In my opinion, AI’s most practical applications have remained the same over time:
The two main areas are:
- Learning Assistance – Especially context-aware translations, idiomatic expressions that aren’t covered in textbooks, and so on.
- Summarization – It saves more time than Google, and you can always verify the sources when needed. As for summarizing articles, I usually use it for less critical content—things that are still worth knowing, like the latest trends, tech developments, or news. Another key use case is summarizing new project details, which works best when combined with Cursor. While AI can only help so much when a project is overly chaotic, it still saves a lot of time reading through code. Summarization is essentially a productivity booster, so to put it simply: learning & productivity.
As for all the hype around generative AI replacing jobs, most of it seems far removed from real-world applications. Will AGI really arrive in 2027? For those who keep learning, there’s no need to worry. In fact, I’m looking forward to finally escaping from legacy code! (That said, I personally don’t think much will change by 2027—it’s not just a matter of coding and comprehension.)
No matter how busy things get, continuous learning and staying updated are key to moving in the right direction.
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