What can expertise look like when AI can answer almost anything?

For young people today, asking AI to explain a complex concept, come up with a business plan in minutes, write code, or build a prototype feels as normal as using a search engine did a decade ago.

Meanwhile, Gulf governments are making big investments in AI, and its use is spreading quickly from offices to classrooms, universities, startups, and businesses.

But a more fundamental question is emerging.

If AI gives us answers right away, how do we make sure people still learn to find, question, and improve those answers themselves?

AI brings big opportunities, but there’s also a risk of confusing output with real understanding.

Ankita Garg, Founder and Program Director of Founder Mode Bootcamp, has seen this tension emerge among young entrepreneurs. “AI has dramatically reduced the distance between an idea and a prototype,” she says. “A young founder can now research a market, analyze competitors, generate a brand identity, and build an early product in days rather than months.”

But the risk is that, Garg adds, “AI can make shallow thinking look sophisticated.

“A polished pitch deck, detailed business plan, or functioning prototype may create the impression of competence even when the student cannot explain the customer, defend the assumptions, or demonstrate real demand.”

The quality of what one produces is only part of the story. “AI is a tool that extends a founder’s judgment; it doesn’t replace it,” Garg says. “It should amplify a founder’s thinking, not substitute for it.”

THE STRUGGLE THAT CREATES JUDGMENT

It’s easy to see why people want to automate learning. If AI can provide solutions instantly, why struggle through the process of finding them? Because that struggle is often where expertise develops.

For entrepreneurs, AI can help analyze markets and create investor materials. It can draft a sales message. AI can prepare someone for a customer meeting. It cannot sit across from a customer and recognize uncertainty.

“Young founders should not outsource customer conversations, rejection, negotiation, difficult decisions, or accountability,” Garg says.

“Customer discovery isn’t about proving an idea is good,” Garg adds. “It’s about acting like a detective and uncovering whether the problem is real in the first place.”

This lesson applies to all careers. AI should make things easier, but it shouldn’t take away the experiences that build real expertise.

DOMAIN EXPERTISE WILL DEFINE THE AI ECONOMY

As AI tools become easier to use, the real advantage is understanding.

Avinav Nigam, Founder and CEO of TERN Group, says the strongest AI companies will begin with a deep knowledge of a problem. “The companies that started with technology and went looking for a problem to solve are going to struggle significantly over the next five years,” he says.

“The best AI products are built by people who understood a broken system so deeply that the technology became the obvious answer rather than the starting point.”

The question, he says, should not be: What can AI do? It should be: Why does this problem keep failing?

This difference is most important in complex industries.

In healthcare, for example, AI cannot simply be deployed into existing systems without understanding regulation, human behavior, and operational reality.

“You cannot parachute a general-purpose AI into a credentialing process you do not understand, or into a cultural integration challenge you have never experienced, and expect it to work,” Nigam says.

“The domain knowledge is what tells you where the model should operate and where a human must remain in the loop.”

The rarest skill right now, Nigam says, is “not machine learning expertise or data engineering.”

“It is people who understand both AI and a specific industry deeply enough to know where the two should and should not meet.”

AI-POWERED VS. AI-DEPENDENT DEVELOPERS

The same issue comes up for software engineers. As AI coding assistants get better, developers can create more software in less time.

But speed is not the same as capability. Mohammad Abu Sheikh, CEO of CNTXT AI, believes the difference between AI-powered and AI-dependent developers becomes clear when things go wrong.

“An AI-powered developer understands why it broke and knows what to do next. An AI-dependent developer opens a new chat window and asks again,” he says.

There’s already a generation that’s great at using AI tools, Sheikh adds. The real challenge is building deeper technical understanding.

“What we see across the region is a generation that has learned to use these tools remarkably well but has rarely been asked to debug, to reason from first principles, or to own a system end to end,” he says.

“That is not a failure of talent, but an environment that rewards using the tools, shipping fast, and moving on.”

The solution is not resisting AI. It deliberately creates situations where humans still have to think. “Give engineers problems where the tools alone are not enough, where they must understand what the model is actually doing to get unstuck,” he says.

“Measure reasoning, not only output. And promote the engineer who finds the root cause as highly as the one who ships the fastest fix.”

At CNTXT AI, that philosophy shapes how the company develops Arabic AI systems. “We are building Arabic voice models that do not exist anywhere else, and that work starts with the data: the dialect coverage, the training datasets, the evaluation frameworks,” Abu Sheikh says.

“The absence of a shortcut is sometimes the best education an engineer can receive.”

Applying AI effectively will become a core professional skill. But if every engineer focuses only on using existing models, the region risks remaining dependent on innovations built elsewhere.

“With AI, the raw materials are data, compute, and talent, and the region has all three in abundance,” Abu Sheikh says. “What it must now build is the capability to turn them into models, not just consume models built elsewhere.”

The AI era will need people who know their fields well enough to guide intelligent systems.

The future doctor will not simply use AI diagnostics. They will understand when the recommendation makes sense. The future engineer will not simply generate code. They will understand why it works. The future founder will not simply build faster. They will understand which problems deserve to be solved.

The future AI researcher will not simply adapt existing models. They will create new ones.

For the Gulf, the opportunity is significant. The region has capital, ambition, infrastructure, and a young digital workforce. The next step is to make sure these strengths lead to more than just technically skilled users.