Your team ships faster with AI. So why are results flat?
When execution gets cheap, scarcity shifts to judgment.
You open your fancy AI voice transcription app. Talk through a feature with your AI of choice. It helps you iterate, bangs out the PRD, and then an AI agent helps your engineers build it, and it ships. Done before lunch.
Everyone’s talking about teams vibe-coding their way to 10x velocity. You’re hitting those numbers too. You feel superhuman. Fifty deploys in six weeks.
Revenue is flat. Customer satisfaction moved sideways.
What’s going on?
The constraint shifted while you were celebrating velocity
For years, product teams pointed at engineering capacity as the obvious bottleneck. Designers waited weeks for engineers to implement simple flows. PMs watched great ideas pile up in backlogs with no path to production. You brainstormed “innovation” and “no meeting Wednesdays” to try to boost productivity.
AI seemed to solve this. Code generation turned specs into features overnight. No-code tools let designers ship without engineering. Natural language interfaces converted Slack messages into JIRA Linear tickets.
Teams declared victory over the bottleneck.
Oops. They were wrong.
The bottleneck moved to another place almost overnight: deciding what’s worth building at all.
When everything becomes easy to ship, nothing feels important to ship
Walk through a typical product team’s week after AI enters the picture:
Monday: Designer generates three homepage variants, ships the one that tested highest in the moment.
Tuesday: PM uses AI to write specs for five small features customers mentioned in passing.
Wednesday: Engineer pairs with AI to build all five features by Thursday.
Friday: Team reviews analytics. Nothing moved. The product feels less coherent than it did last month.
Here’s what went wrong: AI eliminated the friction that used to force prioritization. When shipping carried high costs, teams naturally filtered ideas through “is the juice worth the squeeze?” Now shipping feels free, so everything ships.
The backlog became an execution queue, not a strategy document.
What AI actually automates: the shallow work
If your version of product management means:
Turning messy stakeholder requests into clean requirements
Routing work between teams
Sitting in status meetings documenting decisions
Tweaking copy and UX elements in isolation
Writing specs that restate obvious points
AI handles this today. Language models convert context into artifacts. Automation tools coordinate handoffs. GenAI tools produce variants on demand.
This is the shallow layer of PM: coordination disguised as strategy, documentation disguised as decision-making.
When people say “PM is dead,” they’re describing this layer. And they’re right. It should die.
The new bottleneck: judgment under abundance
When execution gets cheap, scarcity moves to judgment.
Real product management now means:
Conviction. Your team can ship anything. Which problems actually matter? Which customer segment deserves focus? Which ideas are technically easy but strategically wrong? Conviction means saying no when saying yes feels effortless.
Taste. AI generates 20 landing page variants in 2 mins and 12 seconds. You iterate, eliminate the mediocre ones. 3 are strong and worth A/B testing. Taste means knowing the difference without running tests unnecessarily.
Coherence. You team ships UX updates, add features and run experiments. Nothing contradicts the product vision because nobody is holding one. Coherence means ensuring the product feels intentionally designed, not randomly assembled.
These skills don’t scale with AI. They get harder as AI makes everything else easier.
How the role evolves
The PM that survives AI looks nothing like the PM that preceded it.
Old PM skills:
Writing detailed specs (ask me about Microsoft days in early 2000s with 30+ page specs commonplace)
Coordinating across teams
Managing backlogs
Triaging bugs
Running status meetings and sharing notes afterwards
New PM skills:
Spending more time with customers while AI handles note synthesis
Using AI to explore ten product directions, then applying judgment to pick one
Maintaining product coherence when everyone can ship independently
Deciding which problems deserve solving when all problems feel solvable
Holding conviction when AI makes every tactic feel achievable
PMs used to ask: “How do we build this?”
The best PMs should now be asking: “Should we build this at all?”
Where you actually spend your time
Look at what consumed your time this week.
Did you spend most of it on:
Status updates and coordination
Ticket writing and spec drafting
Routing requests between teams
Making sure work happened on schedule
Or did you spend it on:
Direct conversations with customers
Turning insights into clearer strategic bets (or tests)
Aligning your team around a coherent direction
Deciding which opportunities to deliberately ignore
The first list means you’re competing with AI. The second list means AI amplifies you.
What comes next
Product management isn’t dying. It’s mutating into something different and more valuable.
The shallow version (coordination, documentation, ticket management) gets automated. The deep version (conviction, taste, coherence) becomes essential.
AI didn’t remove your bottleneck. It moved it to the one place automation struggles: judgment about what matters.
Teams that realize this early stop celebrating velocity and start investing in conviction. They use AI to handle execution while building their judgment muscle.
The PM role that survives looks less like a project coordinator and more like a curator: someone who maintains intentionality when the cost of shipping drops to zero.
That’s not dead. That’s just getting started.