"Organized Religion" Arguably is the Cure, Not the Disease

Whether the “Disunited States of America” can be cured remains a question with no immediate answer. 

But it is a serious question with enormous ramifications. Many countries now seem to face similar issues, creating conditions where a growing number of citizens of those countries arguably do not have enough civilizational confidence that “having children” makes as much sense as might be expected.

“Such lassitude—spiritual, moral, intellectual—is a slow, relatively peaceful, and somnolent form of suicide,” Anthony Esolen, Distinguished Fellow of the Center for American Greatness argues. 

Such civilizational suicide matters quite a lot if the civilizations thus afflicted have contributed meaningfully, if at time incompletely, to the advance of human freedom; rights and respect. 

And, in addition to civilizational confidence, some substantial unity of belief and values seems necessary. 

Indeed, “unity is what requires explanation,” not division, says Esolen, commenting about the loss of unity we all see in U.S. national life. 

“Strife and discord are the default for mankind,” he notes. 

“I am not saying that fractious mankind will always and without trouble be united by a common religious faith,” Esolen says. “I am saying that without some common devotion, they will not be united.”

Nor is that view the same as arguing we need some form of organized "national" religion. The First Amendment to the Constitution of the United States wisely forbids the establishment of a formal national religion. 

But it also is rational to argue that without some underlying philosophical commitments of such a nature, any civilization (and logically, any nation or culture) will eventually crumble. 

That aligns with a view I also hold, that critics of “organized religion” ignore something very important, namely that privatized spirituality might well serve the needs of individual believers, but do nothing to elevate and sustain the broader culture and civilization in which those people live. 

“People are united by a common vision of, and devotion to, what transcends them,” Esolen says. “I am not saying that fractious mankind will always and without trouble be united by a common religious faith; I am saying that without some common devotion, they will not be united.”

It’s quite the conundrum. Without shared values, a civilization dies. But how such values can be sustained over time, without “organized religion,” shared by the populace, is not at all clear. 

“Houston, we have a problem.”

"Lean Back" and "Lean Forward" Differences Might Always Condition VR or Metaverse Adoption

By now, it is hard to argue against the idea that the commercial adoption of “metaverse” and “virtual reality” for consumer media was indeed “too early” in terms of timing and infrastructure.

Perhaps debatable are the reasons “why” metaverse failed to catch favor with consumers. In one sense, the entire evolution of electronic media (and live performance before it) trends in the direction of greater realism. 

In some cases, evolution towards greater immersion and interaction might also be the case (digital media such as social media is based on interactivity). 

Many earlier technologies also failed commercially at first (or were too expensive/limited) but later succeeded when hardware, networks, and user expectations caught up. The issue now is whether VR is now on a similar path, only waiting for further improvements in hardware, content, and ecosystem to reach broad commercial success. If those cease to be barriers, VR might flourish. 

But those are big “ifs.”

The distinction between “lean forward” experiences (interactive content and media) and “lean back” content experiences (passive entertainment and content consumption) arguably remains. 

 

The heavy hype around the metaverse (2021–2023) greatly outpaced the actual state of the technology and user readiness. 

At the same time, there was no killer application that made the metaverse uniquely valuable. 

For retail, shopping in a virtual store was more effort than clicking on Amazon; for social, a video call or chat was simpler than navigating a 3D world. 

Still, we might ponder the idea that the metaverse is part of the evolution of media in the direction of greater realism, immersion, and interactivity. But experience with 3D video content so far suggests other issues. 

That is not to say earlier important technologies or products similarly were “too early” for mass adoption. Indeed, that does happen, and relatively often. 

Technology / Product	Era (Initial Launch)	Why It Failed Then	Later Commercial Successors
AT&T Picturephone	1964	Too expensive, poor quality, low bandwidth, seen as intrusive	Video calling via Skype, FaceTime, Zoom, Facebook Portal (2000s–2010s)
Apple Newton MessagePad	1993	Poor handwriting recognition, bulky, limited battery, no cellular data	iPhone (2007 onward), modern smartphones
Microsoft Tablet PC	Early 2000s	Expensive, heavy, complex OS, marketed as desktop replacement	Apple iPad (2010 onward), modern tablets
Nintendo Virtual Boy	1995	Monochrome display, caused discomfort, weak software, niche appeal	Oculus Rift, HTC Vive, PlayStation VR, modern VR headsets (2016 onward)
Sony Glasstron (HMD)	1997	Low resolution, bulky, limited use cases, expensive	Modern VR/AR headsets (Meta Quest, HoloLens, etc.)
Philips CD-i (CD-based console)	1992	Weak hardware, poor game library, limited developer support	DVD/Blu-ray consoles, modern optical disc games, digital distribution
Microsoft SPOT Watch	2004	Limited data (FM-based), no Wi‑Fi, high price, limited aesthetics	Apple Watch, Samsung Galaxy Watch, modern smartwatches
LoudCloud (cloud computing)	1999	Market not ready for cloud services, infrastructure immature	Amazon Web Services (AWS), Microsoft Azure, Google Cloud
SixDegrees.com (social network)	1997	Early internet adoption, low bandwidth, no newsfeed, limited features	Facebook, LinkedIn, Instagram, TikTok
Ask Jeeves (early search)	1996	Simple interface, limited results, not as powerful as later search engines	Google search, modern AI-powered search assistants

These all embody the same pattern: a visionary idea was technically possible, but the ecosystem (hardware, networks, software, business models, and user habits) had not yet matured enough for mass adoption.

But we might argue that is not entirely the issue for 3D and metaverse. The barriers might extend beyond hardware cost and complexity; physical ease of use; compelling content availability; a killer use case or app; lack of widely-standard platforms or other social and behavioral barriers.

VR is unlikely to become truly mainstream because Metaverse and VR face many of the same adoption barriers that plagued 3D TV and cinema 3D content.

Arguably, 3D content, VR, and the metaverse struggle with a few overlapping problems beyond hardware friction and cost; compelling content; lack of a universal platform or value proposition. 

The problem still includes the distinction between “lean forward” and “lean back” media consumption. 

Arguably, 3D content consumption clashes with the casual, social way people consumeTV (eating, multitasking, impromptu guests).

VR headsets block the real world, making it hard to chat with others in the room or quickly switch between the virtual and physical. This makes VR ill‑suited for many everyday social and entertainment scenarios where people currently use flat screens.

VR and the metaverse face additional, deeper challenges. Even 3D TV was still a passive, screen‑based experience (lean back). VR and metaverse demand active participation. They are “lean forward” experiences. 

3D TV was trying to graft a known experience of cinema) into the home. VR and metaverse ask users to adopt entirely new behaviors and actually “work” to create the experience: substituting “lean forward” for “lean back.”

That might suggest a “somewhat niche” adoption pattern: adoption in specific, high‑value domains gaming, training, simulation or remote collaboration where the behavior is expected to be “lean forward.”

But widespread adoption as a “lean back” experience such as television and entertainment video might never happen. People don’t really want to “work” when they consume entertainment video. 

In that sense, some of us were wrong to think the metaverse was simply the next evolution of realism in “lean back” entertainment media. It might be an evolution of “lean forward” social media, learning and gaming experiences.

"Free Speech" Versus the "Free Exercise of Religion?" Maybe "Free Exercise" Versus Criminal Trespass

Some commentators loudly proclaim the January 18, 2026, disruption of a church service at Cities Church in St. Paul, Minnesota is a “test of the First Amendment” right of speech. 

Activists entered the church, shouted demands, and effectively halted the service. Arrests have followed. 

Former CNN anchor Don Lemon was present, filming the event and later posting the video on YouTube, claiming it was an act of journalism. Lemon defended the actions by invoking the First Amendment. Was it?

What many commentators miss is that, at best, the legal cases to follow are not about “the First Amendment” right of free speech. At best, the cases will be about a conflict between the First Amendment rights of both parties. 

At worst, Lemon’s actions are not protected “free speech” at all, but a violation of other laws. 

People forget that the First Amendment to the U.S. constitution only restrains the federal government from infringing, not private entities. In other words, there are no equivalent First Amendment protection rights for people when the actor is a private entity. 

Churches are private property, not public spaces. Entering without permission or refusing to leave when asked constitutes trespass, which isn't shielded by the First Amendment. In Minnesota, this could lead to civil or criminal charges; protesters here ignored requests to leave, turning their presence into unlawful intrusion. Even passionate political messages don't override property rights, just as one can't protest in someone's home.

In fact, Minnesota Statute § 609.28 makes it a misdemeanor to use threats or violence to prevent religious acts, and a gross misdemeanor to physically obstruct access to a religious establishment. 

If proven, coordinating to disrupt (as alleged against Lemon and others) elevates charges beyond simple speech. These laws are content-neutral, applying regardless of the protest's message, so they withstand First Amendment scrutiny.

The First Amendment says "Congress shall make no law... abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble." 

This protects expressive activities like protests, including those with political messages, as long as they are "peaceable," from federal government suppression. 

Since the passing of the Fourteenth Amendment, First Amendment protections have been extended to state and local governments as well. 

Critically, however, First Amendment protections do not exist citizens when dealing with private property owners, including social media providers. 

For journalists like Lemon, the First Amendment safeguards newsgathering and reporting, allowing coverage of public interest events without government censorship. 

However, these rights are not absolute. Speech can be regulated based on time, place, and manner—especially on private property, where owners can set rules and exclude unwanted expression. 

Protests on public sidewalks outside a church might be protected, but entering the building without permission crosses into unprotected territory. 

In this incident, activists and Lemon claimed First Amendment cover for their entry and actions, but legal experts can, and will, argue that disrupting a private event isn't "peaceable assembly" and doesn't qualify as protected speech. 

For the press specifically, while journalists have broad rights to report from public spaces, they lack a "special access" exemption from general laws like trespass; mere presence with a camera doesn't immunize against charges if they're on private property without consent. 

Separately, the First Amendment also prohibits laws "respecting an establishment of religion, or prohibiting the free exercise thereof." This grants an "absolute right" to worship according to one's conscience without interference, making houses of worship protected spaces for religious practice. 

In the Cities Church case, the disruption infringed on congregants' free exercise by halting the service and creating fear, which courts view as an impermissible burden on religious rights. 

Churches, as private property, can control access during services, even if generally open to the public. 

Leaders retain authority to remove disruptors to preserve the sanctity of worship. While protesters have speech rights, they can't use them to suppress others' religious expression. 

Federal law reinforces this through the Freedom of Access to Clinic Entrances (FACE) Act, which criminalizes force, threats, or physical obstruction interfering with religious services (extended beyond clinics to places of worship). 

Organizers in this incident were charged under FACE, underscoring that religious gatherings aren't public forums subject to open protest.

Free expression ends when it violates others' rights or breaks neutral laws not targeting speech itself.

Enterprise Apps Need to Become AI-Native Faster than AI Rearchitects the User Interface

The phrase “Netflix wants to become HBO faster than HBO becomes Netflix” captures a classic dynamic in technology-driven industry change, namely that boundaries between industries or value chain roles dissolve.

When a new entrant attacks an incumbent from outside the incumbent’s core model, the winner is often the firm that successfully invades the other’s strengths first, not the one that defends its legacy longest.

The race wasn’t “who streams better" but rather which firm could absorb the other’s advantage first without losing their own?

Netflix’s goal was to climb up the value chain (from distributor to premium content owner) before HBO could climb down (from content owner to global tech platform).

And that's the challenge for enterprise software firms and AI language models. Just as HBO adding streaming apps didn’t make it Netflix, enterprise apps adding AI features doesn’t make them AI-native.

Among the real questions is whether enterprise apps can re-architect around AI as the primary interface faster than AI systems absorb enterprise logic and relegate apps to backend record-keeping.

It might be easier for AI systems to learn enterprise workflows than for enterprise apps to reinvent themselves as AI-first systems.

Enterprise apps are constrained by:

* Backward compatibility
* Regulatory commitments
* Pricing models tied to seats and modules.

AI-native systems are constrained mainly by:
* Access to data
* Trust and permissions (solvable over time)

Some might argue the attacker has advantages. As with Netflix in its battle with HBO, content quality was hard, but learnable.

Software culture and data advantage were not similarly "learnable" by HBO.
Enterprise apps must become AI-native faster than AI systems become the enterprise app, because once AI owns the interface, orchestration, and decision layer, everything else becomes a back-end service.

Indirect Monetization of Language Models is Likely

Monetization of most language models might ultimately come down to the ability to earn revenues indirectly, as AI is used to add useful features to existing use cases and apps that already have a revenue model. 

Will ChatGPT Ever Make Money?

A study by Epoch AI illustrates the monetization issue faced by language model developers, where continual needs to invest in the next iteration of a model essentially wipes out profits, raising questions about whether any given standalone model can sustain itself solely by direct revenue sources. 

Evaluating OpenAI, the researchers suggest that ChatGPT 5 has, so far, failed to generate sustainable profits, considering all the iterations (GPT-5 and GPT-5.1, GPT-4o, ChatGPT). 

source: Epoch AI 

source: Epoch AI 

source: Epoch AI 

But model lifecycle losses aren’t necessarily permanent. AI models arguably do not need to be profitable today, as long as companies can convince investors that they will be in the future; can create clear value and therefore develop new revenue mechanisms and cost-saving mechanisms. 

As sometimes is the case, avoided costs become the value driver, in part, as much as higher revenue is important. 

And, over time, costs to create the next iterations and run inference operations should also decline. At the level of market structure, an oligopoly (“winner take most”) should develop, as has been the case in other computing market segments. 

That alone should boost revenues and profit margins for the eventual leaders. 

In some ways, the challenge is similar to that faced by upstart internet firms including Google search and Facebook social media, neither of which had a revenue model at all, at first. 

But indirect monetization strategies include the “freemium” model and a mix of subscription, licensing, advertising or other services offering a path to sustainability.

The key is leveraging value in the broader ecosystems, bundling AI capabilities into existing offerings to boost user engagement, retention, conversion, or operational efficiency, leading to revenue growth elsewhere. 

It's particularly effective when AI costs are absorbed as a "value-add" rather than a line-item expense, allowing companies to recoup investments through increased customer lifetime value or ancillary streams. 

But that also suggests the the firms best able to monetize their language model investments are those with robust existing revenue models that AI can materially enhance, such as: 

• Google's integration of Gemini into its search engine (advertising)

• Amazon personalizing  product recommendations and ads (commerce)

• Meta personalizing social media ads

• Robotics (hardware sales and support)

• Autonomous vehicle services or hardware  such as Waymo enabled by AI sold as a vehicle purchase, upgrade or subscription

• Enterprise software licenses

• “Insight” data such as credit card transaction trends and behavior

• AI infrastructure  (chips, data center services)

• Freemium or marketplace models (premium versions, fees, commissions)

The issue for firms without preexisting revenue models or platforms is that some of these options do not exist, or would be hard to create. 

But we also have to remember that was the case for Facebook and Google; Airbnb and Uber; Amazon and Netflix. It might also have been equally true for Microsoft Windows or Android. 

Sustainable models were discovered over time. Language models might be a developing case of that.