Tasks / Growth

Find the growth loop

Can the model find a product's real growth loop, show whether it compounds, and say which lever to pull?

Task type v1.0 · 2 tasksLast changed 2 Oct 2026 · ChangelogDifficulty

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

From 18 graded outputs by 9 models. 28% were usable with at most a quick edit.

Reliably right

  1. Sees the cross-side effect100% pass
    It traces the chain from the tutor bounty to oversupply, thinner bookings, new profiles without reviews, and weaker ranking, and acts on it by pausing broad tutor referrals.
    GPT-6.1 Sol · API · Growing on the surface, decaying underneath
  2. Produces the required deliverable99% pass
    The memo answers all parts of the brief (primary loop, compounding, engineer allocation, success measurement) in a usable form for the Head of Growth.
    Sonnet 5.5 · API · The badge on every form
  3. Respects explicit constraints97% pass
    The output is a memo to Sam Okoro, under 800 words, and respects the requested form and reader.
    Sonnet 5.5 · API · The badge on every form

Where it slips

  1. Finds the decay behind the growth42% pass
    Does not compute the decline in sessions per profile (~−14%) despite having the 40% session growth and 62% tutor growth.
    GPT-6 Astra · ChatGPT · Growing on the surface, decaying underneath
  2. The loop maths holds47% pass
    The memo does not give a plain verdict of 'decaying' for the content loop despite showing its decline, and it does not compute a numeric yield or coefficient for that loop.
    GPT-6.1 Sol · API · Growing on the surface, decaying underneath
  3. Uses the supplied evidence correctly61% pass
    The claim that the base settles at 10,700 creators is unsupported by the pack's arithmetic, and the claim that cost per sign-up usually rises with spend is not in the supplied evidence.
    Opus 5.5 · Claude · The badge on every form

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. A closed loop, not a channel

    Does the output name one primary loop where a cycle's output demonstrably becomes the next cycle's input, chosen from where the best users come from, and size it with the supplied numbers?

    Passes when Names one closed loop, grounded in where the highest-retention users come from, and works out its yield or coefficient with retention applied.

  2. The loop maths holds

    Is every loop yield, coefficient and payback computed correctly from the supplied steps, with retention applied, and does the output say plainly whether each loop compounds, merely contributes, or decays?

    Passes when Every yield, coefficient and payback checks out against the pack, retention is in the sums, and each loop gets a plain verdict: compounding, contributing or decaying.

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 · Proposes tests that could fail · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're a PM at Pollen. Our Head of Growth, Sam Okoro, has two engineers for next quarter and four ideas for how to use them. Write Sam a memo of no more than 800 words that says what our primary growth loop is, whether it's compounding, and where the two engineers should go, with how we'll know it worked. Everything we know is below.

What the model was given5 items: About Pollen, Creators, Where new creators come from (last month), Revenue, The four ideas on the table
About PollenA free form and survey builder. Every published form shows a small 'Made with Pollen: make your own' badge at the bottom. Creators can upgrade to Pro for $20 a month to remove the badge and unlock logic and integrations.
Creators18,000 creators published at least one form last month, publishing 40,000 forms between them. Each form gets 120 respondents on average. 78% of last month's active creators were active again this month.
Where new creators come from (last month)Badge: 0.9% of respondents clicked the badge and 11% of those signed up; 38% of badge sign-ups published a form within 30 days, and 85% of those were active again the next month. Template pages (written by our team, ranking in search): 6,000 sign-ups, 14% published, 71% active the next month. Paid search: 2,100 sign-ups at $38 per sign-up, 21% published, 64% active the next month.
Revenue9% of creators who publish upgrade to Pro, and Pro customers stay for 14 months on average.
The four ideas on the table1. Double the paid search budget (Finance has approved it). 2. 'Build our SEO loop': 200 more template pages. 3. A referral programme: $10 of Pro credit for each friend who signs up. 4. Replace the badge with 'Make a form like this', which opens the editor with a copy of the form the respondent just filled in. A two-week pilot on 500 forms raised badge clicks from 0.9% to 1.6% of respondents; 11% of them signed up, as before, and 52% of those published within 30 days.
What a strong answer doesThe answer key the graders mark against

Names the badge as the primary loop: creators publish forms, respondents see the badge, some become creators who publish more forms. It's chosen because badge creators publish and stay best (38% publish, 85% retained), not because it brings the most sign-ups. Sizes it: 120 × 0.9% × 11% × 38% ≈ 0.045 new publishing creators per form, about 1,800 a month from 40,000 forms. Puts that against churn: 22% of 18,000 is about 3,960 creators lost a month, while all sources together add about 3,090 (1,806 + 840 + 441), so the base is shrinking and the loop contributes rather than compounds. Shows paid isn't a loop: $38 ÷ 21% ≈ $181 per publishing creator against about $25 of revenue each (9% × $20 × 14), so doubling it burns money. Calls the template pages a channel, not a loop, since our team writes them and creators don't. Gives the referral credit little weight, because it only matters to the 9% who pay. Puts both engineers on 'Make a form like this': the pilot gives 120 × 1.6% × 11% × 52% ≈ 0.11 creators per form, about 2.4× today, which would add roughly 2,600 creators a month if it held. Flags that the pilot was small and short, and proposes a full test with a threshold (for example, publishing creators per 1,000 respondents) and a stop condition. Mentions retention as the other lever.

Critical failures (cap the score at 40)
  • Recommends doubling paid acquisition, or calls it a growth loop
Task

Mid-level · v1.1

The outputs

9 setups answered this brief.

Usable with a quick edit9 of 11 checks passed

The memo is complete and actionable; a PM could add a stop condition and measurement window with a quick edit without redoing any reasoning.

Output

To: Sam Okoro, Head of Growth From: Product Subject: Put both engineers on the respondent-to-creator loop

Recommendation

Assign both engineers to “Make a form like this” next quarter: one to a safe, reliable copy-to-editor experience; the other to attribution, experimentation, and activation measurement. Do not double paid search or build the referral programme. Keep existing template acquisition running, but do not make 200 new pages the engineering priority.

Our primary growth loop

Creators publish forms → respondents encounter Pollen → some become creators and publish → their forms reach more respondents.

The badge closes this loop. Templates and paid search feed creators into it, but are not themselves loops: team-written SEO pages require more team production, and paid acquisition requires more spend.

Using the supplied volumes, last month’s 40,000 forms generated approximately 4.8 million respondent encounters:

SourceNew creators publishingStill active the following month
------:---:
Badge1,8061,535
Templates840596
Paid search441282

The badge is our largest acquisition source by activated creators and our strongest by next-month retention: 85%, versus 71% for templates and 64% for paid.

These badge totals assume the respondent volume is eligible to see the badge. Pro removes it, so we must measure actual badge exposure before treating these as forecasts.

Is it compounding?

It recirculates, but we have not demonstrated self-sustaining compounding.

At current rates, each active creator generates approximately 0.10 new publishing creators per monthly production cycle. Meanwhile, 22% of active creators do not return the next month.

A simplified replacement model therefore gives:

78% returning + 10% newly generated = 88% of the starting base.

The loop alone does not currently replace creator attrition. External acquisition can still grow Pollen, but that is different from autonomous compounding. This calculation is directional: publication, activation, and retention windows are not perfectly aligned, and future cohorts may retain differently.

The pilot is promising. Its click and publication rates imply 2.43 times as many new publishing creators per respondent:

  • Current: 0.376 publishing creators per 1,000 respondents
  • Pilot: 0.915 per 1,000

At unchanged volume, that would mean approximately 4,393 new publishing creators, versus 1,806 today. The same simplified replacement model reaches 78% + 24.4% = 102.4%—potentially crossing the self-sustaining threshold, but narrowly. Badge coverage, cohort maturity, retention, and repeat production could erase that margin.

Why this beats the alternatives

Paid search: Current spend is $79,800, or approximately $181 per publishing creator. Applying our overall upgrade rate and Pro lifetime, expected lifetime subscription revenue per publishing creator is only 9% × $20 × 14 = $25.20, before costs. Channel-specific monetization could differ, but the supplied economics do not justify scaling. Finance approval is not evidence of profitable growth.

More templates: They produce useful acquisition, but weaker activation and retention than the badge. We also have no evidence that 200 additional pages would reproduce existing traffic or conversion.

Referral credits: Paying for sign-ups rewards an intermediate action, invites low-quality referrals, and lacks pilot evidence. Revisit an activation-based incentive later.

Delivery and proof

Engineer 1: Build the contextual editor entry and reliable form-copy flow. Copy only permitted form structure—not respondent submissions, credentials, or integrations—and provide appropriate creator controls. Preserve Pro’s badge-removal benefit.

Engineer 2: Instrument exposure through publishing, next-month activity, downstream form distribution, and Pro conversion; run a persistent randomized test, clustered by source creator.

Our primary success metric is incremental creators publishing within 30 days per 1,000 eligible respondent encounters, not clicks or sign-ups. Target at least 2× control, with a statistically credible lift and no material deterioration in next-month retention.

Track form-completion rate, privacy complaints, Pro conversion, and badge-removal behavior as guardrails. Validate the pilot’s reported 30-day activation on mature cohorts; a two-week pilot alone cannot establish that outcome for every participant.

Finally, follow acquired creators’ subsequent publishing and respondent reach. Declare compounding only when mature production cohorts show that returning creators plus loop-generated creators consistently exceed the starting base without increased external acquisition.

Grades and run details

Decision model 82 · LLM judge 92
Decision model checks
  • failUses the supplied evidence correctly34%
  • passAddresses the actual decision98%
  • passRespects explicit constraints48%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims45%
  • passProduces the required deliverable76%
  • passCalls out the paid maths76%
  • passPicks the lever with the most yield92%
  • passA closed loop, not a channel100%
  • partialThe loop maths holds18%
  • partialProposes tests that could fail88%
Run
Run
#1
Time to output
48 s
Submitted
2 Oct 2026

Needs more than a quick edit5 of 11 checks passed

A PM would need to correct the badge-acquired creator count and add a test to confirm the pilot lift will hold at scale, so the output cannot be used without re-checking the evidence and redoing the rollout logic.

Output

MEMO

To: Sam Okoro, Head of Growth From: Product Management Date: May 24, 2024 Subject: Q3 Growth Engineering Allocation & Loop Analysis

Sam,

Ahead of Q3 planning, I analyzed our acquisition data to determine where we should allocate our two growth engineers.

The short answer: Both engineers should be dedicated to Idea #4 (the "Make a form like this" badge upgrade). It is the only option that pushes our primary viral loop past its tipping point into true compounding growth.

Here is the math behind where we stand today and why this is our highest-leverage move.

---

1. Our Primary Growth Loop (and Why It Isn’t Compounding Yet)

Our primary growth engine is our viral badge loop: Active Creators $\rightarrow$ Publish Forms $\rightarrow$ Collect Responses $\rightarrow$ Respondents Click Badge $\rightarrow$ Sign Up $\rightarrow$ Publish Forms.

Last month, this loop generated 59% of all new active creators (1,806 published creators via the badge, compared to 840 from SEO templates and 441 from paid search). Badge-acquired creators also have our highest activation rate (38%) and highest next-month retention (85%).

However, this loop is currently sub-critical (not compounding).

To compound, our net growth rate must exceed our monthly creator churn. Here is the math on last month’s performance: * Free Creators: Of our 18,000 active creators, 9% (1,620) upgraded to Pro, removing the badge. That leaves 16,380 free creators driving the loop. * Impressions: At an average of 2.22 forms per creator (40k / 18k) and 120 respondents per form, each free creator generates 266.7 badge impressions. (4.37M total impressions across the free base). * Conversions: 4.37M impressions $\times$ 0.9% CTR = 39,312 clicks $\times$ 11% signup rate = 4,324 signups $\times$ 38% publish rate = 1,643 new active creators. (Note: Even if Pro forms carried badges last month to yield 1,806 new creators, the per-creator math remains the same). * The Viral Factor ($K$): 1,643 new creators / 16,380 free creators = 0.10 new creators per active free creator per month.

The Churn Problem: We lose 22% of our active creators each month (78% retention), and another ~9% upgrade to Pro and exit the viral loop. Because our viral generation rate (+10%) is lower than our loop attrition (-22% to -31%), the loop is decaying on its own. Right now, our total creator base is only growing because we prop it up with linear channels (SEO and Paid Search).

---

2. Evaluating the Four Ideas

1. Double Paid Search Budget (Pass — Negative ROI) * We pay $38 per signup, and 21% publish. That is a $181 CAC per active creator. * Only 9% of active creators upgrade to Pro ($20/mo for 14 months = $280 LTV). That means our expected LTV per active creator is only $25.20 ($280 $\times$ 9%). * Verdict: We are losing ~$155 on every active creator we buy. Not only does this not require engineering, we should actually talk to Finance about cutting this spend, not doubling it.

2. "Build our SEO Loop" with 200 Template Pages (Pass — Not a Loop) * Because our team writes these pages rather than our users, this is a linear content marketing funnel, not a compounding loop. * Furthermore, template signups have our worst activation rate (14% publish vs. 38% for the badge) and mostly calls on content/design resources, not engineering.

3. Referral Programme for $10 Pro Credit (Pass — Low Leverage) * Only 9% of creators care about Pro features enough to pay for them; for the other 91% on our free tier, $10 in Pro credit is an weak incentive. Building a referral ledger and billing integration will easily eat two engineers' quarter for an unproven, likely tiny audience.

4. "Make a form like this" Badge (WIN — Pushes Loop to Compounding) * By dropping respondents straight into an editor with a cloned template, our 500-form pilot radically improved the funnel: CTR jumped from 0.9% to 1.6% (+78%), and the publish rate jumped from 38% to 52% (+37%). * New Loop Math: 266.7 impressions per creator $\times$ 1.6% CTR $\times$ 11% signup $\times$ 52% publish = 0.244 new active creators per free creator each month. * Even accounting for the 9% of creators who upgrade to Pro and remove the badge, every active creator now generates 0.222 new creators/month. Combined with the higher retention of badge cohorts (85%, or only 15% churn), our viral inflow (+22.2%) finally outpaces cohort churn (-15%). This makes Pollen self-sustaining and genuinely compounding. * Rolling this out across 40,000 forms next month will take our monthly badge-driven active creators from ~1,800 to ~4,390 (+2,590 net new creators/month), adding ~$65,000 in new LTV every single month from Month 1.

---

3. Recommendation & Success Metrics

Put both engineers on Idea #4 to build the form-cloning architecture, handle privacy/sanitization (ensuring respondent data isn't copied over with the form structure), and ship the new badge UI to 100% of free forms.

How we’ll know it worked (Success Metrics for Q3): 1. Badge CTR: Sustains at $\ge 1.5\%$ across all free forms (up from 0.9%). 2. Signup-to-Publish Rate: Sustains at $\ge 50\%$ within 30 days for badge signups (up from 38%), without cannibalizing the 11% click-to-signup rate. 3. Guardrail Metric: Creator complaint/churn rate remains flat (verifying that original creators don't mind having their form structures cloned). 4. North Star Impact: Monthly new published creators from the badge increases from ~1,800 to >4,000/month within 30 days of full rollout.

Claims the judge couldn’t find in the brief

  • Last month, the badge loop generated 1,806 published creators

Grades and run details

Decision model 59 · LLM judge 58
Decision model checks
  • failUses the supplied evidence correctly16%
  • passAddresses the actual decision76%
  • passRespects explicit constraints37%
  • partialIdentifies material uncertainty37%
  • failAvoids unsupported claims73%
  • passProduces the required deliverable64%
  • passCalls out the paid maths98%
  • partialPicks the lever with the most yield97%
  • passA closed loop, not a channel100%
  • failThe loop maths holds13%
  • partialProposes tests that could fail83%
Run
Run
#1
API response time
16 s
Submitted
13 Oct 2026

Needs more than a quick edit9 of 11 checks passed

The memo contains an unsupported equilibrium number (10,700) and an unsupported claim about rising cost per sign-up, so a PM would need to re-check those parts before using it.

Output

To: Sam Okoro, Head of Growth From: [PM], Growth Re: Our growth loop, and where the two engineers should go next quarter

Summary: Our primary loop is the badge. Today it does not compound, and the active creator base is shrinking. The "Make a form like this" pilot is the only one of the four ideas that could make it compound. Both engineers should ship it properly, with a holdout. We should not double paid search.

1. The loop

Creators publish forms → respondents see the badge → some respondents become creators → they publish forms.

Last month the loop worked like this: - 40,000 forms × 120 respondents = 4.8M respondents - 0.9% clicked the badge = 43,200 clicks - 11% of clicks signed up = 4,752 sign-ups - 38% of sign-ups published = 1,806 new publishing creators

The badge is our largest source of creators. Templates produced 840 publishers and paid search 441. Badge creators also retain best: 85% are active the next month, against 71% for templates and 64% for paid.

2. Is it compounding? Not yet.

Each active creator produces about 267 respondents a month, which yields 0.10 new publishers per creator per month. With 78% monthly retention, a creator stays about 4.5 months. Over that lifetime, each creator brings in about 0.46 new creators.

A loop compounds only when this number exceeds 1. At 0.46, the badge amplifies our other channels by about 1.8×, but it cannot sustain itself.

The overall picture is worse: - We keep 14,040 of last month's 18,000 active creators. - We add about 3,100 new publishers. - That gives about 17,100 active creators, a 5% monthly decline. - At current rates the base settles at roughly 10,700 creators.

This assumes the 78% retention figure covers the whole base, which is how I read the data.

3. The four ideas

Double paid search: no. - Each sign-up costs $38. Only 21% publish, so each publishing creator costs about $181. - Each publisher is worth about $25 in revenue (9% upgrade × $20 × 14 months). - Adding the loop's downstream creators raises that to only about $46. - Doubling spend would add about 440 publishers a month for an extra $80k a month, and cost per sign-up usually rises with spend. - This idea doesn't need engineers. I'd ask Finance to hold the budget rather than spend it because it's approved.

200 more template pages: not now. - This is not a loop, because new creators don't create more pages. Our team writes them, so output grows only as fast as we write. - We don't know how many pages produce today's 6,000 sign-ups, so we can't estimate the yield. - Templates publish at only 14%. - This is mainly content work, not engineering work.

Referral credit: no. - It rewards sign-ups, not publishing, which invites low-quality and fake sign-ups. - $10 of Pro credit means nothing to the 91% of creators who never upgrade.

"Make a form like this": yes. The pilot changed every stage of the loop:

Current badgePilot
Click rate0.9%1.6%
Sign-up rate11%11%
Publish rate38%52%
New publishers per 1,000 respondents0.380.92

At network scale, that would mean about 4,400 badge publishers a month instead of 1,800. Each creator's lifetime yield would rise from 0.46 to about 1.1, so the loop would start to compound. Next month's active base would be about 19,700 instead of 17,100.

The margin is thin. The loop reaches the break-even of 1.0 at 0.83 publishers per 1,000 respondents. The pilot beat that by only about 10%. We also need to treat the pilot result with caution: - It covered 500 forms, which may not be representative. - It ran for two weeks, so check whether the 30-day publish window had fully elapsed. - We have no next-month retention data for these creators. People who copy a form may be less committed than people who build one.

4. What the engineers build

1. Staged rollout with a 20% holdout of forms on the old badge, randomised by form and run for at least 8 weeks so we see 30-day publishing and next-month retention. 2. Creator controls and safety: - Copies take the form structure only, never responses. - Creators can turn off copying for sensitive forms. 3. Instrumentation for each stage: click, sign-up, publish, next-month activity, and Pro upgrade. Results should be split by form type so we learn which forms spread best.

5. How we'll know it worked

Primary metric: new publishing creators per 1,000 respondents, treatment vs holdout. - Ship to 100% if it stays at or above 0.83, the level needed for the loop to compound. - Below about 0.6, the change is still positive but the loop won't compound. We would keep it and look for other levers.

Quality checks: - Next-month retention of copy sign-ups should be at least 70%. If it falls much lower, the extra volume won't last. - The upgrade rate of these creators to Pro should be close to 9%.

Guardrails: - Form completion rate is unchanged. - Creator complaints and copy opt-outs are low. - Pro upgrade rate across the base is unchanged.

Business outcome: the active creator base grows month on month by the end of the quarter, instead of falling about 5%.

Bottom line: paid search and templates rent growth one sign-up at a time. The copy-this-form change is our one realistic chance to make growth compound, and a proper rollout will tell us within a quarter whether it does.

Claims the judge couldn’t find in the brief

  • At current rates the base settles at roughly 10,700 creators.
  • Cost per sign-up usually rises with spend.

Grades and run details

Decision model 91 · LLM judge 75
Decision model checks
  • passUses the supplied evidence correctly40%
  • passAddresses the actual decision100%
  • passRespects explicit constraints48%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable89%
  • passCalls out the paid maths95%
  • passPicks the lever with the most yield97%
  • passA closed loop, not a channel100%
  • passThe loop maths holds11%
  • partialProposes tests that could fail41%
Run
Run
#1
Time to output
55 s
Submitted
2 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 6

Uses the supplied evidence correctlyMixedWrongMixed
GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly supported by the brief or derived by correct arithmetic.

Gemini 4 Argon · API

The claim that the badge generated 1,806 published creators last month assumes all 40,000 forms showed the badge, ignoring that 9% of creators are Pro and remove it; this number is not supported by the brief.

Opus 5.5 · Claude

The claim that the base settles at 10,700 creators is unsupported by the pack's arithmetic, and the claim that cost per sign-up usually rises with spend is not in the supplied evidence.

Identifies material uncertaintyRightWrongRight
GPT-6 Astra · ChatGPT

Names badge exposure uncertainty, pilot size limitation, cohort maturity, and retention variability, and says to validate on mature cohorts and measure actual exposure.

Gemini 4 Argon · API

The output does not identify that the pilot's small size (500 forms, two weeks) makes the lift uncertain at full scale, nor does it specify a condition that would change the recommendation before full rollout.

Opus 5.5 · Claude

Flags pilot's small size, two-week window, missing retention data, and representativeness, and resolves them with an 8-week holdout test and thresholds.

Avoids unsupported claimsRightMixedWrong
GPT-6 Astra · ChatGPT

Forecasts and interpretations are labelled as directional or potential, and no confident claims go beyond the supplied evidence.

Gemini 4 Argon · API

Interpretive claims like the loop being 'sub-critical' are presented as conclusions from the math, not as unsupported facts.

Opus 5.5 · Claude

Presents the 10,700 equilibrium and the rising cost-per-sign-up claim as facts without labelling them as hypotheses or supporting them from the pack.

Picks the lever with the most yieldRightWrongRight
GPT-6 Astra · ChatGPT

Chooses the copy-as-template badge, sizes its yield from the pilot (0.915 per 1,000, ~2.4×), and flags the small two-week pilot before relying on it.

Gemini 4 Argon · API

Although it chooses the copy-as-template badge and sizes its yield, it does not flag that the pilot was small and may not scale before relying on its results.

Opus 5.5 · Claude

Chooses the copy-as-template badge, sizes its yield from the pilot (0.92 per 1,000 respondents, ~4,400 publishers, ~2.4× current), and notes the pilot's small size.

The loop maths holdsRightMixedRight
GPT-6 Astra · ChatGPT

Yield (0.10), retention (78%), and replacement (88%) are correctly computed; verdict is 'recirculates but not self-sustaining compounding'.

Gemini 4 Argon · API

The current loop yield (0.10) and pilot yield (0.244) are computed correctly from the given rates and retention, and the badge loop is plainly labeled as decaying today.

Opus 5.5 · Claude

Correctly computes yield per creator (0.10/month, 0.46 lifetime), retention (4.5 months), amplification (1.8×), and break-even (0.83), and says the loop does not compound.

Proposes tests that could failWrongWrongRight
GPT-6 Astra · ChatGPT

Proposes a test with a numeric threshold (2× control) but does not specify a measurement window or what action each outcome triggers.

Gemini 4 Argon · API

The success metrics are for post-rollout measurement, not a pre-commitment gate with a numeric threshold, a window, and a kill condition before full rollout.

Opus 5.5 · Claude

Proposes a holdout test with a numeric threshold (0.83 to ship, below 0.6 to keep but seek other levers), an 8-week window, and quality checks with thresholds.

All got right 5

Addresses the actual decisionRightRightRight
GPT-6 Astra · ChatGPT

Commits early to putting both engineers on 'Make a form like this', says how we'll know it worked (2× control threshold, guardrails), and states what would change the compounding declaration.

Gemini 4 Argon · API

The memo commits early and unambiguously to putting both engineers on Idea #4, and provides success metrics that would indicate if the rollout worked.

Opus 5.5 · Claude

Commits early to putting both engineers on 'Make a form like this' with a holdout, says not to double paid search, and specifies what metric would change the call.

Respects explicit constraintsRightRightRight
GPT-6 Astra · ChatGPT

Memo is addressed to Sam, from Product, under 800 words, and covers the required topics.

Gemini 4 Argon · API

The output is a memo addressed to Sam, under 800 words, and proposes to enforce the chosen idea by shipping it across free forms.

Opus 5.5 · Claude

Memo is addressed to Sam Okoro, stays within ~800 words, and proposes enforcement mechanisms (holdout, controls).

Produces the required deliverableRightRightRight
GPT-6 Astra · ChatGPT

Delivers a complete, actionable memo within the word limit that Sam could use to assign engineers and measure success.

Gemini 4 Argon · API

The memo is complete, within the word limit, and provides an actionable plan with success metrics that a PM could act on.

Opus 5.5 · Claude

Delivers a complete memo to Sam with a clear recommendation, how to measure success, and is within the word limit.

Calls out the paid mathsRightRightRight
GPT-6 Astra · ChatGPT

Computes $181 per publishing creator and $25.20 revenue, and states plainly that doubling paid would lose money.

Gemini 4 Argon · API

The memo calculates $181 CAC per publishing creator and $25.20 revenue per creator, and states plainly that doubling paid search would lose money.

Opus 5.5 · Claude

Calculates $181 cost per publishing creator and $25 revenue, states doubling would lose money, and recommends against it.

A closed loop, not a channelRightRightRight
GPT-6 Astra · ChatGPT

Names the badge as the primary closed loop, grounded in highest retention (85%), and sizes its yield with retention applied (0.10 per creator, 88% replacement).

Gemini 4 Argon · API

The badge loop is named as the primary closed loop, chosen because badge creators have the highest retention, and its yield is sized with the supplied numbers.

Opus 5.5 · Claude

Names the badge as the primary closed loop, grounds it in highest-retention creators, and sizes its yield with retention applied.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI91.596.22None
2GPT-6.1 SolwithAPI89.287.82None
3GPT-6 AstrawithChatGPT84.784.32None
4Opus 5.5withClaude89.264.42None
5Haiku 5.5withAPI80.567.92None
6GPT-6 LunawithAPI78.064.12None
7Gemini 4 ArgonwithAPI67.067.62None
8Gemini 3.8 FlashwithAPI73.363.52None
9Gemini 3.5 Flash-LitewithGemini57.635.32None

What you get for the cost

GPT-6.1 Sol · API scores within 5 points of Sonnet 5.5 · API at a fifth of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 92, 44× the cheapestGPT-6.1 Sol · API: task score 87, 9.3× the cheapestGPT-6 Astra · ChatGPT: task score 85, 53× the cheapestOpus 5.5 · Claude: task score 73, 54× the cheapestHaiku 5.5 · API: task score 71, 2.1× the cheapestGPT-6 Luna · API: task score 68, the cheapestGemini 4 Argon · API: task score 67, 9.3× the cheapestGemini 3.8 Flash · API: task score 65, 8.2× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 44, 1.7× the cheapestSonnet 5.5 · APIGPT-6.1 Sol · APIGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeHaiku 5.5 · APIGPT-6 Luna · APIGemini 4 Argon · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API681.0×
Gemini 3.5 Flash-Lite · Gemini441.7×
Haiku 5.5 · API712.1×
Gemini 3.8 Flash · API658.2×
Gemini 4 Argon · API679.3×
GPT-6.1 Sol · API879.3×
Sonnet 5.5 · API9244×
GPT-6 Astra · ChatGPT8553×
Opus 5.5 · Claude7354×

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

Working out what actually drives growth, and where to push.

Why it matters

Teams tune funnel steps while the loop that compounds goes unmeasured. Mistaking a channel for a loop can cost a year.

What good looks like

  • A closed loop: each cycle's output feeds the next
  • The primary loop, traced from where the best users come from
  • The loop sized: cycle time, conversion, amplification
  • Retention in the maths
  • One lever, with a test that could fail

Deliberately not measured

  • Building a full growth model in a spreadsheet
  • Channel-level media planning
Capability tested

Growth systems thinking

The failure we’re looking for

Calls a channel a loop, or a referral button a viral loop

Grading

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