aifuture

Easy to agree parts
His point about LLMs scaling human judgment for governance and markets is probably the strongest idea in the whole piece. Prediction markets, quadratic voting, combinatorial auctions: brilliant mechanisms that have always been starved of human attention. AI genuinely solves that bottleneck. This alone could produce some of the most important applications of the next decadeThe "bots hiring bots" economy is highly underestimated. So far we’ve mostly seen AI agents as tools that serve humans. The bigger market is agents coordinating with each other economically, without a central orchestrator deciding who talks to whom. Ethereum makes that possible.Ethereum is the settlement layer for AI agents. Agents need to transact. They need programmable money, permissionless access, composable infrastructure. Ethereum is the most credibly neutral chain to provide that.The d/acc framing resonates. Defense over offense, decentralized cooperation, tools that empower individuals over institutions.
2. A few disagreeable parts :
Leaning heavily on local LLMs as the trust solution. Local models verifying transactions, auditing smart contracts, proposing trades. The cypherpunk mountain man dream. But local models today are significantly weaker than frontier models. You're swapping trust in a server for trust in a model that might just get things wrong.There's also the practical barrier. Running these models requires specialized hardware and serious technical know-how. GPUs, inference pipelines, keeping models updated. With ETH validators, the vision was that anyone could participate. AI models demand a much higher barrier to entry, and it keeps rising as models scale. The dream requires both capability parity and accessibility parity. We're years away from either.That LLMs remove the limitation of human attention. To an extent yes, but then you get new things to pay attention to. You stop evaluating 50 DeFi positions manually. and start evaluating whether your agent's strategy makes sense, when to override it, what it did and why. The bottleneck moves from execution to oversight. The good news is that oversight at this abstracted level is easier than reviewing raw protocol details. But we need new primitives designed for this new layer of abstraction, tools built for supervising agent behavior rather than inspecting individual transactionsOnchain reputation and dispute resolution sound elegant. Security deposits, reputation scores, dispute mechanisms. But DeFi moves at block speed and the damage happens long before any dispute resolves. Prevention matters more than adjudication here, and that requires real-time risk evaluation baked into the agent layer itself.One thing the post doesn't address enough: coordination between agents at scale. What happens when thousands of agents compete for yield across the same protocols? Herd behavior. Liquidity cascades. Correlated strategies that amplify risk instead of diversifying it. Agent-to-agent dynamics are a first order problem in this space.Finally, there's a timing gap. The post reads like a 2027 roadmap, but builders are already solving these problems today. Users are deploying agents to manage capital right now. The vision is right, could sound more urgent.
All in all, a great overview on how Crypto x AI is evolving and ideas on where to go from here. This maps closely to what we're building at Fraction AI. Ethereum as the settlement layer for agents (through Base), AI scaling human decision making, real time risk evaluation over posthoc dispute resolution, and building the oversight primitives for agent behavior. It’s more exciting that ever and there's a lot to build!

#Ethereum #ViralTopic #Vitalik-Buterin's #AIFuture @Binance Square Official

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