AI trading is the use of machine-learning models to help read market data, form a view, or execute trades — instead of a human doing all of it by hand. That is the whole definition. Everything else is detail about which part the AI touches.
The three places AI shows up
It helps to split “AI trading” into three jobs: reading the market (turning raw data into a structured picture), deciding what to do, and executing the order. A product might use AI for one, two, or all three — and most only touch the first. A model that summarizes the tape is doing something very different from one that actually places and manages orders in your account.
The word “AI” tells you almost nothing. The question is which job it does — reading, deciding, or executing — and whether anything acts on the output.
What it is not
AI trading is not a crystal ball. Markets are adversarial and mostly noise, so no model “knows” tomorrow's price. What good AI does is process more data, faster and more consistently, than a person can — and, critically, it does not get tired, tilted, or greedy. That is a real edge over the human version of you, but it is not a guarantee of profit.
Where the value actually sits
The most durable value is in execution — taking a defined rule set and running it live, the same way every time, without hesitation. That is the harder, less glamorous half most tools skip. See AI signal vs AI execution for why that gap matters, AI vs a rules engine for how the two combine, and why a raw LLM can't trade on its own.