In the absence of ground-truth verifiers, the foundation of modern frontier AI systems is human expressions of preference (i.e ‘taste’), deployed at scale.
Gwern argues that this is what he sees as his least replaceable skill.
The “je ne sais quois” of senior researchers is also often described as their ‘taste’, i.e. ability to choose interesting and tractable things to do
Even when AI becomes superhuman and can do most things better than you can, it’s unlikely that AI can understand your whole life experience well enough to make the same subjective value judgements that you can. Therefore expressing and honing this capacity is one of the few ways you will remain relevant as AI increasingly drives knowledge work. (This last point is also made by Gwern).
Concrete example: Even in the present, when using AI to aid in knowledge work is catching on, the expression of your own preferences is (IMO) the key difference between AI slop and fundamentally authentic work
“Taste” as a hard-to-automate skill.
In the absence of ground-truth verifiers, the foundation of modern frontier AI systems is human expressions of preference (i.e ‘taste’), deployed at scale.
Gwern argues that this is what he sees as his least replaceable skill.
The “je ne sais quois” of senior researchers is also often described as their ‘taste’, i.e. ability to choose interesting and tractable things to do
Even when AI becomes superhuman and can do most things better than you can, it’s unlikely that AI can understand your whole life experience well enough to make the same subjective value judgements that you can. Therefore expressing and honing this capacity is one of the few ways you will remain relevant as AI increasingly drives knowledge work. (This last point is also made by Gwern).
Ask what should be, not what is.
Concrete example: Even in the present, when using AI to aid in knowledge work is catching on, the expression of your own preferences is (IMO) the key difference between AI slop and fundamentally authentic work