Good - Bad AI? c/o FT Magazine et al.
My source: Tim Harford, How can we tell good AI from bad? FT Magazine. 21 March 2026. #1168: pp.9-10.
'Two new working papers address the tricky issue of verifying quality. In "Some Simple Economics of AGI", Christian Catalini, xiang Hui and Jane Wu (assisted, sometimes gratingly, by generative AI) propose the inevitable 2x2 matrix in which economic activity can be easy to automate, easy to verify, both or neither. Automatable, verifiable output is the stuff that computers do for us. The non-automatable stuff remains reassuringly artisanal.
The difficult quadrant is where tasks seem easy to complete but are hard to check. Catalini, Hui and Wu call this the "runaway risk zone". It is not a reassuring label and it is not meant to be.
...
In the second paper, "A model of Artificial Jagged Intelligence", Joshua Gans offers an analogy in which asking AI to perform task is like trying to cross a river over a network of planks supported by occasional pylons. The jagged frontier is represented by the fact that some planks are long and wobbly, while others are short and sturdy. Problem one: even if the planks are typically sturdy, the wobbly planks will require most of your time and attention. Problem two: if you can't predict in advance which planks will let you down, you may quite sensibly prefer to eschew the AI entirely and row yourself across the old-fashioned way.'
Catalini, C., Hui, X., & Wu, J. (2026). Some Simple Economics of AGI. arXiv preprint
arXiv:2602.20946.
orcid.org/0000-0002-0192-8965
