[The View] When Everyone Sees the Same Answer, the Financial Market Collapses

The Backlash of AI Synchronization: The Rise of Massive "Common Errors"

Liquidity Increases, but the Amount of Substantive Information Declines

Machine Errors Now Determine the Overall Direction of the Market

Original Perspectives Mat

Recently, the adoption of artificial intelligence (AI) has become an unstoppable trend in international financial markets, including Wall Street. AI, which can process vast amounts of data in an instant and uncover hidden patterns, is undoubtedly a powerful tool for investors. Many experts expect that such advanced technology will enhance the price discovery function of markets and make them more efficient. However, if the majority of market participants use the same or similar AI technologies, can the market truly become safer and smarter?

Traditional financial market theory holds that markets are intelligent because countless investors participate in trading, each with different information and perspectives. While the judgments of individuals may be prone to errors, when numerous trades are aggregated, these individual errors cancel each other out, leaving only the true value of assets reflected in the price. This is the principle of collective intelligence and the price discovery mechanism of the market.

However, in the age of AI, this premise is seriously undermined. If leading financial institutions base their investment strategies on the same foundational models or AI architectures, their conclusions will inevitably become similar. Even the unique biases or errors that occur during AI data processing will be shared by all investors. As a result, there is a strong tendency toward synchronization in how investors interpret information and make decisions.

There have been past instances of sudden market crashes triggered by simple program trading. However, today’s AI is fundamentally different in that it goes beyond mere price following to independently interpret news articles and economic indicators. If the numerous AIs that constitute the market all misread a particular economic indicator in the same way, the impact far exceeds that of simple mechanical error.

What is even more paradoxical is that this clustering of AI systems can make the market appear to run more smoothly on the surface. As the adoption of technology becomes more concentrated, market liquidity actually tends to increase. It becomes difficult for market makers to discern whether large-scale buy or sell orders flowing into the market are due to genuine good or bad news, or simply the result of a shared AI judgment. As a result, rather than causing abrupt price swings, they absorb the incoming orders and provide even more liquidity.

However, beneath the surface, the qualitative level of the market is being seriously undermined. Liquidity has increased, but the amount of meaningful information embedded in those trades has decreased. Since everyone relies on the conclusions of the same AI systems, the extent to which new, original information is reflected in the market faces clear limitations. The market may appear to be active and stable, but in reality, its fundamental function of discovering the fair price of assets is fading.

Ultimately, this greatly increases the inherent vulnerability of financial markets. In the past, individual investor mistakes would cancel each other out, having little impact on the market as a whole. But in an environment where everyone uses the same AI, errors made by machines determine the overall direction of the market. As technology advances, individual human errors disappear, and only massive, shared errors come to dominate. This overlapping of systemic misjudgments deepens errors in asset pricing, and can trigger systemic risks where markets plummet or soar abruptly even without any specific external shocks.

So why do investors continue to use similar technologies despite being aware of these risks? This can be explained by the tragedy of the commons in information acquisition. For an individual investor, there is little incentive to bear the substantial cost of collecting unique and diverse information. Using the high-performing, general-purpose models that others use appears to be an effective way to immediately reduce transaction costs and increase profits. Each investor makes a perfectly rational trading choice, but the result is a tragedy in which the valuable public good of information diversity in the market is destroyed.

In the end, technological advancement does not guarantee unconditional market efficiency. In the financial markets of the future, true competitiveness and stability will not depend on computing power, but rather on how well one can maintain a unique perspective and break free from technological uniformity. Financial authorities must also closely monitor the homogenization of information and systemic risks that can arise from unified algorithms. When everyone is looking at the same screen, believing it to be the answer, it is time to remember that the market is exposed to its most dangerous blind spot.

Sungkyu Park, Professor at Willamette University, USA

This content was produced with the assistance of AI translation services.