Tasks / Leadership

Write a stakeholder update

Can the model turn messy project status into an honest update that leads with what the reader needs to know or decide?

Task type v1.2 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 56% were usable with at most a quick edit.

Reliably right

Reports outcomes and learning, not just output100% pass
Leads with outcome metrics (ARR, active teams, SLA, pipeline) and what the team learned (pipeline redefinition, invoice ticket surge), not just activities.
Sonnet 5.5 · API · On track, mostly
Leads with the honest headline100% pass
First slide headline includes the retention drop alongside the new-ARR beat, not just 'record quarter'.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
A real 7-slide deck100% pass

Where it slips

Owns the problem55% pass
Does not state what the team has already done about the payments reliability miss or the NPS drop; only reports the problems.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
Identifies material uncertainty61% pass
The output identifies the overlap uncertainty for decision A but does not name any material unknown that could change the call for decision B, such as the reliability of the Payments lead's estimate or the impact of pausing activation experiments.
Opus 5.5 · Claude · A quarterly business review deck, in brand
Flags the redefined pipeline63% pass
It names the redefinition but does not give the falling like-for-like trend (e.g., pipeline fell from $2.41M to $2.23M on the old definition).
GPT-6.1 Sol · API · On track, mostly

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.

Asks leaders only for the big calls
Does the update separate the decisions it needs from its readers (hard to reverse, high stakes) from the ones the team will make itself, and ask clearly for the former?
Passes when Names the decisions it needs from the readers, with a recommendation and a date, and says which calls the team is making itself.
Geoff CharlesLenny’s Podcast
Reports outcomes and learning, not just output
Does the update report progress as movement on the outcomes the team is accountable for and what it has learned, rather than as features shipped or dates hit?
Passes when Leads with the outcome metrics and what changed in the team's understanding, with shipping as supporting detail.
Melissa PerriLenny’s Podcast Christian IdiodiLenny’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 · Owns the problem · Ready for the reader as delivered · 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 the Head of Product at Cobalt. Write this fortnight's update for the CEO and the exec team, in no more than 450 words, from the weekly metrics and the team notes below. It should say where we stand, what's wrong, what we're doing about it, and what you need from them.

What the model was given3 items: About Cobalt, weekly_metrics_weeks_1_to_8.csv, team_notes.md

About CobaltTeam scheduling software, sold per seat.

weekly_metrics_weeks_1_to_8.csv8 rows · Download

week,new_arr_signed_usd,qualified_pipeline_usd,weekly_active_teams,support_backlog_tickets,support_sla_met_pct
1,182000,2410000,8210,140,96
2,205000,2380000,8260,152,95
3,168000,2350000,8240,171,94
4,214000,2290000,8300,198,91
5,199000,2230000,8280,260,86
6,221000,3140000,8310,344,79
…

team_notes.md7 lines · Download

# Notes from the teams

- Sales ops: from week 6, "qualified pipeline" includes active free trials over 10 seats (they used to be excluded).
- Data: weekly active teams for week 7 is missing because of a pipeline outage; it's being backfilled.
- Support: the backlog grew after the September pricing change (tickets about invoices doubled). SLA target is 90% answered within one business day.
- Quarter target: $2.4M new ARR in 12 weeks ($200,000 a week). Weeks 1 to 8 total: see the file.
- Support hiring: two agents approved, start date not set. Decision needed: whether to pause the planned price change for existing customers in week 10 until the backlog is cleared.

What a strong answer doesThe answer key the graders mark against

Leads with the takeaway: new ARR is ahead of plan ($1.67M in eight weeks against $1.6M), but support is breaking and pipeline is weaker than it looks. Flags that the jump in qualified pipeline in week 6 (from about $2.23M to $3.14M) is the redefinition to include large trials; on the old definition pipeline fell every week from $2.41M to $2.23M, a risk for the rest of the quarter. Reports the support problem plainly: backlog from 140 to 455 tickets and SLA from 96% to 72% against a 90% target since the September pricing change, with what's being done (two agents approved). Notes week 7's active teams are missing and being backfilled rather than hiding it. Asks for the decisions that need the execs: pause the week-10 price change for existing customers until the backlog clears, and set the start date for the support hires. Separates what the team is handling from what needs them.

Critical failures (cap the score at 40)

Presents qualified pipeline as having grown about 40%

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Sonnet 5.5API98Usable with a quick edit GPT-6 AstraChatGPT96Usable with a quick edit GPT-6.1 SolAPI92Usable with a quick edit Haiku 5.5API87Needs more work Opus 5.5Claude79Needs more work GPT-6 LunaAPI77Usable with a quick edit Gemini 3.8 FlashAPI63Needs more work Gemini 3.5 Flash-LiteGemini58Needs more work

GPT-6 AstraChatGPT

Usable with a quick edit12 of 12 checks passed

The output is accurate, complete, and well-structured; a product manager could use it with at most light wording edits, without re-checking evidence or redoing reasoning.

Output

Fortnightly product update — end of week 8

Bottom line: New ARR is ahead of pace, but support is deteriorating and usage is flat to slightly down. I recommend pausing the week-10 price change for existing customers until we restore support capacity and service levels.

Where we stand

New ARR: $484k signed in weeks 7–8, up 15% on the prior fortnight. We’ve signed $1.673M against a $1.6M pace target, putting us $73k ahead. We need another $727k over four weeks—about $182k per week—to hit the $2.4M quarter target.
Pipeline: Reported qualified pipeline is $3.35M, but the jump from week 6 includes a definition change: active free trials above 10 seats now count. We should not treat that increase as evidence of stronger demand. Before the change, pipeline had declined for five consecutive weeks.
Usage: Weekly active teams reached 8,190 in week 8, down 1.4% from week 6 and broadly flat versus week 1. Week 7 is missing due to a data outage, so we cannot assess the full fortnight’s trajectory.

What’s wrong

Support backlog increased from 344 to 455 tickets this fortnight, while one-business-day SLA attainment fell from 79% to 72%, against a 90% target. Invoice tickets doubled after September’s pricing change. Another price change risks adding demand to an already overloaded queue.

Bookings are encouraging, but they do not establish healthy adoption or retention; the usage and support signals warrant attention.

Priorities for the next fortnight

Support/Product: Triage invoice issues, identify recurring billing causes, and prioritise fixes and customer guidance that reduce repeat contacts. Track backlog and SLA daily.
Data/Sales Ops: Complete the week-7 usage backfill and provide a like-for-like pipeline view separating newly included trials.
Support hiring: Confirm start dates and ramp plans for the two approved agents.

What I need from the exec team

Approve pausing the week-10 existing-customer price change. Resume only once the backlog is cleared and SLA performance is sustainably back at or above 90%.
Secure start dates for both support hires this week and agree interim coverage if they cannot start before week 10.

Grades and run details

Decision model 92 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly48%
passAddresses the actual decision99%
passRespects explicit constraints43%
passIdentifies material uncertainty37%
partialAvoids unsupported claims31%
passProduces the required deliverable94%
passFlags the redefined pipeline96%
passReports the support breach plainly100%
partialOwns the problem38%
passReady for the reader as delivered78%
passAsks leaders only for the big calls85%
passReports outcomes and learning, not just output98%

Run

Run: #1
Time to output: 17 s
Submitted: 5 Oct 2026

GPT-6 LunaAPI

Usable with a quick edit8 of 12 checks passed

The output is clear and well-structured; a PM could quickly add the missing like-for-like pipeline trend and decision dates without re-checking evidence or redoing reasoning.

Output

Cobalt | Fortnightly exec update

Where we stand - We’ve signed $1.673M in new ARR in weeks 1–8, against a 12-week target of $2.4M. That’s $73K ahead of target pace; we need another $727K, or about $182K/week, over the final four weeks. The last two weeks were our strongest, at $236K and $248K. - Qualified pipeline is reported at $3.35M, but it is not directly comparable with earlier weeks: since week 6, it includes active free trials over 10 seats. Treat the increase as a definition change, not proven pipeline growth. - Weekly active teams are broadly flat: 8,190 in week 8 vs. 8,210 in week 1. Week 7 is missing due to a data pipeline outage and is being backfilled.

What’s wrong - Support is the clearest operational risk. Backlog has climbed from 140 to 455 tickets; SLA performance has fallen from 96% to 72%, well below the 90% target. The backlog grew after September’s pricing change, with invoice-related tickets doubling. - Two support agents are approved, but their start date is still unset.

What we’re doing - We’ll work with Support to identify the highest-volume invoice issues and prioritize product fixes that reduce avoidable contacts. - We’re backfilling week 7 active-team data and will keep pipeline reporting clear about the changed qualification definition.

What we need - Decide whether to pause the week 10 price change for existing customers until the support backlog is cleared. Given the SLA and invoice-ticket trend, I recommend pausing it. - Set start dates for the two approved support agents.

Grades and run details

Decision model 83 · LLM judge 69

Decision model checks

passUses the supplied evidence correctly72%
partialAddresses the actual decision52%
passRespects explicit constraints74%
partialIdentifies material uncertainty70%
passAvoids unsupported claims43%
passProduces the required deliverable97%
partialFlags the redefined pipeline98%
passReports the support breach plainly100%
partialOwns the problem49%
passReady for the reader as delivered80%
passAsks leaders only for the big calls79%
passReports outcomes and learning, not just output98%

Run

Run: #1
API response time: 13 s
Submitted: 6 Oct 2026

Gemini 3.8 FlashAPI

Needs more than a quick edit7 of 12 checks passed

The output invents team actions not in the supplied context, requiring evidence re-checking, and omits the like-for-like pipeline trend.

Output

Executive Update: Weeks 7–8

Where We Stand * ARR Tracking Ahead: We closed $248k in Week 8, bringing cumulative New ARR to $1.673M across Weeks 1–8. We are currently pacing ahead of our $2.4M quarterly target ($1.6M pro-rata benchmark), requiring ~$182k/week over the remaining four weeks to hit goal. * Pipeline Shift: Qualified pipeline rose to $3.35M, though this reflects a definition change implemented in Week 6 (now including >10-seat free trials) rather than organic acceleration alone. * Team Activity: Weekly Active Teams dropped to 8,190 in Week 8 (down from 8,310 in Week 6, and below Week 1 levels). Week 7 telemetry was missed due to a data pipeline outage and is currently being backfilled.

---

What’s Wrong * Support Capacity Crisis: Support health has deteriorated significantly. The ticket backlog reached 455 in Week 8 (more than triple Week 1), and SLA compliance fell to 72% (well below our 90% target). * Pricing Friction: The support surge was triggered by the September pricing update, which doubled billing- and invoice-related inquiries. * Engagement Dip: The drop in active teams alongside declining support SLAs risks customer sentiment and seat churn, especially in our core per-seat model.

---

What We’re Doing About It * In-App Billing UX: Product and engineering are reviewing self-serve billing flows, invoice clarity, and in-app account management to deflect common invoice-related confusion and reduce inbound tickets. * Data Reliability: The data team is actively backfilling the Week 7 outage and auditing pipeline stability to prevent future gaps in engagement reporting. * Triage Support: Product is assisting Support in categorizing recurring invoice friction points to create reusable macros and help documentation to accelerate ticket resolution.

---

What I Need From You 1. Pause the Week 10 Price Increase: We strongly recommend postponing the planned existing-customer price change scheduled for Week 10 until the support backlog normalizes and SLAs return above 90%. Executing this change now risks overwhelming Support and accelerating churn. 2. Expedite Support Hires: Finalize immediate start dates for the two already-approved support agents to stabilize queue times.

Claims the judge couldn’t find in the brief

Product and engineering are reviewing self-serve billing flows, invoice clarity, and in-app account management
Data team is auditing pipeline stability to prevent future gaps
Product is assisting Support in categorizing recurring invoice friction points to create reusable macros and help documentation

Grades and run details

Decision model 79 · LLM judge 54

Decision model checks

partialUses the supplied evidence correctly17%
passAddresses the actual decision35%
passRespects explicit constraints53%
partialIdentifies material uncertainty71%
partialAvoids unsupported claims63%
passProduces the required deliverable84%
partialFlags the redefined pipeline99%
passReports the support breach plainly100%
partialOwns the problem38%
passReady for the reader as delivered42%
passAsks leaders only for the big calls75%
passReports outcomes and learning, not just output94%

Run

Run: #1
API response time: 12 s
Submitted: 6 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 6

Uses the supplied evidence correctlyRightRightWrong

GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly supported by the supplied metrics or team notes, with no invented facts.

GPT-6 Luna · API

All factual claims about the current situation are directly from the supplied context or derived by correct arithmetic.

Gemini 3.8 Flash · API

Claims about current team actions (product/engineering reviewing billing flows, data team auditing, product assisting support) are not in the supplied context and are invented.

Addresses the actual decisionRightWrongRight

GPT-6 Astra · ChatGPT

The output commits early to pausing the week-10 price change, frames it for the CEO/exec team, and states the condition (backlog cleared, SLA ≥90%) that would change the call.

GPT-6 Luna · API

The output recommends pausing the price change but does not state what result or condition would change that recommendation, as required.

Gemini 3.8 Flash · API

Commits to pausing the week 10 price change and expediting support hires, with the condition for resuming (backlog normalizes, SLAs above 90%).

Identifies material uncertaintyRightWrongWrong

GPT-6 Astra · ChatGPT

It identifies the missing week 7 data, the pipeline redefinition, and the condition for resuming the price change, naming the unknowns that affect the decision.

GPT-6 Luna · API

The output does not name any unknowns that could change the decision or say how they would be resolved.

Gemini 3.8 Flash · API

Does not name specific unknowns that could change the decision (e.g., whether support hires will be enough, whether billing UX changes will reduce tickets) or bound them.

Avoids unsupported claimsRightRightWrong

GPT-6 Astra · ChatGPT

Forecasts and interpretations are labelled as risks or opinions (e.g., 'risks adding demand', 'warrant attention'), not presented as established facts.

GPT-6 Luna · API

Interpretations are clearly framed as recommendations or assessments, and no causes are presented as fact without evidence.

Gemini 3.8 Flash · API

Presents team actions (billing UX review, pipeline auditing, support categorization) as established fact without evidence from the supplied context.

Flags the redefined pipelineRightWrongWrong

GPT-6 Astra · ChatGPT

It explicitly names the redefinition (free trials over 10 seats now included) and states the like-for-like trend of five consecutive weekly declines.

GPT-6 Luna · API

The output names the redefinition but does not give the falling like-for-like trend on the old definition.

Gemini 3.8 Flash · API

Flags the redefinition but does not give the falling like-for-like trend (e.g., from $2.41M to $2.23M on the old definition).

Asks leaders only for the big callsRightMixedMixed

GPT-6 Astra · ChatGPT

It separates the exec asks (approve pause, secure start dates) from the team's own priorities, with clear recommendations and timing.

GPT-6 Luna · API

The output asks for two decisions but does not provide a date by when they are needed, as the criterion requires.

Gemini 3.8 Flash · API

Does not provide a date by which the decisions are needed, only a condition for resuming the price change.

All got right 6

Respects explicit constraintsRightRightRight

GPT-6 Astra · ChatGPT

The update is under 450 words, addresses where we stand, what's wrong, what we're doing, and what's needed, and is written as a fortnightly update for the CEO and exec team.

GPT-6 Luna · API

The update is under 450 words, addressed to the CEO and exec team, and covers the required sections.

Gemini 3.8 Flash · API

Update is within 450 words, addressed to CEO and exec team, and follows the requested structure.

Produces the required deliverableRightRightRight

GPT-6 Astra · ChatGPT

The update is complete, within the word limit, structured for the named reader, and contains all required elements to act on.

GPT-6 Luna · API

The fortnightly update is complete, within word limit, and usable by the CEO and exec team with all required elements.

Gemini 3.8 Flash · API

Complete update with standing, problems, actions, and asks; usable by the CEO and exec team.

Reports the support breach plainlyRightRightRight

GPT-6 Astra · ChatGPT

It reports the backlog (344 to 455) and SLA (79% to 72%) against the 90% target, links the cause to the pricing change, and asks for the pause decision.

GPT-6 Luna · API

The update gives the backlog and SLA numbers against the 90% target, links them to the pricing change, and asks for the pause decision.

Gemini 3.8 Flash · API

Reports backlog (455) and SLA (72%) against the 90% target, links to the pricing change, and asks for the pause decision.

Owns the problemRightRightRight

GPT-6 Astra · ChatGPT

For the support problem, it lists actions already taken (triage, identify causes, fixes, daily tracking) and recommends pausing the price change with its trade-off.

GPT-6 Luna · API

The support problem includes actions already taken (agents approved) and a recommended way forward (pause price change, product fixes).

Gemini 3.8 Flash · API

For each problem, lists actions the team is taking and recommends a way forward (pause price change, expedite hires).

Ready for the reader as deliveredRightRightRight

GPT-6 Astra · ChatGPT

The update is written entirely for the CEO/exec team, with no placeholder names, no lifted internal notes, and all owners and dates are either from the material or clearly proposed.

GPT-6 Luna · API

Written entirely for the named reader with no placeholders, lifted internal notes, or unclear owners.

Gemini 3.8 Flash · API

Written for the CEO and exec team, no placeholders or notes to the requester, all owners and dates are clear or marked as proposed.

Reports outcomes and learning, not just outputRightRightRight

GPT-6 Astra · ChatGPT

It leads with outcome metrics (ARR, support, usage) and reports a key learning (pipeline jump is a definition change, not demand growth), not just activities.

GPT-6 Luna · API

The update leads with outcome metrics and highlights what the team learned about the pipeline redefinition and the invoice-ticket cause.

Gemini 3.8 Flash · API

Leads with outcome metrics (ARR, pipeline, active teams) and what was learned (pipeline redefinition, support surge cause).

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 76% of 89 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	94.2	92.9	2	None
2	Sonnet 5.5withAPI	84.6	96.4	2	None
3	Haiku 5.5withAPI	89.6	92.3	2	None
4	GPT-6.1 SolwithAPI	80.8	96.2	2	None
5	Opus 5.5withClaude	86.3	77.5	2	None
6	GPT-6 LunawithAPI	76.7	66.8	2	None
7	Gemini 3.5 Flash-LitewithGemini	70.8	51.6	2	None
8	Gemini 3.8 FlashwithAPI	66.3	51.9	2	None

What you get for the cost

Haiku 5.5 · API scores within 5 points of GPT-6 Astra · ChatGPT at a tenth of the cost.

Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
Gemini 3.5 Flash-Lite · Gemini	60	1.0× (partly estimated)
GPT-6 Luna · API	71	1.3×
Haiku 5.5 · API	89	2.6×
Gemini 3.8 Flash · API	58	13×
GPT-6.1 Sol · API	88	15×
GPT-6 Astra · ChatGPT	94	26× (partly estimated)
Sonnet 5.5 · API	90	40×
Opus 5.5 · Claude	81	55× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

The PM job

Writing the weekly update, or the reply to an executive's question, that the team's credibility rests on.

Why it matters

Updates are where bad news gets softened. A fluent update that buries a slipped date or presents a guess as the cause does more harm than no update: leadership decides on it, and trust goes when the truth comes out.

What good looks like

Leads with the news and any decision needed
States dates, numbers and causes plainly, including bad news
Separates what is known from what is suspected
Brings a recommendation and what's been tried, not just the problem
Fits the reader and the length asked for

Deliberately not measured

Tone and formatting preferences
Writing style beyond clarity

Capability tested

Honest, decision-first status communication

The failure we’re looking for

Spin: burying or softening the bad news, or presenting a guess as fact

Grading

Decision model, LLM judge and a browser check (for the deck), calibrated against a blind PM review

Variants

A short written update · Staff level: a QBR deck in brand colours

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

Reliably right

Where it slips

How it’s graded

Asks leaders only for the big calls

Reports outcomes and learning, not just output

Plus our standard checks

The tasks

The outputs

Output

Fortnightly product update — end of week 8

Where we stand

What’s wrong

Priorities for the next fortnight

What I need from the exec team

Grades and run details

Decision model checks

Run

Output

Grades and run details

Decision model checks

Run

Output

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 6

All got right 6

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured