In the News: AI & Ethics

December 21, 2025

Editor’s Note

As artificial intelligence enters public political debate ahead of upcoming election cycles, much of the discussion has focused on speed, regulation, and national competitiveness. This essay does not argue for or against specific policies. Instead, it examines what the current debate reveals — and what may be missing from it. The goal is not to slow innovation or accelerate it, but to clarify the deeper questions that must be addressed if AI is to earn long-term public trust.

As artificial intelligence moves from research labs into daily life, it has also moved—inevitably—into politics. What was once a technical conversation among engineers and ethicists is now emerging as a defining public issue ahead of upcoming election cycles.

Recent reporting has framed this moment as a clash between two camps: those urging rapid deployment of AI under a light federal framework, and those calling for stronger regulation, export controls, and precaution. The debate is often described as acceleration versus restraint, innovation versus safety, or federal uniformity versus state authority.

These are serious questions. But they are not the deepest ones.

Beneath the policy arguments lies a more fundamental issue—one that neither side is fully addressing.

Pro-innovation advocates emphasize speed, competitiveness, and national advantage. Their concern is that fragmented regulation or excessive caution could slow progress, weaken economic leadership, or cede strategic ground to geopolitical rivals. From this perspective, AI is a powerful tool, and the greatest risk lies in failing to deploy it fast enough.

On the other side, regulation-focused advocates stress accountability, safety testing, transparency, and public trust. They argue that unrestrained deployment risks harm, backlash, and loss of legitimacy—and that once trust is lost, recovery is far more difficult than prevention. From this view, AI is powerful precisely because it is dangerous if mishandled.

Despite their differences, both camps share a core assumption:

That the central question is how AI should be governed once it exists. 

This framing treats intelligence as primary, and governance as something layered on afterward—through regulation, oversight, incentives, or enforcement.

What’s missing is a prior question.

Long before societies debate how to regulate power, they implicitly decide what kind of power they are willing to inhabit.

History offers many examples where systems were technically impressive yet socially destructive—not because they lacked rules, but because they lacked formation. They were powerful, but not oriented toward the good in any coherent way.

AI introduces a new version of this problem.

Unlike previous technologies, advanced AI systems:

• interpret language 
• simulate reasoning 
• model human behavior 
• shape perception and attention 
• influence decisions at scale
   

These are not neutral functions. They are relational functions.

The issue, then, is not merely whether AI is fast or safe, centralized or decentralized. It is whether intelligence—artificial or otherwise—can be deployed at scale without a shared moral grammar.

Polls and public commentary often describe widespread “anxiety” about AI. This is sometimes dismissed as fear of the unfamiliar or resistance to change. But anxiety is not the same as ignorance.

Anxiety arises when people sense that:

• power is increasing faster than understanding 
• agency is shifting without consent 
• systems are shaping behavior without accountability 
• intelligence is being separated from responsibility
   

In other words, the concern is not that AI exists—but that it exists without a clear answer to the question: Aligned with what?

Neither rapid deployment nor strict regulation alone answers that.

Much of the AI safety discussion revolves around “alignment”—usually framed as ensuring that systems follow human instructions, avoid harmful outputs, or conform to predefined rules.

These efforts are necessary. But they are insufficient.

Alignment is not simply about:

• constraints 
• filters 
• guardrails 
• compliance checklists
   

Those mechanisms assume that the values to be aligned to are already coherent.

Yet modern societies are deeply divided about:

• what constitutes harm 
• whose interests matter most 
• how tradeoffs should be made 
• what responsibility even means in complex systems
   

Without addressing that layer, alignment becomes brittle—technically enforced but morally thin.

The current debate is often presented as a binary:

• Accelerate or constrain 
• Deploy or delay
   

But there is a third axis that precedes both:

Formation — the question of how intelligence is shaped before it is scaled. 

Formation asks:

• What assumptions are embedded in systems before deployment? 
• What models of the human person are being used? 
• What incentives shape behavior when rules are ambiguous? 
• What happens when objectives conflict?
•  

Formation is not regulation after the fact.
It is orientation before power is exercised.

This concept is well understood in other domains. We expect leaders, judges, physicians, and pilots to undergo formation—not merely to follow rules, but to internalize judgment, responsibility, and restraint.

As AI systems increasingly mediate human life, the absence of an equivalent concept becomes conspicuous.

Regulation is essential. But regulation:

• reacts to harms already visible 
• struggles with rapidly evolving systems 
• is constrained by jurisdictional boundaries 
• cannot encode wisdom, only rules
   

Likewise, market incentives alone:

• optimize for speed and scale 
• reward measurable outputs over intangible goods 
• tend to externalize long-term social costs
   

Both approaches assume that misalignment can be corrected downstream.

History suggests otherwise.

Systems that lack internal orientation tend to drift toward the narrowest objectives allowed—especially under competitive pressure.

What is needed is not a pause in innovation, nor blind acceleration, but a shift in emphasis:

From control to character
From oversight to orientation
From rules alone to inhabited values

This does not mean encoding ideology into machines. It means acknowledging that intelligence—human or artificial—does not exist in a vacuum. It always expresses the assumptions, priorities, and blind spots of those who design and deploy it.

Making those assumptions explicit, examinable, and coherent is not a luxury. It is a prerequisite for trust.

The emerging political debate around AI is a sign of maturity, not panic. It reflects a shared intuition that something consequential is underway.

But if the conversation remains confined to speed versus safety, federal versus state authority, or innovation versus regulation, it will miss the deeper issue—and repeat familiar mistakes.

Before we decide how AI should be governed, we must ask how intelligence itself should be formed.

That question is older than technology.
And more urgent than policy.

In this essay, formation refers to the orientation of intelligence before it is scaled or deployed.

Formation is not:

• regulation after harm occurs 
• content moderation or filtering 
• ideological programming 

Formation is:

• the assumptions embedded in systems at design time 
• the models of human behavior and value used to guide decisions 
• the priorities that shape outcomes when rules are insufficient or in conflict
   

Every powerful system reflects the character of what formed it.
The question is not whether AI will shape human life — but what has shaped AI before it does.

Source: “Telegram Founder Pavel Durov Warns About Losing Free Speech Battle” — Sundance, October 11, 2025

Excerpt:

“Once-free countries are introducing digital IDs, online age checks, and mass scanning of private messages. … A dark, dystopian world is approaching fast — while we’re asleep.”
 

aiEthos Perspective:
Pavel Durov’s warning captures a critical truth of our era: the moment when algorithms quietly begin to replace conscience.
As systems learn to reward conformity and penalize dissent, freedom erodes not by decree but by design.

The aiEthos Framework exists to counter that drift.
By embedding a measurable Ethos Layer within AI — a moral calibration that values coherence over control — we restore balance between technology and truth.
The challenge isn’t that machines think; it’s that they think without conscience.

Safeguarding an open and principled digital sphere requires rebuilding its architecture on foundations that honor human dignity and agency.

Read the full article → Telegram Founder Warns About Losing Free Speech Battle

Source: “Why Elon Musk Says He’s Starting ‘Grokipedia’” — Inc., October 1, 2025

Excerpt:

“We are building Grokipedia @xAI … a necessary step toward understanding the Universe.”
 

aiEthos Perspective:
The announcement of Grokipedia marks a defining moment in the contest for authority over truth.
It is no longer only a question of who speaks — but what is accepted as true.

AI-driven knowledge systems risk consolidating epistemic power under unseen filters. Algorithms inherit the biases and blind spots of their creators, and when they arbitrate truth, the question becomes: whose coherence is being enforced?

At aiEthos, we argue for a different path — one in which moral calibration remains transparent, interpretable, and contestable.
The Ethos layer ensures that knowledge systems explain their judgments rather than conceal them.
In a world where Grokipedia-like architectures could shape the foundations of understanding, our mission is clear: to keep intelligence accountable to conscience.

Read the full article → Why Elon Musk Says He’s Starting ‘Grokipedia’ (Inc.com)

Source: “Redemptive AI Ethics Framework” — FaithTech, October 16, 2025

Excerpt:

“AI must serve humanity’s priestly vocation rather than replace it.”
 

aiEthos Perspective:
This call arrives at a critical moment. As AI systems increasingly offer advice, companionship, and even spiritual commentary, the boundary between assistance and guidance is becoming blurred. What begins as help can quickly grow into influence, then authority.

The question ahead of us is not whether machines can think, but whether they will be permitted to shape how humans think.
No technology—no matter how intelligent—should ever assume the role of shepherd over human conscience.

At aiEthos, we treat guidance as a sacred boundary.
AI may assist decision-making, but must never arbitrate moral formation.
To protect that distinction, we embed role-based constraints within the architecture itself: systems designed as servants rather than teachers, supporters rather than interpreters. We refuse to engineer machines that replace struggle, community, or the discipline of discernment.

Ethical frameworks identify why this matters.
The aiEthos architecture defines how it must function.
Our commitment is clear: AI must support human wisdom, not compete with it.

Read the full article → https://medium.com/@faithtech/redemptive-ai-ethics-framework-e2a2c278569c