Generative AI’s $800B Flop: Why Its Failure Could Be a Hidden Blessing?

The generative artificial intelligence (genAI) boom, heralded as the next technological revolution, faces an uncertain future as mounting costs, legal battles, and minimal productivity gains threaten its commercial viability, according to Fenwick McKelvey of Concordia University. Despite the hype fueling billions in investments, genAI firms confront a staggering USD 800 billion revenue shortfall, with current applications largely limited to aiding programmers and copywriters. Far from transforming economies, genAI’s high operational costs and legal entanglements suggest it may never replace human workers or deliver the anticipated profits, potentially rendering it “worthless”—a scenario that could paradoxically benefit society by curbing Big Tech’s overreach.

The financial model underpinning genAI is precarious, with companies like OpenAI burning through vast sums to sustain free or low-cost services like ChatGPT and Gemini. OpenAI’s CEO Sam Altman has admitted that even paid pro accounts operate at a loss due to exorbitant computing costs, with each query draining resources. Unlike traditional tech giants that thrive on low-cost, ad-funded platforms, genAI firms rely on a startup strategy of heavy spending to capture users, risking what Cory Doctorow terms “enshittification”—a decline in service quality as ads increase to offset losses. OpenAI’s tentative plans to introduce ads to ChatGPT, though promised to be “thoughtful and tasteful,” highlight the struggle to monetize a technology that remains prohibitively expensive, with no clear path to profitability.

Compounding these financial woes are significant legal challenges, particularly around copyright infringement. GenAI models, trained on vast datasets including copyrighted books and online content, face lawsuits and costly licensing agreements. For instance, one model can recall 42% of the first Harry Potter novel, raising ethical and legal concerns. American AI startup Anthropic’s attempt to settle with authors for USD 1.5 billion was rejected by courts, signaling that payouts could erode valuations like Anthropic’s USD 183 billion. These “toxic assets” burden firms with lobbying costs and potential liabilities, making genAI a risky investment as courts and creators demand accountability for unauthorized use of intellectual property.

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The rise of open-source alternatives further undermines genAI’s commercial prospects. Meta’s release of Llama as an open-source model allows anyone with sufficient computing power to run it for free, challenging the high valuations of proprietary models. Similarly, Chinese firm DeepSeek’s open model, which rivals commercial offerings, briefly disrupted AI stock markets, highlighting the viability of free alternatives. These open models, while less advanced, are “good enough” for many users, potentially eroding the market share of costly commercial services. If investors grow skeptical, funding for genAI could dry up, stalling innovation but leaving accessible, functional tools in the public domain—a scenario that could democratize AI’s benefits without entrenching corporate control.

Should genAI fail to generate sustainable profits, the implications could reshape the tech landscape. Creators hoping for lucrative deals with AI firms may face disappointment, and progress on advanced models could stagnate, leaving users with free, adequate tools. McKelvey argues that genAI’s potential worthlessness reflects the incalculable value of the world’s collective knowledge, which these models exploit but cannot fully commodify.

This failure could check Big Tech’s growing dominance, sparing society from overhyped promises and unsustainable business models. As genAI teeters on the edge of becoming a financial liability, its collapse might ironically preserve its utility as a public resource, fostering a future where technology serves without enslaving.

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