AI crypto trading uses machine-learning methods within a cryptocurrency trading process. Models may estimate returns, classify market conditions, assess volatility or help execute orders. The phrase describes the use of a technology, not a particular level of profitability.
Different models do different jobs
A signal model asks whether there may be an opportunity. A market-context model asks what environment the signal is operating in. An execution model addresses how an existing order should reach the market. Treating those questions as interchangeable makes a trading system harder to evaluate.
For example, a positive price forecast does not specify a position size or account for the cost of entering the trade. Those decisions need their own logic.
AI does not have to mean a chatbot
AlphaNet distinguishes numerical trading models from general-purpose language-model agents. Its research describes structured market inputs and researcher-designed factors, rather than relying on a persuasive written explanation as evidence of a trading edge.
A useful assessment asks what the model controls, which data it was tested on and whether the results include realistic costs. AI can make a process more adaptive, but it can also overfit historical patterns. New market conditions can expose weaknesses that a backtest did not reveal.