Why Coding Skills Are More Valuable Than Ever in the Age of AI

Modern programming is nothing like what we had in the simpler days of Web 2.0. Back then, code was easier to read and follow — a bit like reading step-by-step instructions in English. Each line explained exactly what was happening, making it straightforward for anyone with a little curiosity to understand the logic. Today, programming has evolved into a more shorthand-heavy, abstract process.

Tools like asynchronous programming with promises and async/await, dependency injection in application frameworks, and highly abstracted data manipulation with libraries like Lodash or modern functional JavaScript methods (e.g., .map(), .reduce()) have added layers of complexity that make understanding the logic behind a program even more critical. These modern techniques prioritize brevity, modularity, and performance but often obscure the underlying processes, making it harder for someone unfamiliar with them to follow. If you don't have that programming background, you'll quickly feel lost trying to decipher everything, especially when complications or errors arise.

So, let’s talk about errors. Not all errors in programming are created equal. Some are syntax errors, where the code simply won’t run because it doesn’t meet the technical rules of a programming language. Others are programming logic errors, like an infinite loop or a miscalculated condition. But there’s another type of error that’s more subtle — and more dangerous in business-critical applications: the business logic error. These mistakes happen when the code doesn’t align with the organization's real-world processes or requirements. For example, imagine an e-commerce app applying the wrong discount to premium users, charging too much (or too little). While AI can write code seamlessly, it can’t always interpret complex business rules with the precision a skilled programmer can — especially when edge cases sneak in.

Now, to be fair, a business domain expert can certainly help reduce these errors. People with deep knowledge of the company’s rules and workflows can use AI tools to write detailed prompts that describe what their code should do. They can also guide the development process step by step: using unit tests to validate each new feature and integration tests to ensure everything works well with the existing system. These tests could even be auto-generated by AI based on English descriptions of the scenarios the program should handle, such as:

# Example: Testing the edge case for a discount logic

def test_premium_discount():

assert calculateDiscount("premium", 100) == 85 # 15% discount applied

def test_no_discount():

assert calculateDiscount("standard", 100) == 100 # No discount

This process works great — for routine logic. But when we start talking about edge cases (and every business has those), you can run into big trouble without a programmer. For example, what happens when multiple discounts, overlapping exemptions, or simultaneous transactions occur? While business experts can describe rules, programmers have the ability to dive into code, isolate tricky situations, and craft targeted fixes at a level AI or domain users cannot. They understand how to handle errors gracefully, design workflows for rare conditions, and ensure that patches don't create chaotic "spaghetti code "— broken, tangled logic that becomes impossible to maintain.

Speaking of spaghetti code, this is one of the biggest dangers of putting AI in the hands of coding beginners who might not fully understand the structure they’re building. Here’s why it happens: inexperienced coders rely on high-level prompts to modify their code, but without knowing what each part does, they can unintentionally overwrite or duplicate logic. Every new change builds on an unstable foundation until you end up with a tangled mess that breaks whenever someone tries to fix or extend it. Skilled programmers, on the other hand, know exactly where to step into the code and make surgical changes, saving time and reducing technical debt.

Here’s the bottom line: AI is a powerful tool, but it's only as reliable as the person using it. AI won’t replace programmers; it will empower them. Businesses should lean on programmers’ expertise to create smarter, more efficient systems while letting domain experts contribute their knowledge in meaningful ways. At the enterprise level, it’s the synergy between programming experience and domain expertise — not inexperience with an over-reliance on AI — that delivers reliable, scalable solutions. To anyone nervous about the future, take this as a positive reminder: coders aren’t going anywhere anytime soon. In fact, their value may be higher than ever before. Let’s embrace the tools, but let’s also double down on the skills that make us indispensable.