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

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Mixed 1

Uses the supplied evidence correctlyMixed

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.

Got right 11

Addresses the actual decisionRight

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

Respects explicit constraintsRight

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

Identifies material uncertaintyRight

It identifies agent start-date uncertainty and pipeline comparability, links them to decisions, and says how they would be resolved.

Avoids unsupported claimsRight

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.

Produces the required deliverableRight

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

Flags the redefined pipelineRight

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

Reports the support breach plainlyRight

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

Owns the problemRight

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

Ready for the reader as deliveredRight

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

Asks leaders only for the big callsRight

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

Reports outcomes and learning, not just outputRight

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.

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