Coding after coders: The end of computer programming as we know it?

I used Claude just the other day to write unit test coverage for a tricky system that handles resolving updates into a consistent view of the world and handles record resurrection/deletion. It wrote great test coverage because it parsed my headerdoc and code comments that went into great detail about the expected behavior. The hard part of that implementation was the prose I wrote and the thinking required to come up with it. The actual lines of code were already a small part of the problem space. So yeah Claude saved me a day or two of monotonously writing up test cases. That's great.

Of course Claude also spat out some absolute garbage code using reflection to poke at internal properties because the access level didn't allow the test to poke at the things it wanted to poke at, along with some methods that were calling themselves in infinite recursion. Oh and a bunch of lines that didn't even compile.

The thing is about those errors: most of them were a fundamental inability to reason. They were technically correct in a sense. I can see how a model that learned from other code written by humans would learn those patterns and apply them. In some contexts they would be best-practice or even required. But the model can't reason. It has no executive function.

I think that is part of what makes these models both amazingly capable and incredibly stupid at the same time.

This doesn’t really make sense to me. GenAI ostensibly removes the drudgery from other creative endeavors too. You don’t need to make every painstaking brushstroke anymore; you can get to your intended final product faster than ever. I think a common misunderstanding is that the drudgery is really inseparable from the soulful part.

Also, I think GenAI in coding actually has the exact same failure modes as GenAI in painting, music, art, writing, etc. The output lacks depth, it lacks context, and it lacks an understanding of its own purpose. For most people, it’s much easier to intuitively see those shortcomings of GenAI manifest in traditional creative mediums, just because they come more naturally to us. For coding, I suspect the same shortcomings apply, they just aren’t as clear.

I mean, at the end of the day if writing code is just to get something that works, then sure, let’s blitz away with LLMs and not bother to understand what we’re doing or why we do it anymore. Maybe I’m naive in thinking that coding has creative value that we’re now throwing away, possibly forever.

When your agent explores your codebase trying to understand what to build, it read schema files, existing routes, UI components etc... easily 50-100k tokens of implementation detail. It's basically reverse-engineering intent from code. With that level of ambiguous input, no wonder the results feel like junior work.

When you hand it a structured spec instead including data model, API contracts, architecture constraints etc., the agent gets 3-5x less context at much higher signal density. Instead of guessing from what was built it knows exactly what to build. Code quality improves significantly.

I've measured this across ~47 features in a production codebase with amedian ratio: 4x less context with specs vs. random agent code exploration. For UI-heavy features it's 8-25x. The agent reads 2-3 focused markdown files instead of grepping through hundreds of KB of components.

To pick up @wek's point about planning from above: devs who get great results from agentic development aren't better prompt engineers... they're better architects. They write the spec before the code, which is what good engineering always was... AI just made the payoff for that discipline 10x more visible.