The complex effects of AI are causing traditional market relationships, like yields reacting to economic surprises, to break down. In this new regime, broad diversification and passive strategies are ineffective as winners and losers become more distinct and dispersion explodes.

If AI wealth management becomes mainstream and models rely on similar data signals, it could create a "herd problem." All AIs might execute the same buy or sell trades simultaneously, leading to synchronized panics or euphoric bubbles and unprecedented market volatility.

David Kaiser of Methodical Investments posits a contrarian view on AI's market impact. Instead of creating perfect efficiency, he argues AI and the data it processes might actually create more mispricings and inefficiencies. This provides opportunities for disciplined, rules-based strategies that don't constantly adapt to short-term noise.

Widespread use of similar AI models by average investors will likely lead to herd behavior and crowding in certain securities. This pushes prices away from fundamental value, creating predictable inefficiencies and new alpha opportunities for sophisticated investors who can model these effects.

As quantitative models and AI dominate traditional strategies, the only remaining source of alpha is in "weird" situations. These are unique, non-replicable events, like the Elon Musk-Twitter saga, that lack historical parallels for machines to model. Investors must shift from finding undervalued assets to identifying structurally strange opportunities where human judgment has an edge.

The capital financing AI—from venture and credit to public markets—is so deeply interwoven that the system is fragile. Experts warn this creates systemic risk where a single negative event, like a major struggling AI IPO, could rapidly shift sentiment from the current "peak buoyancy" and trigger a broad market correction.

As AI becomes capable of improving itself, capital may concentrate on these systems, seeking exponential returns. This creates a new paradigm where traditional value investing strategies, which rely on mean reversion, could fail as certain sectors get permanently disrupted while others achieve sustained, compounding growth.

Advanced AIs, like those in Starcraft, can dominate human experts in controlled scenarios but collapse when faced with a minor surprise. This reveals a critical vulnerability. Human investors can generate alpha by focusing on situations where unforeseen events or "thick tail" risks are likely, as these are the blind spots for purely algorithmic strategies.

Rather than commoditizing alpha, AI tools will initially create more disparity between investors. They empower users with good intuition but limited quantitative skills to test complex ideas efficiently. This makes the quality of one's questions, not just their analytical process, a key differentiator.