Can AI Escape the Enshittification Trap in Tech Platforms?

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Can AI Escape the Enshittification Trap in Tech Platforms?

Enshittification—a term popularized to describe gradual declines in platform quality as services chase increasingly narrow business incentives—frames a core tension in modern technology. Platforms often start by delivering high-value experiences to users, only to drift toward optimizing for advertisers, data harvest, or powerful intermediaries. This pattern, identified by researchers and commentators, raises urgent questions about whether AI-driven interfaces and governance can reverse or at least temper the drift toward friction, manipulation, and reduced utility.

Understanding the trap: how platforms degrade over time

Enshittification describes a lifecycle where two-sided marketplaces improve for early adopters but deteriorate for the broader user base. In practice, these platforms shift priorities toward business customers, paid partners, and engagement metrics that favor quantity over quality. The consequences are visible: noisier recommendation streams, opaque ranking signals, feature bundling that narrows user choice, and a drift toward gamified attention traps rather than meaningful utility. The term’s traction across publications underscores a growing awareness that technology’s promise of perpetual improvement can collide with structural incentives that reward degradation in the long run.

From a systems perspective, the trap emerges when network effects outpace user value. When a platform generates substantial revenue by selling access or data, it gains the leverage to shape the user experience in ways that may undermine initial quality. Researchers and journalists have traced these dynamics across social networks, app stores, and software ecosystems, highlighting how quickly player incentives can realign after rapid growth. The challenge for AI-enabled platforms is to avoid presiding over a similar decline while still leveraging the efficiencies that intelligent systems offer.

Can AI help reverse or cushion the decline?

Artificial intelligence holds both promise and risk in this context. On the promising side, AI can enable:

Transparent, user-centric ranking that prioritizes usefulness and trust, not just engagement metrics.
Interoperability and open standards that reduce vendor lock-in and invite external verification.
Personalization that respects user intent and consent, rather than inflicting opaque transformations on the feed.
Auditable algorithms with explainability features that empower users to understand what they’re seeing and why.
Decentralized governance models that incorporate diverse stakeholder input, balancing business viability with user welfare.

On the flip side, AI can accelerate enshittification if platforms weaponize predictive models to maximize retention, push proprietary ecosystems, or obscure the true costs of recommendations. When AI becomes a tool for optimizing downstream revenue rather than user value, it risks deepening the very dynamics it could otherwise mitigate. The critical path lies in building architectures that align AI incentives with long-term user benefit, not short-term monetization alone.

Principles that help AI resist decline

To design AI-driven platforms that resist the enshittification trap, practitioners should ground decisions in these principles:

User sovereignty: empower users with control over data, preferences, and the scope of personalization.
Transparency: communicate how AI influences recommendations and results, with accessible explanations.
Open standards: embrace interoperability to reduce lock-in and foster independent scrutiny.
Accountability: implement independent audits and clear redress paths for algorithmic harms or errors.
Value-driven metrics: prioritize quality, trust, and retention that reflect genuine utility rather than mere engagement.
Modular design: decouple critical features so improvements in one area don’t force trade-offs across the system.

Paths for AI-powered platforms to stay trustworthy and useful

Realizing these principles requires deliberate design choices and governance mechanisms. Consider the following strategies as practical steps toward resilience against degradation:

Adopt data portability and user-centric data consent, allowing people to move their configurations and histories between platforms with minimal friction.
Implement visible, auditable ranking criteria and give users controls to customize or override AI guidance when desired.
Foster competing ecosystems by supporting third-party integrations, plugins, and open APIs that reduce platform dominance.
Invest in bias and fairness checks within AI systems to prevent systemic harms or favoritism that harms minority users or creators.
Publish regular impact assessments that assess user welfare, not just revenue, and adjust strategies accordingly.
Design for longevity: avoid feature bundling that locks users into a single vendor’s ecosystem; offer graceful deprecation paths.

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Image credits are sourced from the referenced image collection; this image credit belongs to X-05.com.

What users and designers should demand

Users can influence platform trajectories by demanding:

Clear, accessible explanations of how AI affects content and suggestions.
Control over what data is used for personalization and how long it is retained.
Open access to alternative algorithms or recommended content sources when possible.
Visible indicators of sponsored or paid influence within feeds and search results.
Robust privacy protections and opt-out options for non-essential data collection.

Designers and product leaders, meanwhile, should prioritize transparent roadmaps, independent audits, and collaboration with diverse stakeholders to sustain platform health while pursuing growth.

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