Create Your AI-Powered Business
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Lesson 2: Finding Leverage
How to Find the Leverageable Problem
Not every problem is worth solving.
Once people stop chasing startup ideas and start noticing problems, a new challenge appears: there are suddenly too many problems. Complaints, inefficiencies, frustrations, emotional drains — they are everywhere. The temptation is to pick the most interesting one, the most dramatic one, or the one that feels most “important.”
That instinct is usually wrong.
The purpose of the second stage of problem discovery is not to find the best problem in some abstract sense. It is to find the leverageable problem: one that a single person can realistically address using modern tools, without teams, infrastructure, or years of buildup.
This stage is about constraints, not creativity.
Step 1: Separate Importance from Leverage
One of the most common mistakes early founders make is choosing problems based on moral weight or social importance rather than leverage. They feel drawn to problems that matter deeply — climate change, education reform, healthcare access, political polarization — and assume that importance alone justifies starting there.
Importance is not the same as leverage.
A leverageable problem is one where:
action can begin immediately
feedback is available quickly
progress does not require institutional permission
Many important problems fail these tests. They are large, entangled, and slow-moving. They require coordination across systems rather than intervention at a specific point.
Leverage, by contrast, comes from local solvability. A problem is leverageable when it can be addressed for one person or one group without solving the entire system it exists within.
For example, “healthcare is broken” is not leverageable. “People don’t understand their medical bills and feel intimidated asking questions” is.
The second version does not fix healthcare, but it relieves a specific, repeated burden experienced by real people. That relief is leverage.
When evaluating problems, ask:
Can this problem be reduced to a specific moment of friction?
Does solving it for one person create meaningful relief, even if the broader system remains flawed?
Would solving it locally still be valuable, even if nothing else changes?
This step requires intellectual humility. You are not here to solve the world’s biggest problems. You are here to solve a real problem in a way that creates momentum.
Leverage comes from specificity, not scale.
Step 2: Apply the One-Person Constraint
A powerful way to filter problems is to apply a strict constraint: Could one person realistically help someone with this problem?
This is not a philosophical question. It is practical.
If solving the problem requires:
a large team
specialized credentials
regulatory approval
extensive capital
long development cycles
…it may be a worthwhile problem, but it is not a leverageable starting point.
The one-person constraint forces focus. It strips away grandiosity and reveals whether the problem can be addressed through understanding, judgment, coordination, or clarity — things individuals can provide.
Many viable businesses begin as manual services precisely because they are leverageable. They do not start with automation; they start with insight.
Ask yourself:
Could I personally help one person with this problem in the next 30 days?
Could I do it using my existing skills, plus modern tools?
Could I learn enough by doing it once to improve the next attempt?
If the answer is yes, the problem has leverage. If the answer is no, the problem may need to be narrowed.
This constraint is not permanent. Scale comes later. But early leverage is about reducing uncertainty, not maximizing reach.
Businesses that succeed early do so not because they are scalable, but because they work.
Step 3: Evaluate AI as a Leverage Layer, Not the Product
In the current environment, it is tempting to evaluate problems based on whether they “use AI.” This framing leads people astray. AI is not the solution. It is the multiplier.
The right question is not “Can AI solve this problem?” but “Can AI reduce the effort required to solve this problem?”
Leverageable problems are often those where:
information must be gathered or summarized
communication must be drafted or refined
patterns must be recognized
coordination must be maintained
These tasks are cognitively expensive for humans and well-suited to automation or assistance. AI reduces the cost of performing them consistently, but the value still comes from understanding the problem and delivering a meaningful outcome.
Importantly, the customer does not need to know or care that AI is involved. In many cases, emphasizing AI actually reduces trust.
A useful test is to imagine the business without mentioning technology at all. If the value proposition still makes sense, the problem is likely real.
AI should operate behind the scenes, allowing one person to do work that would otherwise require multiple roles. That is leverage.
If AI disappears from the description and the problem still feels worth solving, you are on solid ground.
Step 4: Test for Speed of Feedback
Leverageable problems provide feedback quickly. This matters because early businesses do not fail from lack of vision; they fail from prolonged uncertainty.
A problem is more leverageable if:
customers can tell you immediately whether it helped
improvement is visible or felt quickly
iteration does not require long delays
Fast feedback allows learning. Slow feedback breeds false confidence or premature discouragement.
Ask:
How quickly would someone know if this worked?
What would “success” feel like from the customer’s perspective?
Could I adjust the approach after one interaction?
Problems that involve long timelines, delayed outcomes, or diffuse results are harder to validate and slower to improve. They may be valuable later, but they are poor starting points.
Early leverage favors immediacy.
Step 5: Choose the Problem That Reduces Your Own Uncertainty
The final filter is personal but practical: which problem reduces your uncertainty the fastest?
This is not about passion. It is about learning.
Choose a problem where:
you already understand the context reasonably well
access to potential customers is realistic
conversations can happen without awkward pitching
The goal of early problem selection is not to be right. It is to learn efficiently.
Leverageable problems are those that allow you to act, observe, adjust, and act again without friction.
If a problem requires too much explanation before you can even test it, it is probably not the right one to start with.
The best problem to choose is often the one that feels almost obvious once you see it — not impressive, not glamorous, but clearly unresolved.
That is leverage.
Why This Step Matters
Finding the leverageable problem is where entrepreneurship becomes practical. It is the moment when ambition meets constraint, and clarity replaces abstraction.
You are not choosing what to build forever. You are choosing what to learn from first.
Leverage is what allows one person to move forward instead of waiting. And movement, not certainty, is what creates real businesses.