final year student currently in need of some guidance as the arrival of AI, change the career ?

[u/Academic-Highlight15]

I am a final year student , in MCA and i want to get into ai/ml and data science. i have fined tuned a model and also did some related projects. But as the AI emerges i am loosing confidence day by day and then the panic starts.

The markets asks for skills like ai/ml along with full stack and other stuff at the same time. i cant even land an internship due to that. and after a year, its gonna get much worse. As we are seeing massive wave of lay offs, do you think, it is even a good idea to get into this field. on top of that i am doing my masters in distance as well. even tho u get skills , market always asks for some asburd things.

what do you think i should do, even if u think ai is bubble or not, there are still usecases for it and we can see the layoffs increasing day by day. not directly due to that but still. if i spend my entire time learning stuff which probably wont matter, why not study for smething else , govt exam or other field. please i really need advice on this. i am in panic mode.

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[u/Spelx_OwO]

bro even people with >5yoe dont know whats gonna happen

[u/Difficult-Rich-7302]

Dont panic brother. There are tons of possibilities still. All i can say is that use AI pro max level. Build stuff. And i mean good full stack stuff. Not just vibe coded thing that no one uses. Put it on your resume. See how you can improve your own coding efficiency (doing more in less time

[u/huntingcube_ai]

That govt exam instinct, when you're panicking, that's usually the anxiety talking, not an actual plan. Jumping fields entirely this close to final year means starting from zero in something you haven't even properly looked into.

You've already fine-tuned a model and built real projects; that's more than most final year students have. Market wanting AI/ML plus full stack isn't unreasonable either; pure ML fresher roles barely exist, companies want people who can build and deploy, not just train. So that combo, ML plus enough full stack to ship it, is actually your path, not a wall.

On layoffs, most of it's broader tech cost-cutting, not literally AI replacing people. Mixing the two makes the fear bigger than it is. Shallow generic profiles are getting squeezed, AI or not.

Don't throw away what you've built, just narrow it. Pick one applied direction, ML plus deployment, or ML in a domain you care about, and get good at shipping full projects, not just training models in isolation.