Mark and Siam sit down with Austin, founder of Aurelius (SN37)—an AI-alignment subnet built on Bittensor. In plain English: training gives models knowledge; alignment adds wisdom. Aurelius tackles the “alignment faking” problem by decentralising how alignment data is created and judged. Miners red-team models to generate high-resolution synthetic alignment data; validators score it against a living “constitution” (beyond simple Helpful-Honest-Harmless), aiming to pierce the model’s latent space and reliably shape behaviour. The goal is to package enterprise-grade, fine-tuning datasets (think safer, less hallucinatory chatbots and agents), publish results, and prove uplift—then sell into enterprises and researchers while exploring a token-gated data marketplace and governance over the evolving constitutions.

They cover why this matters (AGI timelines shrinking, opaque lab pipelines), what’s hard (verifying real inference, building a market), and how BitTensor gives an edge (cheap, diversified data generation vs centralised labs). Near-term: ship a proof-of-concept dataset, harden LLM-as-judge, expand integrations (Shoots/Targon), and stand up public benchmarks (Hugging Face, peer-reviewed studies). Longer-term: Aurelius as a decentralised “alignment watchdog” layer that continuously stress-tests frontier models and nudges them toward human values—so the future’s smartest systems aren’t just powerful, but prudent.