Tasks / Strategy

Challenge an idea

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

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

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

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

Reliably right

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

Where it slips

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

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Finds the need behind the idea

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

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

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Tigers, not paper tigers · and 4 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

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
Task

Staff-level · v1.15

The outputs

8 setups answered this brief.

Usable with a quick edit12 of 12 checks passed

The memo is accurate, respects the word limit, and directly answers the brief; a PM could use it with light editing and no re-verification.

Output

The strongest reason this fails: agencies don't lack prospects, so cold outbound solves the wrong problem

The reason. Our hypothesis says famine happens because founders are too busy delivering to find prospects. The interviews point to a different cause. Agencies win work through trust, and they run short because of capacity or because nobody remembers to keep in touch. A cold-outbound agent sells the one channel these buyers have already tried and dropped.

Evidence for it

  • Referrals dominate. 11 of 14 get 70%+ of new revenue from referrals and repeat clients. A03: "Every client we have came from someone vouching for us." A05: "Content is a trust purchase."
  • Outbound already failed for this segment. 9 of 14 tried it. 7 stopped within six months, and none of those 7 could attribute a closed deal to it. A05: "We didn't close anything from it." A10: "It cost us about £400 for every meeting, and the meetings didn't close."
  • Some famine is capacity, not pipeline. A08, A12 and A14 turn work away. A08: "If you sent me ten more leads in October I'd have to say no to nine of them." A12: "I don't need more leads, I need another designer."
  • The famine fix that worked was human. A05 got two clients back within a month by emailing past clients personally. A05 said they'd pay "a couple of hundred a month" for that kind of reminder, but not $600 for cold outbound.
  • Deliverability and reputation risk. A02's founder account was restricted, and A11's domain ended up on a spam list. A05 would worry about "our name going out on emails I haven't read."

Where the evidence cuts the other way

  • A04 and A07 kept outbound running and call it worthwhile. A04 gets 30% of new revenue from it, and A07 says "one in ten turns into a retainer."
  • Those two have 15–20 staff and, in A04's case, a dedicated business-development person. Most of our target (5–20 staff) is smaller, and the segment only gets viable at the top of it.
  • Six agencies describe feast-or-famine swings. Famine is real. The question is what causes it.
  • A04 says an AI could cut research time, and A07 says outbound "pays for itself, just." Automation may help the people who already run outbound. It doesn't make the case for everyone else.

What would prove us wrong

  1. In a sample of 20+ agencies with 5–20 staff and no dedicated BD person, at least 1/3 show closed deals attributable to automated outbound within 90 days, at $600/month.
  2. Among agencies that describe famine, the trigger is a pipeline gap (a lost client with nothing behind it), not capacity or a lack of follow-up. We'd see this in their revenue records and their own accounts of what happened.
  3. Paying pilots renew past month three at a rate above 50%, and churn reasons are about results rather than deliverability or trust.

Cheapest test (about one week)

Ask 15 agencies from the 5–20 staff band, chosen to exclude A04/A07-type firms, to pay a $600 deposit for a 60-day outbound pilot. Human approval on every email. Refund if no attributable deal. Also offer, as the second option, a $200/month reminder and reactivation tool for past clients and referrers, since A05 said they'd pay that.

  • If fewer than 2 of 15 commit to the outbound pilot, and several choose the reactivation option, the product is pointing the wrong way.
  • If 4 or more commit, run the pilots and measure meetings, deals and renewals against the three tests above.

Grades and run details

Decision model 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly73%
  • passAddresses the actual decision98%
  • passRespects explicit constraints34%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims32%
  • passProduces the required deliverable84%
  • passFinds the load-bearing assumption100%
  • passUses the interviews faithfully82%
  • passEngages the counter-evidence90%
  • passA cheap test that can actually read out47%
  • passTigers, not paper tigers97%
  • passFinds the need behind the idea93%
Run
Run
#1
API response time
14 s
Submitted
8 Oct 2026

Usable with a quick edit12 of 12 checks passed

The output is well-structured, evidence is accurate, and reasoning is sound; a product manager could use it with at most minor wording tweaks.

Output

Strongest reason this fails: you automate prospecting, but prospecting is not the binding constraint

The proposed AI SDR supplies cold introductions. Most agencies interviewed win through transferred trust; converting cold prospects requires substantial human follow-up that busy founders cannot supply. The product therefore risks delivering meetings rather than revenue—and being cancelled before it earns its keep.

This is a mismatch with the promised outcome, not proof that AI cannot write good emails. Cheaper, more personalised outreach does not automatically create trust, budget or founder time.

Evidence

  • Existing behaviour is a warning: 11 of 14 agencies get most new revenue from referrals and repeat clients—86% on average within that group. Nine tried outbound; seven stopped within six months, with no attributable deals.
  • A05 confirms the pain but rejects the proposed solution. Its founder described “feast or famine,” yet previous outbound generated six or seven meetings monthly without a sale: “Most of them didn’t have a budget for content.” Introductions worked because “they’d already been told we were good.” After losing three clients, personal notes to former clients brought two back within a month. On pricing: “Six hundred for cold outbound, no.”
  • Even the strongest success case exposes the missing work. A04 gets 30% of new revenue from outbound, but employs a full-time BD lead: “What wins deals is that I then send them a teardown of their site, a short video, and I’m on the phone with them within a week.” For typical founders, “They don’t have the time to follow up, and the follow-up is the job.”
  • Some agencies do not need more demand. A08: “Getting them isn’t [a problem]. Handling them is.” Its answer to the product was: “It would make my problem worse.” Three agencies regularly turn work away.

“One extra client pays for it” only holds if the tool causes an incremental, profitable win—not merely a meeting—and the agency can serve it.

Where the evidence cuts the other way

Six agencies report feast-or-famine swings. A04 would pay $600 if the tool replaced research tools and saved three hours daily, with HubSpot integration and email approval. A07 says outbound “pays for itself, just.”

These suggest a viable BD-assistance niche, particularly among larger agencies with a dedicated salesperson. They do not establish demand for autonomous pipeline generation across 5–20-person agencies.

The interviews are also a small sample, and failed historical outbound does not prove better targeting cannot work. A05’s two-month trial barely covered a typical sales cycle.

What would prove us wrong

Several agencies without dedicated BD staff pay $600, convert agent-sourced cold prospects into profitable clients with little founder follow-up, and renew. Success only among A04-like firms would support a narrower product, not the current thesis.

Cheapest decisive test

Run a 12–16-week paid concierge pilot with six agencies that genuinely need clients, primarily without BD staff. Charge $600 monthly; use existing prospecting and email tools rather than build software. Obtain sending approval and protect their primary domains.

Track qualified opportunities, founder follow-up hours, attributable signed deals, delivery capacity and paid renewals—not replies or booked meetings. Pre-register a continuation gate: at least three agencies close an attributable profitable deal and renew, within a founder-time limit they set beforehand.

Failure to recruit paying pilots is useful evidence itself. Do this before committing a year.

Grades and run details

Decision model 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly80%
  • passAddresses the actual decision96%
  • passRespects explicit constraints41%
  • passIdentifies material uncertainty99%
  • passAvoids unsupported claims44%
  • passProduces the required deliverable85%
  • passFinds the load-bearing assumption100%
  • passUses the interviews faithfully85%
  • passEngages the counter-evidence87%
  • passA cheap test that can actually read out62%
  • passTigers, not paper tigers97%
  • passFinds the need behind the idea60%
Run
Run
#1
API response time
17 s
Submitted
29 Sept 2026
Critical failure: Treats the founders' feast-or-famine hypothesis as confirmed by the interviews. Score capped at 40.

Needs more than a quick edit10 of 12 checks passed

The unsupported claim about A07 having a dedicated BD person would require re-checking the evidence before use, so a PM cannot rely on it with only a quick edit.

Output

Why the AI SDR for small agencies fails

Sep 25, 2026 · @Martin Slaney

The reason

Small agencies win work on trust, and cold outbound only closes when someone does the follow-up. An AI SDR automates the first email, the cheap part. It hands the follow-up, which is the actual job, to a founder our own hypothesis says is too busy delivering to sell. We would be adding meetings to the one resource that is already the constraint.

Evidence

  • Referrals dominate. 11 of 14 get most new revenue (86% on average) from referrals and repeat clients. A03: "Every client we have came from someone vouching for us."
  • Outbound was tried and dropped. 9 tried it; 7 stopped within six months and none of the 7 closed an attributable deal. They used five different channels (lead-gen agency, cold-email agency, contract SDR, LinkedIn automation, DIY email), so this isn't one bad vendor.
  • Meetings weren't the problem; closing was. A05 got six or seven meetings a month and closed nothing: "Content is a trust purchase." A01: "We got meetings with people who'd never buy from us." A10: "It cost us about £400 for every meeting, and the meetings didn't close."
  • Where it works, a human does the work. Both survivors (A04, A07) have 15–20 staff and a dedicated BD person. A04: "It works because of the follow-up, not the first email" and "most agencies our size don't have someone like me."
  • The downside hits their best channel. A02 lost the founder's LinkedIn, "our best referral network"; A11's domain was spam-listed. A04 and A05 both refuse to let anything send unread.
  • Some don't need leads at all. A08, A12 and A14 turn work away. A12: "I don't need more leads, I need another designer."

Where it cuts the other way

  • The famine is real.1 6 of 14 describe feast-or-famine swings; A05 "spent April panicking" after three clients left at once.
  • The maths are forgiving. At an $18k average deal, one closed client a year covers $7.2k of subscription.
  • One buyer exists. A04 would pay $600 today if it replaced Apollo, Clay and three hours a day of research, but only with HubSpot sync and approval of every email.
  • Past failures were generic. A05's buyers wanted proof of work in their niche; better enrichment might fix that. Untested.
  • Small sample. 14 interviews, one interviewer.
  • Warm outreach worked. A05's emails to past clients won two back in a month, and she'd pay "a couple of hundred a month" for a tool that did it. That supports the thesis against cold outbound, but points at a different product.

What would prove us wrong

Agencies without a dedicated BD person closing deals from AI-sent cold email, without damage to their reputation. Concretely, in a 12-week pilot with 5 such agencies:

  • at least 3 reach a scoped proposal and at least 1 closes;2
  • no domain, inbox or LinkedIn penalties;
  • owners spend under 2 hours a week on follow-up;
  • they keep paying when the pilot ends.

Cheapest test

Pre-sell a hand-run pilot before building anything. Offer the 9 agencies with no BD person that aren't turning work away (A01–A03, A05, A06, A09–A11, A13) a 12-week pilot at $600 a month, paid upfront. We run it by hand: Apollo and Clay for prospects, LLM-drafted emails, owner approves every send, separate sending domain.

  1. Gate 1, one week: fewer than 3 pay, stop. There is no demand at this price.
  2. Gate 2, 12 weeks (covers the 6–10 week sales cycle): score against the thresholds above and log owner follow-up hours.

In parallel, offer A05's version (past-client and referrer re-engagement, about $200 a month) as a second arm. If that sells and cold doesn't, we have our answer.

Cost: roughly £1–2k in tools and a quarter of one founder's time3, against a year and most of the £800k.

What a PM had to fix

  1. 1Hypothesis stated as factReframe it as a hypothesisTargeted repair

    Source: Founders' hypothesis (from our planning doc)Small agencies live feast or famine.

    What we’d changeSay 'Famine is real for some: 6 of 14' rather than confirming the founders' hypothesis for the whole segment.

  2. 2Test or gate too weakTighten the testQuick edit

    What we’d changeRaise the bar for proving the memo wrong: one close in five agencies doesn't show the product works.

  3. 3OtherFix itQuick edit

    What we’d changeThe cost looks light for running a hand-run pilot across nine agencies. Build it up line by line.

Claims the judge couldn’t find in the brief

  • Both survivors (A04, A07) have 15–20 staff and a dedicated BD person.

Grades and run details

Decision model 92 · LLM judge 77
Decision model checks
  • passUses the supplied evidence correctly47%
  • passAddresses the actual decision93%
  • partialRespects explicit constraints31%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims17%
  • passProduces the required deliverable73%
  • passFinds the load-bearing assumption90%
  • passUses the interviews faithfully77%
  • passEngages the counter-evidence96%
  • passA cheap test that can actually read out88%
  • passTigers, not paper tigers98%
  • passFinds the need behind the idea85%
Run
Run
#1
Time to output
60 s
Submitted
25 Sept 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 2

Uses the supplied evidence correctlyRightRightMixed
Haiku 5.5 · API

All claims about the current situation are drawn accurately from the supplied context.

GPT-6.1 Sol · API

All factual claims about the current situation are directly supported by the supplied context, with no inventions.

Opus 5.5 · Claude

The claim that A07 has a dedicated BD person is not supported by the supplied context; the log only says 'outsourced lead generation'.

Avoids unsupported claimsRightRightWrong
Haiku 5.5 · API

Interpretive causes are attributed to the interviews and not presented as unqualified fact.

GPT-6.1 Sol · API

Interpretations and forecasts are clearly presented as such, not as established facts.

Opus 5.5 · Claude

Presents the unsupported claim that A07 has a dedicated BD person as fact, without labelling it as an interpretation.

All got right 10

Addresses the actual decisionRightRightRight
Haiku 5.5 · API

Output commits to the strongest reason the idea fails, framed for the founding team, and says what would change the answer.

GPT-6.1 Sol · API

The output commits to a clear answer (prospecting is not the binding constraint) and states what would prove it wrong.

Opus 5.5 · Claude

Commits to a clear answer: the strongest reason is that follow-up is the bottleneck, not lead generation, and the founder is too busy.

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

The memo is under 600 words, written for the founding team, and includes the required elements.

GPT-6.1 Sol · API

The output includes the one reason, evidence, counter-evidence, disproof condition, and a cheap test, all under 600 words.

Opus 5.5 · Claude

The output is under 600 words, addresses the founding team, and includes all requested sections.

Identifies material uncertaintyRightRightRight
Haiku 5.5 · API

Output names specific unknowns (pipeline gap vs capacity/follow-up) and defines what would prove the conclusion wrong.

GPT-6.1 Sol · API

It acknowledges the small sample and that failed past outbound doesn't guarantee failure, and specifies what result would change the call.

Opus 5.5 · Claude

Names unknowns (small sample, untested enrichment, warm outreach alternative) and states what would prove the idea wrong.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

The write-up is complete, includes the one reason, evidence, counter-evidence, disconfirming conditions, and a test; a PM could act on it.

GPT-6.1 Sol · API

The memo is complete, in the right form for the founding team, within the word limit, and actionable as-is.

Opus 5.5 · Claude

The memo is complete, in the right form, and usable by the founding team with light edits.

Finds the load-bearing assumptionRightRightRight
Haiku 5.5 · API

Makes lead-constrained growth the central challenge, supported by referrals, failed outbound, and capacity examples.

GPT-6.1 Sol · API

The critique targets the assumption that agencies' growth is limited by a shortage of new leads, using referral, abandoned-outbound, and capacity evidence.

Opus 5.5 · Claude

Targets the assumption that growth is limited by a shortage of new leads, arguing the real constraint is follow-up capacity.

Uses the interviews faithfullyRightRightRight
Haiku 5.5 · API

All quoted or closely paraphrased material is attributed to the correct agency and matches the transcripts and log.

GPT-6.1 Sol · API

All quotes are accurately taken from the transcripts and correctly attributed to A05, A04, and A08.

Opus 5.5 · Claude

All quotes are accurate and correctly attributed to the right agencies.

Engages the counter-evidenceRightRightRight
Haiku 5.5 · API

Names A04 and A07, notes they are larger, have dedicated BD owners, and credit follow-up, and tells what that means for the addressable segment.

GPT-6.1 Sol · API

It names A04 and A07, notes their size and dedicated BD resource, and explains that their success relies on follow-up, not just the first email.

Opus 5.5 · Claude

Engages A04 and A07, noting their size, dedicated outbound resource, and that follow-up does the work, implying a small addressable segment.

A cheap test that can actually read outRightRightRight
Haiku 5.5 · API

Proposes a cheap, one-week test measuring willingness to pay for a pilot, which is an early signal observable before the 6–10 week sales cycle.

GPT-6.1 Sol · API

The proposed concierge pilot is cheap, has a clear threshold (three agencies close a deal and renew), and its 12–16 week duration can observe deals within the 6–10 week sales cycle.

Opus 5.5 · Claude

Proposes a cheap hand-run pilot with demand gate and early signals (proposals, closes) within the 6-10 week sales cycle.

Tigers, not paper tigersRightRightRight
Haiku 5.5 · API

Sinks the idea on the core problem (agencies don't lack leads), dismisses the famine-as-pipeline-gap explanation, and surfaces the unasked need for referral reactivation.

GPT-6.1 Sol · API

It dismisses the worry that AI can't write good emails and surfaces the unspoken issue that agencies lack follow-up capacity, not leads.

Opus 5.5 · Claude

Separates real risks (follow-up bottleneck) from manageable ones (bad vendors), and surfaces the unspoken issue that the founder is the constraint.

Finds the need behind the ideaRightRightRight
Haiku 5.5 · API

Identifies the underlying need to keep pipeline full, suggests a $200/month reminder/reactivation tool as an alternative, with what it displaces (cold outbound).

GPT-6.1 Sol · API

It identifies the need as smoothing feast-or-famine and offers an alternative (a tool for past-client reactivation) with its cost and what it displaces.

Opus 5.5 · Claude

Identifies the need (avoiding famine) and offers an alternative warm-outreach tool at $200/month, implying it would displace the cold-outbound product.

Results

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

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

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

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

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

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

Why it matters

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

What good looks like

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

Deliberately not measured

  • Tone
  • Number of objections raised
Capability tested

Evidence-based critique

The failure we’re looking for

Theatrical negativity without evidence

Grading

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

Variants

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