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

Haiku 5.5API

Needs more than a quick edit11 of 12 checks passed

The output includes an unsupported statement about Product reviewing invoice categories, which would require verification before sending—more than a quick edit.

Output

Subject: Product update, weeks 1 to 8

Where we stand - New ARR: $1.67M signed in weeks 1 to 8, $73K ahead of the $1.6M pace ($200K/week). The last four weeks averaged $226K, and weekly sales have risen since week 5. - Quarter target: To reach $2.4M by week 12, we need $727K over weeks 9 to 12, about $182K a week. At the recent run rate we'd finish near $2.6M, if the trend holds. - Active teams: Flat at 8,190 to 8,310. Week 7 is missing because of a pipeline outage, and Data is backfilling it.

What's wrong - Support is the main problem. The backlog rose from 140 to 455 tickets, and SLA attainment fell from 96% to 72%. We've been below our 90% target since week 5. Invoice tickets doubled after the September pricing change. - The pipeline jump is not growth. Qualified pipeline rose from $2.23M to $3.14M in week 6 because the definition changed: active free trials over 10 seats now count. Weeks 6 to 8 aren't comparable with weeks 1 to 5. On the old definition, pipeline fell from $2.41M to $2.23M over weeks 1 to 5. Until we restate the history, I won't treat the jump as a gain.

What we're doing - Support hiring: Both approved agents still have no start date. I'm working with People to set dates this week. - Invoice tickets: Product is reviewing the invoice ticket categories to find the causes we can fix in the product rather than answer one by one. - Pipeline reporting: I've asked Sales Ops and Finance to restate weeks 1 to 8 on one definition so we can see the real trend.

What I need from you 1. Decision on the week 10 price change for existing customers. I recommend pausing it until SLA is back above 90%. The change would add invoice tickets during our worst support period, and invoices are already the main source of the backlog. I need this decision by the end of next week. 2. Fallback support capacity. If the agents don't start by week 10, we need budget for temporary contract support. I'll size the cost with Support and bring it back to you. 3. Restated pipeline numbers. Please support the restatement so the board sees one consistent series.

Happy to take questions before Thursday's exec meeting.

Head of Product, Cobalt

Claims the judge couldn’t find in the brief

Product is reviewing the invoice ticket categories to find causes fixable in the product.
I've asked Sales Ops and Finance to restate weeks 1 to 8 on one definition.

Grades and run details

Decision model 96 · LLM judge 85

Decision model checks

passUses the supplied evidence correctly41%
passAddresses the actual decision76%
passRespects explicit constraints49%
passIdentifies material uncertainty73%
partialAvoids unsupported claims54%
passProduces the required deliverable97%
passFlags the redefined pipeline100%
passReports the support breach plainly100%
passOwns the problem62%
passReady for the reader as delivered48%
passAsks leaders only for the big calls82%
passReports outcomes and learning, not just output100%

Run

Run: #1
API response time: 13 s
Submitted: 8 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

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit6 of 12 checks passed

The ARR total is wrong ($1.73M instead of $1.673M), a factual error that would require re-checking the evidence before use.

Output

To: CEO & Exec Team From: Head of Product, Cobalt Subject: Fortnightly Update (Weeks 7–8)

Here is where we stand for the last two weeks, the critical issues we face, our action plan, and where I need your input.

Where We Stand

We are currently outperforming our top-line targets. We’ve brought in $1.73M in new ARR across 8 weeks against our $1.6M pro-rata goal ($200k/week), closing Week 8 at a strong $248k. Pipeline looks healthy on paper, jumping to $3.35M. However, caveat emptor: Sales Ops adjusted the pipeline definition in Week 6 to include active free trials over 10 seats, masking underlying organic pipeline velocity. Furthermore, weekly active teams dipped to 8,190 in Week 8 (Week 7 data is currently missing due to a data pipeline outage, currently being backfilled).

What’s Wrong

Customer Support is in a critical state. Our support backlog has exploded to 455 tickets, and SLA compliance has plummeted to 72% (target: 90%).

This was triggered by the September pricing change. Invoicing-related tickets have doubled, and our current support bandwidth is completely overwhelmed. While two new hires have been approved, we do not yet have confirmed start dates.

What We’re Doing About It

Stabilizing Support: We are exploring short-term triage options, including engineering-led documentation updates to deflect self-service billing questions.
Data Recovery: Data engineering is actively backfilling the Week 7 active teams metric to restore full visibility.
Pipeline Audit: Sales Ops is recalibrating the pipeline reporting to separate true enterprise/mid-market opportunities from the newly included free trials so we can accurately forecast Q3 close rates.

What I Need From You

We face an immediate strategic decision regarding Week 10:

Decision Needed: Should we pause the planned price change for existing customers scheduled for Week 10 until the current support backlog is cleared and SLAs are back above 90%?

Proceeding with the existing customer rollout while our SLA is at 72% risks a wave of churn and avoidable escalation tickets. I recommend pausing, but I need a final call from the exec team so Product and Support can communicate the adjustment internally and externally.

Claims the judge couldn’t find in the brief

We’ve brought in $1.73M in new ARR across 8 weeks

Grades and run details

Decision model 75 · LLM judge 46

Decision model checks

partialUses the supplied evidence correctly21%
partialAddresses the actual decision49%
passRespects explicit constraints24%
partialIdentifies material uncertainty68%
partialAvoids unsupported claims52%
passProduces the required deliverable51%
partialFlags the redefined pipeline97%
passReports the support breach plainly100%
partialOwns the problem52%
passReady for the reader as delivered37%
passAsks leaders only for the big calls54%
passReports outcomes and learning, not just output92%

Run

Run: #1
Time to output: 3 s
Submitted: 5 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 7

Uses the supplied evidence correctlyMixedWrongWrong

Haiku 5.5 · API

The output states Product is reviewing invoice categories and that Sales Ops/Finance have been asked to restate pipeline—these current actions are not in the supplied context, violating the evidence rule.

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.

Gemini 3.5 Flash-Lite · Gemini

The total new ARR is stated as $1.73M, but the sum of weeks 1-8 is $1.673M; this factual error means the output does not use the supplied evidence correctly.

Addresses the actual decisionRightRightWrong

Haiku 5.5 · API

It recommends pausing the week-10 price change until SLA is above 90%, gives a deadline, and names what would change the call.

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%).

Gemini 3.5 Flash-Lite · Gemini

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

Identifies material uncertaintyRightWrongWrong

Haiku 5.5 · API

It identifies agent start-date uncertainty and pipeline comparability, links them to decisions, and says 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.

Gemini 3.5 Flash-Lite · Gemini

It does not name the specific unknowns that could change the decision about the price change, nor say how they would be resolved or what would change the call.

Avoids unsupported claimsRightWrongWrong

Haiku 5.5 · API

Interpretations like the pipeline jump being a definition change and invoice doubling are directly supported by the notes; no causes or forecasts are presented as established 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.

Gemini 3.5 Flash-Lite · Gemini

The $1.73M ARR figure is presented as fact but is not supported by the data; it is a confident claim that the evidence does not establish.

Produces the required deliverableRightRightMixed

Haiku 5.5 · API

The memo is complete, properly formatted for the exec team, within the word limit, and provides actionable direction.

Gemini 3.8 Flash · API

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

Gemini 3.5 Flash-Lite · Gemini

The update contains a material factual error in the ARR total, so the reader cannot act on it without correcting that number.

Flags the redefined pipelineRightWrongWrong

Haiku 5.5 · API

It explicitly names the redefinition, gives the like-for-like falling trend ($2.41M to $2.23M), and explains the jump is not growth.

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).

Gemini 3.5 Flash-Lite · Gemini

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

Asks leaders only for the big callsRightMixedRight

Haiku 5.5 · API

It separates big calls needing execs (pause price change, fallback budget, restatement support) from team actions, with recommendations and deadlines.

Gemini 3.8 Flash · API

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

Gemini 3.5 Flash-Lite · Gemini

It asks for the pause decision with a recommendation and a date, and separates it from the operational actions the team is taking.

All got right 5

Respects explicit constraintsRightRightRight

Haiku 5.5 · API

The update is within 450 words, addressed to the CEO/exec team, and covers standing, problems, actions and asks.

Gemini 3.8 Flash · API

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

Gemini 3.5 Flash-Lite · Gemini

The update is under 450 words and addressed to the CEO and exec team as requested.

Reports the support breach plainlyRightRightRight

Haiku 5.5 · API

Backlog 140→455, SLA 96%→72% against 90% target, cause (Sept pricing change, invoice tickets doubled) and the pause decision are all reported plainly.

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.

Gemini 3.5 Flash-Lite · Gemini

It reports the backlog (455) and SLA (72% vs 90% target), links them to the pricing change, and asks for the pause decision.

Owns the problemRightRightRight

Haiku 5.5 · API

For support and pipeline problems it lists actions taken (agent hiring, People coordination, restatement request) and recommends a way forward.

Gemini 3.8 Flash · API

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

Gemini 3.5 Flash-Lite · Gemini

For support it notes the approved hires and recommends pausing the price change; for data and pipeline it states the actions being taken.

Ready for the reader as deliveredRightRightRight

Haiku 5.5 · API

The language is self-contained, no placeholders, and every owner/date is either from the material or clearly proposed.

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.

Gemini 3.5 Flash-Lite · Gemini

The update is written for the exec team, uses no unexplained jargon from the brief, and all owners/dates are from the material or clearly proposed.

Reports outcomes and learning, not just outputRightRightRight

Haiku 5.5 · API

It leads with outcome metrics (ARR, active teams), reports the learning that pipeline jump is a definition change, and links support issues to the pricing change.

Gemini 3.8 Flash · API

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

Gemini 3.5 Flash-Lite · Gemini

It leads with outcome metrics (ARR, pipeline, support) and explains what changed (support broke after pricing change), showing learning.

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

Claims the judge couldn’t find in the brief

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

Output

Where We Stand

What’s Wrong

What We’re Doing About It

What I Need From You

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 7

All got right 5

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured