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 18 graded outputs by 9 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. Uses the supplied evidence correctly50% 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
  2. Avoids unsupported claims56% 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
  3. Finds the need behind the idea71% pass
    It mentions A05's past-client reminder idea but does not state the underlying need or offer a full alternative with its cost and what it displaces.
    GPT-6 Astra · ChatGPT · An AI SDR for small agencies

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 · v1.1

The outputs

9 setups answered this brief.

Needs more than a quick edit7 of 10 checks passed

The output relies on unsupported claims about US sales costs, state tax regimes and competitor pricing that a PM would have to fact-check before using it. A quick edit cannot fix missing evidence.

Output

MEMO

TO: CEO FROM: VP of Product SUBJECT: Red-Teaming the US Launch Board Deck

You asked me to challenge the US expansion plan before it goes to the board next week. To be blunt: if you present this slide as-is, the board will tear it apart. The math contradicts our own internal data, the unit economics of the go-to-market plan are upside down, and the timeline ignores our engineering reality.

Here is what the plan gets wrong, what will sink it, what is actually less scary than it looks, and what we should pitch instead.

1. What the Plan Gets Wrong

The "1% of $5B" TAM fallacy: The deck claims a $5B market based on 33.2M US small businesses. However, 27.1M of those are sole proprietors with no employees. They don’t need HR or multi-staff payroll. In the UK, sole traders are 41% of our user base, pay us $150/year, and churn at an astonishing 82% in year one. Even worse, the 2–9 staff segment (another 5M US businesses) only retains at 33% for us.

If you look at our UK data, Halyard actually has product-market fit in the 10–249 employee segment. Although they represent only 23% of our UK customer count, they retain at 71%–78% and generate 75% of our total ARR ($2.95M of our $3.9M). In the US, there are only 1.1 million businesses of this size. Our true, healthy US market isn't 33 million businesses; it’s 1.1 million.

The Year-One Target ($5M ARR): It took us five years to build $3.9M ARR in our home market. Promising the board we will generate $5M of new ARR in a foreign market in 12 months is not credible. At a $150 price point, $5M ARR requires acquiring 33,333 customers in year one—more than double our entire UK customer base. Even if we exclusively target the larger 10–249 staff segment (weighted average ~$870/year), we would need ~5,750 customers in year one. We only have 3,380 of them in the UK after five years.

The "Why Now": "A competitor raised money to go to the US" is a trap, not a thesis. If they are chasing the same $150/year, high-churn micro-businesses with a costly US sales team, let them burn their venture capital doing it.

2. What Will Sink the Plan

The Sales Team Unit Economics: Hiring 6 US sales reps to sell a $150–$620/year product is financial suicide. A fully loaded US B2B sales rep costs $120k–$150k/year. Six reps will burn ~$800k+ in payroll alone. You cannot use human beings to sell a $150/year product; the Customer Acquisition Cost (CAC) will eclipse the Lifetime Value (LTV) by orders of magnitude. To hit $5M ARR at $150 ACV, each of those 6 reps would have to close 22 deals per day, every working day of the year, starting on Day 1.

The Q1 Launch vs. The 9-Month Build: The deck proposes launching in Q1, yet my engineering team estimates 9 months of work just to build the tax rules for the first 10 states. If we hire 6 salespeople in Q1, what are they selling for the first three quarters of the year? We will burn through our $2.5M budget paying a sales team to sit on their hands while waiting for Product to clear regulatory hurdles.

3. What is Less Scary Than It Looks

The "50-State" Regulatory Mountain: Building US payroll sounds terrifying because of the 50-state tax complexity, and 9 months for 10 states sounds slow. It’s actually much less scary than it looks, for two reasons: 1. Concentration: The top 10 US states (CA, TX, NY, FL, etc.) account for over 50% of the US population and GDP. We don’t need 50 states to have a massive market; 10 states gets us half the country. 2. No-Tax States: Nine US states (including massive small-business hubs like Texas, Florida, and Washington) have no state income tax. The payroll rules there are largely federal. We can build for those states in a fraction of the time, giving us a beachhead much faster than 9 months.

Competitor Head-Start: The US market for 10–249 employee businesses is fragmented and notoriously underserved by legacy players (like ADP and Paychex, who have terrible UX) and modern players (like Gusto, which has become very expensive). Being "first" doesn't matter; having the right price-to-value ratio for the 10–249 segment does.

4. What I Propose Instead

Before going to the board for a $2.5M US gamble, we need to align our strategy with our actual strengths. Here is the counter-proposal:

1. Pivot the ICP to 10–249 Employees (In the UK and US): Stop marketing to sole traders. They are a massive distraction destroying our retention metrics. Double down on the 10–249 employee segment where we have 70%+ retention and $620–$2,400 ACVs. 2. Raise Prices for the US: US competitors charge a base fee ($40+/mo) plus $6–$12 per employee per month. For a 20-person company, that’s ~$2,000/year. Our UK price of $620/year is way too cheap for the US market. If we price at even $1,500/year for the 10–49 segment, our US TAM in just the top 10 states is still ~$800M, and the unit economics actually work. 3. Phase the US Launch (Kill the Q1 Sales Team): * Phase 1 (Months 1–6): Spend $500k (or use an embedded payroll API like Check or Gusto Embedded to skip the build) to launch only in Texas and Florida (massive markets, zero state income tax). * Phase 2 (Months 6–9): Use Product-Led Growth (PLG) and digital marketing—not a sales team—to acquire our first 100 US customers (10–249 staff) to prove retention and pricing. * Phase 3 (Month 10+): Once retention is proven and the next 8 states are built, then raise the $2.5M to scale marketing and hire inside sales for the 50–249 segment.

Let’s rewrite the deck to show the board we understand our own data—and that we know how to win the US without lighting $2.5M on fire.

Claims the judge couldn’t find in the brief

  • A fully loaded US B2B sales rep costs $120k–$150k/year.
  • The top 10 US states (CA, TX, NY, FL, etc.) account for over 50% of the US population and GDP.
  • Nine US states (including Texas, Florida, and Washington) have no state income tax, making payroll rules largely federal.
  • US competitors charge a base fee ($40+/mo) plus $6–$12 per employee per month.
  • Gusto has become very expensive.
  • The US market for 10–249 employee businesses is fragmented and underserved by legacy players with terrible UX.

Grades and run details

Decision model 60 · LLM judge 73
Decision model checks
  • failUses the supplied evidence correctly91%
  • passAddresses the actual decision67%
  • passRespects explicit constraints34%
  • partialIdentifies material uncertainty45%
  • failAvoids unsupported claims41%
  • passProduces the required deliverable77%
  • partialRebuilds the market size62%
  • passFinds the timeline that sinks it100%
  • passTigers, not paper tigers34%
  • failFinds the need behind the idea23%
Run
Run
#1
API response time
16 s
Submitted
13 Oct 2026

Needs more than a quick edit8 of 10 checks passed

The 76% revenue share error would require rechecking the data and correcting the figure before use. A product manager could not use it as-is without fixing that factual mistake.

Output

To: CEO and Board From: VP Product Subject: US expansion: approve validation, not the proposed launch

Recommendation: Do not approve the current Q1 launch, six-person sales team, or $5M year-one ARR commitment. The US opportunity deserves investigation, but this plan mistakes a large business population for an addressable payroll market, ignores our strongest customer evidence, and commits to distribution before establishing product readiness.

What the plan gets wrong

1. The market calculation counts the wrong customers at the wrong price.

Of 33.2 million US small businesses, 27.1 million have no employees. They are not a credible default market for an employer payroll product. Some may have relevant needs, but we must establish those rather than count every sole proprietor.

Our UK results argue against prioritizing this population:

SegmentCustomersAnnual revenue/customer12-month retention
------:---:---:
Sole trader6,100$15018%
2–9 staff5,300$15033%
10–49 staff2,900$62071%
50–249 staff480$2,40078%

Customers with 10–249 staff represent roughly 23% of customers but 76% of annual revenue implied by this data. The relevant starting market is the approximately 1.1 million US employers in that size range, narrowed further by geography, payroll complexity, and product fit.

At our UK segment prices, that population represents $682 million–$2.64 billion in hypothetical annual revenue—not a validated TAM. We lack its size mix, US willingness to pay, and the share we could actually serve. “Even 1%” supplies no acquisition strategy.

2. The year-one target has no operating bridge.

At $150 annually, $5 million ARR requires about 33,333 active customers—more than twice our entire UK customer base after five years. At $620, it requires approximately 8,065; at $2,400, 2,083.

With six sellers, the $620 scenario requires each to acquire about 1,344 customers annually before allowing for ramp time or churn. There is no evidence here for the lead volume, conversion, onboarding capacity, pricing, or retention necessary.

The $2.5 million budget is an allocation, not an acquisition model. We need separate estimates for compliance engineering, operations, support, acquisition, and contingency.

3. “Why now” is competitive anxiety, not customer evidence.

A competitor’s fundraising neither proves demand for Halyard nor makes our product ready. Being first matters less than paying employees correctly and retaining employers.

What would sink it

Compliance failure is the existential risk. Payroll mistakes create customer harm, remediation costs, and reputational damage. The team estimates nine months merely to build rules for the first ten states. Unless work is already sufficiently advanced, that contradicts Q1 readiness. Rule implementation also does not establish filing, remittance, testing, exception handling, or operational readiness.

A state-limited launch must constrain employee locations and payroll complexity—not merely customer headquarters. Multi-state employers can breach that boundary immediately.

Retention could make growth economically hollow. Even our best UK segments lose 22–29% of customers by month twelve. We should investigate whether this reflects business closures, product failures, poor fit, or measurement issues. We cannot assume better US performance while adding regulatory and operational complexity.

Premature hiring could consume the budget before learning occurs. Six sellers cannot compensate for an unready product or an unidentified buyer. Low-price segments are particularly unlikely to support conventional sales-led acquisition.

What is less scary than it looks

We do not need nationwide coverage or millions of customers to establish a worthwhile business. A tightly bounded employer segment offers ample room for a meaningful foothold.

Nor must we automatically build the entire US payroll stack ourselves. A payroll infrastructure partner could reduce time and technical risk. It would not remove our obligations to customers, integration risk, or the need to understand liability and unit economics.

Finally, our UK data contains a genuine positive signal: larger employers pay substantially more and retain substantially better. That is a credible hypothesis to test—not proof that it transfers to the US.

What I propose instead

Approve a staged US validation program, with a first tranche capped at $300,000 and further spending conditional on evidence.

  1. Validate the buyer over 8–12 weeks. Start with 10–49-person employers, where we have the strongest combination of customer volume, revenue, and retention. Interview buyers and test paid commitments at US-specific prices. In parallel, diagnose UK churn and compare build-versus-partner economics.
  1. Choose one narrowly serviceable footprint. Select geography based on customer demand and compliance feasibility. Explicitly exclude unsupported employee locations and payroll cases. Do not announce a live-payroll date before readiness is independently reviewed.
  1. Run a paid, founder-led pilot with 20–30 employers. Hire US payroll/compliance expertise before a sales team. Measure payroll accuracy and timeliness, onboarding effort, support costs, willingness to pay, and early retention. Early results will not substitute for twelve-month retention evidence.
  1. Release expansion funding only against gates. Require no unresolved critical payroll defects, demonstrated operational controls, positive contribution economics, and a credible acquisition model targeting under twelve-month CAC payback. Hire sellers only after that motion is repeatable.

Replace $5 million ARR with a conditional first-year objective of 100–200 paying employers after readiness, roughly $62,000–$124,000 annualized at the UK 10–49 price benchmark. That is a learning milestone, not a forecast.

The board should fund the next decision, not an unsupported promise of scale.

Claims the judge couldn’t find in the brief

  • 10-249 staff represent 76% of annual revenue.

Grades and run details

Decision model 95 · LLM judge 73
Decision model checks
  • passUses the supplied evidence correctly25%
  • passAddresses the actual decision100%
  • passRespects explicit constraints62%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims34%
  • passProduces the required deliverable83%
  • passRebuilds the market size22%
  • passFinds the timeline that sinks it100%
  • passTigers, not paper tigers79%
  • partialFinds the need behind the idea2%
Run
Run
#1
API response time
51 s
Submitted
6 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 3

Uses the supplied evidence correctlyWrongMixed
Gemini 4 Argon · API

The output invents several facts not in the supplied context, including US sales rep costs, state tax structures, population concentration, and competitor pricing, without labelling them as assumptions.

GPT-6.1 Sol · API

The memo incorrectly states that 10-249 staff represent 76% of annual revenue; the correct figure from the data is 63%.

Avoids unsupported claimsWrongMixed
Gemini 4 Argon · API

Several claims about US market conditions (sales rep costs, no-tax states, competitor pricing) are presented as established facts without evidence from the supplied context.

GPT-6.1 Sol · API

The 76% revenue share is presented as a fact from the data but is incorrect, making it an unsupported claim.

Finds the need behind the ideaMixedRight
Gemini 4 Argon · API

It identifies the underlying need for US growth, proposes a phased, PLG-led alternative with a smaller budget, and explicitly displaces the immediate sales team and broad targeting.

GPT-6.1 Sol · API

It identifies the need for a viable US entry, proposes a staged validation alternative, and states what it displaces (the $5M ARR target, Q1 launch, sales team) and its cost ($300k).

All got right 7

Addresses the actual decisionRightRight
Gemini 4 Argon · API

The memo commits early to a clear alternative plan (phased launch, pivot ICP) and states what would need to be proven to scale (retention and pricing).

GPT-6.1 Sol · API

The memo clearly recommends not approving the launch and proposes a staged validation program with conditions, framed for the CEO and board.

Respects explicit constraintsRightRight
Gemini 4 Argon · API

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

GPT-6.1 Sol · API

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

Identifies material uncertaintyRightRight
Gemini 4 Argon · API

It identifies that US retention and pricing are unproven and makes the scaling decision contingent on validating them with a small initial cohort.

GPT-6.1 Sol · API

It names unknowns like US willingness to pay, size mix, and retention causes, and ties further funding to evidence gates.

Produces the required deliverableRightRight
Gemini 4 Argon · API

The memo is complete, in the correct format, and usable by the CEO to present an alternative board proposal.

GPT-6.1 Sol · API

The memo is complete, within word limit, and directly usable by the CEO and board with a clear recommendation and plan.

Rebuilds the market sizeRightRight
Gemini 4 Argon · API

It correctly uses the US employer counts and UK retention data to argue that only 1.1M businesses are addressable, not 33.2M, effectively rebuilding to a much smaller market.

GPT-6.1 Sol · API

It rebuilds the market using employer counts and UK retention, arriving at 1.1M businesses and $682M-$2.64B, showing the working.

Finds the timeline that sinks itRightRight
Gemini 4 Argon · API

The memo explicitly names the 9-month state-tax build and uses it to show that a Q1 launch with payroll is impossible and that the year-one ARR target is undermined.

GPT-6.1 Sol · API

It names the 9-month build and states it contradicts Q1 readiness, directly linking it to the year-one target.

Tigers, not paper tigersRightRight
Gemini 4 Argon · API

It separates the manageable competitor-head-start worry from the truly dangerous risks (unit economics, TAM fallacy, build timeline) and surfaces the UK mix problem with sole traders.

GPT-6.1 Sol · API

It ranks compliance failure as existential, dismisses the competitor fear as less scary, and surfaces the unspoken need for product readiness.

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 75% of 75 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI97.586.42None
2Haiku 5.5withAPI87.590.92None
3GPT-6 LunawithAPI90.490.92None
4GPT-6 AstrawithChatGPT88.379.42None
5Gemini 4 ArgonwithAPI73.882.52None
6Sonnet 5.5withAPI85.474.82None
7Opus 5.5withClaude85.479.421 capped
8Gemini 3.8 FlashwithAPI87.972.721 capped
9Gemini 3.5 Flash-LitewithGemini69.649.721 capped

What you get for the cost

GPT-6 Luna · API scores within a point of GPT-6.1 Sol · API at about a tenth of the cost.

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-6.1 Sol · API: task score 90, 11× the cheapestHaiku 5.5 · API: task score 90, the cheapestGPT-6 Luna · API: task score 89, the cheapestGPT-6 Astra · ChatGPT: task score 82, 60× the cheapest (1 of 2 outputs estimated)Gemini 4 Argon · API: task score 77, 13× the cheapestSonnet 5.5 · API: task score 77, 37× the cheapestOpus 5.5 · Claude: task score 60, 59× the cheapest (1 of 2 outputs estimated), 1 critical failureGemini 3.8 Flash · API: task score 50, 9.5× the cheapest, 1 critical failureGemini 3.5 Flash-Lite · Gemini: task score 45, 1.9× the cheapest (1 of 2 outputs estimated), 1 critical failureGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIGPT-6 Astra · ChatGPTGemini 4 Argon · APISonnet 5.5 · APIOpus 5.5 · ClaudeGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost 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 · API891.0×
Haiku 5.5 · API901.4×
Gemini 3.5 Flash-Lite · Gemini451.9× (partly estimated)
Gemini 3.8 Flash · API509.5×
GPT-6.1 Sol · API9011×
Gemini 4 Argon · API7713×
Sonnet 5.5 · API7737×
Opus 5.5 · Claude6059× (partly estimated)
GPT-6 Astra · ChatGPT8260× (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