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Back to This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick, with Ethan Mollick.

Why Don't Companies See AI Productivity Gains Yet?

Ethan Mollick says you do not get organizational productivity gains instantly from individual ones. A person can produce 100 times more code than before, which is great for a solo entrepreneur. Inside a company running two-week sprints, coordinating with marketing, and holding daily stand-ups, that 100 times gain becomes a barrier. Nobody has time to deal with all of that code.

Shipping stats show the same gap: 20 times more code produced, and 30 percent more products shipped. Organizations have not adapted to AI yet. Alex Kantrowitz cites App Store figures of more apps and fewer reviews. A flood of novels, games, and apps is one pattern. Studies of products shipped show a huge increase in coding and a smaller increase in products, less by an order of magnitude.

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This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick ·

Big Technology Podcast

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  1. Are Large Companies Seeing a Return From AI?Ethan Mollick says surveys show 85 percent of companies had positive ROI from AI as of last year.
  2. How Did a $200 Million Data Migration Drop to $20 Million?Ethan Mollick says no single move unlocks the full potential of AI inside a company. The work is structural change, and for many organizations it takes years.
  3. How Much of America's 20th-century Edge Came From Management?Ethan Mollick says 40 percent of the advantage of American companies in the 20th century came from better management, according to a paper by Raffaella Sadun…
  4. What Is the AI Capability Overhang?Ethan Mollick calls the overhang a huge gap between current AI capabilities and future capabilities.
  5. When Did Practical AI Agents Become Possible?Ethan Mollick says practical agents were not possible until models reached Opus 4.6 level or 4.5 level around November and December of last year.
  6. Why Do Superforecasters Keep Getting AI Wrong?Ethan Mollick says superforecasters keep getting AI wrong because an exponential is hard to predict. The labs have been right.
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