I’ve always liked the idea of crypto networks being used to improve real AI systems instead of just generating more content. At the very least, it’s a more useful application than most speculative AI tokens. @perturbaix is taking that idea pretty far with continuous adversarial testing and hardening. Think of it as turning model vulnerabilities into an ongoing, incentivized process, where independent miners keep finding ways to break models and the findings get used to make those models stronger. That could change how teams approach robustness, since adversarial testing usually: → Happens internally or once in a while → Relies on limited in-house expertise → Stops after a single report Perturb’s idea is to make that pressure continuous, verifiable, and easier for teams to act on. But the upside goes beyond the subnet. The output is tied to making production AI systems less fragile before they reach users. This is the side of decentralized AI I’d love to see grow. $TAO #Perturb #SN26