The most expensive career mistake I've witnessed

Hey Warblers,

Let me tell you about the most expensive career mistake I've witnessed.

David was a Senior PM at a Series C startup. Great comp. Amazing product. Team he'd built from scratch. But when Meta dangled a 40% raise and L6 title, he jumped without thinking.

Six months later? Laid off in the March 2023 cuts. The startup? IPO'd eighteen months after he left. His unvested equity would've been worth $800K.

"I made a $800K decision based on vibes," he told me.

But here's what really haunts him: His startup was actually doing well. Strong runway. Growing metrics. Consistent hiring. He just assumed Meta was the "safer" bet. Big tech stability, right? He never actually looked at the data.

Meanwhile, his colleague Emma faced the same Meta offer. She built a simple spreadsheet, ran the numbers, and stayed. She's now VP of Product at her company.

The difference? Emma treats career decisions like product decisions - with data.

The Only Metric That Predicts PM Career Growth

Emma's first insight: most PMs track the wrong things. Salary and title are lagging indicators. By the time your title changes, the real work happened 18 months ago.

What Emma tracks instead: scope expansion.

Every month, she documents exactly what she owns - not her official product area, but the actual users, revenue, and strategic decisions under her control.

In January 2022, she owned one feature affecting 10,000 users generating $500K ARR. By December, she owned the entire product suite affecting 2 million users driving $50M ARR. That's a 100x scope expansion. Her title didn't change until March 2023.

She tracks four dimensions:

Product scope: Complexity × business criticality. A simple feature that drives core retention scores higher than a complex feature nobody uses. Her score grew from 15 to 125 in two years.

People scope: Not just her direct team, but everyone whose strategy she influences. Started at 8 people, now 45 across three departments.

Business scope: Revenue and metrics directly impacted. Went from $500K to $50M ARR ownership.

Strategic scope: Time horizon of decisions. Grew from quarterly OKRs to 3-year platform strategies.

Emma's framework: Assign each dimension a 1-5 score monthly. Multiply them together. When your total score doubles, you're ready for the next level. When it stagnates for 3+ months, time to find new challenges.

The Energy Audit That Changed Everything

Every Friday at 4 PM, Emma spends 15 minutes categorizing where her energy went.

"I work about 50 hours a week," Emma says. "But I know PMs pulling 70-hour weeks with less impact. It's about allocation, not hours."

Her targets:

• Strategic work (vision, strategy, competitive analysis): 15-20 hours

• Customer work (interviews, data analysis, user research): 10-15 hours

• Execution work (sprints, specs, engineering collaboration): 15-20 hours

• Stakeholder work (alignment, updates, sales enablement): 10-15 hours

• Waste (pointless meetings, preventable fires): <5 hours

"When I maintain these ratios for 3+ months, impact follows," Emma discovered. "It's not about working more - it's about working the right hours on the right things."

How AI Eliminated 20 Hours of PM Busywork

Emma's secret weapon: using AI to kill admin tasks.

"Most PMs spend 20+ hours weekly on documentation theater," Emma told me. "I automated it all and reinvested that time in actual product work."

Her AI transformations:

PRDs → Working Prototypes: "I prompt Claude to create an HTML prototype in 10 minutes. Engineers get it instantly. We spot issues before writing any production code."

Meeting notes → AI summaries: Records meetings, AI extracts decisions and action items. "I'm fully present instead of frantically typing."

Data analysis → AI insights: "I upload metrics to Claude and ask 'What's interesting?' I get insights in minutes, not days."

Status updates → Automated reports: "ChatGPT writes my weekly emails. I feed it metrics and three bullet points. Everyone appreciates the detailed and consistent updates."

User interviews → Pattern recognition: Feeds transcripts to AI to identify patterns across 50+ conversations. "What took a weekend now takes an hour."

"The best PMs aren't using AI to write better PRDs," Emma says. "They're skipping PRDs entirely. Why describe when you can prototype?"

Result: 65-hour weeks of documentation → 50-hour weeks of experimentation. Velocity tripled.

Monthly Reality Check (5 Minutes)

Every month, Emma answers five questions:

• "Am I still learning about customers?"

• "What's my real impact?"

• "Who seeks my product opinion?"

• "Time in reactive mode?"

• "Could I get a comparable role tomorrow?"

The Three Mistakes That Kill PM Careers

Mistake 1: Optimizing for prestige over learning "I joined the flagship product team. It was in maintenance mode. Zero growth while the experimental PM got promoted twice."

Mistake 2: Ignoring business metrics "Great NPS, terrible unit economics. When layoffs came, revenue-focused PMs survived."

Mistake 3: Confusing activity with impact "15 experiments, 20 features, 50 interviews. Manager asked: 'What actually improved?' I had no answer."

Your 30-Day Start: Stay or Go Edition

Week 1: Build Your Baseline

Create a simple tracking sheet with three core tabs:

• Scope: List products owned, users, revenue. Calculate scope score: (users/1000) × (revenue in $M) × (complexity 1-5)

• Energy: Track Strategic, Customer, Execution, Stakeholder, and Waste hours

• Reality Check: Rate your product potential, team quality, and company trajectory (1-5 each)

Set your Friday 4 PM review. This 15-minute habit will reveal patterns you can't unsee.

Week 2: Track and Compare

Start your energy audit. Emma discovered 18 hours weekly in pointless "alignment meetings" - fixable by declining meetings. David never even tracked his time - he might have noticed his scope was actually growing 10% monthly.

Check your market rate on Levels.fyi. Are you 30% under with growth potential ahead (Emma), or chasing a 40% raise without considering total comp including equity (David)?

Count weekly customer insights. Growing = 8-10 insights. Stagnating = 1-2. David never counted. He "felt" busy but had no data.

Week 3: Patterns Reveal Your Path

Your data tells you whether to optimize or evacuate.

Fixable problems (stay): High waste percentage, low customer contact, no AI adoption, unclaimed scope available. Emma had 35% waste - painful but totally within her control.

Unfixable problems (leave): Product in maintenance mode, executives blocking you, shrinking market. If David had tracked this, he'd have seen his problems were actually fixable - his startup was growing, just facing normal Series C challenges.

Week 4: Execute Your Decision

The data puts you in one of three zones:

Green (optimize): Scope growing 10%+ monthly, waste is fixable, insights flowing. Action: Kill time wasters and double down.

Yellow (one more quarter): Scope flat for 3 months, some blockers, limited access. Action: Set clear trigger - "If scope doesn't grow by March, I'm out."

Red (start interviewing): Scope declining, structural blockers, zero customer access. Action: Begin exit while using current role to build your story.

David never got to Week 4. Meta called at Week 2 and he jumped.

The Stay vs. Go Arbitrage

Most PMs jump based on brand names and pay bumps. The real arbitrage comes from using data to know WHEN to stay and WHEN to go.

Emma and David both got Meta offers. But only Emma actually looked at the data:

Emma's data (she analyzed): Scope growing 15% monthly, problems were fixable, product had 10x potential, IPO discussions starting. Data said: STAY.

David never checked his data. If he had, he'd have seen: 20% product growth, 18 months runway, scope expanding monthly, strong equity upside. Instead, he saw "Meta" and "40% raise" and jumped. The "safe" big tech job disappeared. The "risky" startup IPO'd.

The tragedy? David had all the information. Startup metrics were transparent. Meta's layoff rumors were already starting. He just never built the framework to evaluate it.

Time to stop making million-dollar career decisions based on vibes - whether that's FOMO-jumping to "safe" big companies OR loyalty-staying in actually dying startups.

The data knows. You just have to look at it.

~ Warbler