AI Chiefs Walk Back Job Apocalypse Warnings as IPO Pressure Mounts

Jensen Huang called it lazy. Sam Altman called it wrong. Dario Amodei softened the math to 90 percent automation with 10 percent human productivity gains. The three most-quoted voices in artificial intelligence spent the past month walking back the job apocalypse they spent two years selling, and the timing is anything but coincidental.

Speaking to Channel News Asia on Monday, Nvidia’s chief executive took direct aim at fellow executives who have publicly blamed AI for workforce reductions. “The narrative that connects AI to job loss, for many of the CEOs that are doing it, it is just too lazy,” Huang said. “AI has just arrived. How is it possible they’re already losing jobs?”

The Reversal Arrives as IPO Windows Open

Huang’s comments follow a pattern. OpenAI CEO Sam Altman told the Commonwealth Bank of Australia’s Accelerate AI Conference in Sydney last week that he “thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened.” Anthropic boss Dario Amodei, long criticized as an AI doomer by peers including Huang, recently predicted that even if 90 percent of jobs are automated, the remaining 10 percent would be handled by vastly more productive human workers.

The reversals from Altman and Amodei come as their companies, OpenAI and Anthropic, are expected to embark on high-profile initial public offerings that will require broad buy-in from investors to succeed. Public sentiment toward AI has soured in recent polling, particularly in the United States, where voters voice serious discontent over the disruption that tech companies and political leaders predict from the technology.

Huang pushed back against the doom-and-gloom forecasts directly. “How is it possible that AI became productive and useful only six months ago, and they were somehow laying people off two years ago because of AI? It doesn’t make any sense,” he said. “It was just a way for them to sound smart, and I really hate that. I think we’re scaring people and that’s irresponsible.”

Corporate Layoffs Cite AI, Data Shows Otherwise

The disconnect between executive rhetoric and actual AI deployment is stark. British bank Standard Chartered announced plans last week to axe thousands of jobs by 2030 as artificial intelligence replaces employees in administrative roles. Snapchat parent company Snap cut 1,000 jobs last month, citing AI-driven efficiency gains as it pushes toward profitability.

Huang’s argument is that the timeline does not add up. AI tools capable of replacing white-collar workers at scale became widely available in late 2022 with the launch of ChatGPT, yet corporate layoffs citing automation began well before that. The narrative, Huang suggests, was a convenient cover for cost-cutting decisions driven by other factors, including over-hiring during the pandemic and rising interest rates that made growth-at-all-costs strategies untenable.

Federal Reserve Warns the Disruption May Still Be Ahead

Not everyone is convinced the threat has passed. Federal Reserve Governor Lisa Cook warned on Wednesday that the full effects of AI on employment may still be ahead. “We could be approaching the most significant reorganization of work in generations,” she said in a speech at Stanford University, adding that AI-related job losses could precede any gains, even if the overall long-run picture remains positive.

Most economic institutions, including the European Central Bank, say that artificial intelligence has had only minor effects on employment so far. The gap between executive predictions and measurable labor-market impact has widened over the past 18 months, fueling skepticism about whether AI will deliver the productivity revolution its backers promise or the job displacement its critics fear.

The Timing Problem

Cook’s warning highlights a timing problem that Huang’s critique does not fully address. If AI tools are only now becoming capable of replacing knowledge workers at scale, the disruption those tools cause may not show up in employment data for another 12 to 24 months. Corporate adoption cycles are slow, and the integration of AI into workflows that genuinely displace workers, rather than augment them, is still in early stages.

The Productivity Paradox

The productivity gains AI is supposed to deliver have not yet materialized in aggregate economic data. Labor productivity growth in the United States has been modest since 2023, despite widespread deployment of generative AI tools in white-collar settings. The disconnect between hype and measurable output mirrors earlier technology waves, including the internet boom of the late 1990s, which took years to translate into productivity statistics.

Public Sentiment Turns Against AI Hype

The reversals from Altman, Amodei, and Huang’s criticism of peers arrive as public opinion on AI shifts. Polling conducted in the United States over the past six months shows growing skepticism about AI’s benefits and rising concern about its risks, particularly around job displacement and misinformation. The backlash has been sharpest among younger workers, who were initially the most enthusiastic adopters of AI tools.

The shift in sentiment poses a challenge for OpenAI and Anthropic as they prepare for public offerings. Investors will weigh not only the companies’ revenue growth and technical capabilities but also the regulatory and reputational risks that come with being the public face of a technology that large segments of the population view with suspicion.

• Regulatory pressure is mounting. Lawmakers in the United States and European Union are drafting legislation that would impose disclosure requirements, liability standards, and safety testing on AI systems, particularly those used in hiring, lending, and law enforcement.
• Corporate customers are slowing adoption. Enterprise buyers, initially eager to deploy AI tools, are now conducting longer pilot programs and demanding clearer return-on-investment metrics before committing to large-scale rollouts.
• Talent retention is becoming harder. AI researchers and engineers, once drawn to the mission-driven rhetoric of companies like OpenAI and Anthropic, are increasingly skeptical of leadership claims and are leaving for competitors or starting their own ventures.

What the Data Actually Shows

Employment data from the U.S. Bureau of Labor Statistics shows that job losses in sectors most exposed to AI, including customer service, data entry, and basic coding, have been modest. The unemployment rate for workers in computer and mathematical occupations stood at 2.1 percent in April 2026, down from 2.3 percent a year earlier. Administrative support roles, another category frequently cited as vulnerable to AI displacement, saw employment grow by 1.2 percent over the same period.

The disconnect between executive warnings and labor-market outcomes suggests that either the technology is not yet capable of the displacement its backers predicted, or that companies are slower to adopt it than the hype cycle implied. Huang’s argument leans toward the latter, suggesting that executives used AI as a convenient narrative to justify layoffs driven by other factors.

It was just a way for them to sound smart, and I really hate that. I think we’re scaring people and that’s irresponsible.

Huang’s comment, delivered in an interview with Channel News Asia, was unusually blunt for a CEO whose company supplies the chips that power AI systems. Nvidia has been the primary beneficiary of the AI boom, with its data center revenue growing 427 percent year-over-year in fiscal 2025. Huang’s willingness to criticize the job-loss narrative suggests he views the backlash as a threat to the broader AI ecosystem, not just to individual companies.

The IPO Calculus for OpenAI and Anthropic

OpenAI and Anthropic face a delicate balancing act as they prepare for public offerings. Both companies have raised billions in private funding at valuations that assume continued rapid growth in AI adoption. OpenAI was last valued at $157 billion in a funding round led by SoftBank in January 2026. Anthropic raised $7.3 billion in a Series D round in March 2026, valuing the company at $60 billion.

Public investors will scrutinize not only the companies’ financials but also their exposure to regulatory risk, reputational risk, and the sustainability of their growth trajectories. The job-loss narrative, which both companies’ leaders helped amplify in earlier years, now complicates that pitch. If AI does not displace workers at the scale predicted, the addressable market for enterprise AI tools may be smaller than investors assumed. If it does, the regulatory and public backlash could constrain the companies’ ability to operate.

Revenue Growth vs. Profitability

OpenAI reported $3.7 billion in annualized revenue as of December 2025, driven primarily by subscriptions to ChatGPT Plus and enterprise API contracts. The company remains unprofitable, with operating losses estimated at $5 billion in 2025 due to the high cost of training and running large language models. Anthropic’s revenue is smaller, estimated at $1.2 billion annualized as of March 2026, with similar profitability challenges.

Competitive Pressure from Open-Source Models

Both companies face growing competition from open-source models, including Meta’s Llama 4 and Mistral AI’s latest releases, which offer comparable performance at a fraction of the cost. The open-source threat is particularly acute in enterprise markets, where customers are increasingly reluctant to lock themselves into proprietary platforms.

Huang’s Long-Standing Position on AI and Jobs

Huang has consistently argued that AI will create as many jobs as it displaces, a position that puts him at odds with some of his peers. In a 2024 interview, he predicted that AI would enable new categories of work, including roles focused on training, auditing, and managing AI systems. He has also argued that AI will make existing workers more productive, allowing companies to grow without proportionally increasing headcount.

The Nvidia CEO’s criticism of executives who blame AI for layoffs is consistent with that view. If AI is a productivity tool rather than a replacement for workers, then layoffs attributed to AI are either premature or disingenuous. Huang’s comments suggest he believes the latter, and that the narrative has done more harm than good by fueling public fear and regulatory scrutiny.

The reckoning Huang describes is not just for the executives who used AI as cover for cost-cutting. It is also for the AI industry itself, which must now convince a skeptical public and wary investors that the technology’s benefits outweigh its risks. The reversals from Altman and Amodei, and Huang’s blunt criticism, signal that the industry recognizes the problem. Whether the course correction comes in time to salvage public trust, and the IPO valuations that depend on it, remains an open question.

Disclaimer: This article is for informational purposes only and does not constitute investment advice. The views expressed are those of the sources cited and do not reflect the opinions of Oton Technology. Readers considering investments in AI companies should consult a qualified financial advisor. Figures are accurate as of publication.