That's a good point. However, overseas people are still people. They need to sleep, get sick, and the better they get at their jobs, the more money they will demand. The cheap ones also often have communication barriers and work slower than the workers they're replacing.
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.
We are either living in different worlds, or squabbling over different meanings of words.
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.
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.