6,000 CEOs admit that AI “did nothing,” yet this year’s Q1 has already used it to lay off 40,000 people

Author: Claude, Deep Tide TechFlow

Deep Tide Guide: A survey by the U.S. National Bureau of Economic Research (NBER) of 6,000 executives in four countries shows that nearly 90% of companies believe AI has had “no impact” on employment and productivity over the past three years, but by Q1 2026, the tech industry has laid off 78,557 people, with 47.9% attributed to AI. Productivity data is nonexistent, yet layoffs surge under the banner of AI, and economists compare this contradiction to the “computing paradox” proposed by Nobel laureate Robert Solow in 1987, now in an AI version.

Invested $250 billion, nearly 90% of companies say AI has brought no productivity gains. Meanwhile, tech firms are massively laying off workers in the name of AI.

This is the most absurd scene in the current AI industry.

According to Fortune magazine on April 19, a study published by NBER in February this year covering 6,000 corporate executives in the U.S., U.K., Germany, and Australia found that nearly 90% of respondents said AI had no measurable impact on their employment or productivity over the past three years. Although two-thirds of executives are using AI, the average weekly usage is only 1.5 hours, and 25% of respondents say they do not use AI at all in their work.

On the other hand, according to Nikkei Asia citing RationalFX data, from January 1 to early April 2026, the tech industry has laid off 78,557 people, with 47.9% explicitly attributed to AI and workflow automation. Over 76% of layoffs occurred in the U.S.

Apollo’s chief economist Torsten Slok directly quotes Nobel laureate Robert Solow’s classic statement, summarizing the current situation as an AI version of the “Solow paradox.” Solow’s original words were: “You can see the computer age everywhere but in the productivity statistics.”

Slok’s assessment almost verbatim reflects today’s reality. AI is nowhere to be seen in employment data, productivity data, or inflation data.

Ninety percent of companies see no effect from AI, raising doubts about the $250 billion investment returns

The data from this NBER study is quite solid. Among the four countries, 69% of companies use AI to some extent, with the U.S. highest at 78%, and Germany lowest at 65%. But usage does not equal results: over 90% of managers say AI has no impact on their company’s employment scale, and 89% say it has no impact on labor productivity (measured by sales per capita).

According to Stanford University’s 2025 AI Index report, global AI investments exceeded $250 billion in 2024. PwC’s 2026 global CEO survey shows that only 12% of CEOs say AI has brought both cost reductions and revenue growth, while 56% see no significant financial gains.

Slok pointed out in his blog that, apart from the “Big Seven,” AI has no visible impact on profit margins or profit expectations.

This is not an isolated view. A 2024 MIT study predicts that AI will only boost productivity by 0.5% over the next decade. The study’s author, Nobel laureate Daron Acemoglu, admitted at the time: “0.5% is better than zero. But compared to industry and media promises, it’s truly disappointing.”

A March study by Boston Consulting Group (BCG) further reveals an counterintuitive phenomenon: productivity improves when employees use fewer than three AI tools; but after using four or more tools, self-reported productivity sharply declines, with employees reporting “brain fog” and more small errors. BCG calls this “AI brain overload.”

ManpowerGroup’s 2026 Global Talent Sentiment Index shows that among nearly 14,000 employees in 19 countries, the routine use of AI increased by 13% in 2025, but confidence in AI’s practicality plummeted by 18%.

Nearly 80k layoffs in Q1, is AI the scapegoat or the real culprit?

While productivity data remains blank, layoffs are accelerating at an astonishing pace.

According to Nikkei Asia, in Q1 2026, the tech industry laid off 78,557 people, with 47.9% attributed to AI implementation and workflow automation. Oracle recently quietly laid off over 10k employees, with the saved funds redirected to data center construction. Anthropic CEO Dario Amodei and Ford CEO Jim Farley both publicly stated that AI will eliminate half of entry-level white-collar jobs in the U.S. within five years. Stanford research also shows that junior programming and customer service roles are already under pressure, with related hiring dropping 13% over three years.

MIT’s simulation study provides a shocking figure: AI could replace 11.7% of the U.S. workforce, involving about $1.2 trillion in total wages.

But how many of these layoffs are truly driven by AI?

Cognizant’s Chief AI Officer Babak Hodjat bluntly told Nikkei Asia: “I’m not sure if these layoffs are directly related to actual productivity improvements. Sometimes, AI is just a scapegoat financially — companies hire more people, want to downsize, and then blame AI.”

OpenAI CEO Sam Altman also admitted at the India AI Impact Summit that there is an “AI whitewashing” phenomenon: “There’s a certain proportion of ‘AI whitewashing,’ where people blame AI for layoffs that were already planned, but there are also some jobs genuinely being replaced by AI.”

Deutsche Bank analysts more directly call this phenomenon “AI redundancy washing,” believing that companies attribute layoffs to AI because “it’s a better signal to investors than admitting demand weakness or overhiring in the past.”

IBM doubles down on entry-level hiring despite the trend, Cognizant refuses layoffs

Not all companies are going with the flow.

In 2026, IBM doubled its entry-level hiring. Its Chief Human Resources Officer Nickle LaMoreaux explained: “While AI can perform many entry-level tasks, cutting these roles would destroy the talent pipeline for future middle managers, jeopardizing the company’s long-term leadership reserves.”

Cognizant — a process outsourcing giant heavily reliant on human labor — also states it will not lay off workers because of AI. The company has set up AI labs in San Francisco and Bangalore to develop customized AI agents for clients (since off-the-shelf general AI products perform poorly in enterprise environments, with issues of performance and security), but its employees will be trained to work alongside AI rather than be replaced by it.

Hodjat emphasized: “There will be many young people just out of school who can’t find jobs and lack domain expertise. You have to bring them in, let them learn how to use AI across various fields on the job.”

Data from the European Central Bank also supports this view: companies that deploy and invest heavily in AI are more likely to expand recruitment.

J-curve or mirage: When will the AI productivity inflection point arrive?

Historical experience offers some hope.

IT investments in the 1970s and 80s also seemed ineffective, but between 1995 and 2005, productivity driven by IT grew by 1.5%. Erik Brynjolfsson, director of Stanford’s Digital Economy Lab, wrote in the Financial Times that the productivity inflection point for AI may already be emerging: last year, U.S. productivity grew by 2.7%, GDP tracked a 3.7% increase in Q4, but only 181k new jobs were created — the decoupling of employment growth from GDP growth may be a signal that AI is starting to have an effect. Former Pimco CEO Mohamed El-Erian also noted this decoupling.

A study by Stanford’s Institute for Economic Policy Research, analyzing browsing data from 200k U.S. households, found that AI increased efficiency by 76% to 176% in online tasks like job hunting, travel planning, and shopping. But researchers found that users spent the time saved on socializing and watching TV, rather than working or learning new skills.

Slok from Apollo describes AI’s future impact as a “J-curve”: a period of decline followed by exponential growth. But he also points out that, unlike the 1980s IT era when innovators had monopoly pricing power, today’s AI tools are priced lower due to fierce competition. Therefore, AI’s value creation lies not in the product itself but in how “generative AI is used and deployed across various sectors of the economy.”

Hodjat’s judgment may be the most pragmatic: in 6 to 12 months, companies will start to see real productivity gains from AI, but “this transition period will be painful for all of us.”