Display size, tech, resolution, refresh rate, brightness 6.3-inch OLED; 2,424×1,080 pixels; 60 to 120 Hz variable refresh rate 6.3-inch LTPO OLED; 2,856×1,280 pixels; 1 to 120Hz variable refresh rate 6.8-inch LTPO OLED; 2,992×1,344 pixels; 1 to 120Hz variable refresh rate 6.3-inch POLED, 2,424×1,080 pixels, 60-120 Hz variable refresh rate Cover display: 6.4-inch OLED; 2,364×1,080 pixels; 60 to 120Hz refresh rate; Inner display: 8-inch OLED; 2,152 x 2,076 pixels; 1 to 120Hz refresh rate (LTPO)

Camera 48-megapixel (wide), 13-megapixel (ultrawide), 10.8-megapixel (5x telephoto) 50-megapixel (wide), 48-megapixel (ultrawide), 48-megapixel (5x telephoto) 50-megapixel (wide), 48-megapixel (ultrawide), 48-megapixel (5x telephoto) 48-megapixel (wide), 13-megapixel (ultrawide) 48-megapixel (wide), 10.5-megapixel (ultrawide), 10.8-megapixel (5x telephoto)

Special features Gorilla Glass 2 Victus cover glass; 3,000 nits peak brightness; Satellite SOS; Dual-eSIM; Wi-Fi 6E; NFC; Bluetooth 6; 30W fast charging (wall charger not included); Qi2 15W wireless charging; support for PixelSnap magnetic accessories; Google VPN; Super Res Zoom up to 20x; Camera Coach; Add Me; Macro mode; Face Unblur; Auto Best Take; IP68 rating for dust and water resistance; 7 years of OS, security, and Pixel Drop updates; Corning Gorilla Glass Victus 2 polished back
with satin finish aluminum frame Gorilla Glass 2 Victus cover glass; 3,300 nits peak brightness; Satellite SOS; Dual-eSIM; Wi-Fi 7; NFC; Bluetooth 6; 30W fast charging (wall charger not included); Qi2 15W wireless charging; support for PixelSnap magnetic accessories; Google VPN; Pro Res zoom up to 100x; Camera Coach; Add Me; Macro mode; Face Unblur; Auto Best Take; High-Res Portrait mode; IP68 rating for dust and water resistance; 7 years of OS, security, and Pixel Drop updates; Corning Gorilla GlassVictus 2 silky matte back with polished finish aluminum frame; ultrawideband chip Gorilla Glass 2 Victus cover glass; 3,300 nits peak brightness; Satellite SOS; Dual-eSIM; Wi-Fi 7; NFC; Bluetooth 6; 45W fast charging (wall charger not included); Qi2.2 25W wireless charging; support for PixelSnap magnetic accessories; Google VPN; Pro Res zoom up to 100x; Camera Coach; Add Me; Macro mode; Face Unblur; Auto Best Take; High-Res Portrait mode; IP68 rating for dust and water resistance; 7 years of OS, security, and Pixel Drop updates; Corning Gorilla GlassVictus 2 silky matte back with polished finish aluminum frame; ultrawideband chip 7 years of OS, security and Pixel feature drops, Gorilla Glass 3 cover glass, IP68 dust and water resistance, 3,000-nit peak brightness, 2,000,000:1 contrast ratio, 30W fast charging with 45W charging adapter (charger not included), 10W wireless charging Qi certified, Satellite SOS, Wi-Fi 6E, NFC, Bluetooth 6, dual-SIM (nano SIM + eSIM), Camera Coach, Add Me, Best Take, Magic Eraser, Magic Editor, Photo Unblur, Super Res Zoom, Circle to Search; colors: lavender, berry, fog, obsidian (black) IP68 rating, gearless hinge, cover and internal screen 3,000 nits peak brightnes, Corning Gorilla Glass Victus 2 cover and back glass, Satellite SOS, ultra-wideband chip, Qi2-certified, free Google VPN. 7 years of OS, security and Pixel Drop updates

OpenAI made its debut at the Cannes Lions advertising festival this week, but it did not book a beach club on the Croisette alongside Meta, Amazon, and Google. Instead, the company summoned reporters and agency executives to a semi-secluded villa near the harbour, a staging choice that doubles as metaphor: OpenAI is in the ad business, just not in the way the incumbents are.

The pitch was led by David Dugan, a former Meta executive who spent more than twelve years as vice president of global clients and agencies before joining OpenAI as VP of advertising earlier this year. He told attendees the company has already attracted “thousands of advertisers” and that the strongest-performing categories are travel, retail, health, beauty, and financial services.

The revenue gamble

OpenAI began testing sponsored links at the bottom of ChatGPT responses on 10 February, starting with free-tier and Go-plan users in the US. The ads have since expanded to seven countries, including the UK, Canada, Australia, New Zealand, Japan, and South Korea, with plans to add Brazil and Mexico in the coming weeks.

The effort sits inside a much larger financial bet. Axios reported in April that OpenAI has projected $2.5 billion in ad revenue for 2026, rising to $11 billion by 2027 and $100 billion by 2030.

Dugan declined to comment on those figures at Cannes.

To reach that target, OpenAI would need to capture roughly a tenth of the global digital advertising market within four years, a market currently dominated by Google, Meta, and Amazon. The company’s pitch rests on a simple proposition: chatbot users volunteer exactly what they want, which means ChatGPT could deliver the highest-intent ad surface on the internet.

Early cracks in the model

The early data, however, tells a more complicated story. OpenAI launched its ad pilot at a $60 CPM, a premium rate that reflected scarcity and novelty.

Within ten weeks, that figure eroded to as low as $25, and the company shifted its pricing model to cost-per-click bidding with bids between $3 and $5.

The ad pilot crossed $100 million in annualised revenue in under two months with fewer than 600 advertisers. OpenAI opened a self-serve ads manager on 5 May with no minimum spend, a move designed to scale advertiser volume but one that also signals the initial premium pricing could not hold.

At Cannes, agencies were polite but cautious. Michael Cohen, an executive at Horizon Media, said the firm is “still in the early stages of investment” and that the focus is “a little bit less on maximising performance today and more on learning alongside OpenAI as the platform evolves.”

The format question

Benji Shomair, OpenAI’s vice president of monetisation and another former Meta executive, said the company is already experimenting with ad formats beyond sponsored links, though he was not ready to announce specifics.

He offered a hypothetical: a user asking the chatbot how to fix a squeaking door might benefit from a video ad for a hinge lubricant.

“If there’s a product like a grease for the hinge, actually would a video explaining how it works be helpful? Maybe,” he said.

The gap between that vision and what agencies can currently buy is substantial. Omnicom’s chief technology officer Paolo Yuvienco described OpenAI’s ad effort as “relatively successful” but “immature to a certain degree,” noting that agencies buy ad placements in milliseconds while it still takes seconds to generate an AI response.

The competitive landscape

OpenAI’s move into advertising is being watched closely because it breaks a tacit consensus among AI companies that chatbot ads are premature. Anthropic used a Super Bowl ad in February to declare that Claude would remain ad-free, framing the decision as a matter of trust: “Users shouldn’t have to second-guess whether an AI is genuinely helping them or subtly steering the conversation towards something monetisable.”

Google has denied current plans to bring ads to its Gemini chatbot, though SVP Nick Fox has since said the possibility is “not ruled out.” The company already runs ads in AI Overviews and is testing them in AI Mode, which means the question is not whether Google will monetise AI-assisted search but how quickly it moves beyond its existing surfaces.

Why it matters for the IPO

The advertising push is inseparable from OpenAI’s path to a public listing. The company spent $34 billion in 2025 against $13 billion in revenue and does not expect to reach profitability until 2030.

It filed confidentially with the SEC on 8 June, targeting an autumn debut at a valuation that could exceed $1 trillion.

Advertising revenue is the lever OpenAI needs to convince public-market investors that it has multiple revenue streams beyond subscriptions and API access. ChatGPT’s user base, which crossed one billion weekly active users in May 2026, gives it the scale.

What it lacks is proof that the ad model converts at rates that justify the projections.

The company’s $100 billion target assumes ChatGPT’s products will reach 2.75 billion weekly users by 2030, nearly triple the current figure, and that the AI advertising market will grow large enough for a newcomer to take a meaningful share from Google and Meta.

The trust problem

The deeper challenge is whether advertising inside a conversational AI degrades the product itself. OpenAI says it will not allow money to influence ChatGPT’s answers and will keep conversations private from advertisers.

Plus, Pro, Business, Enterprise, and Education subscribers will remain ad-free.

But the company’s own pricing trajectory suggests the market is still working out what a ChatGPT ad is actually worth. A CPM that halves in ten weeks is not the behaviour of a product that has found its fit.

It is the behaviour of a product that is still searching for it, from a villa hidden behind the bushes at the industry’s biggest party.

Take a stroll around San Francisco and it won’t take long to spot an empty autonomous vehicle cruising the city’s streets, waiting to be hailed by a rider or heading off to a distant depot to be charged and cleaned. These deadhead miles — an industry term for miles driven without a paying passenger — are one of the biggest barriers between robotaxi companies and profitability.

Redwood City, California-based startup Aseon Labs thinks it has a fix: parking space-sized automated pods that can be scattered throughout cities to inspect, clean, and charge robotaxis. The company, co-founded by the team behind battery-swapping startup Pushme, calls them robotic pit stops for the robotaxi industry. And the idea has caught the attention of investors.

Aseon Labs has raised $10 million in a seed round led by Crane Venture Partners, TechCrunch has learned. Y Combinator, Uber co-founder Garrett Camp’s venture firm Expa, Robin Hood Ventures, and Founders Capital also participated, along with angel investors such as serial entrepreneur and former Google executive Adrian Aoun, Mercury founder and CEO Immad Akhund, Zimride co-founder Rajat Suri, and operators and founding team members from Anthropic, Nuro, Turo, and Revolut.

Aseon Labs is still in the early stages. The seed funds will be used to build five prototypes of these pods, grow its six-person robotics and engineering team to about a dozen, and secure the real estate needed to build out its network, according to Aseon Labs co-founder and CEO George Kalligeros.

“In order to reach economic parity with ride-hailing — which is where we need to get with self-driving cars — and to stop really subsidizing the cost, you need the utilization to go up,” Kalligeros told TechCrunch. “You need the robotaxi in continuous operation during the entirety of the demand curve of the day.”

Aseon’s pitch is that a network of distributed autonomous pods would slash deadhead miles, and inevitably turn robotaxi services into profitable enterprises.

Kalligeros and co-founder and COO Dan Keene come from outside the autonomous vehicle world. But they bring experience developing and scaling a hardware-and-real estate company. Kalligeros worked as a mechanical design engineer at Bentley Motors and Tesla before he and Keene founded Pushme in 2016 to build battery-swapping infrastructure for micromobility fleets. Pushme was building a battery-swap network in Europe when it was acquired in January 2020 by Tier Mobility.

“The parallel I’ll draw is we were basically tasked by SoftBank to put this across as many markets where it made sense for Tier within a very short and compressed period of time,” Kalligeros said. “The playbook became, how do we sprinkle the locations across the center of the city, where it makes sense, but at the same time, make it easy to deploy as non-permanent infrastructure?”

Aseon Labs is applying the same thinking to autonomous vehicles.

As they researched the industry, the pair visited AV depots, where fleets of robotaxis are inspected, maintained, cleaned, and charged. The cost of real estate often prompts companies to locate these depots outside the city center, where most of the ride-hailing activity occurs.

“Depot infrastructure is the key requirement for the launch of a new city for any AV operator,” he said. “And what happens in the depot right now — the operational backbone of autonomy, really — is not fully baked.”

The founders settled on the idea of creating smaller, independently powered autonomous pods that could be dispersed throughout a city but as importantly, they could also be moved as needed. The units, which include cameras to inspect vehicles and robotic arms to retrieve lost items and clean interiors, are considered temporary structures. That classification helps Aseon Labs avoid a lengthy permitting process and allows the company to relocate units if a location underperforms.

The units are designed to run on a propane generator for power or connect to an existing power source through partnerships with EV charging companies. They are meant to operate autonomously, although early versions will be staffed, according to Kalligeros.

Aseon Labs isn’t trying to tackle every edge case, either. Instead, it leans on computer vision and AI — specifically vision-language-action models common in modern robotics — to detect problems the pod shouldn’t try to solve. For example, if a camera detects melted chocolate on a backseat, the robotic arm stands down since attempting to clean it could make the stain worse. Instead, the vehicle will be charged and dispatched directly to that company’s central depot for a human to handle.

Aseon Labs hasn’t signed contracts with any robotaxi companies yet, but Kalligeros said there is widespread interest in the concept. “Pretty much everyone wants to try it,” he said.

Unya-mt/Getty Images

Imagine what a scammer could do with your personal data in six months. Depending on what data was mined, a scammer could open credit accounts in your name, drain your bank account, and even share your information on the dark web. It’s a potential nightmare that would take you years to resolve, and yes, it could happen to you. The average number of days it takes a company to detect a data breach is a staggering 181, and it then takes, on average, another 60 days to contain it. Meanwhile, you’re guessing who accessed your data and what they plan to do with it.

Three million Texas license buyers have been left wondering just that after a data breach involving a vendor associated with the Texas Parks and Wildlife Department license system. The leak was detected by Texas Cyber Command and involves customers of the hunting and fishing license program in the Lone Star State. While their Social Security numbers were not compromised, an unauthorized person was able to access driver’s license information, email addresses, phone numbers, residential addresses, and even passport numbers. Birth dates and credit card details were reportedly not exposed, but that may be little consolation to those involved.

Texas Parks and Wildlife assured the public that no customers under the age of 18 were affected by the breach, and it has put additional security measures in place. It’s also working with the vendor to ensure such a breach doesn’t happen again, but some customers are questioning why so much personal data is necessary for a hunting or fishing license.

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How can you protect yourself?

Matejmo/Getty Images

These breaches are incredibly frustrating for Americans because we expect our information to be secured. Today, the only surefire way to avoid becoming a victim is to completely opt out of sharing personal data — an impossibility in modern life. When a simple application for a fishing or hunting license can lead to data breaches, what can we do to protect ourselves?

State agencies are a common target for malicious actors. Last year alone, there were thousands of incidents involving these agencies and associated vendors. In Texas, affected customers are being offered one year of free credit monitoring, but there are also steps you can take before a data leak occurs that will help protect your sensitive data.

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First, keep your own accounts secure. Use strong, unique passwords, and don’t share passwords across multiple accounts. Change your passwords regularly. Update apps and software to ensure that you’re taking advantage of critical security updates, and enable two-factor authentication when possible. To protect against identity theft, don’t carry your Social Security card with you, but store it somewhere safe and secure. Shred any documents that contain personal information, and regularly review your financial statements and credit reports for irregularities. You can also request a security freeze that restricts access to your credit reports, and if you think you’re at risk, a fraud alert will require lenders to take extra steps to confirm your identity.

Tonight is your last chance to save up to $190 on your pass to TechCrunch Founder Summit 2026. Early Bird pricing ends today, at 11:59 p.m. PT, after which rates increase.

Founders rarely scale alone. The fastest path to growth comes from learning from founders who’ve already done it, connecting with peers tackling similar challenges, and building relationships with investors who can help accelerate your next stage of growth.

On November 4 in Boston, more than 1,000 founders and investors will gather for a highly curated day of tactical learning, candid conversations, and meaningful networking designed to help founders make smarter decisions and grow faster.

Don’t wait until prices increase. Register before 11:59 p.m. PT tonight to save up to $190. Bringing your team? Groups of four or more can save up to 30%.

A founder-first event built for growth

Every session, discussion, and networking opportunity is designed around the real challenges founders face as they build, fund, and scale their companies.

You’ll connect with:

• Founders navigating similar growth stages

• Experienced operators who have scaled teams, products, and revenue

• Investors sharing what they’re funding and what they’re looking for

Whether you’re preparing to raise capital, refining your go-to-market strategy, or planning your next growth milestone, Founder Summit creates opportunities for conversations that can change the trajectory of your business.

Actionable insights for every stage

Founder Summit focuses on the decisions that define a startup’s future. Through breakout sessions and roundtable discussions, you’ll gain practical guidance on topics including:

• Building pitch decks that resonate with investors

• Knowing when to raise Series C and beyond

• How and when to sell your startup

• Preparing your company to go public

These founder-led conversations deliver practical takeaways you can apply immediately.

Learn from founders and investors who’ve done it

Past speakers have included:

Additional speakers have represented Sequoia Capital, NFX, Glasswing Ventures, Wing Venture Capital, Construct Capital, Greylock, Precursor Ventures, and more.

The 2026 agenda is coming soon, with additional founders, operators, and investors to be announced. Check the event page for agenda updates.

Interested in leading a discussion? Submit a roundtable or breakout session topic for a chance to be voted onto the agenda by the TechCrunch audience.

Save up to $190 before tonight, 11:59 p.m. PT

The deadline is in less than 24 hours. Early Bird pricing ends tonight at 11:59 p.m. PT. Join the founders, operators, and investors shaping the next generation of startups. Gain practical insights, build valuable relationships, and leave with strategies you can put into action immediately.

Register before 11:59 p.m. PT tonight to save up to $190 on your pass and up to 30% when registering as a group.

Interested in exhibiting at TechCrunch Founder Summit 2026? Reserve your exhibit table and connect directly with founders, investors, and startup decision-makers.

Industrialized factories changed how the world produced physical goods: more output, lower costs, faster than anything that came before. Now a similar shift is happening with software. 

LLMs have lowered the barrier to writing code, increased individual output, and pushed organizations to think about software development as a production system. The standard software development lifecycle and CI/CD practices that have held for decades won’t hold up under that pressure. That’s where the software factory comes in — and like physical factories, it needs more than speed to actually work.

The idea of a “software factory” started to solidify over the past year. Luca Rossi’s “The Era of the Software Factory” made the case plainly: AI is not just changing how fast people write code — it’s changing the whole production system around software. 

The concept can mean different things: a collection of coding agents and skills files; faster CI/CD; better review systems; or more automation around software delivery. A better frame is to think of it less as a tool category and more as a set of principles. A software factory can’t just be a loose collection of prompts, agents, and plugins. It needs a platform that defines how work moves through the system and how code is generated, reviewed, tested, traced, deployed, and improved when something goes wrong.

Otherwise all you’re doing is putting yet another one-off machine into an empty room and calling it a factory. 

Why is this happening now?

There are a few forces all hitting at the same time.

Companies have always wanted more software than engineers can produce. That’s why tools like Excel exist: They often fill in the gap for a lot of the software that many companies wish they could make.

AI has also lowered the barrier of entry to creating code, and this is the part everyone focuses on. Code creation is now easier, though not always cheaper or better, as evidenced by many high-profile companies fretting over their high AI bills. The barrier to writing functional code has effectively collapsed.

More importantly, a single engineer can generate more code than they could just a few years ago. That changes the bottleneck: it’s no longer “How fast can someone write this?” or even, in some cases, “Can someone understand how to code?” Instead it becomes, “Should this be written?” 

More importantly, can we actually create end products that are durable and reliable and don’t just build tech debt? Or are we just putting out more AI slop faster than ever? That’s where the danger lies. 

The dangers of the modern software factory

All of this sounds great. Factories, after all, made production faster and more consistent. 

They made it possible to build more cars and products, less expensively, which led to more people being able to afford cars and products. Putting environmental impacts aside, you could argue this was positive.

But like many things in engineering, there are always tradeoffs, and in this case, there are new risks.

When you increase the output of one person with machinery, digital or otherwise, you also increase the mistakes that can be made either by the individual or the machinery. The speed at which code can now be put out is on an industrial scale. Even smaller organizations can suddenly have code bases ballooning up to the size of tech company code bases a decade ago. 

The data is already showing problems. Faros AI found that while task throughput per developer is up 33.7% and PR merge rate is up 16.2%, the incidents-to-PR ratio has risen 242.7% and bugs per developer are up 54%. Google’s DORA research found that more AI adoption was actually associated with worse delivery stability. 

As a fractional head of data, I’ve been brought in to fix these exact issues. In the past year alone, I’ve worked on two projects where AI-generated data infrastructure slowly started to morph over time.

Between multiple engineers trying to move quickly and a lack of standards, these projects became unruly. Code bases tend to go through some level of evolution, but as different styles blend, the LLMs in turn start to create their own mutations. Codebases developed five to six different styles within months — a process that previously took years. Layer by layer, the engineers would slowly stop understanding exactly what was going on.

The pattern echoes what happened a decade ago with self-service tooling: early productivity gains that masked downstream complexity.

And that’s why the software factory can’t just be about speed. 

What makes a software factory work

There are several key principles to consider when building a software factory.

Platform over tools: Many teams are slowly implementing AI into their coding workflows at the edges — adding a PR review agent or a skills file into their repos. But building an actual software factory requires a platform, not a collection of tools at the edges. A platform provides a unified foundation where tools aren’t scattered in separate corners. Instead, they actively share data, talk to each other, and work as a single cohesive system — standards, processes, and the work itself all connected. 

Rerunability and traceability: A real platform requires the ability to go back into any run, identify what went wrong, and rerun it — which is why one-off agents don’t make a factory. The system needs to support taking a serial ID, looking it up, and tracing exactly how it got to the output it produced. This is why state machines make more sense than loops for AI workflows: they make it far easier to rerun a process and understand what happened at each step.

Safety and guardrails: Factories are not safe places. Neither is a software factory. As more people develop on these platforms, better guardrails and safety measures need to be built in. Testing and quality control need to be pushed to the front of the process — catching bugs at the lowest possible stage reduces the cost to fix them and limits the blast radius.

Standardization: At the enterprise level, every codebase has its own flavor. Layering a code assistant on top without standards produces an amalgamation of styles. Standardization has to be built into the process from the start.

Quality control: In older manufacturing models, quality control happened at the end of the line. The product was built, inspected, defects found, and fixed later. Toyota’s approach was different. Quality was pushed into the process itself — workers were expected to stop the line when something was wrong. The goal wasn’t to catch defects at the end; it was to prevent them from flowing downstream in the first place. 

The same is true for the software factory. QC needs to be baked into the entire process, starting with how the spec is written. That means integrating static code analysis that catches obvious errors and providing templates to LLMs so they know the structure the code should follow. Without that, the bottleneck becomes the final review — or teams just push out more AI slop.

Speed without quality isn’t productivity

Improving the speed of your code output is not actual productivity if the downstream issues aren’t managed. A company is not more productive because it produces millions of cars, only to see them all fall apart within 100 miles. It’s also not more productive if all it does is produce an endless stream of proofs-of-concept that never enter production. 

Actual productivity is when the software factory takes ephemeral tokens and turns them into durable outputs. It’s easy to talk about lines of code and how much faster your team is moving.

The software factory that wins isn’t the one that generates the most code. It’s the one that generates the fewest defects downstream.

I took over the role of CNET’s editorial leader earlier this year, and while I’ve participated in Prime Day sales as a TV reviewer and general deals editor here for (literally) decades, this is my first Prime Day as EIC. In case you’re wondering what purchases a person like me is considering this time around, here’s a sampling.

iPad 11-inch A16 ($300): My artistic daughter has been asking for an iPad and if my wife approves, I’ll likely get her this basic version, our top pick for most people. I’d also get her the Apple Pencil (on sale for $60). We’d save both of these for Christmas presents.

Belkin Portable Charger Bank ($38): My family and I always need portable chargers. Half our devices call for Lightning and the other half for USB-C. This does both and I like the built-in cables.

Plaud NotePin S AI Notetaker ($152): In my new role I take more meetings than ever, and I also have plenty of valuable face-to-face conversations in the office and beyond. I currently depend on the Otter app on my phone and Gemini+Google Meet recordings at work to take notes (with appropriate permission, of course). This AI wearable could be my “secret weapon” to consolidate everything in one place.

JBL Go 4 Bluetooth Speaker ($38): I actually bought this one a few days ago when it was $40 – still a great deal, but now even better. It’s no longer one of our best Bluetooth speakers but it’s good enough for my (other) daughter, who wants one for the beach. At this price, I won’t be too annoyed if (when?) it gets destroyed by sand and surf. And yes, I got her the pink one which I know she’ll love. We’re saving this for her birthday.

Anker Solix F2000 portable power station ($749): I own a travel trailer and upgraded to solar with an inverter, but at a recent (shady) campsite, I still had to break out my loud, annoying propane generator. Sure, I could just add more standard 12V LiPo4 batteries, but this portable power station is so much more versatile. It includes a 30A RV outlet, and the wheels make it worth the extra $50 over the Bluetti AC200L. No way my wife approves this one, but it stays on the list anyway because I’m camping tech obsessed.

The social media checks implemented in Australia after the country banned their use for teens under 16 have shown little evidence of being effective, according to a study by the University of Newcastle. Published in the British Medical Journal, the study surveyed participants between 12 and 17 years old before and three months after the law was introduced. It specifically looked at the participants’ use of TikTok, X, Facebook, Instagram, YouTube and Snapchat. 

Based on the information they gathered, more than 85 percent of teens under 16 continued using those social media apps, despite two-thirds of them reporting that they had encountered age checks. Approximately 54 to 68 percent of responders under 16 just kept on using their accounts. How, you ask? Well, the most common age check the Australian teens encountered was to self-declare their age, a method criticized by authorities in the country, as well as in other countries considering implementing the same law, due to its limited effectiveness. Among the responders, 24 to 39 percent encountered self-declared age verification, while 13 to 27 percent got through checks by uploading a selfie. 

That said, the study also showed that affected teenagers found other ways to keep using social medial. Around 15 to 19 percent of the responders said they used fake accounts to access the platforms, while 9 to 29 percent reported going on social media using someone else’s account. Approximately 11 percent of the teens said they used private browsers to get around the restrictions. There were very few teens who reported using a VPN. 

Overall, the study found that social media use remained the same among the 12 to 13 year olds after the law took effect. It declined among the 14 to 15 year olds, but it grew among the responders aged over 16. 

While the researchers admit that it’s early days and the sample size was small and relied on self-reporting, an accompanying editorial of the study stresses that the results are early signals worth tracking. 

“What these figures collectively describe is a partially implemented policy, one in which the mechanism intended to restrict access was not reliably activated,” said Dr. Amrit Kaur Purba, an assistant professor in the Faculty of Public Health and Policy at the London School of Hygiene & Tropical Medicine. “Australia’s experience shows that legislating a restriction is not the same as enforcing one: when age assurance relied on self-declared age, most adolescents continued to access restricted platforms. Countries now adopting similar measures – including the UK, which has committed to comparable restrictions and has tasked its regulator with defining effective age assurance before implementation – will need those mechanisms in place from the outset, rather than retrofitted once circumvention is already widespread. As governments across Europe, North America, and elsewhere consider similar approaches, Australia’s experience suggests that implementation may matter as much as legislation, and that lesson may prove as consequential as any headline result.”

Wireless charging on a power bank has always meant accepting a speed penalty, and for most people that trade-off is invisible right up until the moment they actually need to charge in a hurry.

That gap is exactly what the INIU SnapGo Air is built to close, and at $49.49 down from $54.99 this Prime Day, saving you 10%, it is the slimmest Qi2.2-certified magnetic power bank available.

Qi2.2 certification pushes wireless output to 25W, which is how the SnapGo Air gets an iPhone 17 Pro from flat to 50% in 33 minutes compared with 63 minutes on a standard 7.5W wireless pad.

That half-hour gap compounds fast when you are in an airport, a meeting room, or anywhere else where five minutes of charging has to stretch into something that actually moves the needle on your battery percentage.

When wireless is not fast enough, the attached USB-C GoCord delivers 45W wired output and takes an iPhone 17 Pro from 20% to 78% in 25 minutes, without needing a separate cable retrieved from anywhere.

Recharging the SnapGo Air itself through the same GoCord takes 1.8 hours, which means plugging in before bed gives you a full 10,000mAh bank by morning rather than a partial one that runs out by early afternoon.

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The 13N magnetic grip holds it locked flush against any iPhone 12 through 17 series device, and at 0.5 inches thick the SnapGo Air adds no meaningful bulk to a phone sitting in a jacket or front pocket.

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A side-mounted digital display gives an exact percentage readout of remaining charge, and TempGuard 3.0 monitors temperature 3.2 million times per day to keep output stable as the battery drains toward empty.

For iPhone owners who have settled for slow wireless charging because nothing thinner existed at a sensible price, the INIU SnapGo Air at $49.49 through June 26th on Amazon is the answer that has been missing.