The increasing integration of artificial intelligence (AI) into various industries is approaching a critical juncture, with significant economic risks anticipated for 2026. The term “slop,” defined by Merriam-Webster as “digital content of low quality produced in quantity by AI,” was named the word of the year for 2025. This choice reflects the growing concern about AI’s prevalence in business, especially as companies prioritize cost-cutting measures.
Ed Zitron, a prominent critic of AI, argues that the current “unit economics” of the industry do not support its rapid expansion. He asserts that the costs associated with servicing customers far outweigh the prices companies can charge, describing the situation as “dogshit.” Despite rising revenues from increased subscriptions, these gains have not sufficiently offset the staggering investments, projected to reach $400 billion (£297 billion) in 2025, with expectations of more in the following year.
The skepticism surrounding AI’s profitability is echoed by Cory Doctorow, who claims, “These companies are not profitable. They can’t be profitable.” He highlights that many AI companies rely on substantial external funding to maintain operations, often at unsustainable levels.
As the industry evolves, many large language models (LLMs) have become increasingly costly to develop. Each iteration requires more data, energy, and highly specialized personnel, resulting in a cycle of escalating expenses. The infrastructure needed to support these models, particularly the vast data centers, is financed through debt, which is secured against anticipated future revenues. An analysis by Bloomberg revealed that $178.5 billion was allocated for data center credit deals in 2025 alone, as emerging operators flock to the market in a frenzied pursuit of profits.
Despite the allure of generative AI, which is often touted for its potential to revolutionize industries, the reality is more complex. Brian Merchant, author of *Blood in the Machine*, draws parallels between today’s backlash against AI and the Luddite rebellion of the 19th century. He has compiled numerous accounts from displaced workers in sectors such as writing and coding who have been replaced by AI-generated output. Many of these workers note the inferior quality of AI-produced content, raising concerns about the risks of delegating sensitive tasks to machines.
The implications of widespread AI adoption have emerged more prominently in recent months. In the UK, the High Court cautioned against the use of AI in legal contexts after instances where fictitious case law was cited. Additionally, police in Heber City, Utah, had to learn to manually verify the outputs of a transcription tool that inaccurately reported an officer turning into a frog during a bodycam review—an event that occurred while *Disney’s The Princess and the Frog* played in the background.
These specific instances highlight the broader issue of the “slop layer” of AI content that permeates the internet, complicating the task of discerning credible information from unreliable sources. Doctorow argues that AI should not be seen as a precursor to “impending superintelligence,” but rather as a collection of tools that, while occasionally useful, may not deliver the transformative benefits that many investors anticipate.
The fear of a reevaluation of AI’s potential profitability could have far-reaching consequences for financial markets. The Bank for International Settlements (BIS) recently noted that the “Magnificent Seven” tech stocks, which include major AI players, now account for 35% of the S&P 500, up from 20% three years ago. A significant drop in share prices could impact retail investors globally, as well as tech exporters in Asia and the private equity firms that have financed this sector’s growth.
In the UK, the Office for Budget Responsibility (OBR) has projected that a “global correction” scenario, where stock prices fall by 35% in the coming year, could reduce the country’s GDP by 0.6% and result in a £16 billion decline in public finances. While this impact would be less severe than the 2008 global financial crisis, it would still resonate in an economy striving for stability.
As the AI landscape continues to evolve, the implications of these developments are significant. While some may take satisfaction in the potential humbling of tech giants, the reality remains that these shifts will affect everyone, underscoring the interconnectedness of the global economy.
