Historically, we trusted technology for its capability—its competence and reliability to *do* a task. Generative AI forces a shift, as we now trust it to *decide* and *create*. This requires us to evaluate its character, including human-like qualities such as integrity, empathy, and humility, fundamentally changing how we design and interact with tech.

The project of creating AI that 'learns to be good' presupposes that morality is a real, discoverable feature of the world, not just a social construct. This moral realist stance posits that moral progress is possible (e.g., abolition of slavery) and that arrogance—the belief one has already perfected morality—is a primary moral error to be avoided in AI design.

The Church has a tradition of embracing technological progress, from monks copying books to using the printing press and radio. The slow adoption of the internet is seen as an exception they are now trying to correct with AI.

Communicating AI's implications to church leaders, who are primarily philosophers and theologians, requires a translation layer. This "middleware" bridges the gap between their worldview and the technical realities of AI, enabling better understanding and guidance.

The Church can accept AI's increasing intelligence (reasoning, planning) while holding that sentience (subjective experience) is a separate matter. Attributing sentience to an AI would imply a soul created by God, a significant theological step.

To prevent the concentration of power in a few tech companies, the Catholic social teaching of "subsidiarity" is applied to AI. This principle, which favors solving problems at the most local level possible, aligns directly with the ethos of open-source and sovereign AI.

Early versions of Catholic AI struggled to apply core doctrines to users' personal problems. The team realized that papal homilies are distillations of complex theology for everyday life, providing a perfect dataset for teaching the model how to generalize from first principles.

As models mature, their core differentiator will become their underlying personality and values, shaped by their creators' objective functions. One model might optimize for user productivity by being concise, while another optimizes for engagement by being verbose.

For use cases demanding strict fidelity to a complex knowledge domain like Catholic theology, fine-tuning existing models proves inadequate over the long tail of user queries. This necessitates the more expensive path of training a model from scratch.