What stands out to me is that OpenGradient is not trying to make every validator do the same job. Its HACA setup splits the work: inference nodes run models, full nodes verify proofs, data nodes bring in outside information, and storage sits off-chain on Walrus. That matters because AI work is slow, uneven, and expensive to repeat everywhere, so the network feels more like a relay team than a single overloaded machine.
The token design also looks more useful than decorative. OPG is on Base, and the docs say inference payments, model monetization, app access, staking, and governance are all live from day one, with 40% of supply aimed at ecosystem growth and 10% reserved for staking rewards. That tells me the project is trying to tie value to actual usage instead of just asking people to hold and hope.
For builders, that is the real appeal: if the infrastructure is reliable, they can build around it without constantly patching over trust gaps. The risk is obvious too, because adoption has to stay real after the first wave of attention. The foundation’s current materials point to 2M+ inferences, 500K+ proofs, and 2,000+ models, which is a decent start, but repeat usage matters more than headline numbers.
For builders, what matters more here: the incentive design, or whether the network can stay dependable under real traffic?