Incentive Networks: The Missing Link Between AI and the Real World

Decentralized networks make everything more complicated, but decentralized networks can handle complicated things. When it comes to solving artificial intelligence's (AI) gluttonous demand for computing power, the problem might just be complicated enough for decentralization.

Decentralized networks complicate everything, but they can also handle complicated things. When it comes to artificial intelligence’s (AI) insatiable demand for computing power, the problem might just be knotty enough for decentralization.

Incentive networks are a type of decentralized network that reward individual behavior that benefits the network as a whole, fostering an “ecosystem” mentality. What distinguishes a simple ecosystem from an incentive network is its intentionality and mechanisms. An ecosystem is often a fortunate accident, the sum of competing forces deciding they are better off working within certain parameters than outside the group. An incentive network is designed for shared success from the get-go.

But how does this relate to AI? Think of scalable AI applications as mechanical entities that churn out 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 several times what smaller ones do: OpenAI’s GPT-4 cost $78 million in compute to train, while Google’s Gemini Ultra cost $191 million. With numbers that big, a system that can reduce hardware investments and dynamically allocate resources to reduce overall costs while remaining impartial to the participants is crucial — and that’s what incentive networks do.

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