4 AI development skills you need, according to Andrew Ng - and what experts say he's missing

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Is it time to think outside the box for software engineering AI skills ? One of the planet's most noted AI experts recently pinpointed the four most essential skill areas for those seeking careers in AI development . However, some industry observers say the recommendations don't go far enough, suggesting AI skills extend beyond engineering into other business-focused areas.

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Andrew Ng, founder of Coursera and lecturer at Stanford, formulated a list of essential skills for AI development based on his analysis of more than 10,000 job postings and interviews with AI experts, hiring managers, and recruiters. The list included the following abilities:

With the rise of generative, and now agentic, AI , the process of building software has changed drastically, said Ng. But acquiring the right skills can be a confusing process in the hype-filled environment that has developed around AI. Still, "all developers -- full-stack engineers, data engineers, DevOps engineers, machine learning engineers, and, yes, AI engineers -- will need AI engineering skills," he explained.

However, Ng received pushback that his skills-development recommendations needed to consider the bigger picture when introducing AI for problem-solving abilities. One response to his post puts it this way: "Yikes -- this is way too internally looking. It fails to address the business problem. This is why people hate software engineers."

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While helpful from strictly a development perspective, Ng's recommendations constitute "a dangerously narrow framework for the enterprise, suffering from a massive blind spot of builder bias," said Andy Thurai, founder and AI advisor at The Field CTO.

"Ng's taxonomy is entirely focused on 'Day 1' innovation -- writing the code and getting the model to work. But in enterprise environments, the hardest part of AI is no longer building the intelligence; it is orchestrating, observing, and paying for it. Relying on this narrow set of skills optimizes for the variable that matters least in production."

"The four skill areas Ng outlined are important, but are not enough on their own," agreed Deepika Sidana, senior manager of software engineering at American Express and professional development director for NYSCD at the Society of Women Engineers. "Building an AI application is only one part of the challenge. Engineers also need to understand the business problem, customer workflow, risk, compliance requirements, and the consequences of failure."