Ask HN: Is software engineering still a good career choice for new students?
18 コメント
I am afraid the software engineering industry is increasingly becoming like the music industry, which was killed by being able to get any song for free. Open source, free, fremium apps, lower threshold of entry due to AI, all of that is killing the market. Soon, you will not be able to make any money out of it unless you're a big player.
Unfortunately, yes. Also, talking about open source. With the AI, I saw a couple of posts about people trying to rebuild an open-source project in a different programming language and change the license. Which kind hurts the original open source project
OSS has always been vulnerable to this. AI is accelerating, but changing the license doesn't guarantee adoption. It's not just because someone forked a project and changed license that suddenly everyone will start using that tool/library..
And the comparison to the music industry is fair, but the key difference is that in Software we had a Free/OSS movement for a while, and it did not kill the industry, on the contrary, it benefited from it.
And the comparison to the music industry is fair, but the key difference is that in Software we had a Free/OSS movement for a while, and it did not kill the industry, on the contrary, it benefited from it.
I don't think the question is \is software engineering dead?\" so much as \"what kind of first job still compounds?\" For new students
According to the development trend of AI, the demand for software engineering may decrease, but it will not disappear. In any case, top engineers are always in demand. The demand for software engineers in ordinary positions will not be as great as it is now
Yes, I agree, but for a new grad student. May it be hard for him to find a job in this market to learn and become a top engineer?
demand for software will increase, but it will become a lower skill job
The industry is changing. Coding is becoming a commodity, which means other skills will have to be developed and mastered to meet the future demands. We will still need people to build a platform to run AI. To maintain software to do Inference, to maintain a pipeline to train AI...
The abstraction layer is being pushed higher
The abstraction layer is being pushed higher
Full episode (YouTube): https://www.youtube.com/watch?v=oOt_0N1yQEw
I think that's all correct what they're saying. It's not contradictory - writing code has become a commodity to a certain extent. But someone's still responsible for the production systems when it fails. AWS had a couple of big outages last year because of AI. Even with humans reviewing it, review is review. If you're not writing the code, you'll not have spent enough time thinking about edge cases. That's where the most expensive part of software engineering, not coding. Coding being a commodity is indeed analogous to robots in the production line, but those robots have huge teams of mechanical engineers to keep them running. The calibration, the servicing etc.
What's most interesting is now that vibe coding is expensive, will the AI slop go away, gradually?
What's most interesting is now that vibe coding is expensive, will the AI slop go away, gradually?
Yes, I agreed with your points. we can cover these two points.
"But someone's still responsible for the production systems when it fails. AWS had a couple of big outages last year because of AI. Even with humans reviewing it, review is review. If you're not writing the code, you'll not have spent enough time thinking about edge cases.". This is true and we cover software quality in the age of AI (https://youtu.be/oOt_0N1yQEw?t=2915).
"What's most interesting is now that vibe coding is expensive, will the AI slop go away, gradually?" As we said in this episode, "sometimes, AI is getting more expensive than paying a salary to a software engineer". We cover it here as well (https://youtu.be/oOt_0N1yQEw?si=r5tSHicWpJGWLQDL&t=3890) about AI real cost and AI cost effectiveness
"But someone's still responsible for the production systems when it fails. AWS had a couple of big outages last year because of AI. Even with humans reviewing it, review is review. If you're not writing the code, you'll not have spent enough time thinking about edge cases.". This is true and we cover software quality in the age of AI (https://youtu.be/oOt_0N1yQEw?t=2915).
"What's most interesting is now that vibe coding is expensive, will the AI slop go away, gradually?" As we said in this episode, "sometimes, AI is getting more expensive than paying a salary to a software engineer". We cover it here as well (https://youtu.be/oOt_0N1yQEw?si=r5tSHicWpJGWLQDL&t=3890) about AI real cost and AI cost effectiveness
yes it is but u have to be the top 5% developer, u have to learn and update everyday, u should have the problem solving ability along with product-business mindset. yes AI will affect to software engineering but the right one will always survives.
Main question is: what do you like? Do you like research, maybe automation, solving puzzles, constant learning requirement, fast changing domain? Check the attributes of software engineering and ask yourself if this is what you deeply love and can spend on it 10-14 hours a day.
I joined SE/DS/AI because I loved automation and research. And I have plenty of it on a daily basis no matter on what stage of its development compilers/llms are.
The rest are details and might be subjective. You don't need to understand LLM architecture to be able to use it - however this might be an interesting puzzle to solve. Many SE engineers don't write code and they did not write code even before LLM popped up - but they do research and solve design puzzles instead.
Good luck.
I joined SE/DS/AI because I loved automation and research. And I have plenty of it on a daily basis no matter on what stage of its development compilers/llms are.
The rest are details and might be subjective. You don't need to understand LLM architecture to be able to use it - however this might be an interesting puzzle to solve. Many SE engineers don't write code and they did not write code even before LLM popped up - but they do research and solve design puzzles instead.
Good luck.
Here's what they actually said:
- The Senior Software Engineer said: "LLMs are babies. If you don't understand the architecture behind everything, you won't be able to follow."
- The Google advocate pushed back slightly: "Writing code has become a commodity, like car manufacturing after automation. The question isn't whether to learn to code, it's why you want to."
- The IBM infrastructure engineer had the most actionable take: "Don't treat AI as a ghostwriter. Never commit code you can't explain. Use it as a tutor, not a replacement for your own thinking."
And many more
We also reacted to a clip of a Silicon Valley exec telling university graduates that "AI is the next industrial revolution" and getting booed by the crowd.
One of the more sobering parts: at current usage levels, Anthropic is likely losing money on heavy Claude subscribers. No inference company is profitable today. The Google advocate, who works on LLM inference at scale, put it directly: if we don't get significant inference efficiency improvements in the next five years, this entire ecosystem becomes unaffordable. And the NIVIDA have more details to talk about AI cost effectiveness