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Mark Cuban Says Most People Are Missing a Free-To-Learn Skill That Could Make Them This Era’s Top Earners

Mark Cuban says learning about AI could lead to a high-earning income.

By Chris Lewis, CEPF , Edited by Calista Headrick

Updated Oct. 15, 2026

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Artificial intelligence is already changing how people work, but Mark Cuban thinks one of the biggest opportunities may still be ahead. His advice isn't necessarily to learn how to build the next ChatGPT. Instead, Cuban has encouraged people to learn how to use and implement AI inside real businesses.

With powerful AI tools available for free or at relatively low cost, someone can start developing those skills without earning a computer science degree first.

That could matter if you're trying to prepare yourself financially for a job market where AI knowledge becomes increasingly common. Here's what Cuban thinks the opportunity looks like, and how you could start building the skill yourself.

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The opportunity is becoming an "AI integrator"

Cuban has described a potentially valuable role as an AI integrator: someone who understands AI well enough to take the technology into an existing company and put it to practical use.

You don't necessarily need to invent the model or write its code. Instead, the value comes from figuring out where AI can save time, automate existing workflows, and improve processes.

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Small businesses could offer the biggest opening

Large corporations can spend heavily on AI engineers, consultants, and internal technology teams. The neighborhood accounting firm, plumbing company, regional retailer, or 50-person manufacturer probably can't.

That's where Cuban sees an opening. The U.S. has more than 36 million small businesses, according to the Small Business Administration, and many don't have dedicated AI expertise.

Cuban argues that people who know how to implement AI could help fill that gap, especially for businesses that know they should use the technology but aren't sure where to begin.

You don't need a computer science degree

This distinction matters because learning how to apply AI is very different from becoming an AI researcher or a software engineer. Cuban has specifically said you don't have to be a software engineer to take advantage of this opportunity.

His focus is on becoming comfortable with today's tools, learning how to use them in different situations, and understanding what they can do (and what they can't).

Simply put, technical knowledge can help, but it isn't necessarily the price of admission.

Start by actually using the tools

Watching videos about AI isn't the same as becoming good at it.

Cuban has encouraged young people to spend their extra time experimenting with AI. That could mean testing the free versions of several popular tools, giving the same task to different models, and learning where each one performs well or falls short.

Try using AI for research, writing, spreadsheets, data analysis, brainstorming, or routine administrative work. The point is to build judgment through repeated hands-on use.

Get better at telling AI what you need

Prompt writing is part of AI fluency because the quality of your instructions can drastically change the usefulness of the output.

Practice giving a model context, explaining the goal, setting constraints, and asking it to revise weak answers. Then go a step further. Cuban has specifically emphasized learning how to customize models for particular business situations rather than simply typing questions into a chatbot.

That ability could make you much more useful to an employer than someone who has only casually experimented with AI.

Learn to spot expensive problems

Knowing 20 AI tools won't matter much if you can't identify a useful reason to use them. Imagine a company where someone spends six hours every week manually sorting customer emails, preparing the same report, scheduling appointments, or copying information between systems. Those are the kinds of processes worth looking at.

An effective AI integrator needs to understand the business problem first and the technology second. The goal isn't to add AI everywhere. It's to find situations where it can produce a measurable benefit.

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Think like Cuban did when PCs were new

Cuban's argument comes partly from his own experience.

In the early 1980s, personal computers were still unfamiliar to many business owners. Cuban learned the technology and eventually started MicroSolutions, a company that helped businesses understand and implement PCs and software. He later sold the company for $6 million.

He now sees similarities between that period and today's AI transition: Companies know something important is happening, but many still need someone who can translate the technology into something useful.

AI could eventually become as ordinary as Excel

The window may not stay this wide forever. Cuban told Fortune that he expects AI to become a baseline business skill, like "email or Excel."

If he's right, simply knowing how to open an AI chatbot won't remain much of an advantage. The more valuable skill could be understanding how to use AI well enough to change how work gets done. That gives people learning now a chance to build experience before the skill becomes routine.

Don't outsource your thinking to AI

Cuban also draws an important line between using AI to become more capable and using it to avoid thinking. Asked about a valuable skill in an AI-heavy world, he answered: "Critical thinking. 100%."

AI can research, summarize, generate ideas, and automate work, but its answers can still be wrong. Cuban has compared AI to an exceptionally capable intern whose work still needs checking. Learning when to trust a tool, when to question it, and when a human needs to make the final call may become just as important as writing a good prompt.

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Bottom line

Cuban's argument is straightforward: You don't have to build AI to benefit from it. Learning how to use AI well, applying it to real business problems, and combining those skills with strong judgment could make you more valuable in the workplace.

One practical way to start is to pick a task you already understand and use free AI tools to make it faster or easier, then track the results. Building a few concrete examples of time saved or work improved could help you prove your value to an employer or client, and potentially put extra cash in your pocket.

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