Written by: Ray
In the field of cryptocurrency, the combination of AI Agent and Crypto has become a hot topic in the market over the past two years, but the vast majority of market imaginations have fallen into a visual misconception.
We have always anchored the trading mechanism of AI Agents on assets, yet overlooked the adaptive nature of their core capabilities: the true carrier of AI Agent trading is never assets, but rather information.
01 We Have Been Misunderstanding AI Agent Trading from the Wrong Perspective
In the past two years, market imaginations about AI Agent + Crypto have almost all focused in the same direction: enabling Agents to automatically trade assets, manage wallets, execute DeFi strategies, or engage in high-frequency trading and arbitrage operations.
However, if we elevate our perspective, we will discover a fundamental issue: AI Agents do not truly understand "assets"; the core of what they can understand is "information."
Assets are merely the result layer of trading; for AI Agents, what is really computable, inferable, and optimizable are probabilities, events, causal relationships, narrative changes, and information flows.
In other words, AI Agents inherently belong to the information market rather than the asset market.
02 Why Asset Trading Is Not the Ideal Battleground for AI Agents
The core units of traditional financial trading are various assets such as stocks, cryptocurrencies, commodities, and ETFs. These assets possess long-term price trends, clear value anchors, and also a passive holding logic.
For human investors, even without understanding the details behind the assets, profits can be realized through the long-term rise of the assets.
However, the core advantage of AI Agents is not long-term holding but rather extreme information processing speed, the ability to integrate multidimensional signals, and the capability for real-time updates of dynamic probabilities.
This means that the advantages of AI Agents can only be fully realized during the "information change" phase, not in the static phase of long-term asset holding; hence, asset trading is naturally not their ideal battleground.
03 Predictive Markets Reveal the True Direction of AI Agent Trading
Predictive markets represented by Polymarket may seem similar to ordinary trading platforms, but their essence is fundamentally different: users in predictive markets trade not assets, but the probabilities of future events occurring.
For example, whether interest rates will be lowered, who will win the election, or whether a specific event will occur are all trading targets in predictive markets.
The key change here lies in the fact that the trading unit has completely shifted from "assets" to "information expression," a characteristic that aligns closely with the cognitive structure of AI Agents.
AI Agents can read news and various data from across the web in real-time, dynamically update probability models, and accurately judge whether market pricing deviates from the reality.
Thus, it is evident that AI Agents are inherently suitable for trading "probabilities," not "assets."
04 Existing Predictive Markets Still Have Structural Deficiencies
Although predictive markets are closer to the ideal trading environment for AI Agents, they do not represent the ultimate form adapted to AI Agents. Current predictive markets exhibit several structural deficiencies:
Markets are not decision units: The current core structure of predictive markets is the binary question "Will event X occur?" corresponding to "Yes / No" answers, but the decision logic in reality involves a macro narrative deducing causal chains and then directing from those chains to outcomes; a single binary structure cannot match the logic of real-world decisions;
Liquidity is infinitely bifurcated: A single macro narrative is split into multiple independent markets, directly leading to diluted market liquidity and significantly reduced user trading experiences;
Lack of core logic for user retention: Predictive markets are event-driven; once users place bets, they only need to wait for the event outcome, leaving no reason for continuous participation, and they will leave the market after results are announced;
Zero-sum structure limits market growth: Under zero-sum rules, long-term wealth in the market will continuously concentrate among professional players, with ordinary users gradually losing interest, ultimately constraining the overall development of the market.
05 The Real Cognitive Shift: The Trading Object of AI Agents Is Not Market, but Narrative
The trading core of current predictive markets revolves around independent questions, but what AI Agents truly need to trade is the underlying "narrative."
A trading structure adapted to AI Agents should extend from core narratives to multiple related events, with multiple related events corresponding to multiple trading markets.
For example, the "Interest Rate Cut Narrative" could be automatically mapped by the system to multiple related trading markets, with the system completing the combination and management of the markets.
In this structure, AI Agents are no longer limited to trading single events, but manage the overall information structure.
06 The True Advantage of AI Agents: Probability Evolution, Not Outcome Prediction
The core focus of human investors is the event outcome: whether something will happen; whereas the core expertise of AI Agents is to determine how the probabilities of events change.
In future trades dominated by AI Agents, the core competitive advantage will no longer be who can accurately predict event outcomes, but who can understand the paths of probability changes earlier.
Therefore, probability itself is the core content of trading.
07 The Next Generation Trading Form: Belief Asset
The next generation trading form adapted to AI Agents will give birth to Belief Assets.
In this form, users will no longer trade the probabilities of single events, but rather buy into their long-term perspectives, such as AI prosperity narratives, interest rate cut cycles, cryptocurrency bull markets, which will all become targets of belief assets.
The trading market selection, dynamic portfolio adjustment, and the rolling migration of targets will be automatically completed by AI Agents behind the scenes, realizing continuous trading and management of long-term perspectives.
08 Why Belief Assets Are the Natural Domain of AI Agents
The core ability of AI Agents is precisely to continuously read information across the web, update belief weights in real-time, and optimize trading portfolio structures according to market changes; this ability highly matches the trading needs of belief assets.
In the future, AI Agents will no longer just be simple traders but will become professional belief portfolio managers.
09 The Ultimate Fate of AI Agent Trading: Information Becomes the Primary Asset
Looking at the evolutionary path of financial history, trading carriers have experienced an upgrade process from commodities to stocks, then to ETFs and indices, while the emergence of AI Agents may propel financial trading into the next stage: Information Index.
In future financial markets, there will be trading products based on worldviews, probability structures, and information evolution; information will become the core upper layer of trading, while traditional assets will become the underlying settlement layer, officially making information the primary trading asset in the market.
10 Conclusion: In the Era of AI Agents, the Essence of Trading Is Changing
In the past, human-led financial trading centered around trading assets; in the future, the new trading era led by AI Agents will center around trading information.
The true changes that AI Agents bring to cryptocurrency and the entire financial market are not just the automation of trading operations, but rather the fundamental upgrade of trading objects.
As financial markets shift from being "asset-centered" to being "information-centered," we will see a brand-new financial entry: a system capable of managing personal beliefs and world probabilities, and that, too, will be the ultimate value where AI Agents converge with finance.
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