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“一颗芯片，真正要达到回本，没有几百万颗的出货量肯定做不到。”芯擎科技创始人、董事兼CEO汪凯曾如是坦言。按此行业标准衡量，蔚来主品牌年销30余万辆的体量，远未达到让神玑芯片实现规模摊销的门槛。当竞争对手通过百万级销量将先进制程芯片成本摊薄时，蔚来仍在为每一颗神玑芯片支付高昂的溢价——这部分成本最终将反映在车价上，进一步削弱其市场竞争力。
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The president has spoken of tariffs as a tool to encourage the reshoring of jobs back to the U.S. Although this may be true for large-scale manufacturing—Volvo is increasing production at its Ridgeville plant in South Carolina, for example—it is not true for many firms which rely on China for production. Three-quarters of all U.S. toys are manufactured there.
It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.