The Part of AI Infrastructure I Can’t Ignore
I've been around crypto long enough to see countless narratives promise to change the world. Most of them attracted attention, liquidity, and speculation, but very few solved problems that actually mattered. That's probably why I find myself paying more attention to infrastructure than headlines.
When I look at AI today, I don't think the biggest challenge is generating outputs. Models are getting better, faster, and more accessible every day. The harder question is what happens afterward. How do I verify the result? How do I know where it came from? How can different participants coordinate around machine intelligence without trusting a centralized operator to keep score?
That's why I keep revisiting OpenGradient.
I don't assume decentralized AI automatically wins. In fact, I'm skeptical of most projects that rely heavily on narrative. What interests me here is the decision to focus on hosting, inference, and verification as separate infrastructure layers. To me, that feels like a more realistic way to approach the problem.
I've learned that durable networks are often built around boring but essential challenges. If OpenGradient succeeds, I believe it won't be because people were excited about AI. It'll be because the network made trust easier to coordinate at scale.
And that's a much harder problem worth paying attention to.
