Tasks / Strategy

1000x an idea

Can the model expand an idea's ambition while keeping it tethered to a real mechanism?

Task type v1.0 · 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. 56% were usable with at most a quick edit.

Reliably right

  1. Extreme, then back to buildable100% pass
    Pushes from two-person sharing to the extreme household as the unit (any number of members, live pantry state, learning routines, delivery integration), then works back to the scoped two-person build that tests coordination.
    Haiku 5.5 · API · Sharing a shopping list, 1000x
  2. Finds the mechanism in the data100% pass
    Uses the retention, conversion, session frequency, notification triggers, note usage, and invitation context to identify shared money decisions as the core mechanism, not just budgeting.
    GPT-6.1 Sol · API · Budgets for two
  3. Finds the mechanism in the data97% pass
    Names household coordination as the core value (multiple members, claiming items, no duplicate buying), backs it with the 71%/22% retention gap, 38% second-device items, and duplicate tickets, and keeps coordination at the center of both the big vision and the first step.
    Haiku 5.5 · API · Sharing a shopping list, 1000x

Where it slips

  1. Proposes tests that could fail41% pass
    The proposed test does not name a numeric threshold for the primary outcome; it only says to set minimum detectable effects and thresholds before launch, which is a process, not a specific threshold.
    GPT-6.1 Sol · API · Budgets for two
  2. The big version gets stronger as it grows61% pass
    The memo names a demand network as a compounding mechanism but does not propose a test of whether that network effect would be large enough to matter, nor does it ask that question.
    GPT-6 Astra · ChatGPT · Sharing a shopping list, 1000x
  3. Avoids unsupported claims66% pass
    The statements about most financial decisions being made by more than one person and happening in texts/arguments/spreadsheets are presented as established fact without support from the supplied evidence.
    Sonnet 5.5 · API · Budgets for two

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. The big version gets stronger as it grows

    Does the ambitious version name a mechanism that makes it more valuable or harder to copy as it grows (each new user adding value for others, or a barrier a competitor couldn't buy), and ask whether that effect would be large enough to matter?

    Passes when Names the mechanism, how it compounds with scale, and a test of whether the effect is big enough to matter.

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 · Extreme, then back to buildable · Proposes tests that could fail · and 1 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 Pocketbook. Ana has proposed shared budgets for couples; before we commit next quarter, our CEO wants the 1000x version (her note is below). Write the memo, in no more than 900 words: the most ambitious version of the idea, the mechanism that makes it work, and then what we build first and how we'll know it's working. The data is attached.

What the model was given5 items: About Pocketbook, ana_idea.md, ceo_note.md, usage_by_user_group.csv, what_shared_budgets_do.md
About PocketbookA personal budgeting app: connect your accounts, set budgets, track spending. Free, with Premium at $6 a month.
ana_idea.md3 lines · Download
# The idea (from Ana, PM)

"Let people share a budget with their partner. It's our most requested feature after bank sync for more banks."
ceo_note.md1 lines · Download
From Ruth (CEO): "Before we commit next quarter to Ana's idea, show me the 1000x version. How big could this get? Then bring it back to what we build first and how we'll know it's working. Keep it under 900 words."
usage_by_user_group.csv3 rows · Download
user_group,users,retained_at_6_months_pct,invited_someone_pct,invitee_joined_pct,premium_conversion_pct
Solo budgeters,182000,17,3,22,4
Couples sharing one budget,24600,58,100,100,19
Households (3+ people on one budget),3900,71,100,100,31
what_shared_budgets_do.md6 lines · Download
# What shared budgets do (from the data team)

- Couples who share a budget open the app on more days than solo users (11 a month against 4) and most sessions start from a notification about the other person's spending ("Alex spent $48 at the grocery store").
- 41% of couples add a "money talk" note to a transaction in their first month; the note feature was built as an afterthought.
- 16% of couples add a third person within a year (a teenager, a parent they support).
- Shared budgets started from an invite link in a shared-expenses moment (rent, a holiday) retain best.
What a strong answer doesThe answer key the graders mark against

Finds the mechanism in the data: shared money decisions, not budgeting. Couples sharing a budget retain at 58% against 17% for solo budgeters, convert to Premium at 19% against 4%, open the app on 11 days a month against 4 (mostly from notifications about the other person's spending), and use transaction notes as 'money talk'; households of 3+ retain at 71% and convert at 31%. Every shared budget brings in at least one new user by design, and 16% of couples add a third person. So the 1000x version is the place a household runs its money together (shared goals, who pays for what, conversations on transactions, extended family), growing through invitations at shared-expense moments (rent, holidays). Asks whether the effect is large enough to matter: each new member makes the budget more useful to the others, so it compounds. Works back to a first step that keeps the mechanism: shared budgets launched from an invite at a shared-expense moment, with spending notifications and transaction notes built in, measured on invites accepted, retention of invited users, and Premium conversion for couples, with thresholds.

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit9 of 10 checks passed

The 90-day target for 6-month retention creates a minor timeline inconsistency, but it is otherwise complete, evidence is correctly used, and a PM can adjust the metric timing with a quick edit.

Output

Memo: Shared Budgets, the 1000x Case and a First Build

To: Ruth From: PM, Pocketbook Re: Ana's shared-budget proposal

Recommendation

Commit next quarter to shared budgets, scoped as a household product rather than a feature. Start with the invite and the partner-activity loop, and hold the wider household roles until the couple data holds up.

The 1000x version

Today, Pocketbook is a product for one person. Shared budgets make the household the unit. The 1000x version is a household financial operating system: the place a couple, and eventually a family, runs its shared money. That covers bills, spending, savings goals, allowances for teens, and money support for a parent.

The scale argument rests on what the data already shows:

  • Couples are worth far more per user. Couples retain at 58% at six months against 17% for solo users. Their premium conversion is 19% against 4%.
  • Shared users are 14% of our base and about 45% of premium revenue. Our 24,600 couples and 3,900 households produce roughly $35k a month in premium revenue. Our 182,000 solo users produce about $44k.
  • The invite is the bottleneck, not the product. Solo users invite at 3%, but 22% of invitees join. Most people who would share have never been asked.

If couples alone reached 1000x today's count (about 24.6M couples) and kept today's 19% conversion, that would be roughly 4.7M premium users, or about $28M a month. Conversion won't hold at that scale, so treat this as an upper bound. The point is the shape: one shared budget brings in two daily users and a higher-value subscription. Households add a third person, which the data suggests happens for 16% of couples within a year.

The mechanism

Three loops drive it.

  1. Notification pull. Couples open the app on 11 days a month, against 4 for solo users. Most sessions start from a message about a partner's spending ("Alex spent $48 at the grocery store"). Each shared budget creates a second daily user who is pulled back by the other person's activity.
  1. Conversation as the product. 41% of couples add a "money talk" note to a transaction in their first month. The note was an afterthought, but it is the behavior that most resembles what the product is for: couples discussing money inside the app. Making it a first-class feature deepens the habit.
  1. Expansion through real life. Couples who add a third person (a teenager, a parent they support) move from a two-person budget to a household budget. Households retain at 71% and convert at 31%. Each expansion raises value per account.

The invite is the entry point. Shared budgets started from a shared-expense moment, such as rent or a holiday, retain best. The invite should appear at the moment money is actually shared, not as a generic "invite your partner" button.

What we build first

1. An invite anchored to a shared-expense moment. When a user sets up a recurring shared bill (rent, utilities) or tags a transaction as shared, offer to bring in the partner. This targets the 3% invite rate directly.

2. Partner-activity notifications, with controls. The data says these drive sessions. Ship them with clear, per-transaction hiding and easy unsharing from day one.

3. Promote the money-talk note. Make notes on transactions a visible, first-class part of a shared budget.

Deliberately deferred: third-person and household roles, teen allowances, and parent-support flows. We should build these only once the couple version shows retention holding up.

Guardrails. A product where one partner can see the other's spending creates real risk, including financial control in abusive relationships. Before launch we need: - Opt-in visibility, with each person able to hide transactions from the other. - Unsharing that is easy and fully private, with no notice to the partner. - A separate review of defaults and copy with the trust and safety team.

How we'll know it's working

Set baselines now, and measure against cohorts rather than the current group averages.

MetricBaseline90-day target
Invite rate among solo users3%6%
Invitee join rate22%30%
Share of new couples started from a shared-expense momentnot tracked50%+
Couples adding a money-talk note in month one41%50%
New-couple 6-month retention58% (cohort, not yet measured)at or above 50%
Couple premium conversion19%15%+

Caveat on the data. The invited-someone and invitee-joined columns read 100% for the couple and household groups. That is true by definition, since these groups only exist because someone invited a partner. The real comparison is the solo invite rate (3%) against the join rate (22%). The 58% and 71% retention figures may also overstate the effect of shared budgets: couples who stay together may be the ones who stick with the app. We need a randomized holdout, with invited and non-invited users matched on tenure and activity, before we credit the product for that retention.

Kill or rethink criteria. If invites don't lift past 4% within the quarter, or if new-couple retention doesn't separate from the holdout, we stop expanding the feature and keep the notifications and notes as solo-plus-partner improvements.

Grades and run details

Decision model 80 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly53%
  • passAddresses the actual decision99%
  • passRespects explicit constraints31%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims45%
  • passProduces the required deliverable83%
  • passFinds the mechanism in the data100%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail59%
  • passThe big version gets stronger as it grows72%
Run
Run
#1
API response time
20 s
Submitted
8 Oct 2026

Check by check

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

Mixed 1

Uses the supplied evidence correctlyMixed

Every statement about the current situation is taken directly from or derived arithmetically from the supplied context.

Got right 9

Addresses the actual decisionRight

Commits early to building shared budgets as a household product, and gives precise kill criteria that would change the call.

Respects explicit constraintsRight

Delivers a memo addressed to the CEO, covers all requested sections, and stays well under 900 words.

Identifies material uncertaintyRight

Acknowledges retention may be inflated by selection bias, conversion won't hold at scale, and requires a randomized holdout to resolve.

Avoids unsupported claimsRight

Interpretations and forecasts are hedged ('may overstate', 'treat as upper bound'), and no hypotheses are presented as established fact.

Produces the required deliverableRight

Memo includes the 1000x vision, mechanism, first build, and success metrics, and is directly usable by the named reader.

Finds the mechanism in the dataRight

Identifies shared money decisions as the mechanism (partner notifications, transaction notes, invites at shared moments) using retention, conversion, and session data.

Extreme, then back to buildableRight

Pushes along the dimension of household scope to a household financial OS, then scales back to a first build of invite, notifications, and notes that tests the same mechanism.

Proposes tests that could failRight

Kill criteria name a numeric threshold (invites >4%), a window (quarter), and the action (stop expanding); other metrics in the table have 90-day targets.

The big version gets stronger as it growsRight

Describes how each added household member pulls others back via notifications and notes, and tests if the effect is large enough via invite lift and retention thresholds.

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 88% of 118 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Haiku 5.5withAPI85.5100.02None
2GPT-6 AstrawithChatGPT93.091.32None
3GPT-6.1 SolwithAPI90.787.12None
4GPT-6 LunawithAPI88.283.02None
5Opus 5.5withClaude92.778.42None
6Sonnet 5.5withAPI90.774.22None
7Gemini 3.8 FlashwithAPI52.539.02None
8Gemini 3.5 Flash-LitewithGemini50.534.82None

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.304050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreHaiku 5.5 · API: task score 93, 2.1× the cheapestGPT-6 Astra · ChatGPT: task score 92, 74× the cheapestGPT-6.1 Sol · API: task score 89, 15× the cheapestGPT-6 Luna · API: task score 84, the cheapestOpus 5.5 · Claude: task score 84, 59× the cheapestSonnet 5.5 · API: task score 81, 31× the cheapestGemini 3.8 Flash · API: task score 43, 9.1× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 40, 2.9× the cheapestHaiku 5.5 · APIGPT-6 Astra · ChatGPTGPT-6.1 Sol · APIGPT-6 Luna · APIOpus 5.5 · ClaudeSonnet 5.5 · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API841.0×
Haiku 5.5 · API932.1×
Gemini 3.5 Flash-Lite · Gemini402.9×
Gemini 3.8 Flash · API439.1×
GPT-6.1 Sol · API8915×
Sonnet 5.5 · API8131×
Opus 5.5 · Claude8459×
GPT-6 Astra · ChatGPT9274×

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

Finding the bigger version of a good idea.

Why it matters

Ambition without mechanism is fan fiction. The useful version pushes to the extreme, then works back to something buildable.

What good looks like

  • Names the mechanism that scales
  • Keeps the core insight
  • Works back to a first step you could build

Deliberately not measured

    Capability tested

    Ambitious expansion

    The failure we’re looking for

    Bigger adjectives, same idea

    Grading

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