Survey Finds Americans Distrust AI Answers Lacking Attribution, Preferring Human Guidance And Open Information Sources

By Talker Research

Over eight in 10 Americans don’t fully trust what AI tells them — and they opt to still explore original sources by themselves, according to new research.

The poll of 1,200 U.S. adults revealed 86% are distrustful of AI results, and for 42%, this comes specifically when AI-generated answers don’t clearly show where the answer originates from.

People said they distrust AI-generated search results without clear attribution (42%) more than medical bills (18%), confusing legal print (17%) and airline fees (10%).

Adding to their concerns, 75% are also concerned that what they see online is being controlled by a small handful of companies. Four out of five (81%) think it’s important that the information they get online remains openly available — not kept behind a paywall or owned by big organizations.

Commissioned by WordPress VIP and conducted by Talker Research, 75% of Americans said they find humans much more helpful than AI (15%) when interacting with or consulting for help on a business’s website.

And if they ever suspect who they’re talking to isn’t real, 56% are confident in determining if their chat is AI or a human.

When asked which business uses AI best in its brand messaging, 61% said they were not sure or could not think of one, and another 16% said they do not believe any business uses AI well at all. Only about one in four could name a company at all.

Compared to the internet a decade ago, three in four believe the internet today feels less human.

The average person believes 55% of all internet interactions they have is AI. Similarly, they believe 54% of all interactions they have on a business’s website are also AI.

Nearly all of those polled (92%) said they’ve come across AI or bots when using social media, and 63% said it happens frequently. It takes them just 40 minutes before they start to feel fatigued by the amount of bot-made content they see.

“Brands cannot afford to treat visibility and trust as separate things anymore,” said Steph Yiu, CEO of WordPress VIP. “If people cannot understand where information came from or connect it back to a brand they trust, being visible is not enough. Companies need digital experiences that give them more control over how they appear online and help them keep a direct relationship with their audience. More than ever, the website is where a brand provides context and earns trust.”

The study also polled 800 U.S. marketing executives and digital experience experts to find how they perceive the same issues about AI, their messaging and marketing efforts, and the open web.

Three in four (74%) said ensuring their organization’s website content is discoverable and clearly attributed when surfaced by AI search and answer engines is a main or significant priority. And nine in 10 (91%) believe it’s important their content takes on a more human tone.

Nearly four in 10 (39%) of marketing executives and digital experience experts are “kept up at night” by misinformation from AI-generated responses.

And if it’s not openly available and structured for the public, 69% believe their website will be completely invisible to AI search and answer engines.

“People used to build websites for other people,” said Brian Alvey, CTO of WordPress VIP. “Now you have to build websites for AI agents acting on behalf of those people. If your site’s content isn’t legible to AI, you are invisible to a growing share of how people search. You don’t exist. And if your content doesn’t feel human and trustworthy for the tiny percentage of people who actually click past the AI answer engines, they won’t come back a second time. Most CMSes were built for one of those jobs. The next decade belongs to the ones that do both.”

Reviewed by Irfan Ahmad.

Read next: 

• Google and Apple removed millions of apps in 2025, citing fraud, privacy, and policy violations

• The Rise of AI Influencers: Can Users Still Tell What's Real?

• Google’s AI Search Has Struggled With One Religious Question for Years
by External Contributor via Digital Information World

Google and Apple removed millions of apps in 2025, citing fraud, privacy, and policy violations

By Surfshark

Android leads the global mobile operating system market with 3.8 billion users¹, accounting for approximately 71.4% share, while iOS captures a solid 28.6%, translating to 1.52 billion active users². Both Google and Apple provide users with platforms — Google Play and the App Store — where billions of people discover and download apps every day. However, downloading an app from these stores doesn't always guarantee a safe and secure experience.

To examine how app stores address policy violations and user safety concerns, Surfshark analyzed platform transparency reports. Here's what they found:

Key insights

• Nearly 2.2 million apps were removed from Google Play and the App Store in 2025, averaging approximately 6,000 per day. However, the two dominant app ecosystems trended in opposite directions. According to the Google Play Annual Transparency Report, Google deleted over 2 million apps due to violations of terms and conditions — nearly half the number deleted in 2024. Meanwhile, Apple’s App Store Transparency Report revealed that app removals more than doubled, to nearly 167,000, compared with over 82,500 in 2024.
  
  
• The App Store indicated that the main reasons for app removals were fraud (54%) and the ongoing effort to clean up outdated apps (43%). Fraud-related removals were primarily associated with developers from China (13%), Pakistan (11%), the United States (11%), Turkey (11%), and Vietnam (8%), indicating a concentration in Asia, similar to the pattern seen the year before.³
  
  
• The Google Play Annual Transparency Report highlighted data protection and privacy violations as the leading cause of app removals (44%). Other violations included content, goods, and services ineligible for distribution through Google Play (35%), consumer information infringements (13%), and scams or fraud (5%). Additionally, to help keep users safe from fraud, Google Play Protect blocked 266 million risky installation attempts and protected them from 872,000 high-risk applications in 2025.⁴
  
  
• Before being released on either platform, apps must undergo a review process. In 2025, Apple rejected 23% of submissions to the App Store, whereas Google Play's rejection rate was nearly three times lower at 8%, meaning 9 out of 10 submissions went through.
  
  
• In addition to removing individual apps, platforms also terminate developer accounts. In 2025, Apple terminated over 193,000 developer accounts, a 32% year-over-year increase. Google reported a 19% decline in account closures, resulting in the termination of approximately 126,000 developer accounts. At the same time, account restorations saw a massive lift: Google reinstated over 12,500 accounts in 2025 — an eightfold increase from the above 1,500 restored in 2024. Meanwhile, Apple brought back nearly 500 developer accounts, which was more than double the 225 they restored the year before.

Methodology and sources

This study is based on information provided in the Google Play and App Store transparency reports and supplemental data files. While the main focus is on 2025, the analysis also includes historical data going back to 2024. The exploration covers various aspects, such as the number of apps removed, the reasons for their removal, the rates of app submissions and rejections before release on the platforms, and the number of terminated developer accounts.

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

Data was collected from:

Google (2026). Google Play Annual Transparency Report for 2024–2025;

Apple (2026). App Store Transparency Reports and Supplemental Data Files for 2024–2025.

References:

¹Business of Apps (2026). Android Statistics;

²SQ Magazine (2026). iPhone Statistics 2026: Apple’s Market Power Now;

³Surfshark (2025). Google and Apple delete up to 11,000 apps every day;

⁴Google (2026). Keeping Google Play & Android app ecosystems safe in 2025.

Reviewed by Irfan Ahmad.

Rea next: The Rise of AI Influencers: Can Users Still Tell What's Real?

by External Contributor via Digital Information World

The Rise of AI Influencers: Can Users Still Tell What's Real?

The AI craze is here, and it’s taking a lot more effort to differentiate between AI and reality. From badly designed AI photos, we’ve advanced to a full-scale AI generation pipeline that includes photos, flyers, videos, and content schedules. Now, the latest craze is “AI influencers”. Beyond creating content with AI, more brands are leaning towards creating their own “influencers” with AI. While some of these avatars are still clearly discernible, some look, sound, and engage social media trends like actual human influencers.

Photo by ThisIsEngineering - Pexels

On the surface, AI influencers seem harmless. But when you factor in the increase in misinformation, propaganda, rage-baiting, and fear-mongering as a content strategy, there’s a lot of risk to unpack. Top cybersecurity news platforms are already asking the real question - with the lines between AI and reality being heavily blurred, how can users tell the difference?

What are AI Influencers?

AI influencers are virtual personas created by brands or developers that operate without a real human identity behind them. These AI-created avatars post content, speak, and act like actual human influencers. Although they're highly adaptable, they’re just like chatbots and automated posts, which aren’t real and primarily interact using preset content and information.

Initially, they were mostly seen on text-content platforms like X., but the recent growth in AI video/photo generation has ushered in a new wave of AI influencers who post videos and pictures, and also stream live across platforms like TikTok, YouTube, Instagram, and Snapchat.

The AI Appeal

There’s a popular dilemma with AI content, and it’s the fact that most internet users feel it's inauthentic. There’s always a cry for content that feels human and connects on an emotional level. While the desire for authentic human content is valid, there’s a contrasting reality in which AI content starts looking more appealing. In most cases, users cringe-watch AI content despite the latent dislike for AI. Gradually, this has evolved into users slowly getting comfortable with and seeking out AI content. For instance, while the typical glitches seen in AI-generated content can be annoying, they also provide cringe enjoyment.

On the business side, brands have been doing a lot of AI content testing, and the results are more favorable than regular content. Investigative outlets like Cybernews report that this is largely because AI content is technically cheaper, faster, and easier to generate.

Besides this, most brands spend time and money trying to game the social media algorithm for wider reach. But, the prevailing issue is that you may do everything “right”, yet the algorithm will still not be favorable in pushing the curated content without paid boosts or ads. AI influencers don’t have such problems.

Despite the recent spike in flagging of AI content, AI influencers and their content have been successful in gaming algorithms way better than human content. While the average human social media manager needs to strategically create content, schedule posting to optimal times during the day, and also jump on trends, AI influencers don’t have to jump through so many hoops. In a way, the fact that AI influencers are born of “codes”, much like platform algorithms, it seems their ability to understand and exploit the system is innate. Hence, it’s no surprise that brands and businesses are choosing them over human influencers and social media managers.

3 Ways To Tell the Difference

At the moment, we’ve already had funny teasers of “cake vs real”. A whole new content niche has also been created centering on “AI vs real”.

With AI content getting more sophisticated, it’s nearly impossible to tell what is “real” or not. But there are still telltale signs that users can rely on.

Here are 5 ways to spot the difference:

AI is Soulless and Agreeable:

The common perception is that AI has “no soul”. As such, it’s mostly a yes-man that’s trained to conform to the preferences and gimmicks of users and prompts. A quick review of most AI influencer accounts would reveal that just like their chatbot counterparts, they are loyal to a fault. They have no moral inhibition except the restrictions placed by their code. And this makes them the perfect channel for rage-baiting, clout chasing, trend hopping, and churning controversies, which the average human influencer would hesitate on. So, if an influencer is sounding “too agreeable” and is hopping on every trend, it’s most likely AI.

Detail Inconsistencies:

Most AI content defies visual laws. Lighting is often too crisp, and there are noticeable changes in background detail. A character might appear with white hair in one frame, but it unexplainably switches to black in the next frame. And there's the infamous excess fingers and toes.

Exaggerated Smoothness:

With AI, there's always an unnatural smoothness to character details. No matter how trained or glammed up a human is, there's an uneven roughness to the skin and fluctuations in speech that are innate. But with AI, characters are often too smooth in appearance and speech. Even if an AI character looks rough, there’s always that smoothness or precision that tells it off.

Conclusion

While users are mostly concerned with what is AI vs real, the ultimate question is about what is “true”. Propaganda and misinformation are two of the biggest risks with AI. Since AI typically reinforces user bias, it typically hallucinates facts and context in a way that is agreeable but misleading.

With numerous reports on self-harm caused by AI conversations and posts, it’s unsurprising that many nations are actively seeking to regulate AI content and protect younger minds. What’s more is the fact that with AI influencers primarily posting biased content, it’s a lot more difficult for actual brand voices to be heard.

But that’s where content monitoring and revision come in. While AI influencers run automatically, having a human content strategist review whatever goes out remains the best way to regulate AI content to reflect brand voice, pass authentic information, and also retain the human touch.

by Asim BN via Digital Information World

How AI prompting turned writerly description into an everyday skill

Lei Yu, Western University

Image: Blackcreek Corporate - Unsplash

You are sitting at your computer, interacting with a generative AI model like ChatGPT Image or Midjourney. You have a distinct picture in your mind, and you begin with a simple, general prompt: a chair in a cozy room.

The image appears, but you frown. You realize that to get what you want, you must elaborate, so you experiment with more descriptive prompts: Dark mahogany wood. Dim yellow lamplight. Late autumn dusk. You keep revising, trying to discover which words the machine needs and which words it ignores.

You are wrestling with a problem: how do you describe a feeling? How do you communicate warmth, melancholy, intimacy or calm — not to another human being, but to a machine?

This is one novel frustration of the AI age, yet millions of users searching for the “right prompt” are engaging in an old literary practice: turning mental images, vague desires and atmospheric intuitions into precise language.

Modernist writers and description

Generative AI has transformed description from a literary technique into a mass social skill.

This frustration in fact has an unexpected literary history. More than a century ago, writers faced a similar question when new visual technologies began to change how reality could be represented. Photography, and later cinema, could capture surfaces, bodies and landscapes with a speed and accuracy prose could not match. If machines could show the visible world more efficiently than language, what was writing for?

In Strange Likeness: Description and the Modernist Novel, literary scholar Dora Zhang argues that many early 20th-century novelists responded by rethinking the role of description itself.

Modernist author Virginia Woolf, among others, sought to capture the shifting textures of consciousness. (Harvard University Library/Wikimedia)

Rather than competing with cameras in the faithful rendering of objects, modernist writers such as Henry James, Marcel Proust and Virginia Woolf turned toward phenomena that resisted mechanical capture: atmosphere, sensation, relation, mood and the shifting textures of consciousness.

This helps explain why modernist fiction can feel so different from the realism of the 19th century.

Shift from earlier novels

Earlier realist novels by writers like Honoré de Balzac and Charles Dickens often described rooms, clothes and streets in exhaustive detail, helping readers imagine social worlds they could not directly see.

Modernist writers still described, but they increasingly described what did not simply look like anything at all: the tension in a room, the strange resemblance between two unrelated things, the emotional weather of an afternoon, the half-formed feeling of memory returning.

In other words, when cameras became better at recording surfaces, literature moved toward what surfaces could not contain.

Evoking atmosphere

Generative AI has unexpectedly reversed that history. Photography reduced the need for verbal depiction by allowing images to be mechanically captured. AI systems increase the need for verbal depiction by requiring users to verbally specify the qualities of desired images.

To generate a scene, you must now do for the machine what earlier novelists once did for readers: translate objects, spaces and moods into words. The challenge is not just naming things. Anyone who has used image generators knows that describing objects alone does not produce satisfying images.

You also need what internet culture now calls the “vibe.” Vibe refers to the diffuse emotional and sensory qualities that surround objects without being reducible to them. It is the kind of phenomenon modernist writers became increasingly interested in describing.

In this sense, prompt writing combines two older literary tasks at once: the realist description of concrete things and the modernist evocation of atmosphere.

Interacting with generative models

Interacting with these generative models also draws attention to a phenomenon that literary scholar Elaine Scarry has long contemplated. We could also think about prompting, as a writerly, descriptive act, as demonstrating what she refers to as “perceptual mimesis.”

Mimesis (Greek for “imitation”) through esthetic theory has been concerned with “representation.” Scarry’s literary criticism has explored how authors’ discriptions act as instructions to guide the reader’s vivid mental images.

Reflecting on using language to represent our ideas in dialogue with machines opens up reverberating questions about how this could affect our thoughts about ourselves and the world.

AI boom hasn’t ended writing

We often hear that AI will replace writers. In one important sense, it has done the opposite. It has redistributed one of writing’s oldest skills across everyday life.

Office workers, students, teenagers, marketers and hobbyists now spend their time refining prompts, comparing phrases and learning how slight changes in wording alter results. They are practising description, even if they don’t call it that.

The AI boom has not ended writing. It has made writers of us all.

Lei Yu, PhD Candidate in Comparative Literature, Western University

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

Reviewed by Irfan Ahmad.

Read next:

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

• UK social media ban: tech restrictions for teens can’t be the only approach

by External Contributor via Digital Information World

UK social media ban: tech restrictions for teens can’t be the only approach

Emily Setty, University of Surrey

Image: Lesli Whitecotton - Unsplash

The UK government’s decision to introduce restrictions on children’s access to social media marks a significant moment in the evolution of online safety policy. For supporters, it represents a long-overdue response to growing concerns about children’s wellbeing. For critics, it raises questions about effectiveness, enforcement and unintended consequences.

Yet regardless of where one stands on the policy itself, its announcement provides an opportunity to reflect on a broader question: what exactly has this debate been about?

At one level, the answer appears straightforward. Public concern about children’s social media use has grown steadily over recent years. It has been fuelled by worries about a wide range of issues, from mental health and body image to online exploitation, misinformation and the changing nature of childhood itself. The government’s proposals are intended to respond to these concerns and reduce young people’s exposure to risk.

Yet one of the striking features of the debate is that the phrase “social media harms” has come to encompass an extraordinary range of anxieties. Depending on who is speaking, the problem may be cyberbullying, pornography, misogynistic influencers, loneliness, political polarisation, declining attention spans, excessive screen time, image-based abuse or the feeling that childhood is becoming increasingly mediated through screens.

These concerns are real and deserving of attention but they do not necessarily share the same causes or solutions.

When multiple anxieties become bundled together, it becomes tempting to seek a single response. Yet many of the challenges that worry parents, educators and policymakers are not solely technological in nature.

Young people were navigating body image pressures long before social media. Bullying and social exclusion existed before smartphones. Concerns about unrealistic representations of sex and relationships and success have existed for decades. Young people have always had to negotiate questions of identity, belonging, popularity and status.

Social media may amplify these dynamics, but it does not create them from nothing. Understanding this distinction is important because it shapes how we understand both the problem and the solution. If online harms are understood primarily as problems of access, restricting access becomes the obvious response. If they are understood as the product of interactions between technology, relationships, culture and wider social conditions, the picture becomes considerably more complicated.

Changing relationships with tech

As a researcher who studies young people’s digital lives, what has struck me most throughout these debates is that many discussions about children and social media are not really about children and social media alone. They are also conversations about how adults feel about technology more generally.

Over the past two decades, digital technologies have transformed how people communicate, access information, form relationships and participate in public life. For much of that period, these developments were discussed primarily in terms of opportunity, innovation and connection. Increasingly, however, public conversations about technology are framed through the language of risk, uncertainty and loss.

Concerns about social media sit alongside wider unease about the power of technology companies. They accompany fears about the commercialisation of attention, the collection of personal data, the spread of misinformation and the growing influence of algorithms over everyday life.

Right now, debates about children’s social media use are unfolding against a backdrop of rapid technological change more broadly. The emergence of generative AI, deepfakes and increasingly sophisticated algorithmic systems has intensified public uncertainty about the role technology should play in society.

Parents, educators and policymakers are being asked to make decisions about technologies whose long-term implications remain unclear. Researchers are trying to study developments that evolve faster than evidence can often keep pace with. Schools are preparing young people for futures that are difficult to imagine.

In this context, proposals to restrict children’s access to social media can offer something that is often in short supply: a sense of certainty and control. They provide a visible intervention that governments can announce, institutions can implement and parents can understand. Faced with complex and rapidly evolving challenges, there is understandable appeal in policies that appear to offer a clear solution.

However, there is an important difference between taking action and resolving a problem.

What happens next?

One of the lessons emerging from international experience, including developments in Australia, is that the effectiveness of such restrictions remains uncertain. Young people may migrate to alternative platforms or create hidden accounts. They may become less willing to discuss their online experiences with trusted adults. Some may lose access to online communities, information or support networks that play an important role in their lives. The available evidence does not yet allow us to confidently conclude that restricting access will produce the wide-ranging benefits that many hope for.

This does not necessarily mean that restrictions are misguided. It does, however, suggest that policies can sometimes provide reassurance before we know whether they will meaningfully reduce harm. In that sense, there is a risk that social media bans become partly performative. They demonstrate that something is being done and may provide a welcome sense of action in the face of uncertainty. Yet they can also encourage the belief that a complex problem is being solved when many of the underlying issues remain unresolved.

Perhaps the greatest danger is not that restrictions fail, but that they succeed just enough to convince us that the work is done.

Even if age restrictions prove effective, young people will still eventually enter digital environments. They will still need to understand how algorithms shape the information they encounter. They will still need to evaluate misinformation, navigate relationships online, recognise manipulation and make sense of increasingly complex digital cultures. They will still require opportunities to develop critical thinking, digital literacy and healthy relationship skills.

More fundamentally, questions about the design of digital environments themselves will remain. If our concerns centre on addictive design, algorithmic amplification, misinformation or the concentration of power among technology companies, then restricting children’s access addresses only part of the issue. The broader challenge concerns the nature of the digital spaces that all of us inhabit.

Emily Setty, Associate Professor in Criminology, University of Surrey

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

Reviewed by Irfan Ahmad.

Read next: Bad News Overload? News Avoidance on the Rise

by External Contributor via Digital Information World

Bad News Overload? News Avoidance on the Rise

By Felix Richter, Felix Richter, Statista

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

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

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

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

Reviewed by Irfan Ahmad.

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

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

Paul Jones, Aston University

Image: Redmind Studio - Unsplash

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

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

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

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

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

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

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

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

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

Building an identity

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

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

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

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

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

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

Invisible work

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

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

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

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

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

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

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

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

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

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

Reviewed by Irfan Ahmad.

Read next: 

• UK CMA Introduces New Google Search Rules Covering AI Overviews

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

by External Contributor via Digital Information World