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Why Off-the-Shelf AI Doesn't Understand Money — Udi Menkes, Intuit

Jul 29, 2026 · 19:50

Udi Menkes, principal PM at Intuit, argues that off-the-shelf frontier models deliver a 'fluent bluff' when advising on money: advice that sounds right but is dangerous because models have read about money but lack experience. He shows a rental property example where a frontier model told a landlord in negative cash flow to acquire a second property, while a model grounded in real outcomes recommended raising rent 5-10%. Intuit's head-to-head test across 100,000 businesses found frontier models gave advice that would harm businesses 40% of the time, while a mid-sized grounded model outperformed them by training on millions of state-action-outcome records from QuickBooks data. A Princeton study confirmed frontier models given $1M went bankrupt within 500 days, while a simple rule-based system beat them. Menkes says the moat belongs to whoever owns the best system of context, and advises leaders to find verified outcomes in their own data to ground AI.

Personality Driven Development: Exploring the Frontier of Agents with Attitude

Feb 17, 2025 · 18:00

Ben, from Perpetual, argues that anthropomorphizing AI agents—giving them personalities, forms, and preferences—transforms user adoption and engagement, despite significant challenges. He presents examples like Tech Lead, an artichoke recruiter, and hamster teams, showing how form factors create instant understanding and enable price anchoring (e.g., 1/20th the cost of a junior employee). Benefits include easy problem decomposition (specialized agents outperform generalized), branding, and fun. However, downsides include reinforcing stereotypes (100% of generated software engineers appear male), raised performance expectations, difficulty rebranding, distraction from core value, and stark reminders of job replacement—illustrated by a CEO introducing an IC as the one who 'knows what they're doing.' Ben highlights personality-driven development where every instance is bespoke, with prompt-driven preferences (e.g., 'I like Ivy League schools'), making debugging uniquely challenging.