According to @1HowardWu, when AI agents operate on-chain the botting problem could increase 10x as bad actors automate fraud, and he highlights the need for tools that prove humanity; he cites Aleo’s KYA, which uses zero-knowledge machine learning (ZKML) facial detection, as a preventative solution (source: @1HowardWu, X, Aug 26, 2025). For traders, this flags elevated Sybil and fraud risk as AI agents scale on-chain and positions ZK identity and proof-of-humanity infrastructure like Aleo’s KYA as a key area to monitor for adoption and defense-in-depth across crypto markets (source: @1HowardWu, X, Aug 26, 2025).

According to @VitalikButerin, he is more excited by open-weights AI models with strong editing functionality than models focused solely on creation from scratch [source: @VitalikButerin on X]. Open-weights models are those whose parameters are publicly released, enabling local control, customization, and reproducible deployment for developers, as outlined by Hugging Face documentation and Meta’s Llama open-weight releases [sources: Hugging Face; Meta AI]. The post contains no mention of ETH, tokens, or roadmap changes, so there is no direct trading signal from this statement alone [source: @VitalikButerin on X]. The focus on editable open-weights aligns with current on-chain AI and zkML experiments that rely on modifiable local models for verifiable inference on Ethereum, as demonstrated by Modulus Labs’ proof-of-inference work, which frames where developer attention may translate into future tooling rather than immediate price action [source: Modulus Labs].