Obsessing over endless details is unwise no matter how we look at it.
First, the scope may be too large. When the domain is too vast, we won’t have enough time to achieve mastery of it.
Second, a narrower scope can rapidly change in proportion to its novelty. The current hot trend is machine learning, but even name-dropping Cursor, Gemini, or Sora AI will date this essay. Books on those subjects will likely be entirely obsolete in 10 years.
Further, when a topic is narrower and time-tested, that domain can often become obsolete. Paper-making, for example, has been around a few thousand years, but most people don’t need those skills.
Therefore, the only domains worth learning are narrow, time-tested, and won’t likely grow obsolete in 50 years. It’s a small list.
What to learn, then?
Obviously, we must learn something to make ourselves better at our craft. I propose the one form of knowledge which surpasses all the others is “abstraction”.
Abstraction is within the mind. It’s the essence of something after removing all nonessential components. This term makes “abstraction”, “primitive”, and “high-level concept” close synonyms.
This isn’t as complex as it sounds:
- A JavaScript library, species’ taxonomy, and the periodic table of the elements are practical metaphysics.
- Religion, law, and poitics are practical axiology.
- Everything in art, music, and design is aesthetics.
While this sounds simple, I’m obviously writing an essay on it, and that’s because doing it really isn’t.
This isn’t common
Most people in STEM don’t have a natural intuition for primitives because the realm is a vast composite of hyper-specialization.
I’ll use OS development as an example:
- The computers industry itself is a sprawling realm of the broader niche of electronics. Electronics is a subset of a subset of electrical engineering. We could broadly group all of it as “electrical work” if we wanted.
- Inside the computer world, hardware and software branch off. Software development is a further subset. Data science and cybersecurity straddle alongside software development, and the terms can sometimes overlap. I’m omitting sound engineering, web design, graphic design, CAD, UX, and other inter-related engineering disciplines.
- This expands further into cultural distinctions. The tech industry has a disproportionate ratio of mid/high-functioning autism spectrum. ASD has a known pathology of absolute obsession (and often mastery) of certain domains. They also will completely disregard related domains. So, they’ll frequently explore precisely what they like (e.g., OS development) and disregard everything else (e.g., app development).
Holistic vs. specific
We tend to learn much more by connecting two bits of unrelated information than from learning related information. We learn faster with each part of a wider domain than focusing on every detail of a narrow one.
However, to be radically honest, broad understanding doesn’t make as much money as short-term, granular skills. Therefore, sub-sub-niche information is usually more desirable on a résumé:
- Most of the business world treats workers more like a replaceable cog. They’re actually more like an adaptable memory metal. On occasion, some managers discuss “soft skills” (e.g., customer service, teamwork). But, on the whole, most of them use numbers to measure and corral the human condition.
- Craftsmanship requires exploring hands-on skills and cerebral work in a non-optimized way. Learning the thing invariably means we suck at it. While a handwritten OS is highly educational, don’t use it in production.
- “Tribal knowledge” is the broad list of endless small details that nobody will ever bother documenting. It’s a big part of why we can’t easily automate most labor. It’s also why skilled professionals will often philosophically extrapolate across domains more easily than unskilled people develop skills.
- Well-designed technology gives the luxury of not having to understand how things work. Over time, the workplace culture consists more of affordably-priced “operators” than professionally-priced “experts”.
Most industries have this type of natural ignorance, but STEM more room to foster this niche-focused mindset.
Nobody lives forever, so we can’t gather infinite information. Most trivia becomes obsolete in a decade or two, but some information (e.g., web standards, physical limitations) are nearly timeless. If you understand those things, the endless slew of over-information that destroys our sanity won’t sweep you up with it.
Breadth creates remixes
Beyond skill progression, there’s another less measurable reason for prioritizing abstractions over niche expertise.
All our creations draw from our environment to make something new. Everything ever made is simply a remix of everything else that becomes more useful to a specific purpose.
From a raw technical standpoint, brilliant ideas aren’t as clever as they first appear. An inventor usually has two unrelated domains in their mind at once. Then, once they find a pattern across both domains, they experience the spark of inspiration.
- Machine code is purely logic-based, compounded into 2-based numbers. Everything in computers, therefore, is gradations of black-and-white true/false.
- All automation (like as programming functions) are a technical abstraction of how habits work. Therefore, it runs the same abstracted cycle of formation and adaptation. It also eventually creates a catastrophe when anticipated triggers or inputs deviate too strongly from actual triggers or inputs.
Some of the most brilliant hacks come from combining formerly unrelated domains:
- Ride-sharing combines the need for a one-way trip and how people want a side job.
- Some hackers have redirected the extra processing heat from mining crypto or machine learning to heating their home.
- Cheap hardware (e.g., embedded systems) can be a web server, email client, weather station, and more.
But abstractions are hard
We wish to believe the myths and follow the fashions of a brilliant solution to avoid working too hard. Smart people, in particular, have extra skill in avoiding hard work.
Unfortunately, abstractions are much more difficult to learn than trivia. Rote memorization doesn’t require rewiring our brains much. By contrast, abstractions require understanding. Further, abstractions alone aren’t really useful, since they’re simply theories and guideposts more than action and results.
The dull sausage-making of success is less exciting than the glamorous get-absurdly-rich tech entrepreneur story. Understanding abstractions isn’t easy, and often isn’t as stimulating, but it creates better results.
- A 10,000-foot view of what you’re working on makes all future learning easier.
- Your understanding can help you to avoid wasting time with fashions that will render themselves obsolete within 5 years.
- A simpler view of everything let you see absurdly simple solutions for complicated problems. This will make you look like some sort of genius.
All this will mean you’ll live a more complete, fulfilling life on the things that you want to do.