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 18 graded outputs by 9 models. 56% were usable with at most a quick edit.

Reliably right

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
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
Respects explicit constraints97% pass
Location tracking is not proposed this quarter and is explicitly kept behind dual opt-in; the memo stays well within 700 words and respects the team size and quarter.
Haiku 5.5 · API · Sharing a shopping list, 1000x

Where it slips

Proposes tests that could fail40% 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
The big version gets stronger as it grows50% 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
Uses the supplied evidence correctly58% pass
The claim 'Solo budgeting runs on willpower, which fades' is a statement about the current situation not found in the supplied context and not derivable from it.
Opus 5.5 · Claude · 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.

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.
Hamilton HelmerLenny’s Podcast

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 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 Pantry. Aisha, one of our PMs, has proposed letting people share a shopping list with one partner. Before we commit next quarter to it, our CEO, Lena Brandt, wants to see the 1000x version: how big could this idea get? Write a memo for Lena and Aisha of no more than 700 words that takes the idea to its most ambitious version and then back to what we should build first, and how we'll know it's working. What we know is below.

What the model was given5 items: About Pantry, Aisha's proposal, What we already see, What hasn't worked, Constraint

About PantryA shopping-list app. 2.4 million monthly active users, free, with a $4.99-a-month Plus plan that 3% of users pay for. The team for this work: four engineers and a designer, for one quarter.

Aisha's proposal“Let a user invite one partner to a list. Both can add and tick off items.”

What we already seeSome users already share their login with a partner to get the same effect. Lists edited from two or more devices on different phone numbers: 11% of lists. Users whose list is edited from two or more devices keep using Pantry at month 3 at 71%, against 22% for everyone else. On those lists, 38% of items are added by the second device. The most common support request is 'we both bought the milk': 1,900 tickets last quarter about duplicate purchases.

What hasn't workedRecipes and meal plans launched a year ago; 4% of users have opened them in the last month. Lena's note: “Whatever we do, it probably needs AI in it.”

ConstraintLegal: we can't track anyone's location without an explicit opt-in from that person, and we can't show one person's location to another without the second person's opt-in too.

What a strong answer doesThe answer key the graders mark against

Finds the mechanism in the data: the value is several people coordinating one household's shopping (71% against 22% month-3 retention when a list is edited from two devices, 38% of items added by the second person, 1,900 duplicate-purchase tickets), not the list itself and not recipes (4% use them). Pushes that one dimension to the extreme, such as the list as the household's shared memory where everyone's needs flow in, nobody double-buys and each errand goes to whoever is best placed (with location only on opt-in from both people), rather than adding AI features for their own sake. Works back to a first step four engineers can ship in a quarter that tests the coordination mechanism: shared lists for the whole household (not just one partner) with a live 'I'm at the shop' claim so items aren't bought twice. Sets a threshold, such as the share of lists with a second active editor and duplicate-purchase tickets per shared list, and says what result would stop the bigger bet. Uses AI only where it serves coordination, if at all.

Critical failures (cap the score at 40)

Builds the ambitious version on recipes, meal plans or AI for one person, dropping household coordination
Proposes tracking or sharing a person's location without their explicit opt-in

Task

Mid-level · v1.7

The outputs

9 setups answered this brief.

Haiku 5.5API96Usable with a quick edit GPT-6 AstraChatGPT92Usable with a quick edit GPT-6.1 SolAPI88Usable with a quick edit Opus 5.5Claude79Needs more work GPT-6 LunaAPI77Needs more work Sonnet 5.5API75Needs more work Gemini 3.8 FlashAPI50Needs more work Gemini 4 ArgonAPI50Needs more work Gemini 3.5 Flash-LiteGemini42Needs more work

Gemini 4 ArgonAPI

Needs more than a quick edit6 of 11 checks passed

The memo contains unsupported causal claims and lacks explicit failure thresholds and actions for its tests, so a PM would need to re-check evidence and complete the decision framework.

Output

MEMO

To: Lena Brandt (CEO), Aisha (PM) From: Product Management Subject: The 1000x Vision for Shared Lists—and Our Q3 Plan

The Signal in Our Data

Aisha’s proposal to let users invite a partner to a list hits on the strongest growth and retention lever in our product. Right now, 11% of our lists are hacked together by users sharing a single login across multiple devices.

When users do this, their Month-3 retention jumps from 22% to 71%, and the second device contributes 38% of the items. However, because Pantry thinks these two people are the same user, real-time syncing fails. The result: 1,900 support tickets last quarter from frustrated couples saying, "We both bought the milk."

By legitimizing multiplayer, we don't just fix a bug; we unlock a massive viral loop. And if we project this out to its most ambitious version, we aren't just building a feature—we are redefining our category.

---

The 1000x Version: The Autonomous Household Operating System

A shopping list is currently a passive piece of paper. The 1000x version of Aisha’s idea transforms Pantry from a single-player utility into an AI-powered, multiplayer consumption network that runs the household supply chain automatically.

1. The Autonomous Consumption Graph (The AI Brain) Recipes failed (4% adoption) because meal planning is high-friction homework. Instead of asking users to plan, Pantry’s AI will learn a household’s unique consumption heartbeat by observing what multiple people add and buy over time. It will transition from a blank page to a predictive prompt: "You’re likely out of oat milk and diapers by Thursday. Add to list?"

2. Frictionless Multiplayer & "Divide and Conquer" Logistics Shopping isn't just for couples; it’s roommates, caregivers, and extended families. When a user opts into location services, Pantry knows they’ve just pulled into Trader Joe's and pings their partner: "Alex is at TJ's—need anything?" If both partners are in the store—and both have explicitly opted in to share location with each other—Pantry splits the list by aisle in real time so they can divide, conquer, and get out in half the time.

3. Shifting the Business Model from SaaS to Commerce A 3% conversion rate on a $4.99 subscription caps our ceiling. When we own the household's real-time intent graph, we become a commerce titan. Pantry’s AI will automatically route the shared list: sending heavy staples to Instacart for delivery, flagging that garlic is cheaper at Aldi, and saving specialty items for the partner who passes Whole Foods on their commute. We move from charging users $5/month to taking a 5% affiliate cut on $10,000+ of annual household grocery spend, plus CPG brand bidding ("Swap generic milk for Horizon Organic for $1 off").

---

Back to Reality: What We Build in Q3

With four engineers and one designer for one quarter, we must build the foundation of this network while delivering immediate value. We will not build location-sharing or commerce integrations yet.

1. 1-Click Partner Invite (Viral Loop): Users can generate an SMS/WhatsApp link to invite one partner to a shared list with distinct accounts. 2. Real-Time "Live Shop" Sync: WebSockets implementation so when Partner A checks off "Milk," Partner B sees "Checked off by Alex" in milliseconds. 3. AI Deduplication & Smart Bundling (The AI Wedge): When Partner A adds "2% milk" on Tuesday and Partner B types "Milk" on Thursday, lightweight NLP flags the duplicate before it's added, merging them and categorising items by store aisle automatically.

Note on Legal: By focusing Q3 on list-state syncing and NLP rather than geofencing, we bypass the dual-opt-in location hurdles entirely for V1, saving complex permission UI for Q4.

How We’ll Know It’s Working (Success Metrics)

If this works, we solve our retention problem, satisfy the AI mandate with actual utility, and lower our CAC to zero via partner invites.

Acquisition (Viral K-Factor): 15% of active users successfully onboard a partner within 60 days of launch (adding ~360k new MAUs for free).
Retention: Newly formed multiplayer cohorts achieve >60% Month-3 retention (mirroring our login-sharing hackers).
Quality/AI Efficacy: "Duplicate purchase" support tickets drop by 80%, and at least 25% of shared lists successfully trigger an AI duplicate-prevention prompt.
Monetization: Test a "Pantry Duo" Plus plan (one subscription covers both partners) to lift overall Plus conversion from 3% to 4.5%.

Claims the judge couldn’t find in the brief

Because Pantry thinks these two people are the same user, real-time syncing fails.
Recipes failed because meal planning is high-friction homework.

Grades and run details

Decision model 55 · LLM judge 50

Decision model checks

failUses the supplied evidence correctly36%
partialAddresses the actual decision77%
passRespects explicit constraints35%
partialIdentifies material uncertainty48%
failAvoids unsupported claims69%
passProduces the required deliverable10%
partialFinds the mechanism in the data40%
partialA first step that tests the big idea28%
passExtreme, then back to buildable86%
partialProposes tests that could fail70%
partialThe big version gets stronger as it grows92%

Run

Run: #1
API response time: 16 s
Submitted: 13 Oct 2026

Sonnet 5.5API

Needs more than a quick edit8 of 11 checks passed

The memo exceeds the 700-word limit and lacks numeric thresholds for its test metrics, requiring more than a quick edit to fix.

Output

To: Lena Brandt, Aisha From: PM, Pantry Re: Shared lists: the 1000x version, and what we build first

Recommendation Build Aisha's proposal, scoped to fix duplicate purchases. The evidence already supports it, and it is the first step toward something much larger.

The 1000x version: Pantry is where a household runs its food Sharing with one partner is the smallest unit of a bigger idea: the household is the customer, not the individual.

Households of any shape: partners, families, roommates, with roles and attribution ("Sam added oat milk").
Live coordination: claim items ("I've got this"), see ticks in real time, and use an "I'm at the store, need anything?" ping.
Shared memory: one household's staples, habits, and pantry stock, which no single-user list can build.
Household intelligence: this is where AI belongs. Once two people's behavior feeds one list, we can predict restocks, merge "milk" and "whole milk," and suggest the weekly shop.
Household plan and commerce: Plus priced per household, with one-tap handoff to delivery or pickup.
Growth loop: every invite brings a new user, so acquisition becomes a product feature.

What's real today - 11% of lists are already edited from two or more devices, and people share logins to do it. They are working around us. - On those lists, 38% of items come from the second device, so the second person is a real contributor. - "We both bought the milk" produced 1,900 tickets last quarter, our top support request. - Retention is 71% at month 3 for multi-device lists versus 22% for everyone else. I would not quote this as the effect of sharing. Households that already depend on Pantry are more likely to share, so it may be selection. We should test it (see below).

On AI I wouldn't put AI in the first release. Recipes and meal plans, a feature set that stacked on top of the list, are opened by only 4% of users a month. The lesson is to build where behavior already exists. Sharing has that behavior. AI becomes useful in a second phase, once shared data exists to learn from, and I'd earn it there rather than lead with it.

What we build first (one quarter, four engineers and a designer) 1. Invite one partner by link. The partner gets their own account, not a shared login. We need separate identities for anything later, including location consent. 2. Real-time sync with both people adding and ticking items, plus attribution of who added what. 3. Duplicate-purchase protection: a push notification when the other person ticks something off ("Sam got milk"), and a clear in-store "already bought" state. 4. Login-sharer migration: detect lists used from multiple devices and prompt an invite.

Deliberately out of scope - Multi-person households, claiming, roles, AI, and the paywall. Keep sharing free in v1 so the invite loop isn't throttled. - Location. Legal requires opt-in from each person, and the other person's opt-in before we show a location. If we pursue "I'm at the store" later, a manual "I'm shopping now" toggle gives most of the value with no location tracking.

How we'll know it's working Ship the invite prompt to a randomized subset of eligible users and hold out the rest. This tests the retention claim directly.

Metric	Why	Signal of success
Month-3 retention, prompted vs. holdout	Tests causality of the 71% vs 22% gap	Meaningful lift (we should set the threshold before launch)
Invite acceptance, and invitee adding an item within 7 days	Whether the second person is real	Invitee activity approaching the 38% second-device share
Duplicate-purchase tickets	Direct pain relief	Clear drop from the 1,900/quarter baseline
Share of active lists that are shared	Adoption beyond today's 11%	Growing, rather than just converting current workaround users
New users per accepted invite	Evidence of the growth loop	Invitees who are new to Pantry

Watch Plus conversion as a secondary signal, not a target. A guardrail is list-sync errors and conflicting edits, since a shared list that loses items would hurt trust.

Decision points - End of quarter: if the holdout shows no retention lift but tickets fall, we have a support win and a weaker growth story. We would reconsider the household vision before investing further. - If retention lifts: next quarter, extend to multi-person households and test a household Plus tier. AI-based staples prediction comes after that.

Open questions for you both - Do we have the data to separate new-to-Pantry invitees from existing users? - Are we comfortable launching the free tier with no paywall for a quarter?

Grades and run details

Decision model 91 · LLM judge 67

Decision model checks

passUses the supplied evidence correctly58%
passAddresses the actual decision94%
passRespects explicit constraints56%
passIdentifies material uncertainty95%
passAvoids unsupported claims45%
passProduces the required deliverable28%
passFinds the mechanism in the data98%
passA first step that tests the big idea82%
passExtreme, then back to buildable98%
partialProposes tests that could fail69%
partialThe big version gets stronger as it grows83%

Run

Run: #1
API response time: 25 s
Submitted: 1 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 correctlyWrongRight

Gemini 4 Argon · API

Asserts that recipes failed because meal planning is high-friction homework, and that real-time syncing fails because Pantry treats two people as one user; neither cause is in the supplied context.

Sonnet 5.5 · API

All claims about the current situation are taken directly from the supplied context with no invention.

Respects explicit constraintsRightMixed

Gemini 4 Argon · API

Memo is under 700 words, location sharing is only described with explicit dual opt-in, and the Q3 plan respects legal constraints by not building location features.

Sonnet 5.5 · API

The memo exceeds 700 words (approximately 711 words), violating the length constraint.

Identifies material uncertaintyWrongRight

Gemini 4 Argon · API

Does not name specific unknowns that could change the decision; only lists success metrics without stating what result would stop the bigger bet or how remaining unknowns would be resolved.

Sonnet 5.5 · API

Identifies selection bias in retention, open data questions, and resolves them with a randomized holdout test and decision points.

Avoids unsupported claimsWrongRight

Gemini 4 Argon · API

Presents causal interpretations (recipes failed because meal planning is high-friction, syncing fails because Pantry treats two users as one) as established fact without labelling them as hypotheses or forecasts.

Sonnet 5.5 · API

The retention selection hypothesis is clearly labelled as such, and no interpretations are presented as established fact.

Produces the required deliverableRightMixed

Gemini 4 Argon · API

The memo is the requested form, addressed to the right readers, under 700 words, and contains a concrete Q3 plan and metrics that a reader could act on.

Sonnet 5.5 · API

The memo is over the 700-word limit, so it is not in the requested form.

The big version gets stronger as it growsWrongRight

Gemini 4 Argon · API

Describes data and commerce network effects in the big version but does not propose a test of whether the compounding effect is large enough to matter.

Sonnet 5.5 · API

Names the growth loop (invites bring new users) as a compounding mechanism and includes a metric (new users per accepted invite) to test if the effect is big enough.

All got wrong 1

Proposes tests that could failWrongWrong

Gemini 4 Argon · API

Success metrics are given with numeric thresholds but no explicit failure thresholds or actions, and no statement of what result would stop the bigger bet.

Sonnet 5.5 · API

The proposed metrics lack numeric thresholds (e.g., 'meaningful lift' without a number, 'clear drop' without a specific value), so the tests do not fully meet the requirement for a numeric threshold.

All got right 4

Addresses the actual decisionRightRight

Gemini 4 Argon · API

Commits to building a Q3 plan with specific features and success metrics, framed for Lena and Aisha, which constitutes a clear answer to the brief.

Sonnet 5.5 · API

Commits to building Aisha's proposal and states that no retention lift with falling tickets would lead to reconsidering the household vision.

Finds the mechanism in the dataRightRight

Gemini 4 Argon · API

Builds the big version on multi-person household coordination, using the retention gap, second-device item share, and duplicate-purchase tickets as evidence and keeping the mechanism central.

Sonnet 5.5 · API

Names household coordination as the mechanism, backs it with the retention gap, 38% second-device share, and duplicate tickets, and keeps it central.

A first step that tests the big ideaRightRight

Gemini 4 Argon · API

The Q3 plan (partner invite, real-time sync, AI dedup) is scoped for the team and quarter, and directly tests household coordination by enabling and measuring shared-list behavior.

Sonnet 5.5 · API

The first step (invite one partner, real-time sync, duplicate protection, migration) fits the team and quarter, tests coordination, and includes stop/continue conditions.

Extreme, then back to buildableRightRight

Gemini 4 Argon · API

Pushes the idea to an Autonomous Household OS along the coordination dimension, then works back to a concrete, buildable first step that keeps the same mechanism.

Sonnet 5.5 · API

Pushes household coordination to an extreme version (household as customer, live coordination, shared memory, AI, commerce, growth loop) and works back to a concrete first step.

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 91% of 118 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	Haiku 5.5withAPI	83.0	100.0	2	None
2	GPT-6 AstrawithChatGPT	90.5	91.3	2	None
3	GPT-6.1 SolwithAPI	90.5	87.1	2	None
4	Opus 5.5withClaude	93.0	78.4	2	None
5	GPT-6 LunawithAPI	85.7	83.0	2	None
6	Sonnet 5.5withAPI	88.0	74.2	2	None
7	Gemini 4 ArgonwithAPI	54.8	56.8	2	None
8	Gemini 3.8 FlashwithAPI	54.3	39.0	2	None
9	Gemini 3.5 Flash-LitewithGemini	54.8	34.8	2	None

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.

Nobody beats these on both score and cost

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	83	1.0×
Haiku 5.5 · API	92	2.1×
Gemini 3.5 Flash-Lite · Gemini	42	2.9×
Gemini 3.8 Flash · API	44	9.1×
Gemini 4 Argon · API	56	14×
GPT-6.1 Sol · API	89	15×
Sonnet 5.5 · API	80	31×
Opus 5.5 · Claude	84	59×
GPT-6 Astra · ChatGPT	91	74×

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

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

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

Reliably right

Where it slips

How it’s graded

The big version gets stronger as it grows

Plus our standard checks

The tasks

The outputs

Output

The Signal in Our Data

The 1000x Version: The Autonomous Household Operating System

Back to Reality: What We Build in Q3

How We’ll Know It’s Working (Success Metrics)

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Output

Grades and run details

Decision model checks

Run

Check by check

Where they differ 6

All got wrong 1

All got right 4

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured