I spent an afternoon last month exporting my conversation history from one AI platform and trying to load it into another. The export worked. It gave me a JSON file with thousands of messages. The import did not work at all, because the other platform has no import function, and even if it did, raw conversation logs are not the same as the contextual understanding built from those conversations. I had the data. I did not have the knowledge graph.
That experience crystallised something I had been circling around for four weeks of writing this series. The problem is not that platforms refuse to give you your data. It is that the most valuable thing, the accumulated understanding of how you work and think, is not data in any format you can move.
Google just made this look solved. It is not.
Google recently launched an import feature for Gemini that lets you bring in your memories and chat history from other AI apps. You copy a prompt into your current AI, it generates a summary of your preferences, you paste that into Gemini, and it picks up your context. You can also upload a ZIP file of your full conversation history from ChatGPT or Claude and continue where you left off.
On the surface, that sounds like exactly what I have been asking for in this series. Portability. The ability to switch without starting over.
But look at what is actually happening. Google built an import tool. Not an export tool. The data flows in one direction: toward Google. Once your context is inside Gemini, it gets combined with your Gmail, your Google Photos, your Search history, and whatever else you have given Google access to. That is not portability. That is acquisition. Google is not solving the switching problem for users. It is solving the switching problem for Google.
An import tool without a corresponding export standard is not data portability. It is a one-way valve. The question is not whether you can bring your data into a platform. It is whether you can take it back out again when you want to leave.
This is the pattern we should recognise by now. The platform that makes it easiest to arrive is not necessarily the one that makes it easiest to leave.
What we actually need
The solution I keep coming back to is not complicated in concept. We need an AI-agnostic layer where your personal data, preferences, and interaction history live. A place you own and control. A system of record that AI platforms can read from and write to, with your permission, but that is not tied to any single provider.
Your personal knowledge graph should be infrastructure, not a feature of whichever AI product you happen to use today.
Anuj Ahooja wrote a piece recently called "The Everything Account" that frames this well. His argument is that our accounts are not really our accounts. They are accounts owned by Google, Meta, OpenAI, whoever runs the platform. The data we create is tied directly to the app, and the companies hoard it because they know it is what gives them power over us. His proposal: separate the data from the app entirely. Let your account live on its own, grant access to whichever services you choose, and revoke that access when a platform stops serving you. The apps orbit your data, not the other way around.
That model resonates with everything I have been writing in this series. But I would push it one step further.
The data should flow both ways
Right now, the relationship between you and an AI platform is extractive. You give it your context, it builds a profile, and that profile stays inside the platform. You never see what it has learned about you. You never get the enriched version back.
What if the deal were different? What if every platform you interact with, whether it is an AI assistant, a social network, a commerce agent, or a search engine, was required to write its learnings back into your personal data store? Not just let you export raw logs, but return the enriched profile. The insights, the preference models, the behavioural patterns. The things the platform learned about you that you never explicitly told it.
That would change the power dynamic entirely. You would not just own your data. You would own what your data taught the machines. And you could take those learnings to any other platform, or use them yourself to understand your own patterns better than any single AI does.
The goal is not just portability. It is reciprocity. Every platform that learns from you should be required to teach you back.
People are already building toward this
The Data Transfer Initiative recently published principles for portability of personal data in AI and collaborated with Inflection AI on a data model for transferring conversation histories. Their affiliate Fabric built an open-source tool called "context-use" that converts ChatGPT archives into portable context for other agents. Early work, but pointed in the right direction.
Standards like the Model Context Protocol (MCP) are creating a common interface between AI systems and external data sources. Think of MCP as a universal connector: one standard for accessing databases, files, and APIs instead of bespoke integrations per platform. That kind of interoperability is foundational if we want data that actually moves with us.
On the regulatory side, the EU is further ahead than most realise. The Digital Markets Act is putting pressure on major platforms. The EU Data Act, effective since late 2025, already grants rights to access and port data from connected devices and explicitly prohibits vendor lock-in. The infrastructure for enforcement exists. What is missing is the tooling and market demand to make portability real in practice, not just in law.
What you can do right now
If you use AI seriously in your daily work: export your conversation histories where you can. Be intentional about which platforms get your deepest working knowledge. Favour tools that support data export and open standards.
If you lead a team: demand portability from your AI vendors before the lock-in hardens. Ask them directly: can we export our data in a format another AI can use? If the answer is vague, that tells you something about their business model.
If you build AI products: compete on quality, not moat depth. And consider building the two-way data flow from day one. The companies that give users their enriched profiles back will earn a kind of trust that walled-garden players never will.
The window is still open
AI personalisation is early enough that the lock-in has not fully set in. Most people are still experimenting, splitting usage across tools, not yet deeply committed to any single platform's memory of them.
But that is changing fast. Memory features are improving. Agents are taking on more tasks. Personalisation is deepening. Google is already building import tools to pull you in. The moat gets dug a little deeper with every conversation.
The AI data moat — a series in six parts
← Part 4: Agentic commerce and the profile you did not know you were building · Part 5 (you are here)
The social media era taught us what happens when platforms accumulate personal data without portability or user control. We watched it happen, named it, wrote books about it, and then walked straight into the same pattern with the next technology that came along. Your AI profile is becoming one of the most valuable digital assets you own. The question of who controls it is not a technical curiosity. It is personal. I would rather own mine, enriched profile and all.