Painful Problems are Assets

In the book, How to Make a Few Billion Dollars by Brad Jacobs, there is an interesting paragraph;

“If you want to make money in the business world, you need to get used to problems, because that’s what business is. It’s actually about finding problems, embracing and even enjoying them — because each problem is an opportunity to remove an obstacle and get closer to success.”

Well, people want to avoid pain and indulge in pleasure.

“Problems are an asset — not something to avoid but something to run toward.”

A familiar pattern keeps showing up in the startup world: a team builds a slick demo, investors nod approvingly, and everyone talks about how it will “change the way we work.” Yet, once it’s out in the wild, real customers barely touch it. What’s going on?

At first glance, an occasional problem can look like a big opportunity. For example, “Use natural language to query a database” sounds like magic. You type a sentence, and it spits out data. But ask yourself: how often does the average employee really need that? Most people in a tech company write SQL maybe once a month, if at all. They get stuck on a join or misplace a semicolon, and they wish for a bit of help. But that desire appears so infrequently that hardly anyone sticks around long enough to become a paying user.

Contrast that with the daily grind of a data scientist. They write queries dozens of times every day. For them, typing correct SQL fast is not an occasional annoyance but a core part of their job. They need more than syntax hints. They need version control to roll back reports, performance monitoring to identify slow queries, and collaboration tools for teammates to review each other’s work. A ChatGPT-style interface aimed at beginners won’t cut it. It feels like a toy, not a tool.

This gap between occasional users and power users shows why many LLM-driven demos never translate into real traction. The demos solve low-frequency, low-value problems. They look fun at a conference, but they don’t earn a permanent place in any workflow.

By contrast, language models truly shine when they step into a highly repetitive process. Imagine a customer-support team that answers the same five questions over and over for eight hours a day. Or a legal-review group that flags the same clauses across hundreds of contracts every week. Those tasks eat time and focus. They happen around the clock. Automating even part of that cycle saves hundreds of hours and reduces burnout. That is where you see adoption, retention, and word-of-mouth growth.

If you’re building an LLM-enabled product, ask yourself two questions before you write a single line of code: How often does this problem occur? And what is the real value of solving it? If the answer is “one so-so use case once in a blue moon,” you’re setting yourself up for a flashy launch and a quiet sunset. But if it’s a high-frequency pain point that eats through your customer’s calendar, you’ll create something they can’t live without.

In the end, the secret isn’t about making the most remarkable feature. It’s about solving the recurring headaches that people dread.