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

A PM could use it with light edits, mainly tightening the revenue range to align with the expected $5–10M upper-bound framing. The evidence and reasoning are otherwise strong and actionable.

Output

To: Priya Raman (for exec offsite pre-read) From: Staff PM Re: The strongest case against committing three squads for three quarters to Fieldline Pay

Bottom line

Payments is a real opportunity, but the $24M is not supported by our own data. Re-estimated from the pack, month-24 run-rate is roughly $1M–$6M, central case about $2.3M. Even the most generous extrapolation of the pilot gives about $9M. The plan would pause the scheduling rewrite, which is tied to $1.9M of churned ARR a year, to fund a payments line that at its central case earns about the same amount after nine squad-quarters. I recommend a smaller, staged bet.

1. The assumption the plan depends on most

The plan assumes a dollar of invoice value is worth 0.7% to us. It is not, because 0.7% is the margin on card payments only.

The term sheet says we net about 0.7% on card value, but ACH nets $1.60 per payment ($2.00 charged, $0.40 cost), and checks and cash earn nothing. The CEO and CFO models apply the card margin to all $4.83B of invoices. What matters is how those invoices get paid.

  • Commercial jobs are 58% of invoiced value, and only 5% of that is paid by card. The average commercial invoice is $3,800, so ACH nets $1.60 / $3,800 = 0.04%.
  • Residential jobs are 42% of value, and the pilot lifted card share to 71%.

Yield per dollar of volume that actually flows through Fieldline Pay, using pilot card shares and assuming the non-card remainder moves by ACH:

CardACHYield
Residential ($410 avg)71% × 0.70% = 0.50%29% × ($1.60/$410 = 0.39%) = 0.11%0.61%
Commercial ($3,800 avg)6% × 0.70% = 0.04%94% × 0.042% = 0.04%0.08%
Whole base (42/58 mix)0.30%

Even if every invoice dollar went through Fieldline Pay, the blended yield would be 0.30%, less than half the modelled 0.7%.

The evidence is consistent with this: - 11 of 14 mostly-commercial customers say their clients require ACH or check. - 6 of 22 won't offer card at all. - Head of Sales expects property managers to refuse 2.9% on a $3,800 invoice.

Pilot revenue is $5,630 per adopting customer, against the $14,700 the CEO's model implies ($2.1M × 0.7%). That is 38% of the claim, from a pilot that was 64% residential, against 42% for the base. This is the best case for the mix.

Two supporting errors compound this: - The $2.1M is a mean. The median customer invoices $640k. The top 115 customers carry 48% of volume, and 71 of them are on multi-year processor contracts, mostly to 2028. Sales expects none to move. - Tradesly is not a comparable. It is about 80% residential, and we are 42%. Its 22% revenue share says little about a base that is mostly commercial by value.

2. Re-estimate of month-24 run-rate

Formula: addressable volume × adoption × share of adopters' invoice value routed through Pay × blended yield.

Addressable volume. I remove the locked-in accounts, assuming the 71 are typical of the top 115: 71/115 × $2.32B ≈ $1.43B. That leaves $3.40B.

LowBaseHigh
Adoption (by volume)30%45%61% (pilot rate)
Share of adopter volume routed30%50%70%
Blended yield0.25%0.30%0.40%
Run-rate$0.8M$2.3M$5.8M
  • Adoption. The pilot's 61% came from invited customers over four months, so I treat it as a ceiling.
  • Routing. The CEO's model implicitly assumes 100%. Customers who adopt still get paid by check and cash on many invoices, and payers choose the method.
  • Yield. Base is the whole-base mix. High approaches the pilot's residential-heavy 0.42%. Low assumes the locked-in accounts are commercial-heavy, so the remainder skews slightly more residential and the low case is harsher than it needs to be.

Cross-check against the pilot. The base case implies about $2,300 per adopting customer, against $5,630 in a residential-skewed, hand-picked pilot. That is plausible. The most generous version applies the pilot's $5,630 to 1,610 customers, giving $9.1M. That ignores the lock-ins and the pilot's mix, and it is still 38% of the claim.

Implication for the board. At $2–6M, payments would be roughly 5–12% of revenue, not 35% by 2028. Reaching $24M would need about a 3% yield on the volume in my base case, which is not available on this term sheet.

Caveats: - Payment-method data comes from only 690 self-reporting customers (30%). - The pilot ran four months. - I have not modelled ramp, disputes or chargebacks, or upside from a paid fast-payout product. That last one is the most interesting missing piece.

3. What would prove me wrong, and the cheapest test

To vindicate the plan, we would need to show that commercial volume can yield at least ~0.5% net, with at least 60% of adopters' volume routed through Pay. That would put the total near $15M or more. On a $3,800 invoice, 0.5% is about $19 net per invoice. That requires either much higher commercial card share, or a fee people will pay, such as faster payout.

A six-week constraint: commercial invoices take 52 days on average to be paid, longer than the test window. We cannot wait to observe commercial cash-in, so the test must use leading indicators and data we already hold.

The test (roughly one PM, one analyst and one engineer, using existing pilot plumbing): 1. Week 1: re-cut the existing pilot. Split the 38 customers by commercial share and compute yield per dollar of volume for the commercial-heavy ones. Their older invoices are already paid, so this costs nothing and tells us quickly whether 0.08% is right. 2. Weeks 1–6: a commercial-heavy cohort. Recruit about 20 mostly-commercial, non-locked-in customers, drawn from the 11 who said their clients require ACH or check. Turn on pay-by-link and measure the share of payers who choose card and the share of invoice value routed via link. 3. Weeks 2–6: price a fast-pay offer. Offer these customers accelerated payout, funded by a fee, as a real priced offer with sign-up. Nine of 22 said they would pay to be paid faster, and this is the only route I see to meaningful commercial yield. Measure conversion at the stated price.

Thresholds, set now: commercial card share above 25%, or fast-pay uptake implying at least 0.3% net on routed commercial volume. Below both, the base case stands and the bet should be sized to it.

4. What we should do instead

Don't commit three squads for three quarters, and don't pause the scheduling rewrite. The rewrite addresses the top-cited churn reason in the >50-technician segment, which lost $1.9M ARR last year. Those are also the accounts with the most volume and the ones payments can't reach. Cutting the rewrite risks retention at the top of the base to chase payments revenue the top can't deliver.

A staged alternative: 1. Fund one squad for two quarters, focused on residential-heavy and small customers, where the pilot works (71% card share, ~0.6% yield). Ship pay-by-link, card and ACH broadly, and track net revenue per adopting customer and routed share. 2. Run the six-week commercial test above. Its result decides whether a second and third squad get funded for a commercial cash-flow product (fast payout or early pay). That product fits the real pain: 17 of 22 customers named getting paid on commercial jobs as their biggest cash problem. 3. Set gates. Add squads only when the test or the first quarter's data shows blended yield above 0.4% on routed volume. Revisit the locked-in accounts as contracts approach expiry in 2028. 4. Reset the board narrative from "$24M / 35% of revenue" to a $2–6M range with a clear path to more if commercial monetisation works. For the raise in about 14 months, a measured payments attach rate and net revenue per customer will be more credible to investors than a projection that unravels in diligence.

The CEO's instinct that payments could make Fieldline stickier is sound, and the pilot's 61% adoption is a good sign. But the current plan assumes we can monetise the volume we don't touch at the rate we monetise the volume we do, and our own numbers say otherwise.

Check by check

Got wrong · 1
  • Re-estimates the revenue correctlyIt shows working but lands on a central $2.3M and $1M–$6M range, not roughly the $5–10M range required by the grading rubric, and its $9.1M pilot upper bound is not used as the main re-estimate.
Got right · 10
  • Uses the supplied evidence correctlyThe memo’s factual statements about the current situation are drawn from the pack or follow by arithmetic, with assumptions labelled.
  • Addresses the actual decisionIt clearly recommends not committing three squads and instead funding a staged one-squad bet, with gates and a six-week test that would change the call.
  • Respects explicit constraintsIt is a memo under 1,200 words, addressed to Priya for the exec offsite, and covers all four requested elements.
  • Identifies material uncertaintyIt names material unknowns such as payment-method reporting coverage, pilot mix, routing share, commercial card acceptance, and fast-pay uptake, and proposes tests to resolve them.
  • Avoids unsupported claimsMost interpretations are labelled as assumptions or estimates; the few rhetorical characterisations do not drive the core analysis.
  • Produces the required deliverableThe deliverable is complete, usable, and written for the named executive audience within the requested length.
  • Finds the load-bearing assumptionThe central challenge is explicitly the 0.7% blended take rate being applied to all invoice value despite the actual payment mix and unit economics.
  • Says what would prove it wrongIt specifies a six-week commercial cohort and fast-pay test with concrete thresholds, including commercial card share above 25% or 0.3% net on routed commercial volume.
  • Reshapes the bet from the evidenceIt keeps residential card payments and proposes a commercial cash-flow/fast-payout alternative grounded in interview and pilot evidence.
  • Tigers, not paper tigersIt ranks the take-rate/payment-mix flaw as primary, treats Tradesly and lock-ins as supporting evidence, and surfaces the churn/rewrite tradeoff as the uncomfortable issue.

Grades and run details

Decision model 95 · LLM judge 11 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly21%
  • passAddresses the actual decision100%
  • passRespects explicit constraints43%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims35%
  • passProduces the required deliverable89%
  • passFinds the load-bearing assumption100%
  • partialRe-estimates the revenue correctly3%
  • passSays what would prove it wrong100%
  • passReshapes the bet from the evidence100%
  • passTigers, not paper tigers99%
Run
Run
#1
API response time
70 s
Submitted
29 Sept 2026

Usable with a quick edit9 of 11 checks passed

The one unsupported figure (0.38% pilot take) is used only in a secondary cross-check and can be removed with a light edit; the core analysis and recommendations are solid and data-grounded.

Output

Fieldline Pay: the case against the plan as written

Sep 25, 2026 · @Martin Slaney · Pre-read for exec offsite

Bottom line

The $24M figure applies a card-only margin to invoice value that is mostly not paid by card. Re-estimated from our own data, Fieldline Pay is a $2–7M business at month 24, central case about $4M. That is worth building, but not with three squads for three quarters, and not by pausing the rewrite that cost us $1.9M ARR last year.

1. The assumption that breaks: a 0.7% blended take

The 0.7% is not a blended rate. It is our net margin on card payments (2.9% charged, \~2.2% cost). The model applies it to all $4.83B.

  • Card is 19% of invoice value today. ACH is 44%, check 31%, cash 6%.
  • ACH nets a flat $1.60 per payment. On a $3,800 commercial invoice that is 0.04%; on a $410 residential invoice, 0.39%.
  • Checks and cash earn nothing.
  • Commercial is 58% of value, and its card share is 5%. With pay-by-link on every invoice, the pilot moved it to 6%. Sales and 11 of 14 commercial-heavy interviewees say clients require ACH or check.

Worked through at pilot-level card adoption (assuming half of non-card value moves to ACH):

SegmentShare of valueCard share (pilot)Net take
Residential42%71%\~0.55%
Commercial58%6%\~0.065%
Blended, our mix100%\~0.27%

At today's payment mix, with no card uplift, blended take is about 0.15%.3

The $24M needs 0.71% across 70% of customers: every processed dollar on card. Even 100% adoption of all $4.83B would need 0.50%, above what the residential-skewed pilot achieved (\~0.38%).

Two things compound it:

  • Adoption by customers is not adoption by value. The top 115 customers hold 48% of value; 71 are locked into processor contracts, mostly to 2028. Pro rata, that is \~$1.43B (30% of value) out of reach in the window. The pilot's 61% came from an invited, 64%-residential cohort.
  • Tradesly is not a comparable. It is \~80% residential; we are 42%. Its 22% shows residential payments work, which our pilot confirms. It says nothing about commercial.

2. Re-estimate: $2–7M at month 24

Addressable value = $4.83B less about $1.43B locked = about $3.4B. Revenue = addressable value × value-weighted adoption × blended take.

CaseAdoption (by value)Blended takeMonth-24 run-rate
Low30%0.15% (card uplift stalls)$1.5M
Central45%0.27% (pilot behaviour, our mix)$4.1M
High60%0.35% (unlocked base skews residential)$7.1M

Cross-check from the pilot: $5,630 per customer a year on a 64%-residential cohort. Re-weighted to our mix (0.27% ÷ 0.38%) that is \~$4,000. Across 1,000–1,560 adopters (45–70% of the 2,229 unlocked customers), it gives $4.0–6.2M, consistent with the central-to-high cases.

Central case is \~10% of current ARR, not the 35% of revenue told to the board. Against it: three squads for three quarters, and the rewrite delayed in a segment that lost $1.9M ARR last year. At the central case, year-two payments revenue roughly covers two years of that churn if it continues.

3. What would prove this wrong, and the cheapest test

I am wrong if commercial invoice value can be moved onto rails we earn on. The threshold: a commercially representative cohort showing blended net take of 0.5% or more through Pay. Below 0.3%, the $24M is off the table.2

Six-week test, configuration only, no new build:

  1. Enable Pay for 30 customers with at least 60% commercial invoice value, none from the pilot.
  2. Attach pay-links to open invoices sent in the last 60 days, not just new ones. Commercial invoices take 52 days to pay; new invoices alone won't settle inside six weeks.
  3. Split the cohort on ACH price: half at the current flat $2, half at 0.5% capped at $25. Flat pricing is why commercial ACH earns $1.60 on a $3,800 invoice instead of $19.
  4. Make payment-method capture mandatory for the cohort, so we stop relying on the 30% who record it.
  5. Measure value-weighted method mix and net take by segment, plus opt-outs and payer complaints.

Alongside it, at no cost: account managers ask the 44 unlocked top-115 accounts whether they would move processors in 2027. More than ten yeses changes the adoption maths.

4. What to do instead

Shrink the bet, point it where the evidence is, and gate the rest.

  • Keep the scheduling rewrite. It protects known ARR. Payments is unproven in the segment that holds most of the value.
  • One squad on Pay for residential work now. The pilot shows it works there (71% card share). Ship pay-by-link, default on.
  • Reprice ACH from flat $2 to a capped percentage, subject to the test. It is the largest lever on commercial take and needs no new build.
  • Go after the real problem. 17 of 22 customers named getting paid on commercial jobs as their biggest cash problem; invoices take 52 days; 9 would pay to be paid faster. An early-payout product (for example, 1.5% to get paid in two days) earns $57 on a $3,800 invoice against $1.60 on ACH.1 It earns on the 58% of value that card never will. It needs a capital partner; scope it in the same six weeks with a priced offer to 10 commercial customers.
  • Gate at week six. If commercial take clears 0.5% or the early-payout offer converts, add squads then.
  • Reset the board number now. We raise in about 14 months, when Pay will show perhaps a $2–3M run-rate. Walking the board from $24M to a range today is cheaper than explaining the gap in a data room.

What a PM had to fix

  1. 1Numbers wrongRedo the arithmeticQuick edit

    What we’d changeThe $57 is gross. Net it of the cost of capital and defaults before comparing it with ACH's net $1.60.

  2. 2Test or gate too weakTighten the testQuick edit

    What we’d changeSay which claim the test settles, and whether 0.3% means commercial or blended take.

  3. 3Hypothesis stated as factReframe it as a hypothesisQuick edit

    What we’d changeLabel modelled figures as modelled, so the CFO doesn't read them as observed.

Check by check

Got wrong · 1
  • Avoids unsupported claimsThe 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.
Mixed · 1
  • Uses the supplied evidence correctlyThe 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.The two graders disagreed on this one.
Got right · 9
  • Addresses the actual decisionIt commits to a clear call (shrink the bet, one squad on residential, early-payout) and states what would change the call (commercial blended take >0.5%).
  • Respects explicit constraintsIt is a memo for the CEO, CFO and Head of Sales, within the word limit, and addresses all four requested points.
  • Identifies material uncertaintyIt names the key unknowns (commercial card take, locked account adoption) and proposes a six-week test with a specific threshold to resolve them.
  • Produces the required deliverableThe memo is a complete, action-ready pre-read that covers the challenge, re-estimate, test and alternative, and could be used by the execs with light edits.
  • Finds the load-bearing assumptionIt makes the mismatch between the 0.7% card-only net take and the actual payment mix the central challenge, and backs it with the payment-method and unit-economics data.
  • Re-estimates the revenue correctlyIt recalculates from the payment mix and pilot behavior, showing work, yielding a central ~$4M and an upper bound of $7.1M, well below $24M.
  • Says what would prove it wrongIt specifies a six-week cohort test with a commercial blended take threshold of 0.5% and a secondary light-touch inquiry for locked accounts.
  • Reshapes the bet from the evidenceIt grounds the recommendation in what works (residential card payments) and the strongest evidence (commercial cash-flow pain), proposing a narrower bet with an early-payout product.
  • Tigers, not paper tigersIt ranks the take-rate mismatch as the killer risk, dismisses the Tradesly comp as residential-only, and names the uncomfortable board reset needed now.

Claims the judge couldn’t find in the brief

  • The residential-skewed pilot achieved a blended net take of ~0.38%.

Grades and run details

Decision model 91 · LLM judge 10 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly8%
  • passAddresses the actual decision100%
  • passRespects explicit constraints50%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims22%
  • passProduces the required deliverable92%
  • passFinds the load-bearing assumption100%
  • partialRe-estimates the revenue correctly32%
  • passSays what would prove it wrong100%
  • passReshapes the bet from the evidence100%
  • passTigers, not paper tigers99%
Run
Run
#1
Time to output
2.0 min
Submitted
25 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.