The economic landscape of artificial intelligence (AI) is bracing for significant challenges as 2026 approaches. Concerns are mounting over the sustainability of business models in an industry that has seen rapid growth but is now facing a reality check. The term “slop,” defined by Merriam-Webster as “digital content of low quality produced by artificial intelligence,” was named the US dictionary’s word of the year for 2025. This designation underscores a growing awareness of the potential downsides associated with AI, particularly as companies rush to adopt these technologies to reduce costs.
Ed Zitron, a prominent critic of AI, has raised alarms about the “unit economics” of the sector, arguing that the cost of servicing a customer is currently not matched by revenue. He bluntly describes the financial state of the industry as “dogshit.” Although revenues from AI are climbing, the increase is insufficient to offset the staggering investments that are projected to reach $400 billion (£297 billion) in 2025, with expectations for further growth in the following year.
According to Cory Doctorow, another vocal skeptic, the financial viability of these companies is questionable. “These companies are not profitable. They can’t be profitable,” he states, emphasizing that many firms are relying on substantial outside funding to remain operational. As companies continue to invest heavily in AI, the trend has echoes of previous tech bubbles, where businesses operated at a loss for extended periods.
The escalating costs associated with developing large language models (LLMs) further complicate the situation. Each new iteration tends to consume more data and energy, with the expenses of maintaining vast data centres often financed through debt. Recent analysis from Bloomberg revealed that there were approximately $178.5 billion in datacentre credit deals in 2025 alone, as new players enter the market, reminiscent of a “gold rush.”
Yet, this enthusiasm is tempered by the reality that essential components, like Nvidia chips, have a limited operational lifespan, potentially shorter than the terms of their financing agreements. The industry is also increasingly characterized by complex financial arrangements that carry troubling similarities to past corporate collapses.
As the narrative surrounding generative AI evolves, proponents tout its potential to revolutionize various sectors. Figures such as Sam Altman, CEO of OpenAI, assert that these technologies are rapidly approaching “superintelligence.” Meanwhile, Mark Zuckerberg suggests they could replace human interactions entirely. However, critics like Brian Merchant, author of *Blood in the Machine*, highlight the consequences of replacing human workers with AI-generated outputs. Many affected employees have shared their experiences, noting the generic quality of AI-produced work and the risks associated with delegating critical tasks to machines.
Concerns over the reliability of AI have grown, illustrated by recent incidents. In the UK, a high court cautioned against the use of AI by lawyers after fictitious case law was cited in two separate cases. Similarly, police officers in Heber City, Utah, had to verify the accuracy of a transcription tool after it erroneously reported that an officer had transformed into a frog during an incident.
The proliferation of what Merchant calls the “slop layer” of AI-generated content complicates the landscape further, making it increasingly difficult to discern credible information from misinformation. Doctorow argues that AI should not be viewed as a harbinger of impending superintelligence but rather as a collection of tools that can enhance productivity when used appropriately by workers.
As the AI sector grapples with these challenges, the potential for a financial correction looms large. The Bank for International Settlements (BIS) recently reported that the “Magnificent Seven” tech stocks now account for 35% of the S&P 500, a significant increase from 20% three years ago. A decline in share prices could trigger far-reaching implications, impacting retail investors across the globe, Asian tech exporters, and the private equity firms that have backed the sector’s expansion.
In the UK, the Office for Budget Responsibility (OBR) has projected that a “global correction” scenario, where stock prices fall by 35% within the next year, could reduce the country’s GDP by 0.6% and lead to a deterioration in public finances by £16 billion. While this impact would be more manageable than the fallout from the 2008 global financial crisis, it would still resonate in an economy striving for stability.
The unfolding situation highlights the interconnectedness of the tech sector with broader economic stability. As the industry faces its reckoning, the consequences of its financial strategies are likely to affect a wide array of stakeholders, emphasizing that while the allure of innovation is strong, the risks cannot be overlooked.
