Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:.
Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:.
This explains why the first major AI products appeared in chat, image generation, and code assistance.
Not because these were the hardest human problems, but because their outputs were relatively easy to inspect.
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Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect.
The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed.
The issue is then over how well you can measure something. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind.
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