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

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Follow ZDNET: Add us as a preferred source on Google.ZDNET's key takeawaysCoursera founder Andrew Ng has listed key AI development skills.Some industry experts suggest the list is too focused on building.Engineers must also understand the business problem and risks. 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.Also: 'Specialists aren't required' anymore: How to stay valuable in an AI agent workplace todayAI development essential skillsAndrew 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:Building and deploying AI applications: "Understand the building blocks of AI such as LLMs, context engineering, RAG, agentic workflows, machine learning and deep learning." Add to this knowing "how to use statistical techniques to measure, steer, and govern AI systems so that they behave more predictably."Understanding software engineering fundamentals: Engineering software requires skills related to architecture, testing, and security: "It leads to much better outcomes than those for an inexperienced developer who vibe codes a solution without knowing the tradeoffs their coding agent is making."Using coding agents: "When you have this skill, you have a good mental model for how agents work," said Ng. "You understand their limitations and how to work around them, and are able to quickly steer them -- knowing how much to intervene and how much to leave them alone -- to build robust software without wasting excessive time or tokens."Shaping the build: "Engineers should no longer expect to be given a pixel-perfect design and asked only to implement it. Instead, effective AI engineering requires having product sense and understanding business context and customer goals."Not enough on their ownWith 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."Also: Why replacing staff with AI backfires - and 5 ways smart leaders generate real value insteadWhile 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." Also: Companies embracing AI the most are hiring more people - including entry-levelWhat else is criticalStrong orchestration skills are particularly critical to moving AI into enterprise environments. Such skills encompass "coordinating models, tools, data, evaluations, observability, human approvals, and fallback paths," said Sidana. "The strongest AI engineers will combine software fundamentals with domain knowledge, product judgment, communication, and accountability for measurable business outcomes." Along with orchestration, key skills include "the ability to harness engineering and deterministic governance; multi-agent orchestration; agent arbitration; AI FinOps; runtime economics; agentic observability; agentic security; and socio-technical systems integration," said Thurai. "Innovation gets you to the starting line, but observability, security, resilience, and governance get you to production."The most valuable AI-era engineering skills "increasingly sit outside traditional coding," said Naman Ahuja, software engineer at Meta. "AI can generate implementation quickly; the harder work is framing the right problem, understanding business constraints, decomposing systems, orchestrating AI and software components, and judging whether an output is actually useful in production".Also: AI is getting better at your job, but you have time to adjust, according to MITIn his work at Meta, "infrastructure decisions often involve balancing reliability, compute efficiency, cost, and user impact, not simply writing technically correct code," said Ahuja. "The engineers who become most valuable will be those who can translate ambiguous business objectives into reliable technical systems.""When AI-enabled software engineers don't understand why the software is being written and how it will be used, the productivity boosts of AI are wasted in false starts and rework," cautioned Chris Matteson, head of sales engineering at Union.ai. At the same time, "not all rework is waste," he continued. "When generation is cheap, and the information gained is valuable, prototyping is extremely effective. It takes a holistic view to know which decisions are one-way doors, and to set the criteria under which we'd throw a prototype away. Too often AI-enabled engineers build production-grade software inside a bubble. It's interaction with the real world that builds great software."