A recent article from The Atlantic sheds light on AI distillation, a technique where a smaller or new AI model is trained by querying a more advanced model and using its responses as training data. OpenAI is currently investigating DeepSeek, a Chinese AI startup, for allegedly using distillation to train its model based on OpenAI’s outputs—potentially at a fraction of the usual cost. (Source)
LLMs Learning From Each Other: A Self-Reinforcing Cycle
This case raises a crucial issue: Large Language Models (LLMs) are indirectly learning from each other. When one model generates responses that are then used to train another, it creates a recursive loop of knowledge propagation. Over time, this can lead to:
Homogenization of AI models, where models start to converge toward similar knowledge and reasoning patterns.
Amplification of biases and hallucinations, as errors from one model can be inherited by others.
Legal and ethical dilemmas, particularly regarding intellectual property and fair use.
The Bigger Question: Open-Source vs. Proprietary AI
Open-source models (like Meta’s LLaMA or Mistral) make knowledge-sharing explicit, while proprietary models (like OpenAI's GPT-4 and Google Gemini) guard their outputs. If AI distillation becomes widespread, it challenges the notion of ownership over LLM knowledge. Who truly "owns" the insights generated by an AI model? If distillation is inevitable, should companies embrace it or fight it?
My Take
AI’s evolution has always been about building on previous knowledge—whether human-generated or machine-learned. However, AI models feeding off each other without human oversight could lead to a less diverse, more biased AI ecosystem. Instead of fighting distillation, the focus should shift to ensuring AI models remain transparent, accountable, and verifiable.
This is a turning point for the AI industry: Will companies collaborate to advance knowledge responsibly, or will they engage in an arms race to lock down AI intelligence?
Let’s discuss. How do you see AI distillation shaping the future of LLMs?
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