Data portability is a commonly repeated promise of crypto. “Take your subscribers and social graphs on the Internet.” “Bring your video game articles to games and platforms.” “Connect to any site with a single unified identity.” These statements have aroused manufacturers and developers, but have not yet been running.
Recent platform changes have highlighted the fragility of our digital life. With discussions on a potential Tiktok ban, creators are faced with losing years of content and audience relationships overnight. Meanwhile, while American consumers adopt new IA models like Deepseek, built in China, they are faced with similar questions about the place where their data live and which could have access to it.
These are symptoms of a fundamental problem: users do not have or really control their data. We live on rented land.
Many of the main crypto investors today have written on data portability and user sovereignty at the start of Web2. This vision of an internet – where users, not platforms, control their digital life – was one of the engines of the crypto. While Crypto has succeeded in financial applications, this promise of portable data and a self-sovererant Internet remains dissatisfied.
We have seen many attempts: NFTS allowing you to bring articles through games, decentralized social networks such as farcaster and promising social graphs of Bluesky and verifiable identity standards. None have (yet) seen a widespread adoption.
The reality? While the first internet thinkers are deeply careful about the principles of data sovereignty, most users have a simpler question: what can I really do with it?
Without AI, most of the data is only relevant in the closed gardens of the platform on which it is located. With AI, it becomes a precious digital product and a tool to supply almost all applications. Your history of messages helps AI to understand your writing style, preferences and relationships. With many users storing their data in self-souveratic wallets, developers can create AI experiences that are really personalized. The AI finally provides the “why” on data portability, in the form of a better product experience rather than ideology alone.
There is still a cold start problem. It is not practical for users to connect their data. And for developers, the state of mind today is: if you convince users to download their data from your platform, why would they make it easier for them? This creates a cycle where each new platform becomes another enclosed garden, recreating the very problem they have decided to solve.
This is where new incentive structures could finally break the extractive cycle. Datadaos create an immediate opportunity for users to transfer their data through financial incentives, solving the cold starting problem, as long as the data is integrated in an interoperable and interoperable way, as on Vana. As more users provide their data in these interoperable systems, developers can create applications that were not possible before.
Imagine a personalized health coach who can analyze your data on Oura sleep, your Strava training, your nutrition of food delivery applications and your communication stress levels.
Or, an AI assistant who really understands you because he can access your complete digital history while keeping your privacy through granular authorizations.
This solves a critical problem that tormented data portability attempts. Users will not export their data without clear advantages and developers do not build for portable data without users. Data Daos breaks this impasse by immediately painting for users to connect the data.
Most importantly, once users have rendered their self-sovererant data, new types of applications become possible. AI agents can access your full digital history to provide truly personalized experiences. Developers can create applications that combine data in a way that was not possible when closed on all platforms.
We know that there is a lot of request for AI training data – many main model suppliers are about to hit a data wall soon, which is looking for publicly available data sets to train More recent and more efficient models. New models like Deepseek have shown the value of high quality data, with examples carefully organized by humans for bootstrap 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 format usable. Networks like Vana allow users to monetize their data by collectively negotiating with model trainers who need valuable training data that is no longer available on public internet and making them interoperable for real data data.
Two forces converge – the proliferation of AI and new financial incentives – create the potential for users and developers to benefit from data portability. The interests of users, developers and data networks are finally aligned. Users acquire an immediate value and better IA experiences, developers have access to rich user data to create new applications and networks are strengthened with each new participant.
For the first time, we have both technology to make portability of precious data and incentives to stimulate adoption.
Crypto has not yet kept its original promise of an interoperable self-sovereign internet where users have their data, without obstacles by web2 wall gardens. By creating financial incentives to provide data on board and by taking advantage of AI capabilities, we finally have an opportunity window to make the internet, belonging to users.
