In early 2025, I started seeing people turn off their brain as they use LLMs. They would have an LLM take an action (summarize text, write some code, etc.), and just assume that it worked. This generally didn't work in early 2025 and the result was often quite silly.

As LLMs have gotten better, I've seen more of this. Sometimes, people will try to get the LLM to write some code for them and basically just assume that it works. Sometimes there's a human in the loop and, if the thing doesn't work, they'll ask the LLM to figure out the problem and solve it. Niklas Gruhn calls some variants of doing this being a meat proxy.

Being a for loop meat proxy works better than it did in early 2025 and the software I've tried that's developed like this sometimes actually sort of works. Not well enough that I'd want to use it or that it's successful, but I'm impressed at how effective being a meat proxy is in September 2026. You could imagine LLMs improving enough that brain-off meat-proxy development produces average quality software in the foreseeable future, or even that LLMs improve enough that they produce great software without a human in the loop.

Let's say that happens. What reason is there for the company to employ the meat proxy? The company can just run the LLM in a loop and lay off the employee. There's no point at which this methodology will work for the employee.