Andrew Ng has never been known for mincing words when it comes to the kind of engineer he refuses to bring onto his teams. In his view, the rapid acceleration of the artificial intelligence era is mercilessly revealing which technical professionals are adapting — and which are being quietly left behind. The Stanford University professor and pioneering scientist behind Google Brain recently articulated his perspective on what he considers a new hierarchy of engineering capability during a thought‑provoking episode of the “20VC” podcast released on Monday.
In his analysis, Ng explains that the most exceptional engineers — those operating at the very top of this emerging hierarchy — are not simply competent coders or diligent workers; they are individuals who embraced AI tools early, mastered their use, and integrated them into their workflows with remarkable dexterity. These industry veterans, many with ten or twenty years of real‑world experience, stand out not only because they understand software deeply, but because they have evolved alongside the AI revolution. According to Ng, these professionals are achieving levels of productivity and creative output that surpass anything the technology industry witnessed even a year or two ago. Their willingness to evolve continuously has made them models of agility in a landscape where proficiency with AI can multiply the impact of every line of code.
Just beneath this elite group, Ng places a second tier: newly graduated engineers who have immersed themselves in AI not through formal education but through active participation in online “social network communities,” where open exchange, experimentation, and peer learning drive progress faster than conventional classrooms. Though younger and less experienced, these graduates are conversant in the latest AI tools and approaches. Ng notes that he has already hired several such individuals — and would gladly take on more — because their familiarity with AI has made them immediately valuable. Yet, he also laments, the supply of such well‑trained new graduates is insufficient. Businesses across industries, he observes, are eager to recruit these tech‑savvy candidates who possess both enthusiasm and up‑to‑date technical literacy, but demand continues to outstrip availability.
Below them sits a third class of engineers, one that Ng regards with concern. These are experienced developers who once enjoyed the comfort of stable, well‑paid positions and continue working as though it were still 2022, before the explosion of generative AI forever changed the software development paradigm. Despite their professional maturity, they have resisted adaptation, continuing to code in ways that no longer align with the tools and processes defining modern engineering practice. Ng makes his stance crystal clear: he no longer hires people who display this resistance to change, warning that professionals who fail to evolve may soon find themselves facing serious career trouble as automation sets new standards for efficiency and speed.
Anchoring the very bottom of Ng’s hierarchy are the newest entrants into the field — recent computer science graduates who have emerged from universities without ever learning to apply or even understand modern AI technologies. In his view, this group represents the most vulnerable segment of the workforce. The problem, Ng argues, is not individual laziness but an institutional lag: university curricula have not kept pace with the radical transformation that AI has brought to the industry. As a result, many computer science programs are still teaching outdated fundamentals while neglecting essential AI building blocks that every contemporary software engineer is expected to know. To drive his point home, Ng offers a vivid analogy: graduating a computer‑science major without training them in AI is as shortsighted as producing graduates who have never heard of cloud computing. Such underprepared students step into the job market already struggling to compete, unable to meet the new baseline requirements of the evolving profession.
Ng’s observations feed into a much broader conversation currently unfolding across Silicon Valley: as artificial intelligence redraws the boundaries of work, who will flourish, and who will fade? While some leaders emphasize the adaptability of younger professionals, others express apprehension about how older workers will manage this technological shift. OpenAI CEO Sam Altman, for instance, has publicly stated that his greatest concern is not for recent graduates but for individuals in their sixties who may be reluctant to retrain or acquire new skills — a necessity that the political sphere describes optimistically as “reskilling,” though in practice many resist it. From Altman’s standpoint, recent graduates are in an enviable position. Were he 22 again and about to enter today’s workforce, he said, he would feel like the luckiest individual in history, stepping into a world of unparalleled opportunity shaped by AI.
Other technology executives are taking this transformation one step further by institutionalizing the expectation that AI tools become integral to every employee’s workflow. For example, Coinbase CEO Brian Armstrong has made it clear that failure to embrace these tools is no longer acceptable, even dismissing employees who could neither use nor justify avoiding them. A similar mindset appears to be spreading at Google, where — according to reports shared with Business Insider — CEO Sundar Pichai has encouraged staff during all‑hands meetings to incorporate AI more thoroughly across operations, from day‑to‑day productivity to AI‑assisted software development. The message throughout the industry is consistent and urgent: adapting to AI is no longer optional. Those who learn to wield it effectively will shape the future of engineering, while those who hesitate risk being surpassed by the very tools they ignore.
Sourse: https://www.businessinsider.com/andrew-ng-talent-engineer-ai-hire-college-graduates-computer-science-2025-11
