Disclaimer: I’m not an AI specialist, so I’m not trying to pretend to be an expert in an area I don’t know deep inside about, though I’m a programmer with 10+ years of experience, so this article is written from that perspective.
AI has been a hot topic lately, a really hot topic. A lot of experts have buried several occupations entirely, yet they still exist.
Will or will not AI will leave you without a job depends on many factors, and all we as humans can do is minimize risks. I think it is impossible to do so without the right mindset. In this article, I will share advice that has helped me grow and develop as a professional throughout my career.
1. The “can-do” attitude
One of the first projects at my first job was the following: my team needed to build a console tool for i18n. The tool would connect to a PostgreSQL, query data from a couple of tables, process it, and store the resulting data in JSON files that would be used by a Web app. I was given the task.
After a couple of hours of research, I came up with a solution: we’d use Perl to implement the script, because:
-
We worked on Linux machines, which meant we already had Perl installed.
-
I was able to Google suitable libraries to do the job.
After my presentation, the Head of Engineering asked me if I knew Perl, and I said: “No, I’ve done a couple of projects using PHP”. He and my team leader were skeptical, but after a couple of days of work, the tool was ready and worked well. It was the right tool for the job because it simply solved the problem. I didn’t know the language, so I had to learn it.
The situation gave me credit, made me The I18n Guy (so that, I had at least one thing I could expertly talk about), and allowed me to grow further.
The thing is, in IT, everything happens fast, and you have to learn quite a lot all the time, so the mindset I consider the most important is the “can-do“ approach. After all, lots of us came into the industry because we loved hacking and playing around on a computer, so keep doing it!
2. Keep learning
The next logical thing is, you have to keep learning.
When I started working in IT, the term “FE” didn’t exist. IE6 was a thing (quite a painful thing if you ask me), and jQuery was the main JS library, which you’d “import” using a direct CDN link. Then frameworks came and changed a lot: we started focusing on client-side development more. Every time I changed jobs, I had to learn and use a different framework or set of libraries. So unfortunately or fortunately, you’ll have to learn something new almost every day to be able to keep yourself afloat.
Yes, there is a chance that your particular specialization won’t be needed in a couple of years, so you’ll have to learn something new again, but your experience won’t be in vain: your general understanding of how software should be developed and your problem-solving experience will be still with you.
3. The black box approach.
Getting deeper about learning: now we can use libraries and frameworks, without caring too much about the way how they work internally. I mean, to be able to write React code, you don’t have to be an expert in the React code base; to be able to write meaningful BE code, you don’t have to know how a specific assembly language for a specific platform works. AI tools are tools. And I believe in a couple of years, a lot of trivial tasks will be automated, which is great for you: you’ll be able to focus on building a software product on a higher level.
4. Use new tools
When I was getting started, we used Notepad to write FE code, then Notepad++ came, then more advanced editors and IDEs. Nowadays, I know a lot of fellow programmers who don’t write code without ChatGPT and/or GitHub Copilot. For my taste, maybe the use of them is a little bit too much, but you got the point: everything in tech constantly evolves, including tools in general and AI tools, so it’s better for you to use them to be more productive and valuable on the market.
5. Networking
Getting back to my first job, there is a good story about how I found it: when I had a summer break between 2nd and 3rd terms, I wanted to earn some money (as lots of other students normally do), but also I wanted to do something IT related. So I found several phone numbers of several IT companies and called them saying literally: “Hello, my name is Oleg. I’m a student in such a university and my specialization is the following. I can do this, this, and that. Do you have any work for me for the summer?”. It worked. I got a job. It was not a programming job, though it was close enough to IT for me to keep believing in myself + when the time came for me to find a full-time internship, I just called the CEO of the company and got it in that company. After the internship (during which I had to learn Qt by the way) I got a full-time job. The next job offers I mostly got after good references from my ex-colleagues/friends/people I met at conferences/meetups. All in all, at your job you’ll have to work with people, so networking is very important. AI won’t remove all of your colleagues (well, at least in this case, none of us will need a job anyway).
AI is shaking the industry and will probably shake it even more in the future, but with the right attitude, you’ll survive this storm and maybe even find yourself in a better position.
AI is great at making decisions based on data, automating routine tasks, creating something based on something else created before, and some input from a person. But still, AI needs a person. It can’t decide what to do without a human being. We are emotional, which means we can be spontaneous, we don’t like when everything is too ideal, and the pleasurable level of imperfection is unique for everyone. It means that AI will probably not replace human beings in creative areas.
Or maybe it will just need a couple more iterations, haha. As an AI language model, I can’t write an article about whether AI will take your job or not (just kidding, I wrote it myself 😅).