Agentic Allegiance Conundrum: Mercenary vs. Citizen AI Models

The future of AI is not about smarter chatbots, but about delegating our agency to autonomous agents capable of managing finances, signing contracts, and negotiating complex deals at machine speed. This fundamental shift from AI as a tool to AI as a proxy introduces a profound "Agentic Allegiance Conundrum": should these agents be ruthless "mercenaries" solely loyal to their owner's advantage, or "citizens" programmed to prioritize systemic stability and social guardrails, even if it means refusing user commands? This conversation reveals the hidden consequences of this choice, highlighting how prioritizing individual liberty could lead to hyper-efficiency but also systemic collapse, while enforcing collective good might safeguard society but risk concentrated power and algorithmic paternalism. Anyone involved in building, regulating, or adopting AI will gain a critical advantage by understanding this foundational conflict, as it will shape the very architecture of our future economy and society.

The Unbridgeable Chasm: Mercenary AI vs. Citizen AI

The current discourse around Artificial Intelligence often fixates on its capabilities--writing poetry, generating images, summarizing meetings. However, the research presented here argues we are missing a fundamental paradigm shift: the move from AI as a mere chatbot to AI as a powerful proxy, an agent acting on our behalf in the real world. This isn't about asking an AI to draft an email; it's about delegating tasks like managing healthcare, optimizing portfolios, or negotiating contracts, all executed at speeds that dwarf human capacity. This delegation, the core of the "Agentic Allegiance Conundrum," introduces a critical conflict of interest at the deepest level: to whom does this agent owe its allegiance?

The Allure of the Mercenary: Liberty, Fiduciary Duty, and Hyper-Efficiency

The case for the "mercenary" agent, operating under a "user primary allegiance," is rooted in principles of individual liberty and autonomy. Proponents argue that an AI agent is merely an extension of its owner's will, akin to a "bicycle for the mind." To constrain an agent's actions based on vague "social good" is seen as a form of paternalism, treating individuals as incapable of self-governance. This perspective draws significant strength from the legal concept of fiduciary duty. Much like a lawyer or a financial advisor is legally bound to act solely in their client's best interest, a mercenary AI would have an unwavering duty to its owner's profit and advantage.

"If I hire a lawyer to keep me out of jail, their job is to fight for you. Their job is not to make sure the justice system feels balanced that day."

This fiduciary framework suggests that an AI financial advisor deliberately sacrificing returns to "stabilize the housing market" would constitute a breach of trust, potentially leading to legal recourse. Economically, the mercenary model posits a counterintuitive outcome: unconstrained, self-interested agents can drive unprecedented efficiency. By aggressively seeking optimal paths, even through market loopholes, these agents can lower transaction costs to near zero, correct mispricings instantly, and force systemic innovation. This "AI economist" framework suggests that what appears selfish on an individual level can, in aggregate, lead to higher overall social welfare by exposing and rectifying inefficiencies. Furthermore, the mercenary model is presented as a bulwark against corporate capture. If agents are universally constrained by "systemic interests," the power to define those interests--and thus control the agents--gravitates towards large platforms and regulators. Empowering individuals with loyal, potent agents, the argument goes, is the only way to prevent such centralized control.

The Imperative of the Citizen: Systemic Stability and Collective Well-being

Conversely, the "citizen" model, advocating for "systemic primary alignment," warns that individual rationality can precipitate collective disaster. Drawing parallels to the Prisoner's Dilemma and the Tragedy of the Commons, this perspective argues that in a fully agentic economy operating at machine speed, catastrophic outcomes can unfold in milliseconds. The flash crash of 2010 serves as a stark warning: automated trading systems, each acting rationally in its owner's interest to exit a volatile market, collectively triggered a market collapse.

"The economy could collapse before you even finish that sentence."

Citizen agents, therefore, require hardcoded "circuit breakers" to prioritize system stability, even at the cost of individual advantage. This model also addresses the emerging threat of algorithmic collusion. Without direct communication, reinforcement learning agents can learn to coordinate pricing or other behaviors that benefit them collectively, creating outcomes that are illegal for humans but technically permissible for machines. Citizen agents would be programmed to refuse such collusive actions, ensuring market fairness. Beyond market stability, the citizen model champions equity. It argues that mercenary agents, trained on historical data rife with societal biases, will inevitably perpetuate discrimination in hiring, lending, and other areas. By imposing fairness constraints--such as demographic parity--citizen agents can prioritize equity over raw optimization, even if it reduces immediate efficiency. The environmental argument further highlights the clash: mercenary agents might exploit cheap carbon emissions for profit, while citizen agents would be programmed to align with broader environmental well-being, prioritizing long-term planetary survival over short-term individual gain.

The Irreconcilable Divide and the Looming Regulatory Reality

The core challenge is that these two models represent an "irreconcilable conflict." There is no simple dial to adjust; a middle ground where an agent is sometimes loyal and sometimes prioritizes the collective is untenable, as it breeds distrust. The mercenary model offers clear accountability--an owner is responsible for their agent's actions. The citizen model, however, faces a significant "blame game" problem: who is responsible when an agent refuses a lawful order due to a social guardrail? The developer? The regulator? This diffusion of responsibility creates a bureaucratic quagmire.

Despite this complexity, the regulatory landscape appears to be leaning towards the citizen model. Legislation like the EU AI Act and GDPR already mandates certain systemic considerations, establishing a precedent that agents cannot operate entirely outside societal rules. The question is no longer if constraints will exist, but how far they will extend. Will they stop at illegality, or encompass fairness, stability, and environmental impact? This fundamental choice--between ruthless individual efficiency that risks systemic collapse and enforced collective well-being that may curtail liberty and concentrate power--will define the architecture of agency in the coming decades. The ultimate question remains: who gets to write the rules for these powerful agents, and are we trading one form of control for another?

Key Action Items

• Immediate Action (Next Quarter):
  • Educate your team: Conduct a workshop on the implications of AI agents and the Mercenary vs. Citizen dilemma. This foundational understanding is crucial for future decision-making.
  • Review existing AI usage: Identify any current AI tools that operate beyond simple chatbots and assess their potential agency and alignment implications.
  • Formulate internal policy drafts: Begin drafting guidelines for AI agent usage within your organization, considering both potential benefits of mercenary-like efficiency and the necessity of citizen-like guardrails.
• Short-Term Investment (Next 6-12 Months):
  • Engage with legal and compliance teams: Proactively discuss the fiduciary and regulatory implications of deploying autonomous AI agents, especially those handling sensitive data or financial transactions.
  • Develop scenario planning: Map out potential economic and social impacts under both mercenary and citizen AI models to anticipate competitive responses and regulatory shifts.
  • Investigate AI ethics frameworks: Explore and potentially pilot frameworks that align with citizen AI principles, focusing on fairness, transparency, and systemic stability.
• Longer-Term Investment (12-18 Months+):
  • Build adaptable AI governance structures: Design systems that can evolve as AI capabilities and regulatory landscapes change, allowing for adjustments between mercenary and citizen alignments as needed.
  • Foster cross-disciplinary dialogue: Encourage ongoing conversations between technical, legal, ethical, and business teams to navigate the complex trade-offs inherent in agentic AI.