The Cost of AI Generated Content Could Cause a Rethink that Shakes the Global Economy in 2026

The selection of “slop” as Merriam-Webster’s word of the year for 2025 has underscored growing concerns about the quality of artificial intelligence-generated content flooding digital spaces. Defined as low-quality digital content produced in quantity through artificial intelligence, the term reflects mounting unease about the technology’s limitations even as corporate adoption accelerates.

The economic implications of this technology present a growing risk in 2026, particularly as the fundamental unit economics of the industry appear increasingly questionable. Prominent AI sceptics argue that the cost of servicing individual customer requests far exceeds the prices companies can charge for their services. Despite rapidly rising revenues as more paying clients sign up, these gains remain insufficient to justify the extraordinary investment levels currently under way.

Investment in artificial intelligence reached $400 billion in 2025, with forecasts suggesting even higher expenditure in the coming year. Critics contend that these companies cannot achieve profitability under current conditions, sustaining operations primarily through massive capital injections from external investors. Whilst frontier businesses often operate at a loss for extended periods, profitability typically emerges as costs decline. However, each iteration of large language models has thus far proved more expensive than its predecessor, consuming increasing amounts of data, energy and highly compensated technical expertise.

The vast datacentres required to train and operate these models carry such substantial construction and equipment costs that operators frequently finance them through debt secured against anticipated future revenues. Analysis by Bloomberg identified $178.5 billion in datacentre credit transactions during 2025 alone, with inexperienced operators joining established Wall Street firms in what observers characterise as a gold rush. The Nvidia chips that equip these facilities possess limited shelf lives, potentially shorter than the duration of the loan agreements financing their purchase.

The boom increasingly displays characteristics associated with financial bubbles, including elevated leverage and complex circular funding arrangements reminiscent of previous corporate collapses. Confidence that generative artificial intelligence will eventually produce revenues matching the colossal investments depends on expansive narratives about the scale of transformation under way. Industry leaders variously describe large language models as approaching superintelligence or positioning them as replacements for human relationships.

Evidence suggests that artificial intelligence is indeed displacing human workers in specific sectors. Numerous testimonies from writers, coders and marketing professionals describe redundancies in favour of AI-generated outputs. However, many of these accounts emphasise the bland quality of work produced by digital replacements or highlight risks when sensitive tasks shift beyond human control.

The dangers of wholesale replacement of human workers have become increasingly apparent in recent months. The High Court in the United Kingdom issued warnings regarding lawyers’ use of artificial intelligence following cases involving citations of entirely fictitious case law. Police officers in Heber City, Utah, discovered transcription errors after a tool mistakenly claimed an officer had transformed into a frog, having picked up audio from Disney’s The Princess and the Frog playing in the background.

Such specific examples fail to account for broader costs associated with the proliferation of AI-generated content across online spaces, complicating efforts to identify authentic or truthful information. Some analysts argue that artificial intelligence represents not impending superintelligence but rather a collection of occasionally useful tools that can improve workers’ lives when workers control their deployment.

Viewed through this lens, these technologies may offer significant productivity benefits, yet perhaps insufficient to justify current valuations and the scale of ongoing investment. Any fundamental reassessment would trigger chaos across financial markets. The Bank for International Settlements recently noted that the Magnificent Seven technology stocks now comprise 35% of the S&P 500, up from 20% three years ago. A share price correction would produce real-world consequences extending far beyond Silicon Valley, affecting retail investors on both sides of the Atlantic, Asian technology exporters and lenders, including loosely regulated private equity firms, that financed the sector’s expansion.

The Office for Budget Responsibility estimated in its budget forecasts that a global correction scenario, wherein UK and world stock prices fell 35% over the coming year, would reduce the country’s GDP by 0.6% and cause a £16 billion deterioration in public finances. Whilst this would prove relatively manageable compared with the 2008 global financial crisis, in which UK institutions played leading roles, it would nonetheless be keenly felt in an economy struggling to establish momentum.

Although the prospect of technology sector leadership facing humbling consequences may appeal to some observers, the interconnected nature of modern financial markets ensures that any significant correction would produce widespread economic repercussions extending well beyond Silicon Valley boardrooms.