NYU Researchers Map Where U.S. Data Centers Are Located

By NYU Tandon School of Engineering. Edited by Irfan Ahmad.

Image: Chad Davis - Flickr. CC BY

When people picture a data center, they often imagine something remote: a huge warehouse humming quietly far from the city. New research from NYU Tandon School of Engineering shows that assumption is largely wrong.

The study, published in Nature Cities, examined the locations of 4,283 data centers across the contiguous United States and found that 97.5% of them sit inside metropolitan or micropolitan statistical areas, meaning urban cores and their immediate surroundings.

The roughly 2.5% of facilities technically outside city limits are, on average, just 8.5 miles from the nearest urban edge. The cloud lives downtown.

"There is a prevailing narrative of these data centers being somewhere in the middle of nowhere, in rural areas, being a positive force for employment, and being the future of rural communities," said lead researcher, NYU Tandon Institute Professor Maurizio Porfiri, who is the Director of Tandon’s Center for Urban Science + Progress and of the NYU Urban Institute. "We dramatically challenged this view."

The concentration is striking even within the urban category. Five metro areas, Washington-Arlington-Alexandria, Chicago, Dallas-Fort Worth, New York-Newark-Jersey City, and Phoenix, account for nearly a third of all U.S. facilities. The Washington region alone hosts 610 data centers, reflecting Northern Virginia's status as the global capital of data infrastructure (see appendix below).

So why cities?

The answer, the researchers found, suggests that a lot has to do with what’s already there and what used to be there.

The single strongest predictor is electricity capacity, meaning how much power local generators can produce. Data centers are power-hungry, running thousands of servers around the clock and drawing enormous, steady loads from the grid.

But a notable finding goes beyond electricity supply. Closed coal plants near cities are becoming magnets for new data center builds. When a plant shuts down, the power lines and grid connections built to carry electricity continuously do not disappear. Data center developers can tap directly into that infrastructure, or, in some cases, redevelop the sites themselves.

The numbers bear this out. Among cities that overlap with areas designated as Energy Communities under a 2022 federal policy, data centers under development are twice as likely to be found there than in cities without that designation (see appendix).

The policy was designed to direct clean-energy investment toward regions hurt by coal plant closures. The research suggests the digital economy may be taking root in many of the same places as the fossil fuel economy it is meant to succeed.

Because data centers draw from their local grid, their carbon footprint depends heavily on how that grid generates power. A typical data center in Montana or North Dakota produces more than 350,000 tons of CO2 emissions annually, while the average facility in Vermont, New Hampshire, or Arkansas produces less than 3,000 tons.

Beyond electricity supply, data centers also cluster where IT workers and high-speed internet are concentrated. Local water shortages seem to have less of an impact on data center placement, despite the facilities consuming enormous amounts of water for cooling.

The opacity of the industry compounds all of these problems.

“As this industry rapidly grows, the limited publicly available data on its footprint creates a real challenge,' said Ofek Lauber Bonomo, a postdoctoral researcher in Porfiri’s Dynamical Systems lab and a paper co-author. “That makes it more difficult for planners, for local residents, and for anyone trying to make informed decisions about their community's future."

"The patterns we found were consistent and clear,” added Anton Rozhkov, a CUSP Industry Assistant Professor and paper co-author. “Where the infrastructure already exists, the data centers follow. The question now is whether that is the future we want to build.”

The Nature Cities paper follows the recent announcement that Porfiri and Camilla Ancona – a paper co-author and postdoctoral researcher in Porfiri’s Dynamical Systems Lab – were named among the 2026 cohort of Microsoft Research Fellows, to advance their work using AI-driven simulations to help utilities and regulators decide where to site data centers before breaking ground.

The research in the Nature Cities paper was supported by the NYU Abu Dhabi (NYUAD) Center for Interacting Urban Networks, funded by the Abu Dhabi government through the NYUAD Research Institute.

Note on Methodology and Terminology:

This study analyzed a 2025 dataset of 4,283 commercial data centers in the contiguous United States from the commercial Data Center Map database. The dataset includes operational facilities as well as projects that were planned, under construction, or land-banked at the time of analysis. Throughout this article, "city" refers to metropolitan and micropolitan statistical areas (MSAs and MicroSAs), as defined by the U.S. Office of Management and Budget, which classify counties based on economic integration with an urban core rather than population density or land use. As a result, some counties classified as part of metropolitan or micropolitan areas may have low population density or predominantly rural land use. The study characterizes the overall geographic distribution of U.S. data centers by facility count during the study period.

Appendix

Data centers in each metro area - top 10

#	Metro area	Facilities	Share of U.S. total
1	Washington–Arlington–Alexandria	610	14.2%
2	Chicago–Naperville–Elgin	241	5.6%
3	Dallas–Fort Worth–Arlington	192	4.5%
4	New York–Newark–Jersey City	163	3.8%
5	Phoenix–Mesa–Chandler	154	3.6%
6	Atlanta–Sandy Springs–Roswell	136	3.2%
7	Columbus	133	3.1%
8	San Jose–Sunnyvale–Santa Clara	130	3.0%
9	Los Angeles–Long Beach–Anaheim	90	2.1%
10	Des Moines-West Des Moines	76	1.8%

Data centers in Energy Community (EC) designated areas - top 8

#	Metro area	Under-development data centers
1	Chicago–Naperville–Elgin, IL–IN	108
2	Dallas–Fort Worth–Arlington, TX	58
3	Washington–Arlington–Alexandria, DC–VA–MD–WV	26
4	San Antonio–New Braunfels, TX	24
5	Reno, NV	18
6	New Haven, CT	12
7	Scranton–Wilkes-Barre, PA	11
8	Monroe, LA	10

This article was originally published by NYU Tandon School of Engineering and has been republished with permission.

Read next: Study identifies temptation as crucial link in problematic Internet use
by External Contributor via Digital Information World

Study identifies temptation as crucial link in problematic Internet use

By University of Duisburg-Essen. Reviewed by Irfan Ahmad.

Image: Batuhan Doğan - Unsplash

People who turn to online gaming, social media or shopping apps after a stressful day are often looking for distraction or relief. But what determines whether this develops into problematic Internet use? A research team led by Dr. Silke M. Müller at the UDE investigated the everyday online behaviour of 900 adults. Their findings show that while people with problematic Internet use experience higher levels of stress and negative mood, the decisive factor driving them online is temptation. The study* has now been published in PLOS One.

Problematic Internet use refers to a pattern of behaviour in which online activities such as gaming, pornography use, online shopping or social networking become so dominant that they begin to interfere with everyday life. Researchers have long assumed that people turn to the Internet to cope with stress or negative emotions, potentially leading to problematic patterns of use. However, whether these mechanisms actually unfold in everyday life has remained largely unexplored.

Over the past three years, Dr. Silke M. Müller, from the Department of General Psychology: Cognition, and her colleagues from the FOR2974 Research Unit interviewed more than 1,200 adults. Around 900 participants were then followed over a two-week period. Each evening, they completed questionnaires about their mood, stress levels, temptation to go online, time spent online and the consequences of their Internet use. They also reported whether Internet use had brought them pleasure or relief and whether it had caused them to neglect other activities. Before the study began, participants were classified as having non-problematic, risky or pathological Internet use based on standardized diagnostic interviews.

“Our study shows that the temptation to go online is a crucial link between stress, mood and problematic Internet use. It determines whether people engage in online activities – experiencing short-term pleasure or relief while at the same time neglecting other areas of their lives,” says Müller.

Unlike many previous studies, the researchers followed participants in their everyday lives over a period of 14 days. This allowed them to identify why people choose to go online at specific moments. “Understanding these processes is essential for improving prevention and treatment strategies,” explains first author Andreas Oelker. “Our findings suggest that helping people recognize and manage moments of temptation may be more effective than focusing solely on stress or mood.”

* The study is based on data collected within the German Research Foundation (DFG)-funded Research Unit FOR2974, ‘Affective and Cognitive Mechanisms of Specific Internet-use Disorders’. The Research Unit aims to improve understanding of the psychological and neurobiological mechanisms underlying problematic Internet use in order to develop better prevention and treatment strategies. It is led by Prof. Dr. Matthias Brand, who is also a co-author of the PLOS One publication.

Read next: 

• New Report Finds Just 100 Brands Drive a Quarter of All YouTube Sponsorships, and 59% of Brands Have Fewer Than Ten Deals

• Study finds reducing Facebook and Instagram exposure to untrustworthy sources changed feeds but not beliefs

by External Contributor via Digital Information World

New Report Finds Just 100 Brands Drive a Quarter of All YouTube Sponsorships, and 59% of Brands Have Fewer Than Ten Deals

Written by Daniel Moradkhani. Edited by Irfan Ahmad.

An index tracking over 367,994* detected sponsorships reveals a winner-take-most pattern in YouTube brand partnerships.

Most of the leading sponsors are brands familiar to people in the industry, and the average YouTube viewer will recognize them too. These are brands that sponsor creators in a bunch of different niches and have been doing so for the past couple of years consistently. The top 100 brands on YouTube account for 27% of all 367,994 detected sponsorships and the top 10% of brands are behind about 65% of them. The rest of the market is shared by the rest. Basically a winner-take-most market.

GetSponsored is a YouTube sponsorship database built by Stockholm based founder Daniel Moradkhani, tracking 13,944 brands across 25,485 channels. The way it works is it tracks descriptions across thousands of videos, finding out what videos are getting sponsored by what brands. Finding sponsors used to be quite a hassle for Moradkhani. He had to find competitors of his, open each one of their videos in a separate tab, look in their descriptions for links to brands, then do the entire process of finding contacts and pitching himself. So he built GetSponsored, putting it all in one easily manageable place.

"I was opening each video as a tab, looking through the descriptions for brand links, then searching LinkedIn and other sites for contact info for the right individuals," Moradkhani says.

The Brands That Sponsor the Most Creators Are Not the Biggest Companies

NordVPN being on top of the paid sponsorships with 2,150 detected placements across 305 creators won't come as a surprise to anyone.VPNs, specifically NordVPN, have been around for a very long time across tons of niches. Next is Squarespace with 1,831 across 192 creators, then StreamYard (1,441), Shopify (1,417) and finally Hostinger (1,233). BetterHelp, Insta360, Unacademy, Surfshark and Shopee round out the top ten, all somewhere between 1,000 and 1,250 placements. All of these brands are well known in the industry.

The commonality behind these brands is not the company size, instead it's the business model. Almost every single one of them sell some kind of subscription or direct response product where a discount code ties the sale back to a specific video for tracking.

Also read: The Sale That Never Ends in VPNs

Nord Security and Surfshark merged already in 2022 and Incogni is a Surfshark product. Together the three of them sit on roughly 4,000 detected placements. So three different VPN ads on three different channels is often the same parent company three times.

Reach and frequency are also something worth separating. BetterHelp has sponsored 359 different creators which is more than anyone else on the paid side, with 1,226 placements, about three per channel. Whereas Unacademy has almost the same amount of placements, 1,130, spread over just 73 creators, around fifteen per channel. This shows how some brands opt towards niching down when others go for a broader market. Two completely different strategies, one goes wide, one hits the same channels again and again.

Affiliate Links and Paid Campaigns Produce Two Different Leaderboards

Affiliate links are a factor that mildly skew the database. The most notable example of this is Epidemic Sound being the number one brand with 13,393 placements across 298 creators. That's almost as much as the entire paid top 10 combined. Next is Amazon with 8,366 across 352 creators, then Gamersupps (3,692) and Samsung (2,524), and after that a cluster of gaming and hardware brands all between roughly 1,000 and 1,350, Fortnite, Elgato, Epic Games, DJI, Instant Gaming and Apple.

Their reach is basically the same as NordVPN, 298 channels against 305, but they show up about 45 times per channel. NordVPN shows up seven.

"Affiliate links are usually placed across every single one of my videos to maximize revenue. When a brand pays for an integration, the maximal revenue gained is just from placing it in that one video the brand paid for," Moradkhani explains. Which is why brands like Epidemic Sound, who has a very well established affiliate program, lands on top of a lot of sponsorship databases. An affiliate link costs nothing to leave in, so two brands can have more or less the same relationship with the same amount of creators and end up around 11,000 placements apart anyway.

Technology Leads on Volume, VPNs Lead on Intensity

By category Technology is the biggest bucket, 100,662 tracked sponsorships across 1,805 brands. Lifestyle & Vlog comes second at 53,247 across 973 brands, then Entertainment (32,104), Gaming (31,554), Finance & Business (28,757), Beauty & Fashion (25,342) and Health & Fitness (15,260).

Part of this is just that the Technology label is wide. Software, hardware, apps, services, things that would be their own categories anywhere else.

Placements per brand tells a different story. Beauty & Fashion spreads its 25,342 placements across 799 brands, which is around 32 each. VPN & Privacy has 7,706 placements across just 37 brands, more than 200 each. That's the smallest amount of brands of any big category and by far the most active per brand, basically a handful of companies sponsoring at an industrial scale.

Education Apps Are the Fastest Risers While the Largest Paid Sponsors Pull Back

The last 30 days against the 30 before show some movement as well. Two Indian education platforms are the fastest risers, Adda247 grew 146 percent to 113 placements and Unacademy grew 16 percent to 115. In the same amount of time NordVPN appearances dropped about 35%. StreamYard dropped too, around 26%.

These are quite small numbers, low hundreds of placements against an index of 367,994, so this is probably campaign timing more than anything structural. But it does show that not even the heaviest sponsors spend at a constant rate. NordVPN can be number one in the entire index and still be the biggest faller in the same month.

Deal Volume Does Not Track Subscriber Count at the Top of the Index

Of the channels in the index, 823 are under 100K subscribers (3%), 18,311 are between 100K and 1M (72%) and 6,317 are above 1M (25%).

The most sponsored channel in the whole index is John Coogan with 453K subscribers, 796 sponsored videos from 30 different sponsors, more than channels several times his size.

300K viewers that all care about the same thing is a simpler buy than 3M viewers that came for all sorts of reasons. 59% of brands in the index have less than 10 sponsorships.

"From my own inbox it comes in waves. Subscription boxes for a while, then anime apps, then AI chat apps," Moradkhani says.

Methodology and Limitations

The numbers the database represents should be viewed as minimums. Sponsors that might have been removed from the description, exclusively mentioned in the video, or for any other reason not written in the description will not be caught by the detection system. However this is such a minor part of the ecosystem that it practically doesn't matter.

Almost all of the channels in the index are above 100K subscribers, and there are multiple reasons for this. For one, brands usually appear more often in well established channels rather than up and coming ones. "Second, scanning for channels has been done more thoroughly for larger channels. A decent chunk of smaller channels are still currently missing, but will be filled in in the future," Moradkhani says.

Since Epidemic Sound is a brand who has a very well known affiliate program and also does very little real paid advertising in comparison, labeling it as an affiliate brand is mostly correct. However for other brands such as Gamersupps or Samsung, it gets a bit foggier and isn't as accurate yet since the database doesn't verify link by link at this time.

The database started tracking around May 2026, pulling the 15 latest videos of around 74,000 channels, and has been updating daily since then. This potentially leaves a gap for channels who have consistently uploaded for years. Data will most likely deepen in the future.

* Figures are based on GetSponsored's index at the time of reporting and may change as the database continues to update.

About Author: Daniel Moradkhani is the founder of GetSponsored, a YouTube sponsorship database, and runs a network of YouTube channels with more than one billion total views. He is based in Stockholm.

Read next: Study finds reducing Facebook and Instagram exposure to untrustworthy sources changed feeds but not beliefs
by Guest Contributor via Digital Information World

Study finds reducing Facebook and Instagram exposure to untrustworthy sources changed feeds but not beliefs

By Kenny Ma and Aubrey Spowart, UC Berkeley Letters & Science
Reviewed by Irfan Ahmad.

Image: Gaspar Uhas - Unsplash

For years, social media companies have faced pressure from users and government watchdogs to stop misinformation from distorting elections, deepening political divisions and undermining Americans’ trust in institutions.

But what if dramatically reducing people’s exposure to unreliable sources that frequently spread misinformation doesn’t actually change what they believe?

UC Berkeley Political Science Ph.D. student Olivier Bergeron-Boutin tested that question on Facebook and Instagram during the 2020 presidential election. In a new paper published in Science Advances, titled “Untrustworthy sources on Facebook and Instagram in 2020,” Bergeron-Boutin and his coauthors report findings from a large-scale study in which an independent group of academics partnered with researchers at Meta.

The researchers conducted an experiment among consenting Facebook and Instagram users that reduced social media feed exposure to content from sources that repeatedly spread misinformation.

The experiment was successful in changing what people saw: participants assigned to the intervention were exposed to roughly 70% less content from untrustworthy sources. However, their political beliefs and attitudes did not measurably change, even among those who were previously exposed to the most content from those sources.

“The intervention was successful in the sense that it reduced what it was supposed to reduce, in this case, exposure to information from untrustworthy sources,” Bergeron-Boutin said. “But the resulting changes in attitudes were much smaller than some would have expected.”

These findings complicate the assumption that exposure to content from dubious sources that spread misinformation on social media powerfully shapes people’s beliefs and attitudes. The study instead suggests that while social media companies can change what people see, combating misinformation and political polarization may require confronting deeper forces that shape what people believe.
Feeds changed, beliefs didn’t

The researchers also found that exposure to untrustworthy sources was not evenly spread across Facebook and Instagram users. Most people on both platforms saw relatively little content from these sources. Instead, a relatively small group of users was responsible for most of the exposure — 23% of Facebook users and just 11% of Instagram users — accounted for roughly 80% of all exposure to information from untrustworthy sources.

“Exposure to content from untrustworthy sources on social media is less common and more concentrated than people typically think,” said Brendan Nyhan, the James O. Freedman Presidential Professor in the Department of Government at Dartmouth College and a co-author of the study. “In addition, reducing exposure to content from those sources did not have the effects that many speculated it would have on people’s beliefs, attitudes and behaviors.”

UC Berkeley computational folklorist Tim Tangherlini, who studies rumors and conspiracy theories on social networks but was not involved in the Facebook/Instagram study, said that concentration reflects something researchers have long observed about how information moves through communities.

“Stories don’t spread evenly through a population,” Tangherlini said, noting the timeliness and importance of the study. “They flow on and across social networks predisposed to receive them, retell them and refine them — basically like an echo chamber.”

What happens when you take people out of the echo chamber?

Researchers then tested what happened when people saw far less of that content. For three months surrounding the 2020 election, they reduced participants’ exposure to untrustworthy sources by roughly 70%.

Yet people’s political beliefs and attitudes did not measurably change.

Tangherlini said the finding makes sense when political beliefs are understood as something shaped not just by the information people encounter online, but also by the communities and social networks around them.

“A platform intervention changes what flows into the network,” Tangherlini said, “but it doesn’t change the underlying social network, or the beliefs shared in that network.”

He compared changing deeply rooted beliefs to steering a large oil tanker: small corrections can change its direction, but significant changes may take time.
A debate that has only grown

Since 2020, social media platforms have changed substantially. Meta ended its U.S. third-party fact-checking program in 2025, eliminating the system of fact-checking “strikes” that made the study’s source-level intervention possible. The researchers caution that their 2020 findings, therefore, cannot tell us what misinformation exposure looks like on Facebook and Instagram today.

Nyhan added that the findings point to the need to focus on those most heavily exposed rather than treating misinformation as a problem that affects everyone equally.

“I hope social media companies and policymakers will recognize that exposure to content from untrustworthy sources is heavily concentrated and the harms from it are likely to be similarly concentrated,” Nyhan said. “We should worry less about the average person (who is typically uninterested in politics) and more about potential effects on people who consume large volumes of dubious content online and what it might do to them or inspire them to do.”

Bergeron-Boutin said the study’s results should not discount the problems misinformation poses.

“We shouldn’t interpret these results to mean that misinformation is no big deal,” he said. “But there’s no quick fix. Anyone who proposes a simple solution to misinformation or political polarization should be viewed skeptically. Tinkering at the margins can help, but ultimately, institutional features of American democracy provide fertile ground for these issues to rise.”
More questions, less transparency

The study also highlights another challenge: researchers need access to the platforms themselves to understand how social media affects society, but that access is becoming harder to obtain.

“This study presents a unique opportunity because social media platform data is rarely accessible to outside researchers,” Bergeron-Boutin said. “A study like this is becoming less and less likely to happen again, because since 2020, these platforms have become less transparent.”

For Nyhan, that makes accurately measuring the harms caused by misinformation — and determining how society should respond — one of the most important challenges ahead.

“We need to learn how to better measure the potential harms from dubious content online and address them without compromising the values and freedoms that we hold dear,” he said.

As platforms become less transparent, answering those questions may become increasingly difficult. The study shows that social media companies can change what millions of people see. Understanding how that shapes what they believe is a much harder question.

The study’s other lead co-authors are Jaime Settle, department chair and professor of government at The College of William & Mary; Emily Thorson, associate professor of political science at Syracuse University; and Magdalena Wojcieszak, professor of communication at UC Davis.

Read next: 

• University of Phoenix Survey Highlights AI’s Potential to Advance Accessibility in Work and Learning

• 2 in 5 Americans Feel Like Their Brain Is Fried After a Heavy AI Workday
by External Contributor via Digital Information World

University of Phoenix Survey Highlights AI’s Potential to Advance Accessibility in Work and Learning

By Sharla Hooper, University of Phoenix
Reviewed by Irfan Ahmad

Survey conducted by The Harris Poll on behalf of University of Phoenix finds among those already using AI in the workplace, 60% say AI has improved their knowledge of and ability to use accessibility standards and guidelines.

As artificial intelligence becomes part of how people work, learn and solve problems, a new University of Phoenix survey conducted by The Harris Poll finds that recent working learners see meaningful opportunities for AI to support accessibility. The survey was designed to understand the impact of AI in the workplace and learning environments on accessibility, defined as ensuring digital content, tools and resources, including AI tools and output, are usable by people with different abilities through inclusive design, use of assistive technology or conformance with accessibility standards, such as the Web Content Accessibility Guidelines (WCAG). The findings are being released ahead of the 36th anniversary of the Americans with Disabilities Act (ADA) on July 26.

The survey, conducted among 1,019 U.S. employed adults who completed a professionally presented training or school course in the past 12 months (“recent working learners”), found that, among workers already using AI in the workplace, 3 in 5 (60%) say AI has improved their knowledge of and ability to use accessibility standards and guidelines, including nearly 1 in 5 (19%) who report significant improvement.

While the findings point to optimism about AI’s accessibility potential, they also reveal an opportunity for clearer organizational guidance: 45% of respondents say accessibility is absent from, unclear in, or they are uncertain whether it is covered by their workplace AI policies.

“The reality is that accessibility benefits everyone,” shares Kelly Hermann, Vice President of Accessibility and Student Affairs at University of Phoenix. “If accessibility is built in from the beginning, organizations are more likely to create AI-enabled environments that are universally usable. Clearer content, better summaries, accurate captions, and multiple formats can help workers and learners with disabilities, but they also help busy adults, multilingual learners, mobile users, and anyone trying to absorb information quickly.”

Key findings from the survey include:

• Workers see AI’s accessibility potential: 89% of recent working learners identify workflows that could benefit from AI and accessibility tools, especially creating accessible documents, presentations, websites or learning materials (38%), presenting information in different formats such as plain language, audio, summaries or translations (33%), and training employees or learners on accessibility practices (30%).
• AI may help build accessibility awareness: Among those already using AI in the workplace, 60% say AI has improved their knowledge of and ability to use accessibility standards and guidelines.
• Accessibility is not always clear in workplace AI policies: 45% of recent working learners say accessibility is absent from, unclear in, or they are uncertain whether it is covered by their workplace AI policies.
• AI tools may not yet fully support different access needs: Among those who use workplace AI tools, only about a quarter of survey respondents (27%) say AI tools available through their workplace or professional learning environment support people with disabilities very well.
• Human oversight remains important: 36% of recent working learners say human review for important decisions or high-impact work should be part of responsible AI use at work or school.
• Workers also recognize how AI and accessibility can have an impact on their own career journey: 90% of recent working learners identify AI and accessibility skills that would be valuable in their current or desired career field, including 45% who see value in understanding when AI-generated content needs human review.

Why accessibility is essential to responsible AI adoption

As AI tools are used to draft documents, summarize information, generate captions and transcripts, create image descriptions, support learning and assist with workplace tasks, accessibility becomes central to responsible use. Poorly implemented AI can also create or amplify barriers, including inaccessible content, inaccurate summaries, biased outputs and tools that do not work effectively with assistive technologies.

“Responsible AI is not only about productivity,” Hermann said. “It is about whether the technology works for the people who need to use it. AI can help create more accessible materials and more flexible ways to engage with information, but it still requires clear policies, practical training and human judgment to make sure the outputs are accurate, applicable and usable.”

What the findings mean for employers and educators

The survey suggests that organizations have an opportunity to align AI adoption with supportive design, accessibility practices and workforce training. Employers and educators can take immediate steps by:

• Naming accessibility directly in AI policies and guidance.
• Choosing AI tools with accessibility and assistive technology compatibility in mind.
• Training workers and learners to create, check and improve accessible AI-generated content.
• Making support pathways clear for people who experience barriers using AI tools.
• Keeping human review in place for important decisions, high-impact work and accessibility-sensitive outputs.

The survey also found workers want practical AI training. The most helpful resources identified by recent working learners include real-world examples from their field or industry (36%), hands-on practice using realistic workplace scenarios (34%) and step-by-step demonstrations of common tasks (33%).

Read next: New study finds that one in four Gen Z rely on AI several times a day
by External Contributor via Digital Information World

New study finds that one in four Gen Z rely on AI several times a day

By Nike Herzog-Osikominu. Reviewed by Irfan Ahmad

Forget turning to friends and family, AI is now becoming the first port of call for Gen Z when it comes to advice seeking.

A new survey of around 2,000 Britons by one of Europe’s leading price comparison sites, idealo.co.uk, revealed that almost one in four Gen Z (23.6%) rely on tools such as ChatGPT, Gemini and Meta AI, consulting them several times a day for help with a range of everyday tasks.

While early AI adoption saw tools mainly being used for faster answer finding, the new data shows growing usage, especially within younger generations, for support with everything from studying and idea generation to responding to texts, deciding what to buy and planning social activities.

The everyday tasks Gen Z (in UK) are most likely to turn to AI for include:

Everyday Task	Percentage of Gen Z Respondents
Finding quick answers to everyday questions	58.8%
Doing homework and studying	54.0%
Coming up with ideas	47.8%
Writing emails, texts and social content	43.9%
Substituting reading for summaries	40.9%
Deciding what to buy	34.0%
Planning their social life	32.5%
Translating languages	26.3%

Not only is there a growing engagement with AI amongst Gen Z, but confidence in the tools is on the rise too.

Of the respondents within this demographic, 70% said they have trust in the results that AI tools provide to some degree, over half (55%) said they trust it mostly and a further 15% said they trust it entirely.

When it comes to preferred tools, ChatGPT continues to lead as the most popular AI platform among young people and accounting for 83% of users. Gemini followed closely behind with 53% of users and 37% using Meta AI.

UK Country Manager at idealo, Nike Herzog-Osikominu, said: “As an online price comparison site, changing digital behaviours across different consumer groups are really interesting to us, and the insights for this recent study have really helped to understand how growing AI adoption is shaping search, discovery and engagement across a range of topics.”

“Whilst it wasn’t necessarily surprising that Gen Z were found to be the most active demographic when it came to usage of tools, the reliance on them several times a day was definitely a key learning for us. AI is no longer just seen as another search engine; it’s helping younger generations form opinions and make decisions on everything from education to shopping and even their social lives.”

Sources: Kantar on behalf of idealo, online survey conducted in May 2026, with approximately 2,000 respondents aged between 18 and 64 in each country. The results are representative of consumers in Germany, France, Italy, Austria, Spain and the United Kingdom.

About Author: Nike Herzog-Osikominu is the Country Manager at idealo.co.uk, one of Europe’s leading price comparison sites, leading Austria and the UK across B2C and B2B sectors.

Read next: 

• The Geography Of App Bans In 2026: For The First Time, The Reversals Are Winning

• Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong
by Guest Contributor via Digital Information World

Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong

Kai Riemer, University of Sydney and Sandra Peter, University of Sydney

Image: Waz Lght - Unsplash

“We are now, like, in the singularity”.

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world”.

Days earlier, OpenAI had disclosed that two of its artificial intelligence (AI) models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is “the singularity”? And is Altman right that we are in it?

What is the AI singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI cannot make itself smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights – or “parameters” – is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents – systems that run an LLM in a loop to work through complex tasks step by step – do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding and the prompt, and nothing happens inside of it.

A ladder that doesn’t exist

The second problem with the singularity story is the word “surpass”. It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes, and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimised to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging super intelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping our feet on the ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education – so we start worrying about the right things.

Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

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

Reviewed by Irfan Ahmad.

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