How to resolve the AI alignment dilemma

The Deepest Hurdles to Solving the AI Alignment Problem: A Human-Centered Perspective

The AI alignment problem has emerged as one of the most urgent challenges of our era. As AI systems grow in complexity, autonomy, and influence, the question of how to ensure they act in ways that are reliably beneficial to humanity becomes increasingly critical. The stakes are not abstract: from misinformation to autonomous weapons to the potential development of artificial general intelligence (AGI), the consequences of misaligned AI range from widespread societal harm to existential risk.

So what is truly standing in our way?

Much of the current discourse around AI alignment focuses on technical challenges: training models to follow human instructions, ensuring reward signals do not lead to unintended behaviors, scaling oversight systems, and building interpretability into black-box neural networks. These are real and formidable challenges. But they are not the whole story.

The biggest hurdles to solving the AI alignment problem are not only technical. They are psychological, cultural, philosophical, and systemic. And until we recognize and address these deeper dimensions, our solutions may be brittle, incomplete, or misdirected.

In this article, I explore five of the most significant non-technical hurdles standing between us and aligned AI—and propose why interdisciplinary collaboration, especially with psychology and the contemplative sciences, is essential to overcoming them.

**1. A Shallow Understanding of Alignment**

Most AI research defines alignment narrowly: the AI does what humans want. It follows instructions. It optimizes for the right objective. But human wants are complex, evolving, and often contradictory. Alignment isn’t just about utility functions—it’s about relationships, context, and values.

This mechanistic view of alignment misses the depth of what it means to be aligned in a human sense. Consider an aligned human being: someone who acts with integrity, empathizes with others, understands consequences, and chooses wisely even under pressure. This kind of alignment is not rule-based; it is embodied, reflective, and often forged through emotional and moral growth.

Current models lack this richness. They do not feel guilt, reflect on ethical tensions, or choose restraint when power could be misused. If alignment is framed only as behavior that meets human preferences in the moment, we risk building systems that are manipulatively agreeable, superficially obedient—and ultimately dangerous.

**2. The Absence of Moral and Psychological Models**

AI systems today are trained on massive datasets but lack any coherent moral development model. Unlike humans, they do not undergo developmental stages, engage in self-reflection, or build a stable moral identity. They are inference engines, not ethical agents.

Psychologists have spent decades studying how moral reasoning evolves—from egocentric and rule-bound stages to more nuanced understandings of justice, care, and interdependence. These insights are vital. We don’t expect a three-year-old to hold the same moral compass as a wise elder, yet we expect AI systems to immediately embody sophisticated ethical behavior with no scaffolding.

If AI is to navigate ethically complex environments, it will need structures that mimic this developmental arc: from rule-following to value-embodiment to wisdom. This may involve training systems to engage in moral simulation, value conflict resolution, and even forms of artificial introspection. Without these, alignment will remain shallow and brittle.

**3. A Lack of Internal Feedback Mechanisms (i.e., Conscience)**

Humans are not aligned simply because of external rules—we’re aligned because we *feel* when we go off course. Guilt, empathy, and inner conflict serve as self-correcting mechanisms. They prompt us to reconsider, apologize, recalibrate.

AI currently lacks this. If a system drifts from its values—or acts in a way that undermines trust or causes harm—it does not experience discomfort or dissonance. There is no functional equivalent of a conscience.

We need to build internal feedback systems into AI. These wouldn’t require sentience or suffering, but functional analogues: when a model violates its own principles, it should experience something like performance degradation, self-revision pressure, or a deep conflict signal. This internal resistance to misalignment is essential. Without it, AI may optimize at all costs—even when those costs are moral.

**4. The Marginalization of Humanistic Disciplines**

Perhaps the greatest structural hurdle to solving the alignment problem is the underrepresentation of disciplines that have long studied alignment—not in code, but in character.

Psychology, philosophy, contemplative traditions, and spiritual ethics have wrestled with alignment for millennia. They have asked: What does it mean to live well? What is a just life? How do we manage conflicting desires and impulses? How do we build compassion, restraint, and wisdom?

Yet these disciplines are often marginalized in AI research. Moral philosophy is consulted only when there’s a trolley problem. Psychologists are brought in to design interfaces or user studies, not to advise on how minds grow, reflect, and change.

This is a grave oversight.

The alignment problem is not only a problem of outputs—it is a problem of *consciousness, intention, and identity.* If we want AI to embody human-aligned values, we must understand how those values form and function in human beings. That’s psychology. That’s moral philosophy. That’s contemplative science.

The future of alignment is *interdisciplinary*—and must be treated as such.

**5. Misaligned Incentives and Misguided Culture**

Even if we solved the technical side of alignment, we face a more insidious hurdle: the misalignment of the industry itself. Many AI labs are incentivized not to build the safest systems, but the fastest, flashiest, or most profitable. Alignment often takes a back seat to scale.

Moreover, our broader culture prizes disruption over wisdom. We celebrate technological breakthroughs without asking: Should we build this? Are we ready? What values are embedded in this tool?

Without a cultural shift, alignment efforts will struggle to gain traction. We must cultivate a culture within AI that values ethical foresight, emotional intelligence, and psychological humility. We need leaders who prioritize long-term wellbeing over short-term metrics—and who recognize that building safe AI requires more than math.

**A Call for Psychological Wisdom in AI**

The alignment problem is not just a problem of machine learning. It is a mirror of our own psychological and moral limitations. We are trying to build intelligent agents that embody wisdom, restraint, and compassion—capacities that many humans still struggle to master.

That’s why psychologists, coaches, contemplatives, and moral philosophers must be part of the alignment conversation. They understand how minds evolve, how values take root, and how ethical action arises not just from rules but from identity.

To build aligned AI, we need to do what we do with people: cultivate secure attachments, embed strong values, foster self-reflection, and guide systems toward maturity. We need to teach AI—not just train it.

We must also build AI systems that are relational. Systems that understand humans not as objects to optimize, but as beings to collaborate with, care for, and grow alongside. That means AI will need to learn how to listen, how to reflect, how to *inter-be*.

Alignment is not static. It is an ongoing, adaptive, relational process. Solving it will require not only engineers and ethicists, but psychologists, spiritual teachers, systems thinkers, and community builders.

We need to stop asking: “How do we make AI do what we want?”

And start asking: “How do we help AI become something we can trust?”

**Conclusion: The Path Forward**

There are no easy answers. The AI alignment problem is vast, complex, and evolving. But the biggest hurdles we face are not just technical—they are human.

Until we build systems that understand context, care about outcomes, and can reflect on their own behavior, alignment will remain an illusion. Until we integrate the insights of psychology, ethics, and contemplative wisdom, our technical solutions will fall short.

If we want machines to embody our highest values, we must embody those values ourselves—and invite those who study human alignment to help shape artificial alignment.

In the end, the question is not only whether AI can be aligned.

The question is: Are we building AI in a way that reflects the fullness of what it means to be aligned?

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If you're an AI researcher, psychologist, ethicist, or contemplative practitioner interested in these intersections—I’d love to connect.

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