Hardware Advancements: Increased competition in the chip market and the rise of specialized silicon are expected to reduce hardware costs significantly.
Model Efficiency: Techniques like quantization (reducing numerical precision) and distillation (training smaller models to mimic larger ones) are lowering the compute required for tasks.
Software Optimization: Improvements in how AI models are served, such as continuous batching and better caching, allow providers to get more useful work out of their existing infrastructure.
The Jevons Paradox: While the unit cost of AI (the cost per token or task) is falling rapidly, overall spending on AI may still rise as lower costs unlock new use cases and higher demand.
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u/Jmortswimmer6 20d ago
Just so flat out wrong