1820: 70% of people are farmers. 1920: 20% of people are farmers. 2020: 2% of people are farmers. Tractors and pesticides will eliminate all jobs and create a singularity of total abundance. No money will be necessary!

Nothing changed in any substantial way. Previously, the human did the work and then used the machine to validate it. Today, the machine is doing the work, and the human is validating it. Humans were needed then and are needed now. It's no different from using a tractor, a crane, an electric screwdriver, or a dishwasher.

Extrapolate this into any kind of LLM-produced result for which no human-made information exists, and you will get the percentage of time you are fooled by the LLM when working on something no one else has worked on.

In this paper, a 7B language model trained with reinforcement learning learns to orchestrate larger frontier models like GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro. It does so by writing natural-language subtasks, assigning each to one of the workers, and specifying which previous outputs that worker sees in context. The resulting system outperforms every individual frontier model on benchmarks including GPQA Diamond, LiveCodeBench, and AIME25, while averaging about three model calls per question—fewer than the multi-agent pipelines and self-reflection loops it beats. The work provides evidence that prompt engineering and pipeline design, currently done by hand in commercial AI products, can be learned end-to-end through reward signals alone. Read with an AI tutor: PDF: