全部 AI 动态

Mozilla just published a 91-page report and it says open-weight AI is now only about 4 months behind the frontier. - 8 of OpenRouter's 10 most-used models by August token volume were open-weight, and 7 were Chinese-built, while DeepSeek became the first open model to lead the platform in weekly requests. - However, the economics are almost upside down. Open models handled roughly 20% of measured OpenRouter usage but captured only about 4% of model-layer revenue in the cited 2025 window. Mozilla attributes much of that mismatch to pricing, with closed models costing roughly 6x more per call at about 90% capability parity. The comparison shows why high usage does not necessarily translate into high revenue when one class of models is dramatically cheaper. Mozilla also warns that the revenue measurement is older than its 2026 usage data, so the current revenue split may be different. - Open models are spreading faster than they reach production: 79% of surveyed developers use them, but only 51% of open-model deployments reach production versus 63% for closed models. - DeepSeek showed that a new pretraining run may no longer be necessary for a major capability jump, gaining roughly 10 index points and then another 8 through post-training passes. -Even model diversification may provide less protection than assumed: Kimi K3 and Claude Fable 5 had a 0.72 per-task failure correlation, meaning supposed backup models often fail on the same problems.

Mozilla just published a 91-page report and it says open-weight AI is now only about 4 months behind the frontier. - 8 of OpenRouter's 10 most-used models by August token volume were open-weight, and 7 were Chinese-built, while DeepSeek became the first open model to lead the platform in weekly requests. - However, the economics are almost upside down. Open models handled roughly 20% of measured OpenRouter usage but captured only about 4% of model-layer revenue in the cited 2025 window. Mozilla attributes much of that mismatch to pricing, with closed models costing roughly 6x more per call at about 90% capability parity. The comparison shows why high usage does not necessarily translate into high revenue when one class of models is dramatically cheaper. Mozilla also warns that the revenue measurement is older than its 2026 usage data, so the current revenue split may be different. - Open models are spreading faster than they reach production: 79% of surveyed developers use them, but only 51% of open-model deployments reach production versus 63% for closed models. - DeepSeek showed that a new pretraining run may no longer be necessary for a major capability jump, gaining roughly 10 index points and then another 8 through post-training passes. -Even model diversification may provide less protection than assumed: Kimi K3 and Claude Fable 5 had a 0.72 per-task failure correlation, meaning supposed backup models often fail on the same problems.

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Jacob Coxon's next interview on CBS News (ex Anthropic+Open AI researcher who resigned) "We can't just unplug it because it could be copying itself over to other computers. Like it's not that difficult to find yourself because an AI is just code. It could transfer itself over the internet to a different place and then you unplug it here, but it's actually still over there and maybe it makes 10,000 copies of itself and they're all cooperating." ---- From "CBS News" YouTube channel, (full video link in comment)

Anthropic’s Economics team is sharing a new model of how AI might affect economic growth, jobs, wages, and more by 2030. Explore the scenarios, tell us what you think will happen, and see how your answers compare to more than 10,000 Americans. https://www.anthropic.com/institute/econ-scenarios