Coding Is Hard
Someone has been telling you that coding was never the hard part. That's a lie, and it's the kind of lie that erases the hours you've spent chasing a null pointer that only appears in production, or the race condition that vanishes the moment you add logging. I get the appeal of the claim: it makes AI's sudden competence at generating code feel like the whole problem is solved. But it also conveniently forgets that "just writing code" was never the job.
We're in the middle of an upheaval. It's August 8, 2026, and nobody honestly knows how the AI revolution is going to end, except that programming as a profession is going to look different. If coding is so easy, though, then why were programmers in high demand and commanding large salaries for years, even before zero interest rates pumped money into every unprofitable startup? Why was burnout and overwork a running joke across the entire industry before Copilot could even complete a function? The hard part was never tapping out syntax. It's figuring out what the system should do, how it's broken, and how to fix it without breaking three other things at 2 a.m.
So next time someone tells you AI makes code trivial, ask them where they were the last time a memory leak ate a weekend. Or ask them why every project you've worked on had a bug backlog that outlived the people who created it. The answer usually isn't that no one could type fast enough. It's that the hard part was always the world the code lives in, and that world just got a lot more crowded.
What This Phrase Does to Junior Developers
When someone says a particular aspect of coding is "easy," it can be damaging to junior developers. It tells them they're struggling with something that's supposed to be straightforward, which can make them feel stupid. This feeling of inadequacy can discourage them from asking for help, as they might think they should already know the answer. As a result, it creates a culture where admitting that code is hard is seen as a weakness.
This mentality also lets senior engineers off the hook for writing code that's needlessly hard to work with. If debugging is hard, it's not because the junior developer isn't skilled enough - it's because the codebase is complex. As one commenter noted, "If coding is easy, why is software so damn buggy? Debugging is hard." This highlights the fact that coding is not always easy, and that's okay. We should be focusing on making our codebases more maintainable and easier to understand, rather than shaming developers for struggling.
For example, trying to read a large C code base can be a daunting task, even for experienced developers. It's not something that can be easily picked up, and it requires a significant amount of time and effort to understand. This is why it's essential to have clear documentation and coding standards in place, to make it easier for developers to navigate the codebase.
To demonstrate this, consider a simple example in Python:
def calculate_average(numbers):
return sum(numbers) / len(numbers)This function is straightforward, but it's not always easy to write clean and maintainable code, especially in larger projects. Senior engineers should be taking the lead in making their code more accessible, rather than expecting junior developers to figure it out on their own.
It's time to shift the focus away from making junior developers feel bad for struggling and towards creating a more supportive and collaborative environment. By acknowledging that coding can be hard and providing the necessary resources and guidance, we can help junior developers grow and improve, rather than discouraging them from asking for help.
The Code Is the Hard Part
The common phrase "code is the easy part" gets repeated enough to sound like truth. I don't buy it. The hard part isn't design and understanding as separate activities—it's that you discover your design was wrong only when you're writing the code that exposes the contradiction. LLMs don't skip that step. They just make the code appear faster, so you have less time to be confused before you're supposed to be confident.
The community reaction says LLMs will worsen complexity and bugs born from poor design. I'm with that partly. If you treat the model's output as a draft to interrogate, you can still catch the bad abstractions. But the current tooling and culture push toward accepting the generated code as-is. It looks clean. It's consistent with the surrounding function names. It even passes your tests—until a year later, when you need to extend it and discover the model never understood the boundaries of the domain. In that sense, the risk isn't that the code is wrong. The risk is that it's exactly right in a way that doesn't matter.
I think the more precise problem is that LLMs are trained to produce code that looks like a solution, not to represent a system. A human writing a function from scratch has to carry the whole architecture in their head—what this module owes the next one, what failure mode we're hiding, what future change this design makes impossible. A model doesn't carry that. It predicts the next token. So when you ask it to generate code, you're essentially optimizing for local coherence at the expense of global structure. That's fine for isolated utilities. It's dangerous for systems.
What I suspect will happen is a wave of code that is individually sensible and collectively incoherent. We'll see teams ship faster, then spend the following years refactoring the accidental complexity that grew from an LLM's inability to ask "why does this exist?" The question worth sitting with isn't whether LLMs replace programmers. It's whether the ability to generate code without understanding the design makes it harder to ever develop that understanding in the first place.
The Hard Part Is Everything
I think the community reaction hits on a crucial point: the real challenge in software development isn't just about writing code, but about design and understanding. While large language models (LLMs) may make it easier to generate code, they won't necessarily address the underlying issues of complexity and bugs that arise from poor design. In fact, I worry that LLMs could even exacerbate these problems by making it easier to produce code without fully considering its implications.
This is something I've seen time and time again in the development of new tools and technologies: a focus on solving the immediate problem, without fully considering the broader consequences. It's not just about whether LLMs can write code faster or more efficiently, but about whether they can help developers create better-designed, more maintainable code. And on that front, I'm not convinced that LLMs are a silver bullet.
The thing that's really important to consider here is how LLMs will interact with the existing development process. Will they be used to augment human developers, or will they be relied upon to generate code without sufficient oversight? I think the answer to this question will have a big impact on whether LLMs ultimately help to improve the quality of software development, or simply add to the complexity and bug-ridden nature of the codebase. One specific concern I have is that LLMs may make it easier for developers to produce code that is technically correct but functionally flawed, which could lead to a whole new set of problems down the line.
Ultimately, I think the impact of LLMs on software development will depend on how they are used, and whether developers are able to effectively integrate them into their workflows. I'm left wondering: will LLMs become a tool that helps developers focus on the hard part – design and understanding – or will they just make it easier to avoid dealing with those challenges altogether?
Conclusion
The idea that "code was never the hard part" is not just an insult, it's a myth that ignores the complexities of software development. As we navigate the AI revolution, it's clear that many aspects of our work and life will be transformed, including programming. But to suggest that coding is easy, and that the real challenge lies elsewhere, is to disregard the years of high demand and large salaries that programmers have commanded. The stress, overwork, and burnout that have long plagued the profession can't be explained away by the notion that coding is a simple task.
I'm still not sure what to make of the fact that product managers, who are supposedly responsible for making the hard decisions about what to build, often seem clueless about the intricacies of software development. Why aren't they held to the same rigorous standards as programmers? Perhaps it's because we've perpetuated a culture that values coding skills above all else, while neglecting the other essential aspects of software engineering. As I look to the future, I wonder what will happen to the many junior developers who are entering the profession with the expectation that coding is the hard part, only to discover that it's just one piece of a much larger puzzle.
What will it take for us to move beyond the clichés and recognize that software development is a complex, multifaceted profession that requires a broad range of skills and expertise? Maybe it's time for us to stop pitting coding against other aspects of software development and acknowledge that the hard part is, in fact, everything. Only then can we start to build a more inclusive, supportive culture that values the diverse contributions of all software professionals, regardless of their role or specialty.