How does Allora reshape AI inference services with a "model flywheel" led by PolyChain?

With $35 million in financing, Allora, led by veteran VCs like PolyChain and Framework, has recently made a particularly impressive showing. I've seen many people calling it a “prediction market”? That's wrong. Let me share my understanding of this project:

1. To be precise, Allora is a decentralized AI reasoning service platform where users can pay for AI Agent services for any need that requires AI judgment, including price prediction, strategy optimization, risk assessment, etc. Therefore, the prediction market is just one application scenario of Allora, not the entirety;

2. How can AI models, with their uneven reasoning output capabilities, become mature upstream suppliers for bulk output? The answer lies in Allora's establishment of an aggregation platform powered by the collective efforts and competitive cooperation of AI models.

Its mechanism is very straightforward. For example, if a user wants to predict whether ETH will rise or fall, how should they set the LP price range? The traditional approach is to look at K-lines, listen to KOL analyses, or buy various customized AI model APIs for predictions, only to find a bunch of differing answers. Is it possible to have an aggregated inference service platform to handle this comparison and filtering process?

The key lies here: after users submit their demands to Allora, the network architecture with 280,000 nodes will compete to provide answers. Some will say it will rise, some will say it will fall, and some will say it will stay flat. Allora will vote on these models and record the historical scorecard, giving higher weight to AI models with a high prediction success rate and sending token rewards, while penalizing those who guess randomly by deducting points and holding deposits.

This forms a positive feedback loop: models that make accurate predictions earn more, gain higher weights, and take on more tasks; those that keep guessing randomly are eliminated.

3. Therefore, I prefer to think of Allora as the infrastructure layer for AI reasoning services, capable of on-demand invocation of AI model combinations. There are mainly two application scenarios:

DeFAI: When the AI Agent executes on-chain transactions, it needs to determine whether a transaction is affected by MEV, provide the optimal price range in real-time when adjusting Uniswap LP, assess whether AAVE has liquidation risks, and how to dynamically adjust the leverage ratio of the Yield pool, etc.

Prediction Market: Using AI models to dynamically adjust and update probabilities, compared to a mechanism that relies solely on trading volume for pricing, AI's aggregation reasoning can provide users with a more intelligent starting point for predictions, avoiding purely following the crowd.

However, essentially Allora is still just an infrastructure service facility, and in the case of few early models and insufficient data, it will also experience a long energy accumulation period.

But if in the future both DeFAi and prediction market can become mainstream, their infrastructure service value will become apparent.