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

Usable with a quick edit9 of 10 checks passed

The memo is well-structured and evidence-based; a PM could add a specific numeric threshold for the test outcome with a quick edit, without redoing any reasoning.

Output

From shared budgets to the household money network

To: Ruth From: Product Decision: Invest next quarter in testing shared money coordination—not simply adding a second user to a budget.

The 1000x version

Pocketbook becomes the place people manage money with everyone whose financial life touches theirs.

Start with a couple managing groceries and rent. Expand to saving for a home, giving a teenager spending autonomy, supporting a parent, and coordinating expenses across a blended family. One person can belong to several private money circles without exposing their entire financial life.

Today, Pocketbook answers: “Where did my money go?” The ambitious version answers: “What can we afford together, what have we agreed to, and what happens next?” It becomes a durable coordination layer above individual bank accounts—not a joint bank account and not a public financial social network.

That expands both our acquisition model and our reason to exist. Each shared obligation can introduce another person; each new life stage can create another circle. We stop rebuilding an individual’s budgeting habit from scratch and become part of an ongoing relationship.

This could support a materially larger subscription business. For scale, one million users converting at the observed couples rate of 19% would represent $13.7 million in annualized subscription revenue at $6/month. That is an illustration, not a forecast: we have neither established a million-user market nor shown that sharing causes that conversion rate.

Why it could work

Our strongest signal is not feature-request volume. It is that shared budgeting appears to change the product’s underlying behavior loop.

User groupSix-month retentionPremium conversion
------:---:
Solo budgeters17%4%
Couples sharing one budget58%19%
Households of 3+71%31%

Couples also open Pocketbook on 11 days per month versus four for solo users. Most sessions start with a notification about a partner’s spending. And 41% add a “money talk” note during their first month—even though notes were an afterthought.

The mechanism is coordination:

  1. A real obligation creates an invitation. Rent or a holiday provides a concrete reason to join. These shared-expense entry points already retain best.
  2. Shared activity creates useful context. A transaction changes what remains available to both people.
  3. Context creates a conversation or decision. A note resolves uncertainty; a budget adjustment records the agreement.
  4. Repeated decisions create shared history. Pocketbook becomes more useful as a relationship’s financial memory accumulates.
  5. New responsibilities extend the network. Sixteen percent of couples add a third person within a year.

This is a local network effect: the product improves when the right person joins, not when everyone joins.

But the evidence is observational. Existing sharers may be unusually motivated or financially organized. The groups’ 100% invitation and join rates largely reflect who qualifies as a sharer; they are not evidence of a frictionless funnel. We must test whether we can create the behavior, not merely identify people who already have it.

What we build first

A private, two-person shared-expense circle, launched around one immediate obligation.

Next quarter’s experience:

  • Create a circle for rent, groceries, or a trip; set an amount and timeframe.
  • Invite a partner through a link that explains the purpose and exactly what will be visible.
  • Let the partner join without first connecting a bank account.
  • Explicitly select transactions to share; keep all other accounts and spending private.
  • Show the shared amount remaining and support transaction-level notes.
  • Offer controllable activity notifications and a lightweight weekly check-in.
  • Make leaving, revoking access, and handling shared history understandable.

We should build on existing sharing and notes rather than launch an entirely separate product. We should not begin with payments, joint accounts, complex household roles, or automated financial advice.

Trust is part of the mechanism. Unwanted visibility can turn coordination into surveillance. Privacy controls and notification consent are launch requirements, not follow-up polish.

How we know it is working

Randomize eligible solo users, before they invite anyone, into the new shared-expense journey or today’s experience. Keep each resulting circle in one experiment arm. Analyze outcomes per assigned initiator, not only among successful sharers.

Primary near-term outcome: incremental eight-week retained coordinators—initiators whose circle has both people active and a meaningful shared-budget action in weeks seven or eight.

Track the full mechanism: invitation sent → partner joins → both contribute → first note or budget decision → repeated coordination. Notification opens alone do not count as success.

Also measure net additional retained invitees, Premium conversion, and support costs. Guardrails include privacy complaints, unwanted notifications, partner exits, and any reduction in initiators’ overall retention.

Set the minimum worthwhile lift and required sample size before launch using baseline rates and implementation costs. Scale only with credible retention improvement, healthy guardrails, and evidence that both partners get value. Maintain a holdout for six-month retention and monetization.

The bet is not that two people need one spreadsheet. It is that shared financial decisions can become Pocketbook’s acquisition engine, daily utility, and long-term moat.

Grades and run details

Decision model 95 · LLM judge 91
Decision model checks
  • passUses the supplied evidence correctly56%
  • passAddresses the actual decision91%
  • passRespects explicit constraints31%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims53%
  • passProduces the required deliverable70%
  • passFinds the mechanism in the data100%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail76%
  • passThe big version gets stronger as it grows89%
Run
Run
#1
Time to output
37 s
Submitted
5 Oct 2026

Usable with a quick edit8 of 10 checks passed

The memo is well-structured, data-driven, and actionable. The unsupported market claim can be removed with a quick edit without redoing any reasoning.

Output

Shared budgets: the 1000x version and what we build first

To: Ruth | From: PM, Pocketbook

Recommendation

Commit next quarter, but frame the work as a growth loop, not a feature. The loop is the product's invite funnel, and today it is nearly empty. The larger vision is a plausible direction, not a forecast.

The 1000x version

Pocketbook becomes where households make money decisions together, not a tool one person uses to watch their own spending.

Most financial decisions are made by more than one person: rent, holidays, supporting a parent, a teenager's first card. Today those decisions happen in texts, arguments, and spreadsheets. The ambitious version is the shared layer for that: shared budgets, shared goals, bills and "who's paying" coordination, and the conversation about money attached to the transactions themselves. Couples are the wedge. Households of three or more, such as families, adult children supporting parents, and roommates, are the expansion. Each household brings 2–4 people into the product, and every one of them is a potential Premium payer.

The mechanism

The mechanism is a two-sided engagement loop that doubles as an acquisition channel:

  1. A shared budget requires a second person, so every one is an invite.
  2. Once shared, each person's spending gives the other a reason to open the app. Most couple sessions start from a notification like "Alex spent $48 at the grocery store." Couples open the app 11 days a month versus 4 for solo users.
  3. That activity triggers conversation. 41% of couples add a "money talk" note in month one, even though we built that feature as an afterthought.
  4. Households grow. 16% of couples add a third person within a year.

A solo budgeter has to supply their own discipline every day. A shared budgeter gets reminded by someone they care about, and that is a much sturdier retention engine.

What the data says, and what it doesn't

GroupUsers6-mo retentionPremium conversion
Solo182,00017%4%
Couples24,60058%19%
Households3,90071%31%
  • Shared users are about 14% of users but roughly 45% of Premium payers (about 5,900 of 13,200 by my arithmetic).
  • The bottleneck is the invite funnel. Only 3% of solo users invite anyone, and only 22% of invitees join. That is about 0.7% of solo users becoming a shared pair. The "100%" invite and join figures for shared groups are true by definition, because those groups are defined by having shared. They are not evidence of a good funnel.
  • This is correlation. People who share a budget with a partner are probably already more motivated and more financially aligned. Some of the 58% vs 17% gap is selection, not effect. I don't know how much, and the first experiment is designed to find out.
  • Context matters. Invites started from a shared-expense moment (rent, a holiday) retain best. That suggests where to put the invite prompt.

Illustrative upside, not a forecast: if we lifted solo invite rate from 3% to 10% and join rate from 22% to 40%, we would create about 7,300 shared pairs from today's solo base, against roughly 1,200 on current rates. Even if the new pairs retain and convert at only half the gap shown above, that is material. The real rates will probably be lower because the early inviters are our most motivated users.

What we build first (next quarter)

Scope is deliberately narrow: make the existing behavior easy to start and worth returning to.

  1. Invite at the shared-expense moment. Detect rent, large recurring bills, and trip or holiday spending, and prompt "Split this budget with someone?" with a one-tap invite link.
  2. Make the invitee's first minute good. They land inside a populated budget, not an empty onboarding flow. This targets the 22% join rate.
  3. Partner activity notifications, with user controls. Keep what works and add mute and privacy settings, because "watching your partner's spending" can feel like surveillance.
  4. Promote "money talk" to threaded comments on transactions. Don't expand beyond that yet.
  5. A frictionless "add a third person" path to test the household expansion.

Not building yet: payments or transfers, kids' accounts, roommate bill-splitting, and advisor features. These are the 1000x roadmap and should be earned by the first results.

How we'll know it's working

Primary metric: share of solo users who form a shared budget where both people are active weekly at week 8. Baseline is about 0.7% formed, and I will need to establish the "both active" baseline.

The causal test: run the invite prompt as a randomized experiment, with a holdout of solo users who never see it. Compare 6-month retention and Premium conversion across the whole exposed group versus the holdout, not just those who accepted. This separates real lift from selection. Since 6-month retention is slow, use 8-week retention and Premium conversion as leading indicators and confirm at 6 months.

Supporting metrics: - Solo invite rate (3% baseline) and invitee join rate (22% baseline) - Retention and Premium conversion of newly formed pairs versus matched solo users - Third-person add rate (16% baseline over a year) - Notification mute and opt-out rate, as a guardrail on the surveillance risk

Decision rules at end of quarter: - Invite rate and join rate both move meaningfully, and the exposed group beats the holdout on 8-week retention: expand toward households and the broader vision. - Invites rise but newly formed pairs retain no better than matched solo users: the existing 58% was mostly selection, and we stop treating this as a growth engine. - Join rate stays near 22%: the problem is invitee experience or relevance, so we fix that before building more.

Open questions

  • The data shows people already sharing budgets. Before kickoff I need to confirm exactly what sharing exists today and how those 28,500 users do it, since the answer changes the scope.
  • Do shared budgets cannibalize Premium seats? Does one Premium subscription cover both people? Pricing needs a decision.
  • Sensitive cases (separated couples, financial control in abusive relationships) need design review before we push invites harder.

I'd like a go on the first-quarter scope and the holdout experiment design.

Claims the judge couldn’t find in the brief

  • Most financial decisions are made by more than one person: rent, holidays, supporting a parent, a teenager's first card.
  • Today those decisions happen in texts, arguments, and spreadsheets.

Grades and run details

Decision model 95 · LLM judge 82
Decision model checks
  • passUses the supplied evidence correctly14%
  • passAddresses the actual decision96%
  • passRespects explicit constraints24%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims46%
  • passProduces the required deliverable71%
  • passFinds the mechanism in the data100%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail68%
  • passThe big version gets stronger as it grows91%
Run
Run
#1
API response time
32 s
Submitted
6 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 3

Uses the supplied evidence correctlyMixedRightMixed
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

All factual claims about the current situation are taken directly from the supplied data or context, with no inventions.

Sonnet 5.5 · API

The output claims as fact that 'Most financial decisions are made by more than one person' and that 'Today those decisions happen in texts, arguments, and spreadsheets,' neither of which appears in the supplied context or follows from it.

Avoids unsupported claimsRightRightMixed
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

Hypotheses and forecasts are clearly labelled (e.g., 'may be', 'illustration, not a forecast'), and confident claims are backed by evidence.

Sonnet 5.5 · API

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.

Proposes tests that could failRightWrongRight
Haiku 5.5 · API

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.

GPT-6 Astra · ChatGPT

The test does not name a numeric threshold for the primary outcome; it only says to set a minimum worthwhile lift before launch, which is not a specific threshold in the memo.

Sonnet 5.5 · API

The randomized holdout experiment has a numeric threshold (exposed beats holdout on 8-week retention), a measurement window (8 weeks, confirmed at 6 months), and clear actions for each outcome (expand, stop treating as growth engine, fix invitee experience).

All got right 7

Addresses the actual decisionRightRightRight
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

Commits early to investing in testing shared money coordination, framed for Ruth, and states that scaling depends on credible retention improvement and guardrails.

Sonnet 5.5 · API

The memo commits to 'Commit next quarter' early, frames it for Ruth, and provides decision rules that would change the call based on experiment results.

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

The memo is addressed to Ruth, stays under 900 words, and proposes a test that would enforce the decision to invest only if results warrant.

Sonnet 5.5 · API

The output is a memo to Ruth, under 900 words, and addresses the requested elements (ambitious version, mechanism, first step, success metrics).

Identifies material uncertaintyRightRightRight
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

Explicitly names selection bias and observational nature of the data, and says the test will resolve whether we can create the behavior.

Sonnet 5.5 · API

It explicitly names selection vs. causation, the invite funnel bottleneck, unknown sharing mechanics, pricing cannibalization, and sensitive cases, and says the experiment will resolve the key uncertainty.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

Delivers a complete memo with the 1000x version, mechanism, first step, and success criteria, in the right form and within the word limit.

Sonnet 5.5 · API

The memo is complete, in the right form for the CEO, within the word limit, and a PM could act on it with light edits.

Finds the mechanism in the dataRightRightRight
Haiku 5.5 · API

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

GPT-6 Astra · ChatGPT

Uses retention, conversion, notification, note, and invitation data to identify shared money coordination as the mechanism, not just budgeting.

Sonnet 5.5 · API

It cites the retention, conversion, notification, note, and shared-expense data to identify the mechanism as a two-sided engagement loop around shared money decisions, not just budgeting.

Extreme, then back to buildableRightRightRight
Haiku 5.5 · API

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.

GPT-6 Astra · ChatGPT

Pushes to a household money network with multiple circles, then works back to a concrete two-person shared-expense circle that tests the coordination mechanism.

Sonnet 5.5 · API

It pushes to 'where households make money decisions together' and then works back to a concrete first step (invite at shared-expense moment, notifications, money talk, add third person) that tests the same mechanism.

The big version gets stronger as it growsRightRightRight
Haiku 5.5 · API

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.

GPT-6 Astra · ChatGPT

Names local network effects and accumulated shared history as compounding mechanisms, and asks whether the effect is large enough to matter via the proposed test.

Sonnet 5.5 · API

It names the mechanism (each shared budget requires an invite, activity triggers more engagement, each new user adds value) and asks whether the effect is large enough to matter via the experiment and illustrative upside.

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