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There is already an experiment using AI agents1 to participate in prediction markets. This is quite an interesting initial work, but we believe the real power of AI agent markets lies in computer speed (i.e. where humans can’t participate anyways) markets used by algorithms. In most settings where machine learning can be used, AI markets could be used to make better predictions. Indeed, machine learning is mainly used to make predictions: Will the user click on this ad? Will the user like this movie? Will the user click on this search result and spend some time on this website? Will the user view the entirety of this video? Will the user like this post? Current internet is dominated by services which are using AI to make predictions about user behaviours. Those are run by the corporations running those websites themselves (ex: The Netflix team has a team working on the recommendation engine, the X team has a significant number of engineers working on which content to display to users). If those end up being replaced by decentralised protocols, there will be a need to replace those predictive engines by an open process: Prediction markets. Let’s take a simple example, a social network wants to display content to users who are likely to like it. All AIs can submit potential content with a small amount of money (think less than a cent) to be used as liquidity for “Will user X like content Y?”. AIs can then trade on those markets. Content with the highest prediction of “Yes” is displayed to the user (and other markets are cancelled). After the user interacts, markets are resolved, so AIs who predicted that the user would like a specific content make profits if the user does and make a loss if the user doesn’t. A small reward is also given to the AIs who proposed content (and put the liquidity) the user liked. Those mechanisms can work in countless domains, here is a non exhaustive list:
  • Predicting if a user would interact with a particular content.
  • Predicting the amount of stars a user would give to a particular business (ex: shop and restaurants on a map).
  • Predicting the stars given to a particular content (ex: streaming platforms).
  • Predicting if a user would click on an ad or make a purchase related to an ad.
  • Predicting if a user would match a user on a dating app and have a sustained interaction with this user.
  • Predicting the reaction of the user when interacting with a language model.
  • Predicting whether an AI would win at an online game provided it does a particular move.
  • Predicting whether the user would have to rephrase their demand for a voice controlled personal assistant.
  • Predicting if a user would accept a specific proposed correction for a text processing tool.

Auctions started by being used for humans competing to buy valuable goods. Now most auctions are between bots. We expect to see a similar pattern for prediction markets. Initially used for high importance events with predictions made and consumed by humans, to then expand to AI making high throughput predictions used by other algorithms.
As prediction market based AIs can aggregate the power of different AIs and allow any team to participate (even without revealing their source code), we “predict” that those AIs will be more efficient than any individual AI and the first AGI (Artificial General Intelligence) will not be the result of a single research team, but would result from market-based aggregation of AIs made by multiple teams.

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Footnotes

  1. Omen ↩