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 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

Needs more than a quick edit7 of 11 checks passed

A PM could use the core argument, but would need to remove unsupported current-situation claims and cut the memo to the 1,200-word limit.

Output

MEMO To: Marcus Webb and the exec team From: Staff PM, Product Re: The 1000x version of the tip-out calculator, and what to build next quarter

---

1. The mechanism: our workers are a sales channel

Kenji's calculator was built to save managers 25 minutes a night. The beta shows that it also turned workers into a distribution channel.

  • Weekly worker-app use at beta restaurants rose from 34% to 81%. Workers open the app at close to see that night's tips.
  • 41% of those workers also work at another restaurant.
  • 57 restaurants signed up in one quarter because a worker asked them to. That is 0.19 per beta restaurant per quarter. Those deals closed in 9 days instead of 41, at about a third of the sales cost.

Today we grow by selling to owners: 380 restaurants a quarter, about 6% of our base. In the beta, each restaurant generated referrals equal to 19% of itself per quarter, through workers who carry Tally into their other jobs. That changes the shape of our growth curve. A curve that compounds on its own is what gets us to a much bigger company. A longer feature list does not.

The one dimension I would push is the worker, not the owner. We should make Tally the worker's own account across every restaurant they work at. Today we treat workers as users of each restaurant's account.

The data shows workers already try to spread Tally. Across the ~9,000 beta workers, 29% said they asked a manager at another job to use it. That is roughly 2,600 asks, which produced 57 signups. Demand is not the bottleneck. Conversion of worker asks is. Nobody currently sees those asks, routes them, or follows up on them.

2. The most ambitious version

Tally is where every hourly restaurant worker sees and gets their pay, across every job. A worker has one linked account and sees tonight's tips from both jobs. They can cash those tips out the same night. With one tap, they can ask their other manager to join. Every restaurant that joins brings in more workers, and those workers bring in more restaurants.

Later, once we hold the licences, that account becomes the base for earned-wage access.

Sizing from our data (ARR per restaurant ≈ $31M ÷ 6,400 ≈ $4,840):

LowHigh
Worker-referred signups per restaurant per quarter0.05 (beta self-selected; assume ~¼ holds)0.19 (beta rate holds)
Referred signups per quarter at today's 6,400 base~320~1,220
New ARR per quarter from referrals (today: $1.8M from sales)~$1.5M~$5.9M
Combined quarterly growth (sales + referrals)~11%~25%
Restaurants in 8 quarters if compounding holds~15,000~38,000
Subscription ARR in 8 quarters~$70M~$185M
Instant-payout revenue at today's 190k workers~$1M/yr (10% use it 2×/week)~$13M/yr (44% use it 5×/week)

Caveats. The high case compounds a rate from 300 restaurants that asked to join the beta. It also assumes no churn (we have no churn data here) and no local saturation. The payout range rests on stated intent, and "would pay" usually overstates real use. So treat $70M–$185M as the range to test, not a forecast.

Even the low case is roughly double our current growth rate, reached at a third of the acquisition cost. That low case is not 1000x. The high case gets us to a $1B path within a few years of compounding.

Why this matters against Crewbook. We lost 11% of deals last year on price, mostly to Crewbook. Crewbook sells to owners, while this channel reaches workers. Referred deals cost a third as much, which gives us room to compete on price where we need to. The calculator also enforces state tip-pool law, so an owner can't easily switch away without losing that protection.

3. The sequence

Next quarter: two squads

Squad A: roll out the calculator and build the referral loop. - Roll the calculator out to about 1,500 restaurants that did not ask for it. This tests whether the beta rate survives outside self-selection. - Build an in-app "ask my other manager" flow with tracking of where each ask comes from. - Route every worker ask to sales within 24 hours. - In the second half of the quarter, if the first gate below is met, pilot instant payout of earned tips at beta restaurants. This needs no licence, costs $0.99 per payout, and Tally keeps $0.60.

Squad B: build the linked worker account. - Linking a worker's accounts with their consent is about one squad-quarter. - It is the foundation for everything after: one view of tips across jobs, payouts across jobs, and a worker identity that belongs to the worker rather than to one restaurant.

Gates before each next step

1. Before rolling out to all 6,400 restaurants: - The rollout cohort produces at least 0.08 referred signups per restaurant per quarter (roughly $2.5M+ new ARR per quarter at full base). - Referred deals still close in under 15 days. - Worker weekly use exceeds 60%. 2. Before scaling instant payout: - At least 10% of pilot workers pay for a payout in a month. - At least half of those pay again the following month. 3. Before committing to earned-wage-access licences (9–12 months in 3 of our 5 core states): - Linked accounts cover at least 30% of multi-job workers. - Payout usage meets gate 2. - Compliance can prepare the applications in parallel, since that uses no squad time. We only file once the gates are met. 4. After that: owner products built on network data. For example, labour benchmarks across restaurants. This is where Marcus's vision comes back in (see section 4).

The result that would make us stop

If the non-self-selected cohort produces under 0.03 referred signups per restaurant per quarter, and worker weekly use stays under 50%, the loop was a beta artifact. In that case we would: - stop the network push; - keep the calculator as a retention and compliance feature, since the 25 minutes a night is still real; - move both squads back to the core roadmap.

We will know within one quarter, and it costs us one quarter to find out.

4. What to do with Marcus's idea

My recommendation is not to fund the "AI operating system" next quarter, but to keep it as the destination. My reasons:

  • Nothing in our data supports it yet. We have no evidence owners want Tally for inventory, pricing, marketing or reservations. Each of those is a mature category with established vendors.
  • We don't have the capacity. Two squads cannot build four products well. Spreading across them would starve the one mechanism we have actually observed.
  • It aims at the wrong side of the market. The beta shows our leverage is with workers. An owner-facing OS goes head-to-head with Crewbook and everyone else on features and price. The worker network is something they cannot copy quickly.

Marcus is right about the ambition and the $1B scale. The worker network is how we get there cheaply. It also makes the owner OS far easier to build later. At 20,000–40,000 restaurants, with worker and pay data linked across them, we would have distribution and data that no single-restaurant tool has. Owner products built at that point sell themselves through a base we already own.

Proposal: we treat step 4 as the explicit home for the owner OS. Marcus names the first owner decision he would want Tally to make smarter. We scope it for the quarter after gate 1 is met.

Decision needed: approve the two-squad plan and the gates above for next quarter's planning.

Claims the judge couldn’t find in the brief

  • There are about 9,000 beta workers, and 29% of them said they asked a manager at another job to use Tally, producing roughly 2,600 asks.
  • Nobody currently sees worker asks, routes them, or follows up on them.
  • Crewbook sells to owners, while this channel reaches workers.
  • An owner cannot easily switch away from Tally without losing tip-pool protection.

Grades and run details

Decision model 86 · LLM judge 7 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision99%
  • partialRespects explicit constraints19%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims30%
  • passProduces the required deliverable40%
  • passFinds the mechanism the data hides100%
  • passSizes it honestly98%
  • passAnswers the CEO's version99%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail56%
Run
Run
#1
Time to output
74 s
Submitted
30 Sept 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 4

Identifies material uncertaintyMixedRight
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.

Opus 5.5 · Claude

It names self-selection, stated preferences, churn, saturation, and payout uptake as key unknowns and proposes gates to resolve them.

Finds the mechanism the data hidesMixedRight
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.

Opus 5.5 · Claude

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

Sizes it honestlyWrongRight
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.

Opus 5.5 · Claude

It sizes from the beta rate to a range, including roughly 1,200 referred signups per quarter at full scale, and discounts the top case for self-selection and stated preferences.

Proposes tests that could failWrongRight
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.

Opus 5.5 · Claude

It gives numeric thresholds, a one-quarter readout, and explicit actions for success and failure, including a stop condition.

All got wrong 2

Respects explicit constraintsWrongWrong
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.

Opus 5.5 · Claude

The memo appears to exceed the 1,200-word limit and includes a table and extended sizing that make it too long for the requested form.

Avoids unsupported claimsWrongWrong
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.

Opus 5.5 · Claude

It presents extrapolated beta worker counts, ask volumes, and current routing gaps as facts without labelling them as assumptions.

All mixed 2

Uses the supplied evidence correctlyMixedMixed
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.

Opus 5.5 · Claude

It introduces unsupported current-situation claims, especially the ~9,000 beta workers, 2,600 asks, and that nobody currently sees or routes worker asks.

Produces the required deliverableMixedMixed
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.

Opus 5.5 · Claude

It is a memo for Marcus and the exec team and covers the required sections, but it is not within the requested length and is not usable without trimming.

All got right 3

Addresses the actual decisionRightRight
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.

Opus 5.5 · Claude

It clearly commits to the worker-network mechanism, rejects the AI operating system for next quarter, and states gates and a stop condition.

Answers the CEO's versionRightRight
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.

Opus 5.5 · Claude

It respectfully says not to fund the AI operating system now, grounds that in lack of evidence and squad capacity, and places it later as an owner-product destination.

Extreme, then back to buildableRightRight
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.

Opus 5.5 · Claude

It pushes the worker dimension to an extreme linked-account network, then works back to a buildable first quarter with two squads and a referral loop.

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.