In a recent LinkedIn post, Linas Beliūnas discusses the fundamental role of AI distillation in the future of artificial intelligence, drawing insights from Nvidia CEO Jensen Huang. Beliūnas frames the conversation around Huang’s perspective that learning from AI, other people, and various knowledge sources is essential for intelligence, a process that AI itself must also undertake.
The Core of AI Intelligence: Distillation
Beliūnas highlights Jensen Huang’s assertion that distillation is not merely a byproduct of AI but a core component of its development. He quotes Huang’s perspective on the necessity of learning:
“Distillation – learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from one another. AI also has to learn from something.”
According to Beliūnas, Huang’s argument, as presented in an Axios interview, reframes the debate around AI’s learning processes. Instead of viewing distillation as a threat or theft, Huang suggests it is the very engine that will drive AI advancement.
The Inevitability of Inter-System Learning
A central theme in Beliūnas’s post is the idea that as AI increasingly generates online content, systems will naturally begin to learn from each other, mirroring human learning processes. Beliūnas elaborates on this point:
“As AI starts generating the vast majority of internet content, systems will inevitably distill intelligence from other systems the same way humans learn from books, teachers, and each other.”
Beliūnas, channeling Huang’s view, argues that attempting to block this natural flow of knowledge transfer is counterproductive. He posits that such restrictions do not enhance safety but rather hinder the progress of the entire AI field.
Open vs. Closed Models: A Symbiotic Relationship
The post emphasizes that the development of more sophisticated AI leads to safer AI. Beliūnas points out that open models, in particular, tend to drive broader adoption and usage, which ultimately benefits the entire industry, including hardware manufacturers like Nvidia.
“Smarter AI becomes safer AI, open models drive more usage, and the whole industry (including the people building the chips) benefits.”
Beliūnas concludes by characterizing this continuous knowledge transfer between AI systems as a feature, not a flaw. He suggests that the interplay between open and closed models is a crucial aspect of AI’s evolution, fostering innovation and mutual advancement.
Additionally, Beliūnas shared a link to a resource titled “The Ultimate Guide to Kimi K3 🤖”, indicating a broader interest in exploring the practical applications and guides within the AI landscape.
📝 About This Content
This article is based on insights shared by Linas Beliūnas on LinkedIn.
📅 Originally posted on July 26, 2026 | View original post on LinkedIn →