The Erosion of Portable AI Sessions: What It Means for Users and Developers

The original promise of an inference API was straightforward: send some input, receive some output, and keep the conversation. This simplicity allowed you to inspect, archive, replay, or hand off sessions to different models. However, this abstraction has always had its limits. Prompt caches live on third-party GPUs, tokenization varies between models, and sampling is intentionally non-reproducible.

Despite these limitations, users could still retain a semantic record of their session in the form of a transcript. This transcript would ideally contain instructions, messages, tool calls, and results. Another capable model might not continue identically, but it could understand the context and take over seamlessly.

In recent years, however, inference APIs have started moving away from this property. They now often return a mix of text and provider-bound state that is intentionally non-portable. This shift has significant implications for both users and developers.

• Reasoning Tokens: Users are billed for these tokens, which are returned as opaque, encrypted blobs with only useless summaries at best.
• Web Searches: Models see source material that the client never sees.
• Compacted Context: Only the original provider can decrypt this context.
• Subagent Instructions and Messages: These are hidden from the application running the agents in the form of encrypted payloads.
• File, Vector-Store, Container, and Cache References: These references cannot be resolved anywhere else.
• Response and Conversation State: This state is entirely keyed by IDs stored on the provider's servers.

Each feature comes with a basic justification that providers can easily offer. For example, they might argue that encrypted blobs protect user data or that web searches enhance model accuracy. While these arguments have merit, collectively they change the ownership reality of an AI session. The transcript on your machine is no longer a complete record but a partial view of a session whose operational state belongs to the inference provider.

The Impact on Users and Developers

This shift in ownership has several significant implications:

• Loss of Control: Users lose control over their data, as key parts of the session are locked away in proprietary formats.
• Vendor Lock-In: Switching providers becomes more difficult, as sessions are tightly coupled to specific platforms.
• Transparency Issues: The lack of transparency can lead to trust issues, especially when it comes to how data is used and stored.

For developers, this trend complicates the development of interoperable tools. It becomes harder to build applications that can seamlessly integrate with multiple AI services. This fragmentation can stifle innovation and limit the potential for more flexible and user-centric solutions.

Consider a scenario where you're using an AI assistant for project management. If the session data is locked into one provider's ecosystem, switching to a different model or service becomes a significant hurdle. The new model would lack crucial context, leading to a disjointed user experience.

Key Takeaways

• Portability Matters: A portable session should contain enough intelligible information for another model to continue the work without requiring the old provider to dereference IDs or decrypt blobs.
• User Control and Transparency: Users should have more control over their data and transparency into how it is used.
• Interoperability: Developers need to advocate for and build tools that support interoperability across different AI services.

The erosion of portable AI sessions is a concerning trend. As the AI landscape continues to evolve, it's crucial for both users and developers to push back against practices that undermine control, transparency, and interoperability. By doing so, we can ensure that AI technology remains user-centric and open.