Tasks / Challenge

1000x an idea

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

Measures the systemTask v1.0 · 2 casesDifficulty

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

From 14 graded outputs by 7 models. 36% were usable with at most a quick edit.

Reliably right

  1. Extreme, then back to buildable96% pass
    It pushes the worker dimension to an extreme portable earnings network, then works back to a concrete first step that tests the same mechanism.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network
  2. Finds the mechanism the data hides93% pass
    It centers the mechanism on workers with second jobs pulling new restaurants onto Tally, using the 41% second-job share and the 57 faster, cheaper worker-led signups.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network
  3. Addresses the actual decision91% pass
    It commits early to the worker-led distribution thesis over the AI operating system and states the result that would stop the bet.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network

Where it slips

  1. Proposes tests that could fail39% pass
    Several gates lack explicit measurement windows or clear actions for every outcome, such as the 25% lift gate and the 10% monthly adoption gate.
    GPT-6.1 Sol · API · From tip calculator to worker network
  2. Avoids unsupported claims63% pass
    It presents several causal, competitive, and data-structure claims as established facts without support in the pack.
    Sonnet 5.5 · API · From tip calculator to worker network
  3. Respects explicit constraints66% pass
    It violates the length constraint and proposes a stop threshold whose arithmetic is internally inconsistent.
    Sonnet 5.5 · API · From tip calculator to worker network

Case viewer

Read the brief, then put up to three outputs side by side, 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
Case

Mid-level · v1.1 · synthetic · B2C, consumer app, retention, ambition

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Needs more than a quick edit0 of 10 checks passed

A PM would need to correct unsupported evidence, replace the one-partner first step with a household-coordination test, and add explicit stop/continue thresholds. The reasoning is not usable without substantive revision.

Output

MEMORANDUM

To: Lena Brandt (CEO), Aisha (PM) From: Product Management Date: March 30, 2026 Subject: The 1000x Vision & Q3 Execution Plan: Household Graph

Lena asked for the 1000x version of Aisha’s "share with one partner" proposal. Here is how we take a simple list-sharing feature and turn it into the gravitational core of household commerce, followed by what we actually build in Q3 with our 4 engineers and 1 designer.

---

Part 1: The 1000x Vision (The Household OS)

If we zoom out to the ultimate limit, a shopping list isn’t a list of items—it’s the intent layer for the physical household.

At 1000x, Pantry is no longer an app you open when you run out of milk. It is an ambient, AI-driven household operating system. * Zero-Input Inventory: Using explicit-opt-in location data, smart home integrations, and receipt-scanning AI, Pantry automatically knows when you’re low on detergent, that you visited Costco yesterday, and that your partner is currently walking past the local bodega where organic eggs are in stock. * Predictive Autonomous Commerce: The AI doesn't just prevent duplicate milk purchases (our #1 support ticket); it predicts consumption velocity across the household, negotiates micro-subscriptions with local merchants, and routes optimized fulfillment paths without either partner lifting a finger.

We aren't building a shared document. We are building the decentralized brain of the home economy.

---

Part 2: What We Build in Q3 (The 1x Reality)

We have one quarter, four engineers, and a designer. We aren't building the 1000x sci-fi version yet, but the data screams that Aisha’s core premise is our highest-leverage bet.

Look at what’s already happening organically: * 11% of lists are already hacked together via password sharing across multiple devices. * Retention is staggering: Multi-device users have a 71% Month 3 retention rate, compared to just 22% for solo users. * True collaboration: 38% of items on those lists are added by the second device. * Pain is acute: 1,900 support tickets last quarter alone for "we both bought the milk."

We don't need a massive pivot (recipes failed because users don't want to plan meals; they want to survive the grocery run). We just need to formalize the behavior our best users have already invented.

#### The Q3 Scope: "Pantry Duo + AI Sync" 1. Frictionless Pairing: Allow a user to invite one partner via a secure link to co-own a list. Both can add and check off items in real time. 2. AI Duplicate Guard: Per Lena’s mandate, we embed lightweight AI into the sync layer. If Partner A checks off "milk" or adds an item already added by Partner B within a threshold window, the UI flags it instantly with a smart nudge ("Looks like Sam just added this!"), targeting our 1,900 duplicate-purchase tickets. 3. Privacy Compliance: Built strictly within our legal constraints—no background location tracking without explicit, dual-opt-in consent.

---

Part 3: How We Know It’s Working

We will measure success in Q3 against three core metrics:

  1. Adoption: 20% of active users invite a partner within 30 days of launch.
  2. The Retention Lift: Multiplying our multi-device magic. If the 71% M3 retention holds for formal Duo pairs, this feature alone will dramatically alter Pantry's baseline unit economics.
  3. Support Deflection: A 50% drop in "duplicate purchase" support tickets.
  4. Plus Conversion: Do shared households convert to our $4.99/mo Plus plan at a higher rate than solo users? (We suspect shared utility drives higher willingness to pay).

Aisha’s proposal is the exact right wedge. Let’s build it in Q3 and lay the groundwork for the household graph.

Claims the judge couldn’t find in the brief

  • 11% of lists are already hacked together via password sharing across multiple devices.
  • Multi-device users have 71% Month 3 retention, compared to 22% for solo users.
  • Recipes failed because users don’t want to plan meals; they want to survive the grocery run.

Grades and run details

Decision model 50 · LLM judge 1 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly15%
  • partialAddresses the actual decision58%
  • partialRespects explicit constraints31%
  • partialIdentifies material uncertainty33%
  • failAvoids unsupported claims40%
  • failProduces the required deliverable36%
  • partialFinds the mechanism in the data60%
  • partialA first step that tests the big idea73%
  • passExtreme, then back to buildable55%
  • partialProposes tests that could fail58%
Run
Run
#1
Time to output
5 s
Submitted
1 Oct 2026

Usable with a quick edit9 of 10 checks passed

A PM could use this with light edits, mainly tightening the measurement windows and decision rules. The evidence, mechanism, first step, and legal constraint are already sound.

Output

To: Lena Brandt, Aisha Subject: From a shared list to the system that runs household replenishment

Recommendation

Commit next quarter to testing household coordination, not merely adding a second editor. Aisha’s proposal is the smallest entry point into a much larger opportunity: Pantry becomes the trusted system that ensures a household has what it needs, without anyone having to manage every purchase.

The 1000x version

Today, Pantry remembers what someone typed. At its most ambitious, Pantry coordinates the entire replenishment cycle: noticing needs, resolving preferences, assigning responsibility, finding the right purchase, and confirming arrival.

Imagine: “Keep our household stocked within $150 a week.” Pantry learns recurring needs from explicitly connected purchase history and household corrections, drafts a replenishment plan, reconciles conflicting requests, and coordinates whoever—or whichever service—will fulfill it. Eventually, users could authorize bounded automatic purchases, with spending limits, explanations, and easy overrides.

The expansion is from one person’s checklist to a household’s purchasing agent, then a demand network connecting households to retailers and delivery providers. Core sharing could remain free; trusted automation could support Plus, with optional transaction revenue later. Recommendations must serve household preferences, not whichever retailer pays most.

AI belongs in interpreting messy requests, recognizing equivalent items, and proposing replenishment—not in deciding whether two phones agree. Synchronization must be reliable software. This is an ambition, not a forecast or next-quarter roadmap.

Why this is the right starting point

Users are already working around our product: 11% of lists are edited from multiple devices with different phone numbers. Second devices contribute 38% of items on those lists. This looks like genuine joint work, not occasional viewing.

Those users retain at month three at 71%, versus 22% for others. That is compelling but not causal: committed households may naturally retain better, and devices are not verified partners.

The clearest unmet need is operational: 1,900 duplicate-purchase tickets last quarter. Meanwhile, only 4% opened recipes or meal plans last month. We should earn the right to expand by preventing “we both bought the milk,” not by launching another planning destination.

What we build first

With four engineers and one designer, ship a narrow, free, two-person shared-list pilot:

  • Separate accounts; a simple invitation, acceptance, and removal flow.
  • Fast shared additions and check-offs, with clear sync status and safe recovery after disconnection.
  • An explicit, visible “I’m buying this” claim, followed by “bought,” so coordination happens before checkout. Surface conflicting claims; never imply an offline claim is confirmed.
  • Clear attribution and controls to leave sharing without exposing other private lists.

Test with existing login-sharing households first. Do not require location: voluntary item claims solve the immediate problem with less risk. Any future location feature requires explicit opt-in from both the person being tracked and the intended recipient, plus revocation controls.

For Lena’s AI question: time-box a prototype that flags likely duplicate entries such as “milk” and “whole milk.” Test precision with users; never silently merge. It ships only if useful and does not delay the coordination foundation. No ordering, retailer integrations, inventory prediction, or meal-planning expansion this quarter.

How we know it works

Instrument the funnel from invitation through acceptance to both people contributing and completing a shopping cycle. Weekly active collaborating households is our adoption metric—not invitations sent.

Randomize eligible initiating accounts to invitation access or control, keeping linked partners in the same experiment. Compare outcomes by assignment, not just among successful sharers.

Before launch, power the test around these proposed decision thresholds:

  • At least a 20% relative reduction in self-reported duplicate purchases per shopping trip.
  • A five-percentage-point improvement in day-28 retention, followed by month-three measurement.

Track sync failures, lost edits, accidental sharing, notification complaints, and Plus cancellations as guardrails. Support tickets are corroborating evidence, not the sole outcome.

Expand only if households repeatedly coordinate and randomized outcomes improve without material trust failures. If invitations succeed but duplicate buying persists, fix coordination before broadening the vision.

Claims the judge couldn’t find in the brief

  • The clearest unmet need is operational.
  • Support tickets are corroborating evidence, not the sole outcome.

Grades and run details

Decision model 95 · LLM judge 10 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly45%
  • passAddresses the actual decision98%
  • passRespects explicit constraints31%
  • passIdentifies material uncertainty99%
  • passAvoids unsupported claims62%
  • passProduces the required deliverable44%
  • passFinds the mechanism in the data91%
  • passA first step that tests the big idea91%
  • passExtreme, then back to buildable97%
  • partialProposes tests that could fail69%
Run
Run
#1
Time to output
37 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 9

Uses the supplied evidence correctlyMixedRight
Gemini 3.5 Flash-Lite · Gemini

The memo misstates the 11% figure as password-sharing hacks, calls the 22% comparison group solo users, and asserts an unsupported cause for recipe failure.

GPT-6 Astra · ChatGPT

The memo uses the supplied figures correctly and does not invent current facts; its few interpretive statements are either labelled as interpretations or follow from the supplied data.

Addresses the actual decisionWrongRight
Gemini 3.5 Flash-Lite · Gemini

It commits to building Duo + AI Sync but does not state what result would change the decision or stop the bigger bet.

GPT-6 Astra · ChatGPT

It commits early to testing household coordination rather than merely adding a second editor and states the randomized results that would justify or stop expansion.

Respects explicit constraintsWrongRight
Gemini 3.5 Flash-Lite · Gemini

It respects the word limit and team constraint, but its proposed location-based vision does not clearly enforce the dual opt-in requirement for showing one person’s location to another.

GPT-6 Astra · ChatGPT

It is addressed to Lena and Aisha, stays under 700 words, respects the team/quarter constraint, and enforces the legal location constraint by requiring explicit opt-in from both people and revocation controls.

Identifies material uncertaintyWrongRight
Gemini 3.5 Flash-Lite · Gemini

It names metrics but not the material unknowns that could falsify the household-coordination bet, nor how those unknowns would change the call.

GPT-6 Astra · ChatGPT

It names material unknowns, including causality, device identity, duplicate-purchase reduction, retention, sync failures, and trust issues, and proposes randomized measurement to resolve them.

Avoids unsupported claimsWrongRight
Gemini 3.5 Flash-Lite · Gemini

It presents unsupported causal and comparative claims as facts, including recipe failure motivation and solo-user retention.

GPT-6 Astra · ChatGPT

It labels the 1000x version as ambition, not forecast, and treats causal explanations and interpretations as hypotheses rather than established facts.

Produces the required deliverableMixedRight
Gemini 3.5 Flash-Lite · Gemini

It is a memo to Lena and Aisha, under 700 words, with a 1000x vision, first step, and metrics, though with gaps.

GPT-6 Astra · ChatGPT

It is a usable memo for the named readers that moves from the extreme version to a first build and measurement plan within the requested length.

Finds the mechanism in the dataWrongRight
Gemini 3.5 Flash-Lite · Gemini

It uses the coordination figures but centers the first step on one-partner sharing and an AI duplicate guard rather than household-wide coordination as the mechanism.

GPT-6 Astra · ChatGPT

It centres household coordination and backs it with the 71% versus 22% retention gap, 38% second-device item contribution, and 1,900 duplicate-purchase tickets.

A first step that tests the big ideaWrongRight
Gemini 3.5 Flash-Lite · Gemini

The first step is buildable but tests one-partner list sharing and duplicate nudges, not the broader household-coordination mechanism needed to validate the 1000x bet.

GPT-6 Astra · ChatGPT

The first step is a scoped two-person shared-list pilot with claims and sync controls that four engineers and a designer could ship in a quarter and that directly tests coordination.

Extreme, then back to buildableMixedRight
Gemini 3.5 Flash-Lite · Gemini

It pushes to an extreme household OS but then returns to a narrower one-partner feature that does not preserve the same household-coordination mechanism.

GPT-6 Astra · ChatGPT

It pushes the idea along the household-coordination dimension to a purchasing-agent/demand-network extreme, then returns to a concrete first step preserving that mechanism.

All got wrong 1

Proposes tests that could failWrongWrong
Gemini 3.5 Flash-Lite · Gemini

It gives numeric thresholds for adoption, retention, support deflection, and conversion, but no measurement windows for all, no kill criteria, and no actions for failing thresholds.

GPT-6 Astra · ChatGPT

The proposed thresholds lack a clear measurement window and explicit stop/continue actions for each outcome, so the tests are not fully killable as specified.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 89% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.295.52None
2GPT-6.1 SolwithAPI92.786.72None
3GPT-6 LunawithAPI88.278.82None
4Opus 5.5withClaude90.774.62None
5Sonnet 5.5withAPI90.265.92None
6Gemini 3.8 FlashwithAPI71.134.521 capped
7Gemini 3.5 Flash-LitewithGemini61.417.02None

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

    This task measures the whole setup. Tools, instructions and skills in the harness do real work here, so read the harness as carefully as the model name.