The Silent Revolution: When AI Transforms Investment Value and Institutional Investors' Strategies

The traditional narrative of value investing is changing. Once, an investor's competitive advantage lay in the ability to gather hard-to-obtain information, analyze it patiently, and draw conclusions that the market had not yet understood. Today, however, the landscape of financial markets — from traditional value investing to cryptocurrency investing strategies — is undergoing a radical transformation.

How did investors' informational advantage originate?

In the 1990s, Guy Spier founded Aquamarine Capital with a clear vision as a Warren Buffett disciple. Over three decades, his fund managed about $500 million, achieving an annual compounded performance of over 9%, slightly better than the S&P 500 index but with significantly lower volatility.

What was the secret? Spier himself admitted it after reflecting on his thirty-year career: his superiority came from “meticulous ground research.” Once, acquiring knowledge required superhuman effort. Investors had to read printed annual reports, make direct phone calls to company executives, physically attend Berkshire Hathaway shareholder meetings. For Spier, this even meant flying to London just to discuss long-term investment strategies with the founders of the “Nomad Investment Partnership.”

Every piece of information had to be patiently reconstructed. The accumulation of knowledge took days, sometimes weeks. This “difficulty of acquisition” was precisely the defensive advantage that allowed active managers to outperform the market.

The flattening of information: when AI changes the rules

Today, the situation is radically different. With exponential technological development, obtaining information is no longer a challenge. Email, social media, live streaming, podcasts, videos — anyone can instantly access massive flows of data. The advent of large language models (LLMs) has further accelerated this informational symmetry, creating what could be called an “earthquake” in the sector.

Today, the speed of processing public information tends toward instantaneity. Corporate research, sector analysis, and even the evaluation of emerging projects — including innovative sectors like cryptocurrency investing — are now automated. The ability to analyze data has become a scalable tool available to everyone. Analytical models replicate and spread almost instantly.

A study that once took days or weeks can now be completed in seconds. Research reports and corporate communications, once costly and confidential, are now almost free or easily accessible. The informational gap between a large fund manager and a retail investor has drastically narrowed.

The end of the golden age: what does it really mean?

According to Spier, the “golden age of value investing” — the era when in-depth analysis guaranteed superior market returns — is definitively over. The space to generate returns through better research and analysis is shrinking. The “nuances” hidden in corporate details, which once distinguished top managers, are now easily identifiable by anyone with the right tools.

Visible market consequences:

• Asset allocation tends to become increasingly crowded, with managers converging on similar positions
• Market volatility amplifies as strategies homogenize
• More and more, beta returns are traded for alpha (outperformance relative to the market)
• Among investors, fewer succeed by “seeing deeper” and more succeed by “seeing faster”

In this new race, quantitative investing and algorithms have already sharpened their blades.

From informational advantage to soft power: the necessary transition

However, Spier does not see this transition as a tragedy. On the contrary, he considers it an opportunity for constructive transformation.

AI can indeed flatten the informational advantage, but it cannot replace structured research and critical thinking. In the past, research meant investing enormous amounts of time in collecting, organizing, and analyzing data. Today, in an environment where data collection is automated and analytical tools are ubiquitous, what truly matters is the quality of conceptual frameworks and hypotheses each investor employs.

The added value of analysis is shifting from “information processor” to “structural judge.” True competitive differentiation will once again depend on:

• Who can identify blind spots in common models — when everyone uses similar tools, their errors systematically amplify
• Who can question the underlying premises of the data — not all public data are correct or relevant
• Who can resist the “illusion of consensus” — the ability to think contrarily remains a rare value

This represents the new “soft power” of investors.

The decisive role of discipline and non-cognitive skills

According to Spier, the future success of investors will increasingly derive from soft skills:

• Discipline in investing to maintain stable decisions through market cycles
• Emotional control during periods of extreme volatility
• Ability to hold long-term positions when the market screams otherwise
• Skill to act counter-cyclically when everyone else moves in the opposite direction

These aspects, unlike informational advantage, possess more unique defensive barriers and are much harder to replicate. They cannot be automated or quickly distributed like information.

The revolution continues: not “farewell” but “see you later”

The “end of the golden age” is not a pessimistic prophecy. It is rather a statement of transition. If in the past competition among institutional managers was based on “who is smarter, who has more information,” the future belongs to “who is more solid, who is more farsighted, who is more patient.”

The past era of informational advantages belonged to a few elites capable of managing data flows. The future era will belong to those who can build robust long-term investment systems, coherent organizational disciplines, and lasting structured knowledge.

We will not say goodbye to value investing in the era of artificial intelligence. Rather, we will witness the emergence of an evolved form of value investing — one that integrates AI's analytical powers not as an end in itself, but as a cross-validation tool in service of an increasingly sophisticated human judgment.

Spier himself summarized his response to this transition: continue ground research, use generative models for verification, invest in relational networking. It may seem like “stubborn insistence,” but it could also be exactly what is needed in a world where data is abundant but wisdom remains scarce.