We’ve reached a significant turning point: private AI is no longer just a privilege of Big Tech.
With modern open-source LLMs and tools like Ollama and LocalAI, it is now feasible to run powerful models on-premises or in a private VPC using regular GPUs or capable workstations—no research lab required. This advancement allows you to:
- Run models locally on existing infrastructure.
- Integrate them into real workflows—support, engineering, operations, documentation, business intelligence—without exposing sensitive data to external providers.
- Design privacy by architecture rather than relying on a vendor’s defaults.
My team has been developing these private AI systems for years. The true advantage lies not only in the models but also in workflow design and data ownership, focusing on:
- Security
- Compliance
- Care
Most browser and app-based AI tools transmit your data to their own or third-party backends for processing, logging, or model improvement. For sensitive workloads, the safest approach is straightforward: build it in-house and maintain it on your own stack.
Now, envision enhancing this with Web3-style architectures: decentralized data and applications, ensuring you’re not placing all your resources in one centralized basket.
Incorporating zero-knowledge proofs allows you to prove something about data without revealing the data itself. This leads to a model where:
- Users truly own their data.
- Companies request access instead of assuming it.
- Users can be compensated for sharing validated information.
- Systems are both privacy-preserving and high-integrity.
This is the direction I am passionate about: private AI combined with decentralized verification. If you are interested in discussing the development of secure, private AI workflows—beyond mere data-harvesting “AI features”—I am open to connecting. These are my thoughts and projects for the future.
