I'm actively watching understanding slip away from developers, code review getting paired down to no comment checkmarks, and codebases go to bloated messes that nobody can read. Axioms like engineers must understand and take responsibility for the code they ship are getting torn down, and the products coming out are reflecting conway's law, becoming impenetrably obtuse and always "so complex there are no obvious deficiencies" (as opposed to "so simple there are no obvious deficiencies" which used to be the aim).

The one thing plan mode helped is for the humans to get an understanding of the strategy, and be able to poke around and look at the design and architecture. You can achieve this with some self discipline and keeping shorter leashes on agents, but it feels like a losing battle. The best devs still put out good code, but the poor devs are learning nothing while their metrics look great. I can't help but think we are racking up immense amounts of debt that will very soon become due.

We cannot blame just LLM models, it is brains nature to save energy. If agent did tasks consistently good, our brain try to delegate cognitive load to the model to save energy. After consistent use of LLMs anyone can have tendency commit slop just reviewing at high level, this is specially true with busy lifestyle. Also AI generated code do not give dopamine just like solving problems we did before LLMs, we tend to get lazy. Strict discipline is necessary to make good use of LLMs in order to not commit slop and not to make us dumb.

I feel the same way but I have successfully refactored some of the early experiments. Our team has settled on targeting a double output from the before times but more ambitious product vision because AI can teach us things we don't know. We actually target 2 days of coding and 3 days of learning with Ai so the increased efficiency allows upskilling rather than just pushing more code

100% this. Some people are basically adding AI as a dependency for their projects. They no longer understand the code

I see this complaint frequently about losing track of what the agents are doing, and I agree you do need to understand your system. But there seems to be this baked in assumption that if you lose track, you now need to manually wade through this massive mess to untangle it and maybe that is impossible. I don't agree.

If you don't understand the codebase, ask the agent to explain it to you. I'm not not kidding. Modern frontier models are fantastic as this - even more so than actually writing the code. It can tell you in words. It can generate architectural diagrams and sequence diagrams. It can write tests and scripts that prove it's assumptions. It can happily refactor so that the system design is aligned with your preferences.

Once you accept this, you can stop worrying so much about it and instead focusing on building the architectures and tools that lets the agents succeed better and faster - so called closed loops or agents prompting agents. Build systems that are more easily verifiable and deterministic so the agent can write very powerful property based tests. Focus more on what and why you are building, how to make sure all external properties are verifiable and leave the internals to the agents. The code is not really for us anymore.

Just like rushing made messes in the before times so too does rushing via LLM. The exact same outcome will happen, but at a far greater velocity and scale than anything we've seen in this industry before. Old Testament, Mr. Mayor, real wrath-of-God type stuff!

It's truly bizarre to read about all these people who just give up on any understanding about what they are working on.

I very regularly use plan mode not to even make a plan of action itself, but to better understand what possible issues might come up when implementing some feature or fixing some bug. And it is quite common for me to fix or rewrite certain findings that AI comes up because its assumptions are not quite right or don't align with overall goal.

And yet so many seem to be perfectly fine leaving all the decisions to AI - even if it's going in the wrong direction. I suppose that's all the people who got into software purely for money or status - never really caring about the actual thing they are working on.

"The one thing plan mode helped is for the humans to get an understanding of the strategy"

For me, this phase still happens, but a distinct "plan mode" is unnecessary: I just tell the model, "This is discussion; no code changes yet." and spend hours figuring out what will and will not be done.

And what happens when it becomes due?

> The best devs still put out good code, but the poor devs are learning nothing while their metrics look great. I can't help but think we are racking up immense amounts of debt that will very soon become due.

I agree with this, but the reality is that it's only the result of models empowering devs, and power in good hands amplifies positive results while power in mediocre hands amplifies technical debt.

It's a good time to choose wisely who you work with.