“This is the biggest risk I see in the future of AI: capture of information by a small number of companies through proprietary systems.”
For states, this is a national security concern. For investment managers and corporates, it is a dependency risk. If research and decision-support workflows are mediated by a narrow set of proprietary platforms, trust, resilience, data confidentiality, and bargaining power weaken over time.
LeCun identified “federated learning” as a partial mitigant. In such systems, centralized models avoid needing to see underlying data for training, relying instead on exchanged model parameters.
In principle, this allows a resulting model to perform “…as if it had been trained on the entire set of data…without the data ever leaving (your domain).”
This is not a lightweight solution, however. Federated learning requires a new type of setup with trusted orchestration between parties and central models, as well as secure cloud infrastructure at national or regional scale. It reduces data-sovereignty risk, but does not remove the need for sovereign cloud capacity, reliable energy supply, or sustained capital investment.
