A significant slide in stock market valuations across sectors including software, wealth management, and logistics has coincided with the release of powerful new artificial intelligence models, as described by The Guardian. This downturn has swept up industries from drug distribution to commercial property, intensifying investor fears that AI could render millions of white-collar roles obsolete or severely undercut corporate profits.
The market jitters were amplified by a viral essay from AI entrepreneur Matt Shumer, titled “Something big is happening”, which was viewed 80 million times on X. Shumer, who has a history of promoting AI hype, argued that new models would first come for coding jobs and then “everything else”. This triggered widespread fear and fury online, despite scepticism about his claims.
New Models and Massive Spending
Shumer and the markets were reacting specifically to capabilities demonstrated by recently released AI systems such as Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.3-Codex. The context for this reaction includes colossal planned expenditure by the major US tech firms in AI, known as “hyperscalers”. They collectively intend to spend $660 billion this year, following a period of huge, often circular deals between the world’s biggest technology companies.
However, cracks have appeared in these financial commitments. Nvidia and OpenAI recently appeared to drop a proposed $100 billion deal, replacing it with a smaller, as-yet-unknown commitment. A fundamental concern is that none of the leading AI model-builders—OpenAI, xAI, or Anthropic—currently have a clear path to the enormous revenues needed to justify such spending. For perspective, the entire global software sector’s revenue this year is projected to be only $780 billion.
Divergent Investor Theses
This month has seen investors entertaining two seemingly contradictory arguments about AI: that it represents an unsustainable spending bubble, or that it heralds a destructive revolution in knowledge work. Apparent concerns about a bubble affected shares in Google’s parent company Alphabet and Meta, founded by Mark Zuckerberg. The underlying expectation is that AI firms must recoup their vast investment by selling tools that boost productivity—allowing tasks and jobs to be done by fewer people or in fewer hours.
“The two themes are inherently linked but not necessarily contradictory,” says Jason Borbora-Sheen, a portfolio manager at investment firm Ninety One. He notes that initial investor backing for “hyperscaler” expenditure has flipped to anxiety over cash burn and the scale of investment needed to stay competitive. Simultaneously, share prices in sectors like wealth management have been hit by the perception that AI is “now here, will evolve and can displace”.
Carl Benedikt Frey, an associate professor of AI and work at the University of Oxford, explains the dynamic. “AI turns once-scarce expertise into output that’s cheaper, faster, and increasingly comparable, which compresses margins long before whole jobs disappear,” he says, indicating investors are reassessing companies that sell software or specialist knowledge.
Ambiguous Impact on Jobs
While companies like British American Tobacco have cited AI as an influence on job-cutting plans, there has not yet been a wave of wholesale disruption. Evidence of a tangible AI jobs impact on large western economies remains “quite ambiguous so far”, according to Greg Thwaites, a research director at the Resolution Foundation thinktank. He suggests not all white-collar work will be affected, but AI might test the capitalist concept of “creative destruction”—where new jobs replace old ones—due to the speed and breadth of its capabilities.
“There are some jobs that are going to look very different quite quickly. But the idea that there are going to be bands of unemployed lawyers and accountants roaming around London within a few years seems like a stretch to me,” Thwaites adds.
Analysts caution that the stock market reaction is based more on sentiment than evidence. “It’s a kneejerk reaction,” says Alvin Nguyen of Forrester, pointing out that no one has had time to evaluate, for instance, an Opus 4.6-powered wealth manager. He notes many business leaders initially thought they could replace people with AI, but for a lot of cases, it has not panned out.
Long-Term Shift, Uneven Adoption
The long-term impact of AI may be underestimated, but its adoption will not be uniform, according to Aaron Rosenberg, a partner at venture firm Radical Ventures and former head of strategy at Google’s DeepMind. “History shows a repeated pattern of there being a significant lag between a technology working in a lab and it permeating the wider economy, as well as a chasm between early adopters and the majority of users,” he says.
The environment remains unsettled. More new AI models are expected, and other huge deals could wobble. There have also been low-level rumblings of discontent, with a slew of departures from AI companies for reasons ranging from boredom and “AI doomerism” to concerns over the prospect of adult content in ChatGPT. A nervous, unfocused energy prevails, summarised by Borbora-Sheen’s observation: “There is a strong winners versus losers dynamic.”
