Just a day ago, a senior Anthropic executive claimed the U.S. still held a 6 to 9 month lead in frontier AI models, while calling Chinese model distillation adversarial. One day later, Moonshot’s Kimi K3 beat Claude Fable 5 on Frontend Code Arena. And the funniest part is that it is an open weight model.
So the whole 6 to9 month lead lasted about 24 hours. 🤣
Tbf they (Moonshot) claim it’s not as good as Fable overall. Fable is also held back by US government mandated guards. Anthropic could well be ahead by a lot in terms of research and maybe we just don’t have the full picture. But that puts them in an even worse spot because it would mean they’re already at the limit of what they can compete with in the market.
I think the big picture here is that the difference in quality is largely subjective at this point, while US companies are burning through orders of magnitude of cash which is obviously not sustainable. We shouldn’t underestimate the power of developing things in the open. Chinese open models benefit from the wisdom of an entire global research community while American engineers working on proprietary closed models are working in their own insular silos. It should be no surprise that the scientific community at large would pull ahead of these small teams. On top of that, doing research in the open amortizes the cost. Incidentally, this is exactly the same logic that led open source to dominate in recent years.
OpenAIs bet was that compute was the limiting factor. Sam Altman claimed that essentially no-one could obtain the level of compute necessary to develop something like GPT-4. Then Deepseek came out.
They’re still trying to make the original claim true because if they admit that they were wrong (or full of shit) it all comes crashing down, not just their companies but likely the economy as a whole.
It doesn’t hurt that American frontier models are phenomenally huge and cost an exorbitant amount per token in and out while the main source of real world value created by llms in the past year has been through agentic/harness/other words for massive context systems that show a real benefit from simply using more tokens.
Just a day ago, a senior Anthropic executive claimed the U.S. still held a 6 to 9 month lead in frontier AI models, while calling Chinese model distillation adversarial. One day later, Moonshot’s Kimi K3 beat Claude Fable 5 on Frontend Code Arena. And the funniest part is that it is an open weight model.
So the whole 6 to9 month lead lasted about 24 hours. 🤣
https://hai.stanford.edu/news/inside-the-ai-index-12-takeaways-from-the-2026-report
Kimi K3 is not an open weight model, not yet, at least. They promised to releaso on July 27th, but it’s just a promise for now.
That investment graph is insane
Totally not a bubble
Anything is possible with a sprinkle of fraud!
Tbf they (Moonshot) claim it’s not as good as Fable overall. Fable is also held back by US government mandated guards. Anthropic could well be ahead by a lot in terms of research and maybe we just don’t have the full picture. But that puts them in an even worse spot because it would mean they’re already at the limit of what they can compete with in the market.
I think the big picture here is that the difference in quality is largely subjective at this point, while US companies are burning through orders of magnitude of cash which is obviously not sustainable. We shouldn’t underestimate the power of developing things in the open. Chinese open models benefit from the wisdom of an entire global research community while American engineers working on proprietary closed models are working in their own insular silos. It should be no surprise that the scientific community at large would pull ahead of these small teams. On top of that, doing research in the open amortizes the cost. Incidentally, this is exactly the same logic that led open source to dominate in recent years.
OpenAIs bet was that compute was the limiting factor. Sam Altman claimed that essentially no-one could obtain the level of compute necessary to develop something like GPT-4. Then Deepseek came out.
They’re still trying to make the original claim true because if they admit that they were wrong (or full of shit) it all comes crashing down, not just their companies but likely the economy as a whole.
It’s funny to watch them take the whole US economy down on a bluff.
It doesn’t hurt that American frontier models are phenomenally huge and cost an exorbitant amount per token in and out while the main source of real world value created by llms in the past year has been through agentic/harness/other words for massive context systems that show a real benefit from simply using more tokens.