There's no point at which turning your brain off will work

In early 2025, individuals began utilizing Large Language Models (LLMs) to execute tasks by having the model take action and assuming the results were satisfactory. However, as LLMs improved, this approach became increasingly problematic. Some users attempted to have the LLM write code without verifying its functionality, which often resulted in silly outcomes.

This behavior is referred to as "being a meat proxy." While being a meat proxy has shown some effectiveness in September 2026, it is uncertain if it will consistently produce average quality software. Employing meat proxies could lead companies to lay off employees, as the LLM can perform the task independently. However, the employee's role becomes crucial in ensuring the software's quality and addressing unforeseen issues.

The high throughput of LLMs raises the bar for personal performance, often leading to more thorough testing and polishing. Despite the apparent advantages of hands-off automation, the process requires significant supervision and decision-making, particularly when dealing with complex tasks and unknown unknowns. The model's performance can be significantly affected by out-of-distribution problems, such as encountering unfamiliar code, programming languages, or game scenarios. These scenarios may require human intervention due to the agent's limited understanding.