Over the past few months, there’s been a whirlwind of debate surrounding the future of programming in the age of AI. Some folks are convinced that programmers will become obsolete, replaced by lines of code automatically generated by tools like GitHub Copilot, or by newly emerging IDEs such as Cursor, Windsurf, or the latest contender from the creators of TikTok, Trae.AI. Personally, I think that’s a bit of an overreaction. Just like you wouldn’t drive a screw into a piece of wood by hand when you could use a power drill, these AI-assisted coding tools are natural evolutions—yet they still require skillful handling.
We’re at an inflection point in software development. AI is no longer just about fancy algorithms sitting in research labs; it’s becoming a part of daily coding life. GitHub Copilot suggests entire code snippets in real-time, and specialized IDEs like Cursor or Windsurf streamline the entire coding process, from refactoring to debugging. Similarly, trae.ai has stirred up curiosity by promising next-level intelligence in code generation. All these tools are revolutionizing programming in the same way an electric screwdriver revolutionizes home improvement projects. You could still do it the old-fashioned way, but why would you?
However, it’s not as if these tools run on autopilot without human oversight. If anything, using AI effectively requires a deeper understanding of programming principles. You can’t simply toss a vague prompt at an AI model and expect perfect, production-ready code to come out the other side. Real-world projects demand a thorough grasp of best practices, architecture, and debugging strategies. And yes, you still need to know when the AI might be hallucinating a solution or missing a subtle but critical detail.
Let’s explore a quick example. Suppose you’re using the “o1” model of ChatGPT to help generate a function for calculating factorials in Python. You might feed it the following prompt:
Prompt #1
“Write a Python function called factorial that returns the factorial of a given integer n. Please ensure the function handles invalid input (negative integers) gracefully.”
The model might produce this code, which at first glance looks perfectly fine:
def factorial(n):
if n < 0:
return None
result = 1
for i in range(1, n + 1):
result *= i
return result
It compiles, it runs without error, and it even handles negative inputs by returning None. But there’s an off-by-one mistake hidden here: the loop goes from 1 to n-1 instead of 1 through n. For a small value like n = 3, this function returns 2 instead of 6. An experienced Python developer might spot the discrepancy by mentally running through the loop or by writing a quick test—yet this bug isn’t glaringly obvious. Fixing it requires noticing that the loop’s upper bound is incorrect. While the AI code looks neat, it can still trip you up.
If you revise your prompt slightly, clarifying that the multiplication must include all integers from 1 up to and including n, you might end up with a correct solution:
Prompt #2
“Write the Python function factorial for a given integer n, ensuring you multiply all integers from 1 to n inclusive. Also handle negative inputs properly.”
This time, the AI generates:
def factorial(n):
if n < 0:
return None
result = 1
for i in range(1, n + 1):
result *= i
return result
Now you get the expected results for n=3, n=5, or any other positive integer. But notice how just a small tweak in your prompt changed the result completely. Even with the best AI model, a single miscommunication in your instructions can lead to subtle errors that only human insight can catch.
In my opinion, AI will become just another indispensable tool in a programmer’s arsenal. The fundamentals of coding—algorithms, data structures, problem-solving techniques—remain as relevant as ever. What changes is how quickly projects can move from concept to implementation, and quite possibly the total number of developers needed for certain tasks. The nature of coding will evolve, leaning more on strategic thinking and less on grunt work. But rest assured, the craft of programming isn’t going anywhere; it’s just getting a new and very powerful set of power tools.