Despite widespread fears that artificial intelligence could eliminate jobs, one of AI’s most respected pioneers says the real challenge facing the industry is a shortage of skilled professionals, not a surplus of automation. Andrew Ng, founder of Google Brain and co-founder of Coursera, says demand for people who can build, deploy, and maintain real-world AI systems is growing faster than the supply of talent — creating opportunities for aspiring professionals who prepare strategically.

Ng shared three practical tips to help engineers, researchers, and early-career tech professionals position themselves for success in AI jobs by 2026.

Why There’s an AI Talent Shortage

Ng says that while AI tools are advancing rapidly, human expertise is essential to make them work in real environments. Systems still require design, data preparation, training, evaluation, and ongoing maintenance — tasks that automation can’t fully handle on its own. There simply aren’t enough skilled workers to meet the increasing demand across industries like health, banking, robotics, and software.

According to Ng, this means the industry isn’t shrinking jobs overall but is struggling to find people who can build and scale AI systems effectively. That’s a strong message for anyone wondering whether to invest time in learning AI: the opportunity is real, and skills are in shortage.

Tip #1: Build Complete AI Systems, Not Just Demos

Ng’s first recommendation is to focus on building full AI systems, rather than only creating demos or surface-level experiments. While working with pre-trained models and APIs provides valuable exposure, most employers want candidates who can handle the entire lifecycle of an AI solution:

  • Data collection and preprocessing

  • Model selection and training

  • Evaluation and quality checks

  • Deployment and integration in real environments

  • Monitoring and maintenance over time

This full-stack approach shows employers you understand how complex AI workflows operate beyond simplistic examples.

Tip #2: Practice Building AI Systems Consistently

Ng’s second tip emphasizes consistent hands-on practice. Theory and coursework are necessary, but real skill development comes from tackling practical problems repeatedly. Without this practice, candidates risk building fragile systems that fail when scaled or exposed to new data.

Regularly working on meaningful projects — even small ones — helps bridge the gap between academic knowledge and job-ready capabilities. Consistency transforms learning into reliable skills that organizations value.

Tip #3: Read Research Papers to Gain an Edge

While Ng says reading research papers isn’t mandatory for every AI job seeker, he believes it gives candidates a competitive advantage, particularly for advanced or cutting-edge roles. Research papers reveal why certain techniques exist, how they evolved, and where innovations are headed.

This depth of understanding can set you apart from others who only follow tutorials or basic guides. It also makes you better equipped to solve novel problems and contribute to meaningful improvements in systems.

What This Means for Aspiring AI Professionals

Ng’s message is clear and forward-looking: AI jobs are not disappearing — instead, employers need more skilled builders than ever before. If you want to be competitive in the AI job market by 2026:

  • Focus on real world system building

  • Combine theory with regular hands-on practice

  • Use research to deepen insight and stay ahead

These approaches position you for work that matters and help you stay relevant as AI continues to evolve.