Andrew Ng Says AI Jobs Are Growing, Not Shrinking — And Here’s How to Get Hired by 2026

As concerns grow worldwide about artificial intelligence replacing human jobs, one of the field’s most respected voices is offering a very different perspective. Andrew Ng, founder of Google Brain and co-founder of Coursera, believes the real crisis in the AI industry is not job loss — but a severe shortage of people who can actually build AI systems.

In a recent post on X, Ng argued that despite massive investments and rapid advances in artificial intelligence, companies are struggling to find skilled professionals who can design, deploy, and maintain real-world AI solutions. According to him, demand for capable engineers and system builders is rising faster than the supply of talent.

Ng says he is frequently asked by students and early-career professionals whether it is still worth learning AI when automation seems to be accelerating. His response, he says, is always clear: yes. In fact, he believes that AI adoption across industries has created more opportunities, not fewer.

“Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent,” he wrote.

To help aspiring professionals prepare for AI-related roles by 2026, Ng shared three practical recommendations based on what employers are increasingly looking for.

His first piece of advice is to focus on building complete AI systems, not just surface-level demos. While experimenting with pre-trained models or plugging APIs together is useful, Ng cautions that this alone is no longer enough. Companies want people who understand the full lifecycle of AI — from data collection and model training to evaluation, deployment, and long-term maintenance in production environments.

The second recommendation is consistency in practice. Ng emphasizes the importance of balancing theory with hands-on work. He warns that developers who only study concepts without building often end up creating fragile systems or repeatedly solving problems that already have established solutions. Regular, practical experience is what transforms academic knowledge into skills that are valuable in the job market.

His third tip is optional but powerful: selectively reading research papers. While not mandatory for everyone entering AI, Ng believes research exposure can give professionals an edge, especially those interested in cutting-edge work. Understanding how and why techniques evolved, he says, becomes far more meaningful when paired with real-world system building.

Ng has repeatedly challenged the idea that AI will soon eliminate large numbers of jobs. Speaking to NBC News during his AI Developers Conference in November, he highlighted how dependent today’s AI systems still are on human effort.

“I look at how complex the training recipes are and how manual AI development is today, and there’s no way this is going to take us all the way to AGI just by itself,” he said. He added, “When someone uses AI and the system knows some language, it takes much more work to prepare the data, to train the AI, to learn that one set of things than is widely appreciated.”

For Ng, the message is clear: AI’s future belongs to builders — and there aren’t nearly enough of them yet.