While distillation offers opportunities for cost-effective AI development, it also poses a threat to large proprietary models from companies like OpenAI and Anthropic. These companies may attempt to limit distillation by restricting access to reasoning paths in their models. However, the widespread availability of open-source models and datasets makes it challenging to control the use of distillation. As the technique continues to evolve, it raises questions about the future financial prospects of companies that rely on large foundation models, suggesting a shift towards creating valuable products rather than focusing solely on model development.
Key takeaways:
- The cost of building AI is decreasing, with new techniques like distillation allowing for cheaper development of large language models.
- Distillation involves using a larger "teacher" model to improve a smaller "student" model, making it possible to create efficient models at a lower cost.
- Distillation is gaining significance due to the availability of high-quality open-source models, challenging the dominance of expensive proprietary models.
- While distillation offers opportunities for smaller AI companies, it poses a threat to large foundation model builders, who may attempt to limit its use.