Why Coding Is No Longer the Most Valuable Skill for Developers

I have published this original story on Medium, Linke Below:
AI in software engineering

Like most of us, you’ve probably seen the impressive demos of AI tools. “I just built a fully functional Shopify app in 20 minutes without any code.”

“You were one of the first people who created this product only using AI tools.”

Cognition Labs released a video a while ago showing their AI software engineer, Devin, making API requests, displaying the data, and even deploying the app — autonomously.

If you’re a current software engineer or recent computer science grad, it can feel like the rug’s been pulled out from under you.

You start wondering, did I just waste thousands of dollars and years of effort learning something AI can now do in seconds?

I’ve been in this industry for the last five years. And while I don’t think AI is replacing software engineers anytime soon, I do believe the role of a developer is changing. Certain programming jobs will fade. But new ones — ones that leverage AI — will take their place.

In this article, I want to go over some skills that I believe are going to be in higher demand and will give you a real advantage over the next decade, assuming the adoption of AI tools keeps accelerating. Stick around till the end — I’ll share what I think is the most underrated skill of the future.

Don’t Be an Endpoint

In the book Futureproof, Kevin Roose offers a simple rule for staying valuable in the age of AI: Don’t be an endpoint.
Even though it was written before ChatGPT blew up, it’s still incredibly relevant today.

In programming, an API endpoint is the middleman between two applications. But in real life, there are jobs that exist only to be that middleman — without adding much value.

Think: a receptionist who just connects clients to a scheduling app. Or a data entry clerk who transfers numbers from one spreadsheet to another.

Jobs like these are at high risk of being automated. The same logic applies to programmers who just move code from AI to their IDE. That’s not a long-term strategy.

1. Multidisciplinary Thinking and Complex Problem-Solving

If you want to stand out in a post-AI world, you have to lean into what AI isn’t good at.

AI struggles to generate unique insights by combining knowledge across different domains. That’s still a human strength.

More than ever, developers are expected to solve higher-level problems — not just ship a feature in a CRUD app.

AI is putting upward pressure on what’s expected of junior developers. You can’t just be a code monkey anymore. AI can do that part. And probably faster.

Today, a lot of that grunt work has been outsourced. Now we’re expected to think bigger.

Solve business problems — not just code problems.

Instead of building one specific feature, you might be told:
“Make this page load faster.”

How? Up to you. Maybe it’s component refactoring, maybe it’s progressive rendering, or maybe it’s both. And you’ll have to weigh the trade-offs based on deadlines and team priorities.

AI can tell you how to add a pop-up.

A good dev works with the designer to decide if the pop-up should exist in the first place.

Even a basic understanding of business and user psychology makes you 10x more valuable. You’re not just coding. You’re solving business problems with code.

2. AI Engineering

There’s a ton of hype around AI startups right now. That means a ton of opportunity for engineers who are ready for it.

Nearly all of them are using AI in some form. That means demand for engineers who are comfortable with AI tools and services is at an all-time high.

According to the World Economic Forum, AI and big data are in the top 3 most in-demand skills now — and over the next five years.

You don’t need a PhD in machine learning. You don’t need to become a research scientist. But understanding things like AI agents, RAG (retrieval augmented generation), and prompt engineering will make you way more marketable.

You don’t need to train the models. But knowing how to implement, deploy, and use them effectively? That’s where the demand is.

3. AI Coding Tools and Prompt Engineering

AI coding tools are getting better fast. As they become more widely adopted, expectations for developer productivity will rise — if they haven’t already.

According to Stack Overflow’s latest developer survey, 61% of developers are already using AI coding tools. That number will only go up.

But here’s the catch: AI-generated code is often buggy.
If you can use it without trusting it blindly, you’ll be ahead of the curve.

When you understand how LLMs actually work, you’ll start to see the boundaries more clearly. That’s a huge advantage. You stop being surprised by what it can’t do — and start using it for what it’s actually good at.

4. Deep Domain Knowledge

Ever notice how most AI demos involve building simple apps from scratch?

That’s because LLMs struggle with business context. Especially in big legacy systems.

Let’s say there’s a production outage.
AI can help you interpret an error message.
But it’s not going to tell you that the root cause was a bad data migration from three months ago that only affected a subset of users.

The best engineers I’ve worked with had deep technical expertise in one area and a ton of product-specific knowledge.

They were the go-to people when we needed to decide if a feature was feasible, or when we had to figure out a migration plan.

AI solves well-defined problems in narrow contexts.
Real-world dev work is rarely narrow. Or well-defined.

Being the domain expert in a specific system or vertical makes you indispensable.

5. Humanistic Traits: Empathy, Creativity, Communication

These aren’t fluff skills. These are differentiators.

The more our communication is handled by autosuggest, and the less we interact face to face, the more rare human qualities become.

The latest Apple update can summarize text messages and suggest replies.
What does that say about us? That we don’t even want to read and respond to our own friends?

These tools are pushing us toward a world where bots talk to bots — and we doomscroll through the results.

And before you ask — yes, I’m human.
Yes, I sometimes use ChatGPT to clean up my grammar.
But the point is: real connection is becoming rare. And that makes it valuable.

People want strong opinions. They want narratives. They want someone who can put raw numbers into context and make sense of them.

AI can mimic. But it can’t empathize. Not like another person. Not even like a dog.

Software development is inherently creative.
The people who win are the ones who combine ideas from different fields — and understand humans deeply.

6. Meta Learning

This one’s probably the most underrated skill — and the one with the best long-term ROI.

Anyone confidently telling you “this tech stack will be hot in five years” is making a guess.

Yes — data structures, algorithms, and solid fundamentals are a safe bet.

But beyond that?
The best thing you can do is get really good at learning new things — fast.

Just like WordPress and Wix changed web development in the 2000s, AI is changing software engineering now. And the ones who adapt will keep winning.

One book I always recommend is Ultralearning by Scott Young.
It’ll teach you how to learn hard things faster — like how to code.
And how to stay flexible as the tech landscape shifts.

Being flexible was already important.
Now? It’s everything.

There Will be Even More Demand

This is a huge moment of opportunity for engineers who are ready to ride the AI wave — not run from it.

While some people are fleeing tech because they think AI is taking over, I think the opposite is true:
Good engineers are going to be even more in demand.

Despite all the hype, most people hate change.
Most companies move slow. Really slow.

You don’t need to learn everything all at once.
Just be willing to learn what matters — when it matters.

Thanks for reading.

Let me know your thoughts — I’d love to hear what skills you think will matter most over the next 10 years.

Let’s grow, learn, and build amazing things together!

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