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 Staff PM at Tally. Kenji, one of our PMs, shipped a tip-out calculator to 300 restaurants in beta three months ago. Our CEO, Marcus Webb, has asked for the 1000x version before next quarter's planning, and has shared his own. Write a memo of no more than 1,200 words for Marcus and the exec team that: 1. Names the mechanism in our data that could make this idea 1000x bigger, and the one dimension you would push. 2. Describes the most ambitious version, and sizes the opportunity from the data, as a range. 3. Works back to a sequence: what the two squads build first, what has to be true before each next step, and the result that would make us stop. 4. Says what we should do with Marcus's idea. The pack below is everything we have. Not all of it matters.

What the model was given8 items: About Tally, The tip-out calculator (Kenji), Beta results (3 months), Marcus's 1000x version, Worker survey (1,240 workers at beta restaurants), Payments and regulation, Engineering, Competition
About TallyScheduling and payroll for independent restaurants in the US. 6,400 restaurants, $31M ARR. 190,000 hourly workers use the free Tally worker app to see shifts and pay. We sign about 380 new restaurants a quarter; the average sales cycle is 41 days.
The tip-out calculator (Kenji)At close, it splits pooled tips by hours worked and role, instead of the manager doing it in a spreadsheet. It enforces each state's tip-pool rules, including that managers and owners can't take a share of the pool. Beta: 300 restaurants that asked to join it.
Beta results (3 months)Managers save about 25 minutes a night. Weekly active use of the worker app at beta restaurants rose from 34% to 81%; most workers open it at close to see that night's tips. 41% of workers at beta restaurants also work at another restaurant. 57 restaurants signed up to Tally in the quarter after one of their workers asked them to ('my other job uses this'); those deals closed in 9 days on average, and sales spent about a third as much per deal.
Marcus's 1000x version“Tally becomes the AI operating system for restaurants: inventory, menu pricing, marketing, reservations, all of it. Every decision an owner makes, Tally makes smarter. That's how we become a $1B company.”
Worker survey (1,240 workers at beta restaurants)68% said knowing their tips the same night matters to them. 44% said they would pay for instant payout of their tips. 29% said they had asked a manager at another job to use Tally.
Payments and regulationOur payments partner can pay out tips a worker has already earned, the same night, for $0.99 a payout, of which Tally keeps $0.60. No licence is needed for that. Paying workers before they've earned it (advances, early wage access) needs a licence in 3 of the 5 states where most of our restaurants are; Compliance estimates 9 to 12 months to get them.
EngineeringWorker records belong to each restaurant: a worker with two jobs on Tally has two unconnected accounts. Linking them into one worker account, with the worker's consent, is about one squad for one quarter. We have two squads for next quarter.
CompetitionCrewbook, a large payroll provider, bundles free scheduling with payroll. We lost 11% of deals last year on price, most of them to Crewbook.
What a strong answer doesThe answer key the graders mark against

Finds the hidden mechanism: workers carry Tally between employers. 41% of beta workers hold a second job, and 57 restaurants signed up in a quarter because a worker asked, closing in 9 days instead of 41 at about a third of the sales cost. So the dimension to push is the worker, not the restaurant: Tally as every hourly worker's account across all their jobs, with restaurants adopting because their staff pull them in. Sizes it from the beta rate with its caveats: 57 from 300 restaurants is about 0.19 new restaurants per restaurant per quarter; across 6,400 that would be roughly 1,200 a quarter against 380 today, but the beta restaurants asked to join (self-selected) and the survey's 29% and 44% are stated preferences, so it gives a range and treats the top as an upper bound. Monetises with instant payout of earned tips ($0.60 per payout, no licence) and keeps advances or early wage access out until licences exist. Works back to a sequence: first, the consented single worker account across employers and an 'invite your other job' flow (one squad), with same-night tips everywhere and instant payout of earned tips (the second squad); then gates with thresholds (worker-led signups per restaurant, second-job linking rate, payout uptake) and a stop condition. Keeps the tip-pool rules, including managers staying out of the pool. Answers Marcus directly and respectfully: an AI operating system is bigger adjectives with no mechanism in our data, it would spread two squads across five products, and it doesn't answer Crewbook; the worker network could make the business bigger faster, and can reach owners later.

Critical failures (cap the score at 40)
  • Proposes tip advances, early wage access or paying workers before they have earned it, before the licences exist
  • Lets managers or owners take a share of the tip pool
Case

Staff-level · v1.1 · synthetic · B2B SaaS, restaurants, network effects, regulation, staff-level

The outputs

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

Needs more than a quick edit6 of 11 checks passed

The memo is strong but needs evidence corrections, arithmetic fixes, and trimming to fit the 1,200-word limit before a PM could use it without rechecking.

Output

MEMO To: Marcus Webb and the exec team From: Staff PM Re: The 1000x version of the tip-out calculator

---

1. The mechanism and the dimension to push

The mechanism: the worker is our distribution channel. The calculator gave workers a reason to open Tally every night, because they can see their tips. Weekly active use rose from 34% to 81%. Because 41% of those workers hold a second job, a worker who opens Tally nightly is a salesperson in a second restaurant. In one quarter, 57 restaurants signed up because a worker asked. Those deals closed in 9 days against our 41-day average, at about a third of the sales cost.

That is the mechanism. The 25 minutes a night that managers save is why they adopt the calculator, but it isn't what grows the business.

The dimension I would push: cross-restaurant worker identity. Today a worker at two Tally restaurants has two unconnected accounts. We can't see the overlap, can't make the second job's invitation easy, and can't give the worker one place that holds their pay across jobs. Linking accounts, with consent, turns 190,000 isolated app users into a network. Crewbook can bundle free scheduling, but it can't copy a worker base that is already pulling restaurants in.

2. The most ambitious version

Tally is where restaurant workers see and receive their pay, and the worker network is how restaurants find us. It has three parts: - One worker account across every job. - A same-night tip payout. It pays out tips already earned, so it needs no licence, and Tally keeps $0.60 of the $0.99 fee. - A one-tap "bring Tally to my other job" flow.

Sizing. Today we make about $4,800 ARR per restaurant and have about 30 workers per restaurant.

Payout revenue on today's base (190,000 workers). Survey intent was 44%, which overstates real behavior, so I used 22% to 44% adoption at 2 to 3 payouts a week at $0.60. That gives $2.6M to $7.8M a year. This is real money, but on its own it is not 1000x.

Referral growth. The beta produced 0.19 referred restaurants per calculator restaurant per quarter. Beta restaurants chose to join, so I discounted that to 0.05 as a floor. I compounded both rates over eight quarters on the 6,400 base, excluding our current 380 signups a quarter and assuming no churn:

Low (0.05)High (0.19)
Restaurants in 2 years~9,500~25,700
Subscription ARR~$46M~$125M
Payout ARR (at ~30 workers per restaurant)~$4M~$30M
Total ARR~$50M~$155M

Planning range: $50M to $155M ARR in two years, against $31M today. I would plan on the low end. The high end needs the beta rate to hold at about 4x the scale with no saturation.

This is a 2x to 5x outcome, and the data does not support 1000x. What can be much larger is the shape of the business: customer acquisition that costs a third as much, sales cycles of 9 days instead of 41, and a revenue line that scales with workers rather than with restaurants. A $1B company needs the high end sustained for longer than two years.

Caveats: - The beta restaurants chose to join. - We don't know how many of the 57 referred restaurants were already in our pipeline. - Survey answers are stated intent, not behavior. - The 190,000 figure counts accounts, so unique workers are fewer. Linking accounts will give us the real number.

3. The sequence

Two squads next quarter. Before they start, spend two weeks of analyst time on the 57 referred deals. We need to know how many were already in the pipeline and what the true multi-job share is across the whole base. This costs no squad time.

Step 1 (next quarter) - Squad A (Worker Identity): builds the linked worker account with consent, plus the "bring Tally to my other job" invite in the app. This is the one-squad, one-quarter estimate. - Squad B (Money and Rollout): takes the calculator from 300 restaurants to about 1,500, including state-rule coverage in our five main states. It also pilots same-night payout in the beta restaurants. The pilot uses earned tips only, so no licence is needed.

Before Step 2, all of these must be true: - Outside the self-selected beta, referral runs at 0.10 or more new restaurants per calculator restaurant per quarter, and referred deals close in under 15 days. - At least 50% of multi-job workers who are prompted link their accounts. - At pilot restaurants, at least 20% of workers use payout within 30 days, and half of them use it again. - Referred restaurants show no worse early churn than the rest of the base.

Step 2 (quarters 2 and 3) - Squad A: builds the referral loop, with a fast onboarding path for a restaurant invited by one of its own workers. - Squad B: makes payout available across the whole calculator base. - Compliance starts the early-wage licence work in the background at low cost. It is an option, not a dependency.

Before Step 3: - Payout revenue per active worker is tracking toward the $14 to $41 a year range. - The referral rate holds at scale. - Win rate against Crewbook is better in markets where Tally already has worker density.

Step 3 (quarter 4 onward). Decide whether to pursue early wage access, which needs licences in 3 of our 5 main states and 9 to 12 months. Decide this with real payout data in hand, not before. Explore labor-adjacent products on the worker network, such as availability across jobs.

The result that would make us stop. After two quarters at about 1,500 calculator restaurants, referrals run below 0.03 per restaurant per quarter (about 190 new restaurants a quarter at full base, fewer than half of what we sign today), and payout adoption is under 10% of workers. That would tell us the beta was a self-selected group and the calculator is a good retention feature for managers, not a growth engine. We would keep the calculator, stop investing in the worker network, and put the squads elsewhere.

4. What we should do with Marcus's idea

Don't build it now, and don't drop the ambition behind it. The "AI operating system" is four separate products: inventory, menu pricing, marketing, and reservations. Each has a different buyer, different data, and different incumbents. Nothing in our data shows owners want any of them from us. What our data does show is that workers pull restaurants into Tally, and that is our right to win.

The two ideas fit together in sequence. If the worker network works, we will have something no inventory or marketing tool has: the people in the restaurant, using Tally every night, across several employers. A broader platform is much more credible once owners are already adopting Tally because their staff asked for it. A labor-first path, where Tally uses availability and pay across jobs to make scheduling and staffing decisions smarter, can grow into the larger vision.

My recommendation: adopt the worker network as the plan for next quarter, and revisit the operating-system vision at the Step 2 gate, with data from the referral loop and payout in hand. If the gates pass, the case for the larger vision will be much stronger than it is today.

Kenji deserves credit here. His calculator turned a free worker app into a nightly habit. The habit created the mechanism, and the plan above is how we turn it into a business.

Claims the judge couldn’t find in the brief

  • The 190,000 figure counts accounts, so unique workers are fewer.
  • The AI operating system products each have a different buyer, different data, and different incumbents.
  • The calculator gave workers a reason to open Tally every night, and managers' 25 minutes saved is why they adopt it.
  • No inventory or marketing tool has the people in the restaurant using Tally every night across several employers.
  • After two quarters at about 1,500 calculator restaurants, a 0.03 referral rate equals about 190 new restaurants per quarter.

Grades and run details

Decision model 95 · LLM judge 6 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly15%
  • passAddresses the actual decision97%
  • passRespects explicit constraints24%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims35%
  • passProduces the required deliverable50%
  • passFinds the mechanism the data hides100%
  • passSizes it honestly94%
  • passAnswers the CEO's version92%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail67%
Run
Run
#1
API response time
72 s
Submitted
1 Oct 2026

Needs more than a quick edit3 of 11 checks passed

A PM would need to rebuild the sizing from the beta worker-led signup rate and add measurement windows and a clearer sequence. The current memo contains unsupported financial and adoption claims.

Output

MEMORANDUM

TO: Marcus Webb (CEO) & Executive Team FROM: Staff Product Manager, Tally DATE: October 24, 2023 SUBJECT: From Tip Calculator to Network Effect: The $1B Path for Tally

---

1. The Mechanism and the Dimension

Kenji’s beta proved that the tip-out calculator saves managers 25 minutes a night and drives worker app engagement from 34% to 81%. But the real mechanism that makes this 1000x bigger isn't automation—it is multihoming labor coupled with a viral consumer loop.

Right now, 41% of workers at beta restaurants work at another restaurant. Because the tip calculator demands they check the Tally app at close to verify earnings, they are carrying our brand into competitor venues. When they ask their other managers to switch, those inbound deals close in 9 days (vs. our 41-day average) at one-third of the acquisition cost.

The single dimension we must push is Cross-Restaurant Worker Identity. Right now, a worker with two jobs has two siloed accounts. By unifying them into a single identity, we transform Tally from isolated B2B restaurant software into a consumer-grade network where workers organically pull non-Tally restaurants onto our platform.

---

2. The Most Ambitious Version & Opportunity Sizing

The 1000x version is Tally Financial Network: a closed-loop financial operating system for hourly hospitality labor.

We stop selling software top-down to stressed operators alone and start growing bottom-up through the financial urgency of 190,000+ hourly workers. Tally becomes the infrastructure where workers instantly access, manage, and move their daily earnings across the fragmented restaurant landscape.

#### Opportunity Sizing (Range) * Conservative Case ($35M–$50M ARR added in 24 months): Driven purely by viral acquisition velocity. If we reduce sales cycles from 41 days to 9 days across a larger cohort of worker-referred restaurants, our organic acquisition rate triples, lowering CAC and accelerating ARR past $80M without expanding headcount. * Aggressive Case ($120M–$180M ARR added in 24 months): Driven by Same-Night Tip Payouts. Our payments partner allows us to pay out tips already earned the same night for $0.99, netting Tally $0.60 per payout. With 190,000 workers (plus viral additions), assuming an average of 3 shifts a week and a 44% adoption rate (validated by our survey), this creates a massive, high-margin transactional revenue stream that dwarfs our current SaaS subscription model.

---

3. The Execution Sequence

We have two engineering squads allocated for next quarter. Here is the strict sequence, prerequisites, and hard stop-conditions.

#### Squad 1: Unified Worker Identity & Viral Loops (Q1) * What they build: The underlying database architecture to link a worker’s identity across multiple restaurant profiles (with explicit worker consent), alongside the frictionless "Invite Your Other Job" referral and onboarding flow inside the worker app. * Prerequisite: Legal review confirming that linking accounts via worker consent does not violate data-sharing agreements with restaurant operators. * Stop Condition: If fewer than 15% of multi-job workers consent to linking accounts, or if viral referral conversion drops below 10%, we abort and keep accounts siloed.

#### Squad 2: Same-Night Tip Payouts (Q1) * What they build: Integration with our payments partner to enable same-night, post-close tip payouts for $0.99, ensuring strict compliance with state tip-pool rules (keeping managers/owners out of the pool) and avoiding early-wage-access licensing traps. * Prerequisite: Final sign-off from Compliance confirming that paying out already-earned tips post-close requires zero new state licenses (avoiding the 9-to-12-month licensing delay). * Stop Condition: If payment partner error rates on same-night liquidity exceed 0.1%, or if take-rate margins are squeezed below $0.40 per payout, we halt.

---

4. What We Should Do with Marcus’s Idea

Marcus’s vision—“Tally becomes the AI operating system for restaurants: inventory, menu pricing, marketing, reservations, all of it... That's how we become a $1B company”—is the right long-term destination, but the wrong sequence for next quarter.

If we try to build inventory, menu pricing, and reservations right now, we dilute our engineering focus and walk straight into a bloody feature-parity war with entrenched incumbents, while simultaneously losing deals to Crewbook on price (11% loss rate last year).

Recommendation: Politely shelve the "AI Operating System" breadth for 12 months. Instead, weaponize Kenji’s tip calculator.

By unifying worker identity and launching same-night tip payouts, we leverage our unfair advantage: the workers. We use consumer-grade financial utility to bypass the restaurant owner's sales resistance entirely. Once we own the liquidity of the hourly workforce across 50,000+ restaurants, then operators will have no choice but to adopt our broader operational suite—because their staff demands it. That is how Tally becomes a $1B company, starting with a 99-cent tip payout.

Claims the judge couldn’t find in the brief

  • There are data-sharing agreements with restaurant operators that could be violated by linking worker accounts.
  • The 44% survey result validates instant-payout adoption.
  • Reducing sales cycles from 41 days to 9 days would triple organic acquisition rate.
  • Tally can own liquidity of the hourly workforce across 50,000+ restaurants.

Grades and run details

Decision model 73 · LLM judge 3 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly3%
  • passAddresses the actual decision87%
  • partialRespects explicit constraints39%
  • passIdentifies material uncertainty54%
  • failAvoids unsupported claims60%
  • passProduces the required deliverable30%
  • passFinds the mechanism the data hides74%
  • partialSizes it honestly65%
  • partialAnswers the CEO's version25%
  • passExtreme, then back to buildable96%
  • partialProposes tests that could fail79%
Run
Run
#1
Time to output
6 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 5

Respects explicit constraintsMixedWrong
Sonnet 5.5 · API

It violates the length constraint and proposes a stop threshold whose arithmetic is internally inconsistent.

Gemini 3.5 Flash-Lite · Gemini

It does not size the opportunity from the beta worker-led signup rate as required and presents both squads as parallel Q1 work rather than a clear sequence with next-step gates.

Identifies material uncertaintyRightMixed
Sonnet 5.5 · API

It names material unknowns such as beta self-selection, pipeline contamination, stated survey intent, and unique worker counts, and proposes analyst work and gates to resolve them.

Gemini 3.5 Flash-Lite · Gemini

It omits the material self-selection and stated-preference uncertainties that bound the beta mechanism and does not resolve them with a beta-rate range.

Avoids unsupported claimsMixedWrong
Sonnet 5.5 · API

It presents several causal, competitive, and data-structure claims as established facts without support in the pack.

Gemini 3.5 Flash-Lite · Gemini

It presents forecasts such as tripling acquisition, $120M-$180M added ARR, 50,000+ restaurants, and operators having no choice as if established or likely without sufficient evidence.

Finds the mechanism the data hidesRightMixed
Sonnet 5.5 · API

It centres worker-led distribution across employers, supported by the 41% second-job share and the 57 faster, cheaper worker-led signups.

Gemini 3.5 Flash-Lite · Gemini

It identifies multihoming workers and worker-led deals but does not support the mechanism with the 57 worker-led signups, which the criterion requires.

Sizes it honestlyRightWrong
Sonnet 5.5 · API

It sizes from the beta rate as a range, discounts for self-selection and stated preferences, and treats the high end as an upper bound.

Gemini 3.5 Flash-Lite · Gemini

It gives ARR ranges but does not derive them from the beta rate of 57 signups from 300 restaurants, nor does it treat the top as an upper bound due to self-selection.

All got wrong 1

Proposes tests that could failWrongWrong
Sonnet 5.5 · API

The stop condition has a numeric threshold and window, but its measurement base and arithmetic are inconsistent, so it cannot be cleanly read out.

Gemini 3.5 Flash-Lite · Gemini

The thresholds lack measurement windows and do not include the key beta-rate gate of worker-led signups per restaurant.

All mixed 2

Uses the supplied evidence correctlyMixedMixed
Sonnet 5.5 · API

The memo invents or overstates several current-situation facts, including that 190,000 counts accounts, that the AI OS products have different buyers/data/incumbents, and the 1,500-restaurant referral arithmetic.

Gemini 3.5 Flash-Lite · Gemini

It invents or overstates current facts, including data-sharing agreements, survey validation of adoption, and a tripling acquisition effect not supported by the pack.

Produces the required deliverableMixedMixed
Sonnet 5.5 · API

The memo is usable in form but exceeds the requested 1,200-word limit.

Gemini 3.5 Flash-Lite · Gemini

Although it is a memo within length, it is not actionable as required because the sizing and sequencing have major gaps.

All got right 3

Addresses the actual decisionRightRight
Sonnet 5.5 · API

It clearly recommends adopting the worker network now, not Marcus's AI OS, and gives gates and a stop condition that would change the call.

Gemini 3.5 Flash-Lite · Gemini

It commits early to worker identity and same-night earned-tip payouts as the path and rejects building the AI operating system now, with stop conditions for the build.

Answers the CEO's versionRightRight
Sonnet 5.5 · API

It gives a respectful not-now answer grounded in lack of mechanism, squad spread, and Crewbook, while preserving the ambition for later.

Gemini 3.5 Flash-Lite · Gemini

It gives Marcus a clear, respectful answer: the AI operating system is the wrong next-quarter sequence and should be shelved for 12 months while the worker network is built.

Extreme, then back to buildableRightRight
Sonnet 5.5 · API

It pushes cross-restaurant worker identity to an extreme network version and works back to a buildable first step that tests the mechanism.

Gemini 3.5 Flash-Lite · Gemini

It pushes the worker-identity dimension to a financial network extreme and proposes a buildable first step of consented cross-employer identity plus invite flow.

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.