"Men who look upon themselves born to reign, and others to obey, soon grow insolent; selected from the rest of mankind, their minds are early poisoned by importance; and the world they act in differs so materially from the world at large, that they have but little opportunity of knowing its true interests." 

— Common Sense (1776), Thomas Paine

Thomas Paine, a political philosopher and revolutionary thinker, wrote Common Sense in 1776, a pamphlet that played a pivotal role in advocating for American independence. His arguments challenged the legitimacy of hereditary rule and called for self-governance, a stance that resonated strongly with American colonists frustrated by British rule. Many viewed British policies, such as heavy taxation without representation and economic restrictions, as unjust impositions that prioritized the monarchy’s interests over colonial well-being. Common Sense was widely circulated and became a catalyst for revolutionary sentiment, convincing many undecided individuals to support independence. His work remains relevant today as a critique of concentrated power and its detachment from the needs of the people, a dynamic seen in modern corporate monopolies and large technology firms that wield significant influence over markets and regulatory frameworks.

Paine’s insights apply directly to AI regulation today. Those most vocal in demanding regulatory measures are often the companies and individuals who already hold dominant positions in the market. For instance, OpenAI, Google DeepMind, and Microsoft have advocated for AI safety regulations, yet such policies could also serve to solidify their dominance by making compliance too costly for smaller competitors. A notable example is the European Union’s AI Act, which imposes strict compliance requirements that smaller AI startups struggle to meet, potentially limiting their ability to compete with well-funded incumbents. Similarly, the United States has seen discussions around AI regulation that could place heavier compliance burdens on smaller companies while allowing larger firms to navigate these restrictions with greater ease, further entrenching their dominance. When an incumbent corporation urges regulators to implement strict safety regulations, we must critically assess whether these actions genuinely serve the public good or merely act as a mechanism to cement market dominance.

Striking the Right Balance in AI Regulation

While regulations are crucial for fostering responsible AI development and deployment, they must be carefully designed to prevent their misuse as a tool for large corporations to suppress competition under the guise of public interest. If regulatory barriers disproportionately burden emerging competitors and stifle innovation, they reinforce monopolies rather than protect society. As Paine warned about rulers disconnected from the people’s true interests, we must remain vigilant to ensure AI regulations foster competition, innovation, and genuine public benefit rather than preserving the power of market incumbents.

A successful example of this balance can be seen in the aviation industry, where regulations ensure safety without stifling technological advancements in aircraft design and automation. Similarly, AI regulations should aim to protect consumers and society while fostering a competitive environment that encourages breakthroughs and responsible development.

"Society in every state is a blessing, but government, even in its best state, is but a necessary evil; in its worst state, an intolerable one." 

— Common Sense (1776), Thomas Paine

Paine’s advocacy for self-governance extends beyond political independence to the modern debate on AI regulations. Excessive government control can hinder innovation, limit market dynamism, and create unnecessary bureaucratic obstacles. Rather than relying heavily on centralized regulations, AI development should embrace principles of self-governance, industry-led standards, and ethical frameworks that evolve in response to technological progress. Encouraging voluntary guidelines, transparency, and accountability within the industry can lead to more adaptable and effective solutions without stifling competition and progress.

A notable example of successful self-regulation is the financial technology sector, where industry-led initiatives such as the Payment Card Industry Data Security Standard (PCI DSS) have enhanced security and consumer protection without requiring excessive government intervention. By adopting similar models, AI companies can implement best practices while fostering an open and innovative environment.

Historical Lessons on Regulation and Power

Paine’s words remind us that just because a practice is longstanding does not mean it is just or beneficial. The dominant players in AI may present regulations as necessary for the public good, but we must examine whether these measures genuinely promote fairness and innovation or merely entrench their power. A historical example can be seen in the telecommunications industry, where regulatory frameworks often favored established companies by creating high barriers to entry, effectively limiting competition and innovation. The Telecommunications Act of 1996 in the United States, for instance, was intended to foster competition but, in practice, led to further consolidation as large firms acquired smaller competitors, reducing market diversity and limiting innovation.

As Thomas Paine observed:

"The more men have to lose, the less willing are they to venture. The rich are in general slaves to fear, and submit to courtly power with the trembling duplicity of a Spaniel."

This statement highlights how those with established wealth and influence often resist transformative change, favoring regulatory structures that secure their dominance rather than foster innovation and competition. A historical parallel can be found in the early railroad industry, where powerful companies lobbied for regulations that made it difficult for smaller operators to compete, ensuring their continued dominance over transportation infrastructure.

Similarly, in the AI sector, well-established firms may advocate for stringent regulations that create barriers for new entrants, ultimately shaping policies to protect their market control rather than encourage fair competition. Policymakers could mitigate this issue by ensuring that regulations are proportionate, allowing room for emerging companies to innovate while still maintaining essential safety and ethical standards. Measures such as regulatory sandboxes, tiered compliance requirements, and incentives for responsible AI development can help balance oversight with the need for a dynamic and competitive market.