One problem I think the AI industry will have to take more seriously is what happens to private data while it’s being used, not just where it’s stored.
Encryption can protect information at rest and during transmission, but computation creates another challenge. AI agents and financial applications often need access to sensitive information to do something useful with it. That creates a difficult balance between functionality and privacy.
This is where Nillion caught my attention.
Its focus on blind computation is interesting because it aims to make protected data usable without exposing the underlying information to every participant involved in processing it. In simple terms, the goal is to compute on data without automatically revealing the secrets behind it.
I think this becomes especially relevant as AI agents start handling personal context, financial strategies, and business information. Giving an agent more capabilities is one thing; deciding how much sensitive information its infrastructure should expose is another.
Of course, the real test is execution. Performance, computational costs, security assumptions, and developer adoption will determine whether this approach becomes practical at scale.
What interests me most about Nillion is the problem it addresses. If AI becomes more deeply integrated into everyday decisions, privacy-preserving computation could become a much more important part of the infrastructure supporting it.
#nil $NIL @Nillion
Encryption can protect information at rest and during transmission, but computation creates another challenge. AI agents and financial applications often need access to sensitive information to do something useful with it. That creates a difficult balance between functionality and privacy.
This is where Nillion caught my attention.
Its focus on blind computation is interesting because it aims to make protected data usable without exposing the underlying information to every participant involved in processing it. In simple terms, the goal is to compute on data without automatically revealing the secrets behind it.
I think this becomes especially relevant as AI agents start handling personal context, financial strategies, and business information. Giving an agent more capabilities is one thing; deciding how much sensitive information its infrastructure should expose is another.
Of course, the real test is execution. Performance, computational costs, security assumptions, and developer adoption will determine whether this approach becomes practical at scale.
What interests me most about Nillion is the problem it addresses. If AI becomes more deeply integrated into everyday decisions, privacy-preserving computation could become a much more important part of the infrastructure supporting it.
#nil $NIL @Nillion