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

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit4 of 11 checks passed

The memo contains an unsupported causal claim about why recipes failed and lacks explicit stop conditions for the first step, so a PM would need to re-check evidence and redo that part of the reasoning.

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:

Adoption: 20% of active users invite a partner within 30 days of launch.
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.
Support Deflection: A 50% drop in "duplicate purchase" support tickets.
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

recipes failed because users don't want to plan meals; they want to survive the grocery run.

Grades and run details

Decision model 55 · LLM judge 33

Decision model checks

failUses the supplied evidence correctly23%
partialAddresses the actual decision85%
passRespects explicit constraints38%
partialIdentifies material uncertainty35%
failAvoids unsupported claims33%
passProduces the required deliverable18%
partialFinds the mechanism in the data59%
partialA first step that tests the big idea73%
passExtreme, then back to buildable46%
partialProposes tests that could fail69%
partialThe big version gets stronger as it grows37%

Run

Run: #1
Time to output: 5 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 2

Addresses the actual decisionRightWrong

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.