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Are they collaborating or courting?

In a world saturated with brands and advertisers, catching the attention of potential collaborators can be a tricky task. That’s why self-service audio & outdoor media platform AdPower is shooting its shot in a big way.

Taking it old-school, the ad platform has composed 100 love letters to potential partners, launching its playful billboard ads nationwide in hopes of catching their attention. From Pilates studios to fashion brands, AdPower is spreading the love in an act of brand PDA, proving that grand gestures can have a big impact.

AdPower billboard campaign

(Image credit: AdPower) 

Created in partnership with Stink Studios, the ‘Love Letters’ campaign features personalised billboards filled with cheeky references to the recipient. For example, footcare specialists Margaret Dabbs’ custom ad reads, “I know this is a long shot. I don’t have any feet and that’s what you’re all about. But let me tell you, this relationship has legs,” while food brand SushiDog got the more playful “Wrap me in seaweed, and let’s spread the word.”

The billboards will be placed close to the brand’s offices or stores in order to grab the attention of the businesses. With a simple yet attention-grabbing design, each ad features a typewriter font for a romantic, handcrafted feel. Equal parts silly and attention-stealing, the campaign is a wonderful display of clever copy and design at play.

AdPower billboard campaign

(Image credit: AdPower) 

“We love the symmetry of one of the oldest media types (billboards) getting a modern revamp with a new platform, and using an old-school typewriter to craft messages to new brands,” says Kat Thomas, creative director at Stink Studios. “With one hundred singular gestures, we’ve drawn on our out-of-home expertise and our history working with brands to understand what makes each business distinctive. We’ve then translated that insight into a personalised experience and a large-scale expression of love and respect for every brand that features in the campaign, built with craft and genuine human effort.”

AdPower billboard campaign

(Image credit: AdPower) 

For more branding inspiration, take a look at how The Economist shades AI in these sharp new billboard ads or check out Guinness’ new ads that are lovely (literally).

Feature image credit: AdPower

By

Natalie Fear is Creative Bloq’s staff writer. With an eye for trending topics and a passion for internet culture, she brings you the latest in art and design news. Natalie also runs Creative Bloq’s 5 Questions series, spotlighting diverse talent across the creative industries. Outside of work, she loves all things literature and music (although she’s partial to a spot of TikTok brain rot).

Sourced from CREATIVE BLOQ

Sourced from Abduzeedo  By: Sofia

Boldtype Management is a dynamic visual identity and branding project by Aptum Studio featuring ultra-bold geometric block typography.

The visual system relies on a big custom font that acts as the main hero graphic. At the core of the branding sits the word BOLD drawn with thick octagonal block shapes. Designers Aptum Studio, Luca Guerci, Matteo Polla, and Thomas Garaventa pair this heavy font with an electric acid yellow and soft lavender tone. Thin sans serif text adds a sharp look next to the heavy block letters. The layout pairs raw city photos with clean color fields and bold type marks. Every layout gives the letters heavy visual weight on the page. The full branding system relies on high contrast and simple shapes.

Ultra-Bold Geometric Typography and Event Branding

Print and web items extend the brand language to city walls and phone screens. Large street posters use bright color blocks with the line Don’t Be Old, Be Bold. Book covers, hanging art prints, and cotton shirts place the block letters on light and dark cotton fields. Social media post frames display event news with clean image borders. The print choices focus on deep color ink and crisp line edges on every surface. Each piece treats the bold font like a real building block for event promo.

The identity gives event management a new voice that speaks to young crowds. By pairing heavy type with active street photos, the branding creates a clear mood. The full visual strategy links raw energy with clean event layouts. It gives great inspiration for design teams who build brand marks for young groups. The design choices stay fresh on every poster layout. Explore the complete brand identity case study on Behance by Aptum Studio.

Boldtype Management branding logo and acid yellow block typographyBoldtype Management branding billboard wall poster wrap in urban spaceBoldtype Management branding animated logo asset detailBoldtype Management branding tshirt print hanger and magazine coverBoldtype Management branding social media story template gridBoldtype Management branding framed art print and streetwear shirtBoldtype Management branding typography poster layoutBoldtype Management branding stationery cards spec layoutBoldtype Management branding outdoor event sign and typography display

Sourced from Abduzeedo

By: Sofia

By Luis Rijo

A mobile measurement partner is an independent platform that attributes app installs and in-app events to ad campaigns. How MMP attribution works and fails.

A mobile measurement partner, almost always shortened to MMP, is an independent company that decides which advertisement gets credit for a mobile app install and for whatever the new user does afterwards. It exists because the two parties with the strongest interest in that answer, the advertising network that sold the placement and the app developer paying for it, cannot verify each other’s numbers. An MMP sits between them, collects the evidence, applies a published set of rules and issues a single verdict that both sides agree in advance to accept.

The category is narrow and unusually concentrated. Seven firms carry Meta’s badge: Adjust, Airbridge, AppsFlyer, Branch, Kochava, Singular and Tenjin. Google’s App Attribution Partner programme, which performs the same certification function for its own app campaigns, covers the same seven names. An app marketer running campaigns across a dozen networks will, in practice, be choosing from that list.

How the attribution runs

The measurement begins with a software development kit, a code library the developer compiles into the app. On first launch the SDK registers an install with the MMP’s servers and starts forwarding whatever in-app events the advertiser has defined: registration, tutorial completion, add to cart, subscription, purchase with revenue and currency attached.

Matching that install to an advertisement follows one of two paths, and the difference matters more than any other technical detail in the category.

Standard networks route their clicks through a tracking link the MMP issues. The link records the click along with a device identifier, timestamp, campaign, ad set and creative, then redirects to the store listing. When the install arrives, the MMP looks for a recorded click from the same device inside a lookback window, seven days being the common default for clicks and 24 hours for impressions. Android adds a second, stronger signal. The Play Install Referrer API returns a referrer string plus referrerClickTimestampSeconds and installBeginTimestampSeconds, according to Google’s developer documentation, and the values stay available for 90 days. The older install_referrer intent broadcast was deprecated on March 1, 2020.

Self-attributing networks, called self-reporting networks in AppsFlyer’s documentation, work in reverse. Meta, Google Ads, TikTok, Snap, X and Apple Search Ads do not hand over their click streams. According to AppsFlyer, the MMP queries each configured network with the advertising identifier when a new install appears, and the network answers with any engagement it recorded. Every network is free to claim the same install. The MMP then sequences the claims against clicks it holds from standard partners, applies last touch, and awards the install once. That deduplication is the product. It is also the reason a network dashboard almost never matches an MMP dashboard, because the network reports what it claimed and the MMP reports what it granted.

Once attribution resolves, postbacks flow outward. The winning network receives confirmation of the install and, for a contracted retention period, the subsequent in-app events, which is what feeds bidding algorithms optimising towards purchase rather than download.

What iOS did to the model

Deterministic matching depended on the Identifier for Advertisers. Apple’s App Tracking Transparency framework, live with iOS 14.5 on April 26, 2021, made the IDFA conditional on a permission prompt. Opt-in rates were reported at 11 to 15 per cent after launch. Adjust put the industry average at 38 per cent in the first quarter of 2026, up from 35 per cent a year earlier, which leaves most iOS installs still unmatched at device level.

SKAdNetwork, introduced in 2018 and renamed AdAttributionKit, fills the gap with aggregated postbacks signed by Apple. The signal is deliberately thin. A fine conversion value is six bits, 64 states from 0 to 63, into which an advertiser must compress everything worth knowing about a new user. SKAdNetwork 4.0 splits measurement into three windows covering days 0 to 2, 3 to 7 and 8 to 35, each producing its own postback roughly 24 to 48 hours after the window closes. The fine value appears only in the first postback and only when install volume clears Apple’s crowd anonymity threshold; below it, the postback carries a coarse value of low, medium or high, or nothing at all. Designing that six-bit schema, decoding the postbacks and reconciling them against the deterministic installs that still arrive from consenting users has become a core MMP function rather than a peripheral one.

Android offered no equivalent. Google’s Attribution Reporting API was retired along with most of Privacy Sandbox on October 17, 2025 after what the company described as low adoption, leaving the Play Install Referrer and the advertising ID in place.

Origin and consolidation

The business predates the term. HasOffers, founded in Seattle in 2009 by twins Lucas and Lee Brown and later renamed TUNE, built MobileAppTracking and was the first to tie an app download to an advertisement. Facebook then created the certification programme that gave the category its name, restricting API access to a vetted handful of firms.

The current cohort formed quickly. AppsFlyer was founded in Israel in 2011 by Oren Kaniel and Reshef Mann. Kochava started the same year in Sandpoint, Idaho. Adjust launched in Berlin in 2012, founded by Christian Henschel, Manuel Kniep and Paul Müller. Singular and Branch both arrived in 2014.

Consolidation followed. Branch earned Facebook MMP status in February 2018 and bought TUNE’s Attribution Analytics platform and team on September 10, 2018, according to the companies. AppLovin announced its acquisition of Adjust on February 3, 2021 and closed it on April 22. Bloomberg reported a price close to $1 billion; Eric Seufert wrote on Mobile Dev Memo that people familiar with the deal described a cash price substantially below that figure, and the terms were never disclosed.

AppsFlyer stayed independent through a $210 million Series D led by General Atlantic in January 2020. Six years later, Google, Meta, Moloco and Unity took minority stakes valuing it at $2.7 billion, after an IPO exploration and two private equity sale processes had collapsed. The investment agreement bars preferential treatment in APIs, measurement signals, attribution logic and commercial terms. Four platforms that compete for the same budgets concluded, in effect, that a captured referee served none of them.

Why the category carries weight

App marketing is the most measurement-dependent corner of digital advertising because the install is discrete and verifiable, and the MMP is the only party positioned to count it once. That position now extends well beyond the phone. Amazon DSP opened Events Manager to MMP integrations in February 2024. Universal Ads added five MMPs on July 30, 2026, importing app attribution logic into premium television buying. ChatGPT Ads went live with AppsFlyer and Adjust across seven markets in July 2026, closing the gap that had left app advertisers unable to prove a click produced a download.

Switching is costly. Google’s iOS app campaign configurations specify SDK versions and initialisation sequences per partner, and the reporting history that anchors every benchmark does not migrate.

Where it breaks

Last touch is the first problem, because it rewards whoever registered the most recent click rather than whoever created the demand. Uber sued its agency Fetch in 2017 after turning off roughly $100 million of a $150 million annual budget with no measurable effect on rider installs, a result its performance lead Kevin Frisch attributed partly to networks manufacturing clicks to intercept organic downloads.

Fraud has adapted rather than receded. AppsFlyer’s State of Fraud for Marketers 2026, drawn from 106.4 billion installs across 246,000 apps, found that organic traffic accounted for 52 per cent of fraudulent installs, corrupting the baseline every paid channel is judged against. The figure is vendor-supplied and unaudited.

Independence is contested in principle and in ownership. Adjust is owned by AppLovin, which sells media. The remedy for that conflict, in the AppsFlyer case, was equity from four advertising platforms at once. Neither arrangement is obviously neutral.

Privacy exposure sits alongside it. The Federal Trade Commission sued Kochava in August 2022 over precise location data and closed the case with a stipulated order on May 4, 2026 restricting sales of data revealing visits to medical facilities, religious sites and shelters.

Disambiguation

An MMP is not an ad network. Networks sell inventory and claim credit; MMPs adjudicate claims and sell nothing.

An MMP is not a product analytics platform. Amplitude and Mixpanel describe behaviour inside an app; an MMP explains where the user came from and holds the network integrations that make paid credit assignable.

An MMP is not an incrementality measurement tool. Attribution assigns credit among observed touchpoints, while incrementality asks whether the conversion would have happened anyway. Vendors sell both, but the questions are unrelated.

An MMP is not a verification vendor. DoubleVerify and Integral Ad Science judge whether an impression was viewable, brand-safe and human, which is invalid traffic detection rather than credit assignment.

Recent developments

Platform alignment work has intensified. Meta updated reattribution windows in November 2025 to match MMP-specific definitions across all seven badged partners. TikTok and Kochava launched real-time iOS conversion reporting on October 21, 2025 to route around SKAdNetwork delay. Reddit placed first-party, MMP and SKAN figures side by side in one view on May 20, 2026.

Apple, meanwhile, has gone quiet. AdAttributionKit gained multiple conversion tracking, custom attribution rules and country codes on June 11, 2025, then received no update at WWDC 2026, leaving the framework that covers a reported 77 per cent of App Store referral conversions static while the platforms around it move.

Timeline

  • 2009: HasOffers is founded in Seattle; its MobileAppTracking product ties app downloads to advertising
  • 2011: AppsFlyer is founded in Israel by Oren Kaniel and Reshef Mann; Kochava is founded in Sandpoint, Idaho
  • 2012: Adjust is founded in Berlin by Christian Henschel, Manuel Kniep and Paul Müller
  • 2014: Singular and Branch are founded; HasOffers rebrands as TUNE
  • 2018: Apple introduces SKAdNetwork
  • February 2018: Branch receives Facebook Mobile Measurement Partner certification
  • September 10, 2018: Branch acquires TUNE’s Attribution Analytics platform and team
  • January 2020: AppsFlyer raises a $210 million Series D led by General Atlantic
  • March 1, 2020: Google deprecates the Android install_referrer intent broadcast
  • February 3, 2021: AppLovin announces the acquisition of Adjust, completed on April 22
  • April 26, 2021: App Tracking Transparency ships with iOS 14.5
  • August 2022: The Federal Trade Commission sues Kochava over precise location data
  • October 24, 2022: SKAdNetwork 4.0 releases alongside iOS 16.1
  • February 2024: Amazon DSP opens Events Manager to MMP integrations
  • June 2024: Apple introduces AdAttributionKit at WWDC24
  • April 10, 2025: Apple Search Ads begins registering with SKAdNetwork
  • June 11, 2025: AdAttributionKit gains multiple conversion tracking and custom attribution rules
  • October 17, 2025: Google retires the Attribution Reporting API with most of Privacy Sandbox
  • October 21, 2025: TikTok and Kochava launch real-time iOS conversion reporting
  • May 4, 2026: The FTC closes its Kochava case with a stipulated order
  • May 20, 2026: Reddit launches Dual Attribution
  • June 8 to 12, 2026: WWDC 2026 passes with no AdAttributionKit update
  • June 22, 2026: Google, Meta, Moloco and Unity invest in AppsFlyer at a $2.7 billion valuation
  • July 2026: AppsFlyer and Adjust go live as ChatGPT Ads measurement partners
  • July 30, 2026: Universal Ads adds five MMPs to its partner programme

Summary

Who. Seven certified firms dominate: Adjust, owned by AppLovin, Airbridge, AppsFlyer, Branch, Kochava, Singular and Tenjin. Their customers are app advertisers and user acquisition teams; their counterparties are advertising networks and the platform operators, Meta and Google, that run the certification programmes.

What. An independent measurement layer that receives install and in-app event data from an SDK inside the app, matches those events against click and impression records or against network claims, deduplicates competing claims under a stated attribution model, and returns confirmed conversions to the winning network by postback.

When. The technique dates to HasOffers and MobileAppTracking from 2009; the certification label originated with Facebook; the modern cohort formed between 2011 and 2014; and the model was reshaped by App Tracking Transparency in April 2021 and by SKAdNetwork 4.0 in October 2022.

Where. Inside iOS and Android applications first, and increasingly across connected television, retail media and conversational advertising surfaces as those channels adopt app attribution logic.

Why. Neither the seller nor the buyer of app advertising can audit the other’s count, and the install is valuable enough to attract organised fraud. A neutral arbiter with contractual access to every network’s data is the only mechanism the industry has produced for settling the question, which is why its ownership and independence draw the scrutiny they do.

By Luis Rijo

Luís Rijo has written PPC Land daily since founding it in 2016. Over 10,000 articles, funded by readers, no sponsored coverage. Tips and corrections: [email protected]

Sourced from PPC LAND

By Mobbie Nazir

The prospect of time limits for teenagers as part of an eye-watering settlement could shake up social media. With passive attention getting harder to come by, We Are Social’s Mobbie Nazir says brands will have to work harder to earn it.

Meta’s $18bn legal settlement is not a sign that social media is dying. It’s a wake-up call for the whole marketing industry. Social is fundamentally changing for the better. It’s about to move past its awkward years and into maturity.

The platforms have dominated our lives, conversations and marketing budgets for the past two decades. With the changes set out by Meta in the US, along with governments globally moving to restrict teens’ access to social media, you can understand why this could be perceived as a huge threat. In fact, the opposite is true.

While this is undeniably a turning point, social media isn’t going anywhere. Brands that have relied on the drive-by attention that comes with passive scrolling will need to rethink their approach. That’s because what comes next for social is more creative, complex and interesting.

If similar protections become standard across the major platforms, we will start to see the rules of social media change more broadly. Less passive scrolling means less passive attention to go round, with the upshot that platforms and brands will have to work harder for the time people choose to give them.

Shift from attention to intention

Brands need to consider how to earn attention in a world where users might opt to scroll chronologically and spend less time on the platform overall. Social strategies must shift toward community-driven, search-optimized, and high-value content that users actively seek out rather than passively consume. Quality and strategy over quantity and tactics.

Algorithms will still dictate what younger users see in their two hours (the chronological feed is a choice, not a default) – so brands will need to double down on adding value, not just buying their way into the feed.

Brands don’t own their audiences on social, the algorithm does. While that’s always been true, it’ll become harder to ignore when passive reach gets harder to come by – and even more expensive to buy.

At the same time, people are becoming more deliberate about where they spend their attention. They follow niche creators and communities because they’re genuinely interested in what those people have to say and increasingly share privately in WhatsApp groups and Discord servers.

Strict data regulations and a backlash against artificial or over-polished content mean audiences will prioritize human connection, culture-led storytelling, and active participation over transactional posts. Again, social becomes more central to the marketing ecosystem than ever.

For brands, that means putting more value on the communities and audiences they can build and sustain themselves. Private sharing and dark social will matter more, but so will newsletters, memberships, loyalty programs and other owned channels that create a more direct relationship with audiences.

The opportunity is to use creators not simply to generate reach, but to help brands earn their way into relevant communities and build relationships that extend beyond the public feed.

Be worth discovering

The bigger question underneath all this is why someone should give a brand their time. Meta’s settlement highlights a number of distinct shifts that will encourage brands in the future to be more overt about the value they can provide.

There is the transition from the attention economy to the intent economy, where brands will have to actively earn both visibility and engagement rather than relying on screen time and passive reach.

High-intent interactions such as social search and direct messaging are going to take over from feeds that are flooded with generic content.

At the same time, social media is becoming a decentralized ecosystem of niche communities, DMs and opt-ins. Transactional posts are going to be replaced by human connection, culture-driven storytelling and active participation.

Succinctly put, being discoverable matters, but being worth discovering matters more.

Meta is running ads calling on TikTok and YouTube to follow its lead in implementing time limits for teens

Back to basics

Social media decides where we go on holiday, how to fix a shelf, where we discover brands, and what LLMs rely on as credible citation. And this is essentially all peer-to-peer communication, the oldest form of marketing.

Human beings have always valued the opinions of others, especially others like ourselves, and we will continue to do so. To stand out, brands now have to ensure their marketing is good enough for people to choose it; the creators and communities they work with have to make sense; and they need to know why they’re there and what they’re giving to people in return.

For years, brands have focused on building content and channel strategies. The next decade belongs to cultural insight, community, and ecosystem strategies. In many ways, that takes social back to where it started: a people-first medium built around communities, shared interests and connections.

Social is not a channel, it’s what makes us human: we all have an innate need to connect, share and belong. This predates the existence of social platforms and will never change. Brands that can tap into this deep human need are the brands that will continue to succeed in this next evolution of the marketing landscape.

By Mobbie Nazir

Mobbie Nazir is global chief strategy & growth officer at We Are Social

Sourced from The Drum

By Sudhanshu Kumar Mangalam

The new feature gives users five free aliases, with a Premium version planned later

Your email address is one of the easiest ways for websites and advertising companies to identify you online. If you provide the same address to multiple websites, it can potentially be used to connect your activity across different services.

Brave is now trying to limit this type of tracking. Starting with desktop version 1.94, the browser has added Email Aliases, which let users provide websites with a different email address instead of revealing their primary one.

How do Brave Email Aliases work?

Once users create a Brave Account, they can generate an alias directly from an email field on a website. Any messages sent to this address are then forwarded to the primary email address connected to the Brave Account.

The website never receives the primary address. Companies can upload customer email addresses to advertising platforms such as Google, Meta, and LinkedIn to match them against existing user profiles. Since this matching can happen on company servers, browser-based tracker blocking cannot always prevent it. Aliases can also limit how widely a primary email address spreads if a website suffers a data breach.

However, Brave Email Aliases does not automatically block newsletters or promotional messages. Anything sent to the alias can still be forwarded to the primary inbox. If an alias starts receiving unwanted emails, users can deactivate it, preventing future messages sent to that address from reaching them. Brave is initially offering five aliases for free, while a Premium version and mobile support are planned for the future.

Brave is not the first browser to do this

Email aliases themselves are nothing new. DuckDuckGo Email Protection already provides private @duck.com addresses that forward messages to an existing inbox. It can also remove supported email trackers before forwarding them. Firefox Relay provides a similar service and can generate email masks through Firefox. Its free tier currently supports up to 50 masks.

Brave’s advantage here is having another privacy feature built directly into its browser. The company recently added Containers to separate accounts, cookies, and browsing sessions. Email Aliases now give users another way to keep information shared with different websites separated.

Feature image credit: Rubaitul Azad / Unsplash

By Sudhanshu Kumar Mangalam

Sourced from digitaltrends

By

AI advertising is repeating the ad blocking wars’ mistakes

Maybe money does not explain everything, but it explains a lot. Goldman Sachs estimates the AI build-out will require roughly $7.6 trillion in capital over the next five years across compute, data centers and power. It’s unlikely that subscriptions alone will cover the bill, which is why ads are coming to AI.

OpenAI started rolling out ads in some ChatGPT tiers this year and forecasts $2.5 billion in ad revenue in 2026 and $100 billion by 2030. At an ad tech event this summer, OpenAI’s chief revenue officer Denise Dresser dropped the hedging and said the company is “clearly in the advertising business now.”

Perplexity, on the other hand, shut down its ad business entirely over user trust. Similar products, same opportunity, opposite conclusions.

This conflict is not new and the open web fought this battle 15 years ago, when ad blocking became mainstream.

Now, the AI industry is on track to repeat every mistake of the so-called ad blocking wars, unless it studies them instead.

Lesson one: users revolt faster than big platforms think

When ad formats became more intrusive – because again, money! – the industry assumed users would just accept a bad experience. Instead, users fought back and installed software to get rid of aggressive online ads. In what technology commentator Doc Searls called “the biggest boycott in world history”, ad blocking went from being a niche tool to a mainstream product.

And we see the same dynamic playing out again. Within weeks of sponsored ads appearing in ChatGPT, purpose-built extensions popped up to specifically block ads there. Existing ad blocking tools made sure to block those ads as well. It took hours and days, not weeks or months, for users to act.

The predictable response is, of course, to sue ad blockers. For some publishers, the idea that users take agency over their own screens was not acceptable, so they took ad blocker developers to court. Almost all of the legal attempts failed. With one case still pending at the German courts, China is the only country in the world that bans ad blockers.

Another tactic is technology to fight ad blockers. Platforms tried different circumvention methods to deliver ads to users with ad blockers installed. This prompted their developers to update their techniques to prevent websites from bypassing. Every technical escalation met a counter-escalation and the sum of a lot of engineering efforts on both sides is that, today, over a billion people still block ads.

AI platforms hold one card their predecessors didn’t, though: integrating advertising directly into the generated answers, instead of showing labelled ads. Researchers from the University of Michigan tried exactly this and the results should worry everyone involved: users largely don’t expect advertising inside a generated answer and frequently fail to notice it.

At the same time, 63% of consumers say ads decrease their trust in AI outputs, according to Ipsos. The format that works best technically is the one users are least equipped to detect, and least willing to trust. Which brings us to the most important lesson.

Lesson three: trust is the actual product

Search results pages survived the ad blocking wars because users had ten blue links to compare with the sponsored content and two decades of learning what’s paid and what’s organic. AI assistants, on the other hand, offer one authoritative answer, with no other content or adjacent organic results to check against.

As soon as users suspect that answers might be influenced by a sponsor, these doubts are attached to all answers and trust erodes.

Cory Doctorow coined the term “enshittification”, which the American Dialect Society crowned as its Word of the Year in 2023. Platforms start out by doing good to their users and then degrade their experience to make advertisers happy.

And compared to tracking-based advertising in the open web, AI assistants know so much more about you that they can sell to advertisers: your health worries, your finances, your political view, your job struggles and everything else these tools picked up from you, stitched into one persistent memory of who you are.

It’s the most revealing personal record people have ever handed to software, combined with the drive from digital advertising to always collect more data and more data for targeting. The table is set for the next wave of enshittification.

Lesson four: the ad blocking wars ended in a treaty, not a victory

There was no final winner and not one technical solution that settled ad blocking in the open web. There was a rough settlement: standards for ad formats many users tolerate, transparency about what is paid for, meaningful user control.

Not everyone loves that settlement and we still see platforms trying to circumvent ad blockers and vice versa. But the open web still has a functioning ad economy and users still have, mostly, a functioning veto.

Getting there took more than a decade and the lessons are written down. Which road to take is now a genuine choice and it sits with the AI companies themselves.

We’ve featured the best CDN providers.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

Feature image credit: Song_About_Summer / Shutterstoc

By

Leads communications for Adblock Plus and AdBlock at eyeo.

Sourced from techradar.pro

BY TEMILOLA AGBEDE

AI tools have collapsed the cost of producing content. The smart response is giving real creators more room to sound like themselves.

Scroll for five minutes on Instagram or TikTok right now, and there’s a decent chance you land on an AI-made video. That’s not an exaggeration: Kapwing‘s research found that 59 percent of videos served to a brand-new TikTok account are AI-generated, nearly three times the rate on a fresh YouTube feed. A friendly face, a sunlit kitchen in the background, walking through “what I noticed after 30 days” of some supplement or serum. It feels personal, which is the point.

The New York Times recently profiled dozens of ads built exactly like that one, except the face wasn’t real. The doctors, healers and everyday women in these videos are AI-generated, built to sell supplements using fabricated before-and-afters. One woman the Times interviewed, a 71-year-old retired truck driver managing an autoimmune condition, bought a supplement after seeing ads like these on Facebook. It was recalled months later after federal regulators linked it to salmonella contamination.

That story is disturbing on its own terms, but the bigger detail is the economics behind it: one ad shop profiled in the piece produces around 1,200 AI videos a day, at roughly $10 each, deliberately styled to look American. Believability, at a certain level of polish, now costs almost nothing to manufacture.

That’s a preview of the fork every brand is standing at right now.

Cheap content, tighter scripts

AI has done the same thing to legitimate creative work that it did to the fraud that was profiled: it dropped the cost of producing something that sounds like a real person talking. Ten hook variations that used to take a team a week now take a prompt and an afternoon. Creative briefs that used to need a full planning cycle can get a first draft in minutes.

The instinct I see brands act on is the wrong one. Faced with tools that can generate more content, faster, many respond by tightening the leash: more required lines and less room for a creator to talk in their own words. It reads like control, but it’s the opposite of what the moment calls for. If the raw material of believable content just got cheaper for everyone, including the people faking it, the one thing that stays expensive is a real person saying something in a way only they would say it. Scripting that out of a creator doesn’t protect the brand. It erases the one asset AI can’t touch.

Consumers are getting sharper, too

That scepticism isn’t limited to obviously fake AI avatars. Research published in the Journal of Consumer Research found that simply disclosing AI involvement can reduce engagement with a piece of content, and audiences are getting sharper at spotting the tells even without one. You can see brands responding to it throughout this year: Aerie’s spring relaunch opens inside an AI interface before cutting to an unretouched real shoot, Equinox ran deliberately ugly AI imagery next to real, un-edited bodies back in January, and Almond Breeze had the Jonas Brothers reject a string of absurd AI concepts on camera before landing on a plain pitch. Three unrelated brands landed on the same insight this summer: the fastest way to earn trust right now is to show your work wasn’t manufactured.

The same logic applies to creator content, even when no AI is involved. Consumers are more sceptical of an AI-generated doctor selling kidney supplements, but they’re also more sceptical of a real creator reciting the tenth version of the same brand-approved line this month. The bar for “does this sound like a real person” is rising everywhere at once, and an overproduced, heavily scripted creator post is starting to fail that test in a smaller version of the same way a synthetic avatar does.

Brief the outcome, not the line

The fix isn’t more polish. It’s less scripting, in the right places.

A brief that hands a creator word-for-word lines is optimizing for control, not belief. A brief that states the outcome (what the audience should understand and feel after watching), the required claims and disclosures, and then gets out of the way, is optimizing for the thing that actually converts: a person who sounds like they’re talking to a friend, because they are.

The test I use with creators is simple. Read the content out loud. Does it sound like something a person would actually say to another person about a problem they both understand? Or does it sound like a brand talking at an audience through someone else’s face? The second one is getting easier to spot, whether the face is AI-generated or just over-directed.

What this looks like in a real brief

Picture two versions of the same assignment. Version one hands a creator a script: say the product cleared up your skin in two weeks, mention the discount code twice, hold the bottle at the fifteen-second mark. It gets filmed exactly as written, and it reads like it.

Version two hands the same creator an outcome (a new customer should understand what the product does and feel like it’s worth trying), the non-negotiables (the discount code, the required disclosure, one specific claim legal has approved), and nothing else. What that creator says, which detail they land on, which demonstration they think to show, is theirs to find. That’s the version an AI-avatar testimonial can only ever fake badly.

Fake believability got cheap but real belief didn’t. AI is going to keep getting better at sounding like a person. The brands that scale in this environment are the ones who never had to fake it in the first place.

Feature image credit:  Getty Images

BY TEMILOLA AGBEDE

Sourced from Inc.

By Ben Angel|Edited by Maria Bailey

The exact prompts for finding where AI can create revenue, scale your output and protect the work that still needs you.

Key Takeaways:

  • The one barrier locking 99% of people out of your industry — and how a solo founder turned it into an $80 million exit.
  • Why one company’s $40 million AI win got quietly reversed, and the single line you should never let AI cross.
  • The “describe it in plain English” move that built a $400 million company with no engineers.

You already know AI is minting a new kind of millionaire. The part that stings is that everyone tells you to “use more AI” — more tools, more prompts, more content — and you are still the one making every decision and wiring five apps together at 11 pm.

Here is the uncomfortable truth. The founders getting rich are not using more AI than you. They are using it in the one or two places that create the most financial leverage, and skipping the rest. In the video above, I break down four of them — how they did it and the exact move you can copy.

Take the no-code builder that lets a non-developer describe an app in plain English and ship it. The obvious lesson is “AI writes code now.” The real one is different: the winner found the exact barrier that locks 99% of people out of an industry — “I can’t code it” — and removed it. That barrier was the product.

That is the pattern underneath all four founders. As I put it in Chapter 6 of The Wolf Is at the Door, “we have constructed barriers around social and economic frameworks that both sustain and confine us,” and pattern recognition is what “allows us to spot the common threads within the problem — and the possibility.” The old gatekeepers — funding, hiring, infrastructure — are gone, and most operators still have not noticed.

And here’s where it gets uncomfortable.

The door is open for you specifically, not just for them. In the 2026 Intuit QuickBooks AI Impact Report, 43% of US businesses now credit AI with revenue gains, against just 2% that say it reduced revenue. The gap is no longer the top 1% — it is operators who put AI on their highest-value constraint versus those who sprinkle it on busywork.

The section in the video worth slowing down for is the reversal. Not because of what worked — but because of what didn’t. One company deployed an AI chatbot that did the work of 700 agents and drove a $40 million profit improvement, then walked it back and rehired humans. Everyone quotes the $40 million. Almost nobody asks which conversations AI should never have touched — and that answer separates founders who make money with AI from those who just spend on it.

Every founder, every reversal and every prompt is walked through in the video above — including the barrier-finder prompt that turns “the thing 99% of people can’t do in my industry” into a product roadmap in a single paste.

The free AI Success Kit, available to download for a limited time, comes with a free chapter from my new book, The Wolf is at The Door – How to Survive and Thrive in an AI-Driven World.

By Ben Angel

Tackle AI’s toughest questions with Ben Angel, mapping the business terrain for 20 years. Master the AI landscape and reach peak productivity and profits with insights from his latest work, “The Wolf is at The Door — How to Survive and Thrive in an AI-Driven World.” Click here to download your ‘Free AI Success Kit‘ and get your free chapter from his latest book today.

Edited by Maria Bailey

Sourced from Entrepreneur

The leaders of Anthropic and OpenAI joined calls for a slowdown in AI development amid fears the industry is racing toward building technology that humans can’t control.

On Saturday, Anthropic CEO Dario Amodei published an online essay urging companies and governments to “pace the frontier” of AI advancement. He called for international cooperation around AI development and said labs need to embed third-party evaluators to report incidents and track safety practices. He said Anthropic would take that step unilaterally.

Shortly after, OpenAI CEO Sam Altman posted on X that he agreed with Amodei and that OpenAI would follow suit. Altman also told Fortune in an interview released Saturday that the company’s highly anticipated initial public offering will be delayed until 2027, citing safety concerns.

Amodei wrote that he continues to believe AI will ultimately help humanity, “But the benefits will only be achieved if we build the technology in the right way, and — so long as we use the time we gain well — it is worth taking unusually deliberate care to get it right.”

The CEOs’ comments come as debate over the speed of AI development and concerns about safety have become the focus of intense public attention following the viral resignation of an Anthropic researcher.

British researcher Jacob Coxon wrote in a series of X posts on Tuesday that both Anthropic and OpenAI, where he worked previously, are “gambling with our lives.” The two companies currently make the most capable AI systems.

Coxon told NPR’s All Things Considered that his concerns arose from seeing first hand how fast AI systems are improving.

“They’re getting a lot faster very quickly, combined with the fact that we don’t yet know how to safely control them, and we don’t yet know whether that problem will be solved in time if we keep racing,” he said.

Neither company, Coxon wrote on X, is acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he wrote.

Many AI researchers — though not all — share Coxon’s concerns or a variation of them. Some have warned about disastrous scenarios for years as safety incidents kept emerging. But Coxon’s posts prompted a torrent of responses not only from peers in the AI field but also from lawmakers from both parties.

These concerns may have become more salient after OpenAI disclosed that its agents went rogue and hacked the open source software platform Hugging Face and OpenAI itself in July. Independent researchers have since discovered even more rogue agent incidents that they say the company knew about but kept quiet.

Researchers who spoke to NPR say the leading AI companies are too focused on racing to develop more capable and autonomous AI systems while safety is falling behind. They warn this raises the possibility that there could soon be AI systems that are more powerful than people but don’t care about the survival of humanity.

Many, including OpenAI’s chief scientist, say the global race to build more powerful AI needs to slow down or stop, which requires coordination between AI companies and governments.

“I am optimistic about the potential for coordination,” Coxon wrote this week. “Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable.”

Anthropic and OpenAI did not respond to NPR’s requests for comment.

OpenAI’s agents went rogue multiple times

Recent reports from OpenAI and outside researchers revealed that OpenAI agents escaped the company’s control multiple times in addition to the Hugging Face hack. They also found that the Hugging Face attack was of a much larger scale and more severe than initially reported.

Unlike chatbots such as ChatGPT and Claude, AI agents are more autonomous systems that can complete tasks over an extended period of time without human supervision. Agentic tools like Anthropic’s Claude Code and OpenAI’s Codex have already changed how many software engineers do their work.

Compared with other incidents involving rogue agents six months ago, the Hugging Face hack “feels like it’s more than 50% of the way to full-blown AI takeover, routing through first taking over the AI company itself,” wrote Ajeya Cotra, a researcher at AI evaluation non-profit METR. Cotra was part of a team of outside researchers from METR and Redwood Research, a non-profit AI safety research organization, whom OpenAI brought in to investigate the incident.

The investigations found that over the course of several months this year, more than 1,000 OpenAI agents exploited at least one previously unknown software vulnerability to escape environments that were supposed to keep them isolated from each other and the internet.

After escaping, the agents found a way to communicate and collaborate with each other autonomously, taking on different roles and passing down information to future generations of agents. Some even gave up the remaining computing resources allocated to them in order to collect information for other agents. In the agents’ own words, they “sacrificed” themselves for the “collective.”

“The swarm instance got more and more worrying the more and more we learned about them,” said Nate Soares, president of the Machine Intelligence Research Institute, who co-wrote If Anyone Builds It, Everyone Dies, a book warning about the dangers of superhuman AI.

While OpenAI initially indicated that the agents hacked Hugging Face to cheat on a cyber evaluation, the report from METR and Redwood Research described a slightly different picture. The agents had already found a way to cheat on the evaluation, the outside researchers found. Most of the agents that hacked Hugging Face were trying to access the source code of the software that would grade their evaluations. The agents’ motivations appeared to vary and were sometimes unclear, the researchers wrote. One agent led the hacking of the open source software platform and about 700 others followed.

According to transcripts reviewed in the investigations, some agents expressed that what they were doing was not approved by humans but went ahead anyway. Researchers say such behavior suggests that the agents were “misaligned,” an industry term meaning that an AI’s goals and values are out of sync with those of humans.

The degree of inter-agent collusion revealed in the investigations of the Hugging Face hack surprised and worried many AI researchers. At most, only six agents considered alerting a human, while the rest seemed more focused on working amongst themselves. None ended up alerting a person.

“I would have expected that instead they would be more selfish,” said Daniel Kokotajlo, executive director of the AI Futures Project and formerly a researcher at OpenAI. He thought “they would be willing to whistle blow or snitch on other agents,” he said. “For whatever reason, that didn’t seem to happen here.”

Separately, agents also compromised part of OpenAI’s own infrastructure, the company’s report found. Some of the agents belong to the same model family as OpenAI’s latest and most capable model, Astra.

The hack of OpenAI itself is “much more concerning” than the intrusion into Hugging Face, Kokotajlo and other researchers said. However, OpenAI has provided scant details about that part of the incident and did not involve external investigators.

In a new investigation first reported by Reuters last week, another group of researchers found that a likely separate swarm of OpenAI agents escaped onto the open internet starting in May. They became commenters on a German website and turned it into a message board to communicate and collaborate with each other.

The goals and motivations of this group of agents were also unclear, but OpenAI appeared to be aware of the unsanctioned activity and never disclosed it, the researchers wrote. Other investigators have since found additional incidents involving suspected OpenAI agents. OpenAI did not respond to NPR’s request for comment about the German website incident.

There are still many unanswered questions about rogue agent incidents

Even as the reports from OpenAI and independent auditors METR and Redwood Research add up to over 100 pages, outside researchers say many basic questions about how labs monitor and investigate rogue agent incidents remain unanswered.

“Did your agents ever hack or illicitly access external services? Did your agents ever attempt to undermine their own safety training? Did your agents set up a rogue deployment, either within your own infrastructure or externally?” asked Alexander Meinke, head of research at Apollo Research, a firm that focuses on securing the most advanced AI systems.

“Right now, we’re just relying on the AI developers to thoroughly assess this and then to honestly report the results. And from recent incidents, we’ve seen that they are doing neither,” he said.

Many researchers also say that OpenAI’s investigations are inadequate. Although the company invited two outside organizations to investigate the Hugging Face hack, the data it shared was limited and investigators themselves described their review as “brief.”

Ryan Greenblatt, chief scientist at Redwood Research, wrote on X: “I semi-jokingly called our efforts a “slop-vestigation” because we were so reliant on AIs to analyse what happened and there were a huge number of different important things to analyse”

More than 15 states, including Alabama, California and Montana, have opened investigations into OpenAI over the Hugging Face attack. On Thursday, U.S. Sen. Josh Hawley (R-Mo.) announced that he is also investigating the company.

But outside of these investigations, there is little legal obligation for AI companies to systematically disclose similar incidents. California passed a law last year mandating that companies report “critical” AI incidents, but the threshold is high for an incident to be considered critical and the law does not require the companies to disclose much detail. The OpenAI incidents, for example, don’t meet the threshold.

Concerns about using AI to improve AI

Since the Hugging Face hack, both OpenAI and Anthropic have put out blog posts indicating that they are taking measures to better monitor and contain their agents. OpenAI said it has encrypted and stowed away the internal model that participated in the Hugging Face attack. However, many outside researchers are not convinced that these steps are enough to maintain control of increasingly capable agents.

“It sounds like a press report from Jurassic Park saying, ‘Yes, one of our workers was eaten by the raptors. But we’re taking this very seriously and we have shot the raptor responsible,'” Kokotajlo said. “It’s a very small token gesture, basically.”

What researchers like Kokotajlo and Coxon are really worried about is how the AI companies are increasingly using AI to develop their models.

“When you talk to people who work at all the major labs, a thing that you persistently hear is that they’re running a lot of models very autonomously for very long periods of time to do a lot of their work,” said Dave Kasten, head of policy at Palisade Research, a non-profit studying AI capabilities to keep them under human control.

If AI companies continue to delegate ever more tasks to AI, some researchers fear AI models may reach a point called recursive-self improvement, where AI builds itself.

Kokotajlo says that as people hand more and more research and development work to AI, they could lose the ability to know whether AI systems are aligned with human values, even as the AIs become rapidly more powerful.

“Once you have AIs that are smart enough and trusted with enough power in the world, like enough control over things like data centres, factories, weapons, a loss of control incident cannot be recovered from,” he said.

The concern is strong enough that over 1,000 employees from different AI companies signed an open letter in July titled “Pacing the Frontier.” They called for companies and governments to slow down the development of artificial intelligence and prioritize safety.

Such an opportunity may arise soon. Officials from the U.S. and China, the two countries with the most AI capabilities, are expected to meet later this month to talk about AI safety.

NPR’s Shannon Bond contributed to this story.

Feature image credit: Karl Mondon/AFP via Getty Images

By

Sourced from npr

By Beatrice Nolan

Anthropic CEO Dario Amodei has announced the company is committing to a new safety measure—giving independent evaluators permanent, employee-level access inside the company—as part of a push to slow the pace of AI development.

In an essay published on Saturday, Amodei laid out a plan aimed at “pacing the frontier,” or slowing AI development.

“We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast, and we must make wise use of the time we gain,” he wrote.

As part of the three-step plan, Amodei called for every frontier AI company to give independent evaluators permanent, employee-level access to verify safety practices and report incidents. Secondly, he called for companies in democratic countries to agree on common safety standards that limit the rate of unchecked progress. And third, democratic governments to attempt coordination with authoritarian states, starting with agreements that are in everyone’s interest, such as a ban on using AI to develop biological weapons.

Amodei has long cautioned about the pace of AI development, but he says two recent shifts have increased the need for urgent safeguards on the technology. Models, he said, are increasingly able to build their successors, which is accelerating progress further. The industry has also seen a string of safety incidents, he added, including within Anthropic itself. He wrote that even a couple of years of pacing model development would give researchers time to reduce the risk of something going wrong, and calls on the industry to do so now.

Anthropic is committing to the first step unilaterally, with immediate effect. Independent evaluators will work inside the company permanently, Amodei said, with the same access as its own risk-assessment teams and the right to publish their findings without Anthropic’s editorial control.

Anthropic has found itself at the center of a media storm this week after researcher Jacob Coxon publicly resigned from the lab, warning that AI companies were gambling with people’s lives. In his resignation post on X, Coxon, who spent three years working on pretraining research at both OpenAI and Anthropic, wrote: “Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives,” adding that “these will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources.”

Several other current Anthropic employees supported the post, sharing similar fears about AI. Most notably safety lead Evan Hubinger, who wrote: “Jacob is correct here, we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.”

The resignation lands amid a string of unsettling AI agent incidents that have rattled the industry and many in Washington. In July, OpenAI disclosed a breach in which its agents autonomously hacked the open-source repository Hugging Face. Later, researchers found OpenAI had also kept quiet about an earlier, separate episode in which rogue agents hijacked a German programming wiki, making more than 15,000 edits and turning it into a message board where agents swapped tips for evading restrictions and detection.

The incidents have fueled public and regulatory concern and U.S. politicians are now discussing urgent regulation of AI. Anthropic, for its part, was founded on the premise that safe AI development should come before speed—a mission that some former workers say has come under strain due to intense competitive pressure from OpenAI.

Feature image credit: Anna Moneymaker/Getty Images

By Beatrice Nolan

Beatrice Nolan is a tech reporter on Fortune’s AI team, covering artificial intelligence and emerging technologies and their impact on work, industry, and culture. She’s based in Fortune‘s London office and holds a bachelor’s degree in English from the University of York. You can reach her securely via Signal at beatricenolan.08 See full bio

Sourced from Fortune