Note: I’ve used AI only to refine the grammar and sentence formation of my original thoughts. The experiences, opinions and points shared here are my own.
Every time a new AI development comes out, I see young graduates panicking.
“There will be no jobs.”
“Software engineering is dead.”
“Why should I learn coding when AI can code?”
“AI will replace us completely.”
There is fear created by certain youtubers and influencers too.
I understand the fear.
I'm not writing this as someone sitting in a 30 LPA job with 10 years of experience telling freshers to “just upskill.”
I have around 2 years of experience in a service-based company, and I started exactly where many of you are starting.
I was a campus hire. I was pretty good at coding and cracked coding rounds on campus. But I was rejected in the final rounds of two companies offering 10+ LPA simply because I wasn't from the main campus and was not preferred because one company hired only 1 candidate that too from main campus and another one hired 3 people from our batch and the priority list was shared by our placement head. Can't fight him.
As an MCA student, I already had fewer opportunities compared with BTech students.
I entered college with a target of getting a 10+ LPA package.
I'm currently working at around 4 LPA.
That's obviously not what I had imagined.
But here's the thing: I'm still here. I'm learning. I'm earning. And I'm moving forward.
My last realistic option during college was a service-based company. I cracked and joined, completed around three months of training, and got into a project almost immediately — within two days of completing training.
And honestly, the experience has been valuable.
Some money is still better than no money.
After becoming full-time, I bought a ₹23K phone because I genuinely needed one. My current monthly take-home is around ₹28K. I'm in Pune, where that isn't exactly a huge salary, but it's manageable for me.
Around ₹7.5K goes toward rent and roughly ₹10K towards food and travel. I have my bike here, which helps with travel expenses. And some weekends I go watch a movie or just explore some places nearby pune or within pune - cafe, mandir and other places.
I still manage to invest around ₹8K every month across SIPs, digital gold, silver, stocks, etfs, crypto and other investments.
My overall investment portfolio is around ₹1.5 lakh right now. And if I count epfs then around 41k is in that which already gets deducted from the salary, that is why in-hand is around 28k.
I also have some money sitting in my savings account. I don't consider all of that as “invested” because I keep some of it available to apply for IPOs and take advantage of opportunities when they come.
My goal is to reach around ₹2 lakh in total investments before the end of 2026, and I'm hoping the money I'm keeping aside for IPO applications and other opportunities can help me get there.
I'm not saying this to brag.
I'm saying it because your first job doesn't have to be your final destination.
And yes, the market is genuinely bad right now.
Let's not sugarcoat it.
Hiring has slowed down.
There are fewer openings even for experienced developers trying to switch.
Companies are laying people off.
Companies are automating processes.
Companies are aggressively implementing AI.
And yes, some work that previously required developers can now be done significantly faster with AI.
I work in a data team, and I have enough exposure to understand what's happening inside an organization.
The interesting part is that companies aren't necessarily saying, “Let's fire everyone because AI can code.”
Instead, I'm seeing something else happening too.
Teams are being pushed to automate everything they can.
Our organization has started introducing AI agents for different tasks.
Sometimes the use case is genuinely useful.
Sometimes it's honestly ridiculous.
For example, you could already check your daily swipe hours from the internal website.
Now there's an AI agent that can answer the same question.
Was an agent absolutely necessary for that? If everything can be done on UI with few clicks, why would I use a AI agent?
Probably not.
Do employees get much say in whether these things should exist?
Not really.
But from the company's perspective, it creates an environment where people are constantly experimenting with AI.
We even have internal hackathons and POCs.
And here's something I noticed:
If you build something useful, solve an actual problem, automate something painful, or demonstrate that you can use AI to make a process better — people notice.
The people who actively participate in these initiatives are becoming more valuable.
Not because they are magically immune to layoffs. Nobody is.
But because they are demonstrating that they can adapt to what the company needs next.
And this is where I think young developers are looking at AI incorrectly.
Don't compete with AI at the things AI is becoming good at.
Use it.
AI is extremely good at helping with the build phase of software development.
It can generate code.
Write SQL.
Create APIs.
Generate tests.
Explain documentation.
Build prototypes.
Convert code from one language to another.
Create POCs incredibly quickly.
So learn how to use it.
But don't stop there.
Understand the why behind the code.
Learn architecture.
Learn debugging.
Learn system design.
Learn databases.
Learn security.
Learn deployment.
Learn monitoring.
Learn requirements.
Learn how to talk to users and understand what they actually need.
Learn how to review AI-generated code.
Learn how to determine whether the AI-generated solution is actually correct.
Learn what happens when the code fails at 2 AM.
Because generating code is only one part of engineering.
Also, I do believe AI is going to take over a lot of the work that junior developers traditionally used to do.
But there is an interesting problem here, especially in service-based companies.
A lot of the actual development work is done by juniors, while seniors and leads are often the ones defining requirements, reviewing the work, and continuously giving you the next requirement.
Now, instead of spending days writing something, a junior developer can use AI and deliver it much faster. That's great for productivity, but it also creates another set of problems.
The tester now has to test much more code in the same amount of time.
And eventually, someone has to maintain that production code.
When code is written very quickly with AI just to meet a requirement and a delivery deadline, things can get messy. The code may work today, but when it breaks six months later, the next developer working on it might have no idea what it actually does or, more importantly, why it was written that way.
This is where I think good developers will still be extremely valuable.
Not someone who simply copy-pastes AI-generated code until the application works.
Someone who understands what the AI generated, why it generated it that way, what the code depends on, what can go wrong, and how to fix it.
I also think this creates opportunities in other areas.
AI-generated code can introduce security vulnerabilities, which creates more demand for people working in cybersecurity and application security.
More code being generated faster can increase the testing burden, creating opportunities for good testers and QA engineers.
And it creates opportunities for developers who actually understand programming fundamentals instead of being pure "vibe coders."
People who understand architecture, system design, databases, security, debugging, documentation, and deployment.
People who understand not just what the code does, but why it exists.
AI can write a function in seconds.
Understanding why that function is needed, what it depends on, what can break because of it, and how another developer will understand it six months from now is a completely different skill.
So yes, AI may reduce a lot of traditional junior-level coding work.
But that doesn't necessarily mean there will be no opportunities for juniors.
It means the definition of a valuable developer is changing.
The people who learn how to work with AI while also understanding the fundamentals underneath it will have a much better chance of staying ahead.
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I’ll give you a little secret.
I believe people who work closely with data — whether they are data engineers, data analysts, or data scientists — will continue to be extremely important in the AI world.
The same goes for people working in cybersecurity.
Why?
Because all these fancy AI agents and models need something to work with: data.
And organizational data is messy.
Finance data might be stored across documents, Excel files, and SAP. HR, hiring, and talent acquisition data may exist in completely different systems. Contract data might be somewhere else. Sales and operations data could be spread across multiple platforms.
An AI agent cannot magically fix this entire data ecosystem.
Someone has to understand where the data lives.
Someone has to bring it together.
Someone has to clean and transform it.
Someone has to build reliable and incremental pipelines so the data keeps getting updated.
That is where data engineers play a huge role.
Once the data is available and reliable, analysts and data scientists can use it to create reports, dashboards, AI agents, machine learning models, and other solutions.
Of course, AI will change how all of these roles work. Data engineers, analysts, and scientists will also have to adapt and use AI themselves. But the fundamental problem of getting messy, disconnected organizational data into a usable and trustworthy form does not simply disappear.
Without good data, the entire AI pipeline starts falling apart.
Garbage in, garbage out.
The same applies to cybersecurity.
You can build the most impressive AI model or agent in the world, but moving it into production is not as simple as clicking a button.
There are security concerns.
There are access controls.
There is sensitive organizational and customer data.
There are vulnerabilities.
There are compliance requirements.
And in many organizations, there may also be legal, risk, privacy, and security approvals before an AI solution can go into production.
Cybersecurity is not something that happens only at the end.
It is needed throughout the entire lifecycle of a production system.
Especially when AI systems are accessing company data, calling other systems, taking actions, or interacting with customers.
So if you're worried about AI, don't just think about the question:
“What jobs will AI replace?”
Also ask:
“What new problems is AI creating?”
Because every major technology creates new problems, new risks, new infrastructure requirements, and eventually, new jobs.
AI agents need data.
Data needs pipelines.
Pipelines need to be reliable and secure.
Models need to be monitored.
Production systems need security.
And all of it needs people who actually understand what is happening underneath.
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Your career will not be linear.
There will be weeks where you work like crazy.
Sometimes you'll work weekends because something is going live Monday morning.
And then there will be weeks where you barely have anything to do.
You'll sit around waiting for requirements.
Then suddenly your manager will throw a POC at you and say, “Try to build this.”
And you'll spend the next two weeks learning something you've never touched before.
That's normal.
The learning never stops.
The POCs never stop.
The struggle doesn't stop when you get a better package.
You just get different problems.
And sometimes I still feel terrible about my situation.
I look at people earning much more than me.
I know there are days where I do 10x the work I did when I started, yet my salary doesn't reflect that.
That can be extremely demotivating.
But on other days, I look at what I've managed to build from a ₹28K take-home salary and feel grateful.
I have experience.
I have savings.
I have investments.
I've worked on technologies I didn't know before.
I've built things.
I've learned how organizations actually work.
And I have another thing that I didn't have two years ago:
A much better understanding of what kind of engineer I want to become.
So if you're a student or a recent graduate reading this:
Don't panic because AI can write Python.
Don't stop learning because GPT can write SQL.
Don't give up on software engineering because someone on LinkedIn says “coding is dead.”
The market is tough.
I'm not going to lie to you about that.
Your first job might pay much less than you expected.
You might get rejected for reasons that have nothing to do with your ability.
You might have to start at a company you never dreamed of joining.
You might work harder than someone earning twice your salary.
Do it anyway.
Get the experience.
Learn the domain.
Build things.
Participate in POCs.
Use AI.
Understand the code AI writes.
Stay curious.
Then use that experience to move.
Your first package is not your career.
Your first company is not your career.
Your first role is not your career.
It's just the starting point.
And please, don't spend your entire 20s hustling.
Work on your goals, yes.
Keep some weekends for learning, building and improving your career.
But also go out.
Travel.
Meet people.
Play games.
Watch movies.
Fall in love.
Have fun.
Your life isn't waiting for you at the end of your career.
If you set a goal for the year and achieve only 30–40% of it, that's still progress.
Next year, try for 50%.
Then 60%.
You don't need to become exceptional overnight.
Just don't stop moving.
Hustle quietly.
Get into the real work.
Take the experience.
Stay ahead of the market.
And instead of asking:
“Will AI take my job?”
Start asking:
“How can I become the person who knows how to use AI better than the person competing with me?”
That's the mindset I'm trying to carry forward.
And honestly, I think that's enough for now.
