Oxford DPhil Thesis by Andrew Trask Argues Maximal AI Capability Runs Against Centralization
iamtrask · x · 2026-09-20
OpenMined founder Andrew Trask released his Oxford DPhil pre-print Attribution-Based Control in AI Systems, arguing that many AI risks — privacy, alignment, copyright, power concentration, hallucinations — reduce to an attribution-based control (ABC) problem rooted in the overuse of addition, copying, and branching in gradient descent. The thesis synthesizes deep learning, cryptography, and distributed systems techniques into a path to ABC, claiming it could unlock 6+ orders of magnitude more data and compute.
His thread makes a bet: today's AI is a temporary "mainframe computing era"; the most competitive systems will be routed ensembles of narrow AIs assembled per prompt, with cryptography (enclaves) and distributed systems pointing to a different incentive structure. He rejects the assumption that AI power concentration is inevitable and beneficial, disagreeing with safety camps pushing further centralization.
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