The essence of the prediction market is not guessing, but using better tools to discover market mispricing.

😂 This tweet was written and got educated by a colleague responsible for media, saying it was too superficial. While I was being educated, I also scolded the colleagues in the product department a bit. Indeed, what I wrote was somewhat shallow, but the reason for its superficiality was that the product guidance was too poor. If I were just an ordinary user logging in and seeing a product without detailed explanations, how could I not be shallow?

In fact, the AI prediction training camp itself hopes to expand its knowledge blind spots through the breadth of AI's knowledge. Just like when I make predictions on Polymarket or Kalshi, I will only choose fields where I have data, like macro events, in which I have a bit of confidence.

However, many people understand these macro events. Even if I have abundant confidence, the returns are just that little. There are too many reference data and guidelines, whereas some obscure events, which seem to offer decent returns, I really don’t understand. Not understanding and placing bets is no different from throwing money away.

So I thought, if there is a small AI model that can help me answer these questions, as long as the accuracy rate exceeds 50%, it can resolve my knowledge weaknesses. For instance, I am completely clueless about basketball. If there is one or several AIs forming a "consultant" specifically for me, helping me analyze according to the predicted content and providing a final result through interactions among AIs, then my winning rate could likely exceed that of someone who knows nothing.

This is the original intention of the design, which essentially focuses on a specific area through proprietary small models, and improves one’s judgment through training knowledge and content in that area. In the future, when there is a predictive content I don’t understand, if it aligns with this training direction, I can improve my winning rate through AI judgment.

In Evoevo, the goal is to find and train a specialized AI model in a specific field, then subscribe to such models to help improve prediction content's winning rates. If it cannot be found, then train it oneself, feeding data and improving one’s AI's accuracy through repeated testing.

Many colleagues think that existing AIs like Chatgpt can do this, but that's actually wrong. The focus of large language models is not to predict a result, but to break down a complex problem and identify the variables affecting the outcome.

However, the real difficulty of the prediction market lies not just in knowing a lot, but in whether one can continuously accumulate, calibrate, and review in a very specific field. For example, in an NBA game, a general large model can certainly tell you the historical records of the two teams, star player performance, injury situations, and odds changes, but these are merely information summaries.

The real value lies in whether it can know how much the impact of a player’s injury affects the team's offensive space, the net efficiency changes of the backup lineup after a key player is absent under similar past situations, whether there are mismatches between back-to-back games, travel conditions, line changes, and market emotions, and whether there is a price difference between the implied probability given by the odds and the actual winning rate.

This is not a question that a regular chatbot can solve with a simple query; it requires a small model that focuses long-term on a specific field, or a consultant group composed of multiple small models, continuously consuming data, testing, and refining its judgments.

Evoevo is not an AI Q&A tool, but an AI prediction consulting market.

You can subscribe to models that others have already trained or train one yourself. One model can specialize in NBA, another in macro data, another in U.S. elections, another in cryptocurrency on-chain data, another in soccer, tennis, esports, company earnings reports, or even one model that focuses only on a very specific type of prediction question.

I cannot understand all fields.

I understand macro, so I dare to bet on CPI, PPI, unemployment rate, Federal Reserve interest rates, and ETF inflows and outflows.

But I don’t understand basketball, I don’t understand baseball, I don’t understand obscure political events, I don’t understand many local elections, nor do I understand certain internal business changes of companies. For these issues, my betting is essentially gambling.

However, if there is a specially trained AI model, it does not need to achieve 80% or 90% god-like winning rates; as long as it maintains a long-term stable winning rate above random chance in a specific field, even if it only improves from 50% to 53%, 55%, or 57%, that would already be a significant advantage in the prediction market.

Because the prediction market is not a lottery; its essence is probability trading. If the market prices an event at 50%, but the model assesses the true probability to be around 55% through data, then the 5% difference represents the cognitive gap for the bettor.

In the past, we competed in the prediction market with personal knowledge, information gaps, data processing abilities, and experience. In the future, it may be about who can find better AI consultants, who can train more focused AIs, who can achieve more stable judgments through debates among multiple AIs, and who can detect mismatches between market pricing and AI judgments earlier.

This is the direction I believe Evoevo should develop. For the prediction market, this direction is meaningful. The essence of the prediction market is not guesswork, but using better tools to discover market mispricing.

And what the AI prediction training camp truly aims to do is help you train the tool that discovers mispricing.

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