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So what's changed to make these models so much more capable? Quite a bit, actually. The past year has seen a flurry of advancements not only in model training, but also in the frameworks necessary to harness them. You may recall the market tumbling excitement around DeepSeek R1, which was among the first open-weights frontier models to employ reinforcement learning (RL) to replicate GPT-o1's chain-of-thought reasoning to trade time for higher quality outputs. This approach, now referred to as test-time scaling, has helped smaller models make up for their lower parameter counts by "thinking" fo

The ability to run local agents with access to proprietary data doesn't has particular benefits. For one, while these models are open, there is still a degree of lock-in. Any agents built with these models will have system prompts and tooling that have been tuned to that specific architecture. It's about being able to reach markets that bigger models can't, Buss explained.  "If you have people developing using your technologies and approaches and IP, they're more likely to migrate up and stay in your ecosystem," he said. "It's a matter of basically having a product at the entry point... If you