“You need domain knowledge.” That sounds like hundreds of hours of study before writing any code. But with AI, why not search broadly, narrow the options, and learn from live tests? It is a sensible idea for bots hunting tiny DeFi price gaps. The catch: another bot may collect the profit first. You brought a bag to pick up loose change; next to you is an industrial vacuum cleaner.
1. Domain knowledge means understanding how the field behaves
Domain expertise is familiarity with a field’s rules, habits, exceptions, and failure patterns. Python tells you how to build a bot; domain knowledge helps identify where an opportunity exists and why it might fail. In DeFi, this includes price formation, thin liquidity, network costs, execution reliability, and competition.
2. AI can reverse the learning sequence
Traditionally: study, hypothesize, build, test. An AI-assisted approach can start with many candidates, filter them, test small, and accumulate expertise. Ideas might include exchange price differences, liquidations, redemption mechanisms, bridges, and unusual swap routes. But asking for 100 profitable ideas might yield 100 confident guesses. The filtering rules need evidence.
3. A quoted spread is not a realized profit
Measure required capital, fees, gas, slippage, failed transaction costs, execution rates, and rivals. Net result = price gain − trading fees − network costs − unfavorable price movements − failure costs. If ETH is quoted at ¥300,000 on one market and ¥310,000 on another, the nominal difference is ¥10,000. Prices and costs can change before settlement. The dashboard is not your wallet.
4. MEV is extra value from transaction ordering
Maximal extractable value comes from choosing which transactions enter a block, excluding others, or changing their order. Ethereum describes arbitrage, lending liquidations, and sandwich trading as examples.[1] Searchers might execute before another trade, capture a price change immediately after it, or surround a large trade with their own. The last approach can worsen the other trader’s execution.
5. Not every bot race is MEV
Two bots can independently discover the same spread. One winning the race is not, by itself, proof of MEV. To determine the cause, inspect transaction records and block ordering. Finding a price gap first does not guarantee first execution; competing bids and submission paths can make an opportunity disappear.[1] An optimistic simulation cannot guarantee a completed trade.
6. Small, short-lived opportunities still deserve tests
Widely known arbitrage routes are competitive. Obscure assets and awkward redemption rules might offer overlooked opportunities, but that is a hypothesis, not guaranteed income. Test with small exposure, include every cost, and stop when net gains disappear. Stick to legitimate market differences and authorized mechanisms rather than harming others’ assets. Let AI widen the search, real executions narrow it, and results build domain knowledge. If a MEV bot wins again, your treasure hunt has become a vacuum-cleaner benchmark.
Reference: [1] Ethereum.org, “Maximal extractable value (MEV)” — https://ethereum.org/en/developers/docs/mev/
