1. Companies can hire overseas. There's some cost to it in terms of added friction, but if wages rise enough in C1, then it's worth the friction to hire in C2 instead.

2. Workers also consume and invest, raising demand for other jobs. Employment is not a zero sum game, especially at the macro scale.

Comments URL: https://news.ycombinator.com/item?id=47525989

Points: 4

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But anyways, the order of causation is probably reversed. Cities with high density are forced to invest in good public transport by sheer public demand and pressure.

Not in the sense that AI is replacing current jobs, but that they would rather invest that money in Anthropic or on Data Center buildouts

https://www.electrive.com/2026/01/12/mercedes-pauses-level-3...

I empathize with his sense that if we could just provide the right context and development harness to an AI model, we could be *that* much more productive, but it might just be misplaced hope. Claude Code and Cursor are probably not that far from the current frontier for LLM development environments.

An 8-10 year delay from expectations is not too bad all things considered.

Yes, public transit is not a necessity here like in the States, but it's a nice convenience to have, and plenty of people are wealthy enough to pay for it.

2. Transaction costs for liquidating your wealth is materially different from selling enough to significantly affect the market for an asset. As an extreme example, large holders of a meme cryptocurrency cannot sell the majority of their holdings without crashing the value of their coin.

3. Borrowing works for smaller amounts if you can spread out the sales of your assets over a long period of time (or if you don't need to sell at all, e.g. if investing in something that gives you returns).

Unlike freeform writing tasks, coding also has a strong feedback loop (i.e. does the code compile, run successfully, and output a result?), which means it is probably easier to generate synthetic training data for models.