Your Imagination Is the Real AI Bottleneck (Not the Technology)

The weirdest thing about “AI is amazing”

Every week I hear the same sentence from smart people: “AI is incredible… but I don’t really use it.”

That gap is the whole story of this moment.

Because the models are not the bottleneck anymore. The interfaces are getting smoother. The demos are getting absurd. The outputs are getting good enough to ship.

Yet most people still use AI like a fancier search bar, then conclude it’s “not there yet.”

What’s not there yet is our imagination.

Technology moved. Our mental model didn’t.

When a new tool shows up, we tend to force it into an old shape. We do the same workflows, just a little faster. We ask for summaries, we ask for rewrites, we ask for “10 ideas.” Then we stop.

But the real unlock is not incremental. It’s conceptual.

Think about the first spreadsheet. The breakthrough wasn’t that computers got fast enough to do accounting. The breakthrough was that someone looked at the pain of recalculating numbers by hand and thought: what if the table recalculates itself?

That is the move. Not “make my current job 20% easier.” It’s “change the job.”

The imagination bottleneck is a creativity bottleneck

There’s a line often attributed to Einstein: “Imagination is more important than knowledge.” Whether you love or hate quotes, the point lands in practice. When you can ask an AI for almost anything, the limiting factor becomes the quality of the question, the boldness of the experiment, and the willingness to iterate.

Creativity researchers call this divergent thinking: generating many possibilities and exploring unusual combinations. Most AI usage today is convergent. People ask for the one right answer. They want certainty. They want the AI to be a vending machine.

But AI is closer to a studio.

Prompts aren’t commands. They’re thinking tools.

I don’t care about “prompt engineering” as a buzzword. I care about prompt design as a way to expand your range.

If you treat the model like a junior teammate, you stop asking for magic and start giving context: the goal, the audience, the constraints, the tradeoffs, the tone, what you’ve already tried, what “good” looks like. Suddenly the conversation becomes a working session, not a slot machine pull.

That shift sounds small, but it changes everything. It forces you to articulate what you want, which means you discover what you actually want.

And once you realize the model can role-play, critique, propose alternatives, generate drafts, simulate user objections, and stress-test assumptions, the question becomes: why are we only asking for summaries?

Where this shows up for founders (and honestly, everyone)

Founders usually don’t lose to a competitor because their tools were weaker. They lose because their cycles were slower: slower learning, slower shipping, slower feedback, slower clarity.

AI compresses cycles. Not by doing your work for you, but by removing the friction between an idea and a first version.

A draft PRD. A first landing page. A customer interview guide. A pile of objections. A pricing page rewrite. A ruthless teardown of your onboarding. A set of experiments you can run this week.

You can do this in minutes now.

If you’re not doing it, it’s not because the model can’t. It’s because you didn’t think to try.

The “one percent” trap

Most people never get past the first percent of the tool.

They test AI once, get an answer that feels generic, and decide the technology is hype.

But generic output is usually a mirror of generic input.

If you give zero context, you get zero specificity. If you don’t push on constraints, you don’t get taste. If you don’t iterate, you don’t get depth.

In other words: if you don’t use imagination, you don’t get leverage.

A better way to think: invent workflows, not prompts

The biggest mental upgrade I’ve seen is when people stop collecting prompts and start designing workflows.

A workflow has stages. A workflow has checkpoints. A workflow has a “human taste” moment where you decide what matters. A workflow has a feedback loop where you refine.

Once you think in workflows, AI becomes infrastructure. You can chain steps. You can turn messy information into structured decisions. You can take an idea from fuzzy to shippable.

Alan Kay put it bluntly: “The best way to predict the future is to invent it.” AI is a multiplier for invention, but only if you act like an inventor.

The only real question to ask yourself

When you open an AI tool, don’t ask: “What can it do?”

Ask: “What would I attempt if failure was cheap?”

That question pulls you into the right mode. It makes you playful. It makes you experimental. It makes you fast.

It’s also how you get out of the imagination bottleneck.

What I want you to do next

Pick one part of your work you complain about every week. The recurring thing. The annoying thing. The thing that steals your momentum.

Now reframe it as a system instead of a task. Describe the input, the output, the constraints, the failure modes, and what “good” looks like.

Then open your AI tool and run a real session. Give it context. Ask it to propose three completely different approaches. Ask it to critique its own answers. Ask it what information it needs from you to make the output ten times better.

Do that once and you’ll feel the shift.

Not because the technology changed.

Because you did.