> The first group are still thinking fairly deeply about design and interfaces and data structures, and are doing fairly heavy review in those areas.

I can't speak for others, but I'd go further and say that LLMs allow me to go deeper on the design side. I can survey alternative data structures, brainstorm conversationally, play design golf, work out a consistent domain taxonomy and from there function, data structure and field names, draft and redraft code, and then rewrite or edit the code myself when the AI cost/benefit trade off breaks down.

That’s a little bit of a No True Scotsman. Yes there are people who do not review anything; but even people who are reviewing every line from an LLM do not have the same understanding as someone who wrote it themselves.

I’m not making a judgement call about which is better, but it was widely accepted in tech before the advent of LLMs that you just fundamentally lack a sense of understanding as a reviewer vs an author. It was a meme that engineers would rather just rewrite a complicated feature than fix a bug, because understanding someone else’s code was too much effort.

> and on the other side of things is freaking Steve Yegge pitching a whole new editor that lets you orchestrate a dozen or more agents all vibing away on code you're apparently never going to read more than a line or two of

I find it useful to not listen to people who just talk.

That blog post is surreal. It's like cryptocurrencies and the whole web3 nonsense. Cryptocurrencies basically don't work, so there have been a hundred aimless attempts at fixing self inflicted problems caused by deficiencies of cryptocurrencies with no actual goal that has any impact on the real world.

It's the same thing here. AI has dropped the cost of software development, so developers are now fooling themselves into producing low or zero value software. Since the value of the software is zero or near zero, it doesn't really matter whether you get it right or not. This freedom from external constraints lets you crank up development velocity, which makes you feel super productive, while effectively accomplishing less than if you had to actually pay a meaningful cost to develop something.

Like, what is the purpose of Gas Town? It looks to me like the purpose of Gas Town is to build Gas Town.

> The first group are still thinking fairly deeply about design and interfaces and data structures, and are doing fairly heavy review in those areas

I worry about the first group too, because interfaces and data structures are the map, not the territory. When you create a glossary, it is to compose a message, that transmit a specific idea. I find invariably that people that focus on code that much often forgot the main purpose of the program in favor of small features (the ticket). And that has accelerated with LLM tooling.

I believe most of us that are not so keen on AI tooling are always thinking about the program first, then the various parts, then the code. If you focus on a specific part, you make sure that you have well defined contracts to the orther parts that guarantees the correctness of the whole. If you need to change the contract, you change it with regard to the whole thing, not the specific part.

The issue with most LLM tools is that they’re linear. They can follow patterns well, and agents can have feedback loop that correct it. But contracts are multi dimensional forces that shapes a solution. That solution appears more like a collapsing wave function than a linear prediction.