People like you are rare. It seems like with the ever increasing scale of mechanisation, we are seeing an increasing detachment from everything that makes us human.
The author says to "work on boring problems" but I think what it really boils down to is knowing why you are solving a problem. Just picking a problem depending on how hard or easy it seems is a research mindset. Of course it doesn't make sense to apply that same mindset to more business-oriented problems (unless the business itself is research)!
Both research and business related problems are interesting. You don't have to swing between two extremes every time you have an epiphany.
I'm glad to see this sentiment more vocally expressed lately. It's certainly nothing that hasn't been said before, but I think that reality hasn't caught up to the frustrations faced by knowledge workers. Imagine training as a musician, learning the fundamentals, and mastering your craft, only to be used as a glorified set of fingers for playing chords.
This discussion tends to get muddled whenever I bring it up with others because it's seen as an inability to be a team player, or that I'm just trying to daydream instead of "get things done", but my retort is always that you can't know what to get done until you have done the day-dreaming. We need it back.
Currently, statistical/data-driven approaches work best, and that's what you will be expected to use whether you are building your own products, or working for an employer. Most people don't care about the GOFAI approaches anymore, seeing them as outmoded in all respects.
However, if you are curious and want to understand more of the history of approaches we have tried, and learn some really interesting algorithms along the way, I think studying the old school problems and their solutions can be both intellectually stimulating, and potentially increase your depth of understanding. After all, it's only once you've tried to solve a problem and failed miserably that you start to appreciate the depth of its complexity.
That depth of appreciation is sorely lacking in today's new cohorts, who are basically blinded by the incredibly convincing outputs of our cream-of-the-crop LLMs.
I see this sentiment expressed in almost every venue of discussion, but I never see a concrete plan, or even steps we could take right now to progress towards that future. That's why I'll always remain skeptical about technological utopianism. It simply doesn't work that way in reality.
I've read the article and frankly I don't have the expertise on LLMs to discuss whether it lands on something worth investigating further. What I do notice in the comments here though, are:
1) People nitpicking about the use of mathematical ideas in a loose manner as if every person trying to understand some phenomenon must only open their mouth if they have a watertight theory or shut their mouth otherwise.
2) Getting hung up on the use of the luigi metaphors rather than using it as the basis for a constructive criticism that actually adds to the conversation in an interesting manner.
3) A general snarky attitude towards people exploring ideas on their own. I get it, you might have some expertise that others lack but you're forgetting that you've already made the thousands of mistakes to get to where you are. Do others the courtesy of not judging when they attempt the same.
There's a great book by Culadasa: The mind illuminated. It covers a lot of what ancient meditators thought about the practice, but the essentials can be found in the first chapter.
I'm not trying to be snarky here, but why is the saturated fat in regular whipped cream bad, assuming it is not consumed in excess? This seems to be the main selling point in the article, so I'm genuinely curious.