Labor magnification as a measure of AI systems.
Cursor is Mag(Google SWE, 1.03) if Google would rather have access to Cursor than 3% more SWEs at median talent level.
A Mag(OpenAI, 10) system is a system that OpenAI would rather have than 10x more employees at median talent level
A time based alternative is useful too, in cases where it’s a little hard to envision as many more employees.
A tMag(OpenAI, 100) system is a system that OpenAI would rather have than 100x time-acceleration for its current employee pool.
Given that definition, some notes:
1. Mag(OpenAI, 100) is my preferred watermark for self-improving AI, one where we’d expect takeoff unless there’s a sudden & exceedingly hard scaling wall.
2. I prefer this framework over t-AGI in some contexts—as I expect that we’ll see AI systems able to do plenty of 1-month tasks before it can do all 1-minute tasks. OpenAI’s Deep Research already comes close to confirming this. Magnification is more helpful as it measures shortcomings based on whether they can be cheaply shored up by human labor.
What are all the high-level answers to “What should you, a layperson, do about AI x-risk?”. Happy to receive a link to an existing list.
Mine from 5m of recalling answers I’ve heard:
Don’t work for OpenAI
Found or work for an AI lab that gains a lead on capabilities, while remaining relatively safe
Maybe work for Anthropic, they seem least bad
Don’t work for any AI lab
Don’t take any action which increases revenue of any AI lab
Mourn
Do technical AI alignment
Don’t do technical AI alignment
Do AI governance & advocacy
Donate to AI x-risk funds
Cope
Don’t perform domestic terrorism