The $200 AI Tool Test

I pay $200/month for one AI tool, and I’d happily pay $2,000.

That sentence makes people either laugh or get uncomfortable. Good. Because it’s also the cleanest way I’ve found to separate “AI that changes your output” from “AI that just changes your tabs.”

Most founders are doing AI productivity wrong

Right now, the default founder workflow looks like this: you see a new AI tool launch, you try it, you keep the subscription “just in case,” and a month later you’re paying for a small army of products you barely touch.

It feels like productivity. It’s actually expensive procrastination.

The easiest trap to fall into is believing that your next 10% boost is hidden behind a new UI. The truth is less exciting and more useful: most of what you want is already possible with the tools you have, if you commit long enough to extract the value.

The $200 → $2,000 test

Here’s the test I run on every AI tool, and it’s brutally simple.

Would I pay more than I’m paying today?

Not “is it kind of useful?” Not “is it trending?” Not “could I imagine a future where I use it more?”

I mean right now, today, with my current workflow: would I increase the price and still keep it?

I use Cursor for coding. I pay $200/month. Would I pay $2,000? Honestly, yes. Not because I like spending money, but because the ROI is obvious: it compresses time, reduces cognitive load, and lets me ship like I have a small team behind me. Cursor’s own pricing page makes it clear they’re positioning for serious users, not casual dabblers, and that matches how it feels in practice. (If you’re curious: https://www.trycursor.com/pricing)

When a tool hits that level, you don’t need convincing. You feel it in your calendar.

Subscription sprawl is a signal, not a strategy

If you’re paying for 20 AI subscriptions and you wouldn’t raise the price on a single one of them, you’re not buying productivity.

You’re buying novelty.

And novelty is addictive because it gives you a quick dopamine hit without forcing you to confront the real problem: your biggest bottleneck is still sitting there, untouched.

This is also why the AI tool market feels noisy. A lot of startups are shipping the same capabilities with different wrappers. Models commoditize fast. Interfaces get copied even faster. Differentiation comes from execution, focus, and how deeply a tool plugs into a real workflow.

Stop hunting for “the perfect tool”

The winning move isn’t building the ultimate stack. It’s picking the one area where you’re bleeding time and solving that first.

For some founders, that’s coding. For others, it’s content creation, meeting prep, task organization, research, hiring, customer support, or just keeping their brain from exploding across 15 apps.

The point is not to be “an AI power user.” The point is to remove your biggest constraint.

If you’ve ever read about the Theory of Constraints, the idea is simple: your output is limited by the narrowest part of the system, so improving anything else is mostly a distraction. AI tools are exactly the same game. If you optimize the wrong thing, you just get faster at being stuck. (The classic focusing steps are here: https://www.tocinstitute.org/five-focusing-steps.html)

The 30-day depth experiment

Here’s what I’d do if I were starting from scratch tomorrow.

I’d pick one bottleneck that costs me real hours every week.

Then I’d pick one tool that tackles that bottleneck well.

Then I’d commit to using it for 30 days. Not “testing.” Not “trying.” Actually using it in the messy, real parts of work where the gains live.

At the end of the month, I’d ask one question: did this create clear, measurable leverage?

If the answer is no, I cancel without guilt.

If the answer is yes, I double down. I learn the edge cases. I push it until I understand where it breaks. I build habits around it.

That’s where the compounding happens.

The real bottleneck is not the model

Most people are using these tools at maybe 1% of their potential.

Not because the AI is limited.

Because we haven’t trained our imagination to delegate properly.

The hard part isn’t getting an answer. The hard part is asking a question that turns into an outcome. A clean brief. A tight feedback loop. A workflow that makes the tool better every week.

Founders who learn that skill early get a ridiculous advantage. They’ll run like a 10-person team while keeping startup speed.

The founders collecting AI subscriptions like Pokémon cards will keep wondering why nothing changed.

Find your $200 tool

Quality over quantity. Depth over breadth. Results over experimentation.

Find the one tool that hits so hard you’d pay 10x.

That’s where real productivity lives.