Where is your AI dividend landing?

Most AI programs I see are investing on the wrong side of the one asymmetry that matters.

A guest post in Latent Space this week, written from inside a biotech, put it precisely: language models have collapsed the cost of thinking, while the cost of doing has barely moved. The experiment still takes as long as it takes; only the thinking around it has become cheap.

Transformation works the same way. Analysis, options papers, business cases and strategy decks now arrive in days, yet the throughput of delivery, procurement and change has not moved at all. The result is organisations drowning in beautifully argued analysis while nothing ships, and wondering why.

So before your next AI investment, ask where the dividend lands. If it buys more analysis rather than more delivery capacity, the program is going backwards, no matter how good the thinking looks.

Where is your AI dividend actually landing: more thinking, or more doing?

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