Disciplined AI agents are the disruptor needed to break the exchange churn model
Programmable incentives that allow independent trading agents to earn only when portfolios rise will create a fairer market for retail customers, explains Naja.
The truth is, zero-commission trading isn’t free. In 2025, U.S. market makers paid more than $4.9 billion for order flow in U.S. equity and options, up from approximately $3.8 billion in 2021 across the 12 largest U.S. brokerages. The same principle applies to crypto. The derivatives volume from Q1 of 2026 reached about $18.6 trillion, 70% of global crypto trading, with perpetuals dominating spot trading. Exchange economics reward trading velocity over disciplined decision-making.
At peak, Robinhood relied on more than 75 percent of its revenue from payment for order flow (PFOF), the hidden backbone of “free” trading, in which market makers pay brokers to route customer orders. Every broker using this incentive model needs customers to trade often, even though frequent trading works against long-term returns.
Advisory isn’t better. Robo-advisors charge 0.25 percent of assets a year, whether the account is up or down. Human advisors charge around 1 percent, billed against the principal even in down years. The extraction is built into the model by design: the advisor gets paid even when the customer loses.
Less exchange friction makes bad trades easier to repeat
The harsh truth is that exchanges need customers to trade more, not win. When retail investors lose, the exchanges still get paid. PiP World research found 74% to 89% of retail users lose money trading. Platforms charge at every step, and an AI-enabled exchange could just route you back to the same losing trade faster.
The April 14 SEC approval of FINRA’s elimination of the Pattern Day Trader rule removed the $25,000 minimum-equity friction. Removing the friction results in more trades, which creates more order flow. More order flow means more money for the broker, whether the customer’s profit and loss (P&L) is up or down.
Enter AI agents, paid to improve customer P&Ls
The disruptor to this vicious cycle for retail traders is the agent built to do what the existing exchange model avoids: trade less, size down, wait and protect customers from their worst impulses. In volatile markets, the best move is often refusing the bad trade, cutting exposure before emotion takes over. Ultimately, holding discipline when the market wants a reaction. Discipline is hard to sell for an exchange because it shrinks order flow. An agent that earns by protecting customer P&Ls breaks the current incentive model.
