除了政策限制正在有序解除，促使整车厂转向L4的现实推手是供应链。硬件层面，激光雷达成本大幅下降、高算力芯片规模化应用，特别是大模型让智驾研发提速，厂商们发力L4的时机已经成熟。
习近平主席在上海合作组织成员国元首理事会第二十五次会议上发表重要讲话时指出：“中国始终将自身发展同上海合作组织发展联系起来，同各成员国人民对美好生活的向往联系起来。”
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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?，这一点在同城约会中也有详细论述
大模型市场的格局我们刚刚说过：OpenAI、Anthropic、Google三家吃掉企业端89%的钱包份额，高度集中。但在生成式图像、视频、音频这个赛道，完全是另一幅图景。数据显示，企业生产环境里平均要用14个不同的模型。14个。没有任何一家能通吃，连接近都谈不上。