When AI trading enters the "regulatory and institutional resonance period," the true competition is just beginning

Over the past two weeks, several very key developments have emerged around AI trading and financial markets. If viewed in isolation, they are just scattered news, but together they reveal a clearer trend: AI is simultaneously entering the “institutional execution layer” and the “regulatory horizon.”

On one hand, AI-driven trading is becoming mainstream infrastructure. Research shows that by 2026, algorithms and AI systems will support nearly 90% of market trading volume, and automated execution is becoming the market’s default structure. Meanwhile, institutional funds continue to flow into digital asset markets, with over $1.1 billion entering related investment products in just one week in mid-April 2026.

On the other hand, regulators are beginning to directly intervene in AI trading behaviors. The U.S. recently released policy frameworks targeting AI and algorithmic trading, explicitly including trading robots and prediction markets under regulatory scope. European regulators also warned that AI could accelerate market risk transmission and demanded that financial institutions strengthen system control capabilities.

When “markets are using AI” and “regulators are watching AI” happen simultaneously, it is no longer just a technological trend but a structural change.

Markets Are Being Redefined by “Systems”

At a superficial level, the expansion of AI trading seems merely to improve efficiency. But structurally, this is actually a change in how market participation occurs.

In the past, trading mainly happened between humans, with judgment, emotion, and cognition as the main variables. But now, more and more trading is carried out by systems. High-frequency strategies, automated market making, cross-market arbitrage, and AI agents are becoming the main participants.

This signifies a fundamental shift: the market is no longer a “game between humans,” but a “competition between systems.”

In this environment, price formation no longer relies solely on directional judgment but is jointly determined by capital structure, liquidity distribution, and execution paths. Markets are beginning to possess “engineering attributes,” not just “cognitive attributes.”

An Overlooked Reality: Prediction Is Losing Its Core Position

In this structure, a long-held core trading ability—prediction—is rapidly depreciating.

Traditional logic suggests that as long as the directional judgment is correct, profits can be made. But in actual trading, an increasing number of cases show that: the judgment is correct, but the trading result is wrong.

The reason is not complicated. Short-term price changes are no longer determined by a single direction but by execution quality. Slippage, delays, order paths, and liquidity matching all directly impact final returns.

Research has already clarified that, in the current market environment, the advantage of AI trading is shifting from predictive ability to execution ability and consistency.

This indicates a fundamental shift in trading: from “being right” to “doing it right.”

The Real Issue Behind Regulatory Signals: Is the System Controllable?

Regulators’ focus on AI trading is not just about technical caution but also because a deeper problem is emerging: whether the system is controllable.

Regulators are mainly concerned not with the models themselves but with system behavior. For example, whether multiple AI systems might adopt the same strategy simultaneously, amplifying volatility in extreme market conditions. Such “convergent behavior” can create structural risks.

This reflects a key fact:

AI has transformed from a tool into a market participant.

And once the system becomes a participant, risks are no longer from single-point errors but from the overall structure. This kind of risk is not about “mistakes” but about “loss of control.”

The True Watershed of AI Quantitative Trading: System Capabilities, Not Model Capabilities

When market structure, capital structure, and regulatory environment change simultaneously, the logic of competition also shifts.

In the past, the industry focused on model capabilities and strategy complexity; now, the core issue has become system capability.

AI is fundamentally an amplifier. It can magnify gains but also risks. If the system is stable, AI will reinforce advantages; if there are flaws, AI will accelerate failure.

This is also why many models perform excellently in backtests but fail quickly in real markets. The problem is not prediction but the inability of the system to control execution and risk.

From this perspective, the core of AI quantification is not “being smarter” but “being more controllable.”

Conclusion

As AI enters the trading execution layer, institutional funds continue to flow in, and regulators begin to intervene in system behavior, the market has entered a new phase.

This phase’s change is not reflected in a single technological breakthrough but in a rewriting of the entire structure.

In the past, trading was a competition of cognition; now, it is becoming a competition of systems.

And the true watershed is not in strategies or models, but in a simpler question:

Can your system operate stably and continuously in a market dominated by machines?