As generative AI rapidly becomes part of everyday life, we increasingly interact with AI across various aspects of our work and personal routines. The more we use these systems, the better they learn our habits, preferences, and patterns of judgment—ultimately responding in ways that feel tailored to each individual.
This dynamic creates a new form of lock-in that differs fundamentally from traditional IT services. In this article, I refer to this phenomenon as “personal model lock-in” and explore its implications alongside emerging global discussions, including the future need for AI portability.
How This Differs From Traditional Lock-In
Traditional lock-in has often been about the inconvenience of transferring settings or data—for example, moving browser bookmarks or changing mobile phone numbers.
Generative AI, however, learns from far deeper layers of interaction:
- Personal linguistic habits
- Information preferences
- Judgment tendencies
- Values
- Cognitive rhythms
- Behavioral patterns and context in requests
This is not just configuration data—it’s information close to the user’s internal characteristics.
As a result, switching to another AI can feel like “starting a conversation with someone who knows nothing about you,” creating significant friction.
This is the essence of personal model lock-in.
A New Form of Dependency Created by Generative AI
As generative AI systems evolve, they develop increasingly accurate models of individual users. These models cannot be fully explained by simple rules or datasets—they represent ongoing, personalized relationships.
This leads to structures such as:
- Psychological switching costs, making it harder to change platforms
- Heightened dependency on a specific AI
- Influence of the AI on users’ own decision-making processes
These dependencies are deeper and more personal than those created by past technologies.
Global Policy and Research Trends: AI Lock-In and Data Portability
In response to these developments, discussions around AI lock-in and data rights are progressing internationally.
Europe
The EU’s AI Act and Data Act strengthen users’ rights to access, share, and transfer their data, aiming to prevent excessive platform lock-in.
United States
Research attention is growing around how AI systems remember user behavior and thought patterns. Concepts like “cognitive lock-in” and studies on the effects of AI memory highlight risks around dependency and reduced autonomy.
Japan
Japan is still early in the debate, but ministries such as METI and the Digital Agency are focusing on AI governance, data utilization, transparency, and dependency risks.
A Unique Issue in Generative AI: Cognitive Lock-In
As generative AI begins to internalize users’ thinking styles and values, a phenomenon called cognitive lock-in can occur. This refers to situations where AI-generated suggestions subtly shape human decisions, narrowing options or weakening personal autonomy.
Key concerns include:
- Biased decision-making: AI proposals become the default choice
- Reduced autonomy: Users may rely too heavily on AI judgments
- Psychological switching barriers: Different AIs can no longer “understand” the user without rebuilding the relationship
- Opacity: Users cannot easily see what standards or criteria the AI is applying
- Value fixation: The AI reinforces the user’s current preferences, shrinking cognitive diversity
As AI becomes a cognitive infrastructure, these risks grow more significant.
The Need for AI Portability
Given the intensity of these lock-in mechanisms, AI portability—the ability to take one’s AI-related profile to another platform—will likely become essential.
However, transferring raw chat histories or entire datasets is unrealistic due to privacy concerns, compatibility issues, and differences in model architectures.
A more practical form of AI portability would involve transferring abstracted personal models, such as:
- Embeddings that represent one’s writing style
- Structured preference graphs
- Judgment and decision-making patterns
- Compressed behavioral or interaction profiles
- Contextual behavioral tendencies
In other words, portability would focus on carrying “the essence of the user” that an AI has learned.
Conclusion: AI Will Compete on How Deeply It Understands Individuals
- Generative AI learns users’ internal characteristics
- This leads to strong personal model lock-in
- Global discussions on lock-in and data portability are accelerating
- Cognitive lock-in is emerging as a critical generative-AI-specific issue
- A future with “personal model portability” will be necessary
As generative AI becomes a central part of work and life, society will need to rethink how individuals relate to AI and how much control users should have over their personal models.
Avoiding lock-in entirely may be impossible, but ensuring that users can manage—and move—their own data and personal models will be crucial for a healthy AI ecosystem.
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