Can Horizontal Scaling Get Us Closer to AGI?

Monad Research introduces Fortytwo, a decentralized swarm intelligence model that proposes overcoming the limitations—or "lossiness"—of large, monolithic AI models by orchestrating a network of smaller models working in unison. Inspired by how autonomous vehicles coordinate multiple subsystems, the approach aims for scalable, high-accuracy AI on consumer-grade hardware.

Key Ideas

Swarm-Based Consensus Architecture: Fortytwo enables smaller language models to process queries in parallel, scoring and cross-validating each other’s outputs through encrypted, randomized consensus—resulting in more reliable responses.
Built-in Incentives and Security: The system deters bad actors through economic slashing and peer-review mechanisms that preserve integrity and prevent collusion or Sybil attacks.
Entrepreneurial Advantage: Builders can tap into Fortytwo’s swarm inference model to launch censorship-resistant or high-accuracy AI applications, or even run inference nodes using everyday hardware for performance and rewards.