- The core difference between AI trading and human trading is scale and objectivity, which often determine sentiment and outcome
- AI trading struggles with "herding" mentality because machines typically think alike and are not as flexible when dealing with similar data and market conditions
- AI trading is not flawless and has often led to flash crashes triggered by overreaction to market shifts
AI has significantly transformed how financial markets work. Algorithms that can interpret huge volumes of data in milliseconds are taking over more and more of what used to depend on human intuition, experience, and manual analysis. In this article, we discuss how AI trading works, how it has disrupted the market, how it differs from human trading and its downside.
How AI Has Disrupted Trading
About three decades ago, computer-driven trades began shaping finance. These days, smart systems handle most stock market moves. In big markets, automated programs run nearly eight out of every ten transactions. Reports from global financial groups confirm this shift is now standard practice.
Today, AI trading appears on apps such as Robinhood and eToro, helping everyday people apply strategies once limited to experts. Because of this shift, gaps between buy and sell prices shrink while trading grows smoother, reshaping how exchanges function underneath.
How AI Insights Differ from Human Trading
The core difference lies in scale and objectivity. Human traders rely on experience, intuition, and emotional judgment. These are strengths when interpreting complex geopolitical events or company narratives, but weaknesses when emotions lead to bias or hesitation.
Starting with raw data, machines handle vast streams, including news sentiment, order flow, satellite images of shop parking lot from space, without pause or panic. What stands out? These tools spot hidden links others miss, quietly pulling insights from chaos.
Patterns emerge where humans see noise, thanks to relentless number crunching. Emotion never clouds their judgment, letting cold math guide each move. Their strength lies in repetition, finding edges in shifts across markets. Even overnight, they keep scanning, learning, adjusting, all while people sleep.
Still, humans handle rare surprises and political nuances better than machines. For instance, an algorithm might interpret a sudden 15% tax on imports as negative for currency value. Yet a person could view it as leverage in talks, spotting room to act differently, something code often misses.
Challenging the Consensus
Some traders view AI as a path to flawless performance. Yet another angle to it is that it might be building a new kind of weakness instead. With trading firms piling into identical large language systems and shared data pools, their methods begin to mirror one another. That similarity quietly raises risk across the board. Picture a crowd of algorithms, all fed identical information, rushing for the exit together.
That sudden stampede, called algorithmic herding, is linked to those sharp market drops seen briefly in early 2026. In moments like these, automated systems reach the same decision almost instantly, draining available trading volume within seconds. Once one shifts direction, others follow without pause, amplifying swings unpredictably. Watchdogs such as the SEC and ESMA now highlight risks built into widespread reliance on machine-driven trades.