Why Off-the-Shelf AI Can't Understand Money (And How to…

Udi Menkes explains why LLMs give confident but wrong financial advice and how grounding real data creates trustworthy AI.

Imagine asking a top AI model to boost your rental income — it sounds smart, but the advice can be dangerously off.

⚡ TL;DR: Off‑the‑shelf LLMs give confident‑looking money tips that often lack real‑world grounding. Udi Menkes shows how Intuit builds state‑action‑outcome data to turn raw business experience into trustworthy AI advice.

The Simple Version

Most AI chatbots can talk about money like they’ve lived it. They sound convincing, but they’ve never actually managed a property.

How It Actually Works

These models generate responses based on patterns in text, not on lived experience.

🎯 Think of it this way: It's like a weather app that knows all the words for rain but has never felt a drop.

When you ask about rent hikes, the model blends statistical patterns to produce a plausible answer.

🎯 Think of it this way: A chef can recite a recipe perfectly without ever tasting the dish.

Grounding Adds Real Context

Grounding feeds the model actual outcomes from real businesses.

🎯 Think of it this way: It's like giving the chef a kitchen where every dish is tracked, so the advice is based on what actually worked.

Technical Deep Dive

Intuit transforms business data into millions of state‑action‑outcome records.

These records let the model map a current situation to proven actions and their results.

The architecture resembles a reinforcement‑learning loop that learns from cause‑and‑effect.

Developers can implement similar pipelines by tagging events, logging decisions, and storing outcomes.

Why This Matters

Bad AI advice can cost real customers money and erode trust.

When models are grounded, they separate sounding‑right suggestions from proven strategies.

This shift could redefine how finance, accounting, and even everyday decisions are automated.

Key Takeaways

LLMs excel at fluent language but lack lived experience.
“Fluent bluff” describes confidently wrong advice.
Grounding ties AI answers to real‑world data.
Intuit uses state‑action‑outcome records for grounding.
Outcome‑driven finance relies on measurable results.
Building your own context data creates a competitive moat.
Developers can adopt similar pipelines with event logging.

Why Off-the-Shelf AI Can't Understand Money (And How to Fix It)