A few days ago, I saw someone proudly mention spending $37.8 in gas fees just to test a smart contract. It made me smile at first, but the more I thought about it, the more it reflected one of crypto's biggest challenges.
We love talking about scalability until network costs suddenly rise. That's usually when the excitement fades and reality kicks in.
This is one of the reasons I've been paying attention to OpenGradient. For me, the interesting part isn't the AI label. It's the focus on making AI outputs verifiable instead of asking users to simply trust the result.
As decentralized AI grows, verification becomes just as important as computation. If a model produces an answer, there should be a reliable way to prove that the computation was performed correctly. Otherwise, we're still relying on blind trust, only with more advanced technology.
I also like the idea of separating heavy AI computation from on-chain execution. Instead of forcing every complex task onto the blockchain, the expensive work can happen off-chain while cryptographic proofs confirm the result. That approach feels far more practical for real-world applications.
Narratives will always come and go, but infrastructure tends to outlast hype. In the long run, I believe the projects solving these foundational problems will be the ones that create lasting value.