If $200 a Month for AI Feels Expensive, That's Not a Price Problem

If the $200 a month plan for ChatGPT or Claude feels expensive to you, it is not a pricing problem. It is because you do not know what to do with it. If you are not making money with the most capable tool ever put in front of an individual human being, that is on you, not the price tag.

You Are Not Buying Software. You Are Buying Leverage.

Nobody who is actually using AI to make money is complaining about $200 a month. They are asking how fast they can get to the $2,000 a month plan, because the plan was never the expense. The gap between what you paid and what you got back is the expense.

You do not sell AI. You sell an outcome. The tool is not the product. What the tool lets you build, ship, or close is the product. Sell the customer that, and $200 a month stops being a number you think about at all.

You don't sell AI. You sell an outcome.

The High-Skill Work Already Gone

Most people still think AI is coming for call centers and data entry. Fine, sure, but that is not where this is actually hitting hardest. Here is the work that AI can already do today, right now, that most people have no idea it can touch:

• arrow_forwardResearch. Literature reviews, market analysis, competitive teardown. What took a junior analyst a week takes minutes.
• arrow_forward3D modeling. Text-to-model and image-to-model tools are already producing usable assets for games, product design, and prototyping.
• arrow_forwardEngineering. Circuit layouts, structural calculations, first-pass CAD. Not theoretical. Shipping.
• arrow_forwardAdvanced math and programming. Full applications, debugged and deployed, from a written spec.
• arrow_forwardAnalysts, researchers, and call center agents. The roles built entirely on processing information and giving an answer back. That is the job AI was built for.

School Was Built to Produce Employees, Not Operators

Here is the actual problem with education right now. It is teaching kids something that has already been commoditized: knowledge. The computer knows everything. Every fact, every formula, every date. Knowing things stopped being the scarce skill the day that became true.

Knowing what to do with what the computer knows is the scarce skill now. That is the future, and that is what education should be teaching. Instead, most classrooms are still running on a system built roughly a hundred years ago to produce reliable factory workers: memorize the material, pass the test, move to the next grade. Grading curves in plenty of schools, universities included, are wide enough that almost everyone clears the bar. That is not rigor. That is a system optimized to look like it is working.

The computer already knows everything. Knowing is not the skill anymore.

To the Teachers Actually Paying Attention

I have made education a real part of what I do. Not because it is a good business line, though it is, but because it is critical to the future of this planet. AI does not go back in the bottle. There is no version of the next twenty years where this reverses.

If you are a teacher who sees this and is trying to get involved anyway, I am genuinely grateful for you. It is not your fault you came up inside a system that was not built for this moment. You are a product of it, same as the rest of us. But that does not mean the system cannot change, and you are proof it can start with one person. Showing up. Expanding your own thinking on your own time, without anyone requiring it of you. That is exactly what it takes to make it in the world AI just built.

Teach the Practical Skill: How to Ask

Here is where the fear-mongering and the opportunity are the same sentence. Yes, a lot of high-skill work is already gone. And also, every single business on earth still needs marketing, and most of them are still bad at it.

You do not have to be a good marketer to use AI to do good marketing. You have to know what to tell it. Who you are actually selling to. What keeps that person up at night. What you want them to do the moment they finish reading. That is the entire skill. Everything downstream, the copy, the emails, the ad angles, AI can produce once you can hand it those three things clearly.

That is the whole shift in one example. Not "learn to code" or "learn to model." Learn to direct. The people teaching that, practically, with real businesses and real numbers, are about to matter a lot more than the people still grading essays on a curve.