Why I stopped using AI for speed and started using it for coverage.
An LLM has been trained on more text than I will ever read
More industries than I will ever work in. More roles than I will ever hold. More edge cases than I could encounter in a hundred lifetimes.
And I was using it to write emails faster.
(Probably you too.)
Somewhere along the way, my framing changed.
I stopped thinking of AI as a tool for speed.
I started using it as coverage, a way to explore outside my own experience.
Coverage = surfacing plausible roles, contexts, and constraints I wouldn't think to look for.
Not because it's smarter than me. Because it's been exposed to more than me.
The uncomfortable truth about brainstorming
I rarely generate ideas outside what I've been exposed to. Brains are pattern-matchers; we remix what we know.
When I "brainstorm," I'm not really exploring. I'm rearranging. Shuffling the same experiences into different shapes and calling it strategy.
My blind spots aren't things I'm ignoring. They're things I don't naturally reach.
I hit this wall this month:
I was trying to figure out who to build for. Classic founder problem.
(In my case, I was exploring segments for a knowledge retrieval tool but the method works for any product.)
I brainstormed. I made lists. I talked to people.
And I kept landing on the same 4–5 customer types. The ones that "felt right."
But "felt right" just meant "familiar." They matched my experience. My network. My assumptions.
The full problem space was massive. And I was exploring a tiny corner of it because that's all I could see.
So I changed the question
If I ask the AI "who should my customer be?" it just riffs on my framing. It stays inside my box. It gives me better versions of what I already imagined.
Instead, I asked it to generate the raw building blocks, without my assumptions baked in.
I broke it into 8 dimensions:
* Roles (life stages, expertise levels, underserved niches)
* Problems (what specifically goes wrong?)
* Contexts (where and when does this bite?)
* Triggers (what makes it suddenly urgent?)
* Barriers (what stops them even if they need it?)
* Workarounds (how do they solve it today, painfully?)
* Data types (what information are they drowning in?)
* Value signals (how would they know it's working?)
The rule: 50+ options per dimension.
Why 50? Because the first 10 are the obvious ones. The interesting stuff - the stuff outside my experience - shows up at option 27, option 43.
Then i had it generate combinations.
With 50+ options across 8 dimensions, the space is enormous! So I sampled 100 combinations to review and score.
Some combinations felt natural. Those were the ones I probably would have brainstormed anyway.
Some felt weird. Wrong, even. "That doesn't make sense."
Sometimes it's nonsense. But often "doesn't make sense to me" just means "outside my experience."
I kept the mutations, the ones my gut wanted to discard.
Then I scored everything against my actual constraints: Can I reach them? Will they try something new? Can I serve them today? Will they pay for it?
Three surprising mutations worth validating
A tabletop game master running a years-long D&D campaign. They need instant recall during live sessions. They care obsessively about consistency in their world-building. They have mountains of notes they can't search.
A farmer planning across multi-year seasonal cycles. Institutional knowledge passed down but never written. Decisions made years ago that affect what's possible now. No system to track any of it.
A clinical trial coordinator managing regulatory submissions across dozens of sites. Buried in protocols, amendments, and compliance documents. One missed detail can delay a trial by months.
Different worlds. Same underlying need: recall, consistency, and evidence you can point to.
I had never seriously considered any of them.
And I couldn't have. I've never been a game master. I've never farmed. I've never run a clinical trial.
To be clear: I didn't choose all three as my target. They were high-signal hypotheses worth testing.
These are real communities. The details were hypotheses I could then verify. The AI surfaced them from its training — from forums, articles, and discussions it's seen that I haven't.
That's not magic. It's coverage. Not wiser — wider.
Important caveat
This isn't "AI knows the truth."
It's hypothesis generation. A way to map the problem space faster than my brain can alone.
I still had to validate. Talk to real people. Test assumptions.
But I was testing different assumptions. Better ones. Ones I couldn't have generated on my own.
This is the "unlock" for me now
Not productivity. Not speed.
Cognitive offload.
Using AI to explore the problem space that exists beyond the limits of my own experience. The space I can't brainstorm my way into because I don't know what I don't know.
I turned this process into a tool
I've been building Premisia, a platform that helps founders stress-test strategic decisions with structured frameworks and AI.
This segment discovery workflow is now part of it. Describe what you're building. It generates the segment space systematically, - way beyond what you'd come up with alone - scores them against your real constraints, and tells you where to start.
What can't you see because of where you've been?
First 50 beta users get free access. Comment "BETA" and I'll send you the link.
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