The Dilemma of the Oracle Machine: Starting from the Zelensky Suit Incident

Recently, a prediction market regarding Ukrainian President Zelensky's attire has garnered widespread attention. This prediction market, with a trading volume of 200 million dollars, seems to revolve around a simple question but has produced unexpected results, revealing fundamental flaws in the current human-controlled Oracle Machine systems.

The cause of the incident was the appearance of the prediction option "Will Zelensky wear a suit in July" on a certain prediction platform. When Zelensky attended the NATO summit, mainstream media widely reported that he was wearing a suit. However, the Oracle Machine responsible for the verdict gave a contradictory "no" result.

This result is not a system malfunction, but rather the result of human manipulation. It is reported that some token holders placed huge bets on the 'no' option. To avoid losses, they manipulated the final result using their voting rights. More than 23 million tokens (worth about $25 million) were used to reverse the expected outcome.

This event exposed the fatal weakness of human control over the Oracle Machine: when the profits from manipulation exceed the costs, the truth becomes a commodity that can be bought at a high price. This not only goes against the original intention of decentralization but also represents the blatant manipulation of the market by oligarchic powers.

The problem is not limited to individual platforms. All Oracle Machine systems that rely on human judgment face similar challenges. Whether it is predicting markets, validating elections, achieving consensus in scientific research, or determining the truthfulness of news, as long as humans control the definition of truth, it is inevitable that they will become tools for profit.

To fundamentally solve this problem, the key lies in eliminating human intervention. AI-driven Oracle Machine systems may provide a breakthrough:

1. No vested interest: The AI model does not hold positions and has no preference for the results.
2. Consistent decision rules: Based on the same parameters, AI will consistently evaluate evidence.
3. The reasoning process is transparent: every decision can be recorded and reviewed.
4. Efficient processing capability: capable of parallel analysis of massive data sources, not restricted by human factors.

Although AI systems still have errors, these are random noise and are difficult to be intentionally exploited. With clear standards and reliable data support, advanced AI models have achieved considerable accuracy.

The future prediction market architecture may include: predefined information source hierarchy, cryptographic proof of data sources, independent judgments from multiple AI systems, traceable reasoning processes, and immutable blockchain evidence.

The significance of this issue goes far beyond the prediction market itself. In the "post-truth" era, we urgently need to establish a mechanism for determining objective truth to address various challenges.

The prediction market is facing a choice: either trust that profit-driven humans can make neutral judgments, or build a truth determination system that eliminates human bias. The Zelensky suit incident has already provided the answer—when $200 million cannot sway an obvious outcome, the inherent flaws of the system are laid bare.

The technology to solve this dilemma already exists. The determination of truth is of great importance and should not be decided by the highest bidder. We need a more reliable and fair mechanism to maintain the market's credibility.