Compute costs for AI are going up. Incentive-network-driven compute could be the key to saving you and your investors millions of dollars.
Decentralized networks make everything more complicated, but decentralized networks can handle complicated things. When it comes to solvinggluttonous demand for computing power, the problem might just be complicated enough for decentralization.
But how does this tie back into AI? Think of scalable AI applications as something mechanical that produces simple answers from ludicrously large data sets using computational power, like gas in a car. The more data you haul and the faster you want the answers, the more gas you burn, and the biggest and most complex AI models burn multiple times what smaller ones do: OpenAI’s GPT-4 cost $78 million in compute to train, while Google’s Gemini Ultra cost $191 million.
But let’s get back to incentive networks. To be truly decentralized, the incentives not only need to encourage behavior that is beneficial to the network but they must also be programmed and then accepted by the majority, not decided and adapted by the few. They must be fair but scalable, tailored to the user but accepting of diversity. They must also be simple enough to be understood or at least trusted. This implies either simple use cases or a prodigious amount of foresight.
What can you practically do about this information? As always, DYOR . Check out projects like ThoughtAI , Bittensor , and Ocean Protocol and see how you want to get involved, either as an investor, a builder, or part of the community. Or, if you’re more of an entrepreneur and you’re dreaming of disrupting an entire industry with AI, get into the nitty-gritty of what it will take to train and update that AI. Incentive-network-driven compute could save you and your investors millions of dollars.
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