Not Everyone Needs Claude Max

One person who uses AI best, helping twenty people — that is the more efficient arrangement.

Look around these days and many people feel pressured to learn AI.

Small business owners, CEOs, professionals — all studying prompts, learning automation, attempting to code, on top of their actual work. Out of anxiety about falling behind, some start by paying for premium AI plans costing hundreds of thousands of won a month.

Of course, understanding AI and experiencing it firsthand matters.

But whether it's truly efficient for everyone to subscribe to a top-tier plan like Claude Max and try to become an AI expert while their real work sits idle — that deserves scrutiny.

Watching people around me, it often wasn't.

Paying 400,000 won a month for Claude Max is no small expense in itself.

But the real cost isn't the subscription fee.

It's the time spent learning AI.

Here is someone who has already built expertise in their field. Serving customers, running a shop, selling, making content, providing services — that is their real work.

Then, to make use of AI, they spend weeks studying prompts, setting up development environments, wiring together automation tools, and troubleshooting the endless errors that follow.

During that time, the real work stops.

An hour spent studying AI doesn't just mean an hour consumed. It means giving up the customer they could have met, the revenue they could have made, the expertise they could have deepened in that hour.

Calculated as opportunity cost, it's far more expensive than 400,000 won a month.

And after all that time and money, the output often never reaches a level usable in the actual field.

A simple demo, they can build.

But the moment users arrive, data accumulates, errors occur, and security and operations enter the picture, the story changes.

Having AI generate code once and building a product that actually works are entirely different things.

We don't build a power plant in every home to use electricity.

We don't each design an engine to drive a car.

Needing accounting doesn't make every business owner become a tax accountant, and a legal problem doesn't send every CEO off to study for the bar.

In domains that require expertise, division of labor is more efficient.

AI is no different.

Everyone can understand AI at a basic level and apply it lightly to their work. But not everyone needs to pay for the most expensive plan and personally take on complex automation and development.

It can be far more efficient for someone who truly masters AI to use the high-performance tools and deliver the results to many people.

If one person uses Claude Max properly and improves the work of twenty people, that beats twenty people each paying subscription fees and repeating the same trial and error.

Costs fall, time is saved, and the quality of the output rises.

Most people don't actually want AI itself.

They want higher revenue.

They want less repetitive work.

They want easier customer management, automated bookings and inquiries, and the idea in their head turned into a real service.

Becoming a Claude power user is not the end goal.

They want their problem solved.

Yet in the market, means and ends often get swapped.

Instead of solving the problem, people push AI education first. Instead of delivering results, they teach tool usage. Instead of building the product, they tell the customer to study until they can build it themselves.

It's like a restaurant that, instead of serving the customer a meal, tells them to start with knife skills and flame control.

Of course, those who want to learn should learn.

But for someone whose real work matters, entrusting it to someone who does it well can be more economical than learning it all directly.

That person can focus on their own craft.

They understand their customers, explain what problems arise in the field, and specify what results they need.

The person who excels at AI and development turns that expertise into systems and products that actually work.

One side knows the field; the other knows the technology.

Value is created when the two combine.

The arrival of AI doesn't mean an era where everyone must do everything themselves.

Rather the opposite.

The more powerful AI becomes, the wider the productivity gap between those who wield it properly and those who don't. So the structure that matters is one where skilled people use the tools deeply and spread the results to many.

What matters is not the number of AI users.

It's the number of problems actually solved through AI.

Millions of people each paying for premium AI plans and repeating similar trial and error is less economically efficient than skilled developers and experts using AI to solve problems across industries.

A structure where everyone personally occupies top-tier compute and repeats similar requests is less efficient than one where a single well-built product and system serves hundreds or thousands.

The ability to take something built once and extend it to countless people.

If one person good at AI helps only one person, that's close to simple outsourcing.

But discover the recurring problems in that work and turn them into products and systems, and one person's capability extends into the productivity of hundreds.

This is not to say you can ignore AI.

Going forward, a basic understanding of AI will be necessary for everyone — enough to judge what can be delegated in your own work and what results to expect.

And naturally, the better anyone uses AI, the higher their own productivity climbs.

But not everyone needs to benchmark AI models, wire up APIs, run servers, fix errors, and own deployments.

Each person can simply do what they do best.

Entrepreneurs run businesses, doctors see patients, lawyers solve legal problems, creators create.

And the people who handle AI best build the products and systems that amplify everyone else's expertise.

That is the most natural and most economical division of labor.

For people whose real work matters, explaining the problem they want solved to a company and entrusting it was often far faster and cheaper than struggling to learn AI themselves.

These people do not exist to learn AI.

They exist to help their customers, grow their businesses, and exercise their expertise.

Our company cannot do their real work for them, either.

What we can do is make their experience and expertise faster and stronger through AI and development.

The customer does what they do best.

Our company does AI and development best.

When the customer's field knowledge combines with the company's technology, products and systems emerge that neither could have built alone.

And when a problem solved for one customer repeats in other companies and industries, the company turns it into a product again.

A solution built for one person helps twenty; a system built for twenty extends to hundreds.

Nature does not design every being to do the same work.

Each has different abilities and roles, does what it does best, and exchanges the results. Through that division and connection, the efficiency of the whole rises.

The AI era doesn't change this principle.

If anything, AI is the most powerful amplifier ever built for carrying one skilled person's ability to many more people.

Not everyone needs to use Claude Max.

If one person who truly masters it can help twenty people with it, that is the more efficient choice — for individuals, for companies, and for the country.