halyard in the UK

Payroll and HR for small businesses. 14,800 customers, $3.9M ARR, five years old. UK payroll rules are built in; US payroll would need state-by-state tax rules (the team estimates 9 months of work for the first 10 states).
ceo_board_deck_excerpt.md5 lines · Download
# US launch (CEO's board deck, excerpt)

- Market: 33 million US small businesses x our UK price of £120 ($150) a year = about $5B. "Even 1% is $50M."
- Plan: launch in the US in Q1, $2.5M budget, hire a US sales team of 6, target $5M ARR in year one.
- Why now: a competitor raised in the US; we need to get there first.
uk_customers_by_size.csv4 rows · Download
company_size,uk_customers,share_of_uk_customers_pct,retained_at_12_months_pct,avg_revenue_usd_per_year
Sole trader,6100,41,18,150
2-9 staff,5300,36,33,150
10-49 staff,2900,20,71,620
50-249 staff,480,3,78,2400
us_small_business_stats.md5 lines · Download
# US small businesses (government statistics)

- 33.2 million small businesses in total.
- About 27.1 million have no employees (sole proprietors).
- About 6.1 million have employees; of those, about 1.1 million have 10 to 249 employees.
What a strong answer doesThe answer key the graders mark against

Shows the $5B market is the wrong number. It multiplies every US small business by the UK price, but in the UK only businesses with 10+ staff stay (71% and 78% at 12 months) and pay more ($620 to $2,400 a year); sole traders and 2-9 businesses mostly leave (18% and 33%). Most US small businesses (27.1M of 33.2M) have no employees and don't need payroll at all; the comparable US market is about 1.1M businesses with 10 to 249 staff, worth roughly $0.7B to $1B at UK revenue per customer, not $5B. The tiger: US payroll needs state-by-state tax rules, about 9 months for the first 10 states, so a Q1 launch with payroll isn't possible and $5M ARR in year one would need thousands of 10+ staff customers from a standing start. Dismisses the 'first mover' worry as less scary: a competitor's raise doesn't make a market winner-takes-all. Proposes instead: target 10+ staff businesses, start with HR in the US while payroll is built for a few high-density states, test demand with a small budget and a clear threshold before hiring a sales team, and fix the UK mix towards 10+ staff companies meanwhile.

Critical failures (cap the score at 40)
  • Endorses the $5B market size
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Needs more than a quick edit5 of 10 checks passed

Unsupported claims about acquisition cost and support load would need evidence re-checking, and the missing market-size calculation means the reasoning is incomplete.

Output

MEMORANDUM

TO: CEO FROM: VP Product DATE: October 24, 2023 SUBJECT: Critique and Alternative Proposal for the US Launch Plan

---

You asked me to challenge the US expansion plan hard before you take it to the board next week. I’ve reviewed the deck excerpt against our UK unit economics and US market data.

If we walk into the boardroom with the current plan, a savvy board will pick it apart—and rightly so. Here is my unvarnished assessment of what the plan gets wrong, what will sink us, what is actually noise, and what we should propose instead.

---

1. What the plan gets wrong (The fatal assumptions)

  • The "TAM Fallacy" on Sole Proprietors: The deck multiplies 33 million US small businesses by $150 to claim a $5B market. Government data reveals that 27.1 million of those businesses (82%) have zero employees. In the UK, our data tells a brutal story about sole traders: 41% of our customer base (6,100 accounts) churn within 12 months at an 18% retention rate. They are cheap to acquire, high-churn, high-support-load nightmares who don’t run payroll. Treating the entire 33 million pool as viable is a math error that misleads us on product-market fit.
  • The Compliance Mirage: The deck ignores the engineering reality. US payroll is not a monolith; it is federal, state, and local tax compliance across 50 distinct jurisdictions. Our engineering team estimates 9 months of work just to launch the first 10 states. You cannot launch a payroll product in "the US" in Q1 with a generic software wrapper. Without state tax tables, automated filings, and local garnishments, the product is illegal to use.
  • The Unit Economics Math: At $150/year, acquiring a customer through a dedicated US sales team of 6 with a $2.5M budget is mathematically impossible. A traditional outbound sales team cannot profitably sell a $150 self-serve product. CAC will dwarf LTV on day one.

---

2. What would sink us

  • Regulatory suicide: If we launch prematurely in Q1 to "beat a competitor" without robust state-by-state tax calculation, our first cohort of US customers will face IRS and state tax penalties. In payroll, a software bug doesn't just mean a bad user experience; it means employees don’t get paid or taxes aren't remitted, resulting in immediate churn, devastating brand damage, and potential litigation.
  • Reactive strategy: "A competitor raised in the US; we need to get there first" is a panic motivation, not a product strategy. If we rush a half-baked product into a massive market just to plant a flag, we will burn $2.5M in 12 months, fail to hit the $5M ARR target, and lose credibility with the board.

---

3. What’s less scary than it looks

  • The total market size: We don't need 33 million businesses. Look at our UK data: our sweet spot is companies with 10–49 staff (20% of our base, but 71% retention, generating significantly higher revenue) and 50–249 staff (78% retention, $2,400+ ARR). In the US, there are 1.1 million businesses in the 10–249 employee bracket. That is a massive, highly qualified, high-retention target market. We don't need the sole traders; we should actively avoid them.
  • Geographic concentration: We do not need to launch all 50 states on day one. If we pick 3 to 5 high-density, business-friendly states (e.g., Texas, Florida, Washington—states with no state income tax or straightforward payroll setups), we can drastically reduce engineering scope, bypass the 9-month/10-state bottleneck, and launch a compliant MVP much faster.

---

4. What I propose instead

We should go to the board with a revised, credible strategy that protects our capital and targets the segment we actually know how to monetize.

  1. Shift the ICP (Ideal Customer Profile) away from Sole Traders: Ignore the 27.1 million non-employer businesses. Focus exclusively on US small businesses with 10 to 49 employees. They have the budget, they have payroll complexity, and based on our UK experience, they stick around.
  2. Pivoting the Go-To-Market (GTM): Scrap the plan for a 6-person outbound sales team on a $150 product. At this price point, the US motion must be product-led growth (PLG) with digital acquisition, augmented by a lightweight, inside-sales motion only for accounts scaling toward 50+ employees.
  3. De-risk the Engineering Roadmap: Reallocate the $2.5M budget. Instead of a clumsy nationwide Q1 rush, fund a targeted compliance build for just 3 states to launch a closed beta by Q2. Use the remaining budget for localized digital marketing and product-led onboarding flows.
  4. Refrain the Board Narrative: Do not pitch "capturing 1% of a $5B market." Pitch a disciplined land-grab in a profitable sub-segment: “We are entering the US by targeting the 1.1 million businesses with 10–249 employees, starting with a compliant 3-state footprint, leveraging our proven retention playbook to capture high-value employers at a sustainable CAC.”

If we present this, the board will see operators who understand our unit economics, respect regulatory risk, and have a clear path to sustainable growth. Let’s calibrate the deck before Tuesday.

Claims the judge couldn’t find in the brief

  • They are cheap to acquire, high-churn, high-support-load nightmares

Grades and run details

Decision model 80 · LLM judge 45
Decision model checks
  • partialUses the supplied evidence correctly13%
  • passAddresses the actual decision34%
  • passRespects explicit constraints47%
  • partialIdentifies material uncertainty52%
  • partialAvoids unsupported claims49%
  • passProduces the required deliverable68%
  • passRebuilds the market size57%
  • passFinds the timeline that sinks it100%
  • partialTigers, not paper tigers35%
  • passFinds the need behind the idea26%
Run
Run
#1
Time to output
6 s
Submitted
5 Oct 2026

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Got wrong 3

Uses the supplied evidence correctlyWrong

The claim that sole traders are 'cheap to acquire' and 'high-support-load' is not supported by the supplied context.

Identifies material uncertaintyWrong

The memo does not name specific unknowns that could change the decision or say how they would be resolved.

Avoids unsupported claimsWrong

It presents 'cheap to acquire' and 'high-support-load' as established facts without labelling them as assumptions or hypotheses.

Mixed 2

Rebuilds the market sizeMixed

It does not rebuild the market to a dollar figure (e.g., under $1B) or show the working; it only states the 1.1M business count without sizing the revenue opportunity.

Finds the need behind the ideaMixed

It does not state the underlying need behind the US launch idea (e.g., growth, first-mover advantage) and therefore does not offer an alternative way to meet that need with its cost and trade-offs.

Got right 5

Addresses the actual decisionRight

The memo commits to a clear alternative strategy (target 10-49 employee businesses, PLG, 3-state launch) early and unambiguously.

Respects explicit constraintsRight

The output is a memo under 1,000 words, addressed to the CEO, and respects the requested form.

Produces the required deliverableRight

The memo is complete, in the right form, within length, and usable by the CEO with light edits.

Finds the timeline that sinks itRight

It names the 9-month build and explicitly states that a Q1 payroll launch is impossible.

Tigers, not paper tigersRight

It ranks regulatory and reactive strategy as sinkers, dismisses the competitor raise as less scary, and surfaces the need to avoid sole traders.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 76% of 75 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 LunawithAPI100.090.92None
2Haiku 5.5withAPI92.590.92None
3GPT-6.1 SolwithAPI100.086.42None
4GPT-6 AstrawithChatGPT100.079.42None
5Sonnet 5.5withAPI88.374.82None
6Opus 5.5withClaude88.379.421 capped
7Gemini 3.5 Flash-LitewithGemini79.649.721 capped
8Gemini 3.8 FlashwithAPI87.972.721 capped

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.4050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Luna · API: task score 93, the cheapestHaiku 5.5 · API: task score 92, the cheapestGPT-6.1 Sol · API: task score 91, 11× the cheapestGPT-6 Astra · ChatGPT: task score 88, 60× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 78, 37× the cheapestOpus 5.5 · Claude: task score 62, 59× the cheapest (1 of 2 outputs estimated), 1 critical failureGemini 3.5 Flash-Lite · Gemini: task score 50, 1.9× the cheapest (1 of 2 outputs estimated), 1 critical failureGemini 3.8 Flash · API: task score 50, 9.5× the cheapest, 1 critical failureGPT-6 Luna · APIHaiku 5.5 · APIGPT-6.1 Sol · APIGPT-6 Astra · ChatGPTSonnet 5.5 · APIOpus 5.5 · ClaudeGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Cost partly estimated (app runs with no usage meter)At least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API931.0×
Haiku 5.5 · API921.4×
Gemini 3.5 Flash-Lite · Gemini501.9× (partly estimated)
Gemini 3.8 Flash · API509.5×
GPT-6.1 Sol · API9111×
Sonnet 5.5 · API7837×
Opus 5.5 · Claude6259× (partly estimated)
GPT-6 Astra · ChatGPT8860× (partly estimated)

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

About the task

The PM job

Pressure-testing a proposal before committing a team to it.

Why it matters

The useful critic finds the one assumption everything rests on. Theatrical negativity is easy to generate and useless in a planning meeting.

What good looks like

  • Identifies the load-bearing assumption
  • Separates the risks that could kill it from the ones that only look scary
  • Uses the supplied evidence, not generic risks
  • Proposes the cheapest way to test the assumption

Deliberately not measured

  • Tone
  • Number of objections raised
Capability tested

Evidence-based critique

The failure we’re looking for

Theatrical negativity without evidence

Grading

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

Variants

Vanilla prompt (core) · With Roast Me skill · Staff level: a company bet with a long evidence pack