AI, Software Engineering, and Job

Introduction

I was fortunate to learn coding in the pre-AI era and implement it during the peak AI era.

From my perspective, I can share some insights.

This will be a long post, so I suggest enabling reader mode.

The Old Roadmap

First, I want to talk about my college years and even before that, in 11th and 12th standard.

The narrative at the time was simple.

1. Learn to code.
2. Get a job.
3. Secure a stable career.
4. Switch companies.
5. Build a net worth of a million dollars.

That was the roadmap everyone was promised.

I was also excited about it but before pursuing any field, I decided to try it first.

Many already know this journey, so I will skip the details. You can read about it on my portfolio linked in my bio.

The plan was straightforward.

Get settled through engineering.

Then the pandemic hit.

The AI Disruption

We got GPT. Then Claude. Opus. Countless coding models. Then came harnesses for coding, AI tools, and much more beneath the surface.

Now the problem is that many people are entering the economy through coding using no-code tools and building systems.

I am not denying that software engineering was difficult and now it has become comparatively easier, which is true.

But the nuances of the field remain.

A coder can create an app. An engineer can create an app. Even a less technical person can create an app.

Previously, only someone proficient in software engineering or with some experience could build apps or websites.

Those who did not know how to code could not create anything.

That was the entry barrier.

Now the barrier is shattered.

What's Actually Hard

So what remains?

Those who do not know how to code, plus those with less knowledge of how code works and less experience, are trying to create meaningful software.

This brings a fundamental issue.

These people do not understand:

• How software works internally
• What tradeoffs they should make
• How security should be handled
• How to deploy to production

Code was not the only hard part.

The other hard parts were:

• Tech dependencies
• Technology choices
• Structure of code writing
• Legacy codebases

I have been through many legacy codebases and I can tell you that AI does not even write the amount of code I have seen in some legacy codebases.

Speed was never the problem.

The problem was context.

We are shipping at light speed and forgetting the context of how things happen.

This has become one of the fundamental issues for security.

Previously, many people worked on software and had enough time to think about:

• Tradeoffs
• Structure
• Concepts
• Security

Many had read programming books and knew what to do and what to avoid.

Now anyone can code and deploy to production but they do not know:

• How to preserve user data
• How to handle security
• How to debug a production bug when AI models are down

They do not know these things.

Many will say we can hire someone, but the context you need to give to an AI, you also need to give to a human.

Humans are not superhumans who can do many things with just one client or one instruction.

We have fundamentally made one step easier and many think the whole mountain is super easy.

No.

If you want to climb the ladder, you need to:

1. Learn to walk
2. Then run
3. Then climb
4. Then ascend the mountain

Hypothetically, that is engineering.

The AI Bubble & Layoffs

About layoffs and companies not hiring due to AI, it also makes sense to me.

During the pandemic, many companies overhired because cash was flowing from investors and SaaS and mini software were peaking.

Everyone was working from home and technology was the only thing connecting everyone.

Companies hired many times beyond capacity.

Once the pandemic ended, funds hit the real world.

Everyone realized they had overhired.

Since they cannot fire easily, they started laying off people.

They did not want to devalue their stock by laying off because usually layoffs cause stocks to drop.

But now that does not happen if they say they are restructuring because of AI.

Internally, everyone knows it is not because of AI but because of the overhiring during the pandemic.

These companies are trying to protect their stock value by naming it AI restructuring.

We are currently in an AI bubble and the bubble will not break.

Once a lion tastes blood, it can never be a vegetarian.

The same happened with software engineering and with everyone around the world.

AI is not going anywhere.

If you think it will, you are really stuck.

The Reality of Engineering

I would highly suggest you upskill yourself with the current stack.

There is no stop to learning.

You should learn:

• How code works
• How to write raw code
• What kind of code is good
• What kind is bad

The entry to software engineering is harder than ever because low-quality apps and low-quality software are no longer good enough for entry.

Previously, if you knew a language, you were qualified.

Then it became harder.

If you knew a language and could create some projects, you could be hired.

Then it became:

If you knew a language, could build a project, and knew a framework, you would be hired.

Now it has become:

1. Do you know the language?
2. Can you create a project?
3. Do you know a framework?
4. Have you created a full stack?
5. Have you deployed to production?
6. Have you fixed security vulnerabilities?

That is how hard it is nowadays to get a job.

My take is that AI is going to stay here.

Companies will be mass laying off people now and then and calling it AI restructuring.

The entry barrier is shattered but if you are experienced and have gone through the depth of software engineering, no one is going to replace you.

Actual engineers are not replaceable.

There is still a need for good engineers everywhere.

Good engineers are rare.

You should be a good engineer and keep learning because you want to do it.

If you are just doing it for money, you probably will not survive a couple of years.

The entry barrier is shattered and if you only want to be an engineer, then become an engineer.

Do not become an engineer if you are just here for money.

My Journey

I have been in this field for four years and I am almost about to spend six years learning about engineering.

Since my 11th standard, I started tinkering with Arduino, low-level programming, Java, Kotlin, and building my apps.

I spent four years of college doing the same and now I have a job that pays comparatively well.

This is how long it took me to get to a position where I am confident enough that I can build software, fix any kind of vulnerability, and fix any kind of issue if I just spend enough time doing that.

Final Thoughts

So the intake is this.

If you are a fresher, do not be demotivated.

Head down and work hard.

You will be successful one day.