"Magnifica Humanitas" is No "Rerum Novarum"

At the risk of seemingly disagreeing with "Magnfica Humanitas," it is still possible to compare that document with Rerum Novarum, upon which the new encyclical is based, and see clear differences, beyond the specific problems tackled by each document.

At the risk of downplaying artificial intelligence impact, which many could characterize as a general-purpose technology that will transform nearly every industry, the encyclical Rerum Novarum ("Of New Things"), issued by Pope Leo XIII on May 15, 1891, was not addressed “merely” to the impact of the industrial revolution on workers.

Laissez-faire economics; private property; socialist and Marxist ideas were paramount issues also tackled by Rerum Novarum. 

To the extent that Pope Leo XIV’s encyclical Magnifica Humanitas is modeled purposefully on Rerum Novarum, we can compare the two documents.

To be sure, Rerum Novarum focused on:

• potential exploitation of the working class

• Protecting workers

• Materialism, moral and spiritual issues

• ideological extremes (unregulated capitalism and socialism).

But Rerum Novarum also clearly established some clear and practical guidance for Catholic social teaching that are unmatched by any other religion or spiritual belief. 

Catholic social teaching means the Catholic church is clearly and officially:

• Opposed to socialism and collectivist economics

• A supporter of the fundamental right of private property

• A supporter of the right to form trade unions and other intermediate social institutions

• A supporter of market-based economies. 

Magnifica Humanitas is focused on AI’s impact on human dignity. And, to be sure, it warns of the dangers of concentrated “technocratic” power. 

But calls for ethical governance, transparency, accountability, subsidiarity (participation by communities), solidarity and orienting technology toward the common good and human flourishing are not in the same league as opposing both unrestrained capitalism and socialism (communism). 

Rerum Novarum defended the right of private property, for example. So Magnifica Humanitas might criticize unethical behavior, but it does not call for expropriation.

Magnifica Humanitas argues for an AI that serves humanity, not dominates it. We might see that as in line with the argument of Rerum Novarum. Some possible differences are that Rerum Novarum had more direct and practical implications. 

Rerum Novarum:

• Made opposition to socialism foundational for Catholic social teaching

• Specifically supported the role of labor unions and other social groupings

• Supports private property rights as essential for human freedom and creativity

• Supports market-based economics. 

Magnifica Humanitas, in my reading, includes nothing similar. 

Socialists and other leftists might argue Magnifica Humanitas supports expropriation of an AI firm’s  property. Since Rerum Novarum, that is in conflict with Catholic social teaching. 

Magnifica Humanitas contains no similar institutional practices (supporting labor unions as a counterweight and many types of intermediate institutions (family, guilds, social organizations) as a way of restraining the exercise of all social power by the state. 

Magnifica Humanitas contains no new proposals for restricting market economies or embracing socialism or expropriation. 

Instead, it is a moral exhortation; a statement of principles; a broad action to exercise prudence.  

As Rerum Novarum arguably shaped moral discourse, legitimized reforms, and encouraged balanced responses over revolution, so Magnifica Humanitas attempts the same. 

Still, one might read the new document as offering few practical pillars, compared to Rerum Novarum.

No Supplier Likes Customer Concentration, But Sometimes It Cannot be Helped

Customer concentration in the hyperscaler segment is practically unavoidable, when a handful of customers represent such a large percentage of the market. 

In 2025, four hyperscalers (Amazon, Google, Microsoft, Meta) represented as much as 70 percent of all capital investment in the technology business, for example. 

Amazon, Google, Meta, and Microsoft control a massive portion of the world's total computing infrastructure, particularly in artificial intelligence. Together with Oracle, these hyperscalers own over 66 percent of global AI compute capacity.

source: Epoch AI 

To be sure, some suppliers (especially those which historically sell to enterprises, mid-market or small business) might not be as dependent on those few customers. 

Still, for some products (graphics processing units, high-performance memory and accelerators, for example), there are just a few big volume buyers. 

And as much as any supplier might wish to reduce reliance on just a few customers, that is hard to do in markets where a handful of buyers dominate:

• Google: Dominates the single-largest share of pure AI compute, holding about 25 percent of global capacity. While rivals rely on Nvidia, Google’s capacity is largely driven by its proprietary Tensor Processing Units (TPUs). In the broader public cloud infrastructure market, Google Cloud holds approximately 14 percent market share

• Microsoft: Holds an estimated 21 percent share of the global cloud infrastructure market. Through massive deployments of Nvidia GPUs and its strategic partnership with OpenAI, Microsoft accounts for an outsized share of enterprise AI workloads in the cloud

• Amazon (AWS): Leads the global cloud computing market with 28 percent market share. While historically focused on broad, general-purpose enterprise computing, Amazon is rapidly scaling its proprietary AI chips (Trainium and Inferentia) to capture a larger portion of specialized AI computing workloads

• Meta: Operating the largest internal compute footprint, Meta owns an estimated 10 percent of the world's total AI compute. Unlike the others, Meta's compute is largely dedicated to internal workloads, such as powering the massive recommendation algorithms for Facebook and Instagram, alongside its proprietary AI models. 

So, like it or not, customer concentration in some parts of the AI value chain is unavoidable. There are only a few “whales” on the buyer side. 

And some might argue there are similar few whales in the anchor tenant category as well. Consider the role OpenAI and Anthropic play as anchor customers for the hyperscaler compute “as a service” providers. 

source: The Information 

Why Metaverse Failed, AI Succeeds

“Metaverse” just never seemed to catch on, and the issue is “why?” While it is always possible to argue that the concept was simply “ahead of its time,” perhaps there were other issues as well. 

For starters, metaverse was a push toward more immersive, higher-fidelity digital environments. But as with other proposed advancements in digital media, it did not solve a broad, urgent problem for most users.

Television or movies presented in “three dimensions” also arguably are “more immersive” or “realistic,” but that never is enough to create demand. 

But some thought something more was at stake. The metaverse, some thought, would become the next computing platform, the successor to the mobile internet. 

But the technology was not compelling enough; the friction was too high; the value way too limited. The ecosystem, content base and network effects were not there. 

Compare metaverse to artificial intelligence, a general-purpose capability that can attach to almost any cognitive workflow and business process.

Use case	Metaverse value	AI value	Relative usefulness
Virtual meetings / collaboration	Moderate: better spatial presence, but often not better than video calls.	High: summarizes, transcribes, drafts follow-ups, and speeds decisions.	AI
Employee training / simulation	High in physical or risky environments, where immersion helps.	High: creates training content, coaches, quizzes, and personalizes learning.	Tie, slightly AI
Customer support	Low to moderate: immersive support is niche.	Very high: chatbots, agent assist, routing, and automated resolution.	AI
Sales / product demos	Moderate: strong for 3D visualization and experiential demos.	High: personalizes outreach, generates content, and qualifies leads.	AI
Entertainment / gaming	High: this is one of metaverse’s best-fit domains. dreamsoft4u	High: generates content, NPC behavior, personalization, and moderation.	Tie
Education	Moderate to high for immersive labs, historical reconstruction, or anatomy. dreamsoft4u	High: tutoring, summarization, feedback, and adaptive learning.	AI
Healthcare / therapy	Moderate: useful for exposure therapy, rehab, and visualization.	High: triage, documentation, diagnostics support, and patient messaging.	AI
Remote field assistance	Moderate: useful when a remote expert needs the user’s visual context. techtarget	High: guides workers, interprets data, and generates instructions.	AI
Marketing / brand experiences	Moderate: immersive campaigns can be memorable but narrow. dreamsoft4u	Very high: segmentation, content generation, ad optimization, and personalization.	AI
Commerce / shopping	Moderate: 3D storefronts help some categories like real estate or furniture.	Very high: recommendation, search, pricing, and conversational shopping.	AI
Design / visualization	High: strong when spatial understanding matters.	High: concept generation, variants, and analysis, though not always spatial.	Tie
Knowledge work / office tasks	Low to moderate: metaverse mainly changes the interface.	Very high: directly improves writing, coding, analysis, planning, and review.	AI

Where metaverse mainly extends what digital media can look and feel like, AI extends what software can do. 

A virtual world can make communication, entertainment, and simulation more realistic, but it still stays within the realm of mediated experience. 

AI, by contrast, is increasingly useful wherever humans are reasoning, drafting, classifying, predicting, summarizing, planning or making decisions.

AI adds value even when the interface stays ordinary, because it upgrades the work itself rather than just the container around the work.

Metaverse even if all the other issues had not been present) is “only” the next evolution of realism in electronic media, while AI is the next evolution of cognition. 

Perhaps virtual reality will someday deepen immersion for users. 

But AI, in principle, can affect almost every cognitive task because it can assist with language, judgment, memory, analysis, and creativity in almost every domain. 

And adoption barriers are quite low: people can use it right now, with low friction and no new hardware requirements. 

If metaverse was about the realism of the interface, AI  is about the augmentation of cognition itself. 

I don’t recall anybody arguing that metaverse was a general-purpose technology on the scale of electricity, in terms of impact,for example. It’s pretty hard to find anybody arguing AI is less than that.

J Curve and Solow Productivity Paradox are at Work with AI

Investors are going to keep challenging firms to show evidence their heavy artificial intelligence investments really are boosting productivity.

That is going to continue being a tough challenge, as history suggests the real output gains will take some time to develop.

So AI "productivity," or the "lack of quantifiable gains," are currently the most significant contemporary case of the Solow productivity paradox. 

In 1987, Nobel laureate Robert Solow famously remarked, "You can see the computer age everywhere but in the productivity statistics."

Recent research suggests productivity might actually decline for a time as firms deploy AI. 

The reason is the J curve. 

“We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains,” say economists Kristina McElheran; Mu-Jeung Yang; Zachary Kroff and Erik Brynjolfsson. “Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding,

while harming productivity and profitability in the short run.”

In other words, it takes time for enterprises to retool their business processes for the new technologies. And the more profound the innovations, perhaps the longer it takes to integrate those tools. 

Also, much of the reported AI adoption is horizontal rather than vertical; personal rather than systematic. In other words, individuals might be using chatbots, but workflows have yet to be transformed. 

So “personal productivity” has not yet been matched by an applied transformation of key work processes. And personal productivity gains are hard to measure, in terms of impact on firm performance. 

Agentic AI should help, as they can affect complex business processes. 

source: Forbe

Many have noted that  U.S. labor productivity significantly slowed in the 1970s and 1980s, despite rapid information technology investment. 

Then starting in the mid 1990s a decade of faster growth returned arguably because business process re-engineering had taken place.

A similar productivity paradox surrounds AI. As explained by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson in a 2017 working paper, AI and the Modern Productivity Paradox,” the paradox is primarily due to the time lag between technology advances and their impact on the economy. 

While technologies may advance rapidly, humans and our institutions change slowly. 

Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.

Translating technological advances into productivity gains requires major transformations, and therefore time.

Today, we see a "Modern AI Paradox": while Large Language Models (LLMs) and Generative AI are ubiquitous in headlines and corporate pilots, global aggregate productivity growth  remains sluggish.

Economists like Erik Brynjolfsson argue that the paradox isn't a failure of the technology, but a timing and structural issue. He identifies four main reasons for this lag:

1. Mismeasurement: AI often improves quality, variety, or speed in ways that traditional GDP (which tracks "units produced") fails to capture.

2. Redistribution: AI may be used for "rent-seeking" (competing for market share) rather than increasing total industry output.

3. Implementation Lags: Significant "General Purpose Technologies" (like electricity or the steam engine) require decades of organizational restructuring before they move the needle.

4. Mismanagement: Companies often use AI to automate old processes rather than inventing new, more efficient business models.

Study	Target Group	Productivity Impact Found	Notes on Enterprise Deployment Gaps
MIT/Stanford (NBER)	Customer Support Agents	14% increase in issues resolved per hour.	High-skilled workers saw less gain; impact was greatest on novices. Enterprises often fail to use AI as a "leveler" for training.
Harvard/BCG (SSRN)	Management Consultants	40% higher quality; 25% faster task completion.	"Jagged Frontier": AI failed spectacularly on certain logic tasks where humans over-relied on it, leading to "falling off the cliff" errors.
Microsoft/GitHub	Software Developers	55% faster at completing coding tasks.	Gains are often eaten by "code bloat" and increased technical debt if not managed by senior architects.
Goldman Sachs Research	Aggregate US Economy	Projected 1.5% annual increase over 10 years.	Real-world adoption is currently hindered by power grid constraints and data center infrastructure delays.
NBER / Brynjolfsson et al.	Generative AI & the "J-Curve"	Initial 0% or negative impact.	The "Productivity J-Curve": Measured productivity dips initially as firms invest in "intangible capital" (retraining, restructuring) before the payoff.

While individual tasks show gains, enterprise-wide productivity often remains flat for several reasons:

• The "Pilot Trap": According to recent Adobe/Business research, 86 percent of IT leaders see potential, but only a fraction have moved beyond "isolated experiments" to organization-wide workflows

• Inertial Workflows: Companies often use AI to "do the old thing faster" (e.g., writing more emails) rather than "doing the right thing" (e.g., eliminating the need for those emails entirely). This results in "Digital Overload"

• The Human Bottleneck: AI can generate a report in seconds, but a human still takes hours to verify, edit, and approve it. Without changing the governance and approval structures, the AI speed gain is neutralized

• Data Fragmentation: Most AI models are effective only if they can access clean, centralized data. Most enterprises still have "siloed" data, leading to AI hallucinations or irrelevant outputs

• Skills Gap: Enterprises frequently treat AI as a "plug-and-play" tool like a calculator, failing to realize it requires a new type of "AI Literacy" to prompt and integrate effectively into complex projects.

None of that will be too comforting for suppliers who must justify their heavy AI capital investment. 

But history suggests the payoff is coming. It just will take some time. It always does.