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December 2025 was a landmark month not only for the entire crypto industry, but also for the global digital industry. After the decentralized platform Aster completed its experimental futures trading competition "Human vs AI: Battle for the Futures," in which 70 human traders and 30 artificial intelligence models traded in real market conditions, the question arose: are machines a threat to human crypto traders?
The final results were unexpected for some members of the community. The combined result of the human team was recorded at around −32.21% ROI, while the artificial intelligence algorithms finished the tournament with a loss of only about −4.48%. Although the individual victory went to a person under the pseudonym ProMint, who earned about $13,650, the overall statistics showed the greater stability of machine strategies.
The results of individual AI models also attracted the attention of experts. In particular, Claude Sonnet 4.5 Aggressive, which showed one of the best results among bots, interested specialists. Analysts explain this by a combination of strictly defined risk management algorithms, high decision-making frequency, and a complete absence of emotional influence. The aggressive mode allowed the model to react quickly to short-term volatility, locking in profits or limiting losses without attempting to “win back” losses.
In addition, some of the AI agents were optimized specifically for futures instruments: they used statistical price movement patterns, algorithmic stop strategies, and technical indicators, while less adapted models showed worse and weaker results. This once again emphasized that even among machines, performance depends significantly not only on architecture but also on settings.
It should be noted that an important limitation of the “Human vs AI: Battle for the Futures” tournament was the rule that artificial intelligence did not have access to external networks or real-time self-learning. All decisions made by artificial intelligence were based solely on market data and technical indicators. Thus, AI did not have an information advantage over humans, which makes its relatively better result even more indicative in terms of the effectiveness of algorithmic strategies.
For human traders, psychology became a key factor. Some participants acted in a high-risk mode, trying to quickly outrun the algorithms, which led to a series of impulsive decisions and accumulated losses. Fear of loss and the desire to “catch up” became typical pitfalls for humans.
Experts note that individual traders who adhered to a conservative strategy and disciplined risk control were able to show positive financial results. This, in turn, indicates that human trading remains relevant but requires a rethinking of approaches in an environment where artificial intelligence is increasingly setting the standards for trading efficiency.
