I open LinkedIn — and there's another post: "AI writes code better than humans, the programming profession is dying." I check Twitter — same story. YouTube — experts predicting the end of the developer era.
Then I look at job postings. Companies offering $150-200K annually for mid-level developers. AWS paying 31% more to programmers who can work with AI. Demand for machine learning specialists up 82% in the last two years.
Something doesn't add up, right?
Let's figure out what's actually happening. No panic, no hype — just facts.
What AI Actually Is (Spoiler: It's Dumber Than It Looks)
You know what AI reminds me of? That straight-A student who crams before exams. They memorize tons of information but don't understand how it actually works.
When ChatGPT generates code, it's not "thinking" through a solution. It's recalling similar examples from millions of lines of code it was trained on. GitHub, Stack Overflow, documentation — all sitting in its "memory" like a giant database of patterns.
Imagine a high school student preparing for a test. They've solved 500 math problems. When they get a similar problem on the exam, they think: "Oh, this is like problem #347, just with different numbers." They solve it by analogy.
AI works exactly the same way. Except instead of 500 problems, it has millions of code examples.
One developer decided to run an experiment. He started answering technical questions using ChatGPT. The first few weeks seemed great — responses sounded professional. But then he noticed a pattern: the AI was simply rephrasing popular answers that were already in the same discussion a few posts above.
So AI wasn't inventing new solutions. It was working like an advanced search engine with a paraphrase function.
Where AI Falls Flat (And It Happens Constantly)
Now let's talk about what neural networks can't do. And it's a long list.
It Doesn't Understand Your Project's Context
AI only sees what you show it. It doesn't know you already have three different ways to handle dates in your project. That your API works with a specific data structure. That certain libraries conflict with each other.
Ask it to write an email validation function:
function validateEmail(email) {
const regex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
return regex.test(email);
}Looks decent, right? Now imagine your system needs to handle Cyrillic in emails. Or distinguish disposable email addresses from permanent ones. Or you already have a centralized validator, and adding another one creates confusion.
AI doesn't know this. It wrote "correct" code that's useless for your specific case.
It Creates Security Vulnerabilities
Neural networks trained on public code. And public code is full of bugs and outdated practices.
Ask AI to create a database function:
def get_user_by_id(user_id):
query = "SELECT * FROM users WHERE id = " + str(user_id)
return database.execute(query)This code opens the door to SQL injection. Any attacker can steal or delete data. But the AI doesn't understand the danger — it just copied a pattern it's seen thousands of times in old tutorials.
Or take authentication. AI might suggest storing tokens in localStorage:
localStorage.setItem('authToken', token);Does it work? Yes. Is it secure? No. This creates an XSS vulnerability. A professional developer uses httpOnly cookies. AI doesn't, because the simple way shows up more often.
It Writes Slow Code
Performance is something AI doesn't even think about.
Here's a function to find duplicates in an array:
function findDuplicates(arr) {
let duplicates = [];
for (let i = 0; i < arr.length; i++) {
for (let j = i + 1; j < arr.length; j++) {
if (arr[i] === arr[j] && !duplicates.includes(arr[i])) {
duplicates.push(arr[i]);
}
}
}
return duplicates;
}AI will generate something like this. O(n³) complexity. On an array of 1000 elements, this takes seconds. A programmer writes it with a Set at O(n) — thousands of times faster.
On test data, the difference is invisible. In production, your app turns into a turtle.
It Can't Handle Unusual Tasks
There's research testing how AI writes code in languages rarely found online. They used Ada, for example — a language for critical systems in aviation and medicine.
Result? Half the generated code wouldn't even compile. The rest compiled but worked incorrectly. AI was literally making up syntax that "looked similar" to real code.
Same thing happens with niche frameworks, internal company libraries, domain-specific tasks. If there aren't many examples online — AI is helpless.
Real Failures: When Companies Tried Replacing People with AI
Theory is one thing, but let's look at what actually happened.
US Fintech Companies
In 2023-2024, several fintech startups decided to "optimize." They laid off developers, kept managers and a couple seniors who would "just review AI code."
First months seemed fine. Features shipped fast. Managers celebrated the cost savings.
Then problems started. Production bugs. Critical security vulnerabilities. Service outages. Customer complaints. Regulatory fines.
When they dug into it, they found: AI code looked fine on the surface, but inside was full of technical debt. Architecture mismatches. Logic duplication. Non-obvious bugs that only appeared under certain conditions.
Companies urgently started looking for developers again. But the best specialists had already found other jobs. They had to hire new people at double or triple the salary. Plus spend months cleaning up the technical mess.
The savings turned into losses.
The Klarna Story from Sweden
Klarna — a major fintech company — announced in 2024 they were laying off 700 people and replacing them with AI. Media wrote enthusiastic articles about "the future without programmers."
A year later, the company quietly started bringing people back. Because service quality dropped critically. Customers complained. Competitors surged ahead in functionality. New feature development stalled.
Turned out AI could churn out simple code, but couldn't:
- Understand complex business logic
- Coordinate work across different parts of the system
- Make architectural decisions
- React quickly to unexpected problems
Carnegie Mellon Experiment
Researchers decided to test: can a team of AI agents work like a real IT company?
They created a virtual office. Each role — a separate AI agent: developers, testers, managers, designers. Gave them tasks of varying complexity.
Best result? Claude 3.5 completed 24% of tasks. GPT-4 — less than 9%. Other models failed even harder.
But the funniest part — how they failed:
- One "developer" got stuck on a browser dialog box and couldn't continue working
- Another couldn't find the right person in Slack, so just created a new user with the needed name
- A third closed a task as complete without actually doing anything
Researchers' conclusion: AI catastrophically lacks common sense. It doesn't understand what it's doing.
What's Actually Happening in the Job Market
Now here's the interesting part. While everyone's saying "AI is killing the profession," the opposite is happening in the market.
Salaries Are Rising
The World Economic Forum published a forecast: demand for AI/ML specialists will jump 82% in the next couple years. Yeah, you read that right — more than four-fifths.
AWS adds an extra 31% to salaries for programmers with AI experience. And in the US, every second AI-related job posting promises $174K annually — nearly double what the average developer makes.
What about Western Europe? Pretty much the same story:
- Germany: regular senior pulls €70-90K, but if you understand machine learning — expect €85-110K
- UK: classic developer — £50-70K, AI specialist — £70-95K
- Netherlands: development — €60-80K, data science — €75-100K
Even in Eastern Europe, salaries grew:
- Poland: regular mid-level — $25-35K, AI developer — $35-50K
- Czech Republic: development — $30-45K, machine learning — $40-60K
- Romania: classic coding — $20-30K, AI — $30-45K
The Skills Shortage Hasn't Gone Anywhere
Companies are complaining: finding a good mid-level or senior developer has become even harder than before.
Why? Because requirements changed. Now they need people who:
- Understand the entire system architecture
- Know how to work with AI as a tool
- Can verify generated code for security and performance
- Make decisions, not just write code from specs
These specialists cost money. And there genuinely aren't enough of them.
AI Isn't Replacing Programmers — It's Changing the Profession
Before, lots of time went to routine work. Write another REST API. Create a form with validation. Configure a config file. These were mechanical tasks.
Now AI does those. And the programmer focuses on what requires intelligence:
- Design architecture
- Choose the right technology for a specific task
- Optimize performance
- Ensure security
- Communicate with clients and translate business requirements into technical solutions
Essentially, the profession is becoming more intellectual. Less coding for coding's sake, more decision-making.
And that's great news for those just starting to learn.
Why Now Is the Best Time to Start Learning Programming
Yeah, I'm serious. Right now is the perfect moment.
1. High Demand and Good Money
Lots of jobs. High salaries. Ability to work remotely for the global market.
Comparison with other professions (average annual US salaries):
- Doctor: $200-250K
- Lawyer: $120-150K
- Programmer: $90-120K (but in big companies up to $300K+)
- Teacher: $50-60K
- Nurse: $70-80K
In Europe, the difference is even more noticeable — programmers in the top 3 for salaries.
2. You Can Learn Faster Than Before
Before, to become a programmer, you had to:
- Study 4 years at university
- Or spend years independently navigating a chaos of information
Now with a good structured course and practice, you can reach junior level in 6-12 months.
Yes, the market has become more demanding. But that only means one thing: you need to learn properly. Not just watch video lectures, but solve problems. Lots of problems. Write code. Build projects.
For example, on CodeGym students solve over 1200 practical tasks. Because programming is a skill. And skills develop only through practice, not theory.
You can read a book about swimming. But you'll only learn in the pool.
3. The Entry Bar Got Lower
A university degree is no longer mandatory. Companies look at:
- What you can do
- What projects you can show
- How you solve problems in interviews
A portfolio of several working projects is valued more than a diploma.
4. AI Is a Tool, Not a Competitor
Imagine: you're an architect. Before, blueprints were drawn by hand — slow and tedious. Then AutoCAD appeared — now you can draw faster and more accurately.
Did the number of architects decrease? No. There are more of them. But what's required of them changed. Now it's not enough to just press a button — you need to understand composition, light, processing.
Same thing with programming.
AI takes over boring mechanical work. Programmers focus on interesting challenges.
Moreover: those who learn to work effectively with AI will earn more than others. It's a new skill that few have mastered yet.
5. Global Job Market Is Open
Programming is one of the few professions where geography doesn't matter.
A doctor from Poland can't work in Germany without requalification. A lawyer from Romania is useless in the US — different laws there.
But a programmer can:
- Live in Eastern Europe
- Work for a US or Western European company
- Get Western salary (or close to it)
- Travel and work from anywhere in the world
That's a unique advantage.
Who Actually Risks Losing Their Job Because of AI
Here's the paradox: AI threatens not beginning programmers, but those who've been doing the same thing for years and aren't developing.
If you've spent 10 years writing typical CRUD operations, setting up forms, copying solutions — AI really might replace you. Because you're doing mechanical work.
But if you:
- Understand how to design a system from scratch
- Know how to choose the right technical solution for a specific task
- Can sense where code might break
- See weaknesses in application performance and security
- Can actually talk to people and turn their wishes into concrete technical tasks
...then you're irreplaceable. And your value is only growing.
And for those just starting to learn, there's a huge advantage: you'll be learning programming in a world where AI already exists. For you, it'll be a natural tool — like Google and Stack Overflow were for the previous generation.
You'll immediately learn to work properly with AI. While those who learned "the old way" are now trying to relearn.
Where to Start If You've Decided to Become a Programmer
Here's my honest advice.
Learn not just to write code, but to think like a programmer.
What does that mean?
Solve lots of problems. Theory is important, but skills develop only through practice. 100 tutorials read won't replace 100 programs written.
Build projects. Employers need to see you can create working things. A couple of actually working projects in your portfolio are worth more than any certificate.
Learn to read other people's code. Seriously, this is just as important as writing your own. In real jobs, you'll spend most of your time digging through what other people wrote before you, not creating everything from scratch.
Try to understand the logic of solutions. Don't just copy code from the internet — take time to figure out how it works inside. Why did they use this specific data structure? What are the alternatives? What are the tradeoffs?
Don't fear mistakes. Every professional programmer was once a beginner googling "how to create a variable." Everyone went through this.
AI can generate a function in a second. But it can't:
- Understand which exact function is needed in your situation
- See how it fits into the overall architecture
- Predict where problems will arise
- Explain to the client why this is the right solution
- Take responsibility for the result
People do all this. And will continue to for a long time.
Bottom Line: Programmers Are Becoming Elite
You know what happens when technology automates simple tasks? Demand for specialists doesn't drop — it grows. But requirements increase.
When digital photography appeared, there weren't fewer photographers — there were more. But what's required of them changed. Now it's not enough to just press a button — you need to understand composition, light, editing.
Same thing is happening with programming.
AI won't destroy the profession — it'll make it more valuable. Machines will take routine. People handle the intellectual work.
Those who understand how systems work will get salaries other professions don't even dream about. They'll create products that change the world. They'll solve complex interesting challenges.
The market is far from saturated. Digitalization is happening in all sectors — from medicine to agriculture. Companies needing programmers are growing faster than programmers themselves.
So if you're wondering whether you should start learning programming in the AI era — the answer is clear: absolutely.
But do it right:
- Focus on practice, not theory
- Solve real problems, not just watch lectures
- Learn to understand how technologies work, not just use them
- Develop the ability to think, not copy code
The future belongs to those who can solve problems. And AI will just make this work faster and more interesting — if you learn to use it, of course.
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