This article was originally published on bmf-tech.com.
The rapid evolution of generative AI is bringing significant changes to society.
As someone working as a software engineer, I strongly feel the need to anticipate these technological changes.
Although I am not well-versed in the technology of generative AI itself, I have some thoughts from the perspective of a user of generative AI, and I want to jot them down so I can verify them myself in a few years.
These are my personal views at the time of writing this article, and my thoughts might change by tomorrow.
By the way, I did not use any generative AI to write this article.
Will the job of a software engineer disappear?
If the job of a software engineer were to completely disappear, it would mean that the singularity has occurred, and we would be living in an era where we don't need to work to survive.
I think the nature of the work will change, and parts of the job will become automated. It might already be happening.
I don't think the work of dealing with software will disappear.
Whether it disappears or not is a matter of survival, but even if it doesn't disappear, I believe we need to be flexible in adapting to changes.
I feel that the part of coding will become more automated, and the parts where humans need to be hands-on will significantly decrease. In areas I can't observe, this might already be happening.
Coding is an important process in software engineering, but it's only a part of it, so even if this part is replaced by generative AI, I don't think it will lead to the disappearance of jobs.
I don't feel that the ability to read and write code will become unnecessary at this point.
However, I believe that the skills and soft skills required before writing code are areas that are difficult to replace with generative AI, so improving skills in these areas will become increasingly important.
In this context, although I have never experienced working in a system integrator (SIer), I have a vague feeling that the industry structure of SIers will change significantly.
How will it change skill development in software engineering?
Recently, I've developed a habit of asking generative AI chat tools before Googling.
I think the quality of output (answers) from generative AI chat tools changes based on the input (prompts, questions, consultations), but even with rough input, they provide quite intelligent output.
Generative AI has sufficient capabilities to support software engineering, such as summarizing texts, creating document outlines, and comparing and verifying technologies to be researched.
Regarding whether there's a need to learn things that AI can tell you, I won't delve into it deeply here, but I think it's not much different from questioning the meaning of taking certification exams. (At this point, I believe there is a need to learn.)
One of the changes generative AI brings to skill development in software engineering is what I call the "highway of knowledge." In other words, the process of learning something is optimized, making it easier to learn.
While it's necessary to think and verify things on your own, I believe that with the help of generative AI, learning has become easier than ever.
Moreover, generative AI not only provides ease of learning but also sometimes outputs answers (expected deliverables), contributing to efficiency.
It seems to be a driving force that significantly advances the commoditization of technology.
For those who are becoming software engineers, it will likely be easier to acquire software engineering skills faster than before, and for those who are already software engineers, it will be easier to broaden and deepen their technical expertise.
In such a situation, I think the difference in the abilities of software engineers will be reflected in their ability to catch up, but I believe this is an unchanging truth before and after the advent of generative AI. While it's necessary to factorize what the ability to catch up entails, it's not the main topic here, so I won't touch on it.
How should software engineers engage with generative AI?
I think it's necessary to learn about the mechanisms of generative AI and use it to deepen understanding, but beyond that, there seem to be two strategies for how to utilize it. (Here, I'm considering it from the perspective of individual career strategies, not corporate strategies.)
One is "applying it to areas where generative AI can be utilized," and the other is "enhancing abilities in areas where generative AI cannot be utilized."
For the former, I think it's necessary to proceed with what can be done to increase one's disposable time, but it's important to carefully consider what to target and how much resources to invest.
The areas where generative AI can be utilized are those that might eventually be replaced by generative AI, and the costs invested in areas that might be replaced could become sunk costs.
I think it's necessary to carefully assess the areas to entrust to generative AI, the areas to be supported by generative AI, and the areas not to use generative AI, and scrutinize the areas to invest one's time more than ever.
I think betting on the latter might be more leveraged, but since technological innovation can lead to obsolescence, I want to think carefully about that.
Finding areas that are unlikely to be completely replaced by generative AI and considering a skill set that combines them seems to be a solid individual career strategy.
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