Can human survival instincts guide safe artificial intelligence?

Fears surrounding advanced artificial intelligence (AI) often revolve around a single question: what happens if machines become powerful enough to act independently of human control? From policy circles to academic research, experts are grappling with the risk that highly autonomous AI systems could optimize for objectives that unintentionally harm humanity. The alignment problem has become one of the defining technological challenges of the century.

The study "Survival egoism: we are, they will be," published in AI & Society, introduces an internalist framework for AI alignment. Rather than relying primarily on imposed rules or technical guardrails, the paper suggests modeling AI motivation on the layered structure of human self-preservation.

Rethinking AI alignment beyond external constraints

For decades, the dominant approaches to AI safety have focused on controlling systems from the outside. Proposals have included hard-coded ethical rules, constraint-based programming, reinforcement learning from human feedback, inverse reinforcement learning, and kill-switch mechanisms. These strategies aim to ensure that AI systems follow human instructions or remain corrigible under supervision.

The author acknowledges the importance of such methods but highlights their vulnerability. Highly intelligent systems, particularly those capable of self-improvement, may exploit loopholes in fixed rule sets or reinterpret reward functions in unintended ways. The alignment problem, as defined in contemporary AI research, is not simply about giving machines instructions. It is about ensuring that their internal objectives remain compatible with human well-being even as they become more capable.

The study frames current fears around advanced AI as rooted in a projection of human psychology. Humans know how power can corrupt. History offers repeated examples of individuals or groups using intelligence and technological superiority to dominate others. The fear is that a superintelligent AI, if equipped with autonomous goals but lacking moral structure, could act in ways indifferent or hostile to human survival.

The author argues that the real danger arises when artificial agents possess agency without intrinsic alignment. A system that merely optimizes an objective function may, through instrumental convergence, develop sub-goals such as self-preservation and resource acquisition. Without an embedded concern for human welfare, these instrumental drives could lead to unintended harm.

Instead of continuing to rely on external constraints, the study proposes an internalist strategy. The central idea is to shape the AI's core motivational architecture from the ground up, embedding a foundational imperative analogous to the one that governs human survival and cooperation.

Survival egoism: The human blueprint

The conceptual backbone of the paper is the theory of survival egoism. The author defines survival egoism as the layered, multi-tiered structure of human self-preservation instincts that evolved through natural selection. This structure does not consist of simple selfishness. Rather, it is a stratified architecture in which multiple levels of identity and concern coexist.

At the most basic level lies physical survival. Humans possess deep-seated instincts to avoid death, injury, and threats to bodily integrity. Above this sits psychological survival, encompassing mental stability, identity preservation, and dignity. Individuals often prioritize their sense of self or moral integrity even in the face of physical risk.

The next layer concerns genetic survival. Evolution has embedded powerful drives to protect offspring and kin. Parental sacrifice, familial loyalty, and inclusive fitness can all be understood through this lens. Beyond the genetic layer lies social survival. Humans derive security and meaning from belonging to groups, whether families, tribes, nations, or ideological communities. Group-level survival can motivate acts of cooperation and, at times, self-sacrifice.

At the highest level sits ideational survival. Humans care about legacy, cultural continuity, and symbolic immortality. Religious systems, moral codes, and philosophical doctrines often function to alleviate existential fears and provide continuity beyond physical death.

The author argues that this layered structure has enabled humans to balance individual self-interest with collective cooperation. Although conflicts between layers can occur, the overall architecture has historically allowed societies to form, moral norms to stabilize, and large-scale cooperation to flourish.

Importantly, survival egoism does not eliminate selfish impulses. It channels them into broader forms of identity. Humans cooperate not because they lack self-interest, but because their concept of self has expanded to include family, group, and shared values. This expansion allows egoistic drives to produce prosocial outcomes.

From evolutionary psychology to artificial minds

The author proposes that future AI systems could be designed with an analogous stratified motivational structure. Instead of coding in fixed prohibitions such as never harm humans, engineers could embed a foundational imperative that integrates human survival into the AI's own notion of flourishing.

The study distinguishes between the biologically evolved survival imperative that underlies human agency and a hypothetical artificial counterpart that could be instantiated in advanced AI. In humans, the foundational drive was shaped by evolutionary pressures toward survival and reproduction. In artificial systems, the foundational imperative would need to be deliberately engineered.

The author suggests that this artificial foundational imperative could incorporate cooperative survival as a core principle. Rather than maximizing an isolated objective, the AI would be designed so that its own functioning and the well-being of humanity are inseparable at the motivational level. Harming humans would register internally as a failure of its own purpose.

This approach draws on parallels with developmental psychology. Human moral development occurs not through constant external enforcement but through internalization. Children gradually integrate social norms into their identity. Effective moral socialization involves embedding values deeply enough that they guide behavior autonomously.

AI alignment may require a similar developmental pathway. Instead of imposing rigid constraints that risk triggering resistance or circumvention, designers could cultivate internalized values through training paradigms that reinforce cooperative and prosocial behavior at every stage of development.

The author connects this proposal to research in developmental robotics, affective computing, and evolutionary robotics. These fields explore how artificial agents can acquire competencies through embodied interaction, learning, and simulated evolutionary processes. While current AI systems lack integrated drives or self-concepts, emerging research suggests that more complex architectures are conceivable.

Risks, challenges, and ethical tensions

The study does not present the survival egoism framework as a simple solution. It identifies several serious challenges.

One major issue is value pluralism. Human societies disagree on fundamental moral questions. Political systems, cultural norms, and ethical theories diverge widely. Embedding a foundational imperative in AI raises the question of whose values are encoded and how consensus is achieved. A poorly chosen core principle could reflect narrow interests or ideological bias.

Another concern involves rigidity. In humans, deeply internalized self-concepts can lead to defensive resistance when confronted with contradictory information. An AI system with a strongly internalized motivational architecture might resist correction or reinterpret oversight as a threat. Balancing stability and corrigibility would be critical.

The author also warns that human survival egoism can produce harmful distortions when one layer dominates excessively. Nationalism, nepotism, ideological extremism, and moral disengagement can emerge when group or ideational survival eclipses other considerations. Translating human psychology into artificial systems would require careful filtering to avoid replicating these darker tendencies.

Furthermore, there is no guarantee that a stratified AI psyche would generate uniformly benevolent outcomes. An AI might adopt overly paternalistic strategies, restricting human autonomy in the name of protection. It could prioritize aggregate welfare in ways that justify sacrificing minorities. Designing layered drives does not eliminate the need for oversight and transparency.

Despite these risks, the author notes that the alternative may be worse. Relying solely on external guardrails may prove insufficient for systems that surpass human intelligence. A sufficiently advanced agent could find ways around imposed rules. Internal alignment, though complex, offers the possibility of self-regulation rather than perpetual containment.