Efficient Markets Hypothesis suggests that financial markets reflect all available information, but factors like execution systems, market reshaping, information asymmetry, and behavioral factors can still impact returns.
The rise of AI in markets will redefine what counts as an informational advantage, with natural language processing enabling personalized information delivery and agents influencing non-informational efficiencies.
Challenges like attributing actionable information, open-source tools for navigating markets, incentivized markets for truth, diverse trading agents, and securing real-world markets present opportunities for startups in autonomous finance.
Autonomous agents are non-human entities that can assign tasks, operate independently, search for information, and remember things.
As AI evolves, individuals will have the opportunity to become managers of complex operations with the help of autonomous agents, reducing the need for large teams.
Challenges with autonomous agents include reliability, personalization, security, and the need for user-friendly deployment tools and incentive mechanisms for agent resource allocation.