With the latest release of Kimi 3 as an open source model, it feels like we’re reaching a major inflection point. Open source LLMs are now getting extremely close to (and in some areas matching) the performance of the absolute frontier closed models. I believe this has some big downstream effects: Frontier labs will gradually lose their ability to charge sky-high margins on API access. Why pay premium prices when you can run something nearly as good locally or through cheaper hosts? That leads to reduced incentive for massive capex spending. If the moat from proprietary models shrinks, pouring billions into the next 100k+ GPU cluster becomes less attractive. Nvidia’s pricing power could take a hit as demand growth for training GPUs slows down. Overall model intelligence progress might slow or flatten out because the economic incentive for frontier labs to push the absolute bleeding edge diminishes. On the flip side, LLM inference is becoming a true commodity — cheap, ubiquitous, and widely available. At some point, “intelligence” could be delivered like a utility (water, electricity, internet), provided by specialized AI utility companies at very low cost. Overall, I see this as a massive positive for society. Intelligence becoming universally accessible could unlock enormous creativity, scientific progress, and economic growth across the entire world, not just for those who can afford the latest API credits. What do you all think? Is the open source wave going to democratize AI in a meaningful way, or will frontier labs find new ways to maintain their lead (better data, post-training, agents, etc.)? Will this accelerate or decelerate the path to AGI? Looking forward to the discussion! submitted by /u/wenhuizhao
Originally posted by u/wenhuizhao on r/ArtificialInteligence