Battery life has been stellar on the Pro models over the last few years, and that remains true here. The Pro Max is the more notable of the two because it packs a denser battery. With average use (around 5 hours of screen-on time), I hovered around 50 percent by the end of the day, and easily made it through day two on a single charge. I spent a little less time with the smaller Pro, but so far it has also comfortably gotten me through a day with around 40 percent left by bedtime. Safe to say, most people will very much enjoy the battery life, but the update doesn’t feel drastic.

Next, there’s the processor: the A20 Pro chip. I hardly run into problems with Apple’s flagship silicon, and that hasn’t changed. The phones run every app fluidly, and my benchmark tests show a surprisingly sizable leap over the A19 Pro in the 17 series.

It runs Control—a AAA game from 2019 ported to iOS earlier this year—decently well. However, aggressive upscaling makes the graphics look grainy, and character movements can feel a little sluggish. (I maxed out the graphics settings.) It’s certainly not perfect, but I still can’t believe I’m playing such a demanding title on an iPhone.

Control on the iPhone 18 Pro Max.

Photograph: Julian Chokkattu

More impressive is Apple’s thermal management on these phones. The company has expanded the vapor chamber’s surface area by three times—the phone’s cooling technology that helps spread heat around—meaning the entire phone felt toasty after close to an hour of gameplay in Control, with no uncomfortable localized hot spots. This larger vapor chamber also lets you game longer before phone’s thermal throttling kicks in and slows everything down.

The iPhone 18 Pro models come with iOS 27 out of the box, which features the all-new Siri AI. Yes, Siri AI is infinitely more capable than any version of Siri that Apple has offered before. It still doesn’t quite have the spark or versatility of other AI assistants, but you might be fine with that. If you throw Siri AI a question, there’s a good chance it can actually give you a satisfactory answer now. Remember that Siri AI and other Apple Intelligence features aren’t exclusive to the latest models, though you do need an iPhone 15 Pro or newer to access them.

iOS 27 is now available to install on iPhone 11 and newer, and includes plenty of new features. One that’s particularly caught our eye is found within Safari.

With iOS 27, you can now receive notifications whenever a specific webpage changes within the Safari app. That means you can be notified if a product you’ve had your eye on for ages finally comes back in stock, or if an artist updates touring dates on their website. 

It’s easy to set up and could save you from missing out. Follow our steps below to learn how to get notified when a website changes in iOS 27.

Want more about iOS 27? We’ve rounded up 14 tips and tricks of the new update, and explained how to adjust the transparency of Liquid Glass too. Or, if your phone won’t support the update, visit our round-up of the best iPhones to find your next purchase.

Over the past two years, I’ve spent countless hours talking with teachers, parents, students and school leaders about artificial intelligence. The questions they have are remarkably consistent: How should schools respond? When should students use AI? How do we preserve authentic learning in a world where answers have become nearly instantaneous?

They’re important questions, but I’ve noticed something interesting about them. Our conversations rarely stay focused on the technology itself. Before long, they drift somewhere deeper. They become conversations about thinking. About curiosity. About judgment. About what learning is actually for when a machine can generate an essay, solve a problem, or summarize a chapter in seconds.

Oddly enough, the most helpful answers I’ve found didn’t come from AI research. They came from Rwanda and Yellowstone National Park.

For years, those sites occupied completely different parts of my life. One was a trip that changed me. The other was an ecological story I couldn’t stop thinking about. It took me much longer to realize they were helping me answer the very questions I was now asking about artificial intelligence in schools. 

That realization began on a dusty road, on an otherwise ordinary day in Rwanda.

I went to Rwanda as part of a group helping schools transition from French to English in the classroom. That day, I happened to be carrying a yellow jerrycan of water from the watering hole back to the village alongside people who make that miles-long journey every day. Before long, my hands ached and my shoulders tightened. The hard plastic handle dug into my fingers as I wondered how much further we had to walk. The people beside me never seemed to notice the weight. For me, carrying water was extraordinary. For them, it was simply another walk home.

That contrast stayed with me long after I returned home. The walk answered very little. Instead, it left me with a question that has followed me ever since: How do we help students understand realities they have never lived?

That question would resurface years later in a sixth-grade classroom. Our teaching team designed an interdisciplinary unit around the novel “A Long Walk to Water,” hoping literature, conversations with a friend of mine from Sudan, and local field experiences would help students move beyond learning about water insecurity and begin to care about the people living it.

By most measures, the lesson was a success. Then we sat down to debrief.

“I don’t think it worked,” one teacher said.

Almost immediately, heads began nodding around the table.

“I was thinking the same thing.”

“Me too.”

No one defended the lesson. No one blamed the students. Instead, we realized we had been measuring engagement when what we really cared about was understanding.

Looking back, I think that meeting changed our teaching far more than the lesson itself. Years of working together had built enough trust for one honest observation to give everyone else permission to be honest too. We stopped trying to protect the lesson and started asking a different question.

Not, How do we make this activity better? But, How does understanding actually grow?

We’ve been refining that project ever since, and that conversation forced us to confront a much larger question: If meaningful change doesn’t happen because of a single lesson, how does it happen?

To understand how meaningful change happens, consider what happened in Yellowstone National Park.

When scientists reintroduced gray wolves to Yellowstone National Park in 1995, they expected the elk population to change. They didn’t expect rivers to change as well. As elk altered where they grazed, willow and aspen trees recovered. Beavers returned. Wetlands expanded. Vegetation stabilized riverbanks. An entire ecosystem began reorganizing itself in ways few people had anticipated.

The wolves didn’t directly reshape Yellowstone. They changed the relationships within the ecosystem. And everything else followed.

Learning about Yellowstone changed the way I thought about change itself. It made me realize that the most meaningful transformations rarely begin by fixing one thing. They begin by changing the relationships within a system. Once I saw that pattern, I couldn’t stop seeing it in schools.

We often assume meaningful change happens by improving the thing right in front of us. We redesign a lesson. We adopt a new curriculum. We write a new AI policy. We expect better outcomes because we’ve improved one part of the system.

The wolves didn’t change the rivers directly. They changed how elk moved through the landscape. That altered where trees could grow, which brought back beavers, reshaped wetlands, stabilized riverbanks, and eventually changed the rivers themselves. One dramatic intervention didn’t drive the transformation. It emerged because relationships within the ecosystem had changed.

I’m beginning to think schools work the same way.

Looking back, our sixth-grade project didn’t improve because we discovered a better activity. It improved because the relationships around the project changed. Teachers trusted one another enough to challenge assumptions. Students were invited to wrestle with ideas instead of simply completing assignments. Learning became less about delivering an experience and more about cultivating understanding.

Once I began seeing schools that way, I couldn’t think about artificial intelligence in quite the same way anymore.

AI isn’t simply introducing another tool into classrooms. It’s changing relationships: The relationship between students and thinking. Between effort and accomplishment. Between teachers and expertise. Between knowing something and finding it instantly.

Those may prove to be the relationships that shape the future of learning far more than any particular AI model or school policy.

That’s why my conversations about AI are almost never about the technology itself. They’re about what we choose to value.

Parents wonder whether students will gradually stop wrestling with difficult ideas if a chatbot can do the work for them. Teachers ask how to preserve productive struggle when every answer is just a prompt away. School leaders wrestle with an even deeper question: If AI can do more of the thinking, what kinds of thinking should remain unmistakably human?

I don’t think there’s a simple answer. What I do know is that answering those questions has required me to revisit experiences that had nothing to do with artificial intelligence — at least not at first.

I keep finding myself returning to that dusty road in Rwanda.

Carrying a jerrycan didn’t help me understand what it means to live with water insecurity. It wasn’t meant to. What it changed was my relationship to the people carrying it beside me. 

Our sixth-grade project taught me something similar. We eventually realized our goal wasn’t to create a memorable activity. It was to create the conditions where students might begin asking deeper questions about lives very different from their own.

Increasingly, I think AI presents schools with that same choice. We can use it to remove every obstacle from learning, making every task faster and easier. Or we can become even more intentional about preserving the kinds of thinking that develop judgment, curiosity, empathy and discernment.

Looking back, I don’t think Rwanda, Yellowstone, and AI were ever separate stories.

Rwanda changed my relationship to people whose lives I couldn’t fully understand. Our faculty meeting changed the relationships among teachers. AI is changing the relationship students have with thinking itself.

Rwanda taught me that understanding begins with humility. Our sixth-grade team taught me that meaningful change begins with the courage to question our own assumptions. And Yellowstone taught me that lasting change happens by changing relationships within a system.

Technology will continue to change. Our responsibility as educators won’t.

We still have to create experiences that ask students to think, question, struggle, and understand in ways no technology can do for them. Those were the questions before AI. I suspect they’ll still be the questions long after today’s technology has been replaced by something none of us can yet imagine.

The European Union plans to stop children under 13 from using social media and to prevent anyone under 15 from having a personal account, Ursula von der Leyen announced in her State of the Union address on Wednesday.

“No social media under the age of 13. No personal account under the age of 15,” the Commission president said, according to Bloomberg.

Still, kids who are 13 or 14 could still have accounts, but only with parental supervision and limited features. But this announcement answers a question that has divided EU countries in recent weeks, as the age limit remained undecided until Monday, with Commission experts supporting 13 and France opting for 15.

The Commission president’s announcement includes both ages if the text remains as stated today.

More details will be shared on Thursday, when the Commission will present the EU Kids Act. As for now, we have a few details on what the draft contains, such as that the law will require strict age checks on social media, video platforms, app stores, online games, AI companions, and chatbots,

Companies that will not follow the rules could face fines up to 6% of their annual sales.

Euronews, having received the draft the following day, outlined a system under which children under the age of three would be excluded, and teenagers aged 13 and 14 would have restricted accounts.

The draft also states that services must switch off features such as infinite scrolling and artificial notifications, and that “technology companies bear primary responsibility for making their products safe”.

To verify ages, platforms could use the Commission’s age verification app, which Brussels said was ready in April. The app uses zero-knowledge proofs, allowing users to prove they are old enough without sharing their identity documents.

Privacy concerns have already halted one national effort: in August, France’s Constitutional Council rejected the country’s under-15 ban, saying it was too harsh and did not protect users’ data well enough.

President Emmanuel Macron then asked Brussels to create an EU-wide rule. Other EU countries have been making their own rules since Australia banned under-16s in December, and the Commission has faced more calls to set a single standard.

No agreement will be reached quickly since the governments are still divided, with both Estonia and Belgium opposing age-based bans in full, and the European Parliament having earlier called for an age limit of 16; the proposal must have the support of both sides before it can become law, a process which Bloomberg has pointed out can take years.

The text that will be unveiled on Thursday will illustrate the extent to which the 13-and-15 formula remains valid when exposed to the draft and how far the new rules apply to AI chatbots.

The first benchmarks for the M6 Mac mini have emerged. While Moore’s Law is in fact dead, the Mac mini is still getting a massive and noticeable boost in processing performance.

Ahead of the release of Apple’s product launches to consumers, benchmarks for the models frequently surface, hinting at what’s on the way. That has seemingly happened for the M6 Mac mini, which Apple introduced at the end of August.

The Geekbench 7 listing for a “Mac18,5” posted on September 15 mentions it is a Mac with an M6 chip, complete with 12 cores. The listing adds that it has a base frequency of 4.78GHz, up from the 4.4Ghz M4 Mac mini and the 3.2GHz M1 Mac mini.

At the time of reporting, there is only one listing appearing for a “Mac18,5” for Geekbench 7’s CPU benchmarks, with none for GPU or AI testing so far.

While we would prefer to see multiple benchmarks that can back up these figures, they do seem plausible enough to be real. As consumers get their hardware, expect more results to come out and to solidify just how much better the M6 is over its predecessors.

Single-core scores under Geekbench 7

On the single-core test, the M6 Mac mini is shown to have achieved a score of 4,071. By comparison to the previous base Mac mini model, the M4, that works out to be a 24% improvement.

It’s also 69% better than the M2 Mac mini. The 89% improvement almost doubles the score of the M1 Mac mini.

Multi-core scores under Geekbench 7

The change is more startling on the multi-core side of things. At 22,783, the M6 Mac mini is 48% better at multi-core tasks than the M4 Mac mini.

Compared against the M2 Mac mini, the M6 is 132% better. That extends to 165% when the M1 Mac mini is taken into consideration.

It’s said that consumers will only really notice a difference in processing performance if it is about 15% better or worse. If these figures are correct, the first buyers to receive their new Mac mini will definitely be able to feel the difference.