This may be evidence that it's more difficult than evangelists first imagine, but it's not evidence of a technical obstacle. Generally, "automation failed" does not imply that "automation is impossible".

To your individual points:

- OOP and UML are domain-specific abstractions. Aside from still being very much used in expanding niches [1], they have failed to automate much work because their proponents failed to cover enough cases to have a useful general-purpose abstraction.

- Outsourcing is a labor strategy. There's nothing technical that prevents another similarly capable person from doing your job, at least in the next town, if not another country. The obstacles were/are social and political, and the WFH movement shows that. Also, outsourcing is not going anywhere, it's just reduced and converted to nearsourcing due to backlash.

- By contrast, software is a general-purpose abstraction [2]. Databases are a type of software. You can see LLMs [3] as schema-less databases that contain millions of abstractions connected to each other. You can get a UML model or Python code or text by querying the LLM's query engine in a language much more flexible than SQL.

Vibe coding makes it seem like the funny intermediate bullshit is the end result of using LLMs, but it's not. Sure, I agree that LLMs don't make sense to use when a calculator is enough, but I don't see any functional limitations to improving LLMs. Maybe new algorithms or combinations are needed, but no matter how slowly, quality is expected to reach at least human level for the majority of current tasks (on which many jobs depend).

Which leads to my point: we need political, social, philosophical reasons to limit or integrate automation in our civilization, not just watch and hope there's a big enough technical obstacle so we can keep our current jobs.

[1] For example, model-based software engineering is still a growing; slowly, but growing.

[2] So is the organization of mechanical machines or analog computers, but it's faster to reorganize and orchestrate electrical signals.

[3] More precisely, foundation models, because it's far more than natural language processing.

I'm not saying there is a technical obstacle, I'm saying there's a practical obstacle that LLMs and vibecoding don't overcome.

The evidence isn't in that it failed but why it failed. It's not that the problem was more difficult than they anticipated, it's that they didn't comprehend the nature of the problem they were solving from the outset. Software development is an incremental creative activity that involves good taste, constantly shifting and vague requirements, and keen understanding of human factors and ergonomics. LLMs fail at all three. And in fact the shifting and ambiguous requirement element is fatal to the notion of automating the process.

Outsourcing and oop and uml and LLMs and vibecoding --- they're all the same futile attempt at the same thing: abstracting the human out of a loop built for the benefit of humans. It's nonsensical, and the only people who want to do it are capitalists, so it's doomed to fail yet again.