AI Will Change Jobs in Nepal. How Prepared Are You?

“AI will take all the jobs” is the loud version of a quieter truth. On Future of Work Ep. 2, Sanjay Khanal (working at the intersection of technology, leadership, and hiring) argues that entire industries do not disappear, but the skills inside each role change fast. For Nepal’s +2 graduates, the useful question is not panic. It is preparation.

Will AI take your job after +2?

AI will not erase every career path in Nepal, but it will eliminate or shrink many repetitive tasks inside jobs: data entry, basic transcription, basic bookkeeping, and other rule-heavy work. Sanjay’s view is that roles remain (software still needs shipping, hospitals still need care, accounts still need judgment) while the day-to-day skill mix shifts toward communication, learning attitude, and directing tools well.

He also rejects the idea that recovery is automatic. Some eliminated work will not return at the same rate it disappears. Students who only memorize today’s tool stack without learning how to adapt will feel that squeeze first.

What hiring managers look for now

Beyond role-specific fundamentals, Sanjay highlights two signals that matter more as AI handles more hard-skill execution:

1. Communication skills — can you clarify requirements, write clearly, and work with clients or teammates?
2. Learning attitude — can you pick up new tools without waiting for a syllabus update?

In software teams he describes, work that once needed a five-person, three-month squad can compress when AI assists planning, coding, and reporting. That does not remove engineers. It changes how many people and hours a problem needs, and it raises the bar on people who can steer the work.

“Not everyone’s job disappears. The role stays, but it demands different skills.” — Paraphrased from Sanjay Khanal on Future of Work Ep. 2

What to do in the next two weeks

Sanjay’s advice to students who just finished exams is deliberately not a single magic skill. Trajectories differ. The first move is exploration:

1. Write down a 2–3 year aspiration (role type, company type, or problem you want to work on).
2. List the skills that aspiration seems to need.
3. Spend the next couple of weeks exploring those skills (including AI tools, communication practice, and leadership basics).
4. Only then lock a focused learning sprint.

College can lay a foundation and should help with internships when you ask. It cannot personalize every aspiration. Proactivity is on you.

Frequently asked questions

Should I still pursue a four-year IT degree if AI writes code?

Yes, if you want depth and credentials, but treat the degree as fundamentals plus projects, not as a guarantee. Sanjay notes people feared software engineering would vanish even as more software got built. Understanding systems still matters when you review and direct AI output.

Which jobs are most exposed right now?

He points to mechanical, rule-based work: data entry, transcription/translation pipelines, and basic bookkeeping/reporting. Reviewer and judgment-heavy versions of those roles may remain longer than pure execution versions.

Is learning AI enough on its own?

No. In the same conversation, communication and learning attitude stay central because hard skills keep getting delegated to tools. AI literacy without human collaboration skills is incomplete preparation.

Build AI literacy on purpose

If you want a structured, hands-on path instead of random YouTube tabs, see The StudyPort’s AI Power User course for +2 graduates.

See AI Power User: 0 → 1