Technology

The current narrative around enterprise AI is rapidly shifting from the excitement of the pilot phase to the sobering reality of production. As organizations race to integrate generative AI into their workflows, they are hitting a wall that has less to do with technology capability and everything to do with how those models are deployed.

Increasingly, companies are discovering that the problem isn't AI itself, but the assumption that every task requires the most powerful model available. This has led to widespread "tokenmaxxing" - the tendency to default to the largest and most expensive models even when a smaller, cheaper alternative could complete a task.

Rather than matching the right model to the right job, many organizations assume every workflow requires frontier-level reasoning power.

This over-engineering of automation creates a structural drag on profitability. When companies treat every problem as if it requires a frontier model, infrastructure costs inevitably outpace the value of output.

The market is witnessing the consequences of this approach, with reports of major enterprises burning through entire AI budgets in months and canceling internal licenses as costs spiral.

As enterprises experience AI sticker shock, it is becoming clear that AI spending is often outpacing the tangible value it delivers.

Moving beyond the pilot trap

The core issue is that the success of isolated, controlled pilots often serves as the benchmark for current enterprise AI initiatives. In a pilot, the variables are limited, and the cost per process looks manageable. But the moment those floodgates open to enterprise-wide usage, the messy reality of production, edge cases, multistep retries, and high-volume variability, takes hold.

Because probabilistic AI generates a different cost for every run, it creates an unpredictable expense that finance departments cannot forecast. With traditional software, a fixed budget aligns with a predictable cost per task. With AI, that stability is missing. When the same process costs one dollar one day and a hundred dollars the next, it cannot be safely moved onto an operating budget.

This is why 80% of enterprises admit they miss AI cost forecasts by more than 25%, and over 95% of GenAI pilots fail to reach meaningful production. Enterprises are not currently optimizing a cost-to-value ratio; they are discovering that ratio the hard way, often after the financial damage is already done. For many leaders, the only perceived lever left is to "use less", throttling usage or restricting access.

But this response is flawed. The real shock is not the size of the bill itself, but the realization that the only lever management has left is to restrict consumption. By rationing access, the organization is effectively admitting that its AI implementation is too costly to run at scale, turning a potential competitive advantage into a defensive retreat.

Rethinking the economics of automation

Today, most organizations focus on optimizing model selection, essentially deciding which model should handle a task, but the bigger opportunity lies in optimizing the work itself. Because costs reset every time a request starts from scratch inside a large model, assuming every task requires a frontier-level LLM quickly becomes an expensive mistake.

Standard routing tools operate at the request level: they look at an incoming task and forward it to whichever model seems adequate, but that model still performs the entire task as a single, opaque generation. The only thing being optimized is which model answers, and nothing produced makes the next run any cheaper. True scalability requires going one level deeper.

This requires shifting toward an architecture that owns the business process rather than relying entirely on the underlying model. Rather than simply routing work to an endpoint, this architectural approach manages the process itself. It sits above individual models, breaking a workflow into discrete, code-backed steps to generate an auditable trace at every stage.

This single architectural choice is what separates structural optimization from surface-level cost management, offering a depth of efficiency that conventional routing tools cannot reach. This shift leads to a more efficient cost structure. Rather than relying on a single model to perform every task, computation is distributed across specialized, code-backed steps, improving resource utilization and changing how the workflow is executed.

In a high-volume loan- processing workflow, for example, this approach improves overall economics by reducing reliance on expensive model inference where it is not required. Moving toward this modular, process-driven architecture provides a sustainable path for managing the costs of high-volume operations.

Because the process is decoupled from any specific provider, organizations retain full flexibility. They can freely apply optimization strategies, rotating between frontier LLMs, open-source weights, and smaller specialized models as performance and cost needs evolve, without having to rebuild their core infrastructure.

And because every execution produces a deterministic, auditable trace of reasoning steps, tool calls, and outcomes, those records become valuable data assets, improving specialized alternatives that already know how to handle predictable tasks without defaulting to general-purpose calls.

Elevating human capacity through precision

The goal of this new approach is to transition from AI as a capped experiment to AI as the standard means of performing work. When repetitive cognitive tasks, such as verifying data or confirming disclosures, are handled by automated digital workers, the human role changes entirely. Analysts are no longer forced to spend their day manually opening files and keying in data; instead, they shift their focus to high-judgment exceptions, complex strategy, and creative problem-solving.

This is the promise of sustainable AI adoption. By converting unpredictable expenses into a known cost base and focusing on re-architecting workflows rather than just swapping models, companies can finally open the floodgates. This gives organizations a path toward sustainable and measurable ROI as adoption scales, empowering their workforce to focus on the high- value, evaluative work that technology cannot replicate.

The future of enterprise AI will not be determined by model capability alone, but by the ability to deploy integral AI systems that finally make the technology economically viable at scale. It is time to leave behind the era of unsustainable experimentation and embrace a disciplined, architectural approach to AI adoption. For organizations ready to move beyond the pilot stage, the challenge is no longer proving that AI works. It's making it economical enough to scale.

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• The 2026 World Humanoid Robot Games are happening this week
• Robots are performing better than ever across 51 events
• Stopping a sprinting robot remains a major engineering challenge

The World Humanoid Robot Games are underway in Beijing, China, and records set by flesh and blood humans are tumbling: these bots have now beaten our best efforts at the 100-meter sprint and the high jump.

As the Associated Press reports, the robot record for the 100 meters now stands at 9.39 seconds (beating Usain Bolt's 9.58 seconds), while a humanoid bot has reached 2.88 meters on the high jump (above Javier Sotomayor's 2.45 meters).

This is only the second year of the Robot Games, but the progress from 2025 is noticeable — these robots were only reaching 0.95 meters on the high jump last year, for example. One engineer told the South China Morning Post that improved networking capabilities, meaning better access to cloud AI processing, is one reason for the advances in the tech.

These humanoid robots will be put to the test across 51 different disciplines over the course of the five-day event, with sports like tennis, table tennis, and kickboxing newly added for 2026. More practical trials are also included, covering tasks across industrial production, logistics, and hospitality.

Stop right there

Unitree humanoid robot achieving a top speed of 28.3 mph failed to brake in time, veered off the track, and crashed into trackside pic.twitter.com/1JRxoELl2kAugust 20, 2026

As impressive as a lot of these robot athletes are, we're still seeing plenty of fails — such as sprinting robots smashing into safety barriers to stop. Getting these machines to slow down is actually a harder engineering challenge than you might think: we're talking about high-speed, complex movement that needs to be managed with exact precision.

There's a lot of energy that needs dissipating, a lot of rebalancing that's required, and a lot of transitioning from one state to another to be done. Researchers have managed it in simulations, but not yet in actual sprinting robots — right now the speeds and movement mechanisms are too much for the robot AIs to cope with.

It comes back to a perennial problem for robots: avoiding falling over. We take staying upright for granted, but it involves a lot of split-second calculations. When a robot is running, the problem gets exponentially more difficult.

Those watching don't seem too concerned by the abrupt stopping mechanisms. "These sports are perfectly normal for humans, but now robots can do them. I find it amazing," spectator Yang Shangzheng told the Associated Press.

• Apollo confirms July 2026 cyberattack via social engineering exposed PII in its cloud environment
• Data included names, DOB, contact info, addresses, and Social Security numbers
• Firm offers two years of identity protection; no evidence of dark web leaks yet

Apollo, one of the biggest private equity firms in the world, has confirmed it suffered a cyberattack which compromised some people’s personally identifiable information.

The company notified California’s Attorney General’s Office about the breach and shared a copy of the letter it is now sending out to affected individuals. It is impossible to discern from the letter if the victims are Apollo employees, customers, or someone else entirely, but the company did clearly explain what happened.

As per the letter, an unidentified threat actor tricked an Apollo employee into granting them access to the company’s cloud environment. The attackers used social engineering (usually phishing), which means the victim either tried logging in using a spoofed landing page, unknowingly installed an infostealer, or was convinced to grant the attackers access via remote monitoring and management software.

Was there really a hack?

The company spotted the attack a few days later, and after activating its safety protocols (notifying the police, enhancing its security protocols, and bringing in third-party forensic experts), launched an investigation which showed that the attackers accessed its cloud platform between July 6 and 10.

“During our investigation, we learned on August 12, 2026 that the information potentially impacted by this incident included your name, date of birth, contact information, home address, and your Social Security Number (SSN),” the company said. This means that financial data such as credit card or bank account information, was not compromised.

Still, cybercriminals can make use of this type of information, as is often the case in identity theft, business email compromise, and even wire fraud.

Apollo is now offering two years of free identity theft protection and monitoring for affected individuals through Cyberscout.

At press time, no threat actors claimed responsibility for the attack, and the data has not yet surfaced anywhere on the dark web.

Via TechCrunch

Oppo Reno 16 5G: two-minute review

The Oppo Reno 16 5G is a stunning mid-range phone with a lush display, fantastic camera and strong performance. If you’re looking for a phone with “summer looks” and “star vibes,” that’s exactly how Oppo is describing its new mid-range handset, which is aiming to be among the most trendy, flashy looking models on the market.

But it’s not all marketing hype — I really do think that the Oppo Reno 16 5G is a gorgeous phone. It's slim, comes in some beautiful color options, and has a striking 3D holographic design on the reverse side, making it the ideal option for those seeking an aesthetically pleasing and stylish digital companion. It's also lightweight and compact for those who prefer a smaller device.

Don’t be fooled, though, this thing isn’t just a pretty face. Like all of the best phones, the Oppo Reno 16 5G offers solid performance (thanks to its Qualcomm Snapdragon 7 Gen 4 processor), a clean and easy-to-use UI, and a 6,000mAh battery for seamless everyday use.

Another highlight for me, though, was the camera quality on the Reno 16 5G. It has a highly capable system of cameras, featuring a 50MP wide, ultra-wide, and selfie camera, as well as a 50MP telephoto lens. And for a mid-range phone, this thing produces really impressive results. The telephoto lens — which in itself isn’t always a guarantee to get at this price-point — can capture detailed, rich photos with surprisingly little compromise, and the phone is also able to capture 4K 60fps footage with its main or front camera — not bad, eh?

The display, though small, is also vibrant and punchy, and finer intricacies come through nicely when watching movies or YouTube videos in full HD. You won’t get the perfect contrast and detailing that the priciest flagships plate up, sure, but compared to rivals, the Reno 16 5G’s display is very decent indeed.

On the software side, things are pretty good — albeit slightly flawed. ColorOS, which runs on Android 16, runs like a dream the vast majority of the time, and is both well-laid out and easy to customize. Bloatware is relatively limited too, which was a relief for me, even if a handful of the usual offenders (like Temu and AliExpress) are pre-installed.

There are some small oddities, and the default AI-generated background carousel is truly awful, though it can be deactivated if you prefer (and yes, I do indeed prefer). Other AI features are more palatable, and all centralized in the Mind Pilot app, which is fine enough.

In fact, most of my gripes with the Oppo Reno 16 5G are relatively minor. I would’ve liked to have seen wireless charging onboard, and the device can get a little toasty when the processor is hard at work. But overall, this is an excellent phone.

The one thing that I consider a sticking point is the device’s price. At £649 (about $880), it’s more expensive than its recently released predecessor, and is certainly towards the top-end of that mid-range bracket. When rivals like the Nothing Phone (4a) Pro offer similar performance across the board (as well as a stunning design), the high cost could prove to be a turn off for a lot of consumers. Still, the Oppo Reno 16 5G is certainly a highly capable mid-range phone with the substance to back up its style, and I’d recommend it — especially if you spot it on sale!

(Image credit: Future)Oppo Reno 16 5G review: price and availability

• Priced at £649 (about $880)
• Released in July 2026
• Not sold directly in the US, but could conceivably be imported

The Oppo Reno 16 5G was released in July 2026, just half a year after its predecessor. In the UK, the Oppo Reno 16 5G is priced at £649 (about $880), and comes with 512GB of storage and 8GB of RAM. However, some different storage options are available in other markets, such as India. The Reno 16 5G is quite expensive for a mid-range phone, and costs about £50 more than its predecessor did at launch.

The standard Reno 16 5G is not sold in Australia, but a similar model is available called the Oppo Reno 16F 5G. Although Oppo’s phones aren’t directly sold in the US, you could potentially import it. The Reno 16 5G is available in a range of colors across different markets, but in the UK there are two options: Pop White and Purple Black.

Oppo Reno 16 5G review: specs

Dimensions

6 x 2.9 x 0.3 inches / 151.2 x 72.4 x 8.4mm

Weight

6.6oz / 188g

Screen

6.32-inch AMOLED

Resolution

2640 x 1216

Refresh rate

120Hz

Chipset

Qualcomm Snapdragon 7 Gen 4

RAM

8GB

Storage

512GB

OS

ColorOS 16.0 (based on Android 16)

Rear cameras

50MP f/1.8 wide; 50MP f/2.0 ultra-wide; 50MP f/2.8 telephoto

Front camera

50MP f/2.0

Battery

6,000mAh

Charging

80W wired, no wireless charging

(Image credit: Future)Oppo Reno 16 5G review: design

• Impressive build quality
• Attractive look with 3D pattern
• Ultimate IP69K dust and waterproof rating

It really feels like a lot of the Oppo Reno 16 5G’s appeal is in its design. Even on Oppo’s website, a substantial amount of attention is placed on its aesthetic, which is meant to radiate “summer looks” and “star vibes”. And I have to say, this is a really great-looking phone.

The Pop White variant I tested is slim, lightweight, and has an excellent standard of build — it's constructed from aerospace-grade aluminum, according to Oppo. The Reno 16 5G is a fairly compact device, with a 6.32-inch display, and it feels incredibly comfortable in-hand. It's worth noting that it's very similar form-wise to my Xiaomi 17, so moving across to the Reno 16 5G felt natural for me, but if you’re accustomed to a larger phone, like the iPhone 17 Pro Max, say, then it may feel a little undersized to you.

Something that’s unique about this device is its use of ‘Holoverse 3D technology’. On the glass back cover of the phone, you’ll see a visual of a floating planet that actually pops out at you, much like graphics on a 3D display but without having to wear the glasses. It looks fantastic, I’ve never seen this on a phone before, and it adds some nice flair to the Reno 16 5G’s aesthetic.

Right, we’ve ascertained that this is an excellent-looking phone, but does the Oppo Reno 16 5G have the practical elements to make it a true design marvel? Well, yes.

It has well-sized buttons, the camera module is pretty low-profile, and perhaps best of all, the device has an IP69K rating. IP69K is the pinnacle of the dust and waterproof rating tree, and means that the phone is fully protected against dust ingress and can survive high-pressure, high-temperature water jets from close range. It will even survive being submerged underwater, meaning it's pretty accident-proof — though I still wouldn’t recommend scrolling in the shower or anything.

• Design score: 5/5

(Image credit: Future)Oppo Reno 16 5G review: display

• 6.32-inch AMOLED display
• 120Hz refresh rate
• Solid peak brightness of 3,600 nits

The Oppo Reno 16 5G has the sort of screen you’d expect from a mid-range device. It has a 6.32-inch AMOLED display, with a 2640 x 1216 — sometimes known as Full HD+ — resolution.

But what does this mean in-use? Well, I found that the Reno 16 5G produced vibrant, punchy colors, solid contrast, and decent detailing. When watching a wildlife video on YouTube in 1080p HDR, I was struck by rich green tones, intense reds, and azure blues that helped every part of the environment pop. When tuning into The Dark Knight Rises on Netflix, black levels weren’t the best I’ve seen, but the picture was still relatively detailed, and finer elements, like lines on faces were still perfectly visible.

As you’d expect from a mid-range model, the Reno 16 5G’s display has a 120Hz refresh rate. That means that keen mobile gamers can enjoy 120fps gameplay, if software supports it that is. More generally, this meant that scrolling and typical use was incredibly smooth and fluid.

In addition, the Reno 16 5G has a commendable peak brightness of 3,600 nits. This meant that I had no issues using the phone outdoors during an incredibly bright (and sweltering) summer in the UK.

All in all, you’re getting good performance for a phone in the mid-range category. Don’t expect flagship quality in terms of detailing and resolution, but when comparing it against rivals, you’ll likely be satisfied with the Reno 16 5G’s display.

• Display score: 4/5

(Image credit: Future)Oppo Reno 16 5G review: software

• Runs ColorOS 16.0, with five years of OS and six years of security updates
• Lots of AI functionality — but some parts are more welcome than others
• Relatively light on bloatware

The software experience on the Oppo Reno 16 5G is pretty good overall, but it isn’t without its flaws.

This phone runs ColorOS 16.0 out of the box, which is running on Android 16. You’ll get five years of OS updates, and six years worth of security updates. This is pretty good for a phone in this price-range, and will likely serve most users across their time with the device.

ColorOS 16.0 provides the kind of user experience that those familiar with Android would expect. The UI is clear and looks pretty stylish, accessing apps and settings is quick and easy, and you can customize the home screen swiftly.

But let’s take a deeper look at what the Reno 16 5G has to offer. Like a lot of rivals, Oppo is going pretty hard on AI functionality. To be honest, I don’t ever use AI functions on my usual phone, but there are a few options worth highlighting.

Most functions sit in the Mind Pilot app, which lets you make use of Gemini, Perplexity, or ChatGPT, or as I like to call them, the three horsemen of the apocalypse. You can insert basic prompts, ask it to remember vocal prompts and store them as memories, or manage your transactions. Essentially, it’s able to record and collect content on-screen, and help you organize it all in one central hub. Does it work? Sure. But I’d rather handle these sorts of tasks manually

The Reno 16 5G also uses AI to generate some wallpaper options for you — at least that’s what it did for me by default. The results are often ugly and unlikable. The prompts are also left on the wallpaper, a decision I find baffling decision. A lowlight was ‘Farmer saves kittens using a broom handle’, which literally just displayed a ginger cat swimming underwater. This is the sort of AI use that leaves a sour taste, and almost no one actually wants it. Thankfully, you can disable this, but Oppo, please just get rid of this feature.

Something that I did appreciate, however, was that the Reno 16 5G was pretty light on bloatware. You’ll see a few unwanted apps — AliExpress, Temu, and Block Blast to name a few. But I wasn’t subjected to a swathe of shoddy mobile games or unwanted AI chatbots — something I got on the Xiaomi Redmi Note 15 Pro 5G — and most of the pre-installed stuff was things you’d expect, like Google and Meta apps, for instance.

And to finish on a high, I want to shout-out the Snap Key. This is a button on the left side of the phone that enables you to instantly trigger one of many functions. Options include: taking a screenshot, turning on the torch, opening the camera, and more. It's fully customizable and works like a dream, and I wish my flagship Xiaomi phone had this.

• Software score: 3.5/5

(Image credit: Future)Oppo Reno 16 5G review: cameras

• Very solid 50MP main camera
• But the telephoto lens is a real highlight
• Solid selfie camera and video performance

I used the Oppo Reno 16 5G to take photos in a wide variety of lighting conditions, both inside and outside — testing all of its cameras and video performance in the process. And after checking the final results, I have to say that the Reno 16 5G is a pretty nifty phone for photography.

The main 50MP camera is really solid, delivering the exact kind of quality I’d expect from a mid-range device. In bright outdoor conditions, the camera is at its best, producing vibrant colors and plenty of detail. I snapped some trees by my office, and the richness of the greens and gorgeous blue sky were captured fantastically well. Taking photos also feels incredibly easy, with image stabilization on board.

On a more overcast day, the main camera maintained impressive color accuracy and delivered great contrast. Some finer details could’ve looked clearer, but the final results were strong overall. The main camera also did great indoors with, or without, artificial lighting. It replicated the striking emerald green color of my strange yet beloved frog ornament, for instance.

Something that truly exceeded my expectations, though, was the Reno 16 5G’s telephoto lens. Usually, budget or mid-range phones struggle to provide clear and detailed images when zooming in too far, but the Reno 16 5G is an exception. Even when taking a photo at 7x zoom, the intricacies of a building in the distance were surprisingly clear, and details of surrounding foliage were captured with relative ease.

You also get a good quality, 50MP selfie camera, which can zoom out to easily fit you and all of your friends in frame. No more weird arm craning needed.

Video is also great on the Reno 16 5G. You can capture footage in 4K at 60fps — either with the main or selfie camera. Advanced stabilization tech and tilt correction help you to get smooth, fluid, and quality footage at all times.

There are a number of editing and processing tools that also help you get impressive results on the Reno 16 5G. These include ProXDR, which shows your photos with enhanced dynamic range, and AI Remix Collage, which includes options like an AI eraser, and Perfect Shot (which detects stuff like closed eyes).

• Camera score: 4.5/5

A church hidden among trees, taken in natural light outdoorsFutureThe same photo, but using 7x zoomFuturePhoto of trees by my officeFutureFrog ornament, taken in low-light indoorsFutureA photo of buildings taken in the eveningFutureA street in Bath, England, taken in the morningFutureOppo Reno 16 5G review: performance

• Smooth everyday performance with Qualcomm Snapdragon 7 Gen 4 processor
• Really good built-in speakers, good codec support
• 8GB of RAM enough for multi-tasking, but gets a little hot with higher-demand stuff

Like a lot of its recently released fellow mid-rangers, the Oppo Reno 16 5G makes use of the Snapdragon 7 Gen 4 chipset. This is a pretty solid option shared by other models we’ve rated highly, like the Nothing Phone (4a) Pro. But how does it perform in practice?

Well, I found the Reno 16 5G to run nicely, and experienced very few performance blips during my intensive testing. Of course, it handles the standard stuff like social media scrolling, menu navigation, and surfing the web with ease. But I also found it up-to-the-task for gaming. I hopped onto Genshin Impact — a relatively demanding, 3D game — and the Reno 16 5G handled it without a hitch. Whether I was dashing around sprawling areas, doing battle with mysterious creatures, or watching cinematics, the phone held its own with frame drops kept pretty minimal.

Even when multi-tasking between a lot of apps at once, then Reno 16 5G seemed to keep things under control. I had one occasion where there was a bit of slow-down on the home screen, but this only lasted for a very brief moment. The model I used has 8GB of RAM, which is the standard for a mid-range handset.

One thing I will say is that when the processor is put to work, the device can get pretty hot to the touch. It’s something I’ve also experienced with my Xiaomi 17, and after I left the phone to cool off for a few minutes, it was fine to use again, but this is an issue that some more powerful phones may not pose.

An area of the phone’s performance that impressed me was its audio capabilities. I review a lot of audio gear here at TechRadar, so it's fair to say that I have high standards. But I found this to be a great lil’ device sound-wise, right across the board. The built-in speakers provide surprisingly clean, articulate sound, and although there’s little in the way of bass, I’d never expect impactful low-end from phone speakers.

There’s also excellent Bluetooth codec support, with both LDAC and aptx (thanks to the Snapdragon chip). That means you can enjoy higher-res wireless streaming on the go, if you have compatible headphones or speakers, like the Sony WH-1000XM6 or Bose SoundLink Max.

• Performance score: 4/5

(Image credit: Future)Oppo Reno 16 5G review: battery life

• 6,000mAh battery is good for a day’s use
• Supports 80W wired charging
• No wireless charging

The Oppo Reno 16 5G has a 6,000mAh battery, and while I’ve tested phones with more than this, that’s still quite large. I found it to be good for a day’s worth of mixed usage — gaming, watching videos, scrolling the web, and general use. Your mileage will vary depending on the applications you use and how intensive they are, but Oppo says you get over 60 hours worth of calling time with the Reno 16 5G. Not bad, is it?

When your phone does run dry, it doesn’t take all too long to bring it back to life, either. It supports very fast 80W wired charging, and I found it could charge to full in around an hour.

Something that I found slightly disappointing, though, is that the Reno 16 5G does not support wireless charging. At this price, it would’ve been a nice feature to have, especially as my wireless charger stand is a staple of my office desk setup.

It’s also worth noting you get the standard battery preservation options, battery saving settings, and a hub where you can view battery use by app.

• Battery life score: 4/5

(Image credit: Future)Should I buy the Oppo Reno 16 5G?

Attribute

Notes

Score

Design

Gorgeous looks, great standard of build, top-tier IP69K dust and waterproofing.

5/5

Display

Solid display at this price-point with 120Hz refresh rate.

4/5

Software

Neat OS with easy-to-use UI, relatively light on bloat, but some small quirks and odd AI functionality.

3.5/5

Cameras

Great quality for a mid-range phone with an especially admirable telephoto lens.

4.5/5

Performance

Steady performance across the board with impressive audio.

4/5

Battery life

Large battery for all-day use, speedy charging, but wired only.

4/5

Buy it if…

You want a super-stylish handset
If there’s one thing Oppo wants you to know about the Reno 16 5G, it's how stylish and trendy it looks. And I’d have to agree, whether it’s the pretty color options, slim build, or 3D holographic design, this phone is very easy on the eye.

You want great camera quality
I was really impressed by the Oppo Reno 16 5G’s camera quality. Its telephoto lens is the real highlight, and even at 7x zoom, my photos had great levels of detail and color accuracy.

Don’t buy it if…

You need wireless charging
Given that the Reno 16 5G is on the pricer side for a mid-ranger, I was hoping that it would have wireless charging. If this is a deal-breaker for you, then I’d recommend the Google Pixel 10, which is often on sale now that its successor has arrived.

You’re on a budget
The Reno 16 5G is pretty pricey, and it faces some red-hot competition from more affordable mid-rangers like the Nothing Phone (4a) Pro, for instance, which you can read about down below. If you’re on a budget, then that might be the better pick.

Oppo Reno 16 5G review: also consider

Oppo Reno 16 5G

Nothing Phone (4a) Pro

iPhone 17

Price

£649 (about $880)

$499 / £499

$799 / £799

Dimensions

6 x 2.9 x 0.3 inches / 151.2 x 72.4 x 8.4mm

6.4 x 3 x 0.3 inches / 163.7 x 76.6 x 8mm

5.9 x 2.8 x 0.3 inches / 149.6 × 71.5 × 8mm

Weight

6.6oz / 188g

7.41oz / 210g

6.2oz / 177g

Cameras

50MP wide, 50MP ultra-wide, 50MP telephoto

50MP wide, 8MP ultra-wide, 50MP periscope

48MP wide, 48MP ultra-wide

Battery

6,000mAh

5,080mAh

3,692mAh

Nothing Phone (4a) Pro
The Nothing Phone (4a) Pro is easily one of our favorite mid-rangers on the market. It’s oozing in style, has a gorgeous 6.8-inch display, offers solid performance, and has a clean software experience too. It's a bit cheaper than the Reno 16 5G, although Oppo’s phone arguably boasts better cameras. Read our full Nothing Phone (4a) Pro review.

iPhone 17
It’s a slight step-up in price from the Oppo Reno 16 5G, but the iPhone 17 is a great phone worth considering if you want a mixture of great design, performance, and a seamless user experience. Though its battery life is nothing special, and it lacks a telephoto lens, it still offers a stylish and simple user experience backed up by the powerful A19 chip. Read our full iPhone 17 review.

How I tested the Oppo Reno 16 5G

• Tested over the course of a week
• Used for gaming, photography, and general productivity
• Made full use of various features and camera settings

I spent a week testing the Oppo Reno 16 5G, during which time I tested out its various features, put it through its paces with mobile games, and snapped a whole lot of photos.

When taking photos, I stuck mainly to the 16:9 aspect ratio, and tried snapping buildings, foliage, household objects and more in a range of lighting conditions. These included: artificial light, low-light indoors, natural light outdoors and indoors, and nighttime outdoors. I also made sure to assess the phone’s battery life across a full day of mixed usage, compare its software to that of my Xiaomi and Nothing phones, and test its display side by side with some rivals.

More generally, I’ve spent years testing gadgets here at TechRadar, where I serve as a Senior Reviews Writer. I’ve reviewed a number of phones including the budget-friendly Xiaomi Redmi Note 15 Pro 5G and the flashy Nothing Phone (4a).

• Read more about how we test
• First reviewed: August 2026

New images of the sun seem to support a long-held theory about what’s happening on the sun’s surface.

The Nano also features SmoothiePilot, Vitamix’s first adaptive smoothie program.

Take a look back at Apple’s black box and everything it’s added (or not) over the past decade.

• Android Pulse has appeared on the Google Play Store
• It's available as an update for some users, showing that it's already on their phones
• The description suggests this runs in the background to check for misbehaving apps

Android Pulse is an app that you probably don’t know you have — but if you’ve got an Android phone then there’s a chance it’s already running in the background, and there’s an easy way to find out.

As spotted by 9to5Google, just such an app has appeared on the Google Play Store, and more tellingly, you might find it in your list of available Google Play Store app updates — meaning it’s already on your phone, even though you won’t have knowingly downloaded it.

This isn’t actually a completely new app, with versions of it dating back to March appearing on APK Mirror, but it has seemingly only now got itself a Google Play listing — and with the listing mentioning over ten million downloads, a lot of people must already have it.

Keeping your Android's pulse healthy

(Image credit: Google / 9to5Google)

So what is this mystery app? Well, the listing says “this app facilitates rule distribution for anomaly detection in critical system resource consumption by the Android operating system and installed applications.” That’s a bit unclear, but 9to5Google’s interpretation — and we’re inclined to agree — is that this runs in the background, performing software diagnostics on the operating system and your installed apps.

It’s likely — based on the “anomaly detection” part of the description — that it’s looking for any apps or services that are misbehaving, by for example using more battery or CPU than they should. Though whether it then does anything about them or just alerts Google to it is less clear.

Either way though, the ultimate goal is presumably to keep Android running smoothly, so that’s no bad thing.

But this isn’t an app you can directly interact with or run scans from — if it’s on your phone, you just have to hope and assume it’s doing some good, and not simply wasting resources.

• The first teaser trailer for The Pitt season 3 has been released by HBO Max
• It picks up on Dr. Robby's first day back after his four-month sabbatical
• A release window of January 2027 has been confirmed for the season

The Pitt season 3 first trailer has dropped, and I'm incredibly surprised that we're getting a first look this early. We can expect new episodes to arrive at some point in January 2027 — meaning we've got a five month wait until we find out what's really going on.

But HBO Max is a streaming service that likes to keep us on our toes, simultaneously giving away everything and nothing in the first season 3 teaser trailer (which you can catch up with below).

We already know that season 3 is set in early November, corresponding with the three- or four-month sabbatical Dr. Robby (Noah Wyle) is due to take at the end of season 2.

While creators said in a statement that season 3 is “just before the holidays, ushering in a whole new set of emergencies and confrontations and complications,” Wyle added at PaleyFest, "The thesis of season 1 is the doctor is the patient. Season 2, doctors don’t make very good patients. Season 3, doctors benefit from being patients.”

He also told Deadline, "Well, I think we’ll find out what that rock bottom [for Dr. Robby] looks like next year.”

Is this the impression I get from watching the first few glimpses of footage? No. But instead, I think that another medic is finally about to have their moment in the spotlight.

Opinion: The Pitt season 3 teaser trailer hints that Dr. Javadi will finally step up

With such a truly ensemble cast, there were never going to be any losers in The Pitt season 3... but if I were you I'd keep your eye on Dr. Javadi (Shabana Azeez) in new episodes.

Meek, mild-mannered and constantly second-guessing herself, as the 'baby' of the ER team Javadi has striven to prove herself both to her colleagues and to her disapproving parents, who work in general surgery and endocrinology upstairs.

But in the above clip, she's finally stepping up to take charge... and it suits her. In a split-second clip, we see her working with two other medics to try and resuscitate a patient, asking for help without feeling self-conscious in the process.

It's rare that The Pitt produces positive stories of growth for our team, but as the ER storm whips back up, I've got a good feeling about Javadi.

From what Dr. Robby tells us, though, nothing else is going to be straightforward. How the screaming casualties rolling into the day shift are going to differentiate from seasons 1 and 2 is yet to be established, but put it this way — nobody's looking well-rested.

• Amazon has increased the prices for its best-selling devices, including popular Kindle models
• This is due to the increasing memory and storage component costs
• Users online have found ways to score Kindles for cheaper

Amazon is the latest tech giant to increase prices across its lineup of smart home devices — and this also includes popular Kindle models.

In addition to devices such as the newer Echo Dot Max, Echo Show 21, and some Fire TV sticks, Amazon’s basic Kindle and mid-range Kindle Paperwhite e-readers have shot up by up to 60%. The only Kindle model that hasn’t seen a price hike is the premium Kindle Scribe.

Amazon’s price increases quietly landed over the weekend without an official statement from the retailer, but luckily the online outlet Fortune spotted the new price tags when conducting a review — who also shared that Ring doorbells are the only Amazon devices that haven’t been hit with a price adjustment.

The outlet also provided a full breakdown of the devices affected by the price hike, which you can view below:

Device

Old price

New price

Echo Dot (5th Gen)

$49.99

$79.99

Echo Spot

$79.99

$109.99

Echo Show 21

$399.99

$499.99

Echo Dot Max

$99.99

$119.99

Echo Show 15

$299.99

$349.99

Echo Show 11

$219.99

$249.99

Echo Show 8

$179.99

$199.99

Fire TV Stick 4K Max

$59.99

$84.99

Fire TV Stick HD

$34.99

$39.99

Kindle (16GB)

$109.99

$149.99

Kindle Paperwhite (16GB)

$159.99

$199.99

eero 7 (3-pack)

$349.99

$399.99

eero Pro 7 (3-pack)

$699.99

$799.99

Though Amazon didn’t issue any form of official explanation for this, a spokesperson released a statement to Fortune to offer more insight, sharing that consumer tech is “facing significant increases in memory and storage component costs. After absorbing these increases for as long as we could, we recently adjusted pricing across our product lines”.

2026 really has been the year of the price hike for the tech world, and it’s not just Amazon that’s been affected. Apple raised its Mac, iPad, and HomePod prices in June, while services like Spotify hiked subscription plans earlier this year and the same goes for YouTube Premium.

Price hikes are regular as clockwork at this rate, however some users don’t seem to mind as much if the product in question is of value and has a lot to offer. But take devices such as the standard Echo Dot and basic Kindle for example, two entry-level models in their respective areas, a $30-$40 increase seems a little unfair.

Though the Echo Dot is a basic speaker that gets the job done, its audio output and smart features aren’t worth over $50 in my opinion. The same goes for the basic Kindle, an e-reader with basic functions, which you also have to pay more for if you want an ad-free experience.

As you can imagine, Amazon’s latest price increase hasn’t settled well with Kindle users, especially since Amazon’s e-readers are one of the company’s best sellers. Redditors have been posting non-stop about this, one user replied to a post saying ‘I hope nobody buys these and prices go down’ — however, bookworms are sharing ways to score these devices for cheaper in the wake of the price hike.

Comment from r/ereader

Now, users are reverting to third-party discount sites to get their hands on Kindle e-readers for a fraction of the new prices. Many users replied under the same Reddit post recommending Unclaimed Baggage, a site that sells on devices found in lost luggage from airlines.

For example, Amazon’s premium Kindle Scribe Colorsoft e-reader is the best part of $680, while Unclaimed Baggage has offers for half of that. This isn’t the only third-party retailer however, Redditors have also recommended Woot, an online deals site run by Amazon where you can access hundreds of deals across electronics.

One of the better sides to these third-party options is that they’re not limited to Kindles, they also have a number of listings for other devices in Amazon’s latest price increase. That said, Redditors aren’t ruling out Facebook Marketplace for finding offers on gadgets — you never know what the person around the corner from you wants to get rid of.

• HoverAir launched its new Versa drone on Indiegogo, saying it had FCC approval
• FCC has since stated that certification was granted 'erroneously'
• The Versa is the world's first 2-in-1 pocket gimbal camera and drone

HoverAir launched the Versa drone on Indiegogo last week, and a few words in the promotional material suggested that the world's first 2-in-1 pocket gimbal camera and drone had a distinct advantage over DJI: "FCC (Federal Communications Commission) certification."

The claim of FFC approval implied that the Versa would be permitted to be sold in the US, a market which new DJI drones have been fully locked out of since 2025. World-first features, plus US availability — those were two big leg-ups for the Versa.

However, HoverAir's hopes of having that key advantage in terms of market access have quickly been dashed. The FCC contacted TechRadar to tell us that "certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database".

Furthermore, it said the "FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules."

It's quite the turnaround for HoverAir. The company must have been licking its lips at the prospect of its innovative innovative 4K drone and pocket gimbal camera having unchallenged access to the US market. Turns out, it's in the same position as DJI after all.

Here's the FCC statement in full:

“Importing, marketing, or selling this [HoverAir Versa] device in the United States is a violation of U.S. law, and the FCC has directed HoverAir to cease any such activity immediately.

"The device in question is on the FCC’s Covered List: the Versa camera alone is a prohibited UAS critical component produced in a foreign country and, when the Flight Kit is included, the device falls under the ban on UAS produced in a foreign country.

"The certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database. The FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules.

"Finally, the FCC is sending guidance to all Telecommunications Certification Bodies to strengthen their due diligence of applications to ensure they comply with their legal obligation not to certify equipment on the FCC’s Covered List."

HoverAirHoverAirHoverAirHoverAirMore bad news for drone fans in the US

Following the self-flying X1 and the waterproof Aqua, HoverAir notched up a trio of world-first drones with the Versa, a 2-in-1 pocket gimbal camera and drone.

According to its Indiegogo page, the Versa is tipped for a worldwide launch and for October 2026 delivery to backers. However, despite US pricing (starting at $499) remaining on the crowdfunding page, it looks like the Versa won't be taking to the skies in the US any time soon.

That's a shame for drone fans in the US. Versa's modular design means that its wings (guard-protected propellors) can be removed in a simple motion, switching the device from a 230g self-flying 4K camera to a 163g pocket gimbal camera.

Its key features include 4K video up to 60fps, 4K HDR video up to 30fps with 10-bit color depth, 12MP photos (there's no mention of RAW format) and a 1/1.3-inch sensor.

Controller-free aerial selfies are a breeze, thanks to palm take-off and landing, AI subject tracking, auto framing, plus voice and gesture commands.

Furthermore, the Versa is compatible with HoverAir's Beacon device, which debuted with the Aqua, meaning it can pair with a phone for clear audio. It's said to reach speeds up to 36km/h with a Level 5 wind resistance, and have 14-minute flight times, though realistically battery life will be less than that.

As a gimbal camera, the Versa features a rotating OLED haptic screen, which measures 1.64 inches on the diagonal and hits 800 nits of brightness.

We haven't tested the Versa yet, but we'll share our in-depth review soon. The innovative drone will make its public debut at the IFA consumer electronics show, which takes place in Berlin, Germany, from September 4-8, and you can learn more about it in the YouTube video below.

The first salvo of the war against AI slop on LinkedIn looks to be going the right way for those of us who can actually use our imaginations, the company has said.

LinkedIn Chief Product Officer Hari Srinivasan has revealed over a million people have clicked the ‘seems like AI slop’ report option in the two weeks since its launch - and I've definitely clicked it a few times.

Srinivasan says that these reports, along with changes to LinkedIn's detection systems, have cut views of content classified as AI slop by 40% - although sadly my feed is still pretty full of it.

"Low-quality content" crackdown

In his post, Srinivasan noted the feedback was, "helping us better understand how our community experiences low-quality content" across LinkedIn, suggesting stricter clampdowns to come.

The first stage of this is launching now, so if one of your posts receives enough community feedback (aka is reported as AI slop), you may see a message in your Post Analytics, "to help you understand how your content is being received".

"We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan says.

"Importantly, no single piece of feedback determines how content is distributed," he added. "We look at many signals together, and we've built safeguards to help prevent individual feedback from being used to unfairly target other members."

"Despite the progress, we know we have more to do to ensure LinkedIn remains a place where you can find real people & real perspectives... this all remains very top of mind," Srinivasan concluded, suggesting further actions may come soon.

(Image credit: LinkedIn)

The company announced it was cracking down on AI slop in early August 2026, when Srinivasan declared it was, "a top priority for all of us. We really care about this...people come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

To report a post, users just need to click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

Srinivasan added that Microsoft-owned LinkedIn will, "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

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There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

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Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

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• The title and cast of James Gunn's Superman spin-off have been revealed
• The People v. Gorilla Grodd will be a mockumentary series fronted by Jimmy Olsen
• DC fans think it's the perfect project to introduce The Flash to the DCU

James Gunn's Superman spin-off show is officially in development at DC Studios — and its title, cast, and premise suggest it could be the most unusual DC Universe (DCU) project so far.

Nine months after it emerged that a Superman TV off-shoot was in the works, the actual name of a production tentatively titled DC Crime has finally been revealed. It's called The People v. Gorilla Grodd and, as was previously reported, it'll be a mockumentary series that will center on the hyper-intelligent, telepathic ape known as Gorilla Grodd.

Until now, we'd known next to nothing else about the DCU Chapter One TV show. But, with the eight-episode, half-hour crime comedy series officially greenlit by HBO Max, and DCU overlords Gunn and Peter Safran, we have a much clearer picture of what it's about and who'll star in it.

The People v. Gorilla Grodd cast: who's involved? And where's Rachel Brosnahan's Lois Lane?

Extra! Extra! Read all about it! The new DC Studios series The People v. Gorilla Grodd is coming soon to HBO Max. pic.twitter.com/cMfgyhC2j6August 21, 2026

Is James Gunn writing and directing The People v. Gorilla Grodd?

No. Tony Yacenda and Dan Perrault, the creatives behind Netflix crime mockumentary show American Vandal, have been installed as The People v. Gorilla Grodd's showrunners, writers, and directors. They're part of its executive producing team, too, which also includes Gunn and Safran.

Per an HBO Max press release, The People v. Gorilla Grodd will reunite viewers with four characters from last year's Superman movie, all of whom work for Metropolis news outlet The Daily Planet.

Skyler Gisondo's Jimmy Olsen has been installed as the show's protagonist. Meanwhile, Beck Bennett as Steve Lombard, Mikaela Hoover as Cat Grant, and Wendell Pierce as Perry White, all of whom had minor roles in Superman, are also set to feature.

Curiously, Rachel Brosnahan's Lois Lane isn't part of proceedings. Does this have something to do with her role in 2027 Superman sequel Man of Tomorrow? I guess we'll find out at some point.

There are plenty of other familiar faces among the TV project's ensemble. Jimmy Tatro is the most noteworthy of those new DCU additions, with the American Vandal alumnus set to play Grodd.

Mary Holland (Ghost), Eduardo Franco (Stranger Things), Arian Moayed (Wonder Man), Dan Perrault (American Vandal), Andrew Leeds (Zoe's Extraordinary Playlist), and Tim Baltz (The Righteous Gemstones) are also involved, though their roles are being kept under wraps for now.

What is the plot of The People v. Gorilla Grodd?

The forthcoming crime comedy show will examine a potential miscarriage of justice involving Gorilla Grodd (Image credit: DC Comics)

Here's the official logline for The People v. Gorilla Grodd: "A superintelligent ape is convicted of murdering his father, the King of Gorilla City. In an eight-part 'true' crime docuseries, the Daily Planet’s Jimmy Olsen reopens the case, investigating whether Gorilla Grodd was wrongly convicted in one of Metropolis’ most famous murder trials."

But wait, there's more, because the above X/Twitter post gives us a more detailed look at the show's wider narrative.

"The People v. Gorilla Grodd will take a fresh, 360-degree look at the case, revealing the 'lesser-seen side' of the hyper-intelligent ape, according to the film's first-time director Jimmy Olsen," it reads. "Olsen was granted unprecedented, exclusive access to interview Grodd inside Stryker's Island Penitentiary for the documentary.

"Grodd has long claimed his innocence in the murder and brain consumption of his father, King Grodd — a crime he was convicted of in March 2021 and has remained incarcerated for ever since. The series will take an in-depth look at the investigation, featuring brand new reporting from members of the Daily Planet staff, as well as interviews with witnesses and experts on both Grodd's and the Metropolis District Attorney's side of the case, including Grodd's new counsel at the Metropolis Freedom Project."

Will The Flash appear in The People v. Gorilla Grodd?Comment from r/DCULeaks

Nobody knows, though I can see why DC comic book fans are suddenly excited about such a possibility. After all, Gorilla Grodd is one of The Flash's most notorious foes, so it's perfectly understandable that some believe that the speedy metahuman — real name Barry Allen — could make his DCU debut here.

In fact, some fans have already convinced themselves that The Flash will show up in The People v. Gorilla Grodd. Threads on r/DCULeaks and r/DC_Cinematic are sprinkled with such comments, so it'll be interesting to see if the Scarlett Speedster makes an appearance or not whenever the series is released.

Are you excited for The People v. Gorilla Grodd, or would you rather DC Studios focus on introducing Batman and Wonder Woman to the DCU? Let me know in the comments.

And, if you're after more DCU coverage to consume, check out my Sinestro in Lanterns explained and Lanterns' Manhunters explained pieces.

Lorcana has exploded since it came on the scene, and I had a chance to chat with co-founder Ryan Miller about where it may be going next.

If you’re worried about Flock cameras, start by learning to recognize them.

My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

(Image credit: Nintendo)

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

Commerce in the information age has thus far been one long quest to gather as much information as possible. Customer data, sales data, inventory data, system data, operational data, technical data, every facet of business can and has been tracked and quantified, guided by the idea that knowing more means decisions can be made faster and more conclusively.

While sound in theory, that process has clearly hit a snag, as more and more companies abandon their AI projects after finding that the exponential explosion of data creation isn’t generating comparable value.

Has AI peaked in its utility? Or is the issue one of interoperability? Why exactly has this monumental trend hit such a stumbling block?

Expecting scale to produce clarity

It’s entirely understandable to assume that feeding more information into an AI increases the accuracy and quality of the output. Models need to train on data; after all, that is how they establish reason.

But, like any computer system, input must be structured for it to be understood, and the issue many organizations now find themselves in has come as a result of them giving AI a decade's worth of unformatted, incomplete, non-standardized data and expecting it to read between the lines.

Every system a company uses, from CRMs and internal emails to performance tracking spreadsheets and PDFs, stores different data points and operates on vastly different logic. What they host, why they host it, and how they host it were all purposeful decisions based on their desired function.

While some support import and export options that translate material from one format into another, the developers behind these solutions never envisioned the need for a universal logic to tie everything together in a way that AI can easily parse.

Each document and dataset contains a fragment of the overall picture. While AI excels at processing data, it cannot derive meaning and struggles to understand and incorporate unstructured data into its output, regardless of how many times it is asked to.

An example of fragmentation in action

We have largely solved the problem of data accessibility to the point that many public AI models have run out of new data and are now cannibalizing the output of other agents. The next step is in refining how AIs use the data they have.

Real estate is an excellent showcase of this process in motion. To appraise and list a property, agents need access to ownership records, zoning information, environmental information, and neighborhood demographics, just to name a few. None of these systems was designed to communicate or cooperate, which has meant AI has historically struggled to find its footing in the industry.

Newer approaches, however, prioritize the ability to find, interpret, and synthesize information from across different sources, aiding in the manual searches an analyst would otherwise do by hand.

Data siloing and fragmentation slow down decision-making, and any systems that can account for them will command a premium going forward.

Every industry has its own version of the same problem: a wealth of data at its disposal and no meaningful way to turn it into actionable insight.

A generic, trend-inspired adoption of AI tools won’t necessarily solve these underlying issues, so it’s ultimately no surprise that many are abandoning their projects altogether. It will take models that prioritize context and connection, and companies paying more attention to how they store and format data, to address the glut in productivity and decision-driving insight AI is currently experiencing.

Solving information overload

AI and the challenges it now faces are a classic example of the dangers of prioritizing quantity over quality. To be entirely fair to users, the companies behind these agents share some blame for this predicament.

The technology is still in its infancy, and marketing hype continues to push scale and processing power as key features, while the much more valuable aspects, like contextual understanding and data integration, fly under the radar.

This discrepancy will likely shift as more success stories highlight the competitive advantages of automating manual, time-consuming processes, as the real estate industry has with property research. It is this ability to understand what exists at a deeper, more comprehensive level that yields the greatest decision-supporting insight.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit