One ChatGPT query uses more energy than you think

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

by External Contributor via Digital Information World

World Mismanages 52 Million Tons of Plastic Waste per Year

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
by External Contributor via Digital Information World

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

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?
by Guest Contributor via Digital Information World

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

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.

Read next: 

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

• Many Teenagers Show Symptoms of Excessive Screen Use
by Guest Contributor via Digital Information World

Many Teenagers Show Symptoms of Excessive Screen Use

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?
by External Contributor via Digital Information World

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

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.

Read next:

• WhatsApp Opens Username Reservations Ahead of Feature Launch

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

• Explained: Google Search Caliph Problem and Why Answers Are Inconsistent

by External Contributor via Digital Information World

Teens Encounter a Myriad of Problematic Content Online

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.

Read next:

• WhatsApp Opens Username Reservations Ahead of Feature Launch

• What 20 million bans reveal about the strain on Wikipedia’s volunteers
by External Contributor via Digital Information World