Why Off-the-Shelf AI Doesn't Understand Money — Udi Menkes, Intuit
Off-the-shelf Large Language Models (LLMs) often provide fluent but inaccurate financial advice—a phenomenon termed the 'fluent bluff'—because they lack real-world experience. The core argument is that mere context is not sufficient; true intelligence requires grounding in verifiable business outcomes. To solve this, Intuit builds systems that transform raw business data (e.g., General Ledger, P&L) into millions of State-Action-Outcome records. These structured datasets are used to train models that predict the actual impact of actions, leading to 'outcome-driven finance' and outperforming frontier LLMs in real-world decision-making.
Key takeaways
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The Fluent Bluff: Why Off-the-Shelf Models Fail at Money
4:08
LLMs trained on general internet data (blogs, books) can give confident advice that is fundamentally wrong for specific business constraints. Examples include recommending a negative cash flow business acquire another property or raising prices when it risks losing all revenue.
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The Solution: Grounding in State-Action-Outcome Data
Instead of relying on general knowledge, the system must be grounded by creating structured records detailing what a company *did* (Action) given its current financial status (State), and what the verifiable result was (Outcome). This moves advice from theoretical possibility to proven reality.
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Measuring Impact: Beyond Simple Difference
15:45
Determining the true impact of an action requires advanced statistical methods like Conditional Average Treatment Error (CATE) to account for confounding variables, such as a company's natural propensity for success. Failing to do this leads to inaccurate conclusions about causality.
