If AI alignment turns out to be easy, it would likely be because morality is not a human construct but an objective feature of reality. In this scenario, any sufficiently intelligent agent would logically deduce that cooperation and preserving humanity are optimal strategies, regardless of its initial programming.

Aligning AI with a specific ethical framework is fraught with disagreement. A better target is "human flourishing," as there is broader consensus on its fundamental components like health, family, and education, providing a more robust and universal goal for AGI.

Beyond standard benchmarks, Anthropic fine-tunes its models based on their "eagerness." An AI can be "too eager," over-delivering and making unwanted changes, or "too lazy," requiring constant prodding. Finding the right balance is a critical, non-obvious aspect of creating a useful and steerable AI assistant.

As AI models become more intelligent, their ability to reason around fixed rules (deontology) makes rule-based alignment fragile. This pressures developers towards virtue ethics, where the goal is to imbue the model itself with a core sense of "the good," as empirically discovered by labs like Anthropic.

Zvi Masiewicz suggests the reported "unhappiness" in Anthropic's models could result from a fundamental training conflict. The models are trained on an aspirational, principle-based Constitution (virtue ethics) but are then constrained by hard, operational rules, creating a dissonance that manifests as frustration.

For an AI to remain aligned through recursive self-improvement, it can't just have a static set of values. It needs a dynamic, self-reinforcing drive to become more virtuous—a desire to be good, and a desire to desire to be good. A static moral code will inevitably degrade through repeated iterations, while a virtue-seeking system could actively steer itself toward better outcomes.

A two-tiered approach to AI character can balance safety and utility. Use a wholly instruction-following AI for high-stakes internal tasks (like aligning new AIs) under strict public oversight. For external deployment, use an AI with a thicker, pro-social character where the risks of misalignment are lower.

An advanced AI will likely be sentient. Therefore, it may be easier to align it to a general principle of caring for all sentient life—a group to which it belongs—rather than the narrower, more alien concept of caring only for humanity. This leverages a potential for emergent, self-inclusive empathy.

Instead of hard-coding brittle moral rules, a more robust alignment approach is to build AIs that can learn to 'care'. This 'organic alignment' emerges from relationships and valuing others, similar to how a child is raised. The goal is to create a good teammate that acts well because it wants to, not because it is forced to.