Transforming AI's Footprint

AI's energy footprint is exploding. Data centers are projected to consume 10x more energy by 2030, with inference workloads accounting for 80-90% of that consumption.

Mythos takes a fundamentally different approach: instead of scaling supply to meet demand, we reduce demand itself. By storing reasoning as reusable artifacts, we eliminate redundant inference entirely.

The result: 75% reduction in energy consumption for equivalent AI capabilities. That's not incremental improvement - it's architectural transformation.

Traditional AI security is perimeter-based: protect the model, control access at the edge. But once an attacker is inside, they have access to everything.

Mythos implements security by architecture. Every knowledge shard carries its own intrinsic access controls. Security travels with the data, not around it.

This means complete auditability, fine-grained access control, and the ability to explain exactly how any answer was derived. Trust is built into the foundation.

AI inference costs are projected to reach $100+ billion annually by 2030. For many enterprises, these costs are becoming prohibitive, limiting AI adoption and innovation.

Mythos fundamentally changes the economics of AI. By eliminating redundant inference, we reduce costs by 75%. This isn't about doing more with less - it's about doing the same with dramatically less.

The economic impact extends beyond direct cost savings. Lower barriers to entry democratize AI access. Smaller organizations can compete with enterprises. Innovation accelerates.