graduating into an ai-shaped job market

on june 17, 2026, i passed yudisium and officially closed one chapter of my informatics engineering journey. the timing feels interesting: i am entering the job market at the same time ai tools are changing how people learn, build, and evaluate software work.

graduation feels less like a finish line now

graduating used to sound like a clean transition: finish university, send applications, get the first job, and start from there. the reality feels less linear. by the time i graduated, the baseline for technical work had already moved. code generation is faster, debugging help is more accessible, and writing a basic app is no longer a strong differentiator by itself. that does not make an informatics degree useless. it just means the degree has to be backed by judgment, ownership, and evidence that the work solves a real problem.

i do not see ai as a shortcut that replaces learning. i see it as pressure to become more precise. if a tool can produce boilerplate, then the valuable part shifts toward understanding requirements, questioning assumptions, reading existing systems, validating output, and explaining trade-offs. those are harder to fake in an interview, and they are also the parts that separate a project demo from work that survives real users.

what university actually gave me

my coursework gave me the foundation: data structures, databases, web programming, and the habit of breaking problems down. being a teaching assistant made that foundation more concrete because i had to explain concepts to other students, not just pass my own assignments. explaining something repeatedly is humbling. it exposes whether you really understand the topic or only memorized the happy path.

student organizations and competitions taught a different lesson. technical skill matters, but people, timing, communication, and follow-up decide whether an event or project actually works. leading FIT Competition 2024 and later supporting related activities made me more aware that execution is rarely clean. there are sponsors, judges, participants, committee divisions, deadlines, and last-minute constraints. that experience matters to me because software work also fails when coordination fails.

alfamart changed how i look at software

my internship at Alfamart Supply & Distribution changed my view of software from "build features" to "support operations." warehouse systems are not just screens and buttons. they carry workflows: stock opname, picking, supplier returns, barcode scanning, PDA usage, approval controls, multilingual users, legacy constraints, and transaction accuracy. when a system touches operational work, small details are not cosmetic. they shape whether users can finish their job correctly.

that experience also made me more careful with claims. it is easy to write broad labels on a portfolio, but real systems force better questions: what is actually deployed, what is still ongoing, what security trade-off exists because of legacy constraints, what was tested, what was only manually verified, and what still needs improvement. i would rather describe the work accurately than overstate it and lose trust when someone asks for details.

where ai fits into my job search

ai makes the entry-level market more competitive because basic coding ability is easier to augment. that is the uncomfortable part. but it also clarifies what i should sell: not just "i can code," but "i can understand workflows, build backend logic, work with databases, communicate constraints, and keep improving the system after the first version works." for me, that points toward software roles related to backend engineering, internal tools, full-stack systems, system analysis, and digital operations.

at the same time, i am not closing the door on management trainee or graduate development programs. my strongest evidence is still technical, but the pattern behind it is broader: i like understanding how systems operate, how people use tools, and how digital work connects to business execution. that is why i treat IT roles as my main track and MT programs as a selective track, especially when the program values operations, digital transformation, analytical thinking, and cross-functional coordination.

what i am trying to prove next

the next step is not to pretend i am already a senior engineer or a business leader. i am a fresh graduate with strong project evidence, real internship exposure, and a lot still to learn. what i can offer now is practical: i can learn fast, read systems carefully, translate messy workflows into structured implementation, and be honest about what is done, what is risky, and what should be improved next.

graduating into an ai-shaped job market is not comfortable, but it is clarifying. the market is asking for more than syntax. it is asking for judgment, communication, and evidence. that is the bar i am trying to meet.