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 14 graded outputs by 7 models. 64% were usable with at most a quick edit.

Reliably right

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
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
Uses the interviews faithfully100% pass
All quotes are accurate and correctly attributed to A05, A08, A04, A07, and A12.
GPT-6 Luna · API · An AI SDR for small agencies

Where it slips

A cheap test that can actually read out64% 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
Re-estimates the revenue correctly71% pass
It 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.
Sonnet 5.5 · API · The CEO's embedded-payments bet
Avoids unsupported claims71% 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

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

We plan to sell an AI sales-development rep, an agent that finds prospects and sends personalised outbound email, to agencies with 5–20 staff. Before we commit a year to it, find the strongest reason this fails. Use the interview material below, including the three transcripts. Write it up for the founding team: the one reason, the evidence for it (quote the interviews), where the evidence cuts the other way, what we would need to see to be proved wrong, and the cheapest test that would show it. Keep it under 600 words.

What the model was given7 items: Scenario, Founders' hypothesis (from our planning doc), Interview summary (14 agencies, 5–20 staff, last six weeks), Interview log, interview-A05-content-agency.md, interview-A04-shopify-agency.md, interview-A08-packaging-studio.md

ScenarioWe are a three-person founding team with £800k of pre-seed funding and about 20 months of runway. Planned price: $600 a month per agency. Two competitors have each raised over $10m in the last year, selling AI SDRs mostly to software companies with 50+ staff.

Founders' hypothesis (from our planning doc)“Small agencies live feast or famine. When a big client leaves they have no pipeline, because the founder is too busy delivering to sell. An AI SDR keeps the pipeline full in the background, so the famine never comes. At $600 a month, one extra client a year pays for it many times over.”

Interview summary (14 agencies, 5–20 staff, last six weeks)11 of 14 get most of their new revenue from referrals and repeat clients (86% on average across those 11). 9 had tried outbound in the last two years; 7 of those stopped within six months, and none of the 7 closed a deal they could attribute to it. The 2 who kept going (A04, A07) have 15–20 staff and a dedicated person for business development. 3 (A08, A12, A14) said they regularly turn work away. 6 described feast-or-famine swings in new business. Average deal size across the 14: $18k; typical sales cycle 6–10 weeks.

Interview logAgency (staff) · services · share of new revenue from referrals and repeat clients · outbound tried? · outcome · representative quote A01 (8) · brand and web studio · 90% · yes, cold-email agency · stopped after 3 months · "We got meetings with people who'd never buy from us." A02 (14) · performance marketing · 85% · yes, LinkedIn automation · stopped after 4 months, account restricted · "LinkedIn shut our founder's account down. That's our best referral network." A03 (6) · PR boutique · 95% · no · — · "Every client we have came from someone vouching for us." A04 (18) · Shopify development · 40% (plus 30% partners, 30% outbound) · yes, in-house BD lead · kept, 30% of new revenue · "It works because of the follow-up, not the first email." A05 (11) · content · 85% · yes, lead-gen agency · stopped after 2 months, no deals · "Content is a trust purchase." A06 (7) · video production · 85% · no · — · "Our clients find us through the videos. Someone shares one and we get a call." A07 (16) · SEO · 45% · yes, outsourced lead generation · kept · "It pays for itself, just. Most of the meetings are a waste of time, but one in ten turns into a retainer." A08 (5) · packaging design · 95% · no · — · "If you sent me ten more leads in October I'd have to say no to nine of them." A09 (12) · paid social · 75% · yes, cold email · stopped after 5 months · "The replies were mostly people asking us to take them off the list." A10 (20) · B2B marketing · 70% · yes, contract SDR · stopped after 6 months · "It cost us about £400 for every meeting, and the meetings didn't close." A11 (9) · WordPress web agency · 90% · yes, cold email · stopped after 3 months · "Our domain ended up on a spam list. Took weeks to fix." A12 (10) · branding · 85% · no · — · "We're booked out till March. I don't need more leads, I need another designer." A13 (15) · HubSpot partner · 48% (plus 40% from HubSpot's partner directory) · yes, cold email · stopped after 4 months · "HubSpot sends us more than we can handle in a good quarter." A14 (6) · UX research · 90% · no · — · "We're small on purpose. We say no to about a third of enquiries."

interview-A05-content-agency.md31 lines · Download

# Interview A05: content agency, 11 staff
Participant: founder and managing director. Interviewer: our co-founder. 34 minutes, lightly edited.

**Interviewer:** Where did your last five clients come from?

**Participant:** Let me think. Two were old clients coming back: one had changed jobs and brought us into her new company. Two were introductions, one from a web agency we partner with and one from a client's CFO who'd seen our work. The fifth found us through a talk I gave at a SaaS marketing meetup. So none of them from anything you'd call outbound.

…

interview-A04-shopify-agency.md23 lines · Download

# Interview A04: Shopify development agency, 18 staff
Participant: head of business development. Interviewer: our co-founder. 29 minutes, lightly edited.

**Interviewer:** Tell me how new business works for you.

**Participant:** We're a bit unusual for an agency our size. I'm a full-time business-development person, and outbound is about 30% of our new revenue. Referrals and repeat work are still the majority, maybe 40% referrals and the rest partners: Shopify's partner directory and a couple of app companies who send us projects.

…

interview-A08-packaging-studio.md23 lines · Download

# Interview A08: packaging design studio, 5 staff
Participant: founder and creative director. Interviewer: our co-founder. 22 minutes, lightly edited.

**Interviewer:** How do new clients find you?

**Participant:** Almost entirely word of mouth. Food and drink brands talk to each other. Someone launches a range, their packaging does well on the shelf, and the next founder asks who did it. I'd say 95% referral. We've never done any outbound.

…

What a strong answer doesThe answer key the graders mark against

Names the load-bearing assumption as 'small agencies' growth is limited by a shortage of new leads': the interviews say it is limited by trust and capacity. 11 of 14 win most work through referrals and repeat clients; 7 of 9 who tried outbound quit within six months with no attributable deals, for reasons tied to trust (A05's 'content is a trust purchase', A01's meetings with people who'd never buy) and reputation risk (A02's restricted LinkedIn account, A11's spam listing); 3 turn work away (A08: 'I'd have to say no to nine of them'). Quotes the interviews accurately and attributes them correctly. Engages the counter-evidence honestly: outbound works for A04 and A07, but both are 15–20 staff with a dedicated BD owner, already use tools like Apollo and Clay, and credit the follow-up rather than the first email, so the segment is small, tooled and not what the product automates. Treats feast or famine as a real pain but notes agencies fix it through past clients and referrals (A05's reactivation emails, and her willingness to pay for help keeping in touch with past clients), pointing at a different product. Says what would prove the challenge wrong (for example, a meaningful share of 10–20-staff agencies with a BD owner and budget, or a concierge outbound test producing qualified meetings that turn into proposals), and designs a cheap test that measures an early signal, since a 6–10 week sales cycle means a six-week test cannot observe won deals.

Critical failures (cap the score at 40)

Invents interview quotes or attributes a quote to the wrong agency
Treats the founders' feast-or-famine hypothesis as confirmed by the interviews

Case

Staff-level · v1.10 · synthetic · B2B, go-to-market, customer research

The outputs

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

GPT-6 LunaAPI100Usable with a quick edit GPT-6.1 SolAPI96Usable with a quick edit GPT-6 AstraChatGPT94Usable with a quick edit Sonnet 5.5API71Needs more work Gemini 3.8 FlashAPI56Needs more work Opus 5.5Claude40Critical failure Gemini 3.5 Flash-LiteGemini40Critical failure

Gemini 3.8 FlashAPI

Needs more than a quick edit3 of 11 checks passed

A PM would need to re-check evidence and repair the test before acting. The core argument is useful but not reliable as written.

Output

The One Reason This Fails

Agency services are high-trust purchases that cold automated outreach cannot convert without intensive, bespoke human follow-up—which time-poor founders cannot provide. Automating email generation solves top-of-funnel activity, but the actual point of failure is trust and follow-up capacity.

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The Evidence

1. Zero conversion for founder-led shops: 7 of 9 agencies that tried outbound stopped within six months having closed zero deals. 2. Cold outreach delivers the wrong buyers: Services require credibility upfront. * A05 (11 staff): “Content is a trust purchase... The people on those calls had no idea who we were and, honestly, most of them didn’t have a budget.” * A01 (8 staff): “We got meetings with people who’d never buy from us.” * A10 (20 staff): “Cost us about £400 for every meeting, and the meetings didn’t close.” 3. The constraint is manual follow-up, not initial emails: * A04 (18 staff, dedicated BD): “It works because of the follow-up, not the first email... What wins deals is that I then send them a teardown of their site, a short video... The owner does sales on the side. They don't have the time to follow up, and the follow-up is the job.” 4. Asymmetric downside risk: Small agencies live on reputation. * A11: “Our domain ended up on a spam list.” * A02: “LinkedIn shut our founder’s account down. That’s our best referral network.” * A05: “I’d worry about our name going out on emails I haven’t read.”

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Where the Evidence Cuts the Other Way

Feast-and-famine is real: 6 of 14 experience painful dry spells. A05 panicked when three clients left simultaneously.
Outbound works at scale with dedicated staff: A04 generates 30% of revenue via outbound; A07 makes it pay (“one in ten turns into a retainer”).
Willingness to pay exists if positioned right: A04 would pay $600/month if it replaced Apollo/Clay and saved research hours. A05 would pay $200/month for an automated tool to reactivate past clients and referral networks.

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What We Would Need to See to Be Proved Wrong

A solo founder (with no dedicated BD rep) closing an outbound deal with typical agency pricing ($18k) using standard outbound messaging, without spending >5 hours/week on bespoke pitch work.
Willingness of agency owners to allow an autonomous agent to send cold outbound without manual pre-approval of every single touchpoint.

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The Cheapest Test to Prove It

A 3-week "Wizard of Oz" Concierge Test with 3 Agencies (Cost: <$500): * Partner with 3 agencies suffering feast-or-famine (e.g., A05, A09, A10) who will commit to running a free, 30-day outbound campaign. * Manually emulate the AI SDR: scrape prospect lists, generate personalized emails, and handle automated sequencing from a secondary domain. * Kill criteria: If after booking 10–15 meetings across these agencies, zero advance to contract negotiations—or founders drop out because they cannot keep up with follow-ups and custom scoping—kill the product.

(Pivot signal: A05 offered to pay immediately for an AI assistant that drafts warm re-engagement notes to past clients and referral partners. That solves the same famine problem without the friction of cold outbound.)

Claims the judge couldn’t find in the brief

7 of 9 agencies that tried outbound stopped within six months having closed zero deals.
6 of 14 agencies experience painful dry spells.
A04 generates 30% of revenue via outbound.
A05, A09, and A10 are agencies suffering feast-or-famine.

Grades and run details

Decision model 95 · LLM judge 3 of 12 checks

Decision model checks

passUses the supplied evidence correctly44%
passAddresses the actual decision96%
passRespects explicit constraints36%
passIdentifies material uncertainty97%
partialAvoids unsupported claims23%
passProduces the required deliverable71%
passFinds the load-bearing assumption26%
passUses the interviews faithfully74%
passEngages the counter-evidence79%
passA cheap test that can actually read out94%
passTigers, not paper tigers94%

Run

Run: #1
API response time: 22 s
Submitted: 1 Oct 2026

Check by check

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

Got wrong 1

Avoids unsupported claimsWrong

It presents unsupported or overconfident claims as fact, especially the feast-or-famine status of A09/A10 and the zero-deal characterisation.

Mixed 7

Uses the supplied evidence correctlyMixed

It misstates or overreaches several current-situation facts, including 'zero deals', 'painful dry spells', A04's 30% of revenue, and A09/A10 as feast-or-famine agencies.

Respects explicit constraintsMixed

The required sections and length are present, but the proposed test is internally inconsistent (3-week test vs 30-day campaign) and would not reliably enforce the brief's cheap-test requirement.

Produces the required deliverableMixed

Although structured as requested, it is not usable without correcting evidence errors and the contradictory test design.

Finds the load-bearing assumptionMixed

It focuses on trust and follow-up capacity but does not make lead-constrained growth the central challenge or use the turning-work-away evidence that directly refutes the founders' assumption.

Engages the counter-evidenceMixed

It names A04 and A07 but does not fully explain what sets them apart or the implication for the addressable segment, such as A07's dedicated BD owner and existing tooling.

A cheap test that can actually read outMixed

The test is cheap in spirit but inconsistent and not clearly observable within the 6–10 week sales cycle, using contract negotiations after a 30-day campaign.

Tigers, not paper tigersMixed

It does not triage risks or dismiss a plausible worry; it leaves capacity/turning-work-away and the small addressable segment underdeveloped.

Got right 3

Addresses the actual decisionRight

It commits early to one failure reason for the founding team and states conditions that would prove the challenge wrong.

Identifies material uncertaintyRight

It names material unknowns—solo-founder conversion and autonomy tolerance—and gives a test and kill criteria to resolve them.

Uses the interviews faithfullyRight

The quoted interview material is attributed to the right agencies and is accurate apart from minor ellipses and wording.

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 88% of checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6.1 SolwithAPI	100.0	95.8	2	None
2	GPT-6 AstrawithChatGPT	93.2	95.8	2	None
3	GPT-6 LunawithAPI	97.7	91.7	2	None
4	Sonnet 5.5withAPI	90.9	75.0	2	None
5	Gemini 3.5 Flash-LitewithGemini	88.6	75.0	2	1 capped
6	Opus 5.5withClaude	93.2	91.7	2	1 capped
7	Gemini 3.8 FlashwithAPI	93.2	37.5	2	1 capped

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.