@NewtonProtocol #Newt 圍繞加密貨幣的 AI 討論已經走向了不健康的方向。多數專案都在嘗試說服市場,讓 AI 成為另一種在鏈上運行的應用程式。但這忽略了真正的瓶頸。真正的限制並不是產生決策——大型語言模型已經能比人類更快產生策略。問題在於:證明一個自主系統在與永不停歇的金融基礎設施互動時,確實執行了它所宣稱的那套策略。也就是「智能」與「可被驗證的執行」之間的落差,而 Newton Protocol 正是在這裡定位自身,從市場角度來看,這可能比另一款 AI 產品更有價值。
Everyone seems to focus on the models. I think @OpenGradient points to a different opportunity.
The more AI projects I look at, the more I feel the real challenge isn't building smarter models—it's proving the output can actually be trusted. That's not the flashiest part of AI, but it could end up being one of the most valuable.
What stood out to me is that @OpenGradient isn't competing in the race to release the biggest model. It's exploring a different layer by treating inference and verification as network resources. That changes where value might accumulate if AI usage keeps growing.
Of course, there's a catch. None of this matters if developers continue choosing centralized services because they're cheaper, faster, or simply more convenient. Technology alone rarely wins adoption usually does.
My takeaway is simple: in a few years, trust may become a bigger competitive advantage than model size. That's the thesis I'm paying attention to.