An American Golden Age Requires Freedom to Innovate—

and Government That Can Learn

Competitive enterprise, personal superintelligence, strategic research investment, and constitutional limits offer an alternative to political control of the technology shaping our future.

In his October 20, 2026, Fox News opinion article, “The Far Left Wants to Kill the Tech That Can Create Trump’s Golden Age,” Donald Kendal argues that restrictions on artificial intelligence could sacrifice extraordinary economic opportunities and weaken America’s position against China. Kendal directs the Heartland Institute’s Emerging Issues Center. His recent commentary addresses data centers, technological optimism, and the importance of abundant, affordable, reliable energy. [1, 2]

Kendal identifies three approaches associated with Senator Bernie Sanders and allied lawmakers: a moratorium on new AI data centers, compulsory government ownership of substantial stakes in qualifying AI companies, and federal restrictions on advanced AI development. He contrasts these proposals with potential advances in manufacturing, medicine, agriculture, logistics, and independent living. His strategic warning is that competitors could continue advancing while American development slowed. [1]

The article identifies a consequential policy choice, but leaves much of its economic explanation and alternative governance design to the reader. A fuller argument requires examining how decentralized innovation creates prosperity, how concentrated authority can obstruct correction, and how an innovative society can protect people while preserving individual freedom.

The American alternative rests on a coherent set of principles: private ownership, competitive markets, advancement through merit, broad access to productive tools, strategic research investment, constitutional limits, and continuous learning from consequences.

Three proposals, three concentrations of authority

The proposed data center moratorium would suspend expansion of covered infrastructure until national safeguards addressed AI safety, workers, utility costs, communities, and environmental consequences. It would use restrictions on physical computing capacity to slow technological development while government established its preferred framework. [3]

The American AI Sovereign Wealth Fund Act would require qualifying companies to transfer 50 percent of their equity through a one-time tax payable in stock. A government-appointed commission would manage the resulting fund and exercise its voting rights. The proposal would also require separation of covered AI businesses from other operations. [4]

This is compulsory government ownership. A privately held company and a publicly traded company both belong to the private sector when their ownership remains private. Public trading describes how investors buy and sell shares. Government ownership transfers a financial interest and corporate influence to political institutions.

The proposed Ban Artificial Superintelligence Act would establish a federal department, require development charters and deployment approvals, pause development of covered advanced systems, and prohibit superintelligence and specified precursor capabilities. Certain reckless violations could carry criminal penalties of up to 20 years. [5]

Together, compulsory government ownership, development licensing, and regulatory authority could create a political monopoly—a monopoly of power over artificial intelligence. Companies might remain nominally separate, but government would combine ownership influence with authority to determine which businesses may operate, what technologies they may develop, and how their economic proceeds are distributed. This concentration would undermine independent decision-making and competitive enterprise while giving political leaders extraordinary control over the technology shaping the nation’s future.

Each proposal should therefore be evaluated along two dimensions: the harm it seeks to prevent and the power it creates.

The American foundation: liberty, merit, and bounded government

Thomas Jefferson supplied a clear statement of the governing principle in his First Inaugural Address on March 4, 1801. He called for:

“a wise and frugal Government, which shall restrain men from injuring one another, shall leave them otherwise free to regulate their own pursuits of industry and improvement.” [6]

That formulation joins protection with freedom of productive action. Government protects people against injury while individuals retain authority over their pursuits.

Jefferson also connected opportunity with education and merit. In his October 28, 1813, letter to John Adams, he identified “virtue and talents” as the basis of a natural aristocracy and described an educational system through which “Worth and genius would thus have been sought out from every condition of life.” His immediate subject was preparation for public responsibility, but the principle has wider relevance: society benefits when people across economic circumstances can develop and demonstrate their abilities. [7]

James Madison addressed the protection of individual faculties in Federalist No. 10. Discussing the differing abilities from which property rights and economic interests arise, he wrote: “The protection of these faculties is the first object of government.” His broader argument concerned constitutional arrangements capable of controlling factions while preserving liberty and the rights of others. [8]

These principles support a modern system in which people can improve their circumstances through learning, invention, work, investment, and enterprise. Personal superintelligence could expand access to the tools through which those opportunities become practical.

The Constitution distributes authority among branches and between the federal government and the states. It also established a stronger national framework for commerce than the Articles of Confederation had provided. That allocation of power supplies the institutional setting for both effective government and protection against political domination. [9]

Adam Smith’s The Wealth of Nations, published in 1776, supplies an essential economic explanation. Specialization, voluntary exchange, and competition coordinate productive activity among people pursuing different purposes. Prices, costs, profit, and loss help communicate where resources are wanted and whether particular ventures create sufficient value. Institutions of justice establish the conditions under which those exchanges can occur. [10]

Friedrich A. Hayek explained the information problem more precisely in “The Use of Knowledge in Society,” published in 1945. Economically relevant knowledge resides among people and concerns changing circumstances of time and place: a customer’s need, an employee’s skill, a machine’s condition, a supplier’s reliability, or an opportunity visible to someone working directly with a problem. [11]

Millions of individuals continually discover and act on such information. A national authority receives only a partial and delayed account. Better computing can improve analysis, while experimentation and exchange continue producing knowledge that emerges through action.

An entrepreneur tests an idea, encounters customers, changes the product, and tries again. A researcher revises an unsuccessful hypothesis. A buyer selects another supplier. These decisions form a distributed learning system.

Milton Friedman connected this economic independence with political freedom. Independent businesses and institutions provide alternative sources of livelihood, organization, and influence. Their independence helps disperse power throughout society. [12]

The operating question is therefore practical: who makes the decision, what information guides it, who bears the consequences, and how quickly can a mistake be corrected?

Employment: productivity creates room for expansion

Sanders invokes threats to employment as part of his justification for compulsory government ownership. He also argues that AI draws on humanity’s accumulated knowledge and that its economic gains should support broad improvements in living standards. His proposed fund would turn those arguments into government ownership and voting power. [4]

Employment deserves close attention, especially where particular workers face displacement. The relevant evidence favors examining several effects simultaneously: task substitution, worker augmentation, business expansion, new products, new enterprises, and complementary employment.

The SpartansFirst articles “AI Job Losses Are Not Occurring as Predicted” and “The AI Jobs Apocalypse Has Not Arrived: What the Evidence Actually Shows” develop this distinction. Their central economic point is that AI improves business performance as well as output per employee. Better design, faster development, improved service, lower costs, and access to new markets can increase the amount of economically viable work. [13, 14]

A simple illustration shows the mechanism. If labor required per unit falls by 20 percent while output rises by 50 percent, total labor requirements increase by 20 percent. Employment depends on the relationship between productivity and expansion.

Federal Reserve research by Salomé Baslandze and colleagues closely matches this argument. Their survey of nearly 750 corporate executives reports limited near-term aggregate employment declines associated with AI, productivity improvements connected with innovation and demand, anticipated workforce reductions at larger firms, and modest employment gains expected by smaller firms. These expectations describe executives’ outlooks; subsequent employment data will establish how they develop. [15]

Census Bureau research supplies a longer historical foundation. A relatively small share of young firms grows rapidly and accounts for the sustained contribution of startups to employment growth. Preserving the conditions for these ventures to form and expand is therefore central to economic adaptation. [16]

PwC’s 2026 AI Jobs Barometer reports stronger productivity and headcount growth among the most AI-exposed companies. Its findings support the possibility that productivity and employment can expand together, while further research must establish the precise causal contribution of AI. [17]

These sources support a substantial proposition: AI-driven improvements in productivity and performance create expansion opportunities that can offset labor savings. Measuring the balance specifically between rapidly growing ventures and mature firms requires continued investigation.

The transition also presents identifiable problems. Census research documents weaker early-career hiring at highly AI-exposed firms. Employers need new ways to develop the experienced workers they will later require, including apprenticeships, supervised practice, and assignments that preserve learning while using AI productively. [18]

The policy response should strengthen adaptation and monitor actual conditions. A forecast of displacement provides insufficient justification for a permanent transfer of ownership and political power. The more productive response expands people’s capacity to participate in the changing economy.

Mobility and the danger of a political class

Merit-based opportunity gives individuals several routes to advancement. Education, employment, entrepreneurship, invention, and investment allow people to change their economic position. Success depends partly on judgment and effort, alongside access to resources, institutions, and circumstances.

American mobility research documents upward movement from low-income families and substantial variation among communities. Research on wealth mobility also documents movement in both directions, with business ownership among the assets associated with large changes in financial position. These findings support opportunity and risk as real features of the system and identify access to opportunity as a continuing policy concern. [19, 20]

Inherited wealth remains exposed to consumption, investment losses, business failure, and changing conditions. Entrepreneurial success can create wealth; poor decisions and adverse circumstances can erode it. The essential institutional principle is that economic position can change through activity beyond political appointment.

Communist party systems developed a different mechanism of advancement. The Soviet nomenklatura consisted of influential positions whose occupants required party approval. A contemporary U.S. government assessment described this hierarchy as a privileged class, with superior living standards and advantages that members sought to pass to their children through education and appointments. [21]

Milovan Đilas, a former senior Yugoslav communist official, analyzed this development in The New Class. He argued that control of political authority and productive property had produced a new ruling hierarchy. The promise of class abolition had yielded a class whose privilege depended on its position within the governing system. [22]

Party hierarchy allowed some individuals to rise, but advancement operated through approval, loyalty, and access to political authority. Independent economic power faced severe constraints. That distinction matters more than the terminology used to describe the system.

History also demonstrates how concentrated authority can amplify policy failure. Stalin’s forced collectivization combined economic coercion, repression, and impediments to truthful reporting. Research on China’s Great Famine examines how rigid procurement institutions contributed to catastrophic outcomes under Mao. [23, 24]

These histories demonstrate the importance of independent information, local adaptation, and the ability to challenge governing decisions. They provide an institutional warning about concentrating ownership, permission, and enforcement over a transformative technology.

The specific danger in AI is that access to productive opportunity could increasingly depend on political favor. A governing authority with overlapping ownership and licensing powers could advantage selected organizations, restrict challengers, and shape access to advanced capabilities. Preserving independent enterprise reduces that vulnerability.

Personal superintelligence and the ability to advance

Mark Zuckerberg’s August 10, 2026, manifesto, “The Future Is for Everyone,” presents a philosophy centered on individual empowerment, invention, distributed access, liberty, free enterprise, and equal opportunity. It anticipates personal agents, tutors, creative tools, and scientific capabilities that help people pursue their own ambitions. Zuckerberg also predicts that easier business formation could increase employment over time. These are stated aspirations and forecasts whose results require evaluation. [25]

The earlier “Personal Superintelligence” essay supplies the foundation: increasingly capable AI should help individuals shape their own lives. [26]

The SpartansFirst article “Your Superintelligent Assistant or Someone Else’s Superintelligent Marketing Tool?” translates that philosophy into practical questions. It examines an assistant that gets to know its user, continues working toward the user’s goals, and helps produce finished work. Memory provides continuity, sustained execution supports larger projects, and creation tools turn ideas into usable results. [27]

These capabilities could give a founder access to research, prototyping, documentation, and coordination previously requiring a much larger organization. They could help an employee develop new skills, a student obtain individualized instruction, or a person with a disability participate more fully in productive activity.

Personal superintelligence can therefore become an instrument of merit-based mobility. Its value lies in expanding what the person can understand, create, decide, and accomplish.

Meaningful independence also requires practical control. Users need understandable permissions, correctable memory, protection of sensitive information, clear commercial terms, and workable ways to move projects and useful history between providers. Those characteristics strengthen the customer choice through which competition supplies corrective feedback.

The provider’s incentives deserve scrutiny. Advertising, commercial recommendations, engagement targets, and subscription structures can influence product behavior. Transparent commercial relationships and meaningful user controls help people judge whose interests a recommendation serves.

Two policy visions and their mechanisms

OpenAI’s April 2026 paper, Industrial Policy for the Intelligence Age: Ideas to Keep People First, places greater emphasis on institutional arrangements for economic transition. It explores tax changes, a public wealth fund, benefits, safety nets, auditing, and safeguards. It also advocates affordable AI access, AI-assisted entrepreneurship, scientific research, and protection against concentrated control. [28]

The contrast with Zuckerberg concerns emphasis and proposed mechanisms. Zuckerberg foregrounds widely distributed productive capability and personal direction. OpenAI combines access and entrepreneurship with a broader agenda of industrial policy and economic security.

OpenAI leaves the financing of its proposed wealth fund open for discussion. Sanders specifies a compulsory 50 percent equity transfer. Evaluating the ownership mechanism, voting authority, financing, and limits on discretion establishes the practical difference.

Both documents belong in the discussion because they reveal the choices facing an AI-enabled economy. Access, agency, security, and prosperity depend on how institutional arrangements work. The relevant test is whether they expand independent opportunity or concentrate decision-making power.

Research acceleration: the foundation of abundance

AI’s consequences extend far beyond the AI industry.

The SpartansFirst article “When AI Accelerates Everything” distinguishes two interacting processes. Better AI can assist research that produces subsequent AI. Those improved systems can also become research instruments across science and engineering. Advances in computing, materials, energy, manufacturing, and laboratory equipment can then improve research capability again. [29]

AI can help search literature, develop hypotheses, write experimental software, analyze results, and identify connections across disciplines. The “AI co-scientist” research reports experimental validation of selected biomedical hypotheses, providing evidence for parts of this research-acceleration mechanism. [30]

Investment can expand computing, personnel, experiments, and infrastructure. Improvements in research productivity can accelerate the return from those resources. The rate depends on scientific difficulty, hardware, energy, experimental requirements, and diminishing returns.

Different fields will advance at different rates. Software experiments may run quickly. Clinical studies, growing seasons, physical construction, and manufacturing qualification require longer intervals. Hypothesis generation can accelerate well before proof and implementation.

Abundant, reliable, low-cost energy strengthens this process by supporting computation, laboratories, production, transportation, and household prosperity. Discoveries can spread through trade, scientific collaboration, new businesses, and affordable products.

This is the deeper economic promise of a golden age: technology improves the tools through which society produces further knowledge and capability.

Government as a research enabler

Strategic research investment has a well-established American foundation.

In July 1945, Vannevar Bush delivered Science—The Endless Frontier to President Harry Truman following a request from President Franklin Roosevelt. Bush argued for sustained federal support of scientific research and talent. Fundamental discoveries can produce benefits that are distant, uncertain, and distributed among many users, making their full value difficult for a single organization to finance and capture. [31]

That reasoning supports research capacity from which independent enterprises can develop applications.

President Donald Trump’s Genesis Mission applies this role to AI-driven science. Led by the Department of Energy and its 17 national laboratories, it aims to connect supercomputers, datasets, AI systems, and experimental facilities into an integrated discovery platform. Genesis has established a ten-year goal of doubling the productivity and impact of American research and innovation. [32]

Japan became its first international partner through an announcement on June 4, 2026. The two countries outlined a combined investment of $1 billion over five years, divided equally and subject to future appropriations. Joint teams would connect American laboratories and Japanese institutions in fields including quantum information, fusion, biotechnology, materials, particle physics, and autonomous laboratories. [33]

The National Science Foundation’s X-Labs initiative addresses another gap. NSF announced up to $1.5 billion over a decade for independent, milestone-driven teams pursuing ambitious scientific and technological challenges. Its design emphasizes operational autonomy and development of commercially viable platforms ready for private investment to scale and deploy. [34, 35]

X-Labs occupies a useful position between conventional research grants, venture financing, and large national mission programs. Some foundational technologies require sustained interdisciplinary work and infrastructure before commercial prospects become sufficiently clear for conventional private financing.

The policy distinction is functional. Government-supported research can expand the knowledge and capabilities available to independent innovators. Compulsory ownership transfers influence over commercial decisions to political institutions. Each arrangement should be judged by its incentives, accountability, cost, and results.

Research programs need transparent selection, technical milestones, appropriate treatment of intellectual property, and evaluation. Their contribution is strongest when they create additional opportunities for small, medium, and large private businesses to compete.

Innovate, enable, protect, correct—and innovate again

The SpartansFirst “General Architecture of Technological Consequence” describes how benefits and harms develop through different pathways. [36]

Its positive sequence begins with discovery, invention, innovation, reform, or investment. An initial Impact Radius contains direct benefits. Diffusion spreads the capability. An Emergence Horizon marks increasingly difficult-to-predict possibilities. Synthesis combines capabilities, and Compounding Advancement makes earlier achievements inputs to later progress.

Its negative sequence begins with a triggering event and an immediate Blast Radius. Propagation carries harm through dependencies. A Cascade Horizon marks increasingly complex downstream effects. Interactive Feedback can reinforce failures, potentially progressing toward Systemic Collapse.

Adoption and synthesis determine how benefits compound. Containment, resilience, and correction determine how far failures spread.

The operating principle is Innovate → Enable → Protect → Correct → Innovate.

Genesis and X-Labs strengthen research and enablement. Competitive enterprises develop applications. Individuals use the resulting tools to pursue their own purposes. Protection and correction operate throughout the process.

For data centers, this means predictable approvals with measurable standards for noise, water, emissions, power-system effects, infrastructure costs, and decommissioning. Monitoring verifies performance, and enforcement requires correction. Qualified development continues while communities receive enforceable protection. [37]

For agentic AI, controls should correspond to authority and consequences. A system proposing an experiment presents different risks from one operating equipment or obtaining materials. Analysis of financial accounts differs from authority to transfer money. Permissions, independent checks, containment, monitoring, and revocation should follow those distinctions.

Assessment should focus on capabilities, authority, operating conditions, and potential consequences. Superintelligence increases the importance of effective controls and accountable use. Broad access to useful capability and specific safeguards for consequential activity can develop together.

Government needs sufficient competence to perform essential functions. Faster technical assessment, shared expertise, reliable incident evidence, and coordinated response can shorten the technology–government gap within clearly defined constitutional authority.

Regulation itself requires feedback. Review should measure whether a rule reduces harm, increases costs, restricts entry, delays beneficial applications, or protects established firms. Reassessment and sunset provisions can support correction when interventions outlive their usefulness.

Human cognitive leadership is part of the system

Norbert Wiener’s cybernetics supplies an intellectual foundation for feedback and correction: observe results, compare them with objectives, act, and measure again. [38]

The same discipline applies to human use of AI.

Students and employees need practice setting objectives, examining evidence, challenging assumptions, directing tools, and accepting responsibility for decisions. Assessment must measure both AI-assisted performance and the person’s developing ability to reason and act independently.

Research illustrates why interaction design matters. A randomized study of a purpose-built physics tutor found substantial immediate learning gains. A separate mathematics experiment found that a general AI interface improved practice performance while weakening subsequent unaided performance; a tutoring design with safeguards avoided that disadvantage. [39, 40]

The proposed research program “Is AI Making Us Smarter or Dumber?” would investigate these differences through repeated assessment and feedback. Its purpose is to identify practices that strengthen cognition and adjust those associated with dependency or declining independent capability. The proposed measures require validation. [41]

A research registry can supply another useful feedback mechanism by shortening the interval between publication and discovery by researchers facing relevant problems. Tracking provenance, relevance, supporting and contrary evidence, and connections to active projects can improve the conversion of published knowledge into practical inquiry.

The promise of a golden age rests on this combination: capable individuals, competitive enterprise, personal access to powerful tools, abundant energy, strategic research investment, and government accountable to constitutional limits.

Personal superintelligence should expand people’s ability to improve their economic position through learning, merit, invention, and service to others. The institutions surrounding it should preserve the freedom to choose, build, question, compete, and change course.

America can pursue that future through continuous innovation, effective protection, and rapid correction. Each cycle should expand productive capability, strengthen individual independence, and carry verified lessons into the next advance.

References

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