I think there is a middle ground too. Well organised Engineering and Product teams using AI efficiently whilst actually talking together and making decisions. Neither AI zealots, nor doom and gloom.

I can't find a very good comment from HN from a few months ago with this churn of AI posts, but the idea was quite right: there were tinkerers, who loved a problem solving process, and entrepreneurs, who don't care about the problem itself and beauty of a neat solution, but only the end products to sell.

Entrepreneurs currently are in heaven, and tinkerers are in hell.

The enthusiasts are founders, executives and investors who don’t actually have to use AI to build products and therefore have a somewhat distant opinion about its real capabilities but will capture all gains from AI. They only build prototypes and rough drafts and don’t understand that AI makes the first 80% easier and the last 20% harder, and makes maintainability a nightmare where a codebase that’s gone through vibe coding for 3 months is like the equivalent of a 10 year old legacy codebase.

The burnouts are employees who are being asked to produce 10x the output, do their own project management, design, QA, devops, customer development, work long hours to make up for the gap in how much executives think AI boosts productivity and how much it actually does, and for 0.05% equity at best.

The way I see it LLMs are accelerating trends that have been at play for a decade or two now.

I think we are seeing bloat accelerating and the boom will eventually go bust.

My hope is that from the ashes of the current boom/bust cycle we might actually get "software engineering" once the dust settles and society catches up with technology in terms of culture, regulations, education etc.

Most power tools have a dichotomy like this.

If you don't have a clear mission to pursue with your tools, dissatisfaction is the next mandatory stop on your journey. You can change your tools as often as you want. If the mission is wrong it won't matter.

AI might be unique in that it can force a business into an inevitable failure state much faster than it would have experienced otherwise. From the perspective of long term economic activity, this might actually be a good thing. Most businesses will never be successful and this technology seems to be quite good at eliminating any ambiguity regarding that fact.

The real thing that is being tested here is the competence, discipline and focus of leadership. Executing well on AI in a team setting takes a lot of diplomacy and mentorship to get the nervous energy dealt with properly. Many failure modes with AI look like one lunatic employee going mad with a codex/claude subscription and slowly dragging everyone else into the cartoon fight dustball.

I had a meeting yesterday regarding deploying custom enterprise chatbot for a client wherein everyone was practically yelling over each other. We unanimously considered it an extremely productive call despite the chaos. I cannot imagine what is happening elsewhere. I like to believe the team I am working with is one of the better ones.

> On one hand you have AI enthusiasts who are shipping more than they ever have, they are super excited about being able to use the models and build everything they've ever wanted to. Utopia, amazing.

You can't ship faster than you can comprehend, unless you stop caring about comprehension.

Up until the last year the best developers were generally those who valued comprehension and wanted to really understand the libraries, code and infrastructure of their projects because that comprehension was what led to quality software.

Those who didn't care tended to ship buggy crap and weren't great devs.

In my experience those who didn't really care about deeply understanding the systems they were working on are loving AI. But those who did care are now literally unable comprehend the state of the codebase and are being forced to work in a way that's extremely unnatural to them.

I say this because I've personally struggled with this over the last year as I've was one of those people who spent a lot of time building comprehensive mental models for the systems I worked on. Now I just assume the AI understands and just do what I need to do to get whatever functionality I'm working on over the line. I don't like it and I've lost all technical interest in coding at this point, but it is what it is ultimately. Coding is no longer about comprehension, it's just about asking Claude to do something.

I think the people who are into it are the people who before AI would throw up a pull request that was complete shit, expect everyone else to tell them what to fix, and somehow still keep their job.