The interesting thing about @OpenGradient isn't the AI narrative. It's the tradeoff between trust and efficiency.
What stood out to me wasn't the AI narrative itself, but the economics of verification.
Everyone talks about hosting and inference demand, yet verification may end up being the real bottleneck. As AI models become larger and more expensive to run, the cost of proving outputs can grow faster than many investors expect.
OpenGradient sits in an interesting position because it treats verification as infrastructure rather than an afterthought. That changes how value might accumulate across the network.
The strength is obvious: users increasingly want proof that AI outputs came from the model they paid for.
The limitation is less discussed. Verification only matters if enough economic activity exists to justify the extra cost. A network can have technically sound verification and still struggle with adoption if users prioritize speed and price over trust.
My takeaway: the AI market may not reward the biggest models. It may reward the networks that make trust economically viable at scale. OpenGradient is one project that made me think about that tradeoff differently.