How AI Is Supporting the Next Generation of Small Businesses

By Jelyn Johnson

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

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

A New Formula to Help Business Growth

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

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

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

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

Workflow Automation is Becoming the Primary Driver of Adoption

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

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

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

Time Savings are Being Allocated to Higher-Value Work

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

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

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

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

AI is Quietly Standardizing Small Business Workflows

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

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

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

How Gains in Productivity are Beginning to Show Their Impact

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

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

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

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

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

The Emerging “AI-first” Small Business Model

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

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

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

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

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

Some key insights:

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

The Next Phase of Technology

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

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

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

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

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

Reviewed by Irfan Ahmad.

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• The Highest-Earning Creators of the Internet Content Machine

• “Friendly Reminder”: Top Phrases Professionals Use to Soften Tone in Emails
by Guest Contributor via Digital Information World

The Highest-Earning Creators of the Internet Content Machine

By Katharina Buchholz, Statista

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

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

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

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

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

Reviewed by Irfan Ahmad.

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

• “Friendly Reminder”: Top Phrases Professionals Use to Soften Tone in Emails
by External Contributor via Digital Information World

Spy agencies say AI can help combat AI cyber risks. But don’t forget the basics

Toby Murray, The University of Melbourne

Cybersecurity agencies of Australia, Canada, New Zealand, the United Kingdom and the United States issued a call to action on Monday for cyber defenders. The message was clear: artificial intelligence (AI) is a powerful weapon for cyber attackers; defenders must act urgently to improve their cyber defences.

Image: Trophim Laptev/Unsplash

There is much hype and uncertainty surrounding AI and cybersecurity right now. This latest statement comes little over a week since the US government caused frontier AI provider Anthropic to block access to Mythos and Fable, its most advanced AI technology, over fears they might be misused by foreign adversaries to attack US government systems.

In this torrid environment, it’s important for cyber defenders to look past the noise and prioritise what is truly important in protecting their systems.

A call to arms

The joint statement was issued by the heads of the national cybersecurity agencies of the Five Eyes. It warns that AI is dramatically shifting cyber risk and spells out how defenders must act to secure their organisations.

It notes how powerful AI is already helping adversaries carry out more sophisticated attacks more quickly.

One way this is happening is through automated vulnerability discovery and exploitation. No software is perfect. Adversaries leverage subtle design or implementation flaws in a system’s software to break into that system. They then take control of it and use it as a staging ground to launch further attacks.

This is why it’s so important for cyber defenders to keep up to date with deploying software patches. These are small modifications to system software that close off known vulnerabilities.

AI is enabling adversaries to find flaws orders of magnitude faster, as well as to work out how to exploit those flaws to carry out attacks.

For this reason, the Five Eyes statement warns that AI is dramatically shrinking the time between when a vulnerability is first discovered and when it is first exploited in an attack. Defenders can no longer afford to wait weeks before deploying software patches.

What can defenders do?

The Five Eyes report notes cyber fundamentals are crucial and encourages organisations to use AI to boost defences. But deploying AI without first investing in cybersecurity basics would be a mistake.

The cyber defenders who will be able to weather the AI storm will be those who already have mature practices. They know exactly what assets they need to protect, which systems in their organisation are exposed to attack, and what defences are in place to protect exposed systems. They also know to measure defence effectiveness and determine where defences are missing.

They also use evidence-based processes for tracking known vulnerabilities in their systems and prioritising which are most important to patch. These are backed up by reliable processes for rapidly testing and rolling out software patches, as well as for responding to cyber breaches and incidents.

When AI makes finding software vulnerabilities cheap, the next generation of software needs to be engineered to be secure by construction.

Working out the best methods to do this is what I have devoted my research career to.

Before reaching for AI, defenders should first invest in their fundamentals. Otherwise, they are effectively deploying a robot guard dog to defend an unlocked door.

The role for AI in cyber defence

This doesn’t mean AI can’t play an important role for cyber defence – just that it should augment rather than replace strong cyber fundamentals.

AI benefits attackers and defenders alike. An AI model that can help attackers find software vulnerabilities can also help defenders fix those same vulnerabilities.

AI that can automatically exploit software vulnerabilities is just as useful to defenders in helping them to confirm their software has been correctly patched. AI that can map and discover sensitive assets within a computer network is useful for both offensive and defensive purposes.

This is why it’s so important that defenders have access to AI capabilities, so they can be leveraged to harden and protect systems before that same AI is used to attack them.

Can regulation help?

Working out how to balance the competing benefits and risks of new cybersecurity technology is nothing new.

In the 1990s, society grappled with how to regulate the encryption that protects online communication from adversaries but also allows them to avoid law enforcement.

In the 2000s the rise of cyber exploit kits allowed defenders to better test their systems but also enabled any disaffected teenager with an internet connection to become a “script kiddie” hacker, leading to arms controls debates a decade later.

The 2010s gave us blockchain technologies such as Bitcoin and other cryptocurrencies, which were built on defensive cyber technologies but whose lasting legacy remains the rise of ransomware attacks and online illicit marketplaces.

The rise of AI presents a similar dilemma for regulators.

A blanket export ban on advanced AI models is likely to be counterproductive. Open-source AI models such as DeepSeek lag only months behind the most advanced models of OpenAI and Anthropic. Recent research suggests that much of that gap can be closed by pairing less powerful AI models with complementary technologies.

Defenders should therefore assume their adversaries already have access to AI on par with that used for cyber defence. Only by investing in strong foundations can they hope to escape the cat-and-mouse AI cyber arms race.

Toby Murray, Professor of Cybersecurity, School of Computing and Information Systems, The University of Melbourne

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

Reviewed by Irfan Ahmad.

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

• Top AI Logo Makers in 2026 [Ad]

by External Contributor via Digital Information World

Top AI Logo Makers in 2026 [Ad]

If you had to create a logo today, would you still start from scratch, hire a designer, or try one of the many AI logo tools available online?

The reality is, most people now turn to AI logo makers first. The challenge isn’t access anymore… It’s choosing the right tool from a growing list that all promise fast, professional results but deliver very different experiences.

In this article, we look at the top AI logo makers in 2026 and how they actually perform when used for real branding work.

Quick Verdict

After testing these platforms across various branding scenarios, Design.com emerged as the most complete option overall. It consistently produced strong logo concepts quickly while also offering deeper customization and broader branding tools compared to the rest.

That said, not every tool serves the same purpose. Some are better for quick experimentation, while others are designed for full brand ecosystems or website-first businesses.

We tested these tools based on:

• Speed of logo generation: how quickly usable logo concepts are created
• Design quality and originality: whether outputs feel generic or professionally crafted
• Customization flexibility: how much control users have over the final design
• Branding ecosystem: whether logos extend into other assets like websites or marketing materials
• Export usability: file formats, resolution, and real-world application readiness

Quick Comparison

Tool	Best For	Strength	Limitation
Design.com	Full branding systems	Deep AI + branding tools	No native app yet
BrandCrowd	Template-based logo creation	Fast workflow	Less technical integration
Canva	General design & collaboration	Easy and versatile	Less logo-specialized
Adobe Express	Simple logo creation	Clean interface	Fewer branding features
Shopify (Hatchful)	E-commerce branding	Simple store-focused logos	Limited design depth

1. Design.com – Best Overall AI Logo Maker for Full Branding

Design.com is built as a full branding platform rather than just a logo maker. When we tested it, the system didn’t just create logos, it generated full brand directions, including variations, layouts, and supporting design assets.

What stood out most was how the AI could be adjusted conversationally. Instead of manually editing every element, we could request changes like “make the logo more minimal,” “change to a warmer color palette,” or “try a more modern icon style,” and the system would update the design accordingly.

It feels less like using a template tool and more like iterating with a design assistant.

Key Features

• Conversational AI editing for real-time logo adjustments
• Large library of logo templates and design assets
• Automatic brand consistency across fonts and colors
• Built-in tools for business cards, websites, and marketing materials
• Multiple export formats, including print-ready files

Pros

• Strong AI-driven customization flow
• High-quality logo outputs
• Full branding ecosystem beyond logos
• Fast iteration process

Cons

• Web-based only

Pricing

Free Plan:

Design.com offers a genuinely free logo option with no watermarks, high-quality downloads, full editor access (including AI chat editing), and a solid selection of free logo templates.

Paid Plans:

• Starter: from $3/month (annual) or $9/month
• Value: from $4/month (annual) or $14/month
• Premium: from $5/month (annual) or $19/month

See it in action below:

Logo samples:

Logo customization:

Asking AI to do the changes:

2. BrandCrowd - Best for Template-Driven Logo Creation

BrandCrowd focuses heavily on speed and template variety. When we tested it, the workflow was straightforward: enter a business name, browse logo design concepts, and customize from there.

It works well for users who want something polished quickly without spending too much time refining details.

Key Features

• Large library of logo templates
• Fast keyword-based logo generation
• Simple customization editor
• Export options for digital and print use
• Additional branding assets, like business cards and social media designs

Pros

• Very fast workflow
• Large template selection
• Easy to use

Cons

• Less technical integration

Pricing

Free Plan:

BrandCrowd offers a selection of genuinely free logo templates with no watermarks, full editor access, and high-quality downloads in formats such as PNG, JPG, SVG, PDF, EPS, GIF, and MP4.

Paid Plans:

• Starter: from $3/month (annual) or $9/month (monthly)
• Value: from $4/month (annual) or $14/month (monthly)
• Premium: from $5/month (annual) or $19/month (monthly)

See it in action below:

Logo samples:

Logo Customization:

3. Canva – Best for All-in-One Design Work

Canva is less of a dedicated logo maker and more of a full design platform. In testing, we found that logo creation is only the starting point; most users will likely stay within Canva to build the rest of their design.

Its strength lies in collaboration and versatility.

Key Features

• Drag-and-drop logo editor
• Millions of design templates
• Real-time team collaboration
• Brand kit tools for consistency
• Easy export across formats

Pros

• Extremely easy to use
• Strong collaboration features
• Huge template ecosystem

Cons

• Not specialized in logo design
• Advanced branding requires a paid plan

Pricing

• Free Plan: Includes access to templates, basic design tools, and a large library of free assets for logo creation.
• Pro Plan: around $12.99/month – unlocks premium templates, Brand Kit, background remover, and advanced branding tools.
• Teams: Custom pricing for collaboration and business use.

See it in action below:

4. Adobe Express – Best for Clean, Simple Logo Design

Adobe Express offers a more lightweight logo creation experience compared to Adobe’s professional tools. During testing, it performed best when users wanted something clean, minimal, and fast.

It doesn’t overwhelm users with options, which can be a strength depending on the use case.

Key Features

• Simple logo templates
• Fast editing tools
• Cross-device support
• Basic branding elements
• Adobe ecosystem integration

Pros

• Clean interface
• Fast workflow
• Professional-looking outputs

Cons

• Limited customization depth
• Fewer branding tools compared to competitors

Adobe Express – Pricing

• Free Plan: Includes basic logo templates, editing tools, and standard design features.
• Premium Plan: around $9.99/month – unlocks premium templates, brand tools, and AI features.
• Business Plan: Custom pricing for teams and organizations.

See it in action below:

5. Shopify (Hatchful) – Best for E-commerce Logos

Shopify’s Hatchful tool is designed specifically for e-commerce users. When we tested it, the experience was very structured—users select an industry, visual style, and then receive curated logo options.

It’s simple, but intentionally limited to keep things fast.

Key Features

• Industry-based logo generation
• Pre-designed style categories
• Simple editing options
• E-commerce-focused branding outputs
• Free logo downloads

Pros

• Very easy to use
• Free access
• E-commerce-oriented designs

Cons

• Limited customization
• Not suitable for advanced branding
• Better suited for simple ecommerce logos than full brand identities
• Logo designs can feel less unique for niche industries

Pricing

Free Plan: A completely free logo maker with downloadable logo files included. No paid tiers required for logo creation, making it a simple option for quick ecommerce branding.

See it in action below:

Conclusion

Choosing an AI logo maker is no longer just about generating a logo. The best tools help you create a visual identity that can grow with your business, from social media graphics and business cards to websites and marketing materials.

If you're looking for an AI logo maker that can take you from your first logo concept to a fully branded business presence, Design.com is the best place to start!

by Sponsored Content via Digital Information World

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.

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

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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