Bad News Overload? News Avoidance on the Rise

By Felix Richter, Felix Richter, Statista

These days more than ever, it often feels like there’s no end to bad news. In the age of social media and constant exposure to news, doom scrolling can take a heavy toll on people’s mental wellbeing. As a consequence, more and more people actively try to avoid the news or at least limit their exposure to it.

According to the Reuters Institute’s latest Digital News Report, an average of 42 percent of respondents from 48 countries included in the survey said that they sometimes or often actively avoid the news, a significant increase from 29 percent in 2017, when the question was first asked. As the following chart shows, selective news avoidance, as the Reuters Institute calls it, became significantly more widespread across all markets in recent years, with half of all respondents from the United Kingdom and 45 percent of U.S. respondents making an effort to reduce their news intake.

The Reuters Institute finds that news avoidance is often linked with low trust in the news and that there are generally two types of news avoiders: consistent avoiders who typically have low education levels and little to no interest in the news and selective avoiders who struggle with news overload and try to insulated themselves from certain topic to protect their mental wellbeing.

This post was originally published on Statista and republished here under a Creative Commons BY-ND license.

Reviewed by Irfan Ahmad.

Read next: AI saves time – so why does it make us feel guilty?
by External Contributor via Digital Information World

AI saves time – so why does it make us feel guilty?

Paul Jones, Aston University

Image: Redmind Studio - Unsplash

We have built tools that save us hours in work. So why do so many people feel worse for using them? The answer has less to do with AI and more to do with what we have always believed work is supposed to cost us.

Artificial intelligence (AI) is supposed to save us time. It can draft emails, summarise reports, organise ideas and help complete tasks that once took hours. In theory, that should feel like progress. But the experience is often more complicated. Imagine using AI to draft a report that would normally take half a day.

Twenty minutes later, the report is done. The work may be good. It may even be better than expected. But instead of feeling relieved, you feel faintly uncomfortable. What are you supposed to do with the time you have just saved?

Relax? Move on? Fill the gap with more work?

This feeling might be called productivity guilt: the uneasy sense that time saved through technology has to be justified, filled or repaid. AI does not create this guilt from nowhere. It exposes something that was already there.

Many people already feel guilty when they are not working. Even rest can feel uncomfortable in cultures where busyness is treated as evidence of commitment, ambition and value. The familiar thought, “I should be doing something”, shows how deeply work has become moralised.

For a long time, effort has been one of the clearest ways people signal value. In many workplaces, long hours, full calendars and rapid replies act as evidence of competence and importance.

Psychology helps explain why this matters. Research on effort justification suggests that people often value outcomes more when they required greater effort. Many cultures also treat hard work as virtuous, so what feels easy can also feel less legitimate.

AI unsettles that equation. When a tool allows someone to produce a report, presentation or set of ideas in a fraction of the time, the output may still be useful. But the emotional meaning of the work changes. If something no longer requires the same level of effort, it may feel less earned. And if it feels less earned, it may feel less like “real” work.

Building an identity

The discomfort is not only about having more time. It is also about what that saved time seems to say about us.

Many professionals build identity through work that feels personally produced. A well written report, careful analysis or thoughtful proposal does more than complete a task. It tells a story about being capable, knowledgeable and useful. AI complicates that story.

If an AI tool helps generate the structure, language or analysis, the question can shift from “Is this good work?” to “Is this still my work?”

That question matters because AI changes where competence appears to sit. In the past, professional expertise was often demonstrated through direct effort: writing the document, producing the analysis, solving the problem. With AI, expertise may increasingly involve asking better questions, judging outputs, spotting errors, adding context and taking responsibility for decisions.

This makes expertise more demanding, not less. It is no longer enough simply to produce the work. Professionals also have to judge whether it is accurate, appropriate, ethical and useful. The value does not disappear, but it transforms.

The problem is that many workplace cultures have not caught up. They may encourage employees to use AI while still rewarding visible busyness and constant output. Workers are told to be efficient, but are still expected to prove their worth through effort.

Invisible work

This pressure may not be felt equally. Employees in roles built around responsiveness, support and availability may find saved time particularly hard to protect. Research on emotional labour suggests that workers already expected to manage the feelings of others may be less likely to experience efficiency gains as relief. For them, saved time may simply become an invitation to do more invisible work.

Efficiency gains can therefore become a new source of pressure. If a task now takes 30 minutes instead of three hours, what happens to the remaining time? Does it become space for reflection, learning and recovery? Or does it simply become capacity for more tasks?

Too often, saved time becomes capacity for more work. As tools make work faster, expectations rise. What once seemed impressive becomes normal. What once counted as efficient becomes the baseline.

So AI may not remove pressure. It may simply move it. That is not a technology problem alone. It is a cultural problem. If organisations want AI to improve working life, they need to be clearer about what saved time is for. It should not automatically disappear into an expanding workload.

It could support better judgement, deeper thinking, collaboration, development or recovery. These are not luxuries. They are part of sustainable work. Workers also need to rethink the relationship between effort and worth.

Using AI does not automatically make work less legitimate. The key question is not whether a tool helped, but whether the person using it exercised judgement, responsibility and care.

AI is not just changing how quickly tasks can be completed. It is challenging an older belief that effort is the main proof of value. That may be why saved time can feel so uncomfortable.

If workplaces use AI only to squeeze more output from the same people, productivity guilt will not be a strange side effect. It will be the system working exactly as designed.

Paul Jones, Associate Dean for Education and Student Experience at Aston Business School, Aston University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

Read next: 

• UK CMA Introduces New Google Search Rules Covering AI Overviews

• Passive AI use at work increases feelings of work meaninglessness, study finds

by External Contributor via Digital Information World

How to fight human trafficking and online scams

By Max Planck Society, MPI for Security and Privacy

New AI-based training program raises awareness of fraud among Chinese adults.

Image: Yudi Indrawan - unsplash

To the point

• Human Trafficking and Fraud:New research sheds light on the hidden link between employment scams, human trafficking, and online fraud. Victims are lured with false job offers to fraud hubs in Myanmar, Laos, and Cambodia, where they are forced—through surveillance and the use of violence—to commit online fraud by impersonating others or feigning romantic relationships.
• Insights from social media:Analysis of testimonials on social media reveals both the tactics human traffickers use to recruit and control people, as well as the strategies developed by the community to help them avoid fraudulent job offers abroad.
• Prevention through AI training:The AI-based training program ROLESafe for older Chinese adults uses interactive role-playing to improve awareness of and defense against online fraud.

Under the pretext of employment prospects, hundreds of thousands of job seekers are lured by scammers to cross the border to countries like Myanmar, Laos, or Cambodia. Instead of the promised lucrative positions, they are forced to work for long hours in heavily guarded scam compounds, facing strict quotas and violence as punishment. Their main task is to fabricate online identities and defraud people, for example, by operating “pig-butchering” scams in which they introduce fraudulent investment schemes after establishing romantic relationships with random targets online.

The experiences of victims forced to become perpetrators

Scientists from the Max Planck Institute for Security and Privacy, the University of Edinburgh, the Hong Kong University of Science and Technology, and the University of Kent analyzed posts from the Chinese social media app RedNote. These posts were shared by scam victims or their family members. The researchers identified 158 relevant posts by searching for specific hashtags such as “human trafficking”, “overseas job scam,” or “trafficking experience” and further filtering.

This analysis has revealed the often hidden details about how the victims are recruited and turned into perpetrators themselves. The common targets for recruitment are people who possess skills that can be valuable to fraud schemes, such as fluency in a foreign language, and those who are vulnerable due to their lack of a stable social net, such as children of parents who went abroad to work. Testimonials further revealed that the scam compound operators would control victims and prevent them from escaping by withholding wages, using location monitoring apps such as FindMy, confiscating travel documents, and, in extreme cases, resorting to violence. They further exploit the victim’s social and cultural ties by demanding ransoms from family members.

Online communities share advice on how to avoid being trafficked to scam compounds

The study sheds light on the community strategies to prevent trafficking discussed on RedNote. Survivors and members of the RedNote community list common “red flags” that potential victims can look for while considering job ads. Benefits such as “free trips”, “all-expenses-paid round-trip tickets,” or “high pay for minimal work” should be regarded as scam indicators. Caution is advised when someone boasts extensively about the benefits of the job without showing company videos or photos, or says they can only reveal the job’s specific location after arriving in the country. As safety measures, it is recommended to check the company’s legal registration, demand full labor contracts, and request proper work visas.

What else can be done?

As a result of people being trafficked and forced to run scams, there are people experiencing harms on the other end of the spectrum: those being targeted by scams. In addition to counteracting scam-driven human trafficking, MPI researchers also developed trainings to support consumers to detect online scams. To achieve this, the research team leverages large language models (LLMs) to make the training interactive and provide tailored advice, especially to older users.

ROLESafe: an LLM-based intervention for scam awareness

Raising scam awareness among older Chinese adults generally falls under the responsibility of younger family members. However, their caretakers struggle with several problems, such as seniors withholding details about the scam or being reluctant to accept help. To mitigate these issues, the researchers developed ROLESafe, an LLM-based tool for learning about scam schemes and exercising judgment through conversations with an LLM-simulated persona, specifically designed for older adults. The tool utilizes an interface similar to WeChat, the most popular messaging app in China. ROLESafe aims to improve fraud awareness and defensive skills in the aging population by assigning users different roles in a scam scenario: observers (passively viewing LLM-generated chat records based on real-world scam cases), helpers (persuading an LLM-portrayed victim not to fall for a scam), or experiencers (directly interacting with an LLM posing as a scammer).

144 Chinese older adults participated in evaluating the tool. The results show that engaging older adults in active (experiencer or helper) rather than passive (observer) roles enhances their awareness of scams. Therefore, ROLESafe provides a significant educational framework for older adults that could be used in the future for other high-risk communities as well.

Background Information

The two studies linked in this press release were presented at the ACM Conference on Human Factors in Computing Systems (CHI 2026).

• The paper titled Characterizing Scam-Driven Human Trafficking Across Chinese Borders and Online Community Responses on RedNoteby Jiamin Zheng, Yue Deng, Jessica Chen, Shujun Li, Yixin Zou, Jingjie Li was recognized with a Best Paper Award (awarded to top 1% of all papers)
• The paper titled Experiencer, Helper, or Observer: Online Fraud Intervention for Older Adults Through a Role-Based Simulation Approach by Yue Deng, Xiaowei Chen, Junxiang Liao, Bo Li, Yixin Zou was recognized with a Best Paper Honorable Mention (awarded to top 5% of all papers)

Original Publications

Yue Deng,Xiaowei Chen, Junxiang Liao, Bo Li undYixin Zou

Experiencer, Helper, or Observer: Online Fraud Intervention for Older Adults Through a Role-based Simulation Approach

CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Source DOI

Jiamin Zheng,Yue Deng, Jessica Chen, Shujun Li,Yixin Zouund Jingjie Li

Characterizing Scam-Driven Human Trafficking Across Chinese Borders and Online Community Responses on RedNote

CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Source DOI.

Reviewed by Irfan Ahmad.

This post was originally published on the Max Planck Institute for Security and Privacy, and republished on DIW with permission.

Read next: Passive AI use at work increases feelings of work meaninglessness, study finds

by External Contributor via Digital Information World

Passive AI use at work increases feelings of work meaninglessness, study finds

By Ty Tkacik, The Pennsylvania State University

Upon returning to manual writing, participants who copy-and-pasted AI responses reported a decline in outcome satisfaction of 21%.

Image: Fotos - Unsplash

Approximately 88% of organizations around the world implemented artificial intelligence (AI) into at least one business function by the end of 2025, the latest McKinsey Global Survey on the state of AI found. Despite promised productivity gains, passive AI use at work, where employees copy-and-paste AI responses to complete tasks, can make people doubt their skills and find their work meaningless, according to a study co-authored by a faculty member from Penn State’s Smeal College of Business that published in Scientific Reports.

Using prolific, an internet platform designed to help scientists find research participants, the team recruited about 270 professionals working across human resources, communications and management fields to complete a series of writing tests similar to their day-to-day tasks, both manually and with the help of AI tools. Their study found that AI use — specifically whether participants used AI collaboratively to workshop their own ideas or passively to generate and copy responses — played a significant role in participants’ reported scores of self-efficacy, meaningfulness, and psychological ownership. Specifically, passive AI use led to nearly 20% declines in feelings of ownership and 10% declines in perceived meaningfulness, while collaborative AI use showed scores similar to AI-independent work, according to the researchers.

Although AI use has been reported to improve productivity, Yidan Yin, assistant professor of management and organization at Penn State’s Smeal College of Business, explained that less is known about the deeper psychological impacts of AI use in the workplace. Yin explained that while scientists have begun exploring possible long-term costs, the field is still quite new, and much of the research is very broad

“Previous studies have primarily looked at the positive impacts AI can have on work productivity, as well as how AI use can make workers feel isolated and less motivated,” Yin said. “With this study, though, we really wanted to focus on better understanding how AI use reshapes people’s connection to their work.”

To accomplish this, the team primarily focused on measuring AI use’s impacts on three closely related constructs: self-efficacy, or an individual’s confidence in themself to complete a task without AI assistance; work meaningfulness, or how much an individual perceives their work as purposeful and significant; and psychological ownership, or how much ownership individuals feel over their output. The researchers used two additional variables — task enjoyment and outcome satisfaction — to gain a comprehensive view of how AI use impacted participants' psychology, Yin explained.

The researchers built a series of writing tasks tailored to the occupations of participants in the study. In the first task, participants were assigned to one of three conditions and instructed to complete the task either manually without the use of AI, actively collaborate with AI, or passively copy and paste AI-generated responses to complete the task. Participants then answered questions about their feelings of self-efficacy, work meaningfulness and psychological ownership of the output. In the second task, all participants were required to complete the writing task manually without AI assistance, answering the same survey questions afterwards.

“This two-task design made it possible to examine both the immediate effects of different uses of AI and their lingering effects after participants returned to working without AI, all in an experiment that only took about 20 to 30 minutes to complete,” Yin said.

Passive AI use during the first task reduced people’s feelings of ownership by nearly 20%, and self-efficacy and perceived meaningfulness by nearly 10%, relative to manual writing, whereas collaborative AI use did not differ meaningfully from manual writing. The declines in self-efficacy and meaningfulness persisted after the second task, when all participants returned to manual writing, suggesting that the erosions cannot be easily undone by returning to working without AI assistance.

Interestingly, Yin noted that passive AI use led to a substantial increase in reported task enjoyment and outcome satisfaction after the first task, with gains of up to 29% compared to manual writing. However, when participants returned to manual writing in the second task, they reported a large drop in these ratings. Notably, their outcome satisfaction fell to be 21% lower than participants who had previously wrote manually, whereas collaborative AI use buffered against this drop. Yin explained that this pattern shows how critical it is for employees to be mindful of how they are incorporating AI into their day-to-day work.

“Passively relying on AI can erode employees’ confidence in themselves and could make them enjoy their job less in the long-term,” Yin said. “They have an initial burst of enjoyment because they don’t need to put in a lot of effort to accomplish the task well, but it makes an employee reluctant to do the task manually. It also leads them to feel like they’re not needed — they see firsthand that AI can perform a task effectively and could potentially replace them.”

Yin said that change in organizations is usually difficult for employees to adjust to, and the rapid integration of AI has proven no different. Moving forward, the team plans to continue studying the psychological impacts that AI-driven change at work is having on employees, as well as how businesses can employ these tools in a way that is effective both for the employer and the employees in an organization.

“Our findings reinforce that companies need to do more than just ask employees to use AI to maximize their productivity, which may inadvertently encourage passive reliance on AI because doing so saves time in the short run,” Yin said. “That isn’t effectively utilizing the employees’ skills, and long-term, those employees are going to feel very alienated from their work.”

Additional co-authors on the work include Elena Hayoung Lee, a doctoral candidate in management and organization at the University of Southern California (USC); Nan Jia, professor of strategic management at USC; and Cheryl Wakslak, associate professor of management and organization at USC.

This post was originally published on Penn State and republished here with permission.

Reviewed by Irfan Ahmad.

Read next:

• AI-Skilled Workers Make 60 Percent More Across Sectors

• How Google's AI Overviews Are Changing Local Search for Small Businesses
by External Contributor via Digital Information World

How Google's AI Overviews Are Changing Local Search for Small Businesses in 2026

For more than a decade, local search followed a predictable pattern. A potential customer searched for a service near them, Google returned a map pack with three businesses and a list of organic results below it, and the businesses with the strongest Google Business Profiles and most reviews won the clicks.

That pattern is being disrupted. Not gradually, rapidly. And the small businesses most affected are the ones that have not yet recognised that the change is already happening.

What AI Overviews Actually Are and How They Differ From Traditional Local Results

Google's AI Overviews are AI-generated summaries that appear at the top of search results, above the traditional blue links and above the local pack. They synthesise information from multiple sources, websites, reviews, directories, and knowledge panels into a two to five-sentence narrative response with cited sources linked below.

The critical difference from the traditional local pack is the mechanism. The local pack pulls three listings from Google's database of Business Profiles based primarily on proximity, review signals, and profile completeness. AI Overviews do not pull listings. They construct an answer, and then decide which sources to credit for that answer.

For local businesses, this creates a new visibility dynamic that most small business owners have not yet adapted to.

Image: Growtika - Unsplash

The Numbers Behind the Shift

AI Overviews appear on approximately 48% of tracked queries as of February 2026, up from 31% a year earlier, according to BrightEdge research. That is nearly half of all searches now generating an AI-written summary before any organic result appears.

Around 50% of search queries in the United States now generate AI Overview responses, and roughly 81% of searches that trigger an AI Overview are performed on mobile. This mobile dominance is significant for local businesses specifically, because mobile searches are where local intent is highest.

Brands cited in AI Overviews earn approximately 120% more organic clicks per impression than uncited brands on the same queries, according to Seer Interactive's 2026 analysis. Being cited is not just about visibility; it changes the commercial outcome of search traffic significantly.

However, the picture for local businesses specifically is more nuanced. When it comes to local intent, AI Overviews remain limited, appearing in only about 7% of local searches. This is the data point that gives some local business owners false comfort. The 7% figure represents where things are now, not where they are heading.

By early 2026, AI Overviews are present on roughly 15% of all Google searches globally, according to MapAtlas, and that number is climbing every quarter. For local businesses, the implications are direct and immediate.

How AI Overviews Interact With Local Search, Specifically

Local queries trigger AI Overviews less frequently than informational queries, but the trend is expanding. When AI Overviews do appear for local-intent queries, they typically cite Google Business Profile data and local content sources.

This tells local businesses something specific and actionable: the signals that determine whether a local business gets cited in an AI Overview are not entirely different from the signals that have always determined local search performance. Google Business Profile completeness, review quality and recency, NAP consistency across the web, and structured data are all cited as factors.

GBP is now the primary data layer feeding Google's AI systems, including Gemini, Search, and Maps. AI Overviews appear in 13% of local queries, AI-assisted review summaries have rolled out to 60% of profiles, and GBP Maps views grew 61% year-over-year.

What this means practically is that Google is increasingly using GBP data not just to show a business listing, but to construct AI-generated answers about local businesses. A business with an incomplete profile, outdated photos, and generic responses to reviews is providing low-quality input to the AI systems that determine local search visibility.

The Click-Through Rate Reality

The most significant impact of AI Overviews on local search is not which businesses appear, but what happens to click behaviour.

Zero-click searches jumped from 56% to 69% of all searches between May 2024 and May 2025, according to Similarweb data. When a user gets their answer from an AI Overview, they frequently do not click any link at all.

Organic click-through rates dropped 61% on queries with AI Overviews present, from 1.76% to 0.61%, according to Seer Interactive's September 2025 study.

For local businesses with strong traditional organic rankings, this represents a material reduction in traffic from search. Pages that ranked in positions three to five are losing traffic, not because they dropped in rank, but because fewer users scroll past the AI-generated answer at the top of the page.

On mobile, AI Overviews take up more screen space and push traditional organic results further down the page, meaning the impact on mobile click-through rates may be stronger than desktop data suggests.

What Small Businesses Need to Do Differently

The businesses that will maintain and grow local search visibility in 2026 are the ones making specific changes to how they manage their online presence. The following priorities emerge directly from how AI Overviews select and cite local sources.

Complete and Actively Manage the Google Business Profile

Overall, Google Business Profile actions, including calls, directions, website clicks, and bookings, grew 41% year-over-year, according to Google's own 2026 data. This growth is happening against a backdrop of declining traditional organic traffic, which confirms that GBP visibility is increasingly where local business discovery happens.

Businesses managing their GBP based on practices from 2023 or 2024 may be operating with outdated assumptions. The specific changes that matter in 2026 include responding to every review (AI-assisted review summaries now pull from review content on 60% of profiles), keeping hours and service descriptions current, and uploading photos regularly since user-generated content now plays a larger direct role in ranking signals.

Publish Location-Specific Content That Answers Real Questions

A local business with a clear services and pricing page will show up faster for searches containing local intent than a long blog full of general information. AI considers user intent behind the search, how trustworthy the content is, and how useful the information is compared to competitors.

For a local plumber, this means a page that answers "how much does emergency plumbing cost in [city]" with a specific, honest answer will perform better in AI Overview citations than a page that describes plumbing services generally. The specificity of the answer is what gets cited.

Practical implementation: review your service pages and ask whether a customer who has never heard of your business would find a specific, useful answer to their question on each page. If the answer is no the page is not AI Overview-ready.

Build Citation Consistency Across the Web

AI systems look at a business's Google Business Profile to verify that the business is legitimate, active, and well-regarded in its local area. But legitimacy signals extend beyond GBP. Consistent NAP (Name, Address, Phone) data across directories, industry listing sites, and social profiles all contribute to the entity confidence that determines whether a business gets cited.

Inconsistent business information across the web is not just a traditional local SEO problem. It undermines the confidence AI systems have in the data they cite, which reduces the likelihood of appearing in AI-generated local answers. A comprehensive guide to building local search visibility, including how Google Business Profile, citation consistency, and content structure work together, covers these fundamentals in detail for businesses working through local SEO for the first time. A resource such as this local SEO guide walks through each element practically for small business owners managing their own presence.

Use Structured Data Markup

Local SEO fundamentals remain important in 2026, with the addition of structured data optimisation for AI citation.

LocalBusiness schema markup tells Google and its AI systems the precise details of a business in a format that machines can read directly: name, address, phone number, opening hours, service area, and geographic coordinates. Businesses without this markup are providing their information in a format that requires AI systems to infer details from unstructured text rather than reading confirmed data.

Implementation on WordPress takes approximately 10 minutes using Yoast SEO or RankMath's local SEO settings, both of which generate LocalBusiness schema automatically when the business address and contact details are entered.

Keep Content Fresh

Research shows that AI systems cite older content less frequently in 2026. Updating key pages regularly with fresh data, new examples, and expanded insights maintains authority and visibility.

For a local business, this means service pages should not be written once and left unchanged for two years. Updating the content with current pricing information, recent project examples, and answers to questions that have come up from actual customers keeps the page performing as an AI-citable source.

The Businesses That Will Fall Behind

Industries that depend more on local or transactional intent, such as real estate or local services, show much lower AI Overview adoption compared to informational content categories. This is frequently interpreted as protection from the AI Overview disruption if AI Overviews appear less for local searches, local businesses are less affected.

This interpretation misses the direction of travel. The businesses that adapt their local SEO and content strategy now, before AI Overviews become dominant for local intent queries, will have established the signals and credibility that determine citation when the adoption rate climbs. The businesses that wait will be adapting after the advantage window has closed.

Even websites that rank between positions 11 and 20 have a chance of being cited in AI-generated answers, provided the content directly answers the question being asked. This is the equalising opportunity of the AI search era for small businesses, as being cited in an AI Overview is not exclusively reserved for businesses that already rank in position one.

What This Means for Local Business Owners Right Now

The shift in local search behaviour driven by AI Overviews does not require a complete reinvention of how small businesses approach their online presence. The foundation remains the same: a well-maintained Google Business Profile, accurate and consistent business information across the web, and content that genuinely answers the questions potential customers are asking.

What changes is the bar for content specificity and the importance of structured data. Generic service descriptions that could apply to any business in any city will not get cited. Specific, accurate, locally-relevant content that directly answers real questions will.

63% of businesses report that Google AI Overviews have had a positive effect on their organic traffic, visibility, or search rankings since their rollout. The businesses in that 63% are not the ones that waited to see what happened. They are the ones who recognised the change early and made the adjustments before AI Overview adoption reached the point where the competition for citation became saturated.

For small businesses managing their own local SEO, the practical starting point is an honest audit of the Google Business Profile, a review of whether service pages directly answer real customer questions, and a check on whether business information is consistent across every platform where the business is listed.

Author Bio:

Stuart Cowan is a digital marketing strategist and content contributor with a focus on local search, SEO, and the evolving impact of AI on how businesses get found online. He has worked with small and medium businesses across Australia on web design, digital marketing, and search visibility strategies. His work covers practical, data-driven insights for business owners navigating the changing search landscape.

Edited by Irfan Ahmad.

Read next: 

• AI-Skilled Workers Make 60 Percent More Across Sectors

• Google AI Overviews and the Ahmadiyya Caliphate: Examining a Religious Search Dispute

by Guest Contributor via Digital Information World

AI-Skilled Workers Make 60 Percent More Across Sectors

By Katharina Buchholz, Data Journalist, Statista

PwC's Global AI Jobs Barometer has found that AI-skilled workers earn 62 percent more across the globe than their non-AI-skilled counterparts. The analysis looked at data across one million job ads in 16 different sectors and 27 countries and found that in consumer market jobs, differences were especially stark, with roles requiring AI skills paying 118 percent more than those which don't.

Other high pay gaps could be observed in the areas of technology, telecoms and media (+84 percent earnings for AI-skilled employees), energy, utilities and resources (+75 percent) as well as manufacturing (+73 percent). Below average gains were calculated for the public sector, where (government) employees only earned 16 percent more if they had AI skills. In the health industries, this number stood at 37 percent.

The report also found that the hiring of AI specialists has been rising sharply. While in 2012, 1 percent of jobs posted were for AI specialists, this more than doubled and stood at above 2 percent in 2025. The biggest share of AI jobs was listed in the area of tech, media and telecoms at 11.4 percent of all job listings. Hiring was up in all sectors, with shares of AI jobs reaching between 2 percent and almost 6 percent for the other sectors included in the chart. The exception was the healthcare sector with just around 1 percent of jobs posted having an AI component.

This post was originally published on Statista and republished here with permission.

Edited by Irfan Ahmad.

Read next:

• Being your own boss doesn’t always pay off: What 30 years of data reveal

• Google CEO Pichai’s Stanford Speech Accompanied by Student Protest, Visible in News Coverage but Not in Official Recording
by External Contributor via Digital Information World

Being your own boss doesn’t always pay off: What 30 years of data reveal

Xiaoying Wang, Wilfrid Laurier University and Seok-Woo Kwon, University of Calgary

More than 2.6 million Canadians work for themselves, and according to an annual RBC poll conducted in 2025, 59 per cent of Canadians aspire to own a business — the highest level since 2017.

The appeal is understandable. A recent poll found that nearly two-thirds of people feel they have plateaued at work and see owning a business as their next move. “Being your own boss” has become shorthand for freedom, control and finally getting paid what you are worth.

However, working for yourself does not reliably make people richer or happier. In our new study, we followed 12,686 individuals over three decades, from their late teens into their 50s, to see how self-employment actually played out over a their working lives.

The takeaway should give anyone weighing the leap pause. Simply being self-employed often left people no better off financially, and measurably less satisfied with life, than peers who kept a regular job. Whether the dream pays off comes down to how you do it, not whether you do it.

4 ways through a working life

Not everyone follows the same entrepreneurial path. By tracking people year by year through the National Longitudinal Survey of Youth 1979, run by the U.S. Bureau of Labor Statistics, we found four distinct patterns across adulthood.

Predicted probability of self-employment across adulthood for four distinct entrepreneurial career paths. (Seok-Woo Kwon and Xiaoying Wang), CC BY-ND

The largest group, about 69 per cent, remained in regular employment and rarely worked for themselves. We used them as the benchmark for everyone else.

Another 12 per cent tried self-employment in their 20s, then returned to regular jobs. About 13 per cent did the opposite, entering self-employment in their 40s and becoming more involved over time. A committed six per cent started young and remained self-employed throughout most of their careers.

The same decision — “work for yourself” — produced four very different lives, with different financial and personal outcomes depending on when it happened and what form it took.

It’s not whether, but how

When we looked at both financial and psychological outcomes, one pattern stood out. What set the financially and personally successful apart was not a brilliant idea or sheer grit, but the structure of their business.

People who built a formal, incorporated business — a registered company with its own legal identity — earned more and reported greater life satisfaction than people who never became entrepreneurs. People who simply worked for themselves as solo freelancers did not. On average, they earned no more than non-entrepreneurs and reported lower well-being.

Researchers have long noted that incorporated and unincorporated business owners operate very differently. Incorporated owners tend to run more ambitious and growth-oriented ventures compared to unincorporated owners.

In Canada, people with an incorporated business are more likely to plan to expand (37.6 per cent versus 22.6 per cent of unincorporated owners), and incorporated businesses are more likely to survive and typically earn more along the way.

Our results suggest the difference goes beyond income: the legal structure of a business is closely tied to whether self-employment improves lives or wears people down.

However, it’s important to note this is not a case of incorporate and watch your life improve. People who incorporate often start with advantages like more education, professional experience and skills.

The gap showed up even among people with similar backgrounds, but incorporation usually signals something about how a business was conceived from day one rather than acting as a simple fix after the fact.

Timing and the long game

Age also matters, but not the way popular accounts suggest. The image of the brilliant young founder who drops out of school to build the next big thing is mostly a myth, and experience turns out to be an asset.

Among people who built incorporated businesses, lifelong entrepreneurs earned the most, early starters reported the strongest life satisfaction and those who started in midlife struck the best balance between the two. The most rewarding path appeared to be building a formal business after accumulating skills, savings and industry experience.

Staying in business is not only about talent or timing, either. We found that people who grew up with books, magazines and a library card at home — a form of early-life advantage researchers call cultural capital — were more likely to sustain a business over the long term, even though they were no more likely to start one.

The skills and habits that keep a business alive for decades, it seems, may be formed long before anyone writes a business plan.

So should you take the leap?

None of this argues against self-employment. Instead, it suggests being clear-eyed about which path you are choosing. If the goal is greater financial security and a better quality of life, our findings point to building a registered company after gaining skills, savings and industry experience, rather than immediately after graduation.

Lifelong founders tend to earn the most, but for most people the midlife path offers the best balance of income and well-being.

If that is true for individuals, it may also be true for the systems meant to support them. Most programs are designed to help people launch, but far fewer help them incorporate, grow and survive.

Much of the funding that helps a business grow presumes it is already incorporated, leaving the solo self-employed — the group already earning the least — on the outside. If we want more people to reach the path that pays off, the support has to start earlier and last longer than the launch.

Xiaoying Wang, Assistant Professor of Strategic Management, Wilfrid Laurier University and Seok-Woo Kwon, Robson Professor in Entrepreneurship, University of Calgary

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World