The first question an AI computing network should answer is not “How many resources have connected?” but “Who gets the bill?” Someone providing computing power suggests that supply may exist; a task running successfully only shows that this particular delivery was completed. To assess product demand, we need to know who pays for which service, whether the results can be verified, and whether they are willing to buy again. The materials available do not provide the customers, revenue, computing-power utilization, or formal product progress for these three projects, so I won’t invent commercial achievements for them.
Rolling 24 hours, $TAO +1.63%; rolling 24 hours, $RENDER +1.88%; rolling 24 hours, $FET +1.34%. These are changes in each asset’s spot quote over a rolling 24-hour window—not data on orders, computing-power usage, or repeat purchases. Even if all three rise, that doesn’t mean they’re serving the same kind of buyer, let alone prove that demand for any particular AI product has materialized.
Computing-power supply and demand are about whether resources can be matched to tasks and delivered reliably. Product demand is about who is willing to keep paying. Network incentives can attract resources, but we still need to ask who funds the rewards, what participants deliver, and whether resources will remain once rewards are reduced. Connections driven by incentives are not the same as customer payments; a demo or trial is not the same as steady repeat business.
Token value is another matter: Is using the token essential to the service? Where do the fees go? What specific rights do holders have? Even if people use the product, that doesn’t automatically benefit token holders; nor can growth in a company’s AI business be directly credited to these tokens. Supply, demand, incentives, and token value can’t be checked off on the strength of a single usage or a stretch of price gains.
I’m interested in AI, but more than the “computing network” label, I want to see a verifiable payment: who paid, what they got, and whether they’ll buy again.
Rolling 24 hours, $TAO +1.63%; rolling 24 hours, $RENDER +1.88%; rolling 24 hours, $FET +1.34%. These are changes in each asset’s spot quote over a rolling 24-hour window—not data on orders, computing-power usage, or repeat purchases. Even if all three rise, that doesn’t mean they’re serving the same kind of buyer, let alone prove that demand for any particular AI product has materialized.
Computing-power supply and demand are about whether resources can be matched to tasks and delivered reliably. Product demand is about who is willing to keep paying. Network incentives can attract resources, but we still need to ask who funds the rewards, what participants deliver, and whether resources will remain once rewards are reduced. Connections driven by incentives are not the same as customer payments; a demo or trial is not the same as steady repeat business.
Token value is another matter: Is using the token essential to the service? Where do the fees go? What specific rights do holders have? Even if people use the product, that doesn’t automatically benefit token holders; nor can growth in a company’s AI business be directly credited to these tokens. Supply, demand, incentives, and token value can’t be checked off on the strength of a single usage or a stretch of price gains.
I’m interested in AI, but more than the “computing network” label, I want to see a verifiable payment: who paid, what they got, and whether they’ll buy again.
