I've been around long enough to remember when every new narrative promised to "change everything." Most didn't. That's probably why I pay more attention to what a network chooses to make difficult. In AI, the hard part isn't generating outputs anymore. It's answering the uncomfortable questions afterward: Can the result be verified? Can usage be measured fairly? Can strangers coordinate without relying on a single operator to keep score?
That's why I keep revisiting [OpenGradient](https://www.opengradient.ai?utm_source=chatgpt.com). Not because I think decentralized AI automatically wins, and definitely not because I trust every new infrastructure story. I don't. But something about focusing on hosting, inference, and verification as separate problems feels more grounded than the usual "AI on-chain" pitch.
I've seen plenty of projects optimize for attention before utility. The ones that lasted usually solved a boring problem nobody wanted to talk about. If OpenGradient succeeds, I don't think it'll be because of the AI narrative. It'll be because it quietly made trust easier to coordinate at scale. And if it fails, at least it's failing while trying to address the part of the stack that actually matters.