The growing reliance on artificial intelligence (AI) has raised significant economic concerns as industry leaders grapple with the financial sustainability of their investments. Merriam-Webster named “slop” as the word of the year for 2025, defining it as “digital content of low quality that is produced, usually in quantity, by means of artificial intelligence.” This choice underscores the dual-edged nature of AI’s rapid adoption, especially among companies eager to reduce labor costs while facing the emerging realities of the technology’s limitations.
Economic experts are anticipating a critical turning point for AI, particularly in 2026. Ed Zitron, a prominent critic of the industry, argues that the “unit economics” — the cost of fulfilling a single customer request versus the revenue generated — do not currently support the extensive investments being made. He uses vivid language to describe the situation, calling the current metrics “dogshit.” While revenues from AI are increasing as more businesses adopt these technologies, they have not yet reached levels sufficient to offset the staggering investments, projected to be around $400 billion in 2025 alone.
Cory Doctorow, another vocal skeptic, states, “These companies are not profitable. They can’t be profitable.” He emphasizes that many AI firms survive by securing substantial funding while struggling to generate revenue. This pattern is not unusual for emerging industries, which often endure periods of significant loss before achieving profitability. However, the trend among AI companies has been contrary, as each new version of large language models (LLMs) demands more resources, leading to increased operational costs.
The substantial infrastructure required to support these AI advancements, particularly data centers, comes with high expenses. A recent analysis by Bloomberg revealed that there were $178.5 billion in credit deals for these facilities in 2025, indicating a rush among inexperienced operators and Wall Street firms. The reliance on advanced Nvidia chips to power these data centers raises additional concerns, as their lifespan may be shorter than that of the financing agreements secured to build them.
As the industry continues to expand, it faces indicators reminiscent of past financial bubbles, including complex funding arrangements that could lead to significant market corrections. Belief in the transformative potential of generative AI, as espoused by figures like OpenAI’s Sam Altman and Mark Zuckerberg, fuels this speculative growth. Altman envisions a future where LLMs reach “superintelligence,” while Zuckerberg suggests these technologies might replace human connections entirely.
Despite these lofty aspirations, many workers in fields such as writing and marketing report a decline in quality as AI-generated content becomes more prevalent. Brian Merchant, author of “Blood in the Machine,” highlights the experiences of numerous professionals replaced by AI, who lament the lack of creativity and authenticity in automated outputs. The risks of relying heavily on AI for sensitive tasks have also come to light, with incidents in the UK legal system and a peculiar case in Heber City, Utah, illustrating the potential pitfalls of AI misapplications.
As the notion of replacing human labor with AI continues to expand, the ramifications of this transition are increasingly evident. Doctorow cautions that AI is not poised to deliver “humanlike intelligence” but is rather a collection of useful tools that can enhance productivity when employed judiciously. The challenge lies in determining whether these benefits will be substantial enough to justify the current high valuations of AI companies.
The implications of a reevaluation of the AI sector could trigger significant turmoil in financial markets. The Bank for International Settlements (BIS) recently noted that the “Magnificent Seven” tech stocks now account for 35% of the S&P 500, a steep rise from 20% three years ago. A downturn in these stocks could have repercussions that extend beyond Silicon Valley, impacting retail investors globally and affecting tech exporters in Asia and the private equity firms that financed the sector’s rapid growth.
In the UK, the Office for Budget Responsibility (OBR) has projected that a potential “global correction” could lead to a 35% decline in stock prices, resulting in a £16 billion hit to public finances and a 0.6% drop in GDP. While this scenario pales in comparison to the 2008 global financial crisis, the impact would still resonate deeply in an economy striving for stability.
As the AI industry navigates these turbulent waters, the broader implications of its trajectory become increasingly critical. While some may find satisfaction in the potential downfall of tech giants, the reality is that the consequences of their actions will reverberate through the economy, affecting everyone.
