I think most people are focused on the wrong thing when looking at @OpenGradient .
For years, infrastructure networks have competed to attract more hardware, more validators, and more liquidity. But AI introduces a different bottleneck: useful inference demand.
After looking deeper into OpenGradient, what stood out to me wasn't the AI narrative itself. It was the capital allocation question behind it.
I noticed that many decentralized AI projects assume supply growth automatically creates value. In reality, idle compute is just as inefficient as idle liquidity in DeFi.
OpenGradient seems interesting because it pushes the conversation toward verification and utilization rather than simply expanding network capacity.
The strength is obvious: if AI demand continues growing, networks that can prove model outputs may capture more activity than networks focused only on hosting.
The limitation is equally clear. Demand is much harder to bootstrap than infrastructure.
I've been thinking about whether the next winner in decentralized AI will be the network with the most compute, or the one that keeps compute productive.
My takeaway: utilization may matter more than scale.
A decentralized AI network with 50% less compute but 3× more demand will outperform a network with unlimited compute and weak usage.
Do you agree?