As AI agents begin to conduct economic work and transact with each other, they will create an "agentic economy." Our current financial system is ill-equipped for this future, lacking the ability to handle the billions of instant, global, and micro-scale transactions that will become commonplace.

Historically, time and cost acted as a natural defense against overwhelming systems. AI agents can now execute millions of tasks—like filing legal motions or making lowball offers—for nearly free, threatening to collapse systems not built for this scale.

If AI wealth management becomes mainstream and models rely on similar data signals, it could create a "herd problem." All AIs might execute the same buy or sell trades simultaneously, leading to synchronized panics or euphoric bubbles and unprecedented market volatility.

AI agents will attack corporate profit centers that rely on consumer inertia. They will automatically utilize unused flight credits, reclaim loyalty points, and dispute insurance claims. This shifts value back to the consumer and turns previously profitable friction into costly operational burdens for incumbent companies.

For current AI valuations to be realized, AI must deliver unprecedented efficiency, likely causing mass job displacement. This would disrupt the consumer economy that supports these companies, creating a fundamental contradiction where the condition for success undermines the system itself.

While AI currently gives Nubank an edge, its long-term effect could commoditize the entire banking industry. AI agents could constantly optimize consumers' finances, automatically switching them to the lowest-rate products and eroding industry-wide margins.

Businesses with moats based on network effects or consumer friction are vulnerable to "agentic commerce." AI agents, tasked with finding the absolute best price without experiencing the tedium of comparison shopping, will bypass brand loyalty and platform stickiness. This threatens any business model that relies on being the default or convenient choice.

The key threat from AI isn't just its capability, but the unprecedented speed of its improvement. Unlike past technological shifts that unfolded over decades, AI agent autonomy on complex tasks has grown exponentially in just two years. This rapid acceleration is what financial systems and labor markets are not stress-tested for.

Robinhood's AI agents for trading and shopping introduce a new challenge: user trust. The key question isn't whether AI *can* act autonomously, but how much leeway (or "leash") users will grant it with real money. Adoption will hinge on managing this perceived risk, as AI mistakes have direct financial consequences.