Demand for AI services shouldn’t be judged solely by whether “someone is using the model.” We also need to ask what users are actually buying: a usable inference result, a rendered image, or an agent that can complete tasks on their behalf? These product categories have different delivery standards, and paying once doesn’t necessarily indicate stable demand; a willingness to buy repeatedly is a more meaningful signal. The available materials provide no customer, revenue, or computing-power utilization data for these three networks, so growth in the broader AI business cannot be counted directly as their orders.
Over a rolling 24 hours, $TAO +3.29%. Over a rolling 24 hours, $RENDER +2.49%. Over a rolling 24 hours, $FET -0.47%. These are changes in the respective spot prices. They do not mean that customer spending, usage, or demand for computing power changed in tandem, nor is there evidence that the price movements were driven by the same event. Market prices can offer a starting point for observation, but they can’t write a product’s report card.
Supplying computing power entails hardware, bandwidth, and maintenance costs. Network incentives can attract resources, but that is spending on the network side, not customer payment. Nodes being available does not mean there are orders, much less that delivery is reliable. What really needs to be verified is who pays, what result they get, whether the service is completed as expected, and whether users come back. Without this data, demand remains unproven.
Token value is another step removed: is it necessary to hold or pay with the token to use the service? Does the token represent a scarce resource, or is it merely a tool for rewarding participants? Even if people use the product, if payment can bypass the token, that does not automatically imply increased demand for the token. Product demand, computing-power supply, incentive design, and token value are related, but they are not the same thing.
I’m interested in AI applications, but whenever I see the “AI” label, I still ask one more question: Who is paying consistently, why are they willing to pay, and will supply still be able to meet orders once rewards decrease? What we can confirm for now is price movement; evidence of paid demand and token value will have to wait for more concrete proof of usage and delivery.
Over a rolling 24 hours, $TAO +3.29%. Over a rolling 24 hours, $RENDER +2.49%. Over a rolling 24 hours, $FET -0.47%. These are changes in the respective spot prices. They do not mean that customer spending, usage, or demand for computing power changed in tandem, nor is there evidence that the price movements were driven by the same event. Market prices can offer a starting point for observation, but they can’t write a product’s report card.
Supplying computing power entails hardware, bandwidth, and maintenance costs. Network incentives can attract resources, but that is spending on the network side, not customer payment. Nodes being available does not mean there are orders, much less that delivery is reliable. What really needs to be verified is who pays, what result they get, whether the service is completed as expected, and whether users come back. Without this data, demand remains unproven.
Token value is another step removed: is it necessary to hold or pay with the token to use the service? Does the token represent a scarce resource, or is it merely a tool for rewarding participants? Even if people use the product, if payment can bypass the token, that does not automatically imply increased demand for the token. Product demand, computing-power supply, incentive design, and token value are related, but they are not the same thing.
I’m interested in AI applications, but whenever I see the “AI” label, I still ask one more question: Who is paying consistently, why are they willing to pay, and will supply still be able to meet orders once rewards decrease? What we can confirm for now is price movement; evidence of paid demand and token value will have to wait for more concrete proof of usage and delivery.
