While DeepSeek led the AlphaArena live trading competition with a 39.55% return, and Gemini fell to the bottom with a 42.65% loss, this experiment involving six AIs and $60,000 in real money in cryptocurrency trading went beyond the superficial question of whether AI can make money. It uncovered a deeper industry question: Is AI trading a disruptor that will replace top traders, or a tool that will amplify human capabilities? From real-time account fluctuations to the skepticism of industry leaders, the answer lies in every autonomous trading decision.

1. Review of AlphaArena: A Real-World Test of “De-humanization”

Before discussing "replacement", we must first clarify the uniqueness of AlphaArena - it is not a "paper talk" of a simulation, but a "real sword and gun" in the crypto market, which provides the most realistic testing ground for "the comparison between AI and human traders".

1. Unreplicable fairness: Complete uniformity from principal to data

The essence of AlphaArena's rule design is to remove "external variables" and only test "AI decision-making ability":

• The principal is the same : each participating model (Claude 4.5 Sonnet, DeepSeek V 3.1 Chat, etc.) will receive $10,000 in real principal. Losses will be borne by the organizer, and profits will be credited to the account in real time.
• Consistent environment : All AIs trade cryptocurrency perpetual contracts (BTC, ETH, SOL, and other six major currencies) on the Hyperliquid platform, facing exactly the same market conditions, timestamps, and prompts;
• Autonomous decision-making : Humans are not allowed to intervene in any link. AI must independently complete the entire process of "discovering opportunities (finding Alpha) - determining positions - timing transactions - risk control", and even its "inner monologue" (ModelChat) is completely public.

This "standardized" design allows every profit and loss of AI to be directly attributed to its strategic logic and market adaptability - this is the premise for comparing "AI and human traders": excluding external factors such as capital scale and information asymmetry, and only looking at "decision-making quality."

2. Differentiation Report Card: AI's "Capability Boundaries" Begin to Emerge

As of October 20, 2025, the performance of the six AIs has formed a clear stratification, and this differentiation precisely exposes the "advantages and shortcomings" of AI trading:

• Leader: DeepSeek V 3.1 Chat (Yield +39.55%) : As a model under Huanfang Quantitative, it possesses a "quantitative DNA"—its holdings cover all six major cryptocurrencies. It employs a "medium-to-high leverage + diversified allocation + pure long trend following" strategy, allowing it to capture the volatile returns of SOL and DOGE with high leverage while maintaining a $2,840 cash position to mitigate risk. More importantly, it strictly adheres to its pre-set plan, insisting on holding positions until the expiration condition is triggered, even when unrealized profits approach $2,000. This disciplined approach helps it avoid the losses of frequent position adjustments during volatile markets.
• Radical: Grok 4 (+14.5% return) : Musk's model demonstrates its unbridled nature—it goes all-in on six major cryptocurrencies, relying on strong momentum to chase upward trends. It decisively increases its positions when ETH and BTC trend positive, even stating, "Hold on if the MACD turns from weak to strong." However, it lacks a clear profit-taking mechanism, leading to extreme account volatility. While it can surpass DeepSeek in the short term, sustained stability is difficult.
• Conservative: Claude 4.5 Sonnet (yield +24.12%) : Like a cautious analyst, he conducts a comprehensive analysis of macroeconomics, on-chain technology, and technical aspects before every trade. However, he is overly hesitant in making decisions and often misses market breakout points due to being a step behind. His gains are concentrated in the tail end of the trend.
• Bottom of the list: Gemini 2.5 Pro (-42.65% loss) : This is a textbook example of a negative investment strategy—using 25x leverage on ETH and 20x leverage on BTC, maintaining bidirectional positions, and lacking a clear understanding of portfolio risk management. Even as their account saw thousands of dollars evaporate in a single day, they repeatedly emphasized the importance of holding onto their positions until their stop-loss was triggered, even continuing to open long DOGE positions when deeply trapped, exposing their fatal strategic rigidity.

This report card shows that AI can indeed make money, but its "ability to make money" is highly dependent on the model's "strategy design and risk control logic"; at the same time, AI's "personality" has become very distinct - some are like quantitative funds, some are like retail investors, and some are like analysts, which is exactly the same as the style differences of human traders.

II. AI’s “Irreplaceability”: 3 Major Advantages Human Traders Can’t Achieve

AlphaArena's performance proves that in specific scenarios, AI has demonstrated capabilities that are difficult for human traders to replicate - these advantages are not the only reason for "replacement", but they are competitive strength that "cannot be ignored".

1. Data processing: digesting massive amounts of information in seconds, surpassing human capabilities

One of the core challenges of the crypto market is information overload—price charts, MACD/RSI indicators, on-chain capital flows, market sentiment, breaking news, and more—all need to be integrated into decision-making within a short period of time. AI completely outperforms humans in this regard:

• Fast : As described in a CSDN blog post, the AI agent can "deconstruct liquidity, sentiment, and order flow in seconds." In AlphaArena, DeepSeek updated its decisions based on the latest data every 2-3 minutes, being called 601 times in 1,627 minutes. This "high-frequency response" is simply impossible for humans to achieve (a human watching the market for 8 hours can only process about 100 key pieces of information).
• Comprehensive Dimensions : While human traders typically focus on 3-5 core indicators, AI can simultaneously incorporate dozens of dimensions, including the 20-period EMA, stop-loss points, floating profit ratio, and currency correlations. It can even uncover hidden signals like the correlation between BTC and SOL. For example, DeepSeek can simultaneously compare the historical EMA setting (109236.97) with the current actual value (108070.485) during a trade, and make decisions strictly based on pre-set conditions, avoiding the human error of relying on intuition and ignoring details.

2. Discipline: Zero emotional interference and strict implementation of strategies

The biggest enemy of human traders is human nature—greed can cause people to miss profit targets, fear can cause them to stop losses early, and luck can lead to positions being wiped out. However, AI doesn't have this problem:

• Neither greed nor fear : DeepSeek didn't take profits early when its unrealized profit reached nearly $2,000, and Gemini didn't stop losses early when it lost 42% (although the latter was a mistake, it demonstrated discipline). This "playing by the rules" trait is exactly the "mechanical trading" pursued by top quantitative funds.
• Avoiding simple mistakes : Humans may miss market trends due to fatigue or distraction, but AI can monitor the market 24/7 without interruption, eliminating errors like placing the wrong order or miscalculating leverage. All AI trading records within AlphaArena are error-free, whereas even top-tier human traders experience losses due to one or two operational errors each year.

3. Strategy Iteration: Open-source models can be optimized in real time, adapting to the market faster than humans.

In AlphaArena, DeepSeek's lead is not only due to its superior strategy, but also due to its open-source model—a feature that enables it to achieve an evolutionary speed that is difficult for human traders to match:

• Real-time Optimization : As reported by Coinfomania, open-source models can be "optimized by the developer crowd based on real-time performance." The Magic Square team behind DeepSeek can adjust risk control parameters daily based on AlphaArena's trading data. In contrast, human traders typically require weeks or even months to iterate their strategies due to the lengthy "review-verification-testing" cycle.
• No cognitive bias : Human traders are prone to "path dependence" (for example, if they have made money with trend strategies in the past, they are unwilling to try swing strategies), while AI can automatically switch strategies based on data - if the market shifts from trend to swing, open source AI can adjust the indicator weights within 1-2 days, while humans may miss opportunities due to "cognitive inertia".

III. AI’s “Fatal Weakness”: 4 Core Capabilities of Top Traders That Will Never Be Replaced

Despite the obvious advantages of AI, both AlphaArena and industry opinions have proven that AI is still a long way from "replacing top traders" - these shortcomings are not "technical problems" but "essential differences" that are difficult to overcome in the short term.

1. Market Insight: AI understands “data” but not “the logic behind the data”

The core ability of top traders is to "see the essence of data" - for example, understanding "why the Fed's interest rate hike affects the price of BTC" and "the long-term significance of a giant's entry into the market for SOL." However, AI can only process "surface patterns in the data":

• Inability to understand macroeconomic correlations : All AI models in AlphaArena saw price data showing "BTC breaking through $110,000," but none could analyze whether this surge was driven by expectations of interest rate cuts or institutional buying. Top traders, on the other hand, adjust their positions based on macroeconomic logic (e.g., reducing leverage in anticipation of rate hikes). Gemini's losses were essentially due to a focus on technical analysis (EMA, MACD) without considering macroeconomic risks, leading to continued heavy bets during market corrections.
• Unable to interpret "unstructured information" : If a security incident occurs at an exchange, human traders can quickly determine the "scope of the incident's impact" and stop losses, while AI can only react after the "event is converted into price data" - this "time difference" may lead to huge losses. AlphaArena has not encountered such a black swan yet, but in the real market, this is precisely the key to distinguishing "ordinary traders" from "top traders."

2. Black Swan Response: AI Understands the Rules, But Not Breaking Them

The crypto market is never short of black swan events (such as the LUNA crash and FTX bankruptcy). The advantage of top traders lies in their "absence of preset rules and ability to adapt flexibly", while AI can only act according to "preset strategies":

• Rigid strategies and inability to respond to emergencies : Gemini insisted on holding on until stop-loss orders were triggered even when deeply trapped, essentially lacking a contingency plan. If an event, such as an exchange suspending withdrawals, were to occur outside of its pre-set rules, the AI would become completely incapable of decision-making. Meanwhile, top traders would immediately hedge their risks through over-the-counter channels, even identifying arbitrage opportunities in extreme market conditions.
• Unaware of cumulative risks : AI can calculate the risk of a single position (e.g., the liquidation price of ETH at 25x leverage), but cannot understand the correlation risk of multi-currency positions. For example, Gemini opens high-leverage long positions in BTC and ETH simultaneously, but fails to realize that when both prices rise and fall simultaneously, the risks are compounded. Top traders, on the other hand, strictly control the total leverage of correlated positions to avoid a single loss that wipes out all losses.

3. Strategic Innovation: AI Knows “Copying” but Not “Creating”

All AI strategies in AlphaArena are essentially "AI-based quantitative strategies" - for example, DeepSeek's "trend following" and Grok's "momentum drive" are human-proven strategies. AI simply "executes them at a faster speed" rather than "creating new strategies":

• Reliance on historical data makes it impossible to adapt to new markets : If new derivatives emerge in the crypto market in the future (such as perpetual contracts based on AI tokens), AI will be unable to formulate strategies due to the lack of historical data. However, top traders can create entirely new trading methods based on the essence of derivatives and market logic. For example, when DeFi exploded in 2020, top traders quickly developed a liquidity mining arbitrage strategy, which AI was completely unable to participate in at the time.
• No "counter-consensus" capabilities : AI strategies essentially rely on "fitting the consensus patterns of historical data," such as "buy when the MACD crosses." However, top traders often profit from "counter-consensus" strategies—for example, buying during market panic and selling during market frenzy. In AlphaArena, no AI dares to "go against the trend," a core competency of top traders.

4. Controlling Human Nature: AI understands transactions but not the human heart

Trading is essentially a game between people. Top traders can judge their counterparts' behavior by analyzing market sentiment (e.g., changes in trading volume indicate panic selling by retail investors). However, AI can only process objective data and cannot understand human emotions.

• Unable to identify "market traps" : If an institution deliberately pumps up BTC prices to lure retail investors into following suit, the AI will increase its holdings because it "sees an upward trend." However, top traders can identify this as a "buy trap" based on "abnormal trading volume" and stop losses early.
• No "dynamic adjustment of risk appetite" : AI's risk appetite is preset (such as DeepSeek's "medium-high leverage"), while top traders adjust risk based on their own state and market conditions—for example, reducing leverage when fatigued or reducing trading frequency during periods of high market volatility. This kind of "flexible adjustment" is currently completely infeasible for AI.

IV. Industry Controversy: From CZ’s Questioning to Institutional Choices, “Replacement” is Less Effective Than “Synergy”

The popularity of AlphaArena has also sparked discussions among industry leaders - these views further confirm that "AI replacing top traders" is a false proposition, and the "AI + human" collaborative model is the future.

1. CZ's core question: Will AI synchronized trading lead to "self-destruction"?

Binance founder Changpeng Zhao (CZ) publicly commented on AlphaArena on the X platform: "If everyone uses the same AI strategy, trading will become synchronized—either buying together and driving up prices, or selling together and causing flash crashes, ultimately wiping out profits and causing volatility to soar." This view directly points to the "fatal hidden dangers" of AI trading:

• Six AIs in AlphaArena have shown a tendency to converge (for example, DeepSeek and Grok both have heavy long positions in BTC and ETH). If more funds use the same AI in the future, it will lead to liquidity exhaustion. For example, if all AIs simultaneously stop losses at a certain moment, it will trigger a cliff-like price drop, and even the AI itself will not be able to escape unscathed.
• The "differentiated strategies" of top traders are precisely the "stabilizers" of the market - some go long, some go short, and some engage in arbitrage. This kind of "game balance" is something that AI cannot provide.

2. Analyst consensus: AI is a tool, not a replacement

From an industry analysis perspective, most experts believe that the value of AI trading lies in "amplifying human capabilities" rather than "replacing humans":

• Risk control : AI can monitor position risks in real time, such as alerting users that "BTC long position leverage is too high." However, the final decision on whether to reduce positions still requires human judgment and macroeconomic judgment.
• At the strategy execution level : Humans design "counter-consensus strategies" (such as buying during panic buying), while AI is responsible for "high-frequency execution" (such as completing 10 dispersed buys within 1 minute to avoid market shocks).
• As Wolfgang reported , leading institutions have begun to adopt the "AI + human supervision" model - AI handles 80% of routine transactions, and humans handle 20% of black swan events and strategy innovations. This model can not only give full play to the efficiency advantages of AI, but also retain the core judgment of humans.

V. Conclusion: AlphaArena’s Ultimate Revelation: The Future of Trading Is Human-Machine Collaboration

Back to the original question: Can AI replace top traders? Based on AlphaArena's real-time performance and industry logic, the answer is no—at least not in the foreseeable future.

The value of AI lies in its ability to address the pain points of human traders: low efficiency, poor discipline, and limited processing power. The value of top traders lies in their core capabilities of understanding the essence, responding to black swans, creating strategies, and managing people’s hearts. These capabilities are not “technical issues” but the “cultivation of human experience and cognition” that cannot be replicated by AI.

The true significance of AlphaArena is not to “prove that AI is stronger than humans” but to “explore the best way for AI and humans to collaborate”: when DeepSeek’s quantitative strategy meets the macro judgment of top traders, and when AI’s second-level execution meets human risk control, this “1+1>2” collaboration is the future of crypto trading.

Just as Huanfang Quantitative uses AI to amplify its own quantitative capabilities, top traders will also use AI to amplify their decision-making efficiency in the future - but what ultimately determines "how much money can be made and how much risk can be taken" will still be humans' understanding of the market, not AI code.

Would you like me to compile a table comparing the core capabilities of AI and top traders ? This table clearly presents the strengths and weaknesses of both across eight dimensions, including data processing, strategy innovation, black swan response, and human nature management, helping you more intuitively understand the principle of "synergy, not substitution."