The most practical question about AI computing networks isn’t “Is there computing power?” but “Who gets the bill?” Someone connecting resources suggests supply may exist; getting one task to run only shows that this particular delivery was completed. To assess product demand, we need to know what service buyers are paying for, whether the results can be verified, and whether they’ll buy again. The material available doesn’t provide customers, revenue, computing-power utilization, or formal product progress for these three projects, so I won’t fill in commercial achievements for them.
Rolling 24 hours, $TAO +1.63%; rolling 24 hours, $RENDER +1.99%; rolling 24 hours, $FET +1.45%. These are changes in each asset’s spot price over a rolling 24-hour window, not order, computing-power usage, or repeat-purchase data. Even if all three rise, that doesn’t mean they serve the same types of buyers, much less prove that demand for any particular AI product exists.
For computing supply and demand, the question is whether resources can be matched to tasks and delivered reliably. For product demand, it’s who is willing to pay for which service and whether they’ll keep buying. Network incentives can attract resources, but we still need to ask who pays for the rewards, what participants deliver, and whether resources will stay when rewards decrease. Connections driven by rewards aren’t the same as customer payments; a demo or trial isn’t the same as steady repeat purchases.
Token value is another matter: Must the service use the token? Where do the fees go? What explicit rights do holders have? Even if people use a product, that doesn’t automatically benefit token holders; nor can growth in a company’s AI business be directly credited to these coins. Resource supply, user demand, network incentives, and token value can’t be checked off with a single usage event or a period of price appreciation.
I’m interested in AI, but more than the “computing network” label, I want to see a verifiable payment: who paid, what they bought, and whether they’ll buy again.
Rolling 24 hours, $TAO +1.63%; rolling 24 hours, $RENDER +1.99%; rolling 24 hours, $FET +1.45%. These are changes in each asset’s spot price over a rolling 24-hour window, not order, computing-power usage, or repeat-purchase data. Even if all three rise, that doesn’t mean they serve the same types of buyers, much less prove that demand for any particular AI product exists.
For computing supply and demand, the question is whether resources can be matched to tasks and delivered reliably. For product demand, it’s who is willing to pay for which service and whether they’ll keep buying. Network incentives can attract resources, but we still need to ask who pays for the rewards, what participants deliver, and whether resources will stay when rewards decrease. Connections driven by rewards aren’t the same as customer payments; a demo or trial isn’t the same as steady repeat purchases.
Token value is another matter: Must the service use the token? Where do the fees go? What explicit rights do holders have? Even if people use a product, that doesn’t automatically benefit token holders; nor can growth in a company’s AI business be directly credited to these coins. Resource supply, user demand, network incentives, and token value can’t be checked off with a single usage event or a period of price appreciation.
I’m interested in AI, but more than the “computing network” label, I want to see a verifiable payment: who paid, what they bought, and whether they’ll buy again.
