AI trading adoption is outpacing willingness to hand over execution

AI is becoming familiar in trading research faster than it is gaining authority over money. A new survey from MEXC suggests that many crypto traders are comfortable using AI to understand markets while remaining selective about letting it execute. For platforms serving crypto and equities, adoption alone is therefore an incomplete measure of progress.

MEXC’s October 15 report surveyed 3,708 crypto traders. It found that 84.8% had used AI to analyse or trade digital assets during the preceding six months. Yet only 6.5% would authorize AI to trade more than half their account in a scenario that prohibited transfers and withdrawals and allowed users to stop trading. These are responses from a crypto-trader sample, not a population estimate for investors generally. [1]

Research assistance has found an audience

The report also found that 58.3% of respondents reported better market research, compared with 46% reporting better execution. Those are self-reported experiences, not independently measured investment results. Still, the distinction identifies a useful commercial problem: people can find AI helpful without concluding that it should control their trading. [1]

That is understandable. A research assistant can save time by organizing information or explaining an unfamiliar event. Its benefit may be visible immediately. The standard for a trading system is different. Executing a plausible idea automatically is valuable only if the idea and the way it is traded justify the costs and risks involved.

For an equity investor, an accurate summary of an earnings release is not the same as a profitable response to it. For a crypto trader, a clear explanation of a price move does not establish where the next trade should be placed. In both cases, the explanation can be useful while the trading decision remains unresolved.

As more platforms add AI interfaces, that distinction should become more important. A smooth conversation or a completed order demonstrates that the software works. It does not demonstrate that the software has found a repeatable trading advantage. The market for AI trading will have to distinguish convenience from investment capability.

The model behind the trade

At AlphaNet, our focus is on deep learning models built for trading, with research intended to identify patterns in market data and test whether those patterns support an edge. That is a different task from using an agent to translate instructions into orders. An agent’s ability to carry out a sequence of actions does not, by itself, establish a reason those actions should be profitable. [2]

A model’s evidence has to relate to trading outcomes. A promising pattern in development must survive evaluation on unseen data and the costs of execution. Live results then show how the strategy behaves in conditions that were not selected for the demonstration. Our research process and separate performance stages are intended to make those distinctions visible. [2]

This does not make a deep learning label sufficient evidence either. The relevant question is what the particular strategy has demonstrated. A system can be sophisticated and still fail to produce an advantage; research must be able to reject it.

The survey is useful because it exposes the distance between using AI and trusting it with execution. Platforms will not close that distance simply by making delegation easier. They need to give investors something concrete to evaluate. For AlphaNet, that means making the trading model and the evidence behind its strategy the substance of the product, rather than treating automation itself as the achievement.