The ongoing decline in the cost of AI development is reshaping the landscape of the industry. Techniques such as distillation, which utilize larger models to train smaller ones efficiently, are making it increasingly affordable to create competitive AI models. This trend is positive for many developers but poses challenges for established companies in Big Tech, as they must now justify their expensive offerings. The advancements by groups like DeepSeek and UC Berkeley exemplify this shift, with some models being trained for very low costs, raising concerns about future demand for high-end computing power.
Distillation is fundamentally about using a larger 'teacher' model to train a smaller 'student' model, allowing for the creation of efficient AI systems at reduced costs.
As the cost of AI computing decreases and techniques like distillation gain traction, the landscape of AI development may shift dramatically, presenting challenges for established tech firms.
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