Engineer argues AI elites may hoard intelligence like mercantilists

The mercantilist parallel

In an essay published by The Decoder, engineer Venkatesh Rao draws a comparison between the hoarding of intelligence by AI elites and the mercantilist hoarding of wealth, such as gold. Mercantilism, an economic doctrine that preceded capitalism, treated wealth as finite and labor as a means to accumulate it. Rao argues that AI elites today may adopt a similar mindset, viewing intelligence as a scarce, dangerous resource that requires expert management and protection from rivals.

AI as a catalyst for demand, not obsolescence

Rao challenges the idea that AI will render human labor obsolete. Instead, he suggests that AI could mirror the dynamics of early capitalism, where increased access to goods like tea and sugar created new demand for labor. Similarly, AI might make previously unfeasible projects viable, increasing demand for ideas and human creativity. This shift could create opportunities for startups and operators to explore complex or costly projects that were once out of reach.

Political influence and guardrails

The essay critiques the role of some AI executives in exploiting safety fears to gain political influence. Rao argues that the focus should shift from protecting legacy knowledge jobs to ensuring fair access to AI. He also advocates for guardrails on autonomous AI—not to restrict its capabilities, but to ensure it operates within defined boundaries while pursuing its own projects. This perspective could shape future regulatory frameworks, influencing how startups and operators design and deploy AI-driven products.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

Rao’s argument underscores a critical tension for founders and operators in the AI space: the balance between innovation and control. If intelligence is treated as a scarce resource, startups may face regulatory or market pressures to comply with elite-driven frameworks that prioritize safety and access restrictions. However, the idea that AI could increase demand for ideas—rather than replace labor—opens avenues for startups to explore new use cases or business models that leverage AI’s ability to enable previously impractical projects.

The call for guardrails over outright restrictions on autonomous AI also signals a potential regulatory direction. This could shape product development strategies, particularly for startups building AI-driven tools that require autonomy to function effectively. Meanwhile, the critique of AI executives exploiting safety fears for political influence highlights the need for transparency and ethical considerations in AI advocacy.

Key takeaways

• Founders and operators should prepare for debates over AI access and intelligence scarcity, which may shape future policy and market dynamics.
• The comparison to mercantilism suggests that AI-driven economies may prioritize control over intelligence, potentially impacting startup strategies around regulation and compliance.
• If AI increases demand for ideas rather than replacing labor, startups could focus on enabling previously infeasible projects, creating new markets or niches.
• The emphasis on guardrails for autonomous AI indicates a likely regulatory approach that could influence product development and innovation timelines.

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