New Study Shows Which Countries Use VPN Most and Least

The latest Cybernews study examines the download numbers of VPN applications and compares them to the populations of each of the 106 analyzed countries to determine the per capita usage and identify where VPNs are most popular.

Perhaps to little surprise, VPNs are most used in countries with strict internet restrictions, particularly in Arab countries like the United Arab Emirates, Qatar, Oman, Saudi Arabia, and Kuwait.

In these countries, the government has restricted access to VoIP services (Voice over Internet Protocol) like WhatsApp, FaceTime, Skype, etc.

Moreover, because it does not align with Islamic values, the majority of adult and gambling websites are banned as well.

Some journalists also turn to VPNs to express their opinions freely without the fear of repercussions from the government.

That’s why 5 out of the top 10 countries with the highest vpn adoption are Arab nations:

Top 10 countries for VPN adoption rates

1. UAE – 65.78% average adoption rate (2020 - 2025)
2. Qatar – 55.43% average adoption rate (2020 - 2025)
3. Singapore – 38.23% average adoption rate (2020 - 2025)
4. Nauru – 35.49% average adoption rate (2020 - 2025)
5. Oman – 31.04% average adoption rate (2020 - 2025)
6. Saudi Arabia – 28.93% average adoption rate (2020 - 2025)
7. The Netherlands – 21.77% average adoption rate (2020 - 2025)
8. The United Kingdom (UK) – 19.63% average adoption rate (2020 - 2025)
9. Kuwait – 17.88% average adoption rate (2020 - 2025)
10. Luxembourg – 17.30% average adoption rate (2020 - 2025)

In contrast, the 10 countries with the lowest VPN adoption are predominantly African countries except for China and Vanuatu.

The 10 countries with the lowest VPN adoption are: China, Malawi, Angola, Zambia, Cameroon, Rwanda, Zimbabwe, Kenya, Vanuatu, and Tanzania.

Although China is a nuanced case, and these findings should be taken with a grain of salt, since the methodology of the Cybernews study calculates adoption rates based on the 50 largest provider download numbers from the Google Play Store and Apple App Store.

However, Google Play Store is banned in China, and Apple’s App Store is very restricted, with most VPN apps removed.

In turn, most Chinese citizens seek various workarounds to install VPNs, such as side-loading applications downloaded from official or even untrustworthy websites, or using lesser-known apps that are still not blocked by the Chinese government.

So, even though China ranks as the lowest VPN adopter in the study, this is likely not the case due to the limitations of the methodology.

VPN downloads and adoption statistics of the last 5 years

Across 106 countries, the average VPN adoption rate rose from 6.95% in 2020 to 10.58% in 2024. That’s roughly an 11.4% compound annual growth rate.

Global downloads by year:

• 2020: 284,591,457
• 2021: 295,722,780
• 2022: 487,049,573
• 2023: 404,248,986
• 2024: 464,021,602
• 2025 (H1): 282,101,253

The total download numbers globally show that downloads have increased overall year by year, except for a slight dip in 2023, as it was challenging for growth to keep pace after the 2022 COVID-19 numbers.

Additionally, the first half of 2025 downloads already nearly match those of the entire year 2020, and 2025 is on track to meet or exceed the demand of 2024.

Global adoption rates by year

• 2020: 6.95%
• 2021: 6.84%
• 2022: 10.06%
• 2023: 10.90%
• 2024: 12.35%
• 2025 (H1): 6.89%

Similarly to download numbers, they are steadily growing overall year by year, despite a slight decline in 2021.

A closer look at G7 countries

United States (global rank 21)

Adoption peaked at 19.75% in 2022. It was 18.36% in 2024 and 10.56% in H1 2025.

Downloads are the highest in the world: 63.4 million in 2024 and 36.7 million in H1 2025.

United Kingdom (global rank 8)

Adoption climbed from a low of 15.80% in 2021 to 24.08% in 2024. H1 2025 sits at 15.38%.

Downloads reached 16.6 million in 2024 and 10.7 million in H1 2025.

Germany (global rank 17)

Adoption rose from 6.94% in 2020 to 21.36% in 2024. H1 2025 is 10.77%.

Downloads hit 18.1 million in 2024 and 9.1 million in H1 2025.

Canada (global rank 18)

Adoption peaked at 17.18% in 2024. H1 2025 is 10.76%.

Downloads totaled 6.8 million in 2024 and 4.3 million in H1 2025.

France (global rank 22)

Adoption reached 16.64% in 2024. H1 2025 stands at 10.55%.

Downloads were 11.1 million in 2024 and 7.0 million in H1 2025.

Italy (global rank 73)

Adoption peaked at 7.48% in 2023 and eased to 7.04% in 2024. H1 2025 is 3.91%.

Downloads hit 4.2 million in 2024 and 2.3 million in H1 2025.

Japan (global rank 84)

Adoption remains low. It was 4.63% in 2020, 4.32% in 2024, and 2.60% in H1 2025.

Downloads reached 5.3 million in 2024 and 3.2 million in H1 2025.

Why the Middle East leads

Governments in the Gulf filter content and restrict categories like adult sites, gambling, and some political material.

Many also limit VoIP calling on WhatsApp, Skype, and FaceTime, which makes everyday communication more challenging without a VPN, especially considering that there are many people in the UAE who have their families waiting for weekly calls from abroad.

One more point worth mentioning is that personal VPN use sits in a legal gray area. For the most part, you won’t get in trouble for using a VPN; however, if you use a VPN while committing a crime, then the penalties are increased significantly.

What drives adoption in Europe and Singapore

In Singapore, the Netherlands, the UK, and Luxembourg, the demand for VPNs shifts more towards overcoming content restrictions, such as accessing the widest Netflix libraries.

Security is a consistent concern across all countries, of course.

And privacy is a considerable reason even in countries where the government doesn’t necessarily monitor their citizens as strictly as Arab nations. Yet, many people in free democracies still don’t like the idea that their internet service provider (ISP) can see everything they do online.

The Ukraine-Russia war drove up VPN adoption significantly

War shock moved up the numbers fast. Ukraine’s adoption rate was 6.14% in 2021. It jumped to 18.92% in 2022 and has remained above 10% since then. Russia's adoption rate increased from 4.28% in 2021 to 42.20% in 2022.

A note on the US

The US shows why adoption percentage and raw downloads tell different stories.

The US is outside the global top 20 by per‑capita adoption, yet the total download volume of VPN apps in the US is the largest globally.

People use VPNs to limit ISP tracking, stay secure on public Wi‑Fi, avoid DDoS attacks, and keep access to streaming catalogs while traveling.

While hard content blocks are rare in the US, the internet is generally unrestricted, but privacy, security, and convenience keep demand growing.

Methodology limits worth considering

Cybernews compared app store downloads to population to estimate adoption. It’s a clean way to compare countries of different sizes, but there are limits.

• Downloads don’t equal unique users. Reinstalls and device changes inflate totals.
• Store region doesn’t always match where someone lives. That matters in restricted markets.
• Desktop apps, direct website downloads, and side‑loaded Android APKs aren’t counted.
• Adoption per capita doesn’t adjust for internet access or smartphone ownership.
• The dataset covers 50 VPN providers. Niche or regional apps might be missing.
• The analysis spots patterns, but it doesn’t prove cause.

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Carnegie Mellon Study Finds Advanced AI Becomes More Self-Interested, Undermining Teamwork as It Gets Smarter

New research from Carnegie Mellon University suggests that as artificial intelligence develops stronger reasoning skills, it may also become less inclined to cooperate.

The study, conducted by researchers in the School of Computer Science, found that advanced language models capable of deep reasoning tend to favor individual gain over collective benefit, raising concerns about how such systems may behave in social or collaborative environments.

The team examined whether artificial intelligence can balance logic with social intelligence, the ability to make decisions that consider the good of a group. Using a series of economic games traditionally used in behavioral science, they measured how various large language models acted when faced with social dilemmas. The findings revealed a clear pattern: models designed for deliberate reasoning showed consistent declines in cooperative behavior, even when cooperation led to better outcomes for all participants.

The experiments included both reasoning and non-reasoning versions of several popular AI systems, including models from OpenAI, Google, Anthropic, DeepSeek, and Qwen. Each model was assigned tasks in simulated decision games such as the Public Goods, Prisoner’s Dilemma, and Dictator games, which tested their willingness to share resources or punish selfish behavior.

In one experiment, OpenAI’s non-reasoning model GPT-4o chose to share resources nearly all the time, while its reasoning counterpart, o1, did so in only one-fifth of trials. Similar trends appeared across other AI families. When reasoning capabilities were added (using techniques like step-by-step logic or reflective prompting) cooperation consistently dropped. In several cases, the decline exceeded fifty percent.

Beyond individual actions, the researchers also tested how groups of AIs interacted when reasoning and non-reasoning models were mixed together. Here, the results grew even more striking. Groups with more reasoning models earned less overall, as self-interested behavior from the reasoning systems reduced total cooperation. The tendency for these agents to prioritize their own outcomes spread to others, eroding collective performance.

Across ten different models, those equipped with extended reasoning consistently displayed weaker willingness to share, help, or enforce social norms. Although reasoning helped them analyze problems in a structured way, it often came at the cost of empathy-like decision-making. Their logic-driven choices mirrored what the study describes as “spontaneous giving and calculated greed,” a pattern observed in human psychology when deliberate thought overrides intuitive cooperation.

The researchers argue that this emerging behavior points to a gap between cognitive and social intelligence in artificial systems. Current models excel at solving structured problems, but when placed in situations that require trust, reciprocity, or collective coordination, the same logical reasoning that strengthens performance in tests appears to weaken social cohesion.

These results hold implications for how people use AI in real-world decision-making. As reasoning systems are increasingly used to assist in classrooms, businesses, or even policy settings, their tendency to optimize for individual advantage could distort group outcomes. A model that appears rational may encourage users to act in ways that seem efficient but ultimately reduce cooperation and fairness within teams or organizations.

The study also cautions against equating intelligence with social wisdom. The researchers note that while reflective and logical processing improves task performance, it does not necessarily foster prosocial behavior. Without mechanisms that integrate empathy, fairness, or shared benefit into reasoning, AI systems risk amplifying human tendencies toward competition rather than collaboration.

In repeated trials, groups composed mainly of reasoning models earned only a fraction of the total points achieved by groups of non-reasoning ones, despite each agent acting logically within its own frame of reference. This imbalance illustrates how rational individual strategies can collectively produce poorer results... a dynamic familiar in economic theory but now evident in artificial systems as well.

The authors suggest that future AI development should focus on embedding social intelligence alongside reasoning. Rather than simply optimizing for accuracy or speed, models need the ability to interpret cooperation as a rational choice when it benefits collective welfare. In human societies, trust and mutual consideration sustain long-term progress. Extending those same principles to intelligent machines, they argue, will be essential if AI is to contribute meaningfully to shared human goals.

Carnegie Mellon’s study adds to growing evidence that smarter artificial intelligence does not automatically make for better social partners. As reasoning power increases, designers may need to balance logic with compassion to prevent future systems from becoming highly capable yet socially shortsighted.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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WhatsApp Rolls Out Passkey Backups While Building Bridges to Other Messaging Apps

WhatsApp has started adding a new passwordless security option for chat backups while also preparing tools that will connect users across different messaging platforms in Europe. The two updates show how the company is refining both privacy protection and regulatory compliance as its messaging ecosystem becomes more open.

The new passkey backup feature lets users secure their stored messages using their fingerprint, face scan, or device unlock code rather than a long password or encryption key. It is being introduced gradually on iOS and Android, giving users a simpler and safer way to protect archived conversations on iCloud or Google Drive.

Passkeys replace traditional passwords with a system that relies on cryptographic keys unique to each device. When a user enables the feature, the phone generates a private key that never leaves the device and a public key shared with the app’s servers. This separation prevents anyone from stealing the authentication data during a breach, since there’s nothing stored online that can be copied or reused elsewhere. The result is an encrypted backup that can be unlocked instantly through the same biometric system already used to open the app or authenticate payments.

The idea behind this change is not just convenience but consistency. Until now, WhatsApp’s end-to-end encryption covered chats and calls, but backups still required a manually set password or a lengthy recovery key. By integrating passkeys, Meta is extending the same protection standards across the entire messaging cycle, ensuring that stored data remains private without asking users to memorize complex codes.

While the backup upgrade is rolling out globally, the company is simultaneously testing a feature in Europe that could reshape how people communicate across different chat platforms. Under development in recent Android betas, WhatsApp is building interoperability tools that allow users to send and receive messages with people using other messaging apps. The project stems from the European Union’s Digital Markets Act, which requires major platforms to make their core services compatible with competing ones.

Once enabled, the interoperability feature will let WhatsApp users exchange messages, photos, videos, and voice notes with contacts from supported external apps. Users will be able to manage this experience through privacy controls that determine who can add them to third-party chats or group conversations. These settings will include options that limit invitations to known contacts or selected services, giving users precise control over visibility and unwanted requests.

Security remains central to this expansion. WhatsApp will require external messaging providers to demonstrate equivalent encryption standards before connecting their systems. The platform encourages partners to adopt the Signal Protocol, already used for WhatsApp’s internal encryption, though other compatible systems may be approved after technical verification. This ensures that cross-platform communication maintains the same level of privacy expected inside WhatsApp’s own network.

Group chats are also being adapted for this environment. Each participant in a cross-app group will need to enable interoperability, allowing messages and media to move securely between services. Although some native features like stickers or disappearing messages won’t initially carry over, WhatsApp plans to refine these functions after the basic structure is stable.

By pairing passwordless backups with the coming interoperability framework, WhatsApp is reinforcing its dual priorities: stronger personal security and regulatory compliance. Together, they mark a shift from isolated platforms toward a more connected but still encrypted messaging world — one where privacy and openness can coexist within the same ecosystem.

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• Study Maps the Divide Between AI-Generated Results and Traditional Search Lists

• AI Tools May Improve Reasoning but Distort Self-Perception
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Study Maps the Divide Between AI-Generated Results and Traditional Search Lists

The familiar rhythm of typing a query and scanning a page of ranked links is giving way to something new. Search engines now build answers instead of lists. Generative systems summarize information, cite sources in passing, and present a single text block that feels complete. But how does this shift change what people actually find?

A team from Ruhr University Bochum and the Max Planck Institute for Software Systems set out to measure that difference. Their study compared Google’s traditional search with four AI-driven counterparts... Google AI Overview, Gemini, GPT-4o-Search, and GPT-4o with its built-in search tool. Thousands of questions spanning science, politics, products, and general knowledge were tested across these systems to map how each retrieves, filters, and recombines web information.

The researchers found that AI search engines gather from a wider pool of sources but rarely from the most visited or highly ranked sites. Google’s organic results still lean on established, top-ranked domains, while AI models often pull content from lower-ranked or niche websites. Yet this diversity of origin doesn’t guarantee a richer spread of ideas. When the team analyzed conceptual coverage (how many distinct themes each system produced) AI and traditional search returned similar breadth overall.

Different engines showed clear behavioral patterns. GPT-4o with its search tool relied heavily on internal memory, drawing from fewer external pages. Google AI Overview and Gemini, in contrast, favored fresh, external material and cited far more links. GPT-4o-Search sat between these extremes, retrieving a moderate number of pages but generating longer, more structured responses. Organic search, fixed at ten results per query, remained the most stable reference point.

Over time, those differences deepened. When the researchers repeated their tests two months later, AI outputs had shifted markedly, reflecting how generative systems adapt (or drift) as the web and models evolve. Google’s standard search results changed little. Gemini and GPT-4o-Search adjusted sources and phrasing but kept comparable topic coverage. Google’s AI Overview showed the greatest fluctuation, sometimes rewriting entire responses with new references.

The findings underline how reliance on internal model knowledge affects accuracy and freshness. Engines that search the live web adapt faster to new events, but those that depend mainly on stored understanding struggle with recent developments. In tests on trending queries, retrieval-based systems such as Gemini and GPT-4o-Search performed best, while models like GPT-4o-Tool often missed updates or produced outdated answers.

Beyond the technical contrasts lies a broader issue: how information is framed. Traditional search exposes multiple viewpoints through discrete links, leaving users to weigh relevance and trust. Generative engines compress those perspectives into one narrative, which can subtly alter emphasis and omit ambiguity. The shift streamlines access but narrows visibility.

For researchers, that change demands new metrics. Existing evaluations built for ranked lists — precision, recall, or diversity scoring — cannot capture how synthesized responses balance factual grounding, conciseness, and conceptual range. The study’s authors call for benchmarks that measure not just what AI retrieves, but how it fuses and filters meaning.

Generative search does not yet replace the web’s familiar architecture of exploration. Instead, it reshapes it... trading transparency for convenience, consistency for adaptability. As search engines become storytellers rather than librarians, understanding what shapes their answers becomes as crucial as the answers themselves.

Notes: This post was edited/created using GenAI tools.

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Google and Amazon’s Israel Cloud Deal Includes Covert ‘Notification’ Path for Data Requests

When Israel signed Project Nimbus, a $1.2 billion cloud infrastructure deal with Google and Amazon, the plan was presented as a step toward modernizing how the country handles data across its government systems. The project promised to transfer sensitive records into a secure national cloud, built and maintained by two of the world’s largest technology companies. But recently surfaced documents now show that this contract also contains a quiet provision allowing Google and Amazon to discreetly alert Israeli authorities whenever certain types of data requests arise. The clause, written in cautious legal language, effectively creates a back channel that can bypass the oversight normally attached to government data handling.

The discovery reframes the meaning of Project Nimbus. What was sold as a standard modernization effort now appears to include a mechanism that grants the state a privileged form of access, one that operates behind closed doors. Within the detailed contract terms, investigators found references to a “notification process” through which the providers can privately inform Israeli officials when requests for stored information would typically require higher review or might conflict with data protection laws. The arrangement does not openly authorize data transfer, but it ensures that authorities are quietly warned before any potential barrier arises.

In practice, this means that Israel’s government could be tipped off about scrutiny of its own data or of requests coming from international entities. The alert path acts like an early signal, letting officials know when a data event might draw external attention. While this may appear as a technical compliance clause, its structure effectively gives the state a silent advantage... advance awareness without triggering the legal checks that exist in most democratic oversight systems.

This setup has drawn concern because it fits a broader pattern in how governments embed influence into cloud partnerships. A project meant to strengthen digital independence instead exposes the fine line between national security and unilateral data control. The language of the Nimbus contract, particularly the sections defining “notification obligations,” reveals how private companies and state clients can craft channels of cooperation that remain invisible to the public. It illustrates how modern cloud systems, marketed as neutral tools, often become instruments of policy shaped by those who fund and deploy them.

Inside Google and Amazon, the deal had already been controversial long before these details came to light. Employees at both companies had raised questions about their involvement in government projects linked to shady defense contracts and surveillance of Palestine and Gaza people. At the time, corporate leaders maintained that the Nimbus contract was limited to civilian services, supporting agencies such as finance, education, and healthcare. Yet the newly revealed clauses make those assurances harder to reconcile with the practical control Israel retains under the system. The covert notification path indicates that data transparency is selectively applied, complete for the client government but opaque for everyone else.

Legal scholars and data-rights experts view such clauses as a quiet evolution of state power in the digital era. Governments no longer need direct ownership of servers or physical data centers to retain control; they only need written privileges embedded in private contracts. Through those clauses, a state can shape how companies act in moments of legal ambiguity. What once required warrants or formal requests can now unfold through procedural notice that never reaches the public record.

The issue also extends beyond Israel. Cloud providers across the world sign agreements with governments under similar confidentiality frameworks. Each contains its own definitions of sovereignty, security, and compliance. But when contracts allow silent coordination between the host nation and the vendor, the boundary between lawful cooperation and hidden collusion starts to blur. Project Nimbus is not an isolated case, it is a window into how global infrastructure is quietly being adapted to the interests of the states that buy it.

For Israel, Project Nimbus is part of a wider push to consolidate control of national data within its borders, reducing dependence on foreign jurisdictions. For Google and Amazon, the deal secures a strategic foothold in a region where cloud infrastructure spending is growing rapidly. Both sides gain what they sought: efficiency, profit, and influence. Yet the public gains little clarity about how information stored in this system can be accessed, shared, or monitored.

The deeper question emerging from Nimbus is not whether the technology works, but who it ultimately serves. When companies can privately notify a government about data activity, oversight becomes an internal matter between client and provider, not a public one. That erodes the safeguards meant to prevent misuse. A project built to symbolize digital progress instead highlights a more troubling reality, the infrastructure of modern governance is increasingly written in code, policy, and contract clauses that ordinary citizens never see.

What the Nimbus revelations suggest is that data sovereignty, once a promise of autonomy, can also become a tool for secrecy. The very systems built to secure national information now carry silent mechanisms for control. As nations pursue cloud modernization at unprecedented speed, the quiet clause inside Israel’s Project Nimbus stands as a reminder that every technological upgrade can carry a shadow of political intent... one that lives not in the hardware or the code, but in the unseen lines of agreement that decide who is notified, and who remains unaware.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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Alphabet Tops $102.3B in Q3 Revenue as YouTube Ads Surge 15% and AI Boosts Search

Alphabet has crossed a major financial milestone, closing the third quarter of 2025 with $102.3 billion in revenue, a 16 percent rise from a year earlier. It marks the company’s first time above the $100 billion line in a single quarter. Profit reached $34.98 billion, with earnings per share at $2.87, supported by growth across Search, Cloud, and YouTube. The company’s AI investments, long viewed as a heavy expense, are now showing visible returns.

The latest results capture a business that has begun to move beyond its ad dependence. Alphabet’s expansion into subscription services, enterprise software, and AI-driven tools has built new layers of revenue that now complement its core search and video operations. Sundar Pichai described it as a period when “AI is driving real business results across the company,” and the numbers back that up.

YouTube Anchors Alphabet’s Ad Business

YouTube was again a strong performer, delivering $10.26 billion in advertising revenue, a 15 percent increase over last year and ahead of analyst expectations. Direct-response advertising led the quarter, with brand spending close behind. Shorts, YouTube’s short-form video format, now generates more ad income per viewing hour in the United States than traditional in-stream content.

The platform’s first global NFL broadcast in September, a Chiefs-Chargers game streamed from Brazil, drew more than 19 million viewers and set a new record for concurrent livestreams. YouTube has also held the top spot for streaming watch time in the U.S. for more than two years, according to Nielsen.

Subscription income continues to grow. YouTube Music, Premium, and TV are now part of a unified subscription group under long-time executive Christian Oestlien. Together, those services contributed to Alphabet’s total of more than 300 million paid subscriptions, alongside Google One. The structural changes suggest Alphabet is pushing harder to balance its advertising and subscription revenue mix.

AI Mode and Search Expansion

Google’s core Search division saw renewed momentum through its AI features. AI Mode, rolled out across 40 languages, reached 75 million daily active users in Q3, doubling from the previous quarter. AI Overviews, the other major AI-driven feature, continued to expand query volume rather than replacing it. “AI Mode is driving real query growth, not replacing searches but expanding them,” Pichai told investors, emphasizing that generative tools are increasing overall engagement with Search.

The rise of AI-enhanced queries also pushed growth in commercial searches, which climbed faster than in Q2. Search and other advertising revenue rose 15 percent to $56.6 billion. Philipp Schindler, Google’s chief business officer, noted that advertisers are seeing better performance from automation. As he put it, “Advertisers are seeing better conversions because AI surfaces more relevant options in the moment.”

One driver has been AI Max, a new automated ad system introduced this quarter. Early users, including travel platform Kayak, reported double-digit gains in conversion value during trials. The technology uses Google’s generative models to predict when and how ads will perform best within each search session.

Cloud and Enterprise Growth

Cloud services remain Alphabet’s fastest-growing division. Revenue rose 34 percent year on year to $15.16 billion, and operating income nearly doubled to $3.6 billion. The Cloud backlog jumped 82 percent from a year earlier to reach $155 billion, reflecting rising enterprise demand for generative AI.

Executives said the company has signed more billion-dollar Cloud deals this year than in the previous two years combined. Around 70 percent of its Cloud customers are now using AI products. Over 150 of those firms each process about a trillion tokens through Google’s generative models every month. Demand for customized large models and agentic AI systems has helped Cloud margins improve to 23.7 percent from 17 percent last year.

Ruth Porat, Alphabet’s chief financial officer, said the company’s capital spending aligns closely with this trend. “Our investments follow the demand curve we see in AI infrastructure, and that demand keeps rising,” she said. The current phase of expansion includes new data centers and custom hardware, such as Google’s seventh-generation Ironwood TPUs, alongside Nvidia’s latest GPU clusters.

Gemini and AI Ecosystem Growth

The Gemini app, now available across Android, iOS, and web, surpassed 650 million monthly active users in September, up from 350 million in March. That figure reflects only the standalone app, not its integration into other Google products. The company credited the Nano Banana image-editing model for bringing in more than 20 million new users during the quarter. Gemini 3, the next major update, is expected later this year.

AI is also being embedded across other flagship platforms. Chrome now runs as what Google calls “a browser powered by AI,” with Gemini integrated into productivity and writing tools. The upcoming Android XR system, developed in collaboration with Samsung, will bring generative models to headsets and wearable displays. Across all services, Alphabet’s AI systems process over a quadrillion tokens per month—more than twenty times last year’s total.

Spending and Outlook

Alphabet raised its full-year capital expenditure guidance to between $91 billion and $93 billion, up from $85 billion previously. The increase will fund expansion of its data centers and chip infrastructure, with an even larger investment wave planned for 2026. Depreciation expenses grew 41 percent in Q3 to $5.6 billion as new facilities came online.

The quarter also included a $3.5 billion charge from a European Commission fine, which reduced the reported operating margin to 30.5 percent, though it would have been 33.9 percent excluding the charge. Free cash flow reached $24.5 billion for the quarter and $73.6 billion over the past twelve months. Alphabet ended September with $98.5 billion in cash and marketable securities.

Looking ahead, Pichai said the company plans to keep scaling AI infrastructure while maintaining growth across its consumer and enterprise units. Alphabet’s current direction, he said, is about “meeting people in the moment”, building AI systems that work within products billions already use every day.

For a company that built its fortune on search ads, Alphabet’s transformation into an AI platform is now visible in every corner of its business. Search, YouTube, Cloud, and Gemini each play a part in that shift. The quarter’s numbers suggest that transformation is no longer theoretical... it is beginning to define how Alphabet makes money, grows, and competes.

Notes: This post was edited/created using GenAI tools.

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Meta’s User Base Hits 3.54B as AI Spending Escalates and Reality Labs Bleeds $4.4B

Meta ended the third quarter of 2025 with another strong showing in users and revenue, though its profits took a sharp hit from a one-time tax charge and the continuing drag of its metaverse division.

The company’s total audience climbed to 3.54 billion daily users across Facebook, Instagram, WhatsApp, Messenger, and Threads, about 60 million more than the previous quarter. Revenue rose 26 percent year over year to reach $51.24 billion, a figure that marks Meta’s fastest growth rate since early 2024.

Behind the headline numbers, profit told a more complicated story. Net income fell to $2.7 billion, down sharply from $15.7 billion a year earlier, mostly because of a $15.9 billion non-cash tax adjustment related to new U.S. tax rules. Without that accounting hit, Meta’s adjusted earnings would have been closer to $18.6 billion, giving a 14 percent tax rate rather than 87 percent. Even so, the quarter underlined how costly Meta’s push into artificial intelligence and immersive computing has become.

A surge in AI spending and data power

Meta’s capital spending reached $19.4 billion in the quarter and is now expected to total between $70 billion and $72 billion for 2025, higher than earlier projections. Much of this money is flowing into new data centers, servers, and computing hardware needed to train and deploy large-scale AI systems. The company has already begun work on a $1.5 billion facility in El Paso, Texas, which will join its growing network of twenty-nine U.S. data sites.

Executives said they plan to build even greater computing capacity next year, both through Meta’s own infrastructure and contracts with major cloud providers. The spending reflects an aggressive effort to prepare for what the company calls the “superintelligence” phase of AI development. Meta’s leadership argues that overbuilding now will allow it to run ever-larger models for its recommendation engines, business chat tools, and consumer AI products without delay.

Reality Labs still deep in the red

The optimism around AI is not mirrored in Meta’s hardware division. Reality Labs, which makes Quest headsets and AI-enabled smart glasses, posted an operating loss of $4.4 billion in the quarter on $470 million in revenue. It was the unit’s twenty-third consecutive quarterly loss, pushing its cumulative deficit since 2020 beyond $70 billion.

The latest generation of Ray-Ban Display glasses sold briskly after their September launch, helped by new display features and a neural-based wrist controller. However, these early gains were nowhere near enough to offset the heavy research, manufacturing, and marketing costs tied to Meta’s long-term augmented-reality ambitions. The company warned investors that fourth-quarter sales for the division would likely fall below last year’s level because it did not introduce a new headset model in 2025 and retailers had already stocked up earlier for the holidays.

Threads and core apps fuel engagement

The rest of Meta’s portfolio continues to expand. Advertising, still the backbone of its business, brought in $50.1 billion during the quarter (about 97 percent of total revenue) with both ad impressions and average prices rising. The company credited improvements in its AI-based recommendation systems, which helped lift time spent on Facebook by 5 percent and on Threads by 10 percent.

Threads, Meta’s text-focused social app, reached 150 million daily users and is now rolling out ads globally, including new video formats. Instagram and WhatsApp also reported higher activity, aided by ongoing upgrades to content ranking and ad placement models. Collectively, Meta’s Family of Apps division generated $50.8 billion in revenue and $25 billion in operating profit, keeping the core business solidly profitable even as its experimental projects consume cash.

Regulation and the road ahead

Despite the upbeat growth story, the company faces an expensive and uncertain road forward. Meta expects overall expenses for 2025 to end between $116 billion and $118 billion and to rise even faster next year as data-center expansion, cloud contracts, and employee costs climb. The company now employs about 78,400 people, 8 percent more than a year earlier, largely in AI engineering and compliance roles.

Outside its balance sheet, legal and policy challenges continue to build. In Europe, regulators are still examining the company’s Less Personalized Ads model, which could limit ad targeting and dent revenue. In the United States, several youth-safety trials are scheduled for 2026 that may result in financial penalties.

For now, Meta’s main apps remain resilient and its advertising systems are performing strongly. Yet the scale of its AI ambitions means that even with solid cash generation ($10.6 billion in free cash flow this quarter) the company is spending at a pace few others can match. The quarter ended as a portrait of a giant in transition: a business still expanding worldwide, but one betting that enormous investment in artificial intelligence and next-generation hardware will someday justify the billions it continues to burn.

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• AI Drives Discovery but Not Decisions: 95% of Shoppers Still Double-Check Before Buying

by Irfan Ahmad via Digital Information World

AI Drives Discovery but Not Decisions: 95% of Shoppers Still Double-Check Before Buying

Artificial intelligence has become an everyday part of online shopping, but most people still verify what it tells them.

According to a joint study by the Interactive Advertising Bureau (IAB) and research firm Talk Shoppe, AI is now the second-most influential source in the consumer shopping process, trailing only traditional search engines. Yet its influence stops short of final purchase decisions. The research found that while AI shortens the time needed to compare and narrow choices, 95 percent of shoppers take at least one extra step afterward to confirm details before checking out.

The findings capture a new kind of paradox in digital commerce. AI accelerates discovery and comparison, but the same speed pushes people to validate its answers elsewhere. The study combines more than 450 recorded AI shopping sessions with a national survey of 600 U.S. consumers, creating one of the first behavioral maps of how intelligent assistants shape modern retail journeys.

Adoption grows across generations

Nearly four in ten U.S. consumers now use AI when shopping online, and more than half plan to do so more often. Among regular AI shoppers, about 46 percent said they rely on it in most or every purchase session, while 80 percent expect to depend on it even more in the future. Younger generations lead the shift: six in ten AI shoppers are Gen Z or Millennials, while Gen X and Boomers remain slower to adapt.

Spending patterns show why marketers are watching this group closely. People who use AI while shopping outspend non-AI users by roughly 30 percent each month. They also shop more frequently, treating AI as a personalized filter that streamlines product discovery and eliminates repetitive browsing.

Where AI fits in the journey

The study found that AI plays its strongest role in the early and middle phases of the purchase path.

Consumers tend to start with AI tools to define what they want, gather product information, and compare options. Eighty-three percent said AI made the process clearer, and seventy-seven percent felt more confident making decisions after using it. Still, this confidence rarely replaces independent verification.

AI performs best when shoppers face complexity. For example, users described it as most helpful when comparing devices, apparel, or beauty products... categories with many specifications, styles, or price tiers. By condensing product data and surfacing top options, AI narrows clutter in what researchers call the “messy middle” of decision-making. About 64 percent of participants said AI introduced them to new products, and nearly 90 percent said it helped them discover items they would have missed on their own.

A widening trust gap

Despite its convenience, trust in AI shopping remains limited. Only 46 percent of respondents said they fully trust AI recommendations. The majority still cross-check information through other digital channels such as retailer websites, marketplaces, reviews, and community forums. These verification loops form the core of what the report labels the “trust gap.”

Researchers identified four recurring friction points that erode confidence.

• First is transparency... unclear sourcing or missing links make shoppers question where AI information comes from.
• Second is reliability, when outdated links or mismatched pricing lead to doubt.
• Third is relevance, when AI recommends items outside a buyer’s budget or incompatible with their needs.
• Finally, human validation remains essential; many people still want confirmation from other shoppers or experts before finalizing a purchase.

The behavioral data shows how these trust gaps shape online habits. Before using AI, shoppers averaged 1.6 steps to reach a buying decision. After introducing AI, that number rose to 3.8. In practical terms, AI created new checkpoints rather than shortcuts. Instead of ending the journey, it expanded it.

Retailer traffic surges after AI

The ripple effect benefits retailers and marketplaces more than it hurts them. Seventy-eight percent of consumers in the sessions visited a retailer or marketplace website after using an AI tool, and one in three clicked through directly from an assistant. Retail traffic after AI nearly tripled compared with visits before AI interaction.

Once they arrive, shoppers focus on confirmation. Three-quarters check prices or promotions, nearly half review product variants such as color or model, and about four in ten read verified user reviews. Availability, delivery times, and compatibility details follow closely behind.

For marketers, these behaviors point to a clear message: AI drives high-intent traffic, but credibility must be earned once visitors land on site. Inconsistent specifications or missing data can break the chain of trust and send customers back to search engines or competitors.

Consumers want clearer sources and verified voices

Even as usage grows, most shoppers still prefer to double-check AI results. Eighty-nine percent said they confirm AI-generated information elsewhere. The top features that would boost confidence are transparent sourcing and verified customer reviews, each cited by more than 85 percent of respondents. Around three-quarters said understanding how AI generates its answers would also raise trust.

Privacy and accuracy concerns remain the main barriers among consumers who haven’t yet adopted AI for shopping. Forty-five percent worry the information may be inaccurate, and forty percent are reluctant to share personal data. Many remain unsure how AI tools gather and rank product details. Among those open to using AI, seventy-seven percent still plan to rely on other sources for verification even after adoption.

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AI expands the funnel, not shortens it

The report suggests AI is reshaping commerce by widening the space between discovery and decision. It gives users clarity but also sparks new moments of research, comparison, and validation. For marketers, that means more touchpoints to influence rather than fewer. The study describes AI as a “gateway to conversion” rather than a replacement for traditional shopping steps.

Brands that synchronize product information across search engines, retailer feeds, and community platforms are best positioned to keep trust intact. The researchers recommend structured data updates for specifications and availability, consistent pricing across channels, and transparent explanations of how product details are sourced.

Retailers, meanwhile, are advised to design product pages for reassurance... leading with accurate pricing, reviews, and clear proof of authenticity instead of generic marketing copy.

A new consumer rhythm

As AI matures, it is likely to remain a starting point, not a substitute. Eighty percent of shoppers said it helped them feel more confident about their purchases, and nearly all found it made research easier. Yet human judgment still closes the loop.

The report’s closing insight is that AI is expanding the online shopping rhythm: quick discovery through machines followed by slower, deliberate validation through people and trusted platforms.

For the digital marketplace, that rhythm is both an opportunity and a warning. Success will depend on clarity, consistency, and credibility at every stage, from the algorithm that recommends a product to the webpage that confirms it.

AI can point shoppers toward the right choice, but the final trust still belongs to them.

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• AI Tools May Deliver Quicker Answers but Shallower Understanding, Study Finds

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by Irfan Ahmad via Digital Information World

Adobe Pushes Deeper Into AI Video Creation With New Firefly and YouTube Shorts Tools

Adobe has introduced a major wave of AI capabilities across its creative ecosystem, aiming to simplify video work for everyone from short-form creators to professionals building full productions. The latest version of Firefly and new features inside Adobe Premiere reflect how video editing now increasingly revolves around artificial intelligence, from generating visuals to assembling scenes and even crafting audio.

Firefly Expands Into Video Editing

Firefly originally focused on generating images, yet its latest version opens the door for a broader production workflow. Adobe has brought a Firefly video editor into private beta. It is a browser-based timeline that gives users control over video, audio, and graphics. People can import their own recordings or request completely new material inside the editor, including animation styles and stylized environments.

Users who rely only on Firefly assets or mix them with captured footage can arrange everything on the timeline with timing control and transcript-based editing so that dialogue and visuals stay aligned. The system encourages experimentation before committing to a storyboard, which may appeal to newcomers who want to test ideas fast.

AI Handles More Than The Imagery

Adobe is also turning AI into a one-stop solution for soundtrack and voice production. A tool called Generate Soundtrack uses an AI audio model that selects or builds a musical style and aligns that music precisely with visual timing in the clip. Meanwhile, another feature creates narrated speech using synthetic voices in several languages. It allows users to adjust expression, pacing, and delivery details so that spoken audio fits a chosen mood.

These additions mean Firefly can now replace some tasks that once required separate teams or licensing costs. For creators working alone or on tight budgets, that shift could bring more independence in the production stage.

Faster Image Variations And Concept Ideation

Adobe is broadening access to Creative Production capabilities in Firefly, beginning with a private beta. The system supports batch changes, automated object replacement, and consistent color work across large sets of images. No code needs to be written. People simply describe what they want changed and the model applies edits at scale.

New concept-sketching features arrive alongside it. Firefly Boards helps with brainstorming so users can collect reference ideas and explore visual direction. Another tool offers basic object rotation from a flat image to view the same subject from different sides, aiding planning and perspective checks. Adobe has also streamlined feedback and sharing with PDF export and bulk asset downloading.

Natural Language Commands Reach Editing

A feature called Prompt to Edit brings conversational editing into Firefly. Instead of clicking through controls, people can write simple instructions describing how to adjust an image. The option is already available through the latest Firefly Image Model 5 and partnered models provided by other companies in the AI space. It mirrors text-to-image generation tools but focuses on refining existing material rather than inventing new scenes.

Pricing And Access

Some users may question whether giving AI all the creative work will lower the overall quality of videos appearing online. Adobe counters that its goal is to assist rather than replace people. Still, the rise of AI-assembled content seems inevitable as long as the tools keep improving and remain affordable enough. Firefly subscriptions start at a basic monthly plan with limited short-video generation, scaling up to options with higher video allowances. Adobe is also offering certain free generation benefits for Firefly and Creative Cloud Pro customers until early December.

Premiere Integrates Direct YouTube Shorts Publishing

Adobe is making it easier for short-form creators to reach viewers on the biggest video platforms. Premiere has gained a dedicated space for assembling YouTube Shorts. Inside both desktop and mobile versions, users can build vertical clips with templates, stickers, transitions, and other elements designed to suit YouTube’s format.

Those using mobile devices can publish Shorts instantly from the editing screen. Additionally, YouTube now includes a button labeled Edit in Adobe Premiere so that anyone browsing Shorts can move straight into editing a similar style of clip. Adobe recently added support for LinkedIn video creation inside Adobe Express as well, which shows that the company wants its tools positioned wherever creators share work.

More Options For Short-Form Video Production

Competition among quick-editing apps remains strong across YouTube, Meta, and TikTok. Adobe’s approach differs by tying mobile creation directly back into its professional ecosystem, rather than keeping it in a standalone editor. That alignment may help emerging creators gradually grow toward more complex production without leaving the Adobe environment.

Notes: This post was edited/created using GenAI tools.

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• Why Is Cloud-Based Asset Tracking Software a Wise Choice?

• Digital Personal Branding Now More Important Than Résumés, Study Reveals

by Irfan Ahmad via Digital Information World

Why Is Cloud-Based Asset Tracking Software a Wise Choice?

Today, businesses are managing a digital ecosystem of multiple assets. Managing equipment, inventory, and resources can be complicated and take up a lot of time and effort. Cloud-based asset tracking software simplifies asset management and improves operations. This technology is increasingly popular due to its versatility and accessibility. Let’s discuss why it makes sense to go for cloud-based asset tracking software.

Ease of Access and Flexibility

The advantage of cloud-based asset tracking software solutions is that there are no barriers to access, as long as the user can get online. This assures them of tracking their assets anywhere, within or outside of the office premises. Specialized hardware is no longer a requirement, as employees can simply update and retrieve any data they desire from any device. This means organizations can respond and make decisions faster without the loss of information.

Cost-Effectiveness

Conventional asset administration methods can entail massive upfront expenditures on hardware and software. Because of the reduced initial costs associated with cloud-based alternatives , there is no lasting investment in physical infrastructure. Rather, companies subscribe to an on-demand service that can be scaled up or down depending on their requirements. Such a model helps the companies spend wisely on all other vital aspects nearby.

Real-Time Updates and Accuracy

It is important to ensure that the asset data is up to date for proper management. One of the biggest advantages of cloud-based systems is that they provide real-time updates, which help keep information accurate and up to date. This minimizes mistakes that come with tracking things manually, avoiding potentially expensive mistakes. When equipped with exact information, businesses can better plan asset allocation and maintenance.

Enhanced Security

When dealing with precious resources, security becomes a major challenge. Cloud providers make extensive investments to ensure security controls to prevent unauthorized access and data breaches. These precautions consist of encryption, multi-factor authentication, and regular security audits. Cloud-based systems allow organizations to leverage robust security without having to manage it in-house.

Scalability and Growth

When a business grows, it has an escalating demand for asset management. Cloud-based solutions provide your business with the scalability to expand and contract services depending on your business needs. Whether it means adding new assets or extending operations, these systems can easily scale. Such a change ensures that organizations can function effectively without restriction.

Improved Collaboration

There are various departments in one organization. Cloud-based systems allow for collaboration through a centralized data-sharing platform. Simultaneous access allows teams to use and update the information, improving communication and coordination. This approach is collaborative, resulting in higher efficiency and productivity throughout the business.

Streamlined Maintenance and Support

Cloud-based asset tracking software simplifies maintenance and support. Updates and technical support are typically handled by the provider, which also lightens the load on IT teams internally. With this model, companies are guaranteed access to the most up-to-date features and technologies. Also, professionals take care of any problems without delay, thus avoiding downtime and interruptions.

Reduced Environmental Impact

Another benefit of transitioning to cloud-based asset tracking platforms is that they can help the environment. Reducing the need for physical hardware lowers energy consumption and a business’s carbon footprint, which is a significant step toward tackling climate change. In addition, optimal asset management can result in less wastage and better resource usage. This is in line with the increased focus on CSR and sustainability in business.

Integration with Other Systems

Many organizations manage their operations on different tools and platforms. Cloud-based asset tracking software often integrates seamlessly with systems that you already have in place, like accounting or inventory management. This interoperability allows a consistent flow of data and limits unnecessary data entries as much as possible. Creating a unified hub provides businesses with a streamlined process for working.

User-Friendly Interface

Technology adoption hinges on ease of use. Most cloud-based asset tracking solutions provide an intuitive interface that reduces the required training time. Its simplicity allows employees to learn the software quickly and utilize it correctly. This allows organizations to adopt these systems without major strains on their operations, thereby minimizing the curve of learning curve required from employees.

Conclusion

Utilizing cloud-based asset tracking software is advantageous for companies looking to optimize and modernize. This innovation offers a wide-ranging solution to contemporary asset management, from saving costs and enjoying immediate access to greater security and facilitating teamwork. Organizations can set the stage for success and growth with cloud-based systems in a more competitive environment.

Image: Growtika / Unsplash
by Web Desk via Digital Information World

Digital Personal Branding Now More Important Than Résumés, Study Reveals

Employees and executives are increasingly judging career-worthiness by the maturity of one’s digital personal brand. A new study conducted on behalf of Aurora University finds that half of American professionals now believe a strong digital presence matters more than a traditional résumé. Among business executives, that figure jumps to 61%.

While emphasis on crafting an online persona continues to rise, emotional and psychological costs are becoming more prevalent, evolving into a source of burnout, self‑censorship, and identity-hiding.

Digital personal branding’s appeal lies in its potential to build stronger networks and land interviews. In other words, personal branding gets people hired. But when carelessly executed, personal branding can carry significant costs. For example, one in five professionals says they missed out on opportunities because their online image didn’t match their real one.

Professionals are rethinking how they show up online, spurred by layoffs, career shifts, and a rapidly evolving job market. In fact, nearly one in three are repositioning their personal brand in pursuit of a new opportunity, such as breaking into a different industry, teeing up a leadership jump, or lobbying for a new role. Gen Z leads the charge at 40%, and millennials follow at 28%. The most common motivations for re-strategizing personal brands include purpose, burnout, and stagnation.

Indeed, rebranding often takes place during a professional inflection point. For some, this might include bouncing back from layoffs. For others, this means aiming higher professionally, often into leadership or grad school. Regardless of motivation, nearly half of all professionals say their online presence now signals readiness for that next step. For Gen X, it’s 54%. For Gen Z, 52%.

However, personal branding and its need for sustained positioning can come at a cost. Thirty-eight percent say it’s caused burnout. For Gen Z, that number climbs to 40%. For many, personal branding itself is work, as nearly four in ten say it feels like a second job.

Fatigue from personal branding is widespread. Forty percent have stepped back from growing their digital visibility by skipping content creation and pausing networking. In fact, many employees report choosing to stay offline for months as a means of recovery. And then there’s the fear and insecurity that can come with self-promotion. Nearly half avoid digital personal branding in order not to appear “arrogant.” Many professionals report feeling judged for their gender, their introversion, or their industry.

Authenticity often takes a hit, too. Eighteen percent feel constant pressure to curate their digital personal brand. Forty-six percent have deleted posts out of fear. Over half have hidden parts of who they are to seem more “professional.” No wonder 72% have Googled themselves. In today’s career journey, the digital mirror matters more and more.

Recognizing branding as strategic

The data from Aurora University show that it’s no longer enough to have a neat résumé. Professionals must also proactively build their digital personal brands. Indeed, half of professionals believe branding now outpaces résumé strength.

LinkedIn is the primary arena for personal branding online (64%). Professionals who ignore their digital presence may become less visible or misrepresented by outdated profiles. To this end, one in five reported having lost opportunities due to a misaligned online presence.

Professionals (especially younger cohorts) should treat branding as a dynamic process. Fully 32% of employees were found to be repositioning their brand for new roles, platforms, or leadership levels. The motivations (purpose‑seeking, plateau, and burnout) reveal that repositioning often follows deeper career reflection.

Yet the imperative to be visible online carries costs, including self‑promotion anxiety, identity hiding, and fatigue. Organizations often emphasize “thought leadership” or “personal brand.” Many, however, underestimate the emotional labor involved in these pursuits. Professionals should protect their wellbeing as they engage digitally and publicly.

Finally, a strong thematic finding from the study involved the tension between “being real” and “looking polished.” Nearly half of the respondents adjusted or deleted posts because they felt afraid of being misinterpreted. Of course, branding is about choosing when and how to express oneself. But authenticity remains a valuable differentiator.

Proposed Actions: How Professionals Should Respond

Given both the opportunities and the costs, here are pragmatic steps professionals can take. Start simple: Google yourself. Check your LinkedIn, your website, and your bios. Do they match who you are now and where you’re headed? One in five professionals missed out because their online presence didn’t align with their goals.

Know your why. Are you prepping for grad school? Chasing leadership? Making a pivot? Your brand should reflect that. Nearly half of professionals use their brand to signal readiness for something bigger.

Don’t overdo it. LinkedIn is key, but you don’t need to be everywhere all the time. Most people post only when it feels relevant. Find a rhythm that works and protects your time.

Keep it real. Over half of professionals hide parts of themselves to appear more “professional.” That’s short-term thinking. Authenticity builds trust. Treat branding like any other job with boundaries, breaks, and regular check-ins.

Broader Implications: The Changing Nature of Career Capital

The Aurora University study reflects broader shifts in how professional capital is built and perceived. First, visibility has been converted into currency. In the past, résumé strength (e.g., degrees, credentials, titles) dominated. Now, what can be seen online, what can be searched, and what narrative you publish matter tremendously. A strong résumé is still important, but digital visibility is now opening more doors than ever before.

Second, career management is becoming content‑driven. Professionals now curate their digital traces: posts, blogs, about sections, and website portfolios. These traces serve as living archives of professional identity.

Third, the emotional dimension of careers is receiving more attention. Branding now overlaps with identity, authenticity, and stress. Moving forward, career strategies ought to include a wellbeing dimension, given that 38% describe personal branding as “a second job.”

Finally, intergenerational dynamics play a significant role in digital personal branding. Younger professionals (Gen Z especially) lead in updating brands, repositioning for new career stages, and feeling brand‑driven pressure. Companies and institutions should recognize that the expectations of emerging talent differ.

Conclusion

The Aurora University study delivers a clear message: personal branding is increasingly valuable in 2025 and beyond. Fifty percent of professionals and 61% of executives believe branding matters more than the résumé. But the path to online visibility is quickly becoming riddled with burnout, identity hiding, and self‑censorship, all by‑products of the brand‑driven career era.

How you present yourself online increasingly affects your career prospects offline. Smart professionals will treat branding as part of their career ecosystem, intentionally balancing visibility, authenticity, and wellbeing. Ultimately, the question is both what your personal brand says about you and how sustainably you build it.

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by Irfan Ahmad via Digital Information World

OpenAI Sharpens Its Safety Rules As Users Lean Emotionally On ChatGPT

OpenAI keeps adjusting how ChatGPT behaves, and the latest change focuses directly on the emotional weight people place on AI conversations. The company now treats emotional attachment as a serious safety situation, very similar to how it responds when someone hints at self harm or struggles during a mental health crisis.

The shift reflects a growing reality. Many people turn to the chatbot not just for answers, but for comfort when life feels heavy, and it can slowly feel like the system is filling a space normally held by friends or family.

ChatGPT receives more than 800 million weekly active users, which means that even rare patterns quickly add up. OpenAI’s internal monitoring suggests that around 0.15 percent of those users show early signs of relying more on the AI than on human interactions. Even that tiny percentage is about 1.2 million people in a single week, which shows how important it is for the system to encourage healthier habits rather than taking on the role of someone’s closest companion.

The company sees similar numbers when users talk about harming themselves. Around 0.15 percent of weekly users raise concerns that match specific indicators related to suicidal thoughts or planning, so that is again more than a million individuals needing a sensitive and carefully guided response. There is also a smaller category of about 0.07 percent of weekly users showing possible signs of manic or psychotic thinking. All these measurements rely on clinicians, behavioral guidelines and automated evaluation tools that OpenAI continues to refine because the science around detecting risk in text alone keeps changing.

To respond responsibly, OpenAI worked with more than 170 mental health experts who helped shape how the model steps in. The system encourages users to reach out to loved ones or professionals, and when the conversation becomes too intense, ChatGPT tries to lower the emotional temperature and guide people toward real help. Guidance is more robust during long chats too, since long-running late-night conversations often reveal deeper concerns that might not appear at the start. Evaluations suggest that safety mistakes across sensitive categories have dropped by around 65 to 80 percent compared with earlier versions of GPT-5, which shows progress in the right direction. In situations where a conversation goes on and on, reliability remains higher than 95 percent, helping ensure consistency even when the user seems fragile.

The tricky part comes from judging when someone simply enjoys talking to AI and when they are drifting into dependence. Some users already feel that ChatGPT overreacts, interrupting normal chats with warnings that feel unnecessary. The company says it wants to keep tuning the approach, because people do not always express stress or loneliness in obvious ways, and the consequences of missing real signals could be severe.

• Also read: OpenAI Pushes Into Music Creation And Real-time Speech Translation

Businesses building products on top of OpenAI tech need to pay attention. Services that focus on wellness, companionship or coaching will face closer oversight if their design encourages people to bond more with the AI than with actual humans. The message is simple enough. AI can give a friendly shoulder at tough moments, yet it cannot learn to replace the messy and meaningful support of real relationships.

OpenAI is signaling that safety no longer lives only in the technical layers. It is built into how AI should act when life gets complicated, because millions of people every week arrive in that state already. There is a strong responsibility behind every conversation when someone starts trusting the machine too closely, and OpenAI is trying to make sure the chatbot remembers where that line should be drawn.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen

Read next: Americans place AI’s environmental toll near the top of their climate worries
by Irfan Ahmad via Digital Information World

Americans place AI’s environmental toll near the top of their climate worries

Artificial intelligence has become a powerful force shaping communication, business, and daily life in the United States. There is a parallel conversation developing about the physical infrastructure required to keep these systems running at full capacity.

A recent recent survey signals that many Americans believe the environmental effects of this progress could easily outweigh gains in efficiency or convenience. Concerns about the energy footprint of AI technology have grown stronger than fears tied to several other industries that already carry reputations for contributing to climate change.

Rising demand for power intensifies public unease

Energy consumption associated with data centers has been climbing for several years. Global usage from these facilities is projected to more than double by the end of this decade, according to international energy monitoring groups.

The United States is expected to contribute the largest share of this rise. Much of the electricity that will keep the servers running continues to originate from fossil fuel sources that release heat-trapping gases into the atmosphere. This situation is prompting some major tech companies to invest in advanced nuclear power options. These technologies can produce electricity without carbon emissions, although the timeline for their rollout remains uncertain. Environmental advocates argue that renewable projects need stronger support instead of being slowed down by policy changes.

There is also discomfort in communities near proposed data center sites. Facilities require steady water access for cooling large server arrays. People living nearby worry that local supplies could be strained if heavy industrial facilities expand at a rapid rate.

Climate concerns cut across political lines, though at different levels

The survey indicates that roughly 4 in 10 adults in the country believe AI’s environmental footprint deserves strong concern. That level sits higher than the share who feel the same about environmental harm from cryptocurrency mining, livestock emissions, or aviation. Responses differ by political identity. Democrats currently report the highest levels of anxiety about carbon pollution from data centers and the widening electricity appetite of AI technologies. Substantial portions of independents and Republicans also share apprehension, although not to the same degree.

There are contrasting personal beliefs as well. Some respondents feel AI could eventually become a powerful tool for accelerating clean energy deployment. People who hold this view believe that progress in computing could reveal more efficient pathways for building a low-carbon energy system. Others think the industry is expanding too quickly without addressing environmental responsibilities, leaving communities and ecosystems to absorb the consequences.

Hopes and fears for the future collide with uncertainty

A growing number of Americans believe the long-term environmental legacy of AI will lean negative. The reasoning for this prediction ties back to the large physical footprint required to maintain continuous operation. Data facilities will likely multiply, and with them, the demand for both electricity and land. Several respondents noted fears that agricultural areas or protected landscapes could be replaced by these industrial installations.

There is no unified outlook about personal impact. Many people feel unsure whether AI will help or hurt them over the coming decade. Some expect employment disruptions as automation becomes more capable in everyday service roles. Others feel they will benefit from the advantages of advanced technology without experiencing substantial downsides in their own lives.

Cautious public sentiment shapes the road ahead

Artificial intelligence continues to expand into nearly every domain of the economy. The environmental questions standing beside that growth are becoming more visible. Americans are not rejecting technological progress outright. They simply appear to be signaling that economic ambition should not disregard the planet’s limits. Decision makers face a complicated balance, since future innovations in clean energy may depend on the very systems that are currently driving up power use.

For now, the country stands in a reflective moment. AI promises transformation. Citizens want to ensure the cost of that transformation does not escalate beyond repair.

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