Why Disciplined AI Agents Can Reshape Trading Incentive Models

Through much of the modern brokerage era, retail traders have operated within a structural conflict they rarely recognized: the platforms they trusted to execute their orders profit from activity, not outcomes. A recent analysis by market commentator Saad Naja captures the problem clearly — brokerages and exchanges don't need their clients to win, they need them to keep trading. That dynamic has long been the quiet engine behind the aggressive marketing of options products, leveraged products, and frictionless mobile trading apps.

Biaya Tersembunyi dari Insentif Berbasis Volume

The data doesn't favor retail. Study after study shows that between 74 and 89 percent of retail traders lose money over meaningful time horizons. Yet the engagement loops that drive churn — push notifications, gamified streaks, instant order routing — remain a core revenue mechanic for many platforms. Payment for order flow, the practice of brokerages selling client orders to market makers, simply makes this conflict structural rather than incidental.

Bagaimana Agen AI Mengubah Persamaan

What changes the equation is the arrival of disciplined AI agents whose compensation is tied to portfolio performance rather than trading volume. Picture a software agent that places orders on a user's behalf but earns a fee only when the user's portfolio grows. Such an agent has every reason to stay still when conditions call for patience — an incentive directly at odds with platforms that need you to keep swiping and tapping.

Naja's argument rests on programmable incentives encoded into smart contracts, allowing an agent's compensation to be defined transparently and verifiably. For users of platforms like Suluh Nilaiwan, this matters because it points to a future where the burden of discipline is partly absorbed by software that has no reason to encourage overtrading.

Dukungan dari Sisi Regulasi

There is regulatory support as well. New restrictions on payment for order flow y