In Episode 9 Paul Karner and Dave Mangot tackle a recent Fortune interview (https://fortune.com/2026/06/21/nobel-laureate-daron-acemoglu-ai-productivity-capitalism-democracy/) with Nobel Prize-winning economist Daron Acemoglu, who argues that the massive productivity gains promised by AI are harder to achieve than presumed.

Paul puts on his PhD economist hat to break down what this skepticism means for private equity deal teams trying to manage their AI budgets. He introduces the concept of "economic accounting"—understanding the counterfactual of what a company could achieve without AI simply by adopting solid engineering foundations.

For operating partners, the takeaway is clear: preparing a portfolio company for AI requires cleaning up data, mapping workflows, and establishing guardrails. Even if the AI hype is overstated, doing this foundational work will inherently make the company more profitable.

Key Takeaways:

The AI "Hoax" Analogy: Preparing for AI forces organizations to implement best practices. Even if AI doesn't yield AGI-level miracles, those foundational improvements directly increase EBITDA and profitability.

Economic vs. Financial Accounting: CFOs must look at the "counterfactual": evaluating what productivity gains are actually coming from the AI versus what gains are just the result of getting the company's operational house in order.

Ending the Token Free-For-All: Moving from a subsidized "token-maxing" phase to a mature operating model requires pointing a sustainable budget only at areas where AI truly creates unique value.

Multiple Expansion: Deal teams that stop blindly "rubbing AI on everything" and strategically direct dollars toward genuine tech efficiencies will see the results directly in their exit multiples.