By this definition, pre-LLM "business leaders" circa 2008 with not even an understanding of Excel were already "software engineers" this whole time - just prompting out software through their meatspace agents, instead of their silicon ones.

Dismissal of arguments as "just semantics" is high school level argumentation.

It's funny, that's why COBOL was originally developed in 1960: so that business people could write software themselves without needing software engineers. And it sort of worked, to an extent. History repeats itself.

That's how things work already in every workplace where there's any real danger. The companies construes its policies and paper trail in bad faith so that the employees are always operating contradictory to policy/training and then when something happens blame can be shifted on them.

It's funny how we see some people who claim to have "taste" walking around in public wearing horrible Balenciaga shoes. Are they really just tasteless, or are they doing it ironically to troll the rest of us? I guess we'll never know. Maybe someday AI robots will achieve the same level.

Not sure that's what I was getting at. People in camp 2 don't think an LLM can take over the job of a real software engineer.

It's people in camp 1 that I wonder about. They're convinced that LLMs can accomplish anything and understand a codebase better than anyone (and that may be the case!). However, they're simultaneously convinced that they'll still be needed to do the prompting because ???reasons???.

I was at 2) until the end of last year, then LLM/agent/harnesses had a capability jump that didn't quite bring me to be a 1) but was a big enough jump in that direction that I don't see why I shouldn't believe we get there soonish.

So now I tend to think a lot of people are in heavy denial in thinking that LLMs are going to stop getting better before they personally end up under the steamroller, but I'm not sure what this faith is based on.

I also think people tend to treat the "will LLMs replace <job>" question in too much of a binary manner. LLMs don't have to replace every last person that does a specific job to be wildly disruptive, if they replace 90% of the people that do a particular job by making the last 10% much more productive that's still a cataclysmic amount of job displacement in economic terms.

Even if they replace just 10-30% that's still a huge amount of displacement, for reference the unemployment rate during the Great Depression was 25%.

People need to be careful about buying into the shorthand lingo with LLMs. They do not learn like we do. At the lowest level, they predict which tokens follow a body of tokens. This lets them emulate knowledge in a very useful way. This is similar to a time series model of user activity: the time series model does not keep tabs on users to see when they are active, it has not read studies about user behavior, it just reflects a mathematical relationship between points of data.

For an LLM and this "vague" domain expertise, even if none of the LLM's training material includes certain nuggets of wisdom, if the material includes enough cases of problems and the solutions offered by domain experts, we should expect the model to find a decent relationship between them. That the LLM has never ingested an explicit documentation of the reasoning is irrelevant, because it does not perform reasoning.

>They still have a long way to go before they can master a domain from first principles, which constrains the mastery possible.

Mastery isn't necessary. Why are Waymos lacking drivers? Not because self-driving cars have mastered driving, but because self-driving works sufficiently well that the economics don't play out for the cab driver.