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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.

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A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.

Stock market investors are pricing in rapid, significant productivity gains from AI to justify high valuations. This sets up a binary outcome: either investors are correct, leading to massive productivity growth that could disrupt the job market, or they are wrong, resulting in a painful stock market correction when those gains fail to materialize.

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.

Similar to the dot-com era, the current AI investment cycle is expected to produce a high number of company failures alongside a few generational winners that create more value than ever before in venture capital history.

Historically, investment tech focused on speed. Modern AI, like AlphaGo, offers something new: inhuman intelligence that reveals novel insights and strategies humans miss. For investors, this means moving beyond automation to using AI as a tool for generating genuine alpha through superior inference.

Dismissing AI's current capabilities is a mistake due to its exponential improvement rate, evidenced by rapid advances in video generation. Markets selling off established companies based on nascent AI competitors are rationally pricing in this non-linear progress, rather than overreacting.

For current AI valuations to be realized, AI must deliver unprecedented efficiency, likely causing mass job displacement. This would disrupt the consumer economy that supports these companies, creating a fundamental contradiction where the condition for success undermines the system itself.

Drawing a parallel to the early internet, where initial market-anointed winners like Ask Jeeves failed, the current AI boom presents a similar risk. A more prudent strategy is to invest in companies across various sectors that are effectively adopting AI to enhance productivity, as this is where widespread, long-term value will be created.

Beyond automating tasks, Emad Mostaque's "Intelligence Theory" suggests AI's deepest impact is shifting the foundational axiom of economics. Instead of scarcity, the new core principle is persistence: how complex systems (like firms or AIs) maintain themselves by accurately modeling and predicting reality.

History shows a significant delay between tech investment and productivity gains—10 years for PCs, 5-6 for the internet. The current AI CapEx boom faces a similar risk. An 'AI wobble' may occur when impatient investors begin questioning the long-delayed returns.

AI's Self-Reinforcing Productivity May Invalidate 'Reversion to the Mean' Investing | RiffOn