This summary explains how AI is changing arbitrage, or the business of exploiting inefficiencies. Instead of slowly closing gaps over years, AI can erase old gaps in weeks while opening new ones just as fast, changing the economics of entire industries.

1. Arbitrage Changes in the AI Era

For centuries, markets, jobs, and companies have been built on inefficiencies. AI changes the pace of that process by closing old inefficiencies far faster than before, while creating multiple new ones elsewhere.

2. The Polymarket Bot Case

The clearest example is a Polymarket bot that exploited slow pricing in short-term crypto contracts and generated extraordinary returns. The important point is not prediction, but execution against lagging market updates.

3. The Types of Gaps AI Is Closing

The essay breaks the opportunity into several classes: speed gaps, reasoning gaps, synthesis gaps, discipline gaps, and knowledge-asymmetry gaps. In each case, AI gives an edge by reacting faster, interpreting more consistently, combining more sources, or enforcing better execution than humans can sustain.

4. Continuous Change and Claude Mythos

The bigger lesson is that AI-driven arbitrage will not settle into a stable equilibrium. Each new model release creates fresh temporary advantages, then compresses them again, producing a permanent state of faster strategic turnover.

5. Three Questions for Adaptation

The author suggests asking where the next inefficiency is opening, which advantages are likely to disappear, and what new capabilities matter most once current gaps close. That mindset matters more than defending an old moat.

6. Conclusion

The takeaway is not that AI ends arbitrage, but that it accelerates its cycle. People and companies that learn to spot, exploit, and then quickly move beyond disappearing gaps will be better positioned than those waiting for a stable post-AI world.