We See It Happening, But Do Not Know "Why?"

It is too early to tell whether the trend continues, for how long and at what levels, but young men (18 to 29) are behaving in a way that is sharply different from men and women in other age groups, in terms of interest in religion, according to a survey by Gallup. 

Young men in the United States have now surpassed young women in saying religion is "very important" in their lives, a reversal from trends of recent decades. 

Gallup’s latest data, from 2024-2025, show 42 percent of young men saying religion is very important to them, up sharply from 28 percent in 2022-2023.

source: Gallup 

It probably will surprise nobody that the uptick is disproportionately driven by conservative or Republican men and women, given the secular bent of people who say they are Democrats. Religious attendance (self reported, to be sure) seems nearly twice as common among those who say they are Republicans. 

source: Gallup 

Recent news reports have been relatively uniform in discussing how the trend is playing out for the Catholic Church, for example:

• The Washington Post: Explores how Catholicism is drawing Gen Z men who are disillusioned with modern secular culture and the casual, contemporary worship styles of "big box" churches, finding a strong appeal in traditional worship and clear doctrine. 

• The New York Times: Highlights Gallup polling data that shows a sharp increase in the share of men under 30 who say religion is "very important" to them, rising to 42 percent. 

• The Atlantic: Details the striking double-digit increases in adult converts at metro hubs and college campuses, noting dozens of college Catholic centers welcoming record numbers of young professionals. 

• America Magazine: Examines Gallup data showing young men as an "emerging exception" to the overall decline of religious attendance among youth, with 40 percent of young men now attending religious services frequently. 

• ABC 7 News San Francisco: Highlights local parishes, such as St. Dominic's, experiencing growing numbers of men in their 20s and 30s seeking peace, community, and answers in the Catholic faith. 

• The Week: Rounds up the anecdotal "standing-room only" Easter services across major archdioceses (like Boston and Newark) driven by young adults. 

• Fox News: Covers Bishop Robert Barron's observations of young adults "leading the charge" back to the faith and driving record-breaking numbers of rite of election participants.

• 60 Minutes interviews Catholic bishops about rising conversions. 

Anecdotally, I have seen this at my local parish as well.

AI Undermines "Answer Questions" Business Models

Chegg is one of the clearest early examples of a public company whose core business was rapidly undermined by generative AI.

But those of you who have worked in any content production industry have seen this before, in the impact of the internet on content business models.

Industry	Traditional Revenue Model	AI Threat	Risk Level
Newspapers	Ads + subscriptions	AI summaries replace clicks	Very High
News websites	Programmatic ads	Search traffic declines	Very High
Magazines	Ads + subscriptions	Commodity lifestyle content	High
Trade journals	Subscription + data	AI-generated research summaries	High
Music labels	Streaming + licensing	AI-generated songs and voice clones	High
Stock photography	Licensing fees	Text-to-image generation	Very High
Online reference sites	Ads + subscriptions	Direct AI answers	Very High
Product review sites	Affiliate commissions	AI recommendation engines	Very High
Educational publishers	Textbook sales	Personalized AI tutoring	High
Local journalism	Ads + classifieds	Reduced traffic and lower cost AI content	Very High

Chegg built a subscription business around three core assets:

• A massive library of solved textbook problems and Q&A

• Human experts and tutors

• A recurring subscription model (students paid monthly for homework and study help). 

So the business moat was built on:

• Proprietary content accumulated over years

• Search traffic from students looking for specific solutions

• Willingness to pay for reliable, structured answers

Generative AI changed all three assumptions, and undermined the business model.

Tools like OpenAI ChatGPT and Google Gemini offered:

• Instant answers

• Natural-language explanations

• Low or zero cost

• Broad subject coverage

• Personalized tutoring

This turned Chegg’s premium service into a commodity.

Metric	Peak / Before AI Shock	After AI Disruption
Market capitalization	~$14.7 billion (2021 peak)	~$150–200 million (2025–2026)
Share price	~$113/share	Around $1/share
Revenue trend	Strong pandemic growth	Sustained year-over-year declines
Subscribers	Multi-million paid base	Persistent subscriber losses
2025 layoffs (May)	—	~248 employees (22%)
2025 layoffs (October)	—	~388 employees (45% of remaining workforce)

But Chegg likely will not be alone. Other lines of business might have similar characteristics:

• Sell information rather than physical goods

• Depend on labor-intensive expert work

• Have low switching costs

• Offer outputs that can be generated in text, image, audio, or code. 

Industry	Traditional Value Proposition	AI Substitute	Risk Level	Examples
Homework help	Solved problems and tutoring	ChatGPT-style tutoring	Very High	Chegg
SEO content agencies	Human-written articles	Automated article generation	Very High	Jasper
Translation services	Human translation	Neural machine translation	Very High	DeepL
Basic legal drafting	Contracts and standard documents	AI document drafting	High	Harvey AI
Tax preparation	Form completion and guidance	AI tax copilots	High	Intuit
Customer service BPO	Human support agents	AI chat and voice bots	Very High	Zendesk AI
Coding contractors	Routine software work	Code generation assistants	High	GitHub Copilot
Graphic design for simple tasks	Logos and ad creatives	Image generators	High	Adobe Firefly
Market research summaries	Analyst reports	Automated synthesis	High	AlphaSense
Recruiting screening	Resume review	AI candidate matching	High	LinkedIn Talent Solutions
Medical transcription	Dictation and coding	Speech-to-text + AI coding	Very High	Nuance Communications
Stock photography	Generic images	AI image generation	Very High	Shutterstock

As was the case when the internet disruption began, content suppliers will be in the line of fire:

• Research report subscriptions

• Professional tutoring services

• Basic legal document preparation

• Simple coding tutorials

• Generic content websites

• Q&A platforms charging for access

• Standardized test prep companies. 

If you can describe your service as “We answer questions about X,” danger clearly exists, as AI will provide a substitute.

Maybe AI is Not Such a Big Job Killer

“The extant empirical evidence does not suggest AI is leading to a large-scale replacement of workers by machines in either output or knowledge production,” argue economists Ajay K. Agrawal, University of Toronto; John McHale, University of Galway and Alexander Oettl, Georgia Institute of Technology. 

“Instead, the evidence seems more consistent with AI as a productivity-augmenting tool used by workers,” they argue in a new paper. 

And they argue there are important policy implications. If AI actually displaces workers, then alternative income distribution systems (universal basic income) make sense. 

Human capital-focused responses would be largely futile if AI is going to replace most workers anyway. 

Conversely, human-capital investment policies take on greater importance if AI mostly augments what workers do. 

The public policy implications are important, they argue.

A major implication is that human capital determines whether AI produces broadly-shared prosperity or rising inequality.

If more AI expertise increases the productivity gains from AI, while also reducing some inequality effects, 

policy should emphasize:

• AI literacy across the workforce

• vocational retraining

• continuous mid-career education

• managerial capability to integrate AI into workflows

• higher-order cognitive skills that complement AI.

Also, productivity impact depends heavily on “thinly staffed tasks” (areas where too few skilled workers). 

Policy should therefore:

• identify labor bottlenecks

• accelerate training in constrained occupations

• improve mobility into high-leverage roles.

In other words, AI inequality effects are not mechanically determined, but depend on:

• workforce skill distribution

• task allocation

• education systems

• how AI tools interact with existing human capabilities.

The paper argues AI systems:

• help lower-skilled workers improve faster

• diffuse expert knowledge

• increase output without eliminating all human roles.

If AI augments human knowledge and skills rather than displacing humans, then public policy goals, then AI literacy will matter more than income replacement strategies in general.

Also, specific blockages in some fields hinge on issues other than AI. 

The obvious point, if the trend continues, is that AI might not be the job killer many fear.