You’ve spent weeks teaching ChatGPT about your codebase, your writing style, your business context. It finally gets you. Then you hear Claude handles reasoning better for your use case, or your team standardizes on Gemini.

So you switch. And you start from scratch.

All that context you built up? Trapped inside the tool you just left. According to a Parallels survey of 540 IT professionals, 94% of IT leaders now cite vendor lock-in as a concern. Nearly half say they are “very concerned.” In the AI era, the thing that locks you in is everything you’ve taught the model.

The memory illusion

To their credit, every major AI lab has tried to address this. ChatGPT launched persistent memory in 2024 and expanded it to reference all past conversations by April 2025. Anthropic shipped memory for Claude Teams and Enterprise in September 2025. Google rolled out personal context features with Gemini 2.5 Pro.

These features work. Within their own walls.

Your ChatGPT memory doesn’t transfer to Claude. Your Claude Projects don’t sync with Gemini. Your Gemini personal context can’t even be edited, let alone exported. Each tool builds a richer profile of you over time, and each profile is locked inside a proprietary system with no export path.

This creates an illusion of progress. Your AI remembers you better, but only within one product. The moment you step outside, you’re a stranger again. (For a deeper look at why even within a single tool, context degrades over long conversations, see Why does ChatGPT forget everything?)

This is by design

AI vendors have strong financial incentives to keep your context trapped. The more you invest in one tool’s memory, the more painful it is to leave. Classic switching costs, applied to a new domain.

The numbers show the problem is getting worse, not better. A Zapier survey of 550 C-suite executives found that 28% of enterprises now use more than 10 different AI applications, and 66% plan to add more in the next year. At the same time, 70% have not moved beyond basic integration between those tools. No shared context. No data flowing between them.

The result: 76% of enterprises have experienced negative outcomes from disconnected AI tools. More tools, more context silos, more friction.

This mirrors a pattern we’ve seen before. Early cloud platforms locked in customers through proprietary APIs and data formats. It took years of open standards work (Kubernetes, S3-compatible APIs, OCI containers) to give organizations real portability. AI context is in that same early phase, where every vendor builds walls and interoperability is an afterthought.

What you lose without portable context

The real cost of trapped context isn’t the time it takes to re-explain things. It’s the quality gap.

When you start fresh with a new AI tool, you get generic outputs. The model doesn’t know your terminology, your preferences, your constraints. It hallucinates where it would have been grounded, generalizes where it should be specific, and suggests things you’ve already tried and rejected.

This creates an unfair comparison problem. If you’re evaluating whether Claude handles your use case better than ChatGPT, but Claude has zero context while ChatGPT has months of accumulated knowledge, you’re not comparing models. You’re comparing context. The model with more context will almost always win, regardless of its underlying capability.

For teams, the problem compounds. Five people using three different AI tools means fifteen separate context silos. Knowledge that one person built up in Cursor doesn’t help a teammate in Claude Code. Institutional context gets fragmented across tools and people, with no way to consolidate it.

What portable context looks like

The industry is starting to build the plumbing.

The Model Context Protocol (MCP), originally open-sourced by Anthropic in November 2024, went from roughly 100,000 monthly SDK downloads to 97 million in one year. Over 10,000 MCP servers now exist across the ecosystem. OpenAI adopted MCP in March 2025. Google followed in April. By December 2025, Anthropic donated MCP to the Linux Foundation under the new Agentic AI Foundation, co-founded with OpenAI and Block, with backing from AWS, Google, Microsoft, and Cloudflare.

MCP solves the transport layer. It gives AI tools a standard way to connect to external data sources. But a protocol is a pipe, not a container. You still need something on the other end: a structured, AI-optimized representation of your context that any tool can query.

That’s the layer that’s still missing for most teams. Your context needs to live outside any single AI tool, in a format that’s structured for AI consumption, queryable on demand, and accessible from whatever client you choose. Tools like Wire’s context containers take this approach, processing your documents once and making them available to any MCP-compatible tool. But the principle matters more than any specific product: own your context separately from your model.

What you can do now

The ecosystem is moving fast, but you don’t have to wait for it to mature. A few practical steps:

Audit where your context lives. List every AI tool you use regularly. Where have you built up significant context? How much of it is exportable? For most people, the answer is “none of it.”
Separate context from model. Instead of relying on built-in memory features, store your important context externally: in documents, structured files, or dedicated context tools. If it lives outside the model, it moves with you.
Evaluate models on equal footing. When comparing AI tools, give them identical context. Otherwise you’re measuring the difference in what the model knows about you, not the difference in capability.
Watch the MCP ecosystem. With Linux Foundation governance and adoption from every major AI lab, MCP is becoming the standard transport layer. Tools that support MCP today will be more portable tomorrow.

Your AI should remember you because you gave it the right context, not because you’re locked into one vendor’s memory system. The models will keep getting better. The question is whether your context keeps up, or whether you start over every time you switch.

Your AI Doesn't Remember You. That's by Design.

The memory illusion

This is by design

What you lose without portable context

What portable context looks like

What you can do now

References

Ready to give your AI agents better context?