Everybody's jobs will be changed. Some jobs will be obsolete, but many jobs are going to be created ... Whenever companies are more productive, they hire more people.

Bottom Line
The combined effect of next-gen hardware (Cerebras, Graphcore, etc.) and modern software optimizations can realistically reduce AI’s energy use per task by 70–98% compared to just a few years ago.

This is transformative for both cost and sustainability—especially as AI adoption continues to surge.
The biggest wins will come from deploying both hardware and software advances together.

What’s misleading or limited about the term:

Reductionism:
Calling an LLM a "parrot" can understate the model’s capabilities. Modern LLMs exhibit emergent behaviors, including logical reasoning, basic planning, and few-shot learning, which go far beyond rote repetition.

Implied lack of structure:
The term might suggest LLMs are just random or mechanical mimics, ignoring the rich internal representations they develop, which enable complex tasks, such as coding, translation, summarization, and multi-step problem-solving.

Noisy dismissiveness:
It risks being used pejoratively to dismiss all LLM capabilities, which isn’t scientifically accurate. While LLMs lack understanding in the human sense, they are not just random text generators or shallow copy machines.