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我在我的播客中向四位工程师提出了这个问题：一位来自谷歌的开发者倡导者（斯德哥尔摩），一位高级软件工程师/顾问（巴黎），一位NVIDIA深度学习学院的讲师（摩洛哥），以及一位在IBM工作的基础设施工程师（都柏林）。以下是他们的真实回答： - 高级软件工程师说：“大型语言模型（LLMs）就像婴儿。如果你不理解背后的架构，就无法跟上。” - 谷歌的倡导者稍微反驳了一下：“编写代码已经变成了一种商品，就像自动化后的汽车制造。问题不在于是否要学习编程，而在于你为什么想要学习。” - IBM的基础设施工程师给出了最具可操作性的建议：“不要把人工智能当作代笔。绝不要提交你无法解释的代码。把它当作一个导师，而不是你自己思考的替代品。” 还有许多其他观点。我们还对一段硅谷高管告诉大学毕业生“人工智能是下一个工业革命”的视频进行了反应，结果遭到了观众的嘘声。其中一个更令人警醒的部分是：在当前的使用水平下，Anthropic可能在重度Claude订阅用户上亏损。如今没有任何推理公司是盈利的。那位在大规模LLM推理方面工作的谷歌倡导者直言不讳地表示：如果我们在未来五年内没有显著提高推理效率，这整个生态系统将变得无法承受。而NVIDIA则有更多关于人工智能成本效益的细节可以讨论。

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I asked 4 working engineers this exact question on my podcast: a Google Developer Advocate (Stockholm), a Senior Software Engineer/consultant (Paris), an NVIDIA Deep Learning Institute Instructor (Morocco), and an Infrastructure Engineer at IBM (Dublin).Here's what they actually said:- The Senior Software Engineer said: "LLMs are babies. If you don't understand the architecture behind everything, you won't be able to follow."- The Google advocate pushed back slightly: "Writing code has become a commodity, like car manufacturing after automation. The question isn't whether to learn to code, it's why you want to."- The IBM infrastructure engineer had the most actionable take: "Don't treat AI as a ghostwriter. Never commit code you can't explain. Use it as a tutor, not a replacement for your own thinking."And many moreWe also reacted to a clip of a Silicon Valley exec telling university graduates that "AI is the next industrial revolution" and getting booed by the crowd.One of the more sobering parts: at current usage levels, Anthropic is likely losing money on heavy Claude subscribers. No inference company is profitable today. The Google advocate, who works on LLM inference at scale, put it directly: if we don't get significant inference efficiency improvements in the next five years, this entire ecosystem becomes unaffordable. And the NIVIDA have more details to talk about AI cost effectiveness

请问HN：软件工程对于新学生来说仍然是一个好的职业选择吗？