In the long run, you should invest more in areas of knowledge that are not likely to expire or expire for a long time, such as algorithmic logic, application security, performance optimization and architecture.
I’m a programmer who turned 40 a few months ago. One Saturday morning I was at a React Native tech conference, where the speaker was trying to convince us why React Native is really the next big thing in mobile development. But to me, it’s a bit like JSP fifteen years ago, with all the logic in the demo code. But I’m old, and I think that’s because I haven’t fully understood it yet.
I was a little frustrated by the presentation of powerpoint, live coding, and about half a dozen companion tools I hadn’t heard of.
Of course, I wasn’t really surprised. I’ve also mastered some of the more popular technologies over the past 20 years and enjoyed riding them. New things always bring a bit of excitement to programming and make things easier, faster, and clearer.
But on this particular morning, at the threshold of 40, I have to admit I’m feeling a little behind The Times. I knew that if I wanted to keep up with the pace, I would have to keep giving, discarding some of the things I’d already absorbed, and taking in some of the new stuff in preparation for the next journey.
I was thinking that my programming career was not like that of any other professional, a doctor who didn’t have to worry about his knowledge of the vascular system evaporating at 40, or a lawyer, or a plumber, or an accountant, or an English teacher. Their accumulated knowledge is relatively stable and is rewarded with respect and compensation as they age. But in programming, 20 years of experience doesn’t seem to confer the same advantage.
Two main factors
Of course, our industry isn’t that bad — programmers have a lot to love — and it’s interesting how relentless follow-ups seem to be a more or less unique feature of our field. Is programming life really so different? If so, why? What does it mean for our career trajectory? I would like to try to answer these questions from the following two perspectives.
The first is knowledge decay. Everything we know, not just programming, has an expiration date after which it is no longer useful. I learned to drive when I was 16, and I’m still good at it. This skill can be considered to have a relatively long half-life. The doctor becomes more knowledgeable because everything he learns is based on his previous knowledge. And in programming, for better or worse, I think:
Half of the programming knowledge a programmer accumulates will be useless in ten years.
This may be extreme… But it seems right, at least to me. If I don’t learn anything else during this deadline, I bet that only about half of what I know I can continue to use in 2026 (such as long-lived SQL) and the other half will probably be useless (React Native, maybe?). . Of course, I’m constantly acquiring new knowledge to replace the things that are dying, but is that enough? Will I know more in 2026 than I do now?
This brings us to the second factor, speed of knowledge accumulation. In all fields, certain knowledge thresholds must be met to be “certified” (or at least employable), and early career days are often spent trying to acquire knowledge. In programming, however, due to the rapid decay of knowledge, it seems that we never really get past the “student years” and have to learn forever.
The three stages
If I were to record the rate of knowledge decay and accumulation of programmers, I think it would look something like this:
It is easier to accumulate knowledge at the beginning of a career, in what might be called an eager apprenticeship. Everything is new, and every experience is a tool to gain more knowledge. And, being young, we don’t have too many obligations, so may not mind spending a few evenings and weekends picking up new languages and frameworks. Importantly, employers also have low expectations of us, and we can carve out a little time during our working lives to fill in the knowledge. It’s a funny phase where you don’t even realize how persistent you are.
Then we stopped being novices and became self-sustaining developers. Our investment in learning has paid off. Everything we know is useful. At this intellectual stage, we begin to earn the respect of clients, peers, and managers, and gain titles, salaries, and opportunities. Although we didn’t necessarily see it at the time, it was an important turning point.
Two things happened. First, “senior” promotions are not just about money, but more expectations. Employers need their “star programmers” to be leaders — to help junior developers, review code, conduct interviews, attend more meetings, and help them maintain and build complex older versions of software. This is all very reasonable, but it subtly sacrifices the speed at which we accumulate knowledge. We no longer have time to read tech blogs and articles. Second, this is when for the first time we experience (or at least recognize) the decay of a little knowledge, and some of what we learned earlier becomes obsolete.
Then we enter the third and final stage of decline and loss. Yes, we are knowledgeable and accomplished, but we also know that we actually know less than we did at the beginning. We may not have time to accumulate enough new knowledge to fill in, and that’s frustrating. I think that’s why, at this point, a lot of people start turning to — management, sales, testing or agriculture (my dream). We realize that we need to work again to maintain our level and proficiency – otherwise, our work will be worse in five years than it is now.
Two directions
I’m in phase three. I still enjoy learning, but I understand that without hard work, I might stay on the edge of the “expert” balance.
First, I try to take the long view. Don’t push yourself too hard. Get as many opportunities as you can to gain new experiences. I see a lot of colleagues who are well paid, but have very little exposure to new things in their careers. In five years’ time, they will realize that their valuable knowledge has evaporated and that their compensation is not worth much. In some cases, I think making less money in the short term (with a better employer) will generate more money (and stability) over a long career.
Second, considering the limited time, I try to learn durable knowledge. That is, invest in knowledge that has a longer decay time, such as algorithmic logic, application security, performance optimization, and architecture.
Finally, I’d love to hear your thoughts!
Original address:
www.bennorthrop.com/Essays/2016…
Translation Address:
www.oschina.net/news/84850/…
Follow public accounts
【 Pegasus Club 】
▼
Previous welfare concerns about the pegasus public number, reply to the corresponding keywords package download learning materials; Reply “join the group”, join the Pegasus AI, big data, project manager learning group, and grow together with excellent people!
Microsoft Bull series of lessons
(Click the link to listen or subscribe)
【 Limited-time special 】 Teacher Li Ye – artificial intelligence into the whole walkthrough
From beginning to research, the 10 most Readable books in the field of artificial intelligence
RSVP number “2” machine learning & Data Science must-read classic book with resource pack!
Into AI & ML: Learning machine Learning from Basic Statistics (PDF download)
Answer the number “4” to learn about ARTIFICIAL intelligence, 30 books should not be missed (with electronic PDF download)
Reply number “5” big data learning material download, novice guide, data analysis tools, software use tutorial
Answer number “6” AI AI: 54 Industry Blockbuster Reports
TensorFlow Introduction, Installation tutorial, Image Recognition application (with installation package/guide)
Reply to the number “8” full analysis of big data data (352 cases + big data transaction white paper + Domestic and foreign policy collection)
Reply number “9” dry | selections for 10 big data books (junior/intermediate/advanced) become large data expert!
According to a 160-page McKinsey report, 800 million people around the world could lose their jobs to machines by 2030
AI Artificial Intelligence/Big Data /Database/Linear Algebra/Python/ Machine Learning /Hadoop
Reply number “12” small white | Python + + machine learning Matlab neural network theory + practice + + + depth video + courseware + source code, download attached!
Reply number “13” big data technology tutorial + books +Hadoop video + big data research newspaper + science books
Reply number “14” small white | machine learning and deep learning required books + machine learning field video/PPT + large data analysis books recommend!
Big data Hadoop technology e-books + technical theory + actual combat + source code analysis + experts to share PPT
Reply to the number “16” 100G Python from beginner to Master! Complete video tutorials + Python Classics for self-study!
Answer number “17” 【 dry article 】31 papers on deep learning required reading
526 Industry reports + White papers: AI, Artificial intelligence, robotics, smart mobility, smart home, Internet of Things, VR/AR, blockchain, etc. (download)
Reply number “19” 800G ARTIFICIAL intelligence learning materials :AI ebook +Python language introduction + tutorial + machine learning and other limited time free access!
17 mind maps for machine learning statistics
Ten years ago on This day on Machine Learning Projects.
Machine learning: How to go from beginner to Never Giving up? (With benefits)
Respond to digital “24” flash download | 132 g programming data: Python, JAVA, C, C + +, robot programming, PLC, entry to the proficient in ~
Reply number “25” limited resources | 177 g Python/machine learning/TensorFlow video/deep learning algorithm, introduction to cover/intermediate/project each stage!
Reply number “26” introduction to artificial intelligence book list recommended, learn AI please collect well (attached PDF download)
FMI Artificial Intelligence and Big Data Summit Guest Speech PPT
Top 10 AI Jianghu Fields
Machine Learning Practical Experience Guide
More than 100 Papers on deep Learning
Top ten Classic Algorithms of Data Mining
6.10 Ele. me & Pegasus Project Management Practice PPT