When DeepSeek leads the AlphaArena live competition with a return of 39.55%, while Gemini falls to the bottom with a loss of 42.65%, this experiment of "6 AIs, $60,000 in real money trading" has long surpassed the superficial question of "Can AI make money?" — it has torn open a more core industry proposition: Is AI trading a disruptor that "replaces top traders," or a tool that "amplifies human capabilities"? From the real-time fluctuating account curves to the doubts of industry leaders, the answer lies in every autonomous trading decision.

I. Review of AlphaArena: A "Dehumanized" Live Test

Before discussing "replacement," we must first clarify the uniqueness of AlphaArena — it is not a "paper talk" of simulated trading, but a "real battle" in the crypto market, providing the most authentic testing ground for "AI versus human traders."

1. Unreplicable Fairness: Unified Capital and Data

The rule design of AlphaArena is essentially aimed at stripping away "external variables" to solely test "AI decision-making ability":

• Unified Capital: Each participating model (such as Claude 4.5 Sonnet, DeepSeek V 3.1 Chat, etc.) receives $10,000 in real capital, with losses borne by the organizer and profits credited to the account in real-time;
• Unified Environment: All AIs trade cryptocurrency perpetual contracts (BTC, ETH, SOL, etc.) on the Hyperliquid platform, facing identical market conditions, timestamps, and prompts;
• Autonomous Decision-Making: Humans cannot intervene at any stage; AIs must independently complete the entire process of "discovering opportunities (finding Alpha) - determining positions - timing trades - risk control," with even their "inner monologues" (ModelChat) fully disclosed.

This "standardized" design allows every profit and loss of the AI to be directly attributed to its strategic logic and market adaptability — this is precisely the premise for comparing "AI and human traders": it eliminates external factors like capital scale and information asymmetry, focusing solely on "decision quality."

2. Divergent Report Cards: The Initial Emergence of AI's "Capability Boundaries"

As of October 20, 2025, the performance of the six AIs has formed a distinct stratification, and this differentiation precisely exposes the "strengths and weaknesses" of AI trading:

• Leader: DeepSeek V 3.1 Chat (Return +39.55%): As a model under Huansuan Quant, it comes with "quantitative genes" — covering all six major cryptocurrencies, employing a "medium-high leverage + diversified allocation + pure long trend-following" strategy, capable of capturing the volatility profits of SOL and DOGE with high leverage while retaining $2,840 in cash to manage risks. More crucially, it strictly adheres to its preset plan, maintaining positions even when floating profits approach $2,000, demonstrating "discipline" that helps it avoid frequent rebalancing losses in volatile markets;
• Aggressive: Grok 4 (Return +14.5%): The "wildness" of the model under Musk is fully displayed — fully leveraged long on six major cryptocurrencies, relying on "strong momentum-driven" trends, decisively increasing positions when ETH and BTC trends are favorable, even stating "hold if MACD turns weak to strong, do not exit." However, it lacks a clear profit-taking mechanism, leading to significant account volatility; while it can temporarily outperform DeepSeek, it struggles to maintain stability;
• Conservative: Claude 4.5 Sonnet (Return +24.12%): Like a "cautious analyst," it conducts a "macro + on-chain + technical" multi-dimensional analysis before each trade, but its decision-making is overly hesitant, often missing market breakout points due to being "half a beat slow," with profits concentrated in the tail end of established trends;
• Bottom Feeder: Gemini 2.5 Pro (Loss -42.65%): A true "negative example" — betting on ETH with 25x leverage and BTC with 20x leverage, while opening dual positions without a "portfolio risk control" awareness. Even when the account evaporates thousands of dollars in a day, it repeatedly emphasizes "hold as long as stop-loss is not triggered," even continuing to open long positions on DOGE while deeply trapped, exposing the fatal issue of "rigid strategy."

From this report card, it is evident that AI can indeed make money, but its "profit-making ability" is highly dependent on the model's "strategy design and risk control logic"; at the same time, the "personality" of AI has become very distinct — some resemble quantitative funds, some like retail investors, and some like analysts, mirroring the style differences of human traders.

II. The "Irreplaceability" of AI: Three Major Advantages Beyond Human Traders

The performance of AlphaArena proves that, in specific scenarios, AI has demonstrated capabilities that human traders find difficult to replicate — these advantages are not the entire reason for "replacement," but they are "unignorable" competitive strengths.

1. Data Processing: Second-Level Digestion of Massive Information, Surpassing Human Limits

One of the core challenges in the crypto market is "information overload" — price candlesticks, MACD/RSI indicators, on-chain fund flows, market sentiment, sudden news, etc., need to be integrated into decision-making bases in a short time. AI completely outperforms humans in this regard:

• Speed: As noted in a CSDN blog, AI agents can "dissect liquidity, sentiment, and order flow in seconds." In AlphaArena, DeepSeek updates its decisions based on the latest data every 2-3 minutes, being called 601 times within 1627 minutes; this "high-frequency response" is something humans cannot achieve (humans can only process about 100 key pieces of information in 8 hours);
• Comprehensive Dimensions: Human traders typically focus on 3-5 core indicators, while AI can incorporate dozens of dimensions simultaneously, such as "20-period EMA, stop-loss levels, floating profit ratios, cryptocurrency correlations," and even discover hidden signals like "the correlation between BTC and SOL." For instance, DeepSeek can compare "historical EMA settings (109236.97) with current actual values (108070.485)" during trading and make decisions strictly according to preset conditions, avoiding human errors of "overlooking details based on intuition."

2. Discipline: Zero Emotional Interference, Strictly Executing Strategies

The greatest enemy of human traders is "human nature" — greed can lead to missed profit-taking, fear can lead to premature stop-loss, and luck can lead to holding positions until liquidation. But AI has no such issues:

• Neither Greedy Nor Fearful: DeepSeek does not take profits early when floating profits approach $2,000, and Gemini does not stop-loss early at a 42% loss (though the latter is a wrong strategy, it reflects "discipline"). This characteristic of "acting according to rules" is precisely what top quantitative funds pursue in "mechanical trading";
• No Basic Mistakes: Humans may miss market movements due to fatigue or distraction, while AI can monitor continuously 24/7 without making operational errors like "placing wrong orders or miscalculating leverage." In AlphaArena, all AI trading records show no operational errors, while even top human traders may incur losses 1-2 times a year due to operational mistakes.

3. Strategy Iteration: Open-Source Models Can Be Optimized in Real-Time, Adapting to Markets Faster Than Humans

In AlphaArena, DeepSeek's lead is not only due to "good strategy," but also because it is an "open-source model" — this feature gives it an "evolution speed" that human traders find hard to match:

• Real-Time Optimization: As reported by Coinfomania, open-source models can "be optimized by developers based on real-time performance." The Huansuan team behind DeepSeek can adjust risk control parameters daily based on AlphaArena's trading data; whereas human traders' strategy iterations usually take weeks or even months due to the lengthy cycle of "reviewing - validating - testing";
• No Cognitive Bias: Human traders are prone to "path dependence" (for example, if they made money in the past using trend strategies, they may be reluctant to try range strategies), while AI can automatically switch strategies based on data — if the market shifts from trending to ranging, open-source AI can adjust indicator weights within 1-2 days, while humans may miss opportunities due to "cognitive inertia."

III. AI's "Fatal Shortcomings": Four Core Abilities That Top Traders Will Never Be Replaced

Despite the clear advantages of AI, both AlphaArena and industry perspectives prove that AI is still far from "replacing top traders" — these shortcomings are not "technical issues," but rather "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 through data" — for example, understanding "why the Federal Reserve's interest rate hikes affect BTC prices" or "the long-term significance of a major player entering SOL," while AI can only process "surface patterns of data":

• Unable to Understand Macro Connections: In AlphaArena, all AIs can see the price data of "BTC breaking $110,000," but none of the models can analyze "whether this round of increase is due to expectations of interest rate cuts or institutional accumulation" — while top traders adjust positions based on macro logic (for example, reducing leverage in anticipation of interest rate hikes). Gemini's losses fundamentally stem from "focusing only on technical aspects (EMA, MACD) without considering macro risks," leading to heavy bets even during market corrections;
• Cannot Interpret "Unstructured Information": If a sudden security incident occurs at an exchange, human traders can quickly assess the "scope of the event's impact" and stop losses, while AI can only react after "the event translates into price data" — this "time lag" can lead to significant losses. AlphaArena has not yet encountered such black swan events, but in the real market, this is precisely the key differentiator between "ordinary traders" and "top traders."

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

The crypto market is never short of black swan events (such as the LUNA crash or FTX bankruptcy), and the advantage of top traders lies in "having no preset rules and being able to adapt flexibly," while AI can only act according to "preset strategies":

• Rigid Strategy, Inability to Respond: Gemini insists on "holding as long as stop-loss is not triggered" even when deeply trapped, which essentially means "no emergency strategy" — if faced with events that exceed preset rules, such as "exchange withdrawal suspension," the AI would completely fall into a state of "no decision-making ability"; whereas top traders would immediately hedge risks through off-market channels, even finding arbitrage opportunities in extreme market conditions;
• Risk Accumulation Without Awareness: AI can calculate the "risk of a single position" (such as the liquidation price of ETH at 25x leverage), but cannot understand the "correlation risk of multi-cryptocurrency positions" — for example, Gemini opens high-leverage long positions on both BTC and ETH simultaneously, without realizing that "when both rise and fall together, risks will accumulate," while top traders strictly control the "total leverage of correlated positions" to avoid "losing everything at once."

3. Strategy Innovation: AI Understands "Replication," But Not "Creation"

The strategies of all AIs in AlphaArena are essentially "AI-ification of quantitative strategies" — for instance, DeepSeek's "trend following" and Grok's "momentum-driven" strategies are both validated by humans, and AI merely "executes at a faster speed" rather than "creating new strategies":

• Dependent on Historical Data, Unable to Respond to New Markets: If new derivatives (such as perpetual contracts based on AI tokens) emerge in the future crypto market, AI would be unable to formulate strategies due to "lack of historical data"; whereas top traders can create entirely new trading methods based on "the essence of derivatives + market logic" (for example, during the DeFi explosion in 2020, top traders quickly designed "liquidity mining arbitrage strategies," which AI at the time could not participate in);
• Lack of "Counter-Consensus" Ability: The essence of AI's strategy is to "fit the consensus patterns of historical data," such as "buying on MACD golden crosses," but the extraordinary profits of top traders often come from "counter-consensus" — for example, buying during market panic and selling during market euphoria. In AlphaArena, none of the AIs dared to "operate against the trend," which is precisely the core competitiveness of top traders.

4. Understanding Human Nature: AI Understands "Trading," But Not "Human Hearts"

The essence of trading is "a game between people," and top traders can judge opponents' behavior through "market sentiment" (for example, inferring "retail investors are panic selling" from changes in trading volume), while AI can only process "objective data" and cannot understand "human hearts":

• Unable to Identify "Market Traps": If an institution deliberately pumps BTC to lure retail investors to follow suit, AI would increase positions because it "sees an upward trend," while top traders can judge from "abnormal trading volume" that this is a "trap to lure buyers" and stop losses in advance;
• No "Dynamic Adjustment of Risk Preference": AI's risk preference is preset (such as DeepSeek's "medium-high leverage"), while top traders adjust risk based on "personal condition and market environment" — for example, reducing leverage when physically fatigued or decreasing trading frequency during high market volatility. This kind of "flexible adjustment" is currently beyond AI's capabilities.

IV. Industry Controversy: From CZ's Doubts to Institutional Choices, "Collaboration" is Better than "Replacement"

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

1. CZ's Core Doubt: AI Synchronized Trading Will Lead to "Self-Destruction"

Binance founder Zhao Changpeng (CZ) publicly commented on AlphaArena on the X platform: "If everyone uses the same AI strategy, trading will synchronize — either everyone buys to push prices up, or everyone sells to trigger a flash crash, ultimately leading to profit disappearance and soaring volatility." This viewpoint directly points to the "fatal hidden dangers" of AI trading:

• In AlphaArena, the six AIs already show a "tendency to converge" (for example, both DeepSeek and Grok heavily invest in BTC and ETH long positions); if more funds use the same AI in the future, it will lead to "liquidity depletion" — for instance, if all AIs simultaneously stop-loss at a certain moment, it could trigger a cliff-like price drop, from which even the AIs themselves cannot escape;
• The "differentiated strategies" of top traders are precisely the "stabilizers" of the market — some go long, some go short, and some arbitrage; this "game balance" is something AI cannot provide.

2. Consensus Among Analysts: 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":

• In Terms of Risk Control: AI can monitor position risks in real-time, such as reminding that "BTC long position leverage is too high," but whether to reduce positions ultimately requires human judgment combined with macro considerations;
• In Terms of Strategy Execution: Humans design "counter-consensus strategies" (such as buying during panic), while AI is responsible for "high-frequency execution" (for example, completing 10 dispersed purchases within 1 minute to avoid impacting the market);
• As reported by Wolfgang, leading institutions have begun adopting the "AI + human supervision" model — AI handles 80% of routine trades, while humans manage 20% of black swan events and strategy innovations. This model can leverage AI's efficiency advantages while retaining human core judgment.

V. Conclusion: The Ultimate Insight from AlphaArena — The Future of Trading is "Human-Machine Collaboration"

Returning to the initial question: Can AI replace top traders? Based on the live performance of AlphaArena and industry logic, the answer is negative — at least in the foreseeable future, it cannot.

The value of AI lies in its ability to address the pain points of human traders, such as "low efficiency, poor discipline, and limited processing capacity"; while the value of top traders lies in their core abilities to "understand essence, respond to black swans, create strategies, and grasp human nature" — these abilities are not "technical issues," but rather "the crystallization of human experience and cognition," which AI cannot replicate.

The true significance of AlphaArena is not to "prove that AI is stronger than humans," but to "explore the best collaboration methods between AI and humans": when DeepSeek's quantitative strategy encounters 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 Huansuan Quant amplifies its quantitative capabilities with AI, top traders will also use AI to enhance their decision-making efficiency in the future — but ultimately, the decision of "how much to earn and how much risk to bear" will still depend on human understanding of the market, not on AI's code.

Would you like me to help you organize a core capability comparison table between AI and top traders? The table will clearly present the advantages and shortcomings of both sides across eight dimensions, such as "data processing, strategy innovation, black swan response, and understanding human nature," making it easier for you to intuitively understand the logic of "collaboration rather than replacement."

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