In-Short
- AI adoption in business functions has doubled to 65% in one year.
- AI computational power demand is growing annually by 26-36%.
- Big tech’s dominance in AI development raises concerns about centralization.
- Decentralized infrastructures like Qubic blockchain could democratize AI computational power.
Summary
The landscape of artificial intelligence (AI) is rapidly evolving, with large language models (LLMs) like ChatGPT and Dall-E becoming integral to various tasks. A McKinsey survey highlights a significant leap in AI adoption, with 65% of companies integrating AI into at least one business function, a twofold increase within a year.
Despite this growth, the AI industry faces the challenge of computational power demand, which is increasing at a staggering rate. Training and running AI programs are becoming increasingly expensive, with projections suggesting billion-dollar costs on the horizon. Big tech companies like Microsoft, Google, and Nvidia are investing heavily in AI, raising concerns about the centralization of AI development.
Experts like Stanford’s James Landay suggest that the competition for resources will lead to the development of more affordable hardware solutions. In response to the US chip restrictions, China is supporting AI startups with computing vouchers to mitigate costs.
However, a shift towards decentralization may be on the horizon with blockchain technologies like Qubic. Its unique mining mechanism allows for the use of computational power for productive AI tasks, potentially democratizing access to AI resources and reducing reliance on big tech.
The AI industry is at a critical juncture, with the need for computational power posing a significant challenge. Decentralized infrastructures offer a promising solution to reduce costs and prevent big tech from monopolizing this transformative technology.
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