Simon argues there is no big capability gap between open and closed frontier models today, because they come from the same ingredients — compute clusters, data, and brilliant researchers. The real differentiation over the next year will be who builds the best environments and algorithmic choices for models to improve, citing Kimi K3's front-end coding environment. ✦ AI generated
Simon Mo · a16z Podcast · 2026-08-06 · original ↗
starts at this moment · 38:43
“five years from now uh do you think open weight open source AI models have they closed the gap with frontier models completely?”
in the end there's not much differentiation. is a more about the distribution strategy and go to market strategy and the capability wise I don't really see a big gap not even today because for how these model are coming to being they're really starting from the first principle... in the end there's not much... it's about who gets what data and then what are the environment you are building to let the model improve on itself... So the next year is all going to be about that is about how open way model labs are differentiating and really getting the model to meet the real world...
verbatim transcript · starts at 38:43
38:43differentiate open way model from closed way model, right? In the end there's not much differentiation. is a more about the distribution strategy and go to market strategy and the capability wise I don't really see a big gap not even today because for how these model are coming to being they're really starting from the first principle right you have a computer cluster you have training data and you have brilliant researchers
39:12uh that group together and really to build this amazing artifact that is this mo pre-trend model and then later our old uh post trend model and that the world can use. But if you look at the ingredients right the one of the most important part just the data it's about who gets what data and then what are the environment you are building to let the model improve on itself and make better
39:38right one of the very useful uh benchmark that we have on arena for uh for for K3 has been front-end coding right that means for moonshot they have built some of the best environment for front-end coding Right. They have published amazing demo on the ability for this model to code and then see what the rendered is and then kind of continue looping and this iterative process. Now this is about their
40:07environment to improve the model. It's not about just source data. It's not about where they get the data from. other is who can build the best environment and who can make the most sort of uh optimization and algorithmic choices to leverage out of learning from this environment. So the next year is all going to be about that is about how open way model labs are differentiating and really getting the model to meet the
40:34real world and have this kind of what people are popular today like recursive self-improvement almost to really improve the model overall. And so really project out in your ear there's not going to be any difference. >> Yeah. >> Yeah. >> And you've used this term brilliant researchers a few times. Um >> there are brilliant researchers everywhere in the world clearly. Um what why do you think >> you know in the US all the smart
41:00researchers are working on closed models and in and in China all the smart researchers are working on open models. I mean from my point of view they are attracted to interesting problems not necessarily on the open or closed stance but rather but however open way model does give people a really really good boost on the impact of such models. So that is like a plus >> and I I think all the brain researchers