There was never any value in simply the ability to invert a binary tree from memory. First, contrary to popular belief, this particular challenge is quite trivial, even easier imo than fizzbuzz. The value of testing candidates with easy problems is their usefulness in quickly filtering out potentially problematic coders, not necessarily to identify strong ones.
Second, another common take on coding challenges is that they're about memorization. Somewhat, but only to a point. Data structures and algorithms are a vocabulary. A big part of the challenge of using them "creatively" in real life is your ability to recognize that a particular subset of that vocabulary best matches a particular situation. In many novel contexts an LLM might be able to help you with implementation once the right algorithm has been identified, but only after you yourself have made that insightful connection.
Having said this I generally agree with the philosophy [0] that keeping things simple is enough 95+% of the time.
Come to think of it, domain knowledge should be an LLMs strong suit as long as you can provide the right documentation, which is working pretty well already.
Right now the main issue I see with AI is that it doesn't do well with scaling. It's great for building demos and examples but you have to fix its code for real production work. But for how long?
Post-LLMs, the value of this (as differentiator) has dropped to zero. Domain knowledge (also known as business knowledge) is the obvious area to skill up on. It simply means knowledge about the area your organisation is working in. Whether it is yogurt delivery logistics, clothing manufacturing supply chain systems, etc. That's the real differentiator now. Anyone can invert a binary try in 5 minutes using an LLM. But designing a software system knowing well the domain your organisation is in is invaluable.
At the same time medicine, hardware design, good industrial, and specific domain knowledge (problems you solve in assembly or control loops) that are fundamentally proprietary and aren't well documented will continue to have value even when LLMs make solving the problems around them easier. Those might have increased leverage, at least for this round of LLMs. Now, maybe they succeed in World Models, but that is not today.
Really, I don't know what "kids these days" are going to do. I couldn't have predicted the influencer boom 15 years ago, but I also think there are geopolitical risks that are probably bigger than that shift, and "synergized" with the push to AI Everything, it doesn't look like a good time to be a learning/working human.
> The fact you are getting downvoted to oblivion shows how fucked HN has become.
If you're going to participate here, you need to stop poisoning HN like this. People have worries about their future wellbeing as a result of the dramatic changes currently happening in the industry. We can debate the validity of those worries without trashing the community, which is specifically against the guidelines. The guidelines, and the work that many people put into upholding them, are the main reason this site has ever been anything worth defending.
The guidelines you're breaching in this case are:
Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
When disagreeing, please reply to the argument instead of calling names. "That is idiotic; 1 + 1 is 2, not 3" can be shortened to "1 + 1 is 2, not 3."
Please don't fulminate. Please don't sneer, including at the rest of the community.
Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.
Eschew flamebait. Avoid generic tangents. Omit internet tropes.