Americans Are Losing 2 Months a Year to Scrolling... Here Are 5 Ways to Fix This

By Rachel Perez

Smartphones give humans an infinite amount of connection, whether it be through calling or FaceTime, texts, or messaging via social media apps. While being able to connect with people hundreds or even thousands of miles away is quite the superpower, it can also lead to some brain-damaging habits that affect people’s ability to focus or remain emotionally stable.

According to this recent study by Solitiared, Americans spend 1,460 hours a year scrolling on their phone, which amounts to 61 days or two months out of the year. Imagine the amount of free time people would have if they put their phones down and focused on something else! And it’s not for lack of trying, as this study points out that 70% of Americans surveyed have tried to reduce their screen time recently. It’s just very difficult to get rid of scrolling habits that are so ingrained in the brain.

Yet hope is not lost, as it’s entirely possible to break these habits and build new ones if you have a good strategy. So in this post, you can check out five different ways to reduce screen time with tips and tricks to keep you from reaching for your phone and doomscrolling.

Charts: Solitaired

1. Start Small

Try to set a small screen time goal, whether it’s staying off of one social media app for a whole day or only trying to reduce screen time by a specific increment of time, such as 30 minutes per day. You can adjust to more limits each week so that what starts as something small can make significant change in just weeks.

Rome wasn’t built in a day, and screen time certainly can’t be reduced in a day either. While small goals might seem pointless, they can help make the screen time problem seem a little less daunting, and meeting smaller goals can boost confidence and keep you on track to reduce your screen time.

2. Track Screen Time

Many smartphones allow you to track their screen time in your settings app, mapping out usage over a day, week, or month. By looking at your screen time, you can see which apps are being used the most each day and determine whether your screen time is high because you send tons of emails per day for work on your phone or because you’re spending large chunks of time doomscrolling on Instagram and TikTok. If you find yourself spending too much time on one app (or two or three!), you can target those for reduction.

Tracking screen time also offers a tangible way to hold you accountable for the smaller goals you’ve set. For example, if you decide to spend one less hour on Facebook each day, looking at your screen time for that particular app can help you stay on track. Watching your progress over time can be rewarding as you see yourself reducing the time you spend on the app over weeks or months.

3. Schedule an “Unplugging”

Most people seem tied to their phones these days because much of what we do can be filtered through our phones. You might be switching between scrolling on social media apps, texting with friends and family, or using your device for work or school. While not all screen time is a waste of time, however, being tethered to the beck and call of an incoming message or notification can make you a little too dependent on that device.

To combat this, consider an “unplugging.” It can be as simple as putting the phone away in a drawer for an hour or two or staying off your device for an entire day. Unplugging gives you a chance to spend some time off your device completely, not just off a single app. Use this time to connect with the real world in a purposeful, meaningful way. Whether you meet someone for a coffee, pick up a new hobby, relax outdoors, or just work on that to-do list that keeps growing, giving yourself a break from your phone can help you relax and begin new habits that don’t keep you attached to your phone.

4. Social Media Cleanse

One of the most talked-about approaches to reducing screen time is a cleanse. By permanently deleting social media or locking those apps so access is restricted throughout the day, you can finally resist the urge to doomscroll and focus on other things besides social media because you don’t have access to it.

There are other benefits to taking a break from social media. Social media has long been a source of depression and anxiety, with people focused on generating likes and perfect looks rather than things that truly generate joy and fulfillment. By reducing time on those apps or getting rid of the apps altogether, you can free yourself from that cycle.

5. Take on a Brain Workout

For some, scrolling on their phones is simply a way to pass the time while waiting on that email response form work or taking a lunch break. But you can still be on your phone and put your brain to work by doing a brain workout instead of doomscrolling. It can be as simple as trying a word game or playing a quick game of Solitaire. While it’s tempting to get sucked into your phone and scroll mindlessly, you can choose to engage your brain with some mental gymnastics.

Bad habits are always difficult to break, no matter what they are, and creating new habits is even harder. Thankfully, you don’t need to break the scrolling habit immediately. Starting with a small goal makes adjusting to less time on the phone a lot easier. Plus, there are other things to fill time with, such as games that work the brain or just putting the phone away and going outside. By finding the trick that works best for you, you can help yourself by scrolling less and getting that screen time down!

Reviewed by Irfan Ahmad.

Read next: 

• What everyone gets wrong about the modern job search — and what actually works

• New study explores rise of 'ragebait' and its impact on online accountability
by Guest Contributor via Digital Information World

What everyone gets wrong about the modern job search — and what actually works

Leda Stawnychko, Mount Royal University and Mehnaz Rafi, University of Calgary

Image: Swello - Unsplash

Job searching has never been more accessible — or more confusing. Platforms like LinkedIn, Indeed and employer career pages let candidates submit applications with just a few clicks. What happens after they click “submit,” however, has become fertile ground for misinformation.

Social media is filled with “career influencers,” resume writers, recruiters and companies promising insider knowledge of how hiring really works. Much of this advice focuses on misinformed claims about applicant-tracking systems (ATS) and artificial intelligence.

These services profit from job seekers’ uncertainty and convincing people they need specialized services, tools and products to “beat” the ATS and secure interviews.

The result is that many job seekers spend time and money following advice that has no basis in evidence. Here are four common myths about the job application process, and what the research actually says.

Myth 1: 75 per cent of resumes are rejected

Perhaps the most widely repeated claim online is that 75 per cent of resumes are automatically rejected by an ATS before a human recruiter ever sees them.

The statistic originated from a 2012 sales pitch by Preptel, a resume optimization company that went out of business the following year. No methodology was ever published, yet the figure has spread widely.

In reality, an ATS is software that helps employers manage applications, and its capabilities vary widely. Some systems function as digital filing cabinets, simply storing and organizing applications.

Others automatically screen for basic requirements, such as mandatory eligibility questions. At the most sophisticated end, systems use AI to rank applicants, recommend candidates and analyze asynchronous video interviews.

The advanced AI-powered tools are typically found in large organizations, including many Fortune 500 companies, which receive enormous volumes of applications. In Canada, most employers do not use AI in hiring, and small businesses — which employ more than 60 per cent of the workforce — are especially unlikely to rely on ATS.

Small businesses typically lack both the application volumes that make ATS worthwhile and the procurement infrastructure to adopt and maintain them.

For most Canadian job seekers, the better strategy is to focus on clearly communicating how their skills and experience match the role, and on building relationships within their profession.

Myth 2: AI can write a winning resume

A common message from career influencers is that AI can generate a tailored resume or cover letter that dramatically improves your chances of getting hired. While AI can help candidates prepare application materials more efficiently, it is not a shortcut to a stronger application.

As more candidates rely on the same tools and prompts, applications increasingly sound similar and recruiters take notice.

Far from providing a competitive advantage, AI-generated applications may have the opposite effect. Seventy-four per cent of hiring managers report identifying them, and 80 per cent view them unfavourably.

The best approach is to use AI to augment your own voice. That means using it to refine and sharpen your draft, not replace its substance.

Research on Canadian hiring suggests candidates secure more interviews when their applications contain more detail, clarity and structure. Since today’s recruiters review a myriad of applications that look and sound the same, they tend to respond to the ones that stand out by communicating qualifications in an authentic voice.

Myth 3: Use ‘ATS-friendly’ resume templates

Resume writers and career influencers claim that using an “ATS-friendly” template is essential for “beating” the ATS. Some even sell templates that promise to “optimize” your resume to secure interviews.

In reality, there is no universal ATS-friendly resume because the software employers use varies widely from one company to another. Additionally, modern ATS can extract information from common resume layouts, including columns or tables.

Their main limitation is that they are designed to process text, not images, graphics or icons. That means a clean, readable resume should be the actual target, not a template bought online.

If ATS doesn’t automatically reject resumes the way the influencer economy claims, then optimizing for a system that largely doesn’t work that way is solving the wrong problem. The real audience for your resume is a person, not an algorithm.

The better approach is to write for both systems and people. Use clear headings, relevant keywords and concrete examples that show how your experience matches the role.

Myth 4: More applications, more interviews

Another myth is that, with the right prompts, the job search can be fully automated, allowing candidates to submit hundreds of applications with little effort. More applications should lead to more interviews, the logic goes.

In practice, this approach often comes at the expense of thoughtful job-seeking, such as identifying positions and employers that genuinely match your skills and interests, and crafting applications that reflect that fit.

AI is most effective when it enhances, rather than replaces, a candidate’s work, helping to avoid what has become known as “workslop” — a term for generic, AI-generated content.

Candidates are best served by using AI for brainstorming and polishing while ensuring the final version accurately and authentically reflects your experiences, accomplishments and voice.

The fundamentals haven’t changed

Today’s labour market may look different, but the fundamentals of a successful job search haven’t changed much. In that sense, the best thing job seekers can do may be to ignore most of what they’re being sold.

The strongest applications are those that clearly connect a candidate’s experiences to the role, provide concrete evidence of their abilities and communicate in an authentic voice.

Technology may help employers manage applications, but hiring decisions are ultimately made by people. That makes professional networks, trusted referrals, strong communication and leadership skills more valuable than ever.

Put the time you’d spend on template optimization into one good conversation with someone in your field. The research suggests it’ll go further.

Leda Stawnychko, Associate Professor of Strategy and Organizational Theory, Mount Royal University and Mehnaz Rafi, PhD Candidate, Haskayne School of Business, University of Calgary

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

Reviewed by Irfan Ahmad.

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

by External Contributor via Digital Information World

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

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
by External Contributor via Digital Information World

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

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
by AI Analysis via Digital Information World

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.

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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.

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

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• Is Your Government or Organization Ready to Prevent AI Cyber Attacks—at Scale?
by Guest Contributor via Digital Information World