Andrew Ng: “AI won’t trigger a mass wave of job losses,” while software engineering hiring remains strong

Well-known AI scholar and DeepLearning.AI founder Andrew Ng (吳恩達) posted on X and in The Batch newsletter on May 12, arguing that “AI will not trigger a jobpocalypse,” directly rebutting the prevailing narrative that AI will lead to mass job losses. Based on Andrew Ng’s original post, it received more than 2,600 likes and was one of the most talked-about viewpoints in the AI field that week.

Ng’s core argument: software engineering hiring remains strong, unemployment stays at 4.3%

Ng used three sets of specific data to counter the narrative that “AI will cause large-scale unemployment”:

Software engineering is the industry most deeply affected by AI tools (rapid progress in coding agents), but software engineers’ hiring is still strong

Despite rapid AI progress, the United States’ current unemployment rate remains in a healthy range at 4.3%

From historical experience: the number of new jobs created by AI is clearly higher than the number of jobs it replaces, consistent with past technological waves

Ng said directly: “AI—like any other technology—does affect jobs, but it’s irresponsible and harmful to tell exaggerated stories about mass unemployment. We should stop this kind of narrative.”

Why the “AI unemployment” narrative is so popular: Ng points out 3 incentives

Ng highlights three structural incentives explaining why this narrative keeps being amplified:

First, cutting-edge AI labs themselves have strong incentives to tell stories that “AI can replace employees”—if a technology can replace many employees, that technology appears more valuable. In extreme cases, labs may even push science-fiction scenarios where “AI takes over and leads to human extinction.”

Second, SaaS software companies typically charge $100–$1,000 per user per year, but if AI can replace an employee earning $100k a year, or increases employee productivity by 50%, then they can charge $10k and it still looks reasonable. By anchoring pricing to “employee salaries” rather than “typical SaaS prices,” AI companies can charge more.

Third, companies have strong incentives to package layoffs as “because we introduced AI”—telling the story that AI helps them achieve higher productivity with fewer employees is more presentable than admitting “over-hiring during the pandemic under low interest rates and government stimulus.”

Historical parallels: nuclear power, the population bomb, low-fat diets

Ng uses three historical cases to contrast and show how social narratives can persist for years yet be disconnected from reality:

Panic over nuclear power plant safety led to long-term underinvestment in nuclear power

The “population bomb” panic in the 1960s led multiple countries to implement harsh policies to restrict population

Concerns about the harms of dietary fat led governments to promote high-sugar diets for decades

Ng said: “Now mainstream media are beginning to publicly question jobpocalypse, and I hope the impact of these kinds of stories gradually fades—just like the fear of AI wiping out humans.”

Ng’s counter-prediction: AI jobapalooza

Ng proposes a prediction opposite to the “AI unemployment wave”—“AI jobapalooza” (a massive AI boom):

There will be many good AI engineering job openings, and overall labor market prospects will remain optimistic

AI engineers’ jobs will differ from traditional software engineering, and new roles will be distributed across companies that are “non-traditional big developers” employers

The skills required for non-AI roles will change because of AI, meaning more people need to be trained in “AI literacy” or AI proficiency

Chain News observation: The timing of Ng’s remarks aligns with the week’s AI commercialization acceleration—OpenAI’s Deployment Company, and the joint venture between Anthropic and Blackstone, as well as JPMorgan/BlackRock rolling out tokenized funds. Ng did not deny that AI is reshaping the way people work, but he opposed exaggerated predictions of rapid mass unemployment. For readers in Taiwan, Ng’s arguments can be used to evaluate: which mainstream panics may be amplified by stakeholders for their own interests, and which are truly real risks.

Follow-up events to watch include: whether mainstream media really shifts toward “questioning jobpocalypse,” whether senior figures at labs such as Anthropic and OpenAI respond to Ng’s criticism, and whether labor market data in the second half of 2026 (especially tech industry hiring numbers) can support Ng’s prediction.

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