In the rapidly evolving world of artificial intelligence, a familiar specter looms: the risk of “enshittification,” a term coined by writer Cory Doctorow to describe how digital platforms degrade over time. As AI tools like chatbots and recommendation engines become integral to daily life, industry experts are questioning whether they can avoid the same pitfalls that plagued social media giants.

Doctorow’s theory, detailed in a recent Wired article, outlines a predictable cycle: platforms start by prioritizing user satisfaction to build loyalty, then shift focus to monetizing through advertisers or partners, and finally extract maximum value at the expense of everyone involved, leading to a decline in quality.

The Cycle of Decline in Tech Platforms

This pattern is evident in the history of companies like Facebook and Google, where initial innovations gave way to cluttered interfaces and manipulative algorithms. For AI, the stakes are higher, as these systems influence everything from personal decisions to global economies.

According to the Wired piece, AI’s profitability could accelerate this process. As models grow more sophisticated, companies like OpenAI and Google might prioritize revenue streams, such as premium subscriptions or targeted ads, over pure utility.

AI’s Unique Vulnerabilities

Unlike traditional platforms, AI relies on vast datasets and constant training, making it susceptible to quality erosion through “model collapse” or biased inputs. Doctorow warns that without safeguards, AI could follow the path of search engines, where results are increasingly polluted by sponsored content.

The article cites examples from social media, like Twitter’s transformation under new ownership, as cautionary tales. In AI, early signs include chatbots providing less accurate responses to favor promotional outputs, as noted in analyses from publications like The Verge on related tech decays.

Potential Paths to Avoidance

Yet, there may be ways to break the cycle. Open-source AI initiatives, such as those from Hugging Face, promote transparency and community-driven improvements, potentially resisting corporate capture. Doctorow suggests regulatory interventions, like antitrust measures, to prevent monopolistic behaviors that fuel enshittification.

Wired explores how interoperability standards could allow users to switch AI providers seamlessly, echoing proposals in a Wikipedia entry on the term, which credits Doctorow for popularizing it and highlights calls for policy reforms.

Industry Implications and Future Outlook

For insiders in tech, the implications are profound. Venture capitalists are already scrutinizing AI startups for signs of sustainable models that prioritize long-term value over short-term gains. A piece in Startup News echoes Wired’s concerns, noting how AI recommendations, like those for travel itineraries, could degrade if profit motives dominate.

Ultimately, escaping the enshittification trap will require a cultural shift within the industry. As AI becomes more embedded in society, stakeholders must advocate for ethical frameworks that balance innovation with accountability. Doctorow’s framework, as amplified in Wired, serves as a timely reminder that without vigilance, even the most promising technologies can rot from within, leaving users with diminished tools in an increasingly automated world.