But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. So they naturally turn to using cloud services like AWS, Microsoft Azure, and Google Cloud to train their AI models. Those wanting compute power can establish an Akash account and bid for compute power. The one providing computer power on the Akash Network is called a Provider. The point is this: By buying more AKT than needed to fund a deployment on Akash Network, the Tenant can recover some of the deployment costs through (1) staking rewards and (2) appreciation of the value of AKT (assuming that value increases).

Will artificial intelligence take over the world? Sometimes it seems that way, for everyone has heard of AI models that can compose entire essays or generate realistic face images of people who don't exist or create images from text descriptions. But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. XLNet from Google, for example, can cost around $61,000 to train each time, without guaranteed results. Not all AI models are as complicated as the Google one cited above.