Despite the wide availability of powerful AI models, a sustainable edge in the zero-sum game of investing comes from a combination of unique, curated data sets, bespoke technology for scale, and the experienced human context to ask the right questions of the models.

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.

Contrary to popular belief, the market may be getting less efficient. The dominance of indexing, quant funds, and multi-manager pods—all with short time horizons—creates dislocations. This leaves opportunities for long-term investors to buy valuable assets that are neglected because their path to value creation is uncertain.

A Goldman Sachs quant head reveals that over half of a stock's performance is attributable to non-fundamental factors. These include market sentiment, themes, and trends, which can now be captured with unprecedented accuracy using fine-tuned language models on unstructured data.

AI tools are automating traditional analytical tasks, diminishing the edge from pure technical skill. The most valuable investors will be those who can apply superior judgment, market structure understanding, and pattern recognition to challenge and interpret AI-generated insights.

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.

Contrary to classic theory, markets may be growing less efficient. This is driven not only by passive indexing but also by a structural shift in active management towards short-term, quantitative strategies that prioritize immediate price movements over long-term fundamental value.

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.

The expectation that universal, instant access to information would lead to more efficient markets has been proven wrong. Instead, it has amplified sentiment-driven volatility. Stock prices have become less tethered to fundamentals as information is interpreted through the lens of crowd psychology, not rational analysis.