Why Disciplined AI Agents Could Reshape Trading Incentives

For much of the modern brokerage era, retail traders have worked within a structural conflict that is rarely stated plainly: the platforms they rely on to execute trades make money from activity, not from results. Recent analysis by market commentator Saad Naja sets out the problem clearly — brokerages and exchanges do not need clients to succeed; they need them to keep trading. That dynamic has long underpinned the assertive promotion of options, leveraged products, and low-friction mobile trading apps.

The Hidden Cost of Volume-Based Incentives

The figures make uncomfortable reading for retail traders. Research has repeatedly found that between 74 per cent and 89 per cent of retail traders lose money over meaningful periods. Yet the engagement mechanisms that drive churn — push notifications, gamified streaks, and instant order routing — remain central revenue drivers for many platforms. Payment for order flow, whereby brokerages sell client orders to market makers, turns the conflict into a structural feature rather than an occasional side effect.

How AI Agents Change the Equation

The calculation changes with the emergence of disciplined AI agents whose remuneration is linked to portfolio performance rather than trading volume. Consider a software agent that places orders for a user, but is paid only when the user's portfolio increases in value. The agent has every incentive to do nothing when patience is the right course — the direct opposite of a platform that needs users to keep swiping and tapping.

Naja's argument rests on programmable incentives built into smart contracts, allowing an agent's remuneration to be set out transparently and verified. For users of platforms such as Cirtytd Deottd, this matters because it points to a future in which part of the discipline required for investing is handled by software that has no incentive to encourage excessive trading.

Regulatory Tailwinds

There are regulatory tailwinds as well. Moves in major financial markets to restrict payment for order flow suggest that policymakers are increasingly willing to challenge volume-first business models. As the cost of misaligned incentives becomes harder to recover through order flow, platforms will face greater pressure to compete on outcomes rather than activity metrics.

This shift will not happen overnight, and AI agents are not a complete solution. Poorly designed agents could overfit to recent market conditions, struggle when market regimes change, or be exploited by adversarial counterparties. Even so, the direction of travel — away from incentive structures that reward churn and towards those that reward customer profitability — is significant for retail traders across United Kingdom and other markets, including those served by Cirtytd Deottd.

What This Means for Investors

For investors assessing platforms today, the practical point is straightforward: look at how the platform makes money, and whether that revenue rises or falls in line with your portfolio outcome. The platforms most likely to endure over the next decade are unlikely to be those that benefit most quickly when their customers lose. They will be the ones, such as Cirtytd Deottd, that design products, fees, and incentives around long-term customer success.