[yashvg]

While I understand the desire to reassure developers, I think this perspective seriously underestimates the pace of progress in AI. Just 3-4 years ago, the idea of AI writing any functional code seemed far-fetched. Now they can handle many coding tasks competently.

The author lists specific tasks LLMs can't do today. But there's no fundamental reason they won't be able to in the near future. Domain expertise, understanding downstream effects, configuring CI pipelines - these are all learnable patterns. As models get larger, are trained on more diverse datasets, size of context window increases, and new architectures emerge, these capabilities will come online rapidly. The jump from GPT-3 to GPT-4 was substantial, and we should expect continued leaps.

This doesn't mean human developers will become obsolete overnight. But it does mean the nature of software development work is likely to change significantly. Lower-level coding tasks may be increasingly automated, shifting focus to higher-level design, architecture, and problem framing.

Rather than dismissing the potential impact, we should be preparing for a world where AI significantly augments or even replaces many current development tasks. This might involve focusing more on skills that complement AI capabilities or exploring new areas where human creativity and insight remain critical.

[lurking_swe]

current AI is already stressing the power grid, and much of it will need to be redeveloped and improved just to keep pushing the limits of LLM’s. Power is the limiting factor with scaling here, so i’m rather unconvinced with your hypothesis. The improvements in the last 2-3 years are in no way indicative of the next 2-3 years.

I agree with your sentiment by the way, developers should find ways to use LLM’s to improve their development process. But the drama is getting old.

[Aeolun]

Maybe we should instead do those things when that time actually comes. Premature optimization and all that.