Phase 1 of BlockZero is a training platform: customers bring domain data, miners train expert modules, validators evaluate quality, the expert library grows. Phase 2 is what the library enables: a marketplace where validated domain experts are discoverable, testable, and licensable.

Post-mortems are more useful than press releases. This is our honest account of what we underestimated, what surprised us, and what we'd do differently if we were starting over. The ML architecture worked. The systems problems nearly killed us.

Building a Bittensor subnet for decentralized AI training is not like building a centralized ML system. The constraints are different, the failure modes are different, and the design patterns that work in one environment actively fail in the other. This is a distillation of what we've learned.

Before building BlockZero, we surveyed the literature on distributed Mixture-of-Experts training. This is a practical synthesis of what we found — what methods exist, what problems they solve, where they fall short, and what gap BlockZero fills.

Every decentralized compute network faces the same incentive problem: how do you reward workers for doing actual work, rather than for appearing to do work? BlockZero's Proof-of-Loss mechanism is our answer — and it's the property that makes the network's quality guarantees credible.

Catastrophic forgetting is the central unsolved problem of continual learning. You train a model to be better at task B, and it forgets how to do task A. The more you specialize, the more you destroy. BlockZero's MoE architecture makes this tradeoff avoidable — by construction.

Most AI customization is one-off work. A consulting firm spends six months fine-tuning a model for a client, and when the engagement ends, all that accumulated knowledge walks out the door. The next client starts from scratch. There is no compounding.

BlockZero is built around a fundamentally different model: every training job produces a reusable expert module that compounds in value with every subsequent use.

This post introduces TEFT (Targeted Expert Fine-Tuning) — the protocol at the core of BlockZero — and explains how it achieves communication-efficient, quality-gated distributed MoE fine-tuning over a permissionless network. This is the research paper translated into blog form.

The single most important architectural decision in BlockZero's design was the choice of expert parallelism over data parallel or pipeline parallel training. This post explains why the other approaches would have failed — and why MoE expert parallelism is uniquely suited to decentralized networks.