How Jevons’ Paradox Might Apply to DeepSeek and AI
DeepSeek R1’s advancements, especially in efficiency, seem poised to reduce the computational (and thus financial) cost of running AI models. On the surface, this suggests a reduction in the demand for high-cost resources like GPUs. However, when viewed through the lens of Jevons’ Paradox, a very different scenario could unfold:
1. Lowered Costs Drive Greater Adoption
• DeepSeek’s Efficiency: By reducing the cost of training and inference, more companies, industries, and even individuals could afford to deploy AI models that were previously out of reach.
• Outcome: The cheaper it becomes to operate AI models, the more widespread their adoption becomes, leading to increased aggregate demand for GPUs, even if the demand per individual task decreases.
2. Expansion into New Use Cases
• As efficiency reduces costs, AI can expand into applications that were previously uneconomical or infeasible (e.g., localized AI for small businesses, real-time applications for underserved industries, or consumer-level deployments).
• This would multiply the number of people and companies using AI, resulting in an explosion of new AI workloads.
3. Increased Competition in AI Applications
• With AI becoming more affordable, competitors in various industries (from healthcare to finance to retail) will need to adopt it to stay competitive. The race to leverage AI tools might lead to companies over-utilizing GPUs to improve their models faster.
• This increased competitive pressure could escalate resource consumption overall.
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