How to build AI systems in 2026? Google Brain co-founder shares three crucial ti

As artificial intelligence continues to dominate conversations in the tech world, the demand for skilled professionals in this field remains high, often leading to lucrative pay packages for those with the right expertise. Andrew Ng, a prominent AI innovator and co-founder of Coursera, has recently shared three pivotal strategies for aspiring developers eager to make their mark in AI system creation. Ng, who previously co-founded Google Brain and served as Chief Scientist at Baidu, emphasizes that companies are struggling to recruit enough qualified AI talent. To navigate this competitive landscape, he advises newcomers to adopt a balanced approach that includes structured learning, practical experience, and engagement with current research. His first recommendation is to enroll in AI courses. Ng strongly opposes the notion that one should dive headfirst into building projects without a solid understanding of the underlying principles. He describes this as 'bad advice,' cautioning that attempting to create AI systems without foundational knowledge can result in inefficient and redundant work. "I've encountered developers who reinvented standard document chunking methods or duplicated evaluation techniques, leading to disorganized code," Ng remarked. He believes that structured learning allows developers to grasp existing building blocks and save valuable time. Next, Ng stresses the importance of practical experience. He draws a comparison to pilot training, noting that while theoretical understanding is vital, hands-on practice is irreplaceable. "Eventually, you need to take control of the cockpit! Fortunately, with the advent of advanced coding tools, building AI systems has never been more accessible," he explained. When he finds himself lacking inspiration for projects, Ng often turns to courses or research papers to spark new ideas. Lastly, Ng encourages aspiring AI professionals to delve into research papers. He points out that many of the most competitive candidates in the job market regularly engage with academic literature. Although he acknowledges that research papers can be challenging to comprehend, they often contain cutting-edge insights that have yet to be simplified for broader audiences. "The moments of clarity I gain from reading these papers are incredibly rewarding," Ng concluded. For those keen on stepping into the AI arena, following Ng's guidance could be the key to unlocking their potential in this rapidly evolving field.