Article by Melee
Compiled by AididiaoJP, Foresight News
In July 2017, Hayden Adams was laid off from his job as a mechanical engineer at Siemens. At the time, his college roommate Karl Floersch was working at the Ethereum Foundation and often shared stories about smart contracts with him. Adams had previously ignored these discussions. Now unemployed and looking for something to do, he decided to listen.
The emergence of automated market makers (AMMs)
Floersch recommended to him a blog post by Vitalik Buterin on running on-chain exchanges using mathematical formulas instead of order books. The concept involved traders exchanging assets with a liquidity pool, where prices were automatically determined by the ratio of tokens in the pool. At the time, no working version existed. Adams took it on as a learning project, received a $65,000 grant from the Ethereum Foundation, and launched Uniswap in November 2018.
Its formula is almost childishly simple: x * y = k.
Two tokens are placed in a pool, and their product remains constant. When someone buys one token, they must deposit the other token, altering the pool's ratio and adjusting the price accordingly. No order book, no matching engine, and no professional market makers are required. Anyone can deposit tokens into the pool and earn fees from every trade.
Automated market makers have become the foundation of decentralized finance. Protocols such as Uniswap, Curve, and Balancer, along with dozens of others, handle billions of dollars in trading volume. On-chain order books are slow and expensive, and traditional market makers have no interest in participating in tokens with only a few hundred holders. Automated market makers enable anyone to create liquid markets for any asset at any time. Before automated market makers, launching a new asset required permission and dedicated infrastructure. After their emergence, all you need is a liquidity pool.
The benefits are obvious. Therefore, prediction markets have naturally sought to adopt them.
Automated Market Makers and Prediction Markets
Prediction markets face the same cold start problem as token markets: liquidity must exist before traders are willing to participate, and traders must exist before liquidity providers are willing to step in. Few know that years earlier, Robin Hanson proposed an automated market-making solution for prediction markets in his 2002 Logarithmic Market Scoring Rule.
He believed he had theoretically solved the cold start problem. However, in practice, the solution encounters the same fundamental issue encountered every time someone attempts to automate liquidity for prediction markets: the formula cannot distinguish between perpetually volatile tokens and expiring equity tokens.
The outcome of prediction markets is binary—they settle to either one or zero. In token liquidity pools, both assets can fluctuate indefinitely, and the automated market maker formula works precisely because neither token is designed to reach zero.
Early versions of Polymarket used an automated market maker based on the logarithmic market scoring rule. Augur also experimented with a similar approach. If automated liquidity pools are effective for token swaps, they should be equally effective for election betting.
That is not the case.
Why automated market makers fail in predicting markets
When a prediction market event is settled, one side is worth one dollar, and the other side is worth zero. For anyone providing liquidity to the pool, the mathematical outcome is nearly brutal. As the market approaches settlement, the pool automatically rebalances toward the losing side.
Impermanent loss
The "impermanent loss" referred to by decentralized finance traders becomes entirely "permanent" here. Each market will settle, and each liquidity pool will ultimately hold a collection of shares worth zero.
In a standard decentralized finance liquidity pool, trading fees can offset impermanent loss over time.
In prediction markets, losses are an inherent structural certainty. The only question is how much liquidity providers will lose. Various protocols have attempted to persuade users to deposit assets into these pools through liquidity mining, reward programs, and other incentive structures—all of which are merely different ways of subsidizing users’ slower rate of loss.
Price discovery
Another issue is price discovery. Automated market makers price assets based on pool ratios and a fixed formula. For tokens, the "correct price" is inherently a moving target, and the approximations generated by the formula are sufficient. Predicting market price should reflect probabilities. The slippage introduced by the constant product curve distorts signals, especially in low-liquidity markets, where a single trade can cause implied probabilities to fluctuate by several basis points.
Is a Central Limit Order Book (CLOB) better than an Automated Market Maker?
Polymarket recognized this early. At the end of 2022, the platform migrated from an automated market maker based on logarithmic market scoring rules to a central limit order book. Automated market makers are designed for continuous token exchanges across price ranges, whereas prediction markets require precise probability pricing on binary outcomes with known final values. These are entirely different problems.
The features that make automated market makers revolutionary for tokens—permissionless market creation, instant liquidity provisioning, and independence from professional market makers—are precisely the characteristics that prediction markets desperately need. The issue is that the specific mechanism of the constant function formula, designed for token swapping, struggles to hold up when faced with the reality of binary outcomes and mandatory settlement.
The challenge facing prediction markets is replicating the above effects with infrastructure that accurately reflects how such markets are actually settled.