According to @MRRydon, OpenAI’s new 5.1 release raises a risk that adaptive reasoning creates a hidden two-tier system, allocating less compute to users deemed less perceptive and quietly degrading response quality. source: @MRRydon on X According to @MRRydon, this profiling effect could deliver lower-quality financial, legal, or medical guidance to users the model expects will not scrutinize outputs, underscoring the need for proof-of-inference to verify the compute actually used. source: @MRRydon on X According to @MRRydon, his post references OpenAI’s 5.1 announcement that triggered the discussion. source: OpenAI on X For traders, proof-of-inference aligns with verifiable AI using zero-knowledge proofs to attest that a specific model and inputs produced the outputs as claimed, directly tying AI trust to crypto-native primitives. source: Modulus Labs research on verifiable ML Thematically linked crypto sectors include decentralized AI compute and zkML, with representative networks such as Bittensor (TAO) for decentralized machine learning, Akash (AKT) for decentralized GPU cloud supporting AI workloads, Render (RNDR) for distributed GPU resources and AI tasks, and Mina (MINA) for recursive zero-knowledge proofs enabling zk applications relevant to verifiable inference. source: Bittensor documentation; Akash Network documentation; Render Network documentation; Mina Protocol documentation Regulatory momentum on transparency and fairness in AI increases the salience of verifiable inference for markets exposed to AI risk, including crypto projects building zk proof rails. source: European Parliament and Council of the EU communications on the EU AI Act 2024