Digital Information World

New study explores rise of 'ragebait' and its impact on online accountability

2026-07-04T14:01:23.719+05:00

By Joe Stafford, The University of Manchester

A new study has revealed how social media creators are turning anger into entertainment, and what that means for public debate.

Image: Hendrik Kespohl - Unsplash

Research by Dr Nicholas John from The University of Manchester and Dr CJ Reynolds from the University of Copenhagen has explored the rise of ‘ragebait’ - content deliberately designed to provoke anger - and how it is reshaping the way audiences engage with morality, accountability and online behaviour.

Key insights:

‘Ragebait’ is an increasingly popular strategy for generating attention online
Content creators are engineering confrontations to provoke emotional reactions
Audiences are drawn to feelings of moral superiority and catharsis
Online ‘accountability’ is often reduced to spectacle rather than real change
The trend reflects a shift in how public shaming operates in digital culture

Why this matters

From callout videos to viral confrontations in public spaces, outrage has become a powerful currency in today’s attention economy.

Dr John’s research examines the widely viewed ‘Cart Narcs’ video series, where members of the public are confronted - and often provoked - for failing to return their shopping trolleys to storage bays in supermarket car parks.

While such content appears to promote accountability, the study argues that its real appeal lies in carefully staged conflict.

“Ragebait works because it blurs the line between entertainment and morality,” says Dr John. “It invites viewers to feel they are witnessing justice being done, while actually consuming a highly controlled and repeatable form of provoked outrage.”

Entertainment disguised as accountability

The study identifies a formula behind successful ragebait content - creators construct predictable scenarios, provoke emotional reactions, and then frame themselves as morally justified.

This allows audiences to experience what researchers describe as ‘accountability entertainment’ which stages wrongdoing and its punishment, but without any meaningful consequences beyond the screen.

Rather than encouraging broader social change, the research suggests this format focuses attention on individuals instead of systems.

“Viewers are encouraged to judge and condemn, but not to engage with the wider social conditions that shape people’s behaviour,” Dr John explains. “Accountability becomes something you watch - not something you do.”

The politics of outrage

The research also highlights how ragebait repurposes elements of callout culture – something which is originally rooted in social justice activism - into monetised entertainment.
In doing so, it shifts power dynamics - instead of challenging powerful figures, creators often target ordinary individuals, amplifying their mistakes for mass audiences.

This creates what the study describes as a form of ‘atomised politics’, where collective action is replaced by individual judgement and fleeting moments of online outrage.

What needs to change

The study calls for greater awareness of how emotionally provocative content is produced and consumed, particularly as platforms continue to reward engagement-driven formats.

Understanding the mechanics behind ragebait, says Dr John, is key to recognising its broader social impact.

“Not all outrage is meaningful - if we want healthier public discourse, we need to question content that turns anger into spectacle and ask who benefits from it.” — Dr Nicholas John.

Publication details:

The research is published in Information, Communication & Society.

DOI: https://doi.org/10.1080/1369118X.2026.2665797.

This post was originally published on The University of Manchester and republished here with permission. The title has been edited for clarity.

Reviewed by Irfan Ahmad.

Read next:

• Study Finds 70% of Smartphone Photos Are Never Looked at Again - Citing Overload and Emotional Avoidance

• Google updates Chrome Web Store rules on extension data collection and AI safeguards

Google updates Chrome Web Store rules on extension data collection and AI safeguards

2026-07-04T12:18:08.296+05:00

Reviewed by Irfan Ahmad.

Google has updated the policies governing extensions in the Chrome Web Store, introducing stricter requirements for user data collection, new transparency obligations for developers, and additional restrictions on certain types of extensions.

The changes were announced on July 1, 2026, in a post on the Chrome for Developers blog.

Under the revised Limited Use Policy, any data collected must be strictly necessary for the extension's disclosed single purpose. The updated policy states that collecting user data for purposes beyond that disclosed purpose is prohibited.

Google is also expanding its disclosure requirements. Developers must now prominently inform users about all data collection, regardless of whether it is closely related to an extension's stated purpose. If an extension's data handling practices change after installation, developers will also be required to proactively disclose those changes to users.

The company has also revised its Regulated Goods and Services policy by explicitly adding predictive markets as prohibited products. As part of that change, extensions that facilitate or enable real-money transactions on predictive outcomes will not be allowed in the Chrome Web Store.

Another new policy targets extensions that attempt to bypass protections built into AI-powered services. Google said it will explicitly prohibit extensions designed to circumvent safety guardrails, usage restrictions, or other protective measures implemented by those services.

According to Google, the policy updates are intended to help maintain a trusted Chrome Web Store by strengthening data collection standards and clarifying policy boundaries related to prediction markets and AI safety. The company added that users should have clear visibility into how their data is collected and handled.

Google encouraged developers to review their existing extensions against the revised policies before enforcement begins on August 1, 2026. The company said extensions that are not compliant after that date may face enforcement action through the Chrome Web Store.

Image: Zulfugar Karimov - Unsplash

Read next:

• Study Finds 70% of Smartphone Photos Are Never Looked at Again - Citing Overload and Emotional Avoidance

• One ChatGPT query uses more energy than you think

Study Finds 70% of Smartphone Photos Are Never Looked at Again - Citing Overload and Emotional Avoidance

2026-07-03T18:03:28.880+05:00

By Ardziv Simonian

We take more photos than ever before - but according to new research, the vast majority of those images will never be seen again due to the complicated relationship we have with our overloaded camera rolls as well as with our own memories.

Image: Marek Piwnicki - Unsplash

The Memory Economy Report, a multinational survey of 8,000 consumers across the UK, USA, France and Germany conducted by memory curation app Popsa, found that 70% of all smartphone photos are never revisited - despite capturing our most meaningful moments. The report found that consumers globally take an average of 5.5 billion photos every year, yet only 16% have looked back at more than a quarter of their own photos in the last year.

We look at the reasons behind this disconnection between our passion for photo taking and the reluctance to appreciate them.

Over reliance on smartphones as memory banks

The practical reasons around the over reliance we have on technology are a huge factor - over a third of respondents (37%) say important photos simply get lost among the rest, with the same number finding that sheer volume makes individual memories harder to focus on. Shockingly, one in five stated that they “feel paralysed” by the scale of what they've accumulated. This in turn causes more angst - almost half (49%) say their disorganised camera roll causes them genuine stress and 42% feel actively guilty about it, with younger generations feeling this most.

Also read: 1 in 3 Americans Got Wrong Answers From AI, But 38% Use It as Their Calculator Anyway

Forty-five percent of 18-24 year-olds reported the highest levels of stress from photo overload of any age group. Younger respondents also took significantly more photos, averaging 1,468 per year compared with 491 among those aged 55 and over.

The emotionally complex relationship with our photos

The research points to not only a practical problem with photo storage, but an emotional one too. Almost half of consumers (47%) say they actively avoid photos from certain periods of their lives - not by deleting them, but by keeping them untouched on their devices. Past relationships and breakups (26%) were the most cited reason for this, followed by periods of grief, illness or poor mental health (24%), times when life looked very different (23%) and family conflict or loss (19%).

The emotions involved in looking back are similarly layered: nostalgia (54%) and happiness (44%) are the most common drivers for people, however sadness (17%), loneliness (16%) and processing of loss (14%) were also among the main cited reasons. A third describe the experience as producing mixed emotions, 7% say their feelings are mostly negative, and 6% shockingly report feeling disconnected from the person they see in old photos.

"The camera roll has become a filing system rather than a memory system," said Liam Houghton, Popsa CEO, on the report’s findings. "People aren't just overwhelmed by their photo amounts - in many cases, they're actively avoiding the subjects within them. The emotional cost of photo overload is something we're only beginning to notice and understand."

The importance of photo curation for print and legacy

On a more practical level, the report found that the act of curating photos - choosing what to keep, what to show and what to hold onto - helps restore some of their meaning to the context they were taken in. Nearly half of consumers (48%) stated that they felt more emotionally connected to their memories after printing photos of them.

Printed photos also have another overlooked benefit - as legacy items that can be looked at passed on without needing technology to do so.

The report found that the majority (77%) of consumers have made no plans for their digital photo legacies after death, leaving photo libraries at risk of disappearing entirely. While younger generations are more active towards this - 41% of 18-24 year-olds have a digital legacy plan, more than four times the rate of over-55s (9%) - there’s still a huge photo memory preservation gap in the digital age.

As our digital photos have become heirlooms, their intentional curation will be key to how future generations can appreciate them too.

Reviewed by Irfan Ahmad.

Read next: One ChatGPT query uses more energy than you think

One ChatGPT query uses more energy than you think

2026-07-03T17:25:59.587+05:00

By Surfshak

The updated Surfshark analysis reveals that instead of saying "thank you" to your chatbot, you can run the AC for seven seconds or cool down with a mini fan for three minutes.

Image: Image: AppshunterIO - Unsplash

Key insights

One ChatGPT query consumes energy equivalent to running a 40W mini cooling fan for about three minutes. Similarly, a single query uses the same amount of energy as charging your phone with a 5W charger for 24 minutes. Compared with more powerful appliances, such as a 1000W single-room air conditioner, one ChatGPT query equals about seven seconds of AC use. This means you could run an AC unit for 10 minutes with the energy used by approximately 86 queries. Finally, running a regular 550W household refrigerator for one hour uses roughly the same amount of energy as 277 ChatGPT queries.
If every person in the USA made a single query to ChatGPT, it would use an estimated 685MWh of energy. To put this into perspective, this amount of energy could power approximately 63 average American homes for an entire year, given that the average USA household consumes about 10.8MWh annually¹.
Each ChatGPT query produces an estimated 4.32 grams of CO₂². This is because powering the data centers that run these queries requires electricity, much of which is still generated from fossil fuels that emit carbon dioxide. Multiplied by millions of queries daily, this results in significant carbon emissions. For instance, just one day of everyone in the US making a single query could emit around 1479 metric tons of CO₂ — roughly equivalent to the annual emissions of about 322 average gasoline cars³, or the same carbon footprint as 1,500 people flying from London to New York and back⁴.
The global number of AI users reached approximately one billion⁵ in the first half of 2026, nearly tripling from 378 million⁶ users in the first half of 2025. This represents an increase of nearly 622 million users year over year. As AI adoption grows, optimizing energy efficiency and carbon impact becomes increasingly critical.
ChatGPT’s estimated energy consumption per simple query varies across studies, ranging from 0.3 watt-hours (Wh) (Epoch AI⁷, 2025) to around 3Wh (3Wh — Alex de Vries⁸, 2023; 2.9Wh — BestBrokers/EPRI⁹, 2024). These differences reflect variations in model size, hardware efficiency, and measurement methods. This variation highlights both ongoing improvements in AI infrastructure and the complexity of accurately measuring AI energy use. For this study, we used an average of 2Wh per ChatGPT query. Comparing the 2Wh energy use per ChatGPT query with Google Search shows that ChatGPT is nearly seven times more energy-demanding than Google Search (2 Wh vs. 0.3 Wh¹⁰).

Methodology and sources

The energy consumption estimates per ChatGPT query were compiled from multiple recent studies published between 2023 and 2025. Estimates derive from lifecycle assessments and hardware efficiency models, not direct measurements, due to limited transparency from AI companies. The low estimate of 0.3 watt-hours (Wh) per query comes from Epoch AI’s⁷ 2025 analysis, reflecting improvements in model optimization and infrastructure. The higher estimate of 3Wh per query is based on earlier work by Alex de Vries⁸ (2023) and corroborated by measurements from the Electric Power Research Institute (EPRI) and BestBrokers⁹ in 2024 (2.9 Wh). Recent optimizations in GPT-4o reduced energy use to 0.3Wh, whereas older models consumed significantly more due to inefficient hardware. Equally, complex queries with very long inputs may even exceed 3Wh. For this study, we calculated the average from other studies, which resulted in a value of 2Wh.

Carbon emissions per query, estimated at 4.32 grams of CO₂², were derived from lifecycle analyses of data center electricity use, incorporating regional grid carbon intensity averages, assuming a global average grid intensity of 1.44 kg CO₂/kWh (actual emissions vary regionally, e.g., 0.144–9g CO₂/query). EPA⁶ estimates an average gasoline car emits ~4.6 metric tons of CO₂ annually (387 kg/month).

Appliance power ratings were sourced from publicly available manufacturer specifications representing typical household devices. Energy consumption over five minutes was calculated by multiplying power (in watts) by the fraction of an hour (5/60), yielding watt-hours (Wh).

For the complete research material behind this study, visit here.

References:

¹EnergyBot (2025). Average Energy Consumption per Household [2024 U.S Study]

²Smartly.ai (2024). What is the CO2 emission per ChatGPT query?

³EPA United States Environmental Protection Agency (2025). Greenhouse Gas Emissions from a typical passenger vehicle.

⁴The Guardian. How your flight emits as much CO2 as many people do in a year.

⁵Demandsage. AI Chatbot Statistics 2026

⁶Edge AI and Vision Alliance (2025). Global AI Adoption to Surge 20%, Exceeding 378 Million Users in 2025

⁷EPOCH AI (2025). How much energy does ChatGPT use?

⁸Alex de Vries (2023). The growing energy footprint of artificial intelligence

⁹BestBrokers (2025). AI’s Power Demand: Calculating ChatGPT’s electricity consumption for handling over 365 billion user queries every year..

¹⁰RW DIGITAL (2024). How Much Energy Do Google Search and ChatGPT Use?

Edited by Irfan Ahmad.

Read next:

• World Mismanages 52 Million Tons of Plastic Waste per Year

World Mismanages 52 Million Tons of Plastic Waste per Year

2026-07-03T16:52:38.813+05:00

By Katharina Buchholz, Statista

More than 52 million tons of plastic waste remain unmanaged every year around the world and developing countries bear the brunt of the crisis. This is according to a 2024 research article published in the academic journal Nature. This means that an estimated fifth of all municipal plastic waste in the world ends up in the environment or is burned in an uncontrolled manner.

Looking at the data on a per-capita basis, many nations in Sub-Saharan Africa, but also in Central America, the Pacific, Asia and the Middle East let large amount of plastic waste go unmanaged, causing degradation, health hazards and air pollution (in the event of uncontrolled burning). The study found that the lower levels of plastic pollution in the global North were mostly caused by littering, while in developing countries, waste not being collected was the biggest issue.

In absolute terms, India was the biggest emitter of plastic waste identified in the study, with high absolute volumes also set free in Sub-Saharan Africa and Southeast Asia. China, often named as the major emitter in older studies, ranked fourth for absolute volumes, reflecting the progress the country has made, according to the authors. The makers of the report based on their findings suggest a multi-sectoral approach to reducing plastic waste, including reducing plastic use, improving waste collection and better recycling systems.

Reviewed by Irfan Ahmad.

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

Read next:

• 1 in 3 Americans Got Wrong Answers From AI, But 38% Use It as Their Calculator Anyway

• Why turning off screens is so hard for children – and four tips to make it easier

1 in 3 Americans Got Wrong Answers From AI, But 38% Use It as Their Calculator Anyway

2026-07-02T17:43:00.944+05:00

According to an Omni Calculator survey, more than 6 in 10 Americans use AI for calculations, and about 1 in 3 of them say they've gotten a wrong answer from it at some point. Despite that, more than half still trust AI for math, while the other half remains skeptical.

That trust doesn't run very deep, though. Only 2 in 10 users trust AI "completely," meaning they expect it to be right 90-100% of the time. Nearly half, 46%, only trust it in the 60-90% range, and 34% trust it just slightly or not at all.

Why People Don't Trust It

People doubt AI calculations for several reasons; 57% of respondents said they don't fully trust AI because it can simply make mistakes, 14% pointed to privacy concerns, and 13% can’t trust it simply because they do not understand how AI arrives at its answers in the first place. The other 30% are worried that leaning on it too much will make them worse at math themselves.

What's interesting is that not everyone fits neatly into the "trust it" or "don't trust it" camps. In the same survey, 28% of people who were asked why they distrust AI answered that they actually don't, at least not when it comes to calculations specifically. So even people who are wary of AI in general seem willing to make an exception for math.

Younger People Fear Losing Their Skills Over AI

There's a real generation gap here, which was predictable. Gen Z uses AI for calculations more than others; 73% compared to 63% of Millennials, 58% of Gen X, and 55% of Boomers.

The second most common reason for not trusting AI with calculation for younger generations was their fear of losing their own calculation skills; 46% of Gen Z and 33% of Millennials compared to 20% of Gen X and Boomers. The learning angle makes the gap even clearer. 54% of Gen Z said they use AI because it explains the steps behind a problem, versus only 14% of Boomers.

For Gen Z, AI functions almost like a tutor sitting next to them. For Boomers, it's more of a specialized tool they reach for occasionally, and when they do, they seem to trust it more than younger users do.

What People Actually Use AI For

A lot of the reported use isn't about getting a fast answer so much as checking one. Several respondents said they use AI to verify math they've already done by hand, which says something about the level of trust here: enough to use the tool, not quite enough to fully rely on it. As one respondent to the survey put it: "It can check my work."

AI also gets used for things a regular calculator was never built to handle, like working through word problems or adding context around numbers. Some respondents mentioned using it to think through spending, debt, or interest, since it can walk through the reasoning in a way a plain calculator can't: “I calculate specific things... such as spending/earning, and it gives me more context on those than calculators.” A handful of people also brought up simple conversions, like currency or metric to imperial, saying AI is often quicker than hunting down the right tool.

Even with all that, 38% of Americans now say AI tools are what they use most for calculations, edging out traditional calculators (37%), online calculators (13%), spreadsheets (10%), and pen and paper (2%). Age still shapes which tool people reach for. Gen Z (48%) is about twice as likely as Boomers (22%) to use conversational AI tools like ChatGPT or Copilot, while Boomers lean toward specialized online calculators for things like taxes or mortgages, using them roughly three times as often as Millennials or Gen Z.

Why AI Still Gets Math Wrong

This is really the part that explains everything above it. Omni Calculator's ORCA benchmark looked at what they call the instability metric, which tracks how often an AI gives a different answer when asked the exact same question twice, even when the original answer was wrong to begin with.

That instability shows up in three ways: a wrong answer turns into a different wrong answer, a correct answer flips to wrong, or a wrong answer happens to land on the right one. In testing, ChatGPT changed its answer 65% of the time when asked to redo a mistake, and the new answer was still often incorrect. DeepSeek was the least stable of the group, changing its output 69% of the time, while Gemini and Grok came in at 46% and 55%.

The reason comes down to how these systems actually work. A regular calculator follows fixed rules, so the same input always produces the same output. AI models, on the other hand, are predicting the next likely word rather than performing a calculation the way a calculator does, which means the answer can shift even when nothing about the question changed.

What This Means Going Forward

None of this means AI is useless for math, but it does mean the "just ask AI" instinct needs a bit of a check. Using it to understand the steps of a problem, the way over half of Gen Z already does, is a reasonable habit. Treating whatever number it spits out as final is not, especially since a "corrected" answer isn't automatically the right one; 65% of the time, ChatGPT's corrected answers were still wrong.

For anything involving real money, taxes, a mortgage, or retirement planning, it's still safer to use a dedicated calculator than a conversational AI model, particularly ones like DeepSeek or Grok that showed instability rates as high as 69% in testing. Right now, people are adopting AI for math faster than they're learning to actually trust it, and until these tools can match the consistency of a regular calculator, they're better treated as a second opinion than a first one.

Methodology

This article is based on a survey done by Omni Calculator of 1,014 U.S. adults in 2026, representative across age groups and regions. Respondents were asked about their use of AI for calculations, their trust in AI, their reasons for using or avoiding it, and their experiences with incorrect results. Data was analyzed by age and region, and statistical significance was checked using the Chi-squared test. Results were also compared against Omni's ORCA benchmark to add context around AI accuracy.

Author bio: Reyhaneh Mansouri is a research writer and digital PR specialist at Omni Calculator, where she turns data into stories that help people and journalists. She uses her experience as an academic researcher to create original studies. Email contact: rey.mansouri@omnicalculator.com.

Editor's note: This guest article reflects the author's analysis and interpretations and is based on information supplied by the author.

Edited by Irfan Ahmad.

Read next:

• Google's AI Search Has Struggled With One Caliph Answer for Years

• Why turning off screens is so hard for children – and four tips to make it easier

• Is Your Government or Organization Ready to Prevent AI Cyber Attacks—at Scale?

Why turning off screens is so hard for children – and four tips to make it easier

2026-07-02T16:09:14.938+05:00

Steven Howard, University of Oxford

Image: Annie Spratt - Unsplash

The challenges and consequences surrounding children’s screen use are a leading concern for UK families.

One especially difficult challenge is the resistance, arguments and emotional battles – “techno-tantrums” – that can follow when parents ask children to end a screen-based activity or refuse children’s requests to start one. Most families with young children experience these at least occasionally and, for some, frequently.

It’s especially hard for children to move away from a screen because digital content is designed to be engaging. Content developers’ monetisation models usually require this to justify the expense to develop that content. At a minimum, this typically involves using bright colours and fast-paced content with quick and unpredictable transitions, as well as other cues to capture and hold attention. Algorithms ensure you mostly see what would be most interesting to you.

More contentiously, many examples of digital content for children include features that, in the most generous interpretation, persuade children to make choices they might not otherwise have made and may not be in their interests.

Examples include a beloved character encouraging the child to select a particular in-app option, exploiting the child’s emotional attachment with that character. Limited-time pay-to-play offers are presented to compel a quick decision, when slower deliberation may have led to a different choice.

A consequence is that children’s digital activity no longer unfolds solely based on their preferences and interests. Instead, digital design features aim to capture and hold children’s attention, motivate certain decisions over others, and, after digital activity has ended, hasten their return.

This battle between the gratification of continuing screen use or putting down the device when it is required or in one’s best interest is not unique to children. Parents report high rates of technoference – when their own digital activity impedes, interrupts or worsens interactions with their child. Children have even less ability to resist these lures and tactics to engage and sustain their attention.

This does not mean families should resign to a losing battle, or that all digital activity is inherently bad. There are things we can do to help, and at the same time establish foundations for children to better self-regulate their own digital activity and prepare for the digital demands in their future.

1. Give slower-paced options

Provide children with digital options that have slower paced, educational content and more active engagement. Be wary of apps, platforms and programs high in persuasive design features. This will ensure children engage with digital content better aligned in content, pace and design to their developmental needs. Some reputable sources offer expert reviews and ratings of digital content for children.

2. Join in with children’s screen use

One of the largest reviews of research to date found a small association between children’s screen time and negative outcomes. Notably, though, some effects flipped positive when adults were engaged alongside children. Using a screen together helps families understand what their children are viewing and doing on digital devices. It also allows for shared interests, experiences and conversations during and after digital activity.

3. Be careful using screens as a distraction

Try to limit giving children digital devices to keep them occupied or calm them down. Children’s ability to deal with boredom, frustration, excitement or other emotions at times that might be especially undesirable to parents – in a doctor’s office, at a restaurant or on a long car ride – requires that they encounter and learn to manage these emotions. Children won’t learn to manage challenges they haven’t encountered.

4. Establish a routine

Set clear and shared expectations of your child’s digital activity ahead of time and apply these consistently. This might be set periods in which digital activity is more commonly permissible (such as a family movie night), a restricted range of digital options from which they can choose, and a consistent location and duration of play.

Have a plan and let the child know what comes next. Having even a short yet fun transitional activity can help: an obstacle course, a game or asking your child questions about their interests related to the digital content.

Provide warnings in the lead-up to transition. Give them one or two warnings before the end of the digital play, reminding them what will follow next: “In five minutes we will turn this off and get ready for the park.”

Where possible, give children some control over the transition. No one likes having to stop before (what feels like) an activity’s natural completion. Children might be given some control over this by being given the responsibility to turn off the device when time is up or having a say in what follows next.

New routines are rarely immediately effective or without growing pains as families adjust. However, consistent use of approaches like these are reported to help children’s transitions away from digital devices, while also creating the conditions for beneficial digital engagement.

Steven Howard, Senior Academic Research Leader in Child Development and Education, University of Oxford

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

Reviewed by Irfan Ahmad.

Read next: Many Teenagers Show Symptoms of Excessive Screen Use

Web Directories Are Quietly Becoming AI Citation Sources, and the Data Backs It Up

2026-07-02T15:34:57.286+05:00

AI assistants handle tens of billions of interactions each month globally, and every answer depends on cited web sources. When ChatGPT or Google's AI Overviews explain what a company does or which sites belong in a category, they pull from pages that state facts plainly and have stated them for years. Human-edited web directories, a format most marketers wrote off a decade ago, fit that description almost perfectly.

DirJournal is a useful case study. The directory has operated continuously since 2007, survived the collapse of the format, and now appears as a cited source in ChatGPT, Perplexity, and Grok answers about web directories and business listings.

What Is DirJournal

DirJournal is a human-edited web directory founded in 2007 and operated by DSS Media. It organizes more than 30,000 websites into topical categories covering business, technology, health, travel, and regional listings, and every submission passes through manual editorial review before publication.

That editorial layer is the point. Directories that accepted anything died with the link-scheme era, while the small group that kept human reviewers now holds something AI systems value: curated, structured, machine-readable data about which websites belong to which categories.

Why Do AI Systems Cite Directories at All?

Large language models and AI search features rank sources on consistency, structure, and longevity rather than backlink counts alone. A directory page that has described a category the same way for 15 years is a low-risk citation compared with an affiliate roundup rewritten every quarter.

Directory pages also map cleanly onto question-shaped queries. When a user asks an AI assistant to list reputable sites in a niche, a curated category page is the closest thing to a pre-written answer that exists on the open web.

What Does DirJournal's Citation Footprint Look Like?

Brand Radar data from Ahrefs, pulled in mid-2026, shows DirJournal appearing in AI-generated answers across the major assistants:

Metric	Figure
Founded	2007
Operator	DSS Media
Ahrefs Domain Rating	56
Live referring domains	1,658
Total backlinks	3.28 million
ChatGPT answer citations	251
Perplexity answer citations	46
Grok answer citations	44

The citation numbers matter more than the backlink numbers. A domain rating of 56 puts DirJournal in respectable but unremarkable territory for SEO, yet its citation count in ChatGPT answers exceeds that of many DR 80+ publishers, because assistants reward entity clarity over raw authority.|

How Did a 2007 Directory Survive to 2026?

DMOZ, the largest human-edited directory ever built, shut down in March 2017 and took most of the format's credibility with it. The directories that remained split into two groups: paid-link farms that Google eventually deindexed, and a handful of edited properties that kept publishing.

DirJournal took the second path and then repositioned. Over the past two years the site rebuilt on a modern stack, added structured data across every category and listing page, and shifted its editorial content toward answer-engine optimization, the practice of formatting pages so AI systems can extract and attribute facts from them.

Founder Hasan Saleem, who has written on search and digital business for Entrepreneur, Forbes, and Fast Company, describes the shift as a return to the directory's original job. The web needed human curation in 2007 because search engines were easy to spam. It needs human curation in 2026 because AI training data is easy to pollute.

What Should Marketers Take From This?

The practical lesson is that AI citation share and search ranking are separate games with overlapping rules. Pages win citations by stating verifiable facts in self-contained sentences, holding those facts stable over time, and matching their structured data to their visible copy.

Directories happen to do all of this by design, which explains their reappearance in AI answers years after they stopped mattering for rankings. For brands, a listing in an edited directory now functions less like a backlink and more like a record in the reference layer that AI assistants consult.

The directory format did not come back. The way machines read the web simply caught up with it.

Image: Ben Spray via Unsplash

About Author: Hasan Saleem is the founder of DirJournal and DSS Media. His writing on search and digital business has appeared in Entrepreneur, Forbes, and Fast Company.

Note: Views expressed are solely those of the guest author.

Read next: Is Your Government or Organization Ready to Prevent AI Cyber Attacks—at Scale?

Is Your Government or Organization Ready to Prevent AI Cyber Attacks—at Scale?

2026-07-02T14:57:26.794+05:00

By: Frances Zelazny, General Manager, New Market Initiatives at Prove

Image: Image: Lilartsy - Unsplash

The Five Eyes intelligence alliance between the U.S., U.K., Canada, Australia, and New Zealand recently issued a rare joint statement: the potential for devastating, AI-powered cyberattacks is months (not years) away. The Five Eyes statement comes shortly after the U.S. government temporarily restricted access to Anthropic’s Fable 5 and Mythos 5 models following a jailbreak that exposed access to offensive cybersecurity capabilities. Although those restrictions were lifted on July 1, the incident highlighted how quickly AI security concerns are becoming a matter of national importance.

The scale of cyberattacks has changed, and the stakes have never been higher. Identity represents the single greatest point of leverage. Knowing who and what is accessing your systems, continuously and verifiably, is the main factor in preventing an AI-powered attack or potentially leading to a serious breach.

AI-Powered Cyber Threats Create Outsized Concerns

The timing between the Anthropic news and the Five Eyes statement are no coincidence. Let’s examine the Anthropic issue a little further. Though the U.S. government initially ordered access control based on nationality, that approach wasn’t something Anthropic could achieve because there is no way to ascertain that for most Americans who don’t hold a U.S. passport, let alone others from around the world. Since the AI company couldn’t enforce restrictions selectively, access to Fable 5 and Mythos 5 was temporarily suspended before being restored on July 1.

As the Five Eyes wrote: “Cyber risk can no longer be treated as a purely technical issue. This is a core business risk and leadership responsibility.” I’ve been singing the same tune for a long time. This isn’t about compliance or crossing off the items on a basic checklist. It’s time for governments and businesses the world over to recognize what this caliber of cyber risk represents: we must figure out how to manage identity and its far-reaching effects.

The Time to Shore Up Security is Now

Despite risk, the vast majority of governments and organizations have continued to take their chances on cybersecurity methods that no longer fit the bill. Bad actors’ methods evolve, as should our approaches to identity management. First, it’s time to eliminate phishable credentials from your authentication stack: passwords, OTPs, and push notifications are now AI-friendly attack surfaces. Second, it’s time for a layered approach, for example, privacy-preserving biometrics bound to trusted devices augmented by intelligence and dynamic signals for ongoing, verifiable identification at scale.

It’s also time to get serious about non-human identities. They now vastly outnumber human identities, and the rapid rise of agentic AI is transforming them from passive, deterministic processes into autonomous digital actors capable of making decisions and initiating actions at machine speed.

Every AI agent that operates in your environment requires a governance framework that can verify who authorized it, what it’s permitted to do, and whether it’s still operating within that scope. Such agents should also have bound tokens that can be audited and traced back to a human. We also need to establish lines of accountability as an industry. Who is accountable when an AI agent acts on your behalf? And how do you govern an identity that can replicate, reason, and act independently, often without human oversight?

The greatest concern is that our industry conversations surrounding agentic identity governance and verifiable credential ecosystems have very little to do with what’s actually being deployed in the outside world.

Sometimes the Threat Is Already in the Building

If you can’t continuously verify the identity of the humans and machines that touch your infrastructure, the rest of your efforts are the equivalent of securing the perimeter against an adversary who’s already entered the building. Christina Chapman, an American woman, was sentenced to more than eight years in federal prison for helping North Korean IT workers gain employment at more than 300 U.S. organizations, including government agencies, using the stolen identities of 68 Americans. The Justice Department called it the largest identity-theft case of its kind. Since then, the problem has only escalated. CrowdStrike's 2025 Threat Hunting Report identified more than 320 incidents over the past 12 months, a 220% year-over-year rise, through Famous Chollina alone, in which North Koreans gained fraudulent employment at Western companies working remotely as developers.

These criminals didn't break through firewalls. Instead, they walked right through the proverbial front door via hiring processes that relied on resume screening, video calls, and other forms of verification that can be easily defeated. They also used generative AI to forge thousands of synthetic identities, alter photos, mask their appearances during video interviews, and answer technical coding questions in real time.

However, bad actors live everywhere; this is about a lot more than North Korea. And the window to build the right foundation is narrowing fast. The world still largely runs on passwords, SMS codes, and so-called secret questions about concerts and maiden names. Not only are these not secure, but they also were not designed for AI-powered threats. The scary part is that in some cases we are legally mandating them, even though our own standards bodies have deemed them insecure. As we usher in the near future, this reality should be regarded for what it is: a five-alarm crisis.

The Circle of Identity Way, Continuous and Verifiable

With more than 30 years of witnessing urgency, breakthroughs, and brilliant standards accompanied by complacency, slow adoption, and partial implementation, I keep coming back to the same fundamental truth: we must maintain persistent identity across the user lifecycle in every service channel. This means threading humans through enrollment, device registration, authentication, and account recovery, whether on the phone, in person, online, or via a chat or agent. I call this the Circle of Identity.

The concept is simple: Circle of Identity assures a continuous relationship between a person and the institutions, platforms, and systems that need to verify who they are, across many interactions, over the course of their relationship. This distinction matters because most attacks happen in the gaps between verification events.

Today, those gaps are everywhere. A customer may be verified when opening an account, but when they replace a device, call a service center, or recover their credentials, organizations often fall back on passwords, knowledge-based questions, or information that is already available on the dark web. The original verification and subsequent interactions are rarely connected, creating opportunities for fraudsters to exploit.

A closed Circle of Identity operates very differently. When a foundational biometric-anchored identity is established at enrollment, that verification becomes the persistent reference point for every subsequent interaction. Device provisioning, account recovery, step-up authentication, and high-risk transactions all trace back to that original verification, preserving continuity and dramatically reducing opportunities for account takeover and impersonation.

This foundation is particularly important as organizations embrace agentic AI and digital credentials. These technologies represent the future of digital trust, but they depend on strong identity assurance at the human level. You can’t build a reliable credential ecosystem if individuals can obtain multiple credentials under different identities. You can’t govern AI agents without confidently verifying the humans who authorize and oversee them.

The Five Eyes alliance warned organizations to act now and be prepared for AI-enabled cyber threats. I’ve been saying the same thing with less authority, but the same urgency, for a long time. As technology continues to evolve, the principle remains unchanged: trust begins with knowing, continuously and verifiably, who is on the other side of every interaction.

Will we transform our identity management strategies before it’s too late, or are we willing to risk it all?

About author: Frances Zelazny is the General Manager of New Market Initiatives at Prove. She leads the development and commercialization of Prove’s new privacy-preserving biometric and KYC compliance solutions.

Reviewed by Irfan Ahmad.

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• AI can be a personal trainer in your pocket – but is it safe?

• Many Teenagers Show Symptoms of Excessive Screen Use

Many Teenagers Show Symptoms of Excessive Screen Use

2026-07-01T19:01:37.474+05:00

By Felix Richter - Statista

While much of the debate around young people’s digital habits focuses on social media, screen use extends far beyond individual platforms. Between schoolwork, communication and entertainment, screens have become a near-constant presence in teenagers’ daily lives, making it increasingly difficult to separate between productive and problematic device use.

Data from a recent Eurobarometer survey suggests that this constant exposure is taking a toll. On average, EU teenagers report spending 4.5 hours per day in front of screens on weekdays and more than six hours on weekends. Many also report symptoms commonly associated with excessive screen use, including tired eyes, headaches, difficulty concentrating and sleep problems.

The findings highlight that concerns about young people’s digital wellbeing are not limited to social media alone. Instead, they point to a broader challenge: how to manage the overall volume and intensity of screen time in a way that supports, rather than undermines, health and everyday functioning.

Interestingly, 40 percent of the surveyed adolescents still see screens as a net positive for the lives of young people, versus just 29 percent who think that they have a negative impact. Among parents, screens are seen much more critically: 51 percent think that screens have a negative impact on young people, while just 17 percent think that the positives outweigh the problems.

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

Reviewed by Irfan Ahmad.

Read next: AI can be a personal trainer in your pocket – but is it safe?

AI can be a personal trainer in your pocket – but is it safe?

2026-07-01T16:34:28.759+05:00

Hunter Bennett, Adelaide University

Image: Kobe Clata - Unsplash

Generative artificial intelligence (AI) is changing the fitness industry: people can now ask chatbots to write marathon plans, build gym programs and even adjust workouts based on sleep or heart rate data.

For many, AI feels like the future of fitness coaching because it is fast, cheap and readily available.

But while AI can be helpful, research suggests it still has limitations, especially when compared with experienced human coaches.

So, let’s look at how it all works and the pros and cons.

Why are people using AI for training?

There is very little research examining exactly why people use AI for exercise programs, but researchers have offered some potential explanations.

Firstly, accessibility and cost: a chatbot can create a strength or running program in seconds without you having to wait for an appointment with an exercise professional. Not to mention these can be generated for free.

Secondly, availability. There is some research indicating people appreciate rapid feedback in real-time from AI tools. For example, you could ask an AI tool how to change an exercise due to knee pain and get a response in seconds. However, if you are following a program prescribed by a human coach, you may need to wait a day or two before discussing the issue and receiving feedback.

What are the benefits and risks?

There is a growing body of research looking at the suitability of AI-generated exercise programs across a host of contexts.

One study had ChatGPT design an individualised exercise program for five made-up people, which were then evaluated by a team of experts. They concluded the AI tool could provide safe, basic exercise recommendations, but may not provide enough adaptability to ensure long-term progress.

Similarly, another study had expert running coaches assess AI-generated running programs. They thought the exercise programs were suitable for novices but not great for trained athletes.

The effectiveness of these programs appear to be highly dependent on the level of information provided. In short, the more context you can provide regarding your current capabilities, goals and fitness level, the better the exercise program will be.

However, providing such detailed prompts requires a degree of content-specific knowledge that many people don’t have. This may make AI tools less useful to the average person.

Finally, it is not clear whether AI systems can fully account for injuries or medical conditions. Health screening is important to keep people safe before exercising and something all exercise professionals should do before writing you a program.

If this is being missed, there is the potential for an AI-generated exercise program to be unsafe for your current level of health.

Are human trainers better?

There is a small body of research comparing AI-generated exercise programs to human generated programs and the results are interesting.

One recent study randomly allocated people to one of two groups: a 12-week weight training program under the guidance of ChatGPT or a 12-week program under the guidance of a personal trainer.

There were larger increases in muscle size and strength in the personal trainer group.

Another compared a five-week AI generated fitness program to a five-week human-generated program. It found the human-generated program led to slightly greater increases in fitness and endurance than the AI program.

Finally, a third study compared a ten-week AI generated athletic performance program against a ten-week human generated program on measures of jump performance in volleyball athletes. They found the human program led to slightly greater improvements in jump distance but the same improvements in jump height.

Collectively, these studies suggest that while AI-generated exercise programs can improve your fitness, they might be slightly less effective than programs created by human experts. This may be due to their inability to provide real-time feedback and motivation.

However, it is also important to note these studies were all published in relatively low-quality journals and had some limitations. So, their findings should be interpreted with caution.

What should you watch out for?

If you choose to use AI, there are some key things to keep in mind:

treat AI-generated programs as a starting point. Use them to organise your training, but keep in mind you might need to modify the plan if it feels unrealistic or inappropriate
avoid increasing training volume or intensity too quickly. Sudden jumps in running distance or lifting intensity can increase injury risk, and this may not be factored into AI generated programs
if you are completely new to a gym environment, you may want to spend a couple of sessions with a human trainer to familiarise yourself with good technique before starting your AI-generated program
if you are looking to achieve high levels of performance, you might need to consider a human coach to maximise your progress
be extra cautious if you have injuries, a chronic disease, or complex goals. Current AI tools may not be able to personalise your program perfectly and it might be safest to see a professional.

Hunter Bennett, Lecturer in Exercise Science, Adelaide University

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

Reviewed by Irfan Ahmad.

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Teens Encounter a Myriad of Problematic Content Online

2026-07-01T00:11:18.139+05:00

By Felix Richter, Statista

Established in 2010 by Mashable – a leading website for online culture and tech news at the time – Social Media Day is celebrated annually on June 30 to recognize how social platforms have reshaped the way people connect and communicate across the globe. What began as a celebration of social media’s connecting power has also become a good opportunity to reflect more critically on the role these platforms play in everyday life.

This is particularly relevant when it comes to younger users. As social media has become nearly ubiquitous among children and teenagers, concerns about its impact, and calls for stricter regulation, are growing louder. While platforms like TikTok, Instagram and Snapchat are central to how young people socialize, they are also at the center of an ongoing debate: how to balance the benefits of digital connection with the risks that come with it?

According to a recent Euromonitor survey conducted on behalf of the European Commission, the risks young people face online come in many forms. From misinformation and AI-generated content to exposure to sexual or violent material and the promotion of unhealthy products, lifestyles or body images – teenagers are navigating a digital environment filled with content that most parents would try to keep them away from in offline settings. Yet, many turn a blind eye to the things happening online.

“Social media can connect and inspire. But when one in three young people say it leaves them feeling stressed, sad or excluded, we cannot ignore the impact on their mental health and wellbeing,” European Commission President Ursula von der Leyen said in a statement released alongside the survey results. “And when a quarter of our young people are confronted with problematic content online, it is a clear signal that it is time for change.”

In response, policymakers in the EU and elsewhere are exploring stricter safeguards. Last year, the European Parliament proposed a minimum age of 16 for access to social media, video-sharing platforms and AI chatbots, while the EU is working on a bloc-wide age verification system.

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

Reviewed by Irfan Ahmad.

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• WhatsApp Opens Username Reservations Ahead of Feature Launch

• What 20 million bans reveal about the strain on Wikipedia’s volunteers

WhatsApp Opens Username Reservations Ahead of Feature Launch

2026-06-30T15:57:17.098+05:00

Meta-owned WhatsApp announced Monday that users can begin reserving optional usernames ahead of the feature's launch later this year. The company said reservations are opening this week to give users an opportunity to secure a preferred username before the broader rollout.

In a post published on the WhatsApp Blog, the company said it is opening reservations early because its more than three billion users mean many names overlap, giving people an opportunity to reserve the username that matters to them.

Image: Whatsapp

According to WhatsApp, usernames are intended to let people communicate without sharing their phone numbers. Once the feature launches, users who enable a username will no longer have their phone number shown when messaging a person or business for the first time.

For creators, small businesses and organizations, WhatsApp said it has provided an option to claim an existing Instagram or Facebook username for use on WhatsApp.

Image: Whatsapp

Users with the latest version of WhatsApp can reserve an optional username by going to Settings > Account > Username. The company said usernames will roll out gradually over the coming months, and users will receive an in-app notification when the feature becomes available in their country.

In a separate development, WABetaInfo reported Tuesday that WhatsApp is developing a feature for Android that would allow users to link additional devices using a passkey as an alternative method alongside QR code-based device linking. According to the publication, the feature remains under development, is not yet available for beta testing, and WhatsApp has not announced a timeline for its release.

Reviewed by Irfan Ahmad.

Read next: What 20 million bans reveal about the strain on Wikipedia’s volunteers

What 20 million bans reveal about the strain on Wikipedia’s volunteers

2026-06-29T16:20:32.294+05:00

Ryan McGrady, UMass Amherst

Image: DIW - CC BY-SA

This year, Wikipedia is celebrating 25 years as the internet’s encyclopedia that anyone can edit. In its first decade, the quirky experiment for passionate nerds exploded in popularity. It became a ubiquitous information resource and a homework helper for schoolkids, much to the dismay of skeptical teachers.

In its second decade, amid the public’s growing dissatisfaction with the mangling of facts in popular discourse, it took on a new role as information infrastructure, helping categorize and validate information worldwide. Wired magazine deemed it “the last best place on the internet.” The hope was that the volunteer project could serve as the antidote for misinformation. Platforms from Facebook and Twitter to Alexa and YouTube began embedding Wikipedia material to ensure that users had context for what they read or saw.

That role has become more acute in recent years. Artificial intelligence developers have relied deeply on Wikipedia to train the large language models behind popular chatbots, which weight clean, reasonably reliable information sources more heavily than the rest of the web. Chatbots and AI-powered search engines have intensified Wikipedia’s significance, even as they siphon its readers by answering questions directly, with fewer people going to the source site itself.

But as Wikipedia’s importance – and size – has grown, the size of the volunteer corps that maintains it has not, and the number of volunteer administrators, a key moderation role, has shrunk.

I’m a researcher who studies social media platforms. I analyzed two decades of the site’s moderation records to understand the effect of these conditions. I found changes in behavior that appear to prioritize content quality while weakening the project’s ability to recruit and retain new volunteers.

Under pressure

As Wikipedia has become more prominent, its resistance to top-down control has made it a target for people who have political or financial power. There is frequent news about takedown demands and censorship abroad, investigations and threats to its nonprofit status in the U.S., and, outside the U.S., volunteers have been arrested and imprisoned.

The Wikipedia community is also sensitive to its rising importance, but not in the way you might think. Contributors are keenly aware of political rhetoric that takes aim at their project or threatens volunteers. But the chief effect on volunteers has been a sense of heightened obligation to their global readership, which has gradually increased quality standards.

As a longtime volunteer myself, I’m often taken by the community’s perseverance and the people’s desire, above all, to get on with their work of summarizing the world’s knowledge.

Wikipedia’s rich history busts myths that have risen with it.

The English language Wikipedia has maintained a reasonably steady number of contributors since 2010 – about 40,000 – yet its size and importance have grown. In 2006, it contained 1 million articles; in May 2025, it passed 7 million. A new issue is an influx of low-quality content generated by large language models.

The steady decrease in administrators is especially concerning. Administrators are a subset of trusted users, elected by the community at large, who are given powers such as the ability to delete articles or block users from editing. Unlike moderators at for-profit platforms, Wikipedia cannot simply hire more administrators. There are slightly more than 800, down from almost 1,800 in 2011, and they’re not all active.

So Wikipedia’s role has grown, but it is held together by a relatively small, shrinking community of unpaid volunteers. To keep up, the community in general and administrators in particular have had to raise their efficiency, making trade-offs between maintaining open participation and raising article quality. These trends and their costs are well documented. They are clearly visible in one of the basic administrator routines: blocking.

Shown the door

Blocking is when an administrator determines that a user is so detrimental to the project that they must be prevented from making any further edits. The blocked user can still read Wikipedia, but cannot change it.

Unlike the opaque moderation systems at the large internet platforms that I normally study as a researcher, such as YouTube or TikTok, nearly every administrative action on Wikipedia is recorded in a public log. I used these logs for a study analyzing all 20 million blocks made on the English language Wikipedia over the past two decades. I looked for patterns in frequency, duration and reasons for a block. I also assessed whether those patterns corresponded to the growing trade-offs between openness and quality.

I found that the frequency of blocks has risen sharply in recent years due to administrators using bots to preemptively block proxies. Proxies are services such as virtual private networks, or VPNs, that people use to conceal their identity, often to facilitate abuse or manipulation on Wikipedia. One of these bots, ST47ProxyBot, was so active that it accounted for the most blocks in the site’s history. Preemptive proxy blocking likely prevents damage, but it can also occasionally stop good-faith contributors. Given the increasing popularity of AI agents and their disruptive potential, this practice is likely to continue to expand.

I then removed proxy blocks from the analysis so I could focus on humans who were blocked and why. In the early years, administrators made the majority of blocks for vandalism: intentionally bad or nonsensical edits. That has shrunk to about a quarter of all blocks today. Blocks have risen for promotional editing and for sockpuppetry — when one person creates multiple accounts to manipulate content. These shifts speak to Wikipedia’s increased prominence as a target for influence.

Signs of stress

What I found most interesting was administrators’ greater use of generalized reasons for blocking, such as “disruption.” Wikipedia defines disruption as “a pattern of editing that disrupts progress toward improving an article or building the encyclopedia.” But citing this can mean nearly anything seen as counterproductive. The trend is partly explained by “disruption” being in a list of boilerplate rationales that administrators can choose from instead of entering a customized reason.

But it’s also the kind of trend I would expect to see in a labor force stretching to keep up. Administrators don’t act arbitrarily, and their actions are publicly logged and closely scrutinized. A loss of trust leads to an administrator losing their position. But to be effective, general explanations for blocks rely on shared understandings that new users may not have. Research on blocked users shows that when a sanction feels vague or unfair, volunteers are more likely to walk away – or dig their heels in – rather than reform. Good for efficiency; bad for bringing new users into the fold.

Blocks are also lasting longer on average. That, together with preemptive blocking and generalized rationales, suggests that the volunteer community is increasingly prioritizing prevention, efficiency and content quality over efforts to rehabilitate new users.

And the work is not spread evenly among the roughly 800 administrators: For many years, the most active 10% of administrators have made about 80% of the blocks. That high number dropped to 37% in 2024, largely due to changed activity by a single prolific administrator.

Bearing the cost

Wikipedia’s openness is part of how its volunteer community grew in the first place. Now that Wikipedia has become infrastructure, that community is rationing openness to preserve quality for readers. If Cory Doctorow’s zeitgeist-capturing idea of platform “enshittification” is fundamentally about ruining the experience of end users for the sake of the shareholders, Wikipedia is attempting something like the opposite. The end-user experience is being preserved, and the people behind the scenes are bearing the cost.

Wikipedia has adapted remarkably well in its evolution from early web experiment to one of the most important global sources of information. The open question, for a resource that so many humans – and now machines – rely on, is how long the volunteer system can keep enduring the cost.

Ryan McGrady, Senior Research Fellow, Initiative for Digital Public Infrastructure, UMass Amherst

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

Reviewed by Irfan Ahmad.

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• Research Finds Faster Replies Improve Hiring Prospects When They Appear Authentic in Online Marketplaces

Research Finds Faster Replies Improve Hiring Prospects When They Appear Authentic in Online Marketplaces

2026-06-27T17:52:28.562+05:00

Many people worry that responding too quickly to a prospective employer could make them seem too available or even desperate. New research published in Management Science suggests they may be worrying about the wrong thing.

Image: Jonas Leupe - unsplash

Analyzing 11.6 million marketplace interactions and a series of experiments involving both job candidates and service providers, the researchers found no evidence that delaying a response improves hiring prospects. Instead, employers consistently preferred faster responders.

"People have this intuition that playing hard to get is somehow useful," said On Amir, a professor at the UC San Diego Rady School of Management and co-author of the study. "We find the opposite is true."
Speed matters because it signals responsiveness."

The study combined real-world marketplace data from Fiverr, a platform connecting employers with freelancers, with three main experiments involving more than 3,600 participants and five supplemental studies involving another 5,000 participants.

In the Fiverr data, a one-hour delay was associated with a 46% reduction in hiring likelihood, while a full-day delay reduced hiring likelihood by roughly 90%.

The effect persisted even when participants had access to other information, including ratings and the content of a response.

The experiments suggested that faster responders made better first impressions. They were judged to be warmer and more competent – and most importantly, as likely to be more responsive in the future.

People use reply speed, the researchers conclude, to infer what someone might be like to work with.

"Speed is a signal. People see a quick response as a sign that you'll be attentive to their needs in the future, not just right now," said co-author Einav Hart of George Mason University.

Interestingly, the researchers found a gap between what people said about response speed and what they did. Participants reported that same-day responses would be just fine, yet consistently preferred much faster responders when making their actual hiring decisions.

Authenticity matters, too

The researchers caution against reducing the findings to a simple rule about replying as quickly as possible.

"Speed matters because people use it as information," Amir said. "But there isn't an equal sign between speed and responsiveness. Authenticity matters, too."

The researchers found that while response speed influenced hiring decisions, people also paid attention to whether a response appeared personalized and attentive.

That distinction may become increasingly important as AI makes instant responses easier to generate. While automated replies can eliminate delays, they may not convey the thoughtfulness or engagement that people ultimately value. In the experiments, faster replies lost their appeal when recipients believed them to be generated automatically or by an AI.

The takeaway is straightforward: Once someone reaches out, there appears to be little advantage in making them wait. But a quick response is most effective when it is also genuine.

Read the full study, "Speed Is a Signal: When Faster Replies Increase Hiring Likelihood."

In addition to Hart and Amir, other co-authors of the study are Eric VanEpps of Vanderbilt University and Ovul Sezer of Cornell University.

This post was originally published by the University of California San Diego Today and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: NUS research reveals how parenting styles influence children’s honesty

NUS research reveals how parenting styles influence children’s honesty

2026-06-27T17:05:06.259+05:00

By National University of Singapore

Parents who come down hard on their children for telling lies or misbehaving may believe that they are teaching the child right from wrong. But new research by NUS suggests that both overly strict or punitive parenting could be part of what drives the behaviour in the first place.

Drawing on two long-term studies of Singaporean families, researchers from NUS Psychology found that ‘authoritarian parenting’ and ‘harsh punishments’ were associated with greater dishonesty in children across early and middle childhood. The studies suggest that this is not out of defiance, but a way for the children to cope with self-criticism, the pressure to perform and the fear of making mistakes.

The first study, published in the academic journal Child Development, tracked preschoolers and found that those whose fathers were stricter and enforced rules with little explanation were more likely to cheat later on. The researchers observed that these children also tended to be harder on themselves.

The second study, published in Developmental Psychology, followed school-going children over three years and found that children subjected to physical punishment like spanking were more likely to cheat and lie over time.

The studies were led by NUS Psychology’s Associate Professor Ding Xiao Pan and doctoral student Ms Liwen Yu. The second study was also led by Associate Professor Ryan Y. Hong from NUS Psychology.

Authoritarian parenting promotes cheating through self-criticism

The first study examined 479 families who participated in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO*) birth cohort study, one of Singapore’s largest and most comprehensive birth cohort studies.

Researchers assessed parenting styles via a parental questionnaire when children were four and a half years old and measured cheating behaviour a year and a half later using a dart game.

The study found that 61 per cent of children cheated, with strict paternal parenting at age four and a half years significantly predicting this behaviour.

“Authoritarian parenting is characterised by high control, low warmth and harsh discipline without explanation. While parents may believe this approach instils discipline, our research shows it may actually undermine children’s internalisation of moral values,” said Assoc Prof Ding.

Researchers found that children’s self-criticism helped explain this link. Children with stricter and more controlling fathers were more self-critical in a sketching task done as part of the study, which predicted a greater likelihood of cheating.

“Self-critical children may feel intense pressure to maintain a flawless image and cheating becomes a maladaptive coping strategy. It is a way to avoid feelings of inadequacy and secure external validation," Ms Yu explained.

“To our knowledge, this is the first study to investigate the developmental mechanisms linking a discipline-oriented family environment to cheating behaviour,” she noted.

Harsh punishment breeds deception in school-going children

The second study followed 302 Singaporean families with school-going children aged seven to nine years, examining whether negative parental control predicted children’s deceptive behaviours over time.

Negative parental control comprises harsh punishment, discipline and ignoring. Of the three, only harsh punishment, which includes physical punishment like slapping and spanking, was found to increase children’s lying and cheating over time.

Harsh parental punishment at age seven significantly predicted increased deceptive behaviour at age eight, with this pattern continuing into age nine. The relationship also worked both ways: children’s deceptive behaviour at age eight predicted harsher parental punishment at age nine, suggesting a troubling cycle.

The study also identified children’s dysfunctional attitudes, like believing they must do well to be liked, as an important pathway linking harsh punishment to dishonest behaviour.

“Children exposed to higher levels of negative parental control were more likely to internalise dysfunctional beliefs such as ‘I have to do well to be liked’ or ‘I shouldn’t make mistakes’. They may then resort to lying to meet these unrealistic expectations or avoid further punishment,” said Ms Yu.

Cultural context and practical implications

Singapore is a useful setting for the studies because strict, obedience-oriented parenting and physical discipline remain relatively common.

However, even in Singapore, where authoritarian parenting is more culturally accepted, findings suggest it still poses risks for children’s moral development.

“What both studies reveal is that strict parenting doesn’t directly cause dishonesty. Rather, it changes how children see themselves, and it’s this altered self-view that leads to cheating and lying,” said Assoc Prof Hong.

The research team acknowledges that dishonest behaviour in children is multifaceted and influenced by cognitive development, social factors and individual differences. However, these studies provide crucial evidence that parenting practices play a significant role during critical developmental periods.

Ms Yu said, “Understanding these developmental pathways is essential for designing effective interventions. Rather than responding to children’s dishonesty with harsher punishment, which our research shows may actually worsen the problem, parents and educators need to address the underlying psychological mechanisms.”

This post was originally published by the National University of Singapore (NUS). It has been edited for style and length and is republished here with permission.

Image: Jhonatan Saavedra Perales - Unsplash

Reviewed by Irfan Ahmad.

Read next: ‘Alexa, tell me a joke’: how talking to AI impacts young children’s development

‘Alexa, tell me a joke’: how talking to AI impacts young children’s development

2026-06-26T19:10:15.494+05:00

Clara Macarena Ponce Romero, Universidade de Santiago de Compostela

Image: Vitaly Gariev - unsplash

Children are innately curious, and throughout any given day they come up with all manner of questions: Why don’t fish have hair? Why do flowers wilt so quickly? Their need to understand the world – and develop their language skills and ideas – makes them tireless conversationalists.

While their inquiries would usually be directed at parents or teachers, in modern homes even the youngest kids might now talk to a digital interface like Siri or Alexa. These AI systems are fast becoming part of many children’s everyday lives, as kids ask them to play music, help with their homework, answer questions, or just chat to them.

These kinds of interactions are no longer strange, but we need to ask what happens when they become completely routine. Do they change the way children learn to communicate? Do they change the words they use? And are they a threat to kids’ cognitive abilities?

Language learning

Learning to speak has never been a question of just learning words. Children acquire language through human relationships, and by building emotional ties to other people. They learn to take turns, how to interpret silence and context, and how to tell when someone is tired, annoyed or distracted. They also discover that conversations do not need to be perfect – there will always be interruptions, misunderstandings, and off-the-cuff explanations.

But AI does not think like a human. Think about your interactions with ChatGPT or Gemini. We rarely lose our patience while talking to these virtual assistants, partly because these interactions are, by their very nature, governed by a very different logic to human conversation. These tools are built for quick responses and infinite patience, and this changes the experience of communication.

AI and politeness

In many homes, something very curious is becoming increasingly common: some children (and even adults) are adapting their speech so that virtual assistants will understand them better. They speak in simple sentences, and give direct instructions: “play cartoons, open YouTube, tell me a joke”. This kind of speech – known as instrumental language – aims to get immediate results.

This shift does not necessarily mean children are becoming ruder or less empathetic, but it may influence their expectations of conversation in general. Human interactions are usually slow and ambiguous, and require patience, attention and negotiation. Chatbots, on the other hand, are designed to give quick, fluid responses, or even create a sense of virtual empathy with the user.

This all leads us to a question that may seem minor, but reveals a lot: should we teach children to say “please” and “thank you” to Alexa? Beyond the surface-level question of whether we should be polite to machines, this debate forces us to think about the communicative habits that children develop through daily interactions with technology that always obeys them. The wider question for families and educators alike is: what idea of “conversing” will children construct in this context?

Alongside these doubts, we should not lose sight of the opportunities that these systems present. Many children feel freer to ask questions when they do not fear judgement, and a chatbot will repeat an explanation as many times as is necessary, adjust the level of complexity, or support them as they learn new languages or concepts.

These tools provide a safe space for trial and error, free from the social pressures that often accompany human conversation. This is not just the case for children. Many of us now resort to AI to ask ordinary questions, from “Alexa, how do I recover my password?” to more embarrassing queries that we would rather not voice out loud.

Responding is not understanding

Current AI systems produce extremely convincing answers, but they do not understand the world the way a person does. They do not have experiences, emotions, or intentions – even if they talk like they do. Just like many adults, young children tend to attribute human qualities to the things they interact with. If something can converse, it is easy to presume that it also has understanding or knowledge.

However, a lot of the information in human conversation is unspoken. An adult can tell when a child’s question is the product of curiosity, fear, or a simple need for attention. This pragmatic dimension – consisting of gestures, tone, looks, feelings – is crucial for children’s development. It is difficult to replicate in a machine, which can only offer an answer without capturing any of this nuance.

Humans are not machines

When children grow up surrounded by a particular kind of linguistic exchange – one that consists of quick responses and having every single request obeyed – it ends up shaping habits, expectations and ways of interacting. This can lead children to always expect clear, quick, effortless answers, as though any conversation were something to be resolved on the spot.

The adults who live with young children therefore have a vital role to play. They are the ones who mediate daily use of these tools both at home and at school, who understand their limitations, and who are able to integrate these conversations into general learning.

A child asking Alexa to answer their questions or tell them a joke is not, in and of itself, detrimental to their language development. But we should guide these conversations so that they understand they are dealing with a machine that responds to them, not a person.

We need to show children what separates us from machines, how we should interact with them, and in what situations it is alright to use them. We should accompany them in these everyday interactions, commenting on them and helping them to understand AI’s limitations.

AI can be useful as a support, but under no circumstances should it take the place of replace conversation between people. Despite rapid developments in technology, human interaction remains at the heart of the way we exist in the world.

Clara Macarena Ponce Romero, Profesora del área de Didáctica de la Lengua y la Literatura, Universidade de Santiago de Compostela

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

Reviewed by Irfan Ahmad.

Read next:

• Survey finds influencer-driven purchases highest in Brazil, South Africa, India, and China while growing across many markets

• Research Finds People Better Understood Literal Than Figurative Cybersecurity Language

The Influence of Influencers

2026-06-26T18:24:36.337+05:00

By Katharina Buchholz, Statista

Most people trust the opinion of their social circle when making purchasing decisions. As data from Statista Consumer Insights shows, many also trust the friendly people who freely share their lives with us on social media and at least feel like our acquaintances: influencers.

Influencers currently yield the biggest power over people’s purchasing decisions in Brazil, South Africa, China and India, according to the survey which is representative of the countries’ online populations. While influencers' sway has only become larger in Brazil and India, it has recently decreased in China, but stayed on a high level nonetheless.

In most other countries, the trend to follow influencers' lead when deciding on a purchase gained traction. France and Austria as well as Belgium, the Netherlands, Sweden and Japan were among the countries paying influencers little mind, even though their following was growing in these nations also. Among Europeans, Brits were most "under the influence", at 29 percent saying that they had made a purchase because a celebrity or influencer advertised the product.

Reviewed by Irfan Ahmad.

This article was originally published by Statista and has been republished here under the Creative Commons License CC BY-ND 3.0

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• Research Finds People Better Understood Literal Than Figurative Cybersecurity Language

• The Highest-Earning Creators of the Internet Content Machine

Research Finds People Better Understood Literal Than Figurative Cybersecurity Language

2026-06-26T18:16:46.229+05:00

By Flinders University

Cyberattacks now cost the global economy trillions, yet most people still struggle to understand what actually happens when a breach occurs.

Image: freepik

Research by Associate Professor Sky Marsen, an applied linguist and Communications course director at Flinders University, and Professor Robert Biddle, a computer scientist based from Carleton University, Canada, suggests a surprising reason for this gap: the language used to explain cybersecurity may be part of the problem.

In an experimental study comparing “figurative” cybersecurity language (terms such as phishing, virus, or trojan) with more literal explanations, the authors found that people understood incidents significantly better when the language was clearer and less metaphorical.

This challenges a widespread assumption in science communication – that metaphors help non-experts grasp complex ideas. In cybersecurity, the opposite may be true.

“These terms weren’t designed for the public in the first place,” explains Associate Professor Marsen. “They emerged from inside hacker culture, and terms that may sound creative and playful within expert communities, are often opaque to outsiders. When they are used in public communication, they can obscure rather than clarify what’s happening.”

Given the rise of cybersecurity concerns, Associate Professor Marsen says it’s timely to understand how non-experts understand cybersecurity words and metaphors – especially the figurative language created by computer scientists to describe cybersecurity incidents.

A lack of accurate information makes cybersecurity an issue that is difficult to clearly explain to the public – and this can lead to major losses for individuals and serious reputational damage for organizations.

“Organisations routinely tell customers they’ve been hit by phishing or a malware attack, but if people don’t fully understand what that means, they may not know how to respond or protect themselves,” says Associate Professor Marsen. “Worse is that unclear communication can downplay the responsibility of organisations, or leave users vulnerable.”

Using a set of cyberattack stories composed with figurative words and a set composed with more literal versions, and an online survey, the study examines whether the use of metaphor and neologism clarifies or obfuscates the technical aspects of cybersecurity for non-experts.

The results showed participants in the literal set scored significantly better in comprehension. However, participants made important errors in both literal and figurative versions. This underlines the need for organizations to employ language strategically and provide more effective explanations of cybersecurity situations.

Associate Professor Marsen says a key takeaway from this research is that paying attention to language choices in professional communication is not just a stylistic choice but a public safety issue.

The research – “Grok hackspeak? Communicating cybersecurity with figurative language”, by Sky Marsen and Robert Biddle – has been published by the International Journal of Business Communication. https://journals.sagepub.com/doi/10.1177/23294884251329160.

This post was originally published on Flinders University News and republished here with permission.

Reviewed by Irfan Ahmad.

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• 85% of kids are still using social media despite ban. But we need a new measure to judge its success

• Research Shows ChatGPT Improves Home Productivity but Benefits Are Not Shared Equally

85% of kids are still using social media despite ban. But we need a new measure to judge its success

2026-06-26T15:50:24.392+05:00

Samuel Cornell, The University of Queensland

Image: Sixteen Miles Out - unsplash

Six months on from Australia’s under-16s social media ban taking effect, the early verdict from headlines and children themselves has been blunt: it isn’t working.

A new study published today in the British Medical Journal appears to add even more weight to this judgement.

Led by University of Newcastle public health researcher Courtney Barnes, the study found very little evidence that kids had stopped accessing restricted social media platforms such as TikTok, X, Facebook and Instagram.

But the question “are children evading social media age checks?” might be the wrong one to ask when considering the long-term success of Australia’s world-first experiment.

Isolating the effect of the ban

The team behind the new study followed 408 adolescents aged 12–16, surveying them just before the law took effect in December 2025 and again three months later. They compared teenagers just under the age cutoff with those just over it to isolate the law’s effect.

They found more than 85% of under-16s were still using restricted platforms at follow-up, mostly through their own accounts.

Two thirds had encountered age verification, but the most common form was simply being asked to state their age. A minority used fake accounts or private browsing to access social media. But VPN use to evade the ban was rare.

When the researchers checked whether under-16s used social media any less than the just-over-16s who were free to keep their accounts, they found no meaningful gap at the age cutoff.

The researchers were transparent about the study’s limitations. The analysis was underpowered (which means the study may not have had enough participants to detect an effect if one existed). The sample sizes either side of the cutoff were also small.

Nevertheless, these results square with recent research from the eSafety Commissioner that showed roughly 7 in 10 children kept their accounts after the law came into effect.

So, case closed, right? The ban is a failure? Not quite.

An unrealistic pipe dream

It was an unrealistic pipe dream that the ban would stop all of today’s under-16s from using social media overnight. All online technology comes with inherent capacities to be exploited or its features circumvented.

Instead, the ban enables the government to put pressure on social media companies to comply with their directives – to restrain and contain them with greater power than existed before.

The ban should be considered over a longer timeframe. Its logic is more aligned with another form of public health law: the generational approach now being applied to tobacco control.

Britain’s Tobacco and Vapes Act, which received royal assent in April 2026, bars anyone born on or after January 1 2009 from ever being sold tobacco.

The aim is not to make today’s smokers quit but to raise a generation for whom smoking never becomes normal. Australia’s social media law makes a similar bet: that if access is delayed long enough, social media might lose its grip on childhood the way cigarettes slowly did.

That is the measure that matters, and it’s a far slower and less certain test than counting how many teens still have Instagram six months after the ban took effect.

A benighted idea for future generations

Granted, there’s a catch to this framing.

Tobacco use has been denormalised with a public health approach for decades, and its supply has been squeezed from multiple directions: higher prices, plain packaging, advertising bans.

It’s hard to put pressure on social media use in the same way. Effectively, social media is “free”, practically infinite, and engineered to maximise engagement.

Shifting a generation’s social media norms this way only works if the pressure on platforms is relentless and sustained for years, not abandoned the moment the first headlines call it a failure.

My research into social media use and risk-taking found the same difficulties: norms are sticky. Social media rewards risky content and changes what is deemed as normal or acceptable. Changing norms like these overnight is unlikely.

But viewed in the long term, or even generationally, we can see how social media use for children may become a benighted idea for future generations.

Effects not clear for a decade

Naturally, laws that “ban” things often have unintended or even detrimental consequences. When mandatory bicycle helmet laws were introduced in Australia in the early 1990s, one result was that some people simply cycled less.

The new study in the British Medical Journal reflects this, with small numbers of young people turning to fake accounts, private browsing or messaging apps. Some may drift to less visible corners of the internet that are harder to watch than the mainstream platforms.

We shouldn’t take this to mean the ban is a failure. It means we are judging it on a timeline that does not fit its design.

The researchers make the point themselves: the greatest opportunity may lie with children under eight who have not yet started using social media, rather than teenagers whose habits are already set, whose norms are to use social media.

By their estimate, the full effects may not be clear for a decade.

Australia has volunteered to be the world’s test case, with other countries now following. To do the social media age restrictions justice, we should test the right thing.

Samuel Cornell, Honorary Research Fellow in Public Health, The University of Queensland

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

Reviewed by Irfan Ahmad.

Read next: Research Shows ChatGPT Improves Home Productivity but Benefits Are Not Shared Equally

Research Shows ChatGPT Improves Home Productivity but Benefits Are Not Shared Equally

2026-06-25T18:51:31.417+05:00

By USC Marshall School of Business

A new study co-authored by USC Marshall’s Miao “Ben” Zhang shows that the gap between certain groups’ GenAI usage is growing, leaving older and lower-income Americans behind.

Image: Brecht Corbeel - unsplash

Hardly a day goes by without a story about generative AI and what it means for jobs and economic growth. Yet the relentless focus on AI in the workplace may be obscuring something equally important: how the technology is changing the way people get things done at home.

A new working paper by Miao “Ben” Zhang, assistant professor of finance and business economics at USC Marshall School of Business, along with co-authors Michael Blank of Stanford Graduate School of Business and Gregor Schubert of UCLA’s Anderson School of Management, is among the first studies to examine this largely overlooked dimension of the AI revolution. Using Internet browsing data from more than 200,000 U.S. households tracked between 2021 and 2024, the researchers find compelling evidence that the home — not the office — is currently where generative AI is delivering its largest productivity gains.

The study focuses on ChatGPT adoption and its effect on what the researchers call “productive” digital tasks — activities undertaken online that are not purely for enjoyment, such as searching for a job, planning travel, or managing household finances. Their findings show that ChatGPT users completed these tasks between 76% and 176% more efficiently than non-users. The saved time did not, however, flow into education or skill-building. Instead, users spent their newfound hours on leisure: posting to social media, streaming videos, and other recreational activities.

“The large efficiency gains might sound surprising at first — but they actually make a lot of sense,” Zhang said. “Unlike at work, people doing tasks at home aren’t trained professionals. They’re jumping between websites for various tasks, piecing things together. ChatGPT can replace all of that with a single conversation.”

Alongside those productivity gains, the researchers identify a troubling trend: a widening gap in who is actually using these tools. Younger and wealthier Americans have been far quicker to adopt generative AI than their older and lower-income counterparts. More concerning still, the researchers’ data suggest that this “GenAI digital divide” is not closing; it is widening.

That disparity carries real economic stakes. Much of the public optimism surrounding generative AI rests on the idea that tools like ChatGPT can function as a great equalizer, a “PhD in everyone's pocket” that gives lower-skilled workers access to capabilities once reserved for the highly educated. But that promise only holds if lower-income individuals are actually using these tools. The study’s evidence suggests that many are not, which may mean they are missing out on the potential economic benefits of AI-assisted job searching, financial planning, or skills development.

The researchers emphasize that one of the central hopes for generative AI has been its potential to extend capabilities across the socioeconomic distribution, particularly for those at the lower end. Their findings, however, suggest that the people who stand to benefit most are currently the slowest to adopt these tools.

The slow uptake among older and lower-income Americans is notable precisely because awareness of ChatGPT and similar tools is so high. Low adoption is not, in other words, primarily a problem of awareness. The findings point instead to barriers that policy may need to address, whether through digital literacy programs, access initiatives, or other targeted interventions. The researchers also call on policymakers to account for the substantial share of AI’s productivity impacts that are occurring outside of labor markets, a dimension largely missing from current policy debates.

The study’s use of granular internet browsing data offers a distinctive vantage point, allowing the researchers to observe behavioral changes in near-real time rather than relying on self-reported surveys. That approach makes the case for productivity effects more robust and underscores the urgency of further research into AI’s off-work impacts, which are largely absent from traditional economic indicators like gross domestic product.

Ultimately, the study adds important nuance to the broader conversation about AI and economic opportunity. Productivity gains are already happening at scale, but it is happening on the sofa, not just in the office, and it is not yet being shared equally. Understanding those dynamics, and finding ways to broaden access, may be as consequential as anything happening in corporate AI adoption.

This post was originally published by USC Marshall School of Business and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: How AI Is Supporting the Next Generation of Small Businesses

How AI Is Supporting the Next Generation of Small Businesses

2026-06-25T17:10:11.147+05:00

By Jelyn Johnson

Artificial intelligence is not something that is used occasionally to help small businesses. It is now a part of how these businesses work every day. Research from Adobe shows that artificial intelligence is becoming a part of what makes small businesses run. It is no longer an extra tool that helps with a few things but it is now a key part of everything from marketing, to the way things are done in the office.

This is a deal: artificial intelligence is not just making things a little faster, it is actually changing the way work is done in small businesses. It is changing how all the different parts of a business work and operate with each other. AI is really changing what it means to be a business.

A New Formula to Help Business Growth

Small businesses software often has a specific application. There is one software application for sending emails, there is another one for accounting purposes, and another for designing. The role of AI in small businesses is breaking this trend. The majority of small businesses are embracing AI in their operations for more than one process compared to their previous use for individual processes.

The truly amazing thing about all of this is the fact that the use of software is gradually turning into the integration of software into operations. The AI technology is being applied to automate repetitive operations such as creation of content, media posting, customer communications, and handling administrative tasks.

AI software is not being used alone. Rather it is being added to the existing ways of doing things to make everything run more smoothly. AI technology is being used to complement tasks like content creation, social media creation, customer communication and administration to make these tasks easier for businesses.

This is a significant change in small business operations since small businesses are always looking for ways to save time or be more efficient.

Workflow Automation is Becoming the Primary Driver of Adoption

Perhaps one of the most evident pieces of evidence from research results is the fact that small businesses are not using AI as an experimental tool. The reason why AI adoption among small firms is happening is practical; it means that people want to save their time by making work more efficient.

There were tasks that used to take much time, such as creating social media posts, marketing copywriting, or anything else, which is done for the customers’ benefit. These kinds of tasks are now often partly or completely automated using the power of AI. Often, these processes are not done from scratch anymore, but instead, they are handled as a kind of first draft in collaboration with AI assistance.

This trend is really important because it changes the way people work at businesses. Artificial Intelligence is not taking the place of workers, it is just doing repetitive tasks.

Time Savings are Being Allocated to Higher-Value Work

We can see that when small businesses use Artificial Intelligence they do not get free time. Instead the owners of these businesses are using this newfound time to do things that will help their business continue to grow.

The time saved is being used for thinking about the future, talking to customers and making their business better. AI is helping business owners to focus on these things. This implies that AI technology is not only increasing efficiency and also changing the priority of time usage.

The reallocation serves a very important structural change in the operation of small businesses. As the repetitive work is reduced and time is saved, the constraint moves from being ‘available time’ to ‘quality of decision-making’. The business owner is no longer constrained by their ability to execute as much as they can be by the effective use of time.

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

AI is Quietly Standardizing Small Business Workflows

Another developing trend is the process standardization that occurs within small businesses as they begin to implement the use of AI at scale. With many companies implementing tools that are used to generate content, to communicate, and to manage marketing processes, there are certain patterns of behavior that start to develop.

For instance, the content production process is often becoming increasingly uniform in its pattern, with the development of an idea, automatic generation of the draft, and finally, manual refining of the draft.

Over time, this will lead to a smaller variation of actions taken by the small businesses when performing the tasks, especially marketing and communications tasks. At the same time, it raises the question of differentiation, as all outputs from the use of AI become similar.

How Gains in Productivity are Beginning to Show Their Impact

From our findings, AI positively contributes to business operations on top of streamlining day-to-day processes. Productivity increases lead to drastic improvements on business outputs.

There are observable improvements among small businesses as they deploy artificial intelligence. They are improving their core competencies, which in turn has a positive impact on customer engagement levels and the bottom line. This shows that AI has an impact beyond internal business operations for small businesses.

Customer engagement and revenue generation are other ways artificial intelligence has a positive impact on business performance.

The main process behind this effect seems to be compounded efficiency. The time savings in the execution process allow the company to do more work in terms of marketing, customer communication, etc., without additional costs.

In that way, AI acts not as a device but as a multiplier of business potential.

The Emerging “AI-first” Small Business Model

Together, these developments seem to signal the emergence of a new operational model, the small business that operates on the basis of AI-first. This does not imply that small businesses become fully automated operations.

Decision-making methods will still be a collaborative effort led by human thinking when it comes to strategy development, creative aspects, and relations with customers. Yet, the process of performing the tasks associated with decision-making is done through the assistance of AI algorithms.

Overall, this results in a combined operational model in which humans make decisions, and AI performs the majority of tasks.

Thus, AI becomes a tool that expands the capabilities of an existing business operation.

Take a look at these infographics for more insights on small businesses are leveraging AI today:

Some key insights:

AI usage has climbed to 85% among surveyed small business owners, with 65% citing improved confidence in future growth.
"Small business owners using image generation tools are 32% more likely to cite burnout reduction as their primary motivator".
"47% of small business owners saw an increase in revenue since beginning to use Al tools, with an estimated revenue increase of 21%".
"Small business owners are investing an average $218 on Al training this year to stay competitive, with about one in seven investing $1,000 or more".

The Next Phase of Technology

The more that small businesses use AI technology the harder it will be for other small businesses to keep up. When small businesses use AI technology they can do things without needing to hire more people.

For example AI technology helps with a lot of tasks. This means small businesses can be more productive and efficient with their time. The notion of what software and tools can do for businesses is also changing.

It's not just one thing, people are looking for systems that can automate a lot of things. It's the AI technology that makes all these systems work together. It makes things run smoothly, which in turn makes things happen faster.

If you look at the landscape you can see that AI technology is a part of how small businesses operate online. AI technology is transforming how small businesses do business on the Internet.

Thus, what we can conclude from this data is that the use of AI tech in small businesses is no longer a process of experimenting or making the process more efficient. It becomes an essential part of the change in how small businesses operate.

Reviewed by Irfan Ahmad.

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The Highest-Earning Creators of the Internet Content Machine

2026-06-25T14:35:04.077+05:00

By Katharina Buchholz, Statista

Content creator MrBeast aka Jimmy Donaldson earns more than anyone else in the business. This is according to the newest edition of the Forbes Top Creators list published Tuesday. The 28-year-old who grew up in North Carolina made $300 million in gross earnings between March 2025 and March 2026, according to the source, far outpacing other influencers. Donaldson has had resounding success with his YouTube channel focused on over-the-top challenges (and the occasional grand gesture) and released the second season of his Amazon Prime series Beast Games in January. Earnings also come from a food side business, an analytics tool and toy and clothing licensing. Donaldson's company reportedly has taken on venture investments at a $5 billion valuation as well as purchased a personal finance app for teens.

The latest release of Forbes' list of the most successful influencers shows that YouTubers generally rank high among the best-paid content creators. One aspect of this is sponsored posts and ads earning more if they are in a video format. According to Forbes, Donaldson is in fact capitalizing on this aspect. However, many creators who have earned millions as social media personalities have done so by outside business deals. Rhett McLaughlin and Link Neal of channel Rhett & Link have branched out from YouTube sketch comedy and other entertainment content to streaming deals, live appearances, merch and book sales. Mark Edward Fischbach, known as Markiplier on YouTube, initially uploaded gaming videos, but now also earns cash with merch sales and a clothing line. After some podcast and TV deals, he self-released his first feature film earlier this year.

Also among the highest earners are two creators focusing on personal finance and business tips. Phoenix-native Codie Sanchez pivoted from a career in journalism and finance to teaching small business ownership through her multiple online channels, a podcast and a New York Times Best Seller. She has been a prominent voice in the passive income and vending machine/laundromat hype has has been circulating online. Serial startup founder Steven Bartlett meanwhile became famous for his podcast The Diary of a CEO, which streams on YouTube and audio platforms. The format, which features interviews with CEOs, entrepreneurs and celebrities, became one of the most listened-to podcasts in the world. Since then, Bartlett has continued founding and investing in companies, has authored two books and has become an angel investor on the British TV show Dragon's Den.

Second-ranked Dhar Mann shot to internet fame producing mini dramas that can be viewed on YouTube and social media platforms after trying his hand at other entrepreneurship ventures. Having started out with motivational content, his current video releases have been described as morality plays, featuring stories in which a character has to navigate between good and evil influences. Raised in Oakland, Calif., by Indian parents, Mann employs 200 studio crew who shot video in eight teams simultaneously and collaborates with around 2,000 actors per year.

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

Reviewed by Irfan Ahmad.

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Tech companies hit with $3.5B in AI fines since 2022, led by Anthropic and Meta

2026-06-24T19:14:44.299+05:00

By Surfshark

AI is advancing faster than the laws meant to govern it, and regulators are starting to push back. Since 2022, courts and data protection authorities around the world have begun issuing fines and settlements against major technology companies for AI-related violations, offering the first real glimpse of where AI development is colliding with existing rules.

This chart of the week, created by Surfshark, examines who has been penalized, which violations regulators are prioritizing, and how enforcement has evolved year over year.

Key insights

Since 2022, regulators and courts have imposed over $3.5 billion in fines and settlements on seven major technology companies for AI-related violations. Across ten separate cases, Anthropic's $1.5 billion settlement¹ for training AI on pirated books was the largest single penalty, followed closely by Meta's $1.4 billion² for collecting biometric data to train facial recognition without consent. Together, these two cases alone account for 81% of all AI-related fines and settlements in Surfshark dataset.
The vast majority of cases (9 out of 10) centered on a single issue: using data to train AI without proper consent or legal basis. Anthropic ($1.5B), Google ($291M)³, and OpenAI ($17M, later annulled)⁴ were fined for training AI models on copyrighted or personal data without authorization. Meta ($1.4B), Clearview AI ($105M across four cases), and Amazon ($25M)⁵ were fined for collecting biometric data such as facial images and voice recordings to train AI systems. Apple's $250 million settlement⁶ stands apart as the only case where the violation was not about training data, but about overpromising what AI could actually do.
Not all fines have survived legal challenges. OpenAI's $17 million fine, imposed by Italy's data protection authority in 2024 for training ChatGPT without a legal basis and failing to meet transparency requirements, was later annulled by an Italian court in 2026. The case highlights the legal uncertainty that still surrounds AI regulation: what regulators treat as a violation may not ultimately hold up in court.
Clearview AI stands out in Surfshark analysis as the only company to simply ignore the penalties issued against it by European regulators. In fact, four separate European data protection authorities penalized the company for the exact same violation — scraping billions of facial images to build an AI recognition database. The Dutch DPA⁷ fined Clearview $35 million in 2024, following earlier $23 million fines from the Italian⁸, French⁹, and Greek¹⁰ authorities in 2022, totaling $105 million. To date, Clearview has paid none of them, arguing that because it does not have a physical presence in Europe, it is not subject to European jurisdiction.
Overall, AI-related penalties began to emerge in 2022, with three fines targeting Clearview AI. In 2023, Amazon was fined for retaining children’s voice recordings used to train Alexa. Enforcement intensified in 2024, when regulators took action in four separate cases involving Google, OpenAI, Meta, and Clearview AI. The trend continued in 2025 with the largest AI-related penalty in the analyzed dataset — Anthropic's $1.5 billion settlement. Most recently, in 2026, Apple's $250 million settlement stood apart as the only case about overpromising AI capabilities rather than training data.

Methodology and sources

This study examined AI-related fines and settlements involving major technology companies between 2022 and 2026. We included cases where regulators or courts took formal action over the training, or marketing of AI systems. To qualify, each case had to involve a clearly identified company, a measurable penalty or settlement, and a direct link to AI-related conduct such as unlawful training on copyrighted or personal data, biometric data scraping, or misleading AI claims.

The final dataset contains 10 cases across 7 companies. For each case, we recorded the company, year, penalty amount, and stated reason for the action. We also calculated penalty amounts in both euros and the United States dollars, using a conversion rate of $1 = €0.86. Repeated penalties against the same company were counted separately when they came from different authorities. Annulled penalties were also included, but marked accordingly, to reflect the full enforcement landscape.

For the complete research material behind this study, click here.

References:

¹The Guardian. AI startup Anthropic agrees to pay $1.5bn to settle book piracy lawsuit.

²Ken Paxton. Attorney General Ken Paxton Secures $1.4 Billion Settlement with Meta.

³European Commission. France fines Google for unauthorised use of media content.

⁴Reuters. Italian court scraps 15-million-euro privacy watchdog fine on ChatGPT-maker OpenAI.

⁵Federal Trade Commission. FTC and DOJ Charge Amazon with Violating Children’s Privacy Law by Keeping Kids’ Alexa Voice Recordings Forever.

⁶The New York Times. Apple Reaches $250 Million Settlement Over Claims It Misled People on AI.

⁷Hunton. Dutch Regulator Fines Clearview AI 30.5 Million Euros.

⁸European Data Protection Board. Facial recognition: Italian SA fines Clearview AI EUR 20 million.

⁹European Data Protection Board. The French SA fines Clearview AI EUR 20 million.

¹⁰European Data Protection Board. Hellenic DPA fines Clearview AI 20 million euros.

Reviewed by Irfan Ahmad.

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• “Friendly Reminder”: Top Phrases Professionals Use to Soften Tone in Emails

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“Friendly Reminder”: Top Phrases Professionals Use to Soften Tone in Emails

2026-06-24T18:49:05.814+05:00

Is a reminder less intrusive or irritating if it’s paired with “friendly” or “gentle”? Apparently, that’s the perception for many people. “Friendly reminder” is the most common phrase professionals use to soften their tone in emails, a new study shows, followed by a series of phrases apologizing for late replies.

The study, conducted by email deliverability company ZeroBounce, analyzed more than 1.6 million work emails to determine the phrases professionals use to apologize and soften their tone.

While “friendly reminder” topped the list, most of the next few spots were taken by variations of “sorry for the late reply,” suggesting that responding to emails late is a major source of workplace guilt.

Here are the 10 apology and softening phrases you’re most likely to see in your inbox:

1. “Friendly reminder”
2. “Sorry for the delay”
3. “My apologies”
4. “Apologies for the delay”
5. “Sorry for the late reply’
6. “Sorry for any inconvenience”
7. “My bad”
8. “Sorry for the confusion”
9. “Sorry for the delayed response”
10. “Sorry it took”

Professionals mostly apologize for replying late

“Sorry for the delay” is number two on the list, but if we look beyond the top 10, we see several variations of the same phrase popping up in inboxes everywhere:

• “Apologies for the delayed response” (no. 15)

• “Sorry for the late response” (no. 16)

• “Sorry for my late reply” (no. 18)

• “Sorry for this delay” (no. 22)

• “Apologies for the late response” (no. 23)

Aside from expressing guilt over responding to emails late, professionals often pad their emails with phrases meant to sound less direct, such as “quick nudge,” “gentle reminder,” or “sorry for the inconvenience.”

“Sorry to bother”: people apologize for doing their jobs

One of the most surprising findings of the study was that professionals still apologize for things that are normal in the workplace, like asking a question or following up on a previous email. What’s more, a certain set of phrases indicates that people sometimes apologize for no reason at all.

These phrases rank lower on the list, but their occurrence shows a propensity for overly apologetic language:

“Sorry for bothering you”
“Sorry to bug you”
“Sorry to trouble you”
“Sorry to ask”
“Sorry in advance”
“Sorry to disturb you”

There are situations where an apology like the ones above would perhaps be appropriate, such as emailing on the weekend or reaching out to a colleague or manager late at night for an emergency. But as ZeroBounce founder and CEO Liviu Tanase explains, in many cases, it’s best to be direct and avoid phrases that may weaken the tone of your emails.

“Many professionals apologize for simply doing their jobs – asking questions or sending reminders,” says Liviu Tanase. “The data shows how concerned people still are about sounding polite and not too demanding. Even quick workplace emails carry a surprising amount of emotional calculation,” adds the entrepreneur.

How to avoid over-apologizing in your emails

A couple of days have gone by since a colleague reached out with a question, and your instinct is to start your response with a version of “sorry for the late reply.” It happens to all of us – email cliches have a way of enduring despite generational shifts.

If you find yourself in that situation, here are a few tips that may help.

Set an auto-responder if you take time off

Not only will this prevent repeated follow-ups, but it will also give you peace of mind that whoever emails you knows you won’t be able to respond right away. A short message stating the date of your return will let the person know when to expect a reply.

Ask yourself if your reply is truly late

Sometimes people feel the need to apologize for replying late, although it’s been merely hours since they received an email. So before you start typing “sorry for the delayed reply,” ask yourself if the person was really expecting you to write back within minutes. What may seem late to you may not be late at all to the other person.

If you really are late, own it and move on

There are days or weeks when things get overwhelming – a big project to deliver at work or significant changes in your personal life. Colleagues and peers may not hear back from you for a while, and that may cause delays in their own projects. If that happens, it’s completely normal to apologize, but don’t dwell on it too much.

A short line should be enough to explain your absence, then answer the question. Most likely, your recipient cares less about why you haven’t replied and more about getting an answer to their specific request. If the deadline has long expired, you can still write them back and let them know your schedule has cleared and that you’re happy to answer any questions.

Simple and straightforward emails sound more confident

There’s a whole dictionary of cliches and corporate jargon infesting emails across cultures around the globe. Despite Millennials and Gen Z bringing their own flavor to workplace language, these cliches endure – often because people don’t really know what else to say.

Before you send your next email, take a second to scan it and see if you’re using any of these buzzwords: “just following up,” “I’m circling back,” “I’m touching base,” or “sorry to bother.” Then try editing them out and read your email again. You’ll notice it sounds more clear and to the point.

Clean, straightforward language that simply delivers the message stands out and brims with confidence. Who couldn’t use more of that?

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