Case Study: How it Unraveled
It began innocently enough: a few savvy individuals created a digital art project featuring punks in crypto-themed attire. The project's popularity snowballed, and soon, investors were clamoring to get in on the action.
But, as the project's founders continued to peddle their wares, a pattern emerged: every time a new "limited edition" CryptoPunk was released, the price would skyrocket, only to crash back down when the "next big thing" came around the corner. The cycle repeated, leaving investors bewildered and broke.
The scheme's downfall came when a group of AI researchers, attempting to apply reinforcement learning to predict the project's price movements, discovered the underlying pattern. They created a bot that could predict the exact timing and price fluctuations of every new release, and promptly sold their findings to short-sellers. The market, sensing the end was near, collapsed.
The project's founders, caught in their own web of deceit, found themselves on the wrong end of a lawsuit. The researchers, hailed as heroes, continued to refine their AI model, applying it to other areas of finance with great success.
The moral of the story? Even the most seemingly foolproof AI systems can be gamed by a well-crafted set of incentives and a bit of human ingenuity. Or, as one of the researchers put it, "You can't fool an algorithm, but you can fool the people who built it."
Read the conclusion of this case study to learn more about the lessons learned and the future of AI in finance.