When an AI product is connected to computing power and users get their results, who keeps paying the bill? That’s more specific than saying, “The industry needs more computing power.” Someone needing AI doesn’t mean that demand has landed on a particular decentralized network; a network having resources doesn’t mean customers have bought its services. Product demand, supply and demand for computing power, network incentives, and token value need to be assessed separately—not strung together into one story with a single label.
Over the rolling 24 hours: $TAO -4.14%. Over the rolling 24 hours: $RENDER -4.42%. Over the rolling 24 hours: $FET -6.35%. These are the spot price changes for three different tokens—not customer numbers, revenue, or computing-power utilization. The fact that they all fell doesn’t prove AI demand is weakening, and they can’t explain one another.
To assess product demand, ask who is paying, what they’re buying, and whether they’ll use it repeatedly. To assess supply and demand for computing power, look at how tasks are matched with resources and how providers are compensated. Network incentives can attract supply, but who pays for the rewards? If ongoing customer spending hasn’t been verified, a bustling supply side isn’t evidence that services have been sold. Even if customers are paying, we need to verify whether the fees pass through the relevant network and how they flow before discussing any connection to token value.
There’s currently no verifiable information on the customers, revenue, computing-power utilization, or official product progress of these three projects, so I won’t make up a track record for them. Growth in a company’s AI business doesn’t automatically mean these tokens benefit; a price decline likewise doesn’t prove demand has failed. Neither is a product invoice.
I’m interested in AI, but when I see the “computing power” label, I still want to ask one more question: who keeps paying? I’d rather see clear use cases people pay for, evidence of repeat usage, and clarity on who ultimately bears the cost of compensating providers. These accounts may be connected, but there needs to be evidence linking them step by step. Otherwise, a token price is just a quote—not a customer invoice.
Over the rolling 24 hours: $TAO -4.14%. Over the rolling 24 hours: $RENDER -4.42%. Over the rolling 24 hours: $FET -6.35%. These are the spot price changes for three different tokens—not customer numbers, revenue, or computing-power utilization. The fact that they all fell doesn’t prove AI demand is weakening, and they can’t explain one another.
To assess product demand, ask who is paying, what they’re buying, and whether they’ll use it repeatedly. To assess supply and demand for computing power, look at how tasks are matched with resources and how providers are compensated. Network incentives can attract supply, but who pays for the rewards? If ongoing customer spending hasn’t been verified, a bustling supply side isn’t evidence that services have been sold. Even if customers are paying, we need to verify whether the fees pass through the relevant network and how they flow before discussing any connection to token value.
There’s currently no verifiable information on the customers, revenue, computing-power utilization, or official product progress of these three projects, so I won’t make up a track record for them. Growth in a company’s AI business doesn’t automatically mean these tokens benefit; a price decline likewise doesn’t prove demand has failed. Neither is a product invoice.
I’m interested in AI, but when I see the “computing power” label, I still want to ask one more question: who keeps paying? I’d rather see clear use cases people pay for, evidence of repeat usage, and clarity on who ultimately bears the cost of compensating providers. These accounts may be connected, but there needs to be evidence linking them step by step. Otherwise, a token price is just a quote—not a customer invoice.
