Data portability is a commonly repeated promise of cryptography. “Take your followers and your social chart through the Internet.” “Bring your video game elements in games and platforms.” “Log in anywhere with a single unified identity.” These statements have excited builders and developers, but they have not yet become the main current.
Recent changes on the platform have highlighted the fragility of our digital lives. With the conversations of a possible prohibition of Tiktok, creators face years losers of content and audience relations during the night. Meanwhile, while American consumers adopt new models of AI and Deepseek, built in China, face similar questions about where their data and who could access it.
These are symptoms of a fundamental problem: users really do not have or control their data. We live in rented lands.
Many of today’s main cryptographic investors wrote about the portability of the user’s data and sovereignty in the first days of web2. This vision of an internet, where users, not platforms, control their digital lives, was one of the driving forces behind Crypto. While Crypto has been successful in financial applications, this promise of portable data and an auto-sobrain internet remains breached.
We have seen many attempts: NFTs allow you to carry elements through games, decentralized social networks such as Farcaster and Bluesky Promising portable social graphics and verifiable identity standards. None has (yet) has seen a generalized adoption.
Reality? While the first Internet thinkers are deeply concerned about the principles of data sovereignty, most users have a simpler question: what can I do with it?
Without AI, most of the data are only relevant within the walled gardens of the platform in which it is. With AI, it becomes a valued digital merchandise and a tool to feed almost all applications. The history of his message helps to understand his writing style, his preferences and his relationships. With many users who store their data in auto-obese wallets, developers can build experiences that are really personalized. AI finally provides “why” about the portability of the data, in the form of a better product experience instead of the ideology alone.
There is still a cold start problem. It is inconvenient for users to connect their data. And for developers, today’s mentality is: if it convinces users to load their data on their platform, why would you make it easier for them to take them to another place? This creates a cycle in which each new platform becomes another walled garden, recreating the problem that was proposed to be solved.
This is where the new incentive structures could finally break the extractive cycle. Datadaos create an immediate opportunity for users to behave their data through financial incentives, solving the problem of start of the cold, provided that the data is on board in a self -ble and interoperable manner, as in vain. As more users bring their data to these interoperable systems, developers can create applications that were not possible.
Imagine a personalized health coach who can analyze his Oura sleep data, his training from Strava, his nutrition of food delivery applications and their stress levels from communication patterns.
Or, an assistant of AI who really understands it because he can access his full digital history while maintaining his privacy through granular permits.
This solves a critical problem that has affected the past attempts on data portability. Users will not export their data without clear benefits, and developers will not be built for portable data without users. Data data breaks this dead point by making it worth it that users connect the data.
More importantly, once users make their data auto-sobranos, new types of applications are possible. IA agents can access their full digital history to provide truly personalized experiences. Developers can create applications that combine data so that they were not possible when they harden through platforms.
We know that there is a lot of IA training data: many important models providers are ready to reach a data wall soon, making them look for public data sets not available to train newer and higher models. New models such as Deepseek have demonstrated the value of high quality data, with examples generated carefully generated by humans to arraña their new training method. At the same time, user data policies such as GDPR and CCPA legally require platforms to allow users to export their data in a standardized and usable format. Networks as vain allow users to monetize their data through collective bargaining with models trainers that need valuable training data that are no longer available on public internet, and make it interoperable for the true sovereignty of data.
Two forces that converge, the proliferation of AI and new financial incentives, create the potential for both users and developers to benefit from data portability. The interests of users, developers and data networks are finally aligned. Users obtain immediate value more better experiences, developers get access to data from rich user data to create new applications and networks are strengthened with each new participant.
For the first time, we have technology to make data portability valuable as incentives to boost adoption.
Crypto has not yet fulfilled its original promise of an interoperable and intooperable internet where users possess their data, without restrictions by the walled gardens of web2. By creating financial incentives to incorporate data and take advantage of AI capacities, we finally have a window of opportunity for the Internet to really be owned by users.
