The Confidential Compute Layer AI Needs 🤖
$VVV is building infrastructure for AI agents running on sovereign compute rails, where models execute without exposing their logic.
The same investor thesis is driving capital into specialized AI compute tooling.
$ZAMA is building infrastructure for applications that need to compute sensitive inputs without exposing them.
The common need is compute infrastructure that AI models can actually trust.
Arcium provides that.
The Manticore engine, acquired from Inpher (Amazon and JP Morgan backed, $25M+ raised), is purpose-built for machine learning workloads and optimized for the boolean and scalar operations that power AI inference and training.
Multi-party AI training is the institutional use case. Hospitals, banks, and research labs can contribute confidential datasets to a shared model, with each party's inputs staying confidential throughout training.
The model improves without any party seeing the others' raw data.
ARX TGE soon.


