Both AI Fanatics and AI Luddites need to touch grass.

We work in Software ENGINEERING. Engineering is all about what tools makes sense to solve a specific problem. In some cases, AI tools do show immediate business value (eg. TTS for SDR) and in other cases this is less obvious.

This is all the more reason why learning about AI/ML fundamentals is critical in the same way understanding computer architecture, systems programming, algorithms, and design principles are critical to being a SWE, because then you can make a data-driven judgment on whether an approach works or not.

Given the number of throwaway accounts that commented, it clearly struck a nerve.

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rsrsrs86 16 hours ago | parent [-]

The irony is, AI coding only works after and if you put a lot of work on engineering, like creating a factory.

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alephnerd 15 hours ago | parent [-]

There is a lot of work that goes on before even reaching the point to write code.

For example, being able to vibecode a UI wireframe instead of being blocked for 2 sprints by your UI/UX team or templating an alpha to gauge customer interest in 1 week instead of 1 quarter is a massive operational improvement.

Of course these aren't completed products, but customers in most cases can accept such performance in the short-to-medium term or if it is part of an alpha.

This is why I keep repeating ad nauseum that most decisionmakers don't expect AI to replace jobs. The reality is, professional software engineering is about translating business requirements into tangible products.

It's not the codebase that matters in most cases - it's the requirements and outcomes that do. Like you can refactor and prettify your codebase all you want, but if it isn't directly driving customer revenue or value, then that time could be better spent elsewhere. It's the usecase that your product enables which is why they are purchasing your product.

	▲	rsrsrs86 3 hours ago \| parent [-]
		As a researcher in formal methods, I totally get you