Two-Mode Prioritization for AI Products
Prioritize backward from model magic AND forward from customer needs
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 88%
Building on top of a rapidly improving model requires two distinct prioritization modes running in parallel. Mode one works backward from a new model capability — find the magic thing the tech can now do and make it shine (art, not a PM framework). Mode two is classic product management — listen to customers, find the primitives they share across segments, and build those. Applying a standard PM framework to mode-one work will lead you badly astray.
Origin
Turley describes how he prioritizes across ChatGPT's consumer, enterprise, and general surfaces. He explicitly warns that if you applied a rigid PM framework to a breakthrough capability like GPT-5's front-end coding, 'you would do something horribly wrong.' Voice was a mode-one win: not customer-requested, but a magic capability they found a creative way to productize.
Core principles
- 01Model capabilities and customer needs demand different prioritization logic
- 02When a capability jumps, re-plan and re-prioritize around it — don't force it through segmentation
- 03Working backward from the model is art, not science
- 04Across very different customer segments, look for overlapping primitives before building segment-specific features
- 05Litmus test: a good feature should get ~2x better as the model gets 2x smarter
How to run it
- 1
Inventory the model magic
Ask what breakthrough capability the tech now has — e.g. GPT-5 became genuinely good at front-end coding.
Pro tip Voice and coding weren't customer requests — they were 'wow, we figured out how' moments worth productizing creatively.
- 2
Re-plan around the capability (mode one)
Reprioritize to bring the capability to life in the most awesome way. Do NOT run it through a standard PM framework — that's how you get it horribly wrong.
Pro tip This is more important than any particular audience segmentation for genuinely novel capabilities.
- 3
Find the shared primitives (mode two)
For customer-driven work, look across segments (home, work, school, enterprise) for the large overlap — projects, history, search, sharing, collaboration — and invest there for maximum mileage.
Pro tip General-purpose products have far more cross-segment overlap than you'd expect; exploit it before building bespoke.
- 4
Do the non-negotiable segment work
Handle the genuinely segment-specific requirements separately — HIPAA, SOC 2, deployment/privacy for enterprise are non-negotiable if you want to be a serious player.
Watch out Don't let mode-one excitement crowd out the unglamorous compliance work that gates whole markets.
- 5
Apply the 2x litmus test
For each capability feature, ask: does it get ~2x better as the model gets 2x smarter? If not, it may not be worth shipping.
Pro tip Compliance features (SOC 2) don't scale with model intelligence — that's fine; the test is for core capabilities.
In the wild
Voice wasn't driven by customers begging for it. OpenAI found a way to make the models handle anything-in, anything-out, then asked what a creative way to productize that would be, and shipped to see what people did.
→ A retentive capability that emerged from working backward from model magic, not from a customer request.
Turley found that projects, history, search, sharing, and collaboration are wanted almost identically whether users are at work, home, or school — so they became high-mileage shared investments rather than per-segment builds.
→ Small team ships broadly useful primitives instead of fragmenting effort across segments.
Common mistakes
Forcing a breakthrough capability through a standard PM framework
Turley: 'if you applied some sort of PM framework to that I think you would do something horribly wrong.' Novel capabilities need art and re-planning, not scorecards.
Building segment-specific features before checking for overlap
Different segments look different but share most primitives. Missing the overlap wastes a small team's throughput.
Is it for you?
Best for
PMs building on top of a fast-improving foundation model, juggling multiple very different customer segments
Not ideal for
Mature products with stable capabilities and a single well-understood customer, where classic prioritization suffices
From the transcript
“One is sort of working backwards from the model capabilities and that is much more art than science”
“if you applied to some sort of PM framework to that I think you would do something horribly wrong”
“the other chunk of it really is more like classic product management where you need to listen to customers”
“if you know we're shipping a feature and it doesn't get 2x better as the model gets 2x smarter, it's probably not a feature we…”
From the episode
Inside ChatGPT: The fastest-growing product in history
Nick Turley (Head of ChatGPT at OpenAI)