Ask HN: What will stay the same in the next 50 years?

- The scientific method. It has served us well broadly speaking over the last 3 centuries

- Humans oscillating between greed and fear. Has happened for the last few millennia :-)

- Showing up on time , being reliable and doing your best leading to good outcomes

I usually base my decisions around these 3..

Scientific method is undergoing intense changes in methodology. I suspect in 50 years the way causal inference is done will in many areas be radically different.

Oh absolutely. I often read scientific journal articles which make suggestions of direct relations between different factors when it’s very likely they are correlational.

Example: being overweight increases risk of heart disease.

Alternate Possibility: being inactive/not exercising increases risk of heart disease and being overweight tends to co-occur with not exercising.

In the above example it’s possible that an inactive person who is not overweight has similar liklihood of heart disease.

Thank you, those are some nice suggestions. I particularly like the idea of staying true to the scientific method, showing up on time, being reliable, doing your best, and optimizing for the best outcome. It often seems like the simplest things are the most important.

50 years is a long time. If you're looking for the answer to "what should I learn now so I don't need to learn anything else in the next 50 years?" then you're approaching the whole thing in the wrong way.

There's no conflict between learning new things and "fundamentals" (however we define them). It's useful to know both. You start with the more general stuff that applies everywhere (this is a lot of what you study at university - how a computer works, how code executes, how to design a program, data structures and algorithms etc.) and then over time you pick up details like language details, specific APIs, frameworks etc.

Languages don't matter that much. Learn a few, be aware of what's out there and get comfortable with picking up new ones as you need them. It's really not a big deal.

> If you're looking for the answer to "what should I learn now so I don't need to learn anything else in the next 50 years?"

Sorry that wasn’t my intention. I was thinking more along the lines of compounding my knowledge for something by studying it consistently over a long period of time.

Thank you for your advice.

  Specific technologies that won't change:
    - Unix. It's been in use since 1969, runs on almost every device. It isn't going anywhere.
      - In particular, become fluent with its ideas and the CLI. That group at bell labs was hands down the best programmers doing the best programming.
    - LISP. It's been in less use since 1958 and influences almost every modern language. It's like learning greek classics because of their influence.
    - HTTP. The basics of the internet haven't changed and work pretty well.
    - Text. Text is the best way to convey information. Nothing beats it. Not pictures. Not audio. Not video. Learn text manipulation.

Please elaborate on including LISP. Having been programming since the 1980s, its influence seems more a persistent curiosity than fundamental concept. C/C++ may be the more relevant unchanging language, core concepts applicable to most modern languages (which just re-implement them with more depth of expression, and less room for system-crashing errors).

I have personally focused on C/C++, but I would say that Lisp has more natural interpretation to the way that processors and compilers work best. The last 20-40 years have been focused on making them work well for C/C++, which is made for a very simple type of processor, but it hasn’t been a great fit. Modern compilers look at C code and attempt to recognize intent rather than translate to instructions, and then even the processor re-orders everything based on guesses, whereas Lisp is more like a direct declaration of that intent, although somewhat more limited.

Stepping back, there may be some merit to going directly at the assembly. It would not surprise me if computer architects and programmers of the future could align to basically eliminate the concepts of compiler, optimizer, superscalar pipeline, re-order buffer etc. Its really getting ridiculous.

I would also ask to elaborate. Looking at its history I don't feel like C has been conceived with some concept in mind. C++ OOP implementation doesn't look the best of its kind.

Certainly not best, but among most influential to wit enduring.

Strongly agree with this list. Only thing is I'd swap out Lisp for Awk/Snobol4/Perl (given the last bullet on text). Text manipulation will be timeless. Even in the "modern world" of PowerBI tools I spend least time possible learning manipulation with DAX/M... I just don't see them lasting the way *nix tools have (and will continue to last).

Regarding the importance of communication vs "hard skills", I often think of a quote by Nassim Taleb:

"You cannot possibly trust two classes of people: educators who are better at explaining than understanding because they’re selectively better, and science journalists, who are better at communicating than understanding. Then, you end up with things like scientism." [1]

The pandemic and climate crisis has also shown us the huge importance of thoughtful communication. Having both theoretical and years of practical background in journalism, I do wonder, though, how much of this emphasis is slowly becoming a hype; a source of well-paid bullshit jobs. There are companies and institutions with deliberately honest communication out there, but often times "good communication" leans towards masterful, smooth hiding of wrongdoings or stuff you don't actually grasp. It's way too easy to (accidentally) become better at explaining than understanding in this field. In this light, it's probably always good to have those more straightforward "hard skills people" around you as well.

All in all, I think it's an excellent time to read Dostoevsky and think about the volatility of human temperament. We're often trying to hide our rough edges with (soft) communication, but who knows how well, or for how long, this actually works.

I would also definitely bet on Dostoevsky's fiction being relevant in 50 years, too.

1: https://medium.com/conversations-with-tyler/bryan-caplan-nas...

Technology definitely will not. Fundamental knowledge may be resilient in the face of change, but the skills you need to cultivate to navigate that change and operate effectively in society and business are evergreen.

Learn how to reason about problems from first principles. Understand how back planning works and how to apply it to a project. Cultivate your communication skills and build a habit of constant learning.

Learn about critical thinking and logical fallacies. Build heuristics you can use to evaluate ideas with incomplete information.

These skills will serve you well long after any technical knowledge you have expires.

Thank you, I really like that idea of developing “meta-skills”, if you will. Learning how to learn, critical thinking, clear communication, evaluating ideas with incomplete information, first principles thinking, and so on.

Are you asking about technologies or are you asking about knowledge?

New technology often replaces old technology, but new theory (in math/CS) generally extends older theory, and problem-solving techniques are often timeless. So if you're looking to learn things that will be relevant in the long-term, your answer is likely algorithms, data structures, and the like. They're not going to be rendered irrelevant by the passage of time; they're going to become more fundamental as we build more advanced theories on top of them.

If the kind of stuff you're into is more like Rust, React, or whatever, then the stuff useful 50+ years from now probably won't be those particular technologies, but the ideas behind them. So instead of learning Rust, you'd need to learn about topics like abstract interpretation and formal verification. (Or to put it another way: try to figure out how to make a Rust compiler, vs. learning how to use it.) If you're into event-based technologies or like parallel programming, go learn about the various models of computation (there are lots) and their strengths and weaknesses. There's a lot of prior literature and research in areas you might not expect, and you'll be surprised how often research from half a century ago suddenly becomes relevant again (like it did with parallel computing). If you like PyTorch, go learn about optimization and machine learning (and I'm not referring to neural network architectures here). There are grad courses on all of this stuff if you go searching around. You'll probably have to put in more effort into them than you'd need to put into learning React or Go, but that's life.

> Are you asking about technologies or are you asking about knowledge?

I was trying to get at both. On the technology side, I was wondering if it is wise to focus on a particular technology/language consistently over a long period of time to compound my knowledge, or learn a new and promising framework/language every so often. For example, I assume Java and C++ will have a high likelihood of being relevant my entire career and so it could be valuable to become an “expert” in the language. However, Go and Rust seem promising going forward and could introduce me to new ways of programming. As I write this I get the feeling the answer is not so black and white as I make it out to be. Some combination of both seems like the right approach.

On the knowledge side, I was mainly wondering what are specific concepts that would be relevant years down the road. I was thinking if I could grasp these fundamental concepts well, then it could provide a solid foundation for any type of field I would like to work in. I realize it is was a very vague question and probably should have been more specific (I’m mainly interested in backend development). Nonetheless I found your answer very insightful, thanks.

Ah I see. Concept-wise, see some stuff I listed above (formal methods, models of computation, optimization, statistics & ML, etc.). Pretty much any programming technologies you like right now are backed by interesting academic research (even OOP) and conceptual topics that I would consider worthwhile to learn for the long-term, but it might take some digging for you to find them, especially with older research. IMHO you (should) want to learn the concepts & techniques you'd need to create the technologies you use, and those are timeless. It might not be as "fun" as hot new topics, and there's obviously a lot to learn, but that's science.

Technology-wise, I think the real answer to your question is to narrow the scope of the question down to the next 5-15 years rather than the next 50, in which case I doubt you'll see things like Rust/Python/Linux/etc. becoming irrelevant in that timeframe. There's also GPU and FPGA programming which would probably also stay relevant for quite a while, though it won't necessarily ever have as huge of a job market as something like React. It's important to care about the next 5-15 years though after all—that's part of your life too.

Plain text.

It lets us convey information in the most effective and durable way, with very high signal-to-noise ratio. (NB: I'm not dissing on other communication mediums; I see them as complementary to plain text.)

I think 50 years is way too long to plan ahead for. A ~9-12 year approach makes more sense. Look at these intervals:

1942 - First mass-produced fully-electric analog computer

1951 - First mass-produced commercial computer (using vacuum tubes)

1963 - First mass-produced integrated circuit mainframe computer

1974 - First mass-produced personal computer

1984 - First GUI operating system with widespread adoption

1995 - AOL, CompuServe, and Prodigy bring the internet to the masses

2007 - First smartphone with widespread adoption

~2017 - I'm actually having trouble coming up with something. Maybe cryptocurrency (specifically Ethereum) gaining widespread adoption, but it's too early to judge its significance. Same with VR.

The job market and what it meant to be a programmer changed so much between these intervals.

You might not even be in tech anymore in 11 years. Maybe you'll be retired after a lucky windfall. Or dead.

2012 (if you move your date back a little) - Deep Learning. In 2012 CNN won the ImageNet competition by a wide margin. After that we have some amazing improvements in Speech to Text and machine translation.

Mathematics.

Of course, mathematics will continue to develop in unimaginable directions, but the “base” notions in math will stay the same. I am talking here about notions of relation, function, group, vector space, tensor, category, measure, integral... Even if some of those are known for a long time, formal definitions that we use today are pretty new. And it seems to me that those will stay fundamental for a long time.

(Seems to me that other sciences (like physics and biology) don’t have that “stability”. In those fields, it wouldn't be too surprising if we discovered some fact that changes a lot we know about it)

Also, as these core notions of math will stay the same, math notation will stay the same also. And that is a good sign that we will continue to use TeX for a loooong time

I've effectively forgotten all of the useful math I learned in college, primarily stats and linear algebra along with enough calc to read through mathy CS papers.

Any recommendations for me to properly re-learn this stuff without going to a community college?

It really depends on what you're trying to do with the knowledge. If you mean learn it to the level of a university undergrad, then you'll need to do exercises and solve a lot of problems.

If you're interested in computational or cookbook type math, then it's best to find some textbooks with lots of exercises and student solution manuals available. You can grab lots of homework problems, solutions, and lecture notes from undergrad material posted freely on the internet. I would also recommend learning about how to use some numerical packages or computer algebra systems, so you can check your work.

If you're interested in proof based math, then it's probably best to find a grad student at a local school that you can pay to review your proofs and provide feedback. You might be able to use some online communities to do this as well.

There are often books that are aimed at a very basic level and others that are aimed at a more rigorous approach for the same material. It can help to review the more basic or less rigorous books to help get some intuition when you're stuck or need an alternate explanation.

There are several online math communities where questions about undergrad math have been asked and answered. A couple of examples for math and stats are: https://math.stackexchange.com/ and https://stats.stackexchange.com/

If you decide to follow an undergrad sequence of problems, you can look at starting with the highest level course you want and determining if there is any discussion on prereqs. You can trace the prereqs (which will typically be a subset of the previous course material) back to the beginning and find the minimal amount of material that you would need to get through.

I've written two (nonfree) books that might be useful for re-learning the topics you listed. Links in profile.

Here are the concept maps to give you an idea of the concepts (round boxes) and topics (rect. boxes) covered in the books: https://minireference.com/static/conceptmaps/math_and_physic... https://minireference.com/static/conceptmaps/linear_algebra_...

Khan academy, it’s free and I’ve always thought the pacing of the lessons to be good.

I need similar, but I don’t even remember much of HS math.

See my comment, sibling to yours, for first-year university-level books.

For high school math specifically, I have a shorter book that covers only high school topics, with exercises, and applications (and a SymPy tutorial). See book preview here https://minireference.com/static/excerpts/noBSmath_v5_previe... and website is https://nobsmath.com

One of the constants I've seen in the past 15 years is soft skills and leadership - learn how to work in a team, how to socialize ideas, lead without authority, do cross-functional work.

This will make you stand out from your peers and ensure a good career path for years to come.

What do you mean by ‘socialize’ ?

I cannot speak for him, but to my mind, he's getting at the difference between being able to come up with an idea, and being able to explain it. If you've got a great idea, but you can't explain it so that others can understand it, it's unlikely to be used. Being able to explain yourself is a more useful skill, in more contexts, than any in depth knowledge of a particular problem space.

And that's before we get into navigating the political situation of getting ideas listened to. Knowing who to explain ideas to, and understanding how to learn their motivations are both important skills to navigating a social space like any company and team. And they are skills not usually taught during typical education in my experience.

In short, an idea is only as useful as the people who you can explain it to.

Aptly put :) Thank you!

Study math (linear algebra, geometry, calculus...) and statistics, in the pure form, then again realized in computer languages and visualized for communication in 2D and 3D with computer graphics, animations and compressed streams. And then again as realized in deep learning, how computer vision applies calculus to imagery, and how statistical reasoning and deep learning are siblings. This logical progression of math to building automated reasoning applications is eternal and will serve an entire career.

I would simplify the problem as choosing between a boring but proven approach to things and a more innovative and exciting one that has a bit more risk. Like most things in life, I would try to balance it. Because I don't have the kind of stress resistance and energy that's required to go 100% on the more risky stuff all the time, but at the same time living 100% on the other side could be unbearably boring after some time.

At my day job I work as a Java+Angular developer on an enterprise software, but if there's a chance to do a small freelance gig on the side, or my personal projects, I'd take it as an opportunity to try out the "hip" stuff just to learn what's good about it and if I like it or not (most recently Nuxt.js).

Also the older, more stable tech are still getting cool updates too but at a much more slower rate, and very often adopted from the newer languages/frameworks. So if I've played with the newer stuff too, I would know what the update is about and have a much easier time getting used to it.

Yeah it seems finding a balance between new stuff and old is a good approach. That’s a good point about languages being inspired and adopting features from others. I have personally noticed in my limited experience that learning new languages/frameworks have broadened my perspective as a developer. For example, through JavaScript I found about .map(), .forEach(), .reduce(), and so on. Go taught me dynamic arrays (slices). Then I noticed Java also has functional concepts, and dynamic arrays (ArrayList). C++ taught me the zero-overhead principle and I realized functions such as .map() and .forEach() in JavaScript have significant overhead.

If I may flip your question around and ignore the technical things you seem to be after:

What certainly won't change is the way humans work. That means that the way they use tools also won't change much (modulo maybe some newly established conventions).

As a direct result, the way Don Norman simplifies the process in figure 2.7[1] of "The Design of Everyday Things" will stay relevant. If you intend to build tools for humans -- even if those tools are digital -- it will be a good idea to keep the principles of feedback and feedforward in mind. If you want to learn timeless skills, it might be a good idea to learn more about your audience (i.e. users) instead of new and shiny technologies.

[1]: https://books.google.ch/books?id=I1o4DgAAQBAJ&pg=PT76&dq=des...

Over long horizons, only very structural knowledge retains value. This is why CS courses drag you thru exercises like writing your own compiler and simulating a CPU. This isnt an observation peculiar to computing of course. The practice of biology/genetics are radically different from what they were even in the late 1990s... but the fundamentals are the same.

I coach all my gen Z hires that the skill is not in coding but communication. For example, the act of coding is a dual communication. Once to the computer, where "skill" means transmitting the message simply, unambiguously and without repetition. once again, repeatedly, to the community of readers and maintainers who look at your code (where the skill here is to communicate clearly and without misunderstanding, without needing a shared cultural, organizational or temporal context). Being able to think deeply about what it means to communicate is a true long term life skill.

Seek to find a thread thru the full stack, sand to simulation. Get at least a cursory grasp of silicon-based transistors, binary representations of data, basic binary logic, 4-operation CPUs, simple memory & storage, a toy operating system, C programming, object-oriented C++/Swift/Java programming, memory-mapped I/O & graphics, TCP/IP networking, HTTPS protocols, then pick a major example vertical (PC, Mac, iPhone, Android) and work up to a publishable (if simple) product.

Having seen computing grow from before the IBM PC, my primary lament is new developers rarely have a view of how everything works (however simply) from the very bottom to top, from basic semiconductor electronics to getting something onto an App Store. Most pick a layer and live there, largely unaware of what's happening below or above - and so don't know why things work, and more importantly why they don't.

The only constant I've seen in 20 years is terrible documentation, which highlights the most common mistake all engineers make: we hyperfocus on the how, and forget to consider the why.

Learn how to explain and document why you chose an architecture for a problem. It's a habit that'll set you apart from your peers.

Thank you this is a good one. Clear writing is very important (more so in an increasingly remote world). Also clear writing reflects clear thinking and true understanding of a problem/solution. I read recently an important practice within Google (and I’m sure many other organizations) is to write design documents.

Theory and abstract principles - databases and client/server architecture, data structures and algorithms, distributed design and the two generals problem (also extrapolated as CAP theorem), race conditions and locking, queue theory and cybernetics, etc etc.

We’ll always need plumbers, electricians, builders, mechanics. At least until general AI becomes a thing. A lot of jobs will be eroded by automation except those above in the mean time.

I don’t foresee AI becoming advanced enough to be able to plan out and build most residential homes in the next 30 or so years. Maybe after that it’ll be good enough to design basic / common homes. Same goes for the hardware and control systems needed for fine motor control for those jobs.

I also don’t see AI replacing most software engineers or STEM based jobs until general AI becomes a thing either. I can see testing probably being the first thing to go.

Fundamental physics will always be fundamental ;) and you can build up to most things from first principles.

For always useful basics I recommend the book clean coder, not code, coder, as in worker. It as also helpful to non programmers.

The book talks about what is important in the communication in the workplace, like when, why and how saying no for example.

The consistent, relentless, and joy sucking “professionalization” of everything involving computing.

We’ll do less with more, and then have meetings about it.

If you’re able to do things end to end and actually understand how things work people will think you’re a witch.

Fundamentals about how a CPU works, machine code, caches, memory, etc. have not changed for 50 years. Well, they have and they haven't I guess. But high frequency highly pipelined speculative out of order CPUs with several levels of cache and much slower memory have been pretty "unchanging" for almost 30 years. That is to say they've also changed a lot, but fundamentally extremely recognizable and many of the major basic techniques for extracting good performance are the same. Multiprocessing and SIMD/vector processing are significantly more common now, but they were around back then too.

I never did this but I think for maximum success move to the best city in the world for your career. I think even in 50 years this will be true. Will it be San Fransisco? Probably but maybe not. But the advice stands.

You can't predict how things will evolve in 50 years but, as you have suggested, there are various (often simple) ideas and concepts that are timelessly useful. So develop a personal system for storing knowledge, keep adding to it, and use it to review continuously.

Here's a counterintuitive idea you won't hear often: reading on its own is futile. You'll eventually forget almost all of what you read or even learn if it's not reinforced. In fact, knowledge that isn't being actively recalled or applied can decay shockingly quickly.

The only way to combat this is to have a system. A simple and fairly informal system might be Post-Its and notes in a book that you review sporadically, whenever you happen to remember to do it. A more sophisticated system might leverage spaced repetition, practice problems, and tools like e.g. Anki and Obsidian/Roam Research.

As an example, let's say you didn't want to forget how to differentiate (most) functions. This might sound ridiculous from where you are now, but keep in mind this is about thinking longer term. So how would you achieve this? Well you might have some flash cards with concepts and some graphics and then some more with practice problems. How many problems would you need to do a year to recall differentiation properly? Maybe 50? Add flash cards.

Now differentiation is a poor example insofar as you might never find practical utility for it across the entirety of a software engineering career and you don't necessarily even need to remember how to do it manually (e.g. just use Wolfram Alpha). It can suffice to spend 5-10 minutes a year making sure you recall the most critical ideas.

Broaden out and think about what could be useful for you. Mental models, data structures/algorithms, systems design, etc. Consider what would be needed to retain that knowledge and then extend your system to make it happen. Focus on ideas that are disproportionately useful (or could be) but that you encounter infrequently (so e.g. perhaps you might include 'distributed systems consensus' and not 'git fundamentals'). For most concepts it suffices to have a good high level understanding and assume that you can deep dive and relearn the rest, should the need arise.

You'll be amazed at the surface area you can cover spending even 5 hours a week doing this. It's one of the easiest ways to remain an effective and flexible thinker.

Most languages, especially those you listed, are rather similar. You learn how to program in one(really program, not make something compile from hail marys) , and switching to another isn't that difficult. New libraries, different names, sometimes a new paradigm or two but nothing really groundbreaking.

Unless functional programming takes over the world, then a lot of us are screwed.

As soon as AI learns to write code I guess all bets are off.

Not really AI writing code, but it has only taken about a month for me to get used to just always having GitHub’s GPT-3 based code completion and suggestion always there in PyCharm and VSCode.

The OP asked about the next 50 years ago. If I look back 50 years I was mostly coding in FORTRAN using punched cards. What hasn’t changed is that so much of programming is still persistence in getting things to work. But of course, our tools now are so much better than 50 years ago.

If I only go back 40 years, then I was programming on my own Xerox 1108 Lisp Machine, and loving it.

> On the flip side, I wonder if mastering the fundamentals and the things that will not change in the next 50 years is a wiser approach.

This will never go out of style. All these new frameworks are all built on javascript or such technologies, and knowing the underlying technology will always pay for itself in dividends.

Learning a framework-du-jour will only let you be proficient in that framework

Listening, speaking, and and writing well

https://blog.bradfieldcs.com/cutting-through-to-what-matters...

Data structures, algorithms, fundamentals like how compilers/memory/operating systems work likely won't change, programming languages and frameworks are constantly changing.

Languages and frameworks are constantly “changing”, but are they constantly improving?

If they are generally following an upward trajectory, they seem worthwhile to track and follow, or even contribute.

If it is all just fashion, what is to be gained by being a “dedicated follower of fashion”? Might as well buy a lottery ticket.

C: No matter what the newest hippest toy will be, it will be running on a large layer of C code.

COBOL: No matter what where how, the airlines and banks will still be running on COBOL.

Funny enough, just yesterday I was thinking that cloth hangers will stay the same forever... Don't know why

In 50 years, if you do really well, everything will be different except the life partner you are choosing now.

The core of emacs. It will have new features, but emacs skill will never become obsolete.

Fifty years from now you'll be very dead. Your entire generation will fuck this planet into a coma[1]

[1] https://m.imdb.com/title/tt5463162/quotes/

emacs, ffmpeg, imagemagick, ...

The GIL.

Communication skills.

the fact that most of us will probably be dead :)

1366x768

The way people chew gum

Chewing gum is largely an American phenomenon and seems to be past its peak even there. I'd be surprised if the number of gum chewers doesn't drop over the next 50 years.

I'll readily grant that the motion of the jaws is unlikely to change though, at least until Zuck figures out a way to sell virtual gum in the Metaverse.

Nerds will still be awkward