Visa has laid off around 1,400 people at its technology centre in Bengaluru, nearly 40 percent of its Indian workforce, and it has been unusually direct about why. The cuts, which hit roughly 500 engineers and 900 non-technical staff, are part of a plan to trim 2,600 jobs globally, and the company frames the whole exercise as a drive toward AI-enabled efficiency. In a staff memo, chief executive Ryan McInerney wrote of his "deep conviction that we are doing what is right" as the firm focuses on "driving efficiency" in order to reinvest elsewhere. It is Visa's largest retrenchment in India since it opened the centre in 2015, and India absorbed more than half of the worldwide total.
What makes the Visa cuts notable is not their size but their honesty about the culprit. For years, companies attributed layoffs to restructuring, headwinds or a challenging macro environment. Now "AI efficiency" is the stated reason, and Visa is far from alone. Its rival Mastercard has shed 1,400 roles, PayPal 4,800, Block 4,000 and Intuit 3,000, a wave of financial-technology reductions moving in near lockstep through 2026. When a whole sector reaches for the same explanation in the same year, it starts to look less like a series of independent decisions and more like a shared narrative that everyone has agreed sounds acceptable.
Which raises the uncomfortable question underneath all of it: is the AI actually doing the work? Skeptics have a name for the alternative, "AI washing," the practice of dressing ordinary cost-cutting in the language of technological progress because it plays better with shareholders than admitting to slowing demand or overhiring during the boom years. The suspicion is not paranoia. Blaming a model is a tidy story: it signals that a company is forward-looking rather than struggling, and it sidesteps harder questions about strategy. The trouble is that from the outside, a genuine productivity gain and a convenient excuse look almost identical on a balance sheet.
The human detail is where the story resists both easy readings. One former Visa employee described a manager who had end-to-end ownership of a product and outstanding performance reviews every cycle, "probably the last person you would expect to be let go," gone all the same. That is not obviously the profile of a role a chatbot can absorb, which is exactly what fuels the washing critique. But it is also consistent with a company genuinely restructuring around new tools and deciding it needs fewer people at every level, including good ones. Strong performance has never been much protection when an employer redraws the org chart, whatever the trigger.
The honest verdict is that both things are true at once, and that is what makes this moment hard to police. Some of these jobs are being automated in a real sense; some are being cut for old-fashioned reasons and given a shiny new label. What is missing is any obligation to tell the difference. Layoff-notification laws written decades ago say nothing about AI, and there is no requirement for a company to show its work when it credits software for a decision that upends thousands of lives. Until that changes, "AI efficiency" will remain the most flattering explanation available, and the most impossible to check.