The Day Trading Era Ends: How AGI Could Become the Final Market Maker

The landscape of trading is undergoing significant changes as new research and market trends challenge conventional beliefs about the future of trading.

Key Takeaways

Analysts are discussing the potential decline of day trading as AGI approaches.
Over 70% of trading on major exchanges is now driven by algorithms.
AGI could disrupt traditional trading methods by making predictions with high accuracy.
This could lead to a liquidity black hole where day trading becomes unviable.
The role of humans in trading is evolving toward oversight and interpretation, while automation takes over execution.

The Day Trading Era Ends: How AGI Could Become the Final Market Maker

In both traditional finance and the cryptocurrency sector, analysts are now contemplating a once-unimaginable scenario: the gradual decline of day trading as Artificial General Intelligence (AGI) edges closer to becoming a reality.

While AGI is not yet a reality, advancements in sophisticated multimodal systems and automated trading agents are steering markets toward an environment where machines will take charge of price discovery, leaving little opportunity for human traders.

Current levels of trading automation illustrate how quickly competitive advantages disappear when algorithms take over.

The Impact of Algorithms on Crypto Trading

Over 70% of Trading Flow is Algorithm-Driven

High-frequency trading revolutionized the equities market years ago, and this logic extended into cryptocurrency markets with the emergence of companies like Jump, Wintermute, and GSR.

As of 2024, Kaiko reported that over 70% of trading activity on popular exchanges such as Binance and Coinbase is algorithmically generated rather than conducted by humans.

Crypto Trading Flows

This transformation has fundamentally altered market structures, reducing spreads and speeding up trade execution, while making it increasingly difficult for retail traders to secure profits during periods of high volatility.

Researchers point to these developments as early indicators of growing market efficiency.

During the Solana memecoin boom in 2024, trading bots—specifically "sniper" and "AI" bots—generally outperformed human traders thanks to their impressive speed, automation, and absence of emotional biases.

These small AI systems, which are designed to detect whale behavior and monitor blockchain activity, responded more rapidly than discretionary traders, often positioning themselves ahead of human participants who were still trying to comprehend the market dynamics.

With each leap in automation, the opportunities for retail traders have continued to diminish, leading analysts to suggest that the emergence of AGI would culminate this trend.

Understanding the AGI vs. Narrow AI Debate

The distinction between today's narrow AI and the anticipated AGI is central to this discussion.

Current models excel in specific tasks such as analyzing order books, gauging market sentiment, or spotting arbitrage opportunities. However, they lack the ability to generalize their knowledge across different fields or emulate human reasoning.

AGI, on the other hand, is expected to master new tasks with minimal guidance, adapt to novel environments, and synthesize information from multiple independent sources.

In the realm of finance, this would entail interpreting blockchain flows, understanding global macroeconomic signals, evaluating political risks, identifying whale movements, and analyzing supply chain disruptions—all within a cohesive system capable of generating real-time predictions.

Market theorists refer to this potential outcome as the “Perfect Efficiency Paradox.” If an AGI system can predict price movements with almost perfect accuracy, the market would adjust instantaneously.

When all market participants are armed with the same level of intellect, traditional trading practices would collapse.

Price changes would outpace human reactions, volatility would diminish, arbitrage opportunities would vanish, and liquidity provision would shift from being competitively driven to machine-led.

Experts caution that this dynamic could create a liquidity black hole, where trading persists, but the advantages that once supported day trading are eliminated.

The Shift from Theoretical AI Market Makers to Practical Automation

Concerns about this transition have circulated for years. DWF Labs highlighted in July that AI-powered market makers would enhance liquidity, particularly for smaller cryptocurrencies that typically have thin order books and wide spreads.

Economist Alex Krüger envisioned an era of hyper-efficient markets where mistakes are virtually nonexistent.

BitMEX founder Arthur Hayes asserted that AI will eventually outperform all human traders, while Ethereum co-founder Vitalik Buterin warned that advanced systems could monopolize Extractable Value (MEV) and lessen human involvement in essential market functions.

While these statements were initially regarded as speculative, the increasing prevalence of automation has lent them greater credibility.

As automation continues to accelerate, the role of humans in trading environments is already evolving.

Experts suggest that while humans may not entirely vanish from trading desks, they will transition to roles focused on risk management, regulatory compliance, and interpreting unusual occurrences beyond model predictions.

The execution of trades will increasingly shift to autonomous systems, exemplified by the surge in AI trading agents. These tools can analyze markets, select strategies, adjust risk parameters, execute trades through APIs, and learn from results with minimal human intervention. Predictions indicate that the AI trading bot market may reach around $75.5 billion by 2034.