The Myth of AI as the New Printing Press

Written on 6 October 2025.

The Myth of AI as the New Printing Press

Overview

In a recent interview, Mike Adams and attorney Jonathan Emord discussed the idea that each major technological revolution—such as the invention of the printing press—has ultimately defeated government censorship. They suggested that artificial intelligence (AI) could bring about a similar decentralization of knowledge, making it impossible for governments to suppress information in the long term. However, this comparison between the printing press and AI overlooks fundamental differences in the nature of these technologies. AI, by its very design, depends on vast centralized infrastructure, enormous energy consumption, and financial investment, which makes it less likely to follow the same path toward decentralization.

The Printing Press Analogy

The printing press represented a true democratization of knowledge:

• Once invented, presses and type could be built and used almost anywhere.
• The cost of replication was low, and printed material could be easily hidden or copied.
• The quality of a printed page did not degrade from copy to copy.
• Knowledge became portable, persistent, and uncontrollable by centralized authorities.

The spread of the press allowed reformers like Martin Luther to challenge the Catholic hierarchy and created an unstoppable flow of Bibles, tracts, and pamphlets. By contrast, AI models are not portable in the same sense. They are not self-replicating pieces of hardware or text, but massive statistical systems that must be continuously powered and maintained.

The Centralized Nature of AI

Unlike the printing press, AI’s accuracy and usefulness scale with size and computational power. Frontier-level models require:

• Billions of parameters and terabytes of training data.
• Massive clusters of GPUs or TPUs running around the clock.
• Energy inputs equivalent to small industrial plants.
• Continuous tuning and retraining to prevent obsolescence.

Running smaller “local AI” models on personal computers is possible, but these models often hallucinate, lack reasoning depth, and cannot match corporate-level systems such as GPT-5 or Claude Sonnet. The very architecture of AI makes it capital-intensive and energy-dependent, creating a natural barrier to full decentralization.

Information vs. Infrastructure

There is an important distinction between information and infrastructure:

• Information, once released, can spread freely in static formats such as books, PDFs, or datasets.
• Infrastructure—the systems required to process and create new knowledge at high accuracy—remains centralized because of cost and physical scale.

Mike Adams’ vision of “mining human knowledge” and releasing it through his own AI (Enoch) follows this pattern: once the mined material is distributed, it can circulate freely. But the act of mining itself—training large models—requires industrial resources that cannot be replicated in a basement.

Future Outlook

AI decentralization is possible only in limited ways:

• Small local models may continue to improve through distillation and optimization, offering privacy and independence for basic use cases.
• Open-weight releases (e.g., LLaMA, Mistral) allow enthusiasts to experiment, but they will likely remain one or two generations behind frontier models.
• State and corporate dominance will persist because they control the compute, the power grid, and the data pipelines.

In short, while the printing press distributed knowledge by lowering costs, AI centralizes power by increasing them. The more advanced the model, the greater the dependence on centralized infrastructure. Unless a fundamental algorithmic breakthrough makes high-quality models inexpensive to train and run, AI will not mirror the democratizing effect of the printing press.

Conclusion

Mike Adams and Jonathan Emord’s optimism about AI as an unstoppable “tech revolution” underestimates the material and economic constraints inherent in the technology. While AI may help expose censorship and accelerate information sharing, its core operation is rooted in centralization, not independence. The analogy to book printing is therefore misleading: anyone can print a Bible at home, but few can train or host a large-scale reasoning engine. This revolution, unlike Gutenberg’s, is shaped not by ink and paper—but by energy, silicon, and capital.

References

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