This is the key point for me in all this.

I've never worked in web development, where it seems to me the majority of LLM coding assistants are deployed.

I work on safety critical and life sustaining software and hardware. That's the perspective I have on the world. One question that comes up is "why does it take so long to design and build these systems?" For me, the answer is: that's how long it takes humans to reach a sufficient level of understanding of what they're doing. That's when we ship: when we can provide objective evidence that the systems we've built are safe and effective. These systems we build, which are complex, have to interact with the real world, which is messy and far more complicated.

Writing more code means that's more complexity for humans (note the plurality) to understand. Hiring more people means that's more people who need to understand how the systems work. Want to pull in the schedule? That means humans have to understand in less time. Want to use Agile or this coding tool or that editor or this framework? Fine, these tools might make certain tasks a little easier, but none of that is going to remove the requirement that humans need to understand complex systems before they will work in the real world.

So then we come to LLMs. It's another episode of "finally, we can get these pesky engineers and their time wasting out of the loop". Maybe one day. But we are far from that today. What matters today is still how well do human engineers understand what they're doing. Are you using LLMs to help engineers better understand what they are building? Good. If that's the case you'll probably build more robust systems, and you _might_ even ship faster.

Are you trying to use LLMs to fool yourself into thinking this still isn't the game of humans needing to understand what's going on? "Let's offload some of the understanding of how these systems work onto the AI so we can save time and money". Then I think we're in trouble.

I don't think "understanding" should be the criteria, you can't commit your eyes in the PR. What you can commit is a test that enforces that understanding programatically. And we can do many many more tests now than before. You just need to ensure testing is deep and well designed.

" They make it easier to explore ideas, to set things up, to translate intent into code across many specialized languages. But the real capability—our ability to respond to change—comes not from how fast we can produce code, but from how deeply we understand the system we are shaping. Tools keep getting smarter. The nature of learning loop stays the same."

https://martinfowler.com/articles/llm-learning-loop.html

> Are you trying to use LLMs to fool yourself into thinking this still isn't the game of humans needing to understand what's going on?

This is a key question. If you look at all the anti-AI stuff around software engineering, the pervading sentiment is “this will never be a senior engineer”. Setting aside the possibility of future models actually bridging this gap (this would be AGI), let’s accept this as true.

You don’t need an LLM to be a senior engineer to be an effective tool, though. If an LLM can turn your design into concrete code more quickly than you could, that gives you more time to reason over the design, the potential side effects, etc. If you use the LLM well, it allows you to give more time to the things the LLM can’t do well.

Why can't you use LLMs with formal methods? Mathematicians are using LLMs to develop complex proofs. How is that any different?