This chapter shows the cases that AMAFMMs investigated AI traders and high-frequency trades (HFTs). The first one investigated four well-known market liquidity indicators where an HFT participated and did not participate, and showed that market liquidity is supplied by the HFT’s transactions. The second one investigated how a leveraged ETF rebalance has the least influence on the future market volatility, and showed that increasing the size of the leveraged ETF causes increased market volatility. The third one investigated the spillover of market impacts between ETF markets to future market and showed that a large price impact generated in the ETF market spills over to the future market through arbitrages. The fourth one investigated how HFTs affect the markets in a stable market and an unstable market with a flash crash, and suggested that HFTs’ transactions contribute to more stable prices in the stable market, whereas their transactions are not expected to be effective in stabilizing price in the market with the flash crash. The last one investigated that AI trader discovers market manipulation through learning even when the person who built the AI trader has no intention of market manipulation, and showed that the AI trader discovered market manipulation as an optimal investment strategy.