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Artificial intelligence is no longer a side note in social media. In 2025, it has become the clearest dividing line between trusted brands and those audiences question. New findings from SproutSocial’s Q2 and Q3 Pulse Surveys, spanning more than 2,000 respondents in the US, UK, and Australia, underscore just how much AI shapes user expectations.
AI-generated content disclosure leads all concerns
When asked what worries them most about brand behavior on social platforms, respondents put undisclosed AI-generated content at the top of the list, ahead of data mishandling.
Gen Z and Millennials are the most likely to rank AI disclosure as their number-one issue.
Older generations place stronger emphasis on data privacy, but disclosure still features prominently across groups.
Human-made content holds the trust advantage
Audiences show a measurable bias toward human creators:
55% of consumers say they are more likely to trust brands that commit to publishing content created by people rather than AI.
This rises to 62% among Millennials.
Among those who identify as Liberal, 61% prefer brands that publish human-generated content.
Respondents also value attribution. Brands that credit photographers, stylists, and makers behind their posts gain credibility, reinforcing the idea that transparency is as important as output.
AI influencers remain divisive
Synthetic influencers highlight the sharpest tension between innovation and trust:
46% of consumers say they are not comfortable with brands using AI-generated influencers.
31% say their comfort depends on the campaign.
By demographics: 28% of men say they are comfortable, versus 19% of women.; 32% of Gen Z and 33% of Millennials express comfort, compared with only 23% of Gen X.
These splits show some openness among younger audiences, but discomfort remains the dominant stance.
What the numbers mean for brands
The data suggests that AI is acceptable to audiences only under clear conditions: it must be disclosed, human creators must be acknowledged, and synthetic influencers remain a risk for brand perception. Consumers are not rejecting AI entirely; they are demanding honesty and choice.
The wider landscape in brief
Beyond AI, the same surveys report that 60% of users say social media improved their mental health in the past six months, and 76% say social influenced a purchase during that period. Younger generations also continue to see platforms as tools for financial education. But none of these figures resonate as strongly with trust as the numbers tied to AI.
Notes: This post was edited/created using GenAI tools.
August 2025 App Rankings Show Slower Downloads and Steady Revenues
Mobile app rankings for August reveal a quieter month. According to Appfiguresdata, downloads fell compared with July, while revenues stayed firm. Market leaders held their ground, and streaming platforms gained more space in the earnings chart. The results underline seasonal shifts as users returned from vacations and spending patterns settled into late summer routines.
Downloads Slip as Vacations End
ChatGPT led global downloads in August with about 48 million installs across iOS and Android. Most of its growth came from Google Play, where it added 35 million downloads. Apple’s App Store contributed 13 million. TikTok followed with 38 million installs, supported by its Lite version on Android. Instagram remained close behind with 35 million. Facebook and WhatsApp completed the global top five with 27 million and 24 million installs.
The combined figure for the ten most downloaded apps reached 273 million in August. This marked a decline from July, reflecting reduced activity during the vacation period. Threads moved higher in the overall chart, and Uber entered the iOS top ten for the first time this year.
iOS Download Chart
On the App Store, ChatGPT led with 13 million installs. Threads ranked second with 11 million. Google Maps, CapCut, and Temu followed, each recording about 8 million downloads. Google’s search app and TikTok tied with 7 million. Telegram added 6 million. Uber and Google Chrome closed the list with 5 million each.
Google Play Download Chart
On Google Play, ChatGPT topped the list with 35 million installs. TikTok Lite came second with 32 million, while Instagram reached 30 million. Facebook and WhatsApp followed with 23 million and 19 million. Temu and Snapchat both added 15 million downloads. WhatsApp Business and Telegram each brought in 13 million. CapCut ranked tenth with 12 million installs.
Combined Global Downloads
When both platforms are combined, ChatGPT held first with 48 million installs. TikTok reached 38 million, Instagram 35 million, Facebook 27 million, and WhatsApp 24 million. Temu stood at 22 million, Threads at 21 million, and CapCut at 20 million. Telegram followed with 19 million, while Snapchat closed the top ten with 18 million.
Rank
iOS App Store (Downloads)
Google Play
Combined Total
1
ChatGPT – 13M
ChatGPT – 35M
ChatGPT – 48M
2
Threads – 11M
TikTok Lite – 32M
TikTok – 38M
3
Google Maps – 8M
Instagram – 30M
Instagram – 35M
4
CapCut – 8M
Facebook – 23M
Facebook – 27M
5
Temu – 8M
WhatsApp – 19M
WhatsApp – 24M
6
Google – 7M
Temu – 15M
Temu – 22M
7
TikTok – 7M
Snapchat – 15M
Threads – 21M
8
Telegram – 6M
WhatsApp Business – 13M
CapCut – 20M
9
Uber – 5M
Telegram – 13M
Telegram – 19M
10
Google Chrome – 5M
CapCut – 12M
Snapchat – 18M
Revenue Rankings Hold Steady
While downloads slowed, earnings remained stable. TikTok was again the top revenue generator in August. The app brought in an estimated $335 million after store fees, keeping a clear lead over other services. ChatGPT followed with $170 million, supported mainly by App Store purchases. YouTube held third place with $137 million, relying on iOS transactions rather than standard billing on Android.
Tinder and Disney+ secured fourth and fifth with $130 million and $115 million. HBO Max and Google One posted about $100 million each. CapCut earned $93 million. Peacock TV and Crunchyroll completed the chart with $59 million and $58 million. Together, the ten highest-earning apps generated about $1.3 billion, a slight increase from July.
iOS Revenue Chart
TikTok topped iOS with $213 million in net revenue. YouTube followed with $137 million, and ChatGPT ranked third with $123 million. Tinder added $92 million, while CapCut produced $81 million. Disney+ earned $76 million and HBO Max $72 million. Snapchat contributed $49 million. Peacock TV and Tencent Video each recorded $48 million.
Google Play Revenue Chart
On Google Play, TikTok earned $123 million, the highest on the platform. Google One followed with $99 million. ChatGPT generated $47 million. Amazon’s mobile app was close behind with $46 million. Disney+ collected $40 million, while Tinder reached $37 million. Spotify earned $32 million and HBO Max $29 million. Crunchyroll and Prime Video shared the final places, both at $20 million.
Combined Global Revenue
The combined rankings placed TikTok far ahead with $335 million. ChatGPT held second at $170 million. YouTube stood at $137 million. Tinder and Disney+ followed with $130 million and $115 million. HBO Max reached $101 million, while Google One added $100 million. CapCut ranked eighth with $93 million. Peacock TV recorded $59 million and Crunchyroll $58 million.
Rank
iOS App Store (Revenue)
Google Play
Combined Total
1
TikTok – $213M
TikTok – $123M
TikTok – $335M
2
YouTube – $137M
Google One – $99M
ChatGPT – $170M
3
ChatGPT – $123M
ChatGPT – $47M
YouTube – $137M
4
Tinder – $92M
Amazon – $46M
Tinder – $130M
5
CapCut – $81M
Disney+ – $40M
Disney+ – $115M
6
Disney+ – $76M
Tinder – $37M
HBO Max – $101M
7
HBO Max – $72M
Spotify – $32M
Google One – $100M
8
Snapchat – $49M
HBO Max – $29M
CapCut – $93M
9
Peacock TV – $48M
Crunchyroll – $20M
Peacock TV – $59M
10
Tencent Video – $48M
Prime Video – $20M
Crunchyroll – $58M
Market Trends
Seasonality shaped August’s results. Downloads slowed, but revenue stayed stable, with most mid-tier apps earning more than in July. Streaming platforms performed strongly, with four services in the revenue chart compared with the usual two. These included Disney+, HBO Max, Peacock TV, and Crunchyroll, all supported by summer demand for entertainment.
Outlook
As September begins, the market will show whether download activity returns to July’s levels. The revenue side suggests that subscription models and media services continue to hold ground. TikTok, ChatGPT, and YouTube remained the strongest performers across both categories, reinforcing their positions as leading players in the global app economy.
Notes: This post was edited/created using GenAI tools.
Attempts at communicating what generative artificial intelligence (AI) is and what it does have produced a range of metaphors and analogies.
From a “black box” to “autocomplete on steroids”, a “parrot”, and even a pair of “sneakers”, the goal is to make the understanding of a complex piece of technology accessible by grounding it in everyday experiences – even if the resulting comparison is often oversimplified or misleading.
Image: unsplash
One increasingly widespread analogy describes generative AI as a “calculator for words”. Popularised in part by the chief executive of OpenAI, Sam Altman, the calculator comparison suggests that much like the familiar plastic objects we used to crunch numbers in maths class, the purpose of generative AI tools is to help us crunch large amounts of linguistic data.
The calculator analogy has been rightlycriticised, because it can obscure the more troubling aspects of generative AI. Unlike chatbots, calculators don’t have built-in biases, they don’t make mistakes, and they don’t pose fundamental ethical dilemmas.
Yet there is also danger in dismissing this analogy altogether, given that at its core, generative AI tools are word calculators.
What matters, however, is not the object itself, but the practice of calculating. And calculations in generative AI tools are designed to mimic those that underpin everyday human language use.
Languages have hidden statistics
Most language users are only indirectly aware of the extent to which their interactions are the product of statistical calculations.
Think, for example, about the discomfort of hearing someone say “pepper and salt” rather than “salt and pepper”. Or the odd look you would get if you ordered “powerful tea” rather than “strong tea” at a cafe.
The rules that govern the way we select and order words, and many other sequences in language, come from the frequency of our social encounters with them. The more often you hear something said a certain way, the less viable any alternative will sound. Or rather, the less plausible any other calculated sequence will seem.
In linguistics, the vast field dedicated to the study of language, these sequences are known as “collocations”. They’re just one of many phenomena that show how humans calculate multiword patterns based on whether they “feel right” – whether they sound appropriate, natural and human.
Why chatbot output ‘feels right’
One of the central achievements of large language models (LLMs) – and therefore chatbots – is that they have managed to formalise this “feel right” factor in ways that now successfully deceive human intuition.
In fact, they are some of the most powerful collocation systems in the world.
By calculating statistical dependencies between tokens (be they words, symbols, or dots of color) inside an abstract space that maps their meanings and relations, AI produces sequences that at this point not only pass as human in the Turing test, but perhaps more unsettlingly, can get users to fall in love with them.
A major reason why these developments are possible has to do with the linguistic roots of generative AI, which are often buried in the narrative of the technology’s development. But AI tools are as much a product of computer science as they are of different branches of linguistics.
The ancestors of contemporary LLMs such as GPT-5 and Gemini are the Cold War-era machine translation tools, designed to translate Russian into English. With the development of linguistics under figures such as Noam Chomsky, however, the goal of such machines moved from simple translation to decoding the principles of natural (that is, human) language processing.
The process of LLM development happened in stages, starting from attempts to mechanise the “rules” (such as grammar) of languages, through statistical approaches that measured frequencies of word sequences based on limited data sets, and to current models that use neural networks to generate fluid language.
However, the underlying practice of calculating probabilities has remained the same. Although scale and form have immeasurably changed, contemporary AI tools are still statistical systems of pattern recognition.
They are designed to calculate how we “language” about phenomena such as knowledge, behaviour or emotions, without direct access to any of these. If you prompt a chatbot such as ChatGPT to “reveal” this fact, it will readily oblige.
ChatGPT-5 response when asked if it uses statistical calculations to form its responses.OpenAI/ChatGPT/The Conversation
AI is always just calculating
So why don’t we readily recognise this?
One major reason has to do with the way companies describe and name the practices of generative AI tools. Instead of “calculating”, generative AI tools are “thinking”, “reasoning”, “searching” or even “dreaming”.
The implication is that in cracking the equation for how humans use language patterns, generative AI has gained access to the values we transmit via language.
But at least for now, it has not.
It can calculate that “I” and “you” is most likely to collocate with “love”, but it is neither an “I” (it’s not a person), nor does it understand “love”, nor for that matter, you – the user writing the prompts.
Generative AI is always just calculating. And we should not mistake it for more.
Amnesty International has documented how Pakistan has built a wide-reaching surveillance and censorship system using technology supplied by companies across several regions. The investigation, carried out with media and research partners including Paper Trail Media, DER STANDARD, Follow the Money, The Globe and Mail, Justice for Myanmar, InterSecLab, and the Tor Project, found that tools from North America, Europe, China, and the Middle East form the backbone of Pakistan’s monitoring network.
At the core are two platforms, the Web Monitoring System (WMS 2.0) and the Lawful Intercept Management System (LIMS). Both were developed and upgraded through international suppliers. The first firewall arrived in 2018 with equipment from Canadian firm Sandvine, later renamed AppLogic Networks. Trade records show shipments to local contractors as early as 2017, including Inbox Technologies, SN Skies Pvt Ltd, and A Hamson Inc.
By 2023, WMS 2.0 replaced the original setup. This version was supplied by China’s Geedge Networks with additional components from Niagara Networks in the United States and Thales in France. Amnesty describes it as a commercial export of China’s Great Firewall.
LIMS was built with technology from German company Utimaco, distributed through UAE-based Datafusion. Together, these systems gave Pakistani authorities broad capacity to intercept and review traffic moving across national telecommunications networks.
Surveillance Reach
WMS 2.0 works as both a monitoring and blocking tool. Investigators found it could log emails, VoIP calls, and browsing sessions in real time. It also allows operators to block websites and virtual private networks. With deep packet inspection, data sent over the internet can be broken down and examined in detail. While HTTPS encryption masks content, operators can still identify websites visited and gather metadata.
The firewall runs across all major telecom providers and at Pakistan’s international internet landing station. This positioning ensures that most digital traffic entering or leaving the country is subject to monitoring.
LIMS extends surveillance to phone calls, text messages, and location data. Investigators found that state agencies can initiate monitoring with only a phone number. This provides access to communication logs and, when connections are unencrypted, entire pages of content. Even when encrypted, the system still records websites visited and times of access.
According to Amnesty, LIMS has the capacity to monitor more than four million users at once. Telecom operators are legally required to connect their systems as a condition of their license.
Public Financing
Expansion of the network has relied heavily on public funds. In November 2023, five billion rupees from the Universal Service Fund were redirected to the firewall project through the ICT R&D Fund, also known as Ignite. In February 2024, the Economic Coordination Committee approved another ten billion rupees under the Digital Information Infrastructure initiative, which includes WMS 2.0.
These allocations covered upgrades to the firewall and wider integration of LIMS. Amnesty’s report traces the procurement of equipment through trade databases, showing how suppliers across multiple jurisdictions provided hardware and software despite the human rights risks.
Oversight and Legal Gaps
Pakistan’s Fair Trial Act requires warrants for surveillance, but Amnesty found these safeguards are often bypassed. The Pakistan Telecommunication Authority has acknowledged in court that large-scale interception takes place.
Authorities frame the systems as lawful interception tools for national security. Critics argue that little transparency or independent oversight exists. Citizens are not notified when their data is monitored, and Amnesty concludes that in practice LIMS enables indiscriminate surveillance.
Effect on Citizens
Journalists and civil society groups report that the systems are already narrowing space for free expression. One journalist told investigators that after publishing on corruption, both he and his contacts faced greater scrutiny. The awareness of monitoring led him to self-censor and avoid open communication, even with family members.
Amnesty’s Secretary General Agnès Callamard compared the technologies to watchtowers that constantly observe citizens, warning that unchecked surveillance undermines civic space and democratic participation.
International Role
The investigation highlights the responsibility of companies and governments exporting surveillance tools. Of twenty companies contacted, only Niagara Networks in the United States and AppLogic Networks in Canada provided full responses. Utimaco and Datafusion initially answered in 2024 but did not follow up.
Out of nine government bodies approached, only Germany’s export authority and Canada’s trade bureau acknowledged correspondence. Neither gave substantive replies. Pakistan’s government did not respond.
Amnesty argues that this silence reflects a broader failure to regulate the trade of surveillance equipment. The report notes that suppliers in Europe, North America, China, and the UAE continue to export such tools without safeguards, even where they are used to restrict rights.
Expanding Framework
Together, the firewall and intercept platforms form a nationwide monitoring system. They rely on international technology, domestic funding, and limited oversight. Amnesty concludes that this framework leaves private communication exposed and narrows space for free expression.
Pakistan’s case is presented as part of a larger global debate on how surveillance technologies are exported and used. With procurement continuing and safeguards absent, the country’s monitoring capacity is expected to expand further, raising deeper concerns over privacy and basic freedoms.
Benchmarks for Facebook advertising in 2025 show two trends. Traffic campaigns are becoming cheaper and more effective, while lead campaigns are turning more expensive and harder to convert.
How the data was measured
The analysis covers US campaigns that ran from April 2024 through June 2025. Traffic results came from 554 campaigns. Leads were based on 726 campaigns. Each industry had a minimum of three active traffic campaigns and two active leads campaigns. Averages were calculated as medians to avoid distortion from outliers.
Traffic campaigns
Click-through rates on traffic ads rose to 1.71 percent, up 8 percent from last year. Shopping, collectibles, and gifts performed best at 4.13 percent. Automotive repair was at the bottom with 0.80 percent.
The average cost per click fell by nearly 7 percent to $0.70. Shopping, collectibles, and gifts paid the least at $0.34. Finance and insurance paid the most at $1.22.
Lead campaigns
The average cost per lead reached $27.66, about 21 percent higher than last year. Conversion rates fell from 8.67 percent to 7.72 percent. Click-through rates stayed about the same at 2.59 percent.
Industry differences were wide. Restaurants and food had the best numbers with an 18.25 percent conversion rate and a $3.16 cost per lead. Dental services had weak performance, with a 1.05 percent click-through rate and a $76.71 cost per lead.
The average cost per click for lead ads was $1.92. Restaurants and food paid the lowest at $0.74. Dental services paid the highest at $9.78.
Comparisons with Google
Despite higher costs on Facebook leads this year, the platform is still cheaper than Google. The average Google cost per lead is about $70. Facebook’s $27 looks affordable in comparison. On clicks, Facebook leads average $1.92 while Google’s are more than $5.
Industry swings
Some industries shifted sharply year over year. Shopping, collectibles, and gifts doubled their click-through rates. Sports and recreation also saw large gains. Restaurants and food raised conversion rates by more than threefold. On the losing side, dentists faced a near-doubling of lead costs. Furniture and real estate showed weaker conversion rates.
What it means for advertisers
The results suggest that broad economic pressures and more competition are raising acquisition costs in many fields. Businesses tied to discretionary spending, such as home improvement and beauty, are finding it harder to generate leads.
Practical steps can help. Advertisers are advised to filter out low-quality leads by tightening forms and blocking disposable email domains. Meta’s Advantage+ automation may save time but needs careful oversight to avoid wasted spend. A balanced mix of traffic, brand, and lead campaigns can spread risk across objectives.
Bottom line
Facebook remains an affordable channel compared with search, especially for driving traffic. But lead generation is becoming more expensive and less reliable. Marketers will need sharper targeting and stronger controls to keep performance steady in 2025.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
Meta Platforms and Anduril Industries have been selected to participate in the U.S. Army’s latest effort to develop advanced mixed-reality combat goggles. The project adds another chapter to the ongoing military push to equip soldiers with head-mounted systems that combine augmented and virtual reality.
The Army’s new program will build on the earlier Integrated Visual Augmentation System (IVAS), a multibillion-dollar initiative first led by Microsoft. That effort, despite heavy investment, faced criticism from within the military and was eventually transferred to Anduril. Since taking over, Anduril has restructured the initiative under the name Soldier Borne Mission Command, drawing on years of test data and soldier feedback.
According to available information, the new phase will use more than 260,000 hours of input gathered during the IVAS program. The Army has already committed over $1.3 billion to research, prototypes, and testing in this area, making the current contracts part of a long-running strategy rather than an isolated deal.
The collaboration also marks a renewed partnership between Meta chief executive Mark Zuckerberg and Oculus founder Palmer Luckey, now running Anduril. The two had previously pursued consumer-focused virtual reality projects but are now applying their experience to defense contracts. Bloomberg reported that Meta was included as a partner in Anduril’s proposal, although the company has not disclosed financial details of its share.
Rivet Industries, a defense technology firm led by a former Palantir executive, was also awarded a contract. The company said its deal is valued at around $195 million, suggesting that multiple prototypes will advance in parallel before the Army settles on a final system.
For the Army, the goal remains unchanged that is to field reliable mixed-reality equipment that enhances soldier awareness and mission planning. For the technology firms involved, the contracts represent a shift from consumer markets to defense applications, where timelines are long and budgets remain substantial.
Meta’s Record on Content and Advertising
While Meta’s role in the combat goggles project signals a deeper move into defense technology, the company’s broader record on ethical and moral decision-making remains under scrutiny. Over the past several years, Meta has faced repeated criticism for how it moderates content and manages political advertising, particularly during armed conflicts.
Human Rights Watch and Access Now documented widespread suppression of pro-Palestinian voices during the Gaza genocide by Israel, including removals and account suspensions affecting journalists and activists. In 2024, nearly 200 Meta employees signed an open letter criticizing what they described as systemic censorship of Palestinian content. A lawsuit filed by former Meta engineer Ferras Hamad further alleged discrimination after he raised concerns about mislabeling Gaza-related posts.
The company has also been accused of allowing ads that sought donations for Israeli military campaigns, including equipment such as drones. Reports from The Guardian, Euronews, and Al Jazeera found that dozens of ads linked to the Israeli Defense Forces remained active on Meta platforms, even when they appeared to breach internal advertising rules.
Taken together, these controversies have reinforced perceptions of inconsistency in Meta’s enforcement of its own policies, raising questions about how its role in defense projects will be viewed in light of its track record in global conflicts.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
Google’s lawyers told a federal court that the open web is already in “rapid decline.” The statement appeared in a filing on September 5, where the company opposed a Department of Justice proposal to break up parts of its ad business. Lawyers said forcing Google to sell its AdX marketplace would hurt publishers who depend on display advertising.
The claim stands against recent comments from Google’s leadership. In May, chief executive Sundar Pichai said the number of indexable pages had grown by 45 percent since 2023, pointing to a surge in online content. Nick Fox, vice president of Search, described the web as “thriving.” Elizabeth Reid, who leads the Search division, said last month that traffic to websites has stayed relatively stable, even with new AI features in Google Search.
Google clarifies its position
After criticism, Google explained that its filing referred specifically to advertising. Dan Taylor, vice president for global ads, said the phrase had been taken out of context and pointed to budget shifts toward connected television, retail media, and other formats. A spokesperson added that the full passage was about open-web display ads, not the entire web.
Advertising shift
Industry data shows that display ads on independent sites have lost ground for several years. Google’s own figures indicate these ads accounted for 40 percent of impressions in 2019 but only 11 percent in early 2025. Marketers now spend more on apps, video, and social platforms. For publishers that still rely on display ads, this shift has meant slower revenue growth.
Antitrust pressure
The statement comes as Google defends itself against remedies in a major antitrust case. Judges earlier found the company had tied ad services in ways that disadvantaged rivals and favored its own marketplace. Regulators are pushing for structural changes, and Google is trying to show that breaking up its ad business would harm publishers instead of restoring competition.
Reaction from industry
Observers have pointed out the conflict between Google’s upbeat public messages and the language it used in court. Some industry voices say the company is presenting different stories depending on the audience. For publishers, the core concern remains whether search traffic and advertising revenue can sustain the business models that keep much of the open web alive.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
