Nah, sometimes the expectation and advertisement was that you could let go of the white collar worker because you're paying the overseas person 1/10th the amount. And "overseas person" is pretty general.
"Everybody knew" it was a bad idea to get a CS degree for a bit after the dot-com bust because of that.
(Some white-collar industries did get hit much harder by that; VFX is one I've heard in that context quite a bit.)
This isn't analogous to the threat of AI.
(Even assuming "intelligent" is a sensible label to apply to an LLM holding hands with a shell script in an infinite loop)
AI models get better and more efficient every 3 months, run around the clock, can be copied infinitely, and unprecedented amounts of capital and research talent are being thrown at any limitations we can see with them (such as problems writing correct code in 2024, lack of agency in 2025, autonomy and self-improvement in 2026). That's the difference between labor replacement through outsourcing vs. labor replacement through automation.
The idea that “AI models” have acquired “agency” as of 2025 and are working on “self-improvement” in 2026 is closer to delusion than exaggeration.
Models have absolutely acquired agency as of 2025. Developers are no longer copy-pasting code from ChatGPT into their text editor, they're working with agents like Claude Code and Codex that can edit code, run terminal commands, do web searches, manage their own context windows, sift through gigabytes of logs with datadog MCP, etc.
Self-improvement is also being worked on. Claude Tag learns over time in slack convos. My company also has an agent that updates its own skill files after every conversation so that we don't need to keep reminding it about the same workflows every time. Is it clunky as hell? Yes. Are the labs plowing billions of dollars into "continual learning" and "recursive self improvement"? Also yes.
What you call a model acquiring agency I call plain old software with productivity workflows designed by humans, with deliberate goals. We must separate “model” and an execution environment using a model. [Model] ≠ [A glorified shell script doing API calls in a control flow based on heuristics]. Agents are not AI, they are plain old software. The weights are the model, and that very much remains a static artifact (and pre-post training models haven’t improved much over the last few years).
What you call self improvement is a duck tape hack to imitate persistence and save on inference. Every time you do an API call, anything that needs to be processed is sent to the model. Narrowing that context down saves money. Finding clever ways to do that improves apparent performance and value. The cleverness is still human.
These are all useful innovations on top of LLMs, which remain models that generate text and symbols based on static weights, which in turn represent training data and the provider’s preferences.
"Oh yeah, we're gonna bring in some entry-level graduates, farm some work out to Singapore, that's the usual deal"
Office Space 1999
It was so pervasive that it was satirized by someone that had worked in engineering in the 80s
Evidently, this has not really changed with LLMs and coding agents. It’s what AI companies are betting on though.