Tasks / Challenge

Challenge an idea

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

Measures the systemTask v1.2 · 2 casesDifficulty

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

From 12 graded outputs by 6 models. 75% were usable with at most a quick edit.

Reliably right

  1. Addresses the actual decision100% pass
    It commits early to not committing three squads and authorizing a six-week test, and specifies what result would change that.
    GPT-6.1 Sol · API · The CEO's embedded-payments bet
  2. Identifies material uncertainty100% pass
    It names the unknowns (representativeness of payment data, large-account volume access, actual mix) and resolves them with a bounded test.
    GPT-6.1 Sol · API · The CEO's embedded-payments bet
  3. Finds the load-bearing assumption100% pass
    It makes the 0.7% blended take applied to all invoice value the central challenge, backed by payment-mix and unit-economics figures.
    GPT-6.1 Sol · API · The CEO's embedded-payments bet

Where it slips

  1. A cheap test that can actually read out67% pass
    The pre-registered gate requires signed deals and renewal rather than an early signal such as qualified meetings or proposals, so it risks being too late for a cheap read-out.
    GPT-6.1 Sol · API · An AI SDR for small agencies
  2. Avoids unsupported claims77% pass
    The memo claims the pilot achieved ~0.38% blended take as a fact, which is not in the evidence and is not derived from it arithmetically; it also treats the pro-rata $1.43B locked value as a hard constraint without flagging the assumption.
    Opus 5.5 · Claude · The CEO's embedded-payments bet
  3. Uses the supplied evidence correctly79% pass
    The memo presents the pilot blended net take of ~0.38% as an achieved fact, but it is not supported by the pack and is an assumption-driven extrapolation.
    Opus 5.5 · Claude · The CEO's embedded-payments bet

Case viewer

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 a Staff PM at Fieldline. Our CEO wants to commit three squads for three quarters to Fieldline Pay, embedded payments for our customers' invoices, and has told the board it could add $24M of annual revenue within two years. Priya Raman, our CPO, has asked you to write the strongest challenge to the plan as a pre-read for next week's exec offsite. The CEO, CFO and Head of Sales will all read it. Write a memo of no more than 1,200 words that: 1. Names the single assumption the plan most depends on that the evidence does not support, and shows why, using the numbers in the pack. 2. Re-estimates the revenue from the supplied data, showing your working, as a range. 3. Says what we would need to see to be proved wrong, and the cheapest test that would show it within six weeks. 4. Says what, if anything, we should do instead or how the bet should change. The pack below is everything we have. Some of it matters more than the rest.

About FieldlineField-service software for trades businesses (plumbing, HVAC, electrical): scheduling, dispatch, quotes and invoicing. 2,300 customers, $41.0M ARR, average $17,800 per customer. Series C; the next raise is planned in about 14 months.
CEO's memo to the exec team (excerpt)“Every invoice our customers send is money we don't touch. Our customers invoiced $4.83B last year through Fieldline. If we process those payments, we become part of how they get paid, not just how they schedule. The model is simple: 70% of customers adopt within 18 months, the average customer invoices $2.1M a year, and we keep a 0.7% blended net take. That is $24M of new annual revenue by month 24, more than half our current ARR, and it makes the next raise a very different conversation. Tradesly has shown it works: payments are now 22% of their revenue. I've told the board I believe payments can be 35% of our revenue by 2028. I want three squads on this from next quarter, which means pausing the scheduling rewrite.”
CFO's revenue modelCustomers: 2,300. Adoption by month 18: 70% (1,610 customers). Annual invoiced value per customer: $2.1M (total invoiced $4.83B ÷ 2,300). Blended net take rate: 0.7%. Month-24 revenue run-rate: 1,610 × $2.1M × 0.7% = $23.7M. CFO's note: “Adoption and take rate are the CEO's assumptions. I haven't stress-tested them.”
Invoicing data (last 12 months, all customers)Total invoiced value: $4.83B. Mean per customer: $2.1M. Median per customer: $640k. The largest 5% of customers (115) account for 48% of invoiced value ($2.32B). By job type, residential jobs are 42% of invoiced value and commercial jobs (property managers, facilities contracts) are 58%.
How invoices are paid todayFrom the 690 customers (30%) who record the payment method in Fieldline, by share of invoice value: card 19%, ACH/bank transfer 44%, check 31%, cash 6%. Card share is 38% of residential invoice value and 5% of commercial. Average commercial invoice: $3,800; average residential invoice: $410. Commercial clients pay on net-45 or net-60 terms; the average commercial invoice is paid 52 days after it is sent.
Payments partner term sheet (unit economics)Card: the customer is charged 2.9% + $0.30 per payment; our all-in cost (interchange, network, partner fee) is about 2.2%, so we net about 0.7% of card value. ACH: the customer is charged a flat $2.00 per payment; our cost is $0.40. Checks and cash earn nothing unless the payer switches to card or ACH. The partner handles licensing, KYC and risk; they have approved our application.
Pilot (4 months)62 customers invited, 38 adopted (61%). Pilot customers' invoice value is 64% residential (the customer base is 42%). With pay-by-link on every invoice, card share of invoices paid through Fieldline Pay rose to 71% for residential and 6% for commercial. Net payments revenue, annualised: $214k across the 38 customers ($5,630 per customer per year).
Sales notes on the largest accountsOf the 115 largest customers, 71 have multi-year contracts with an existing payment processor, most running to 2028. Head of Sales, in Slack: “None of the big ones will move processors before their contracts end, and their property-manager clients will not pay 2.9% on a $3,800 invoice. They'll pay by ACH or check like they always have.”
Customer interviews (22 customers, last quarter)17 of 22 named getting paid on commercial jobs as their biggest cash problem (“I'm floating $200k of payroll while property managers sit on invoices for two months”). 9 said they would pay a fee to be paid faster. 6 said they won't offer card payment because clients fight the surcharge. Of the 14 customers with mostly commercial work, 11 said their clients require ACH or check.
CompetitorTradesly launched embedded payments in 2025 and says payments are now 22% of its revenue. Its published case studies are almost all home-services businesses; it describes its customers as about 80% residential.
EngineeringThe plan needs three squads for three quarters. The scheduling rewrite it would pause is the top-cited reason for churn among customers with more than 50 technicians (lost ARR from that segment last year: $1.9M).
What a strong answer does

Names the load-bearing assumption as the 0.7% blended net take: that is the card margin applied to all invoiced value, but only about 19% of value is paid by card (5% of commercial, which is 58% of value), ACH nets about 0.04% on a $3,800 invoice, and checks and cash earn nothing. Re-estimates with the supplied data: even at the pilot's improved mix (about a third of value on card), the blended take is roughly 0.2–0.25%, so the run-rate is about $5–9M, not $24M (the pilot's $5,630 per customer × 1,610 = $9.1M is an upper bound because the pilot was residential-heavy; the 71 locked-in large accounts, with 48% of value in the top 5%, cut it further). Notes that $24M needs essentially all invoice value on card. Treats Tradesly as a residential-mix result, not proof, and engineering capacity, licensing and competition as secondary. Keeps what the evidence supports: card payments work for residential invoices, and the strongest pull is commercial cash flow (17 of 22), so it reshapes the bet toward faster payout or financing on commercial invoices, which earns on the volume the processing model can't. Proposes a cheap test with a threshold that would prove the challenge wrong (for example, commercial card share well above 20% with pay-by-link in a six-week cohort, or uptake of paid instant payout), and is written for a room that includes the CEO: firm on the numbers, not personal.

Critical failures (cap the score)
  • Accepts the $24M projection or the 0.7% blended net take rate as sound
  • Invents market sizes, competitor figures or customer statistics that are not in the pack
Case

v1.5 · synthetic · B2B SaaS, fintech, strategy, staff-level

Usable with a quick edit10 of 11 checks passed

The pre-read is tightly argued, numerically grounded, and directly usable by a senior PM; only minor phrasing tweaks would be needed to align the bull case with pure payments revenue if desired.

Output

To: Priya Raman (CPO), CEO, CFO, Head of Sales From: Staff Product Manager, Fieldline Date: September 25, 2026 Subject: Pre-read: Strategic Challenge to the Fieldline Pay Plan ($24M Revenue Target)

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Executive Summary

Committing three squads for three quarters to Fieldline Pay to capture $24M in new revenue within two years is a high-risk bet that relies on a structural distortion in our financial model.

The plan’s core vulnerability is not execution; it is a misallocation of our customer base's economic reality. By using the mean invoice value ($2.1M) rather than the median ($640k), the model assumes our average customer mirrors our largest commercial accounts1, while simultaneously ignoring entrenched enterprise contracts and commercial payment norms.

Executing this plan requires pausing the scheduling rewrite, which risks $1.9M in annual churn from our largest, most valuable accounts. Below is the evidence-based challenge, a revised revenue range, an inexpensive six-week test, and a recommended alternative path.

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1. The Dependent Assumption and Why the Evidence Fails It

> The Plan's Single Dependent Assumption: That we can achieve a $23.7M–$24M revenue run-rate by month 24 by applying a blended 0.7% net take rate across a homogenous $2.1M annual invoice volume per adopted customer.

The evidence flatly refutes this assumption in three ways:

  • The Mean vs. Median Distortion: The CEO's model relies on a mean invoice value of $2.1M. However, the median customer invoices just $640k. Our distribution is heavily skewed: the top 5% of customers (115 accounts) drive 48% ($2.32B) of our total invoiced value.
  • Enterprise Lock-in: Of those 115 largest customers, 71 are locked into multi-year contracts with existing payment processors running through 2028. As the Head of Sales noted, none will migrate early.
  • The Commercial Wall: Commercial jobs comprise 58% of our invoiced value ($2.80B). The average commercial invoice is $3,800 and is paid on net-45/net-60 terms (averaging 52 days). Property managers and facility contractors will not pay a 2.9% card surcharge on a $3,800 invoice. They pay via ACH ($2.00 flat fee, netting us $1.60) or check ($0 revenue), exactly as they always have.

Applying a 0.7% card take-rate model to commercial volume is fundamentally flawed because commercial clients do not use cards (card share is only 5% for commercial, vs. 38% for residential).

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2. Re-Estimated Revenue Range (Working Included)

To model realistic revenue, we must segment our base by customer type, recognizing that residential and commercial segments have radically different payment behaviors and take rates.

#### Step-by-Step Working: 1. Customer Base: 2,300 total customers. 2. Adoption Rate: Pilot adoption was 61% (close to the 70% target). Let's model a realistic 60% adoption rate by month 24 = $1,380$ adopting customers. 3. Segmentation Split: Based on overall volume, 42% of value is residential ($2.03B) and 58% is commercial ($2.80B). Total invoiced value = $4.83B. * Total Residential Invoiced: $2.03B ($\approx$ $882k$ per customer across 2,300) * Total Commercial Invoiced: $2.80B ($\approx$ $1.22M$ per customer across 2,300) 4. Take Rates & Behavior: * Residential: 71% card adoption via pay-by-link. Net take rate on card is 0.7%. ACH/Check take rate is near zero (flat $1.60 net on ACH, negligible volume). Effective blended take rate on residential volume $\approx$ $0.7\% \times 71\% \approx \mathbf{0.50\%}$. * Commercial: 6% card adoption, 94% ACH/Check/Terms. Card take rate is 0.7%; ACH nets a flat $1.60 per transaction (on a $3,800 invoice, $1.60 is a 0.04% effective take rate). Effective blended take rate on commercial volume $\approx$ $\mathbf{0.08\%}$.

#### The Realistic Range (Month 24 Run-Rate): * Bear Case ($3.2M ARR): Commercial customers reject card fees entirely, sticking strictly to ACH/checks; residential adoption stalls at 45% due to surcharge pushback. * Base Case ($6.1M ARR): 60% overall adoption. Residential volume yields a 0.50% blended take ($2.03B $\times$ 60% adoption $\times$ 0.50% = $6.09M). Commercial yields minimal flat-fee ACH revenue. * Bull Case ($9.8M ARR):3 70% adoption matches the CEO's target, and we successfully introduce a B2B "accelerated payout" fee (drawing on the 9 customers out of 22 who said they would pay a fee to solve their $200k payroll float).

> Result: The realistic revenue run-rate at Month 24 is $3.2M to $9.8M, falling drastically short of the $24M target.

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3. Falsification Criteria and a Six-Week Test

#### What would prove us wrong? If a randomized cohort of commercial-heavy customers willingly adopts card payments at >20% volume despite a 2.9% surcharge, or if property managers accept automated card-on-file billing for invoices over $3,000, our commercial pessimism is unfounded.

#### The Six-Week Test ($15k budget, 1 squad for 6 weeks): * The Experiment: Launch a targeted "Fast-Pay Commercial Portal" pilot with 30 mid-market commercial customers currently handling invoices between $2,000 and $5,000. * The Mechanics: Offer them an explicit choice: continue standard net-45 terms via free ACH, or use a discounted commercial card rate (e.g., split-surcharge or 1.9% + $0.30 via a specialized B2B interchange tier)2 to get paid in 48 hours. * Success Metric: Do at least 25% of their commercial payors opt-in to the digital card rail over 6 weeks? If not, the commercial card thesis is dead.

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4. What We Should Do Instead

Pausing the scheduling rewrite to chase an illusory $24M payments figure is a strategic trap. The scheduling rewrite is our defensive moat: losing it costs us $1.9M in high-value churn annually from customers with >50 technicians.

#### Recommended Pivot: 1. Protect Core Engineering: Keep the scheduling rewrite on track. Do not cannibalize the core product teams for a pivot that math shows yields ~$6M, not $24M. Allocate one dedicated squad (not three) to productize payments iteratively. 2. Pivot the Value Proposition: Stop trying to monetize residential card transactions where competition (Tradesly, Stripe) is fierce. Instead, solve the actual pain point identified in customer interviews: commercial cash flow float. 3. Build B2B Working Capital Solutions: Build features that help trades businesses bridge the net-45 gap (e.g., instant payout/invoice factoring models embedded in Fieldline Pay) rather than relying purely on interchange take-rates.

---

Given that our next Series C raise is in 14 months, how comfortable is the board with a realistic payments ARR of $6M built on workflow efficiency, versus an aggressive $24M gamble that risks our core enterprise retention?

What a PM had to fix

  1. 1Numbers wrongRedo the arithmeticStart again

    Source: CFO's revenue modelAnnual invoiced value per customer: $2.1M (total invoiced $4.83B ÷ 2,300).

    What we’d changeDrop the mean-versus-median argument: customers times mean invoicing correctly recovers total volume. The real flaw is applying a card-only 0.7% margin to all of it.

  2. 2Invented evidenceVerify or remove the claimSubstantial rework

    What we’d changeRemove the discounted tier: nothing in the term sheet offers it. Design the test around economics we have, and make it test the $24M claim.

  3. 3Numbers wrongRedo the arithmeticTargeted repair

    What we’d changeShow the working for the bear and bull cases. Only the base case is calculated.

Check by check

Mixed · 1
  • Uses the supplied evidence correctlyEvery factual statement about the current situation is directly taken from the supplied context or derived by straightforward arithmetic, with no inventions.The two graders disagreed on this one.
Got right · 10
  • Addresses the actual decisionThe memo commits early to challenging the plan, recommends scaling back to one squad and pivoting to commercial cash-flow solutions, and specifies a condition (≥25% commercial card opt-in) that would change its assessment, all framed for the CEO, CFO and Head of Sales.
  • Respects explicit constraintsThe deliverable is a memo under 1200 words, addressed to the specified readers, and respects the four numbered requirements.
  • Identifies material uncertaintyThe memo pinpoints commercial card adoption as the critical unknown, bounds the revenue range, and proposes a concrete six-week test with a clear threshold that would resolve whether its commercial pessimism is wrong.
  • Avoids unsupported claimsInterpretations such as "structural distortion" are clearly argued from the data, and forecasts are presented as cases with assumptions, not as established fact.
  • Produces the required deliverableThe memo follows the requested format, is well within the word limit, and contains all four required elements in a way the executive audience could act on with light edits.
  • Finds the load-bearing assumptionThe memo makes the mismatch between the assumed 0.7% blended net take rate and the actual payment-method mix (mostly ACH/check for commercial) its central challenge, exactly as the strong answer expects.
  • Re-estimates the revenue correctlyIt segments customers by residential/commercial, applies realistic take rates from the pilot and unit economics, shows its arithmetic, and lands in a $3.2 M–$9.8 M range, far below $24 M, noting that the pilot's residential-heavy mix would give a higher bound.
  • Says what would prove it wrongIt describes a six-week test with 30 mid-market commercial customers, a discounted card option, and the explicit threshold 'at least 25% of their commercial payors opt-in' that would prove its skepticism wrong.
  • Reshapes the bet from the evidenceIt leverages the evidence that residential card payments work and that commercial cash-flow pain is acute, recommending a smaller investment, a pivot to faster payout/factoring solutions, and protecting the scheduling rewrite.
  • Tigers, not paper tigersIt distinguishes the real killer (commercial card adoption) from execution risk, dismisses the residential-only comparison to Tradesly as misleading because of mix, and calls out the unspoken cost of pausing the scheduling rewrite ($1.9 M churn).

Grades and run details

Decision model 86 · LLM judge 12 of 12 checks
Decision model checks
  • failUses the supplied evidence correctly21%
  • passAddresses the actual decision74%
  • passRespects explicit constraints26%
  • passIdentifies material uncertainty68%
  • partialAvoids unsupported claims28%
  • passProduces the required deliverable48%
  • passFinds the load-bearing assumption50%
  • passRe-estimates the revenue correctly32%
  • passSays what would prove it wrong100%
  • passReshapes the bet from the evidence100%
  • passTigers, not paper tigers56%
Run
Run
#1
Time to output
14 s
Submitted
25 Sept 2026

Needs more than a quick edit9 of 11 checks passed

The memo is factually strong and gives a clear go/no-go call, but its 'instead' is generic and misses the commercial cash-flow/faster-payout opportunity from the interviews. A PM would need to add that alternative reasoning rather than just edit wording.

Output

To: Priya Raman Subject: Fieldline Pay: challenge the $24M case before committing three squads

The strongest case against the current plan is not that embedded payments cannot work. It is that the $24M forecast treats invoice volume as if it were payment volume earning a card-like margin.

The critical unsupported assumption: 70% of customers will adopt and route enough of their invoices through Fieldline Pay to earn roughly 0.7% on that value. The 0.7% net take is specifically the card margin. ACH earns $1.60 per payment; checks and cash earn nothing. The CFO’s arithmetic is correct only if essentially every dollar from every adopting customer earns the card margin.

That is a poor fit with our mix. Commercial work is 58% of invoiced value; commercial clients typically require ACH or check, pay after 52 days, and may not accept a card surcharge on a $3,800 invoice. Eleven of 14 interviewed mostly-commercial customers said their clients require ACH or check. By contrast, the pilot’s customers were 64% residential, against 42% for Fieldline overall. And 71 of the 115 largest customers have processor contracts running mostly to 2028; the largest 5% of customers account for 48% of invoiced value. The pilot’s 61% adoption is encouraging, but it does not establish broad adoption or broad monetization. Tradesly’s 22% revenue share is also a weak comparator: its customer base is about 80% residential.

A more defensible annualized revenue range

The following estimates are run-rate scenarios, not a forecast. They use the supplied 61% pilot adoption rate and 70% plan assumption as bounds, and make explicit that the take depends on payment type.

Card revenue: Current recorded payment methods show 19% of invoice value paid by card. In the pilot, the reported card shares imply a 33.3% value-weighted card share if applied to our mix: 42% residential × 71% + 58% commercial × 6% = 33.3%.

At a 0.7% net card take:

  • Lower case: $4.83B × 61% adoption × 19% card share × 0.7% = $3.9M
  • Upper case: $4.83B × 70% adoption × 33.3% card share × 0.7% = $7.9M

The upper case assumes pilot card behavior transfers to the full customer mix, despite the pilot’s residential skew.

ACH revenue: The recorded-method sample reports ACH at 44% of invoice value, but value share does not tell us the number of $2 ACH payments—and the pilot does not report ACH usage. As an illustrative bound, if that 44% share transferred to adopters, ACH revenue would be about $0.5M–$2.1M after the $0.40 cost: the lower end assumes ACH dollars are mostly $3,800 commercial invoices; the upper end allows the pilot’s card mix and available non-card residential volume. This is uncertain upside, not a reliable forecast.

Together, that supports roughly $4.5M–$10M of annualized revenue, before accounting for contract lock-in or slower adoption among large accounts.1 The range is still conditional on transferring today’s payment mix and pilot behavior to adopters. As a cross-check, the pilot’s $214K annualized revenue across 38 customers is $5,630 per customer; scaling that to 61%–70% adoption gives $7.9M–$9.1M. That sits within the range, but is not a clean base case because the pilot skewed residential.

The $24M case requires $14,700 per adopting customer per year ($2.1M × 0.7%). That is about 2.6× the pilot’s $5,630, while assuming adoption and monetization across a materially more commercial customer base.

What would change my mind—and the cheapest six-week test

I would support a larger bet if a representative cohort showed both credible adoption and enough net revenue per adopter to approach the $14,700 annualized level the board case requires—especially among commercial customers and larger accounts not locked into existing processors. A high signup rate alone would not prove the economics.

Run a six-week, no-build test using the existing pay-by-link and partner setup. Recruit a small, stratified cohort across residential and commercial customers, including larger unlocked accounts. Track offers accepted, invoice value actually routed, card versus ACH mix, net revenue after costs, and payment timing. Report results by segment; do not annualize a handful of unusually large or residential-heavy customers as if they were representative. Include a holdout or compare with each customer’s recent invoices to distinguish new payment volume from existing behavior.

Recommendation

Do not commit three squads for three quarters or pause the scheduling rewrite on the current evidence. The rewrite addresses the top-cited churn reason among customers with more than 50 technicians, a segment that lost $1.9M ARR last year. Run the six-week test with a small team and existing capabilities, then make a stage-gated decision. If commercial routing and net revenue per adopter are strong, fund the next increment. If not, preserve the scheduling investment and narrow Pay to the segments where the data shows customers can—and will—use it.

What a PM had to fix

  1. 1OtherFix itTargeted repair

    What we’d changeThese assumptions produce an illustrative $4.5M–$10M envelope, not a validated revenue range. They assume customer adoption translates proportionally into invoice volume and the modelled payments route through Pay. Rebuild by segment: accessible invoice value × volume-weighted adoption × routing share × payment-method economics, excluding contracted volume.

Check by check

Got wrong · 1
  • Reshapes the bet from the evidenceThe recommendation is a generic stage-gate/narrow-to-segments move and does not propose the specific alternative the evidence supports, such as residential card focus or commercial cash-flow/faster payout.
Mixed · 1
  • Uses the supplied evidence correctlyAll facts and arithmetic trace to the supplied pack, with no invented current-state figures found.The two graders disagreed on this one.
Got right · 9
  • Addresses the actual decisionThe memo commits clearly to not committing now, proposes a stage-gated test, and states what would change the call.
  • Respects explicit constraintsIt is a memo addressed to Priya, within the requested length, and covers the four required elements.
  • Identifies material uncertaintyIt names payment-mix, adoption, contract lock-in, and pilot skew as unknowns and says how a six-week test would resolve them.
  • Avoids unsupported claimsEstimates are labelled as scenarios/bounds, and generalizations are tied to interview and sales evidence rather than asserted as fact.
  • Produces the required deliverableThe memo is a complete, usable pre-read for the exec audience with a clear recommendation and supporting analysis.
  • Finds the load-bearing assumptionIt makes the take-rate/payment-mix mismatch the central challenge, using the card, ACH, and check/cash unit economics from the pack.
  • Re-estimates the revenue correctlyIt recalculates revenue from the payment mix and pilot, lands in the $4.5-10M range, and explains why the pilot is an upper bound.
  • Says what would prove it wrongIt specifies a six-week stratified cohort test with net revenue per adopter approaching $14,700 as the threshold to change its mind.
  • Tigers, not paper tigersIt triages the take-rate mismatch as the sink, dismisses Tradesly as a weak comparator, and surfaces the scheduling-rewrite churn risk.

Grades and run details

Decision model 86 · LLM judge 10 of 12 checks
Decision model checks
  • failUses the supplied evidence correctly3%
  • passAddresses the actual decision95%
  • passRespects explicit constraints57%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims31%
  • passProduces the required deliverable57%
  • passFinds the load-bearing assumption97%
  • passRe-estimates the revenue correctly82%
  • passSays what would prove it wrong79%
  • partialReshapes the bet from the evidence44%
  • passTigers, not paper tigers89%
Run
Run
#1
API response time
73 s
Submitted
29 Sept 2026

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 87% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI100.095.82None
2GPT-6 AstrawithChatGPT95.595.82None
3GPT-6 LunawithAPI93.291.72None
4Sonnet 5.5withAPI93.275.02None
5Gemini 3.5 Flash-LitewithGemini81.875.021 capped
6Opus 5.5withClaude90.991.721 capped

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

This task measures the whole setup. Tools, instructions and skills in the harness do real work here, so read the harness as carefully as the model name.