Conversations with execs at the World Economic Forum reveal that many are racing toward automation to stay ahead of competition regardless of impact on workers
DAVOS, Switzerland — They'll never admit it in public, but many of your bosses want machines to replace you as soon as possible. Tweets: @reckless , @reckless , @nytimes , @joe_atikian , @noahpinion , @poloolop , @nytimesbusiness , @alexstamos , @hosanlee , @stoweboyd , @nytimesbusiness , @dabeard , @dealbook , @nytimesbusiness , and @simonestolzoff Tweets: Nilay Patel / @reckless : If you think about how many people sit in office parks being paid to point and click repetitive tasks in old software, the case for RPA basically makes itself. But all those people are out of their jobs. Nilay Patel / @reckless : Robotic process automation sounds so boring but it's wild - basically an AI that can use the user interface of a legacy computer system to do tasks after being trained by a human https://www.nytimes.com/... @nytimes : In public, executives wring their hands over automation's negative consequences for workers. In private, they talk about how they are racing to automate, @kevinroose writes http://www.nytimes.com/... Joe Atikian / @joe_atikian : Industry has just completed 100 years of automation, robotics, computers and AI, but here we are at somewhere near full employment. http://twitter.com/... Noah Smith / @noahpinion : Somehow businesses always want to replace humanity with machines, and yet somehow it keeps not happening... http://twitter.com/... Umang Saini / @poloolop : ""Why can't we do it with 1 percent of the people we have?'" Mohit Joshi, the president of Infosys. http://twitter.com/... @nytimesbusiness : A 2017 survey by Deloitte found that 53 percent of companies had already started to use machines to perform tasks previously done by humans. The figure is expected to climb to 72 percent by next year. @kevinroose explores why: http://www.nytimes.com/... Alex Stamos / @alexstamos : @kevinroose None of those CEOs have ever spent a weekend sobbing because the random forest classifier you are trying to apply to a problem a 3yo can get right with 94% precision keeps crapping the bed.We are definitely in an ML optimism bubble at the exec level. @hosanlee : “The choice isn't between automation and non-automation,” said Erik Brynjolfsson, the director of M.I.T.'s Initiative on the Digital Economy. “It's between whether you use the technology in a way that creates shared prosperity, or more concentration of wealth.” http://twitter.com/... Stowe Boyd / @stoweboyd : An open secret is now headline news: The primary goal of ‘digital transformation’ is to cut as many workers as possible, and as soon as possible. http://twitter.com/... @nytimesbusiness : “People are looking to achieve very big numbers,” the president of Infosys said. “Earlier they had incremental, 5 to 10 percent goals in reducing their work force. Now they're saying, 'Why can't we do it with 1 percent of the people we have?'” http://www.nytimes.com/... David Beard / @dabeard : Wisconsin pledged up to $4 billion to Foxconn, which promised to create up to 13,000 jobs. It created 82 as of last week, and wants to automate 80 percent of jobs held by humans http://www.nytimes.com/... http://twitter.com/... @dealbook : Kai-Fu Lee, a longtime technology executive, predicts that artificial intelligence will eliminate 40 percent of the world's jobs within 15 years. Here's what else he and others told our reporter: http://www.nytimes.com/... @nytimesbusiness : At a time of political unrest and anti-elite movements on the progressive left and the nationalist right, it's probably not surprising that all of this automation is happening quietly, out of public view. @kevinroose explores why: http://www.nytimes.com/... Simone Stolzoff / @simonestolzoff : .@kevinroose's dispatch from Davos is a great example of a story hiding in plain site: On stage, execs speak to the potential deleterious effects of automation, but behind closed doors they are trying to automate away labor as fast as they can. https://www.nytimes.com/... Expand More For Next Unexpand More For Next
Context & Ripple Effects
The Davos reporting lands mid-arc in a decade-long shift that began when automation stopped being a factory story — coverage as far back as 2014 documented it displacing knowledge and service workers too. By 2019 the private consensus among executives had hardened into a race: Mohit Joshi of Infosys relayed companies asking why tasks can't be done with 1% of their current headcount, while Kai-Fu Lee projected AI eliminating 40% of world jobs within 15 years.
What makes the piece durable is how precisely later coverage validated its premise. Researchers later narrowed the unit of analysis from occupations to tasks, and by 2025 tech workers at Google, TikTok, Adobe, Dropbox, and CrowdStrike were recounting managers using AI to justify firings — the private Davos attitude made explicit on the record.
First-order effects
- Workers at automating firms bear the immediate cost: Foxconn targeted automating 80% of jobs at its Wisconsin facility after creating far fewer roles than promised, and an [[a:842344|AT&T call center worker describes generating transcripts and suggestions with AI while wondering if she is training her replacement]].
- Competitive dynamics, not efficiency alone, drive adoption — Deloitte found 53% of companies had already deployed machines on human tasks, heading toward 72% — so laggards face pressure to automate regardless of workforce consequences.
Second-order effects
- Rivals are forced into matching moves: once Infosys clients ask why headcount can't drop to 1%, outsourcing and services firms must reprice around machine-delivered output or lose deals to those who do.
- A counter-coalition forms around responsibility: Ben Shneiderman argues full robotic automation absolves humans of ethical accountability, giving regulators and boards a framework for pushing back on the race.
Third-order effects
- Erik Brynjolfsson's framing becomes the structural question: whether automation compounds into shared prosperity or concentrated wealth depends on choices being made privately at events like Davos rather than through public deliberation.
- Because tasks rather than whole occupations get automated, labor statistics and policy lag reality — researchers still disagree on how many jobs AI affects, leaving displaced workers without an accurate map of what is coming.
The trend: Automation decisions are migrating from public strategy decks to private executive consensus, with competitive fear — not capability alone — setting the pace of workforce displacement.