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 more

We 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