When the person who sells the shovels says the gold rush is not what is cutting jobs, leadership should hear a diagnosis, not a defense. That is the useful way to read Jensen Huang, the head of the company whose chips sit under most of the AI boom, telling executives to stop blaming layoffs on his own product. The cuts and the missing returns are both being run through a convenient explanation, and naming AI hides the real, fixable cause underneath: the way the company runs.
What he said, and why it is easy to dismiss
In a Channel News Asia interview reported in late May, Huang called blaming layoffs on AI lazy. His argument is a timing argument: AI became genuinely useful only about six months ago, while the layoffs people keep pinning on it started around two years back, so the technology was not capable of doing those jobs when the jobs were cut. He pointed instead at over-hiring during the COVID years, ordinary cost-cutting, and weak leadership or a lack of ambition, called scaring people about AI irresponsible, and said he expects AI to drive hiring up rather than down. The easy response writes itself: Huang sells AI hardware, every AI boom makes him richer, so of course the man selling the chips says the chips are doing something other than taking jobs. Read him as a vendor protecting his product and you can close the tab.
Hear it as a diagnosis, not a defense
The messenger is exactly why the message is worth a second look. Huang has every commercial reason to take the opposite side. He profits when leaders believe AI is already powerful enough to replace whole teams, because that belief is what funds the spending that flows to him. He is arguing against his own interest, telling the market his product is less capable of cutting headcount today than the headlines assume. When the person with the most to gain from the hype is the one calling it lazy, that is the cue to listen harder, because he is pointing at the unglamorous causes that no consultant gets hired to name. Companies hired too aggressively in the boom years, the macro turned, and someone had to make the cuts. Saying "we are restructuring because we over-hired and the cycle turned" is hard to do on an earnings call, whereas "we are reducing roles because of AI" sounds like foresight rather than a correction, and it costs the person saying it nothing.
AI is the cover story for an older problem
The timing is the whole tell, and it does not line up. The technology people now point to as the reason for the cuts has been genuinely useful for roughly six months. The cuts being explained that way stretch back around two years. A cause cannot run two years ahead of the thing that supposedly caused it, which means for most of that window AI was the label on a decision the technology had no hand in. The decision was a management call about headcount, and the label arrived later to make the call sound strategic.
The hard data points the same way. 61% of organizations report no measurable EBIT impact from AI, in McKinsey's State of AI 2025. If AI were powerful enough to be quietly cutting headcount across the economy, that saving would show up somewhere on the profit line for most of these companies, and it does not. The same technology cannot be the force eliminating roles at scale and the investment that produces nothing measurable. AI is being blamed for the cuts and credited for a transformation, and the financials say it was never the lever doing either job. That makes it a convenient story on both ends, the reason for the layoffs going out and the reason for the spending coming in.
The cover story blocks the fix
A wrong diagnosis is expensive because it points the response at the wrong place. A company that believes AI cut its costs goes looking for the next tool to buy, more pilots, more licences, another platform, because the story says technology is the lever. A company that admits it over-hired and carried a process built for a different size looks at the operating model instead, at how the work is actually organized, where the duplication sits, which decisions take three people because the structure never got rebuilt. One of those responses can recover the money. The other funds more of the spending that already produced nothing measurable. Over-hiring and a heavy structure are problems leadership can act on directly. Naming AI as the cause hides exactly the thing that is in their control, and the comfort of the story is precisely what keeps the fixable problem in place.
The uncomfortable question worth sitting with
If you struck "because of AI" from your last headcount decision, what would the honest reason be, and would it survive being said out loud?