The rise of artificial intelligence (AI) is facing significant economic challenges as the industry approaches a critical juncture in 2026. The word “slop,” defined by Merriam-Webster as “digital content of low quality that is produced, usually in quantity, by means of artificial intelligence,” was selected as the dictionary’s word of the year for 2025. This choice highlights the dual nature of AI’s growth, where corporate enthusiasm coexists with emerging concerns regarding quality and sustainability.
Strong skepticism surrounds the financial viability of the AI industry. Ed Zitron, a vocal critic of the sector, argues that the current “unit economics” are fundamentally flawed. He states that the costs associated with servicing a single customer do not align with the revenue generated, labeling the situation as “dogshit.” While revenues from AI are increasing, they have not risen sufficiently to cover the vast investments, which reached approximately $400 billion in 2025, with projections for even higher figures in the coming year.
Cory Doctorow, another prominent critic, emphasizes that many AI companies are not profitable. He asserts, “These companies are not profitable. They can’t be profitable. They keep the lights on by soaking up hundreds of billions of dollars in other people’s money and then lighting it on fire.” It’s common for emerging industries to operate at a loss, but profitability typically follows a reduction in costs. However, each new version of large language models (LLMs) has thus far demanded greater resources, including data, energy, and skilled labor.
The costs associated with building and maintaining the necessary data centers are staggering. In 2025 alone, there were $178.5 billion in credit deals for these centers, as new operators entered the market amidst a “gold rush.” However, the crucial Nvidia chips that power these facilities may have a limited lifespan, potentially shorter than the terms of the loans taken out to finance their construction.
This rapid expansion is not just about borrowing; it also raises concerns about financial engineering. Complicated funding mechanisms reminiscent of past corporate failures are becoming increasingly prevalent. The belief that generative AI will eventually generate revenue sufficient to justify the substantial investments relies heavily on narratives that promise transformative changes.
Prominent figures like Sam Altman, CEO of OpenAI, claim that LLMs are on the verge of achieving “superintelligence.” Conversely, Mark Zuckerberg suggests they could soon replace human relationships. While some sectors face job displacement due to AI, the quality of AI-generated content has come under scrutiny. Brian Merchant, author of “Blood in the Machine,” draws parallels between the current backlash against big tech and the Luddite rebellion of the 19th century. Many affected workers have recounted the lack of creativity and depth in AI-generated outputs, raising concerns about the consequences of relying on technology for sensitive tasks.
The potential pitfalls of widespread AI adoption have become increasingly evident. In the UK, a high court recently cautioned against the use of AI by legal professionals after cases emerged where fictitious legal precedents were presented. In a separate incident, police in Heber City, Utah, were forced to double-check a transcription tool that inaccurately reported an officer had turned into a frog while a Disney film played in the background.
These examples illustrate the pervasive issue of “slop” in AI content, complicating the task of distinguishing between accurate and misleading information. Doctorow argues that AI should not be viewed as a harbinger of “impending superintelligence,” but rather as a collection of useful tools. He stresses that these tools can enhance workers’ lives when utilized appropriately.
Despite potential productivity benefits, the current valuations of AI companies and the massive influx of investment may not be sustainable. If the market reevaluates the prospects of AI, the consequences could ripple through financial markets. The Bank for International Settlements (BIS) noted that the “Magnificent Seven” tech stocks now constitute 35% of the S&P 500, a rise from 20% three years ago. A correction in share prices could impact retail investors globally, including those in the UK, the United States, and Asia, as well as the private equity firms financing this expansion.
According to the Office for Budget Responsibility (OBR), a “global correction” scenario that sees stock prices decline by 35% could reduce the UK’s GDP by 0.6% and lead to a £16 billion deterioration in public finances. While this outcome would be less severe than the 2008 global financial crisis, it would still be acutely felt in an economy struggling to recover.
The implications of these developments extend beyond the tech sector. While there may be a sense of satisfaction in witnessing the challenges faced by the tech elite, the interconnectedness of the global economy means that the repercussions of a downturn will affect us all. As the industry grapples with its future, the stakes are high for both investors and the broader public.
