It’s always the same story: A new technology appears and everyone starts talking about how it’ll change everything. Then capital rushes in, companies form overnight, and valuations climb faster than anyone can justify. Then, many many months later, the warnings arrive, and people suddenly remember the dot-com crash or crypto.

You’ve probably seen it before. And if you have, you probably think AI is the next bubble. Humans are great at pattern-matching. We’ve evolved to see patterns, so when something familiar emerges, we instinctively map it onto the closest story we already know. We think we’ve seen it before, and we’re confident we know how it ends.

But that instinct can mislead us. AI feels like a bubble because we’re forcing something genuinely discontinuous into a familiar story. The idea that everything that rises quickly must ultimately collapse sounds prudent. But it doesn’t mean it’ll always be true.

Why markets keep overshooting

Every major technological shift produces the same outward symptoms: Inflated expectations, followed by high-visibility failure. Dot-com, mobile, and crypto all went through a phase where the world lost its sense of proportion.

Why does this keep happening? Because markets don’t have a framework for discontinuous change. Discounted cash flow models assume steady, stable growth, and comparable companies assume the category already exists. So people assume the near future looks like the recent past, but that doesn’t work when the underlying category itself is changing.

Most valuation tools are designed for incremental progress, so analysts look at quarterly forecasts and incremental improvements. They don’t know what to do with step changes, and they can’t model nonlinear adoption.

So when you see capital overshooting or extreme dispersion of outcomes, that’s the market trying to value decade-long bets using quarterly logic. (Which doesn’t work.) And that’s what a bubble actually is: An indication that no one yet knows how to price what’s coming. That uncertainty looks like invalidation, but it just exposes the limits of existing frameworks.

The category error we keep making

When something new arrives, we reach for comparisons.

AI is like electricity.

AI is like computers.

AI is like the internet.

AI is like mobile.

These comparisons are comforting because they all produced massive, economy-wide change, and attracted enormous capital. They changed how work got done.

They also share something deeper. Every one of those technologies extended human capability without replacing human cognition. Electricity powered machines, but humans still decided what to build. Computers processed data, but humans interpreted it. The internet moved information, but humans decided what mattered. Mobile put computing in your pocket, but human attention remained the scarce resource. In every case, human intelligence anchored everything. It was also the bottleneck.

AI is different because it performs cognitive work. And if that makes you uneasy, it should. Because if AI can actually think, then a lot of what we’ve built our careers on, like our expertise and our hard-won skills, might not be as defensible as we thought. The junior engineer who spent years developing intuition now works alongside a tool that has it instantly. So does the financial analyst known for their variance analysis. People aren’t completely sure where value actually lives anymore, and that’s terrifying.

I talk to CFOs every week. Six months ago, they asked me abstract questions like “what is AI?” and “should we have an AI strategy?” Now the questions are concrete: “Which parts of my team’s work no longer need to be done this way?” That shift happened so quickly, it’s already changing how resources get allocated.

For example, a founder I know started using Claude to write SQL queries that used to take her analyst a couple of days. Did she replace the analyst? Of course not. But she removed the bottleneck, and doesn’t have to depend on him anymore for quick answers. Then her analyst’s role changed completely. He went from spending 60% of his time writing queries to 10% checking them and 90% on strategic recommendations. The company didn’t reduce headcount or costs, and the analyst went from supporting three stakeholders to supporting fifteen.

This is where historical comparisons really start to fail. Tools like GitHub Copilot are compressing expertise. A junior engineer can now operate at a level that once required years of work experience. And every time the tool is used, it learns. A hammer doesn’t improve just because you built a house with it, but AI tools do. And when tools get better through use, the rate of improvement compounds. That dynamic doesn’t fit cleanly into any prior technological analogy, which is why the instinct to call this a “bubble” misses the actual point.

Previous technologies assumed a fixed ceiling on human cognition. They made us faster and stronger, but the limiting factor was always the same: How many smart people could we put on a problem? AI stretches that ceiling way beyond what we’re used to. Before, understanding your business better usually meant one of three things: More data, more analysts, or more experienced leaders. The constraint was how much human attention and judgment you could afford. With AI, that constraint shifts. When analysis that once took days appears in seconds, the new constraint is knowing what to look for. What questions matter? The limiting factor stops being talent and starts being judgment.

The skeptics are right about the hype, and wrong about what it means

Let’s take the strongest version of the bubble argument at face value. Maybe AI actually is overhyped, and most of these companies will fail. Maybe we’re early, and real impact takes another five or ten years. All of that could be completely true, and it still wouldn’t change the core point, which is this:

Even if the majority of AI startups fail, and even if adoption is way slower than expected, AI is still the first technology that can perform knowledge work. That doesn’t disappear because markets overshoot or expectations reset. The skeptics are right that the hype is inflated. But they’re wrong that inflated hype makes the technology irrelevant. We’ve seen this before: The dot-com bubble was real, and Pets.com crashed and burned, but the internet still changed everything. Both things were true at the same time.

The finance leaders I’m working with are beyond arguing about whether AI matters. Now they’re trying to understand which workflows change first, and how fast they need to adapt. That conversation is happening quietly, underneath all the noise.

And the workflows collapsing first share three properties:

1. They require expertise, but they’re repetitive.

2. They’re bottlenecks to strategic work.

3. They’re easy to verify but hard to generate.

These workflows are important enough to pay for, but not so strategic that automating them threatens competitive advantage. They require skill, but that skill doesn’t compound dramatically with repetition, which makes them economically fragile, and explains why they’re already being automated away.

Where humans still matter (for now)

AI is great at recognizing trends, and terrible at knowing which ones actually matter. It can generate variance analysis, but it can’t tell you whether a 12% swing in spend signals healthy growth or a deeper problem. It can draft strategies, but it can’t tell you which strategy fits this market and this team in this exact moment. Judgment under uncertainty, and high-stakes tradeoffs where the downside is catastrophic, remain human responsibilities. For now.

When the constraint is no longer “do we have enough smart people,” the problem becomes one of priority. What deserves attention? What’s worth building next? That’s where I see many founders get stuck. They ask if this is a bubble and if they’re too early, but those aren’t the most useful questions. The right one is: “What can I build in the next year that creates real value, regardless of what valuations do?”

The companies that last will be the ones quietly iterating and embedding AI into actual workflows that solve actual problems. Take CFOs, for example. They’re buying AI because their board wants faster variance analysis, and they’re tired of hiring analysts who quit after six months. That’s a real-world problem that companies need to solve.

And the same is true for investors. The ones who succeed long-term will be those who tolerate uncertainty long enough to see what actually works.

This time is actually different

In the short term, AI will disappoint. Many use cases won’t deliver what they promise, and a lot of companies formed in this wave won’t survive. But the technology will. And, over the long term, AI will reshape every field that depends on knowledge work. Not all at once, and not evenly, but a decade from now, it will be difficult to find a knowledge-based industry that looks the same as it does today.

AI is different because intelligence itself, which was historically the core constraint of human innovation, has now become scalable. That’s an observable fact with measurable consequences. The conversation about bubbles will fade, as it always does, and what will remain are the systems that quietly adapted while everyone else argued about valuations. The skeptics will have been right about the excess, and wrong about what actually mattered, because, five years from now, we’ll probably look back at today’s panic the same way we look back at people who dismissed the internet because a handful of companies failed. And the winners will be those who were building while everyone else argued about valuations.

In time, those are the only stories anyone remembers.

Siqi Chen is co-founder and CEO of Runway.

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Hewlett Packard Enterprise (HPE) has patched multiple security vulnerabilities in the Aruba Networking AOS-CX operating system, including several authentication and code execution issues.

AOS-CX is a cloud-native network operating system (NOS) developed by HPE subsidiary Aruba Networks for the company’s CX-series campus and data center switch devices.

The most severe security flaw today is a critical authentication bypass vulnerability (tracked as CVE-2026-23813) that attackers without privileges can exploit in low-complexity attacks to reset admin passwords.

“A vulnerability has been identified in the web-based management interface of AOS-CX switches that could potentially allow an unauthenticated remote actor to circumvent existing authentication controls. In some cases this could enable resetting the admin password,” HPE said.

“HPE Aruba Networking is not aware of any public discussion or exploit code targeting these specific vulnerabilities as of the release date of the advisory.”

IT admins who can’t immediately apply today’s security updates to patch vulnerable switches can take one of the following mitigation measures:

• Restrict access to all management interfaces to a dedicated Layer 2 segment or VLAN to isolate management traffic.
• Implement strict policies at Layer 3 and above to control access to management interfaces, allowing only authorized and trusted hosts.
• Disable HTTP(S) interfaces on Switched Virtual Interfaces (SVIs) and routed ports wherever management access is not required.
• Enforce Control Plane Access Control Lists (ACLs) to protect any REST/HTTP-enabled management interfaces, ensuring only trusted clients are allowed to connect to the HTTPS/REST endpoints.
• Enable comprehensive accounting, logging, and monitoring of all management interface activities to detect and respond to unauthorized access attempts.

HPE has yet to find publicly available proof-of-concept exploit code or evidence that attackers are abusing the vulnerabilities in the wild.

In July 2025, the company also warned of hardcoded credentials in Aruba Instant On Access Points that could allow attackers to bypass standard device authentication.

One month earlier, HPE patched eight vulnerabilities in its StoreOnce disk-based backup and deduplication solution, including another critical-severity authentication bypass and three remote code execution flaws.

More recently, in January, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) flagged a maximum-severity HPE OneView vulnerability as exploited in attacks.

HPE has over 61,000 employees worldwide, has reported revenues of $30.1 billion in 2024, and provides services and products to over 55,000 enterprise customers worldwide, including 90% of Fortune 500 companies.

Malware is getting smarter. The Red Report 2026 reveals how new threats use math to detect sandboxes and hide in plain sight.

Download our analysis of 1.1 million malicious samples to uncover the top 10 techniques and see if your security stack is blinded.

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The deal pairs one of the world’s most powerful chip companies with the AI startup founded by OpenAI’s former CTO, and the compute commitment alone runs to tens of billions of dollars.

When Mira Murati left OpenAI in September 2024, she declined to say much about what came next. What has become clear, roughly 18 months on, is that she was building something with serious ambitions,  and that she has found in Nvidia a partner prepared to back them at a scale that would have seemed extravagant even a year ago.

On March 10, 2026, NVIDIA and Thinking Machines Lab announced a multiyear strategic partnership under which Murati’s startup will deploy at least a gigawatt of NVIDIA’s next-generation Vera Rubin systems to train its models.

NVIDIA has also made what both companies describe as a “significant investment” in Thinking Machines, though neither has disclosed the figure.

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According to the Financial Times, the chip supply arrangement alone is worth tens of billions of dollars. Nvidia CEO Jensen Huang has previously said that one gigawatt of AI data centre capacity costs up to $50 billion.

Thinking Machines Lab, which Murati founded in February 2025, has now raised more than $2 billion since its inception. Investors include Andreessen Horowitz, Accel, and NVIDIA,  alongside, somewhat unusually, the venture arm of AMD, NVIDIA’s principal chip rival. The company has grown from roughly 30 employees a year ago to about 120 today.

A lab built on customisability

The company’s stated mission is to build AI systems that are, in its own words, “more widely understood, customizable and generally capable.” The emphasis on customisability is pointed: Murati and her team appear to be positioning

Thinking Machines as something distinct from OpenAI and Anthropic, which sell relatively fixed products, by building infrastructure that companies and developers can shape to their own requirements.

The partnership with NVIDIA includes technical collaboration as well as compute supply, specifically the optimisation of Thinking Machines’ products for NVIDIA’s hardware. That kind of close integration at the chip level has historically proved valuable,  it is, in rough terms, part of what allowed OpenAI to move as quickly as it did in the GPT era.

“NVIDIA’s technology is the foundation on which the entire field is built,” Murati said in a statement accompanying the announcement. “This partnership accelerates our capacity to build AI that people can shape and make their own.”

What this signals about the compute race

Thinking Machines is not the only frontier lab signing gigawatt-scale compute agreements. The broader AI industry is locked in a race to secure the infrastructure necessary to train the next generation of models, and the deals being signed now, in some cases before the hardware even exists,  reflect a bet that whoever secures the most compute earliest will have a durable advantage.

For NVIDIA, the investment serves a dual purpose: it generates revenue from chip sales while also giving the company a stake in a lab it clearly views as a potential long-term customer and strategic partner. NVIDIA has made similar investments in other AI companies, building a portfolio that tracks the industry’s frontier.

Murati, for her part, turned down an acquisition offer from Meta’s Mark Zuckerberg last year. The NVIDIA partnership suggests she intends to remain independent, and that she has secured the resources to make that case credibly. Whether a 120-person lab can genuinely compete with organisations ten times its size remains to be seen. But she is no longer short of compute to try.

[This is a sponsored article with Colony.]

“Location gets people through the door. A premium experience is what they remember.” It’s this philosophy that has brought Colony to Setia Alam. 

Spanning 11,454 sq ft on the 9th floor of Sunsuria Forum, this expansion marks a new chapter for Colony, its first beyond the city centre. Some might find the choice of location a little odd, with it being roughly 40 minutes away from KL, but it was by no means a hasty decision.

In fact, Colony @ Setia Alam has been in the making since December 2022, before the COVID-19 MCO was even lifted.

Predicting the trend, Colony’s team quickly realised that people are no longer separating work, lifestyle, and social experiences in the same way.

“As hybrid work becomes more common, well-planned suburban townships (like Setia Alam) have evolved into self-contained ecosystems that can support premium venues, not just as alternatives to the city, but as destinations in their own right,” said Timothy Tiah, CEO of Colony.

Why Setia Alam, and why now?

From the outset, Colony @ Setia Alam was never treated as a secondary location. It’s designed to deliver the same sense of impact as Colony’s other locations in the city.

The space draws on its signature design-led approach. This latest location takes Neo-Regency influences and reinterprets it for a contemporary space, emphasising on bright and airy interiors, tall ceilings, and spatial clarity. 

Visitors will notice that this space builds upon the visual identity that’s also seen at Colony @ The MET, Mont Kiara. 

It takes on a similar modern-classic interior with a monochromatic colour palette and elegant wall panel mouldings. But where Colony @ The MET featured cosy Japandi aesthetic in some rooms, Colony @ Setia Alam offers event halls in rustic and English styles respectively.

This gives customers more design options when looking for a coworking or event space from the award-winning brand that better suits their needs. But one area that’s not to be missed is Colony @ Setia Alam’s heart—The Conservatory.

The ballroom is decorated with classic black and white checkered tiles, while its glass rooftop brings in natural night, transforming the space throughout the day. This versatility makes the space suitable for weddings, corporate events, and private celebrations of all kinds for up to 200 guests.

“Design is critical because it’s what shapes the first impression. It’s often what removes the initial hesitation people have about choosing a venue outside the city,” Timothy stated. “Guests usually arrive with assumptions about scale, quality or experience, and those assumptions are either reinforced or dismantled within the first few moments.”

This new Setia Alam venue is part of the brand’s renewed strategy, which pays more attention to evolving districts, as well as growing residential and commercial communities. 

With work and social boundaries continuing to blur, flexibility has become central to how people use space. Colony’s future locations are being designed to transition easily between focused work and collaborative events without feeling fragmented.

Taking a more selective approach

In the broader Malaysian coworking and event landscape, Colony observes a growing demand for high-quality work and event spaces beyond central KL.  

“We see Colony playing a role in shifting the idea that premium events and gatherings need to be anchored to city centres. As the way people live and work continues to decentralise, expectations are changing. People want the same quality of experience closer to where their lives already are,” Timothy remarked.

Hence, the brand is making it their mission to bring those standards into emerging locations without diluting the experience. Their hope to support a more balanced ecosystem where work, events and social moments can happen meaningfully across different parts of the country.

Colony @ Setia Alam is now welcoming guests as part of its soft opening period in March 2026. Interested guests can learn more about the space and its offerings here.

• Learn more about Colony here.
• Read other stories we’re written about Malaysian startups here.

Featured Image Credit: Colony

Last year, a Reddit thread emerged suggesting that the iPhone 17 Pro’s vibrant cosmic orange color could somehow turn bright pink and it turns out it’s absolutely true. In a previous version of this article I tested the Redditor’s claims by attacking my phone with various chemicals and at the time I didn’t notice much difference, but a couple of months on, things have changed. And when I put my phone against a fresh iPhone 17 Pro Max at MWC 2026, the difference was clear: mine was now bright pink. Here’s what happened and what you can do to protect yours. 

As PCMag’s Eric Zeman noted, discoloration can be caused by cleaning substances that affect a phone’s finish, with oxidation being to blame for the color shift from cosmic orange to hot pink. Sure, this might technically be a fault, but in all honesty I love pink phones (remember the pink Moto Razr V3?) and the idea of a hot pink iPhone 17 Pro filled me with joy. So I tested the theory with various cleaning fluids.

It’s important to note here that the iPhone 17 Pro I used was bought by CNET for the purposes of testing. Had I paid over $1,000 of my own money, I would never be so reckless in smearing it with chemicals that could potentially do it irreparable harm. And you shouldn’t either. If you need to clean your phone, do it safely. Disclaimer aside, let’s dive in.

The chemicals

I first bought two chemicals to test this out. Zeman explains that oxidation may have caused the color to change and that hydrogen peroxide could do this. I couldn’t find this over the counter in the UK at the time (but more on this later), so I instead bought an “oxy-active” stain remover spray that, among other things, contains “oxygen-based bleaching agents” which sounded ideal. Apple clearly states, “don’t use products containing bleach or hydrogen peroxide” on its support page — so, naturally, I bought some thick bleach too.

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Watch this: iPhone Air, One Month Later: Camera and Battery Worries Put to Rest

05:55

Oxy application

I started by spraying the oxy cleaner on a microfiber cloth until it was noticeably wet from the liquid. I then liberally applied it all over the rear of the iPhone. The Reddit user with the affected phone showed that it only affected the metal parts, not the glass back panel, so I made sure to focus my attention on the sides and camera bar. 

With the phone well and truly doused in chemicals that have no business being anywhere near a phone, I left it to sit and think about what it had done for 30 minutes — after which time I wiped it dry and took a close inspection. Disappointingly, my phone was still factory orange, rather than “what the hell have you done to your phone” pink. Time to move on.

Bleach blast

I opened the bleach and, trying hard not to think about my days as a middle-school cleaner, applied a liberal blob of the stuff to a cloth. Again I smeared it over the defenceless phone, concentrating again on the metal areas. I definitely should have worn protective gloves for all of this, so please make sure you take better care of yourself than I do if you do anything with bleach. 

Again, I gave it a 30-minute settling-in period before cleaning it off and inspecting the results. The phone remained as orange as ever, looking as box fresh as it was the day before when it was, indeed, box fresh. 

Hydrogen Peroxide FTW

While I couldn’t find hydrogen peroxide in shops in the UK, it was readily available on Amazon. I bought a bottle and, using a piece of kitchen towel, rubbed some liberally around the phone’s metal parts. I did this a few times, leaving it to air dry between applications. I was disappointed at first that I didn’t literally see the orange transform into hot pink, but over time I did notice that, in the right light, there was a more pinkish hue going on. 

But it felt subtle and in some lighting it just looked as orange as ever. But a few weeks later, during Mobile World Congress 2026, I compared the phone with Patrick Holland’s cosmic orange iPhone 17 Pro Max and that’s when the difference became wildly apparent. Mine wasn’t just a slight shift to pink, it looked like I’d bought a completely different color variant. All the metal surfaces looked vibrantly pink against the orange of Patrick’s model, with only the glass parts — and the non-metallic slits of the antennas — remaining orange.

Is the pink iPhone 17 real?

My first version of this article had to conclude that maybe that vibrant pink iPhone seen on Reddit wasn’t the real deal. Or that something had gone wrong within Apple’s manufacturing and that customer had simply bought a fault model. But finally seeing my chemically-treated phone against a fresh Cosmic Orange model really showed me what can happen to a phone if not looked after properly. 

So yes, it’s absolutely possible for an iPhone 17 Pro to turn from orange to pink. But the reality is that this isn’t going to be an issue for the vast majority of owners, and I don’t believe this can be considered any kind of fault on Apple’s side. Apple’s guidance is very clear that strong chemicals should not be used on its phones and frankly, I don’t think that’s even guidance that needs to be given. What I did — entirely on purpose — was to attack the phone with chemicals so potent that I couldn’t even buy them in shops. 

If you have an orange iPhone and want to keep it that way, my advice is simple: Don’t cover your phone in hydrogen peroxide. You’re welcome. Sure, I’m being flippant and it’s of course worth keeping in mind that hydrogen peroxide can be found in other products, including hair dyes, so it’s possible you may have some on your hands and then pick up your phone to answer a call without thinking. A case would certainly help protect your phone further. 

But otherwise I don’t think you need to worry about keeping your phone in its original orange state. Of course, if you simply want a pink iPhone 17 Pro then that’s another matter entirely, but my guidance remains that smearing your phone in dangerous chemicals is not sensible. It could do serious harm to you and your phone and you’re much better off just hoping that Apple launches a new pink iPhone Pro in September. And it might — it has just launched a pink iPhone 17E, after all. 

Apple’s affordable MacBook Neo is now available to buy, and we’re keen to learn more about its A18 Pro chip.

First introduced in the iPhone 16 Pro and iPhone 16 Pro Max, A18 Pro promised to usher in “a new era of pro performance”. So, how does the A18 Pro compare to Apple’s first in-house processor, the desktop-level M1? With a four-year age gap between the two, is it a guarantee that the newer A18 Pro offers a faster performance than the 2020 M1? Or, because the M1 is designed for PCs, does it still reign supreme?

We’ve dissected the specs of both the A18 Pro and M1 and highlighted the key differences between the two below. 

Want to know more about the MacBook Neo? Visit our MacBook Neo vs MacBook Air M1 and MacBook Neo vs iPad guides to see how the new affordable laptop measures up to its closest alternatives. 

Otherwise, make sure you check out our list of the best student laptops and overall best laptops to find your next purchase.

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Which devices include the A18 Pro chip?

Apple’s A18 Pro chip can be found in the following devices:

Remember that the A18 Pro is not the same as the A18 Bionic chip, which powers the iPhone 16e, iPhone 16 and iPhone 16 Plus.

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Which devices include the M1 chip?

The M1 chip launched back in 2020 and was found within the MacBook Air and MacBook Pro. Unsurprisingly, as it’s been nearly six years since the launch, the MacBooks sporting M1 aren’t readily available to buy.

M1 has up to 8-core GPU

When Apple launched the M1, it was hailed as including “Apple’s most advanced GPU” and featured up to eight cores (in the MacBook Pro, or seven within the MacBook Air) which could run nearly 25,000 threads at once. This enabled the chip to handle more intensive and demanding tasks like rendering 3D scenes and video editing easier.

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In our MacBook Air M1 review, we concluded that the GPU unit was “very capable” and able to cope brilliantly with everyday tasks and intensive ones like rendering 4K HDR video and opening large Raw files in Lightroom. We also found that its GPU was able to output to the 4K Pro Display XDR Monitor, which was something that previous MacBook Airs had struggled with.

Instead, the A18 Pro is fitted with a six-core GPU. Promised to be up to 20% faster than the previous generation, Apple claims that the A18 Pro’s GPU should drive graphics rendering for Apple Intelligence and visuals for gaming too. While in our experience some titles weren’t quite optimised, overall we found AAA games from the App Store ran brilliantly.

With this in mind, yes the A18 Pro is fitted with a slightly smaller six-core GPU, we still found that the chip runs brilliantly within the iPhone 16 Pro and iPhone 16 Pro Max.

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M1 includes an 8-core CPU

Following on from the above, regardless of whether you opted for the MacBook Air or MacBook Pro, the M1 boasts an eight-core CPU. Made up of four performance and four high-efficiency cores, the CPU was once hailed by Apple as being the “world’s fastest” in a low-power silicon and “the world’s best CPU performance per watt”. 

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A18 Pro, on the other hand, is fitted with a 6-core CPU with two performance and four energy cores. Apple promises that the CPU runs 15% faster while using 20% less power than its predecessor. While we’re yet to review the MacBook Neo, Apple does promise it will see up to 16-hours of battery life on a single charge, which is a promising claim. 

A18 Pro supports hardware accelerated ray tracing

One area where the A18 Pro has the edge over the M1 is with gaming, as it features hardware-accelerated ray tracing which enables the chip to showcase advanced light, shadow and reflection in video games. Essentially, it helps games look more realistic than otherwise.

In fact, the A18 Pro promises its hardware-accelerated ray tracing is up to two times faster than before.

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In comparison, although the M1’s up-to-eight-core GPU will offer support for games, it does lack ray tracing capabilities, which means games won’t boast that photorealistic finish.

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Both support Apple Intelligence

We should disclaim that although the MacBook Neo is powered by A18 Pro, it benefits from macOS and won’t run on iOS found on the iPhone. This is important to note, as it means the MacBook Neo will support desktop apps and not just rely on mobile ones.

Otherwise, both the A18 Pro and M1 chips are fitted with a 16-core Neural Engine and, subsequently, supports the Apple Intelligence toolkit, allowing you access to tools such as Image Playground, Notification Summaries, Live Translation and more.

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Slow home internet rarely comes down to your broadband plan alone, as outdated routers often struggle to keep up with modern streaming, gaming and smart home demands despite quietly running everything in the background.

But with the Netgear Nighthawk WiFi 7 Router (RS700S) now £549.99, down from £699.99, replacing an ageing router with something built for modern streaming and gaming demands becomes a far easier upgrade to justify.

Netgear Nighthawk WiFi 7 router is back to its best price

The Netgear Nighthawk WiFi 7 router is officially back at its best price, offering a rare chance to grab top‑tier wireless performance without paying price.

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The Netgear Nighthawk WiFi 7 Router (RS700S) is built around the WiFi 7 standard, which pushes wireless speeds significantly beyond previous generations while helping reduce latency during heavy network traffic.

In practical terms, that means large game downloads, high-resolution video streaming and connected devices competing for bandwidth should run more smoothly without the frustrating slowdowns common with older routers.

Tri-band connectivity also plays a major role here, allowing devices to distribute themselves across multiple wireless bands so phones, laptops, televisions and consoles are not all fighting for the same signal.

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Where this router really stretches its legs is raw throughput, with wireless speeds rated up to 19Gbps, giving it the headroom needed for 4K streaming, cloud gaming and large file transfers happening simultaneously.

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In fact, we actually ranked the Netgear Nighthawk RS700 as the fastest router in our Best Router guide, thanks to its ability to maintain exceptional speeds across demanding home networks.

Coverage is another major focus, with the antenna design aiming to deliver a reliable signal across up to roughly 3,500 square feet, helping larger homes avoid the frustrating dead zones common with weaker routers.

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The Netgear Nighthawk WiFi 7 Router (RS700S) also includes security features designed to help protect connected devices, an increasingly important consideration as more smart home products rely on constant internet access.

For households packed with streaming devices, gaming systems and smart home tech, this discount makes RS700S a far more compelling upgrade than usual.

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