TechRadar News

• Xbox chief strategy officer Matthew Ball confirms Gears of War: E-Day and Clockwork Revolution "are just the first" exclusive Xbox titles
• Xbox chief content officer Matt Booty says, "There has to be a reason for people to buy an Xbox"
• Ball says this plan doesn't mean every Xbox single-player game will be console exclusive

Xbox has confirmed that the Microsoft-owned company is looking to expand its console-exclusive portfolio, and Gears of War: E-Day and Clockwork Revolution won't be the only two exclusives coming to Xbox consoles and PC.

During the Xbox Games Showcase 2026 last month, Xbox CEO Asha Sharma announced that The Coalition's next Gears of War game will be an Xbox console exclusive, meaning the game won't be coming to PlayStation 5.

Later, Clockwork Revolution, inXile Entertainment's steampunk role-playing game (RPG), will also drop PS5 and launch exclusively on Xbox Series X, Series S, and PC.

Xbox began exploring multi-platform a few years ago, but amidst Microsoft's major Xbox restructuring, which included mass layoffs of 3,200 people, and what Sharma is calling a "reset" of the business, it seems things are reverting to the way things used to be with an emphasis on console exclusivity.

According to Xbox chief content officer Matt Booty, who discussed the matter with GamesRadar, "There has to be a reason for people to buy an Xbox," and console exclusives can act as "a reward" for players.

Xbox chief strategy officer Matthew Ball also confirmed that Gears of War: E-Day and Clockwork Revolution won't be timed exclusives, unlike games like Starfield, but "permanent ones," and the company is looking to embrace exclusivity more.

"I can assure you, Gears of War: E-Day and Clockwork Revolution are not the only titles we're planning," Ball said. "To grow a platform like ours, you have to have exclusive games and services. These are not going to be the only two titles."

Ball added that "large live-service titles" will be multiplatform moving forward, but suggested that this won't mean every Xbox single-player game will be console exclusive.

For instance, Fable will launch on PS5, Xbox and PC on the same day, along with Halo: Campaign Evolved, which is the first Halo game to ever release on PlayStation.

"I wouldn't overly fixate on single-player," he said. "I think that's a good rule of thumb, but it certainly isn't something that's carved in stone."

The executive explained that Microsoft has a "framework" for deciding which games will be exclusive and which ones will be multiplatform and recognizes "this approach is not obvious to all of our players, and we are committed to making this clearer."

Amazon has cut the prices of the Geekom AX8 Max to $619 (was $699) and the flagship Geekom A9 Max is down to $1440 (was $1599) mini PCs, giving you the choice between an affordable all-round performer and a premium AI-focused workstation.

Having tested the two machines, whether you're upgrading a home office, replacing a bulky desktop, or looking for a compact creative machine, both mini PCs offer plenty of performance in a surprisingly small package.

Today's best Geekom mini PC deals

AMD Ryzen 7 8745HS, Radeon 780M graphics, 16GB DDR5 memory, 1TB PCIe Gen4 SSD, Windows 11 Pro, dual USB4 (40Gbps), dual 2.5GbE LAN, SD card slot, support for up to four 8K displays.View Deal

AMD Ryzen AI 9 HX 470, Radeon 890M graphics, 32GB DDR5 memory, 2TB PCIe Gen4 SSD, Windows 11 Pro, Wi-Fi 7, Bluetooth 5.4, dual USB4, HDMI 2.1, dual 2.5GbE LAN, support for up to four displays, up to 86 TOPS AI performance.View Deal

Which mini PC should you buy?

✅ Choose the AX8 Max if...

You want the best balance between performance and price. The Ryzen 7 8745HS processor, Radeon 780M graphics, 16GB of DDR5 memory, and 1TB SSD provide more than enough power for productivity, photo editing, media creation, multitasking, home servers, and light gaming. Dual 2.5GbE networking, USB4 connectivity, quiet operation, and support for four displays should satisfy most buyers while costing considerably less.

In his review, Alastair summed it up saying: "All-metal build, AI-ready, and support for an 8K monitor make this a powerful office-ready option."

✅ Choose the A9 Max if...

You want as much performance as possible from a compact desktop. The Ryzen AI 9 HX 470 processor, Radeon 890M graphics, 32GB of DDR5 memory, 2TB SSD, Wi-Fi 7, and dedicated AI hardware make it a stronger choice for AI workloads, software development, video production, 3D rendering, virtualization, and heavy multitasking. It delivers substantially more computing power for professional work.

Alastair's review for this model declared it: "A feature-packed AI-ready mini PC with surprising performance in a compact, ultra-stylish form."

Why we recommend them

These two mini PCs target different very different buyers. The cheaper AX8 Max combines AMD's Ryzen 7 8745HS processor with Radeon 780M graphics, fast DDR5 memory, dual USB4 ports, dual 2.5GbE networking, and support for up to four displays.

Its IceBlast 2.0 cooling system is designed for quiet operation, making it well suited to offices, home workspaces, or always-on NAS duties where low noise is important.

The A9 Max is more for buyers with deeper pockets who need considerably more horsepower. AMD's Ryzen AI 9 HX 470 processor brings dedicated AI acceleration alongside Radeon 890M graphics, while 32GB of DDR5 memory and a 2TB SSD provide plenty of room for demanding applications.

Wi-Fi 7, Bluetooth 5.4, dual USB4 ports, HDMI 2.1, and dual 2.5GbE networking complete a specification that's built for professional content creation, AI applications, large datasets, and intensive multitasking. If you'll actually use that additional performance, this is the model to go for.

Price Context & Historical Value

The AX8 Max's current $619 price is reasonable. It was $499 in October 2025, and went up to $999 in April 2026. Similarly, the A9 Max at $1,439.10 is more expensive than the $999 it was in August 2025, but much cheaper than the $,1799 Amazon was selling it for in May 2026. Looking at the range of prices, the message here is clear: Whichever model you go for, buying at the right time can save you hundreds of dollars.

The Catch: What to know before you buy

The biggest question isn't which mini PC discount is better, but whether you genuinely need the extra performance.

The cheaper AX8 Max already offers enough power for the vast majority of workloads, making it the smarter buy for many people.

The pricier A9 Max only becomes worthwhile if you'll benefit from its faster processor, larger memory and storage, stronger graphics, and dedicated AI capabilities.

• Shop all mini PCs at Amazon

If you're tired of buying ink cartridges every few months, this is a good time to switch to a cartridge-free tank printer. Right now, the HP Smart Tank 5101 is $170 (was $260) at Amazon, and comes with 2 years of ink already included in the box.

For home, dorm room, home office, and small businesses printing a mix of monochrome and color documents and photos, it's an ideal pick. Particularly as HP's ink tank printers tend to have larger tank reservoirs compared to rivals like Epson's EcoTank range.

Today's top HP printer deal

Cartridge-free supertank inkjet all-in-one with print, scan, and copy functions, wireless connectivity, and a 1.2-inch control display. Includes a 100-sheet input tray and 30-sheet output tray. Ships with a full set of refillable ink bottles rated for approximately two years of typical printing.View Deal

Should you buy it?

✅ Buy it if...

You want to save on running costs because bottled ink is much cheaper than inkjet cartridges and you get four bottles free in the box. We recommend ink tank printers if you're chiefly printing documents and photos.

❌ Skip it if...

You're mostly printing text documents - a high-volume laser printer is a much better option for sharper, bolder on-page text.

Why we recommend it

Having tested HP’s Smart Tank line alongside the Epson EcoTank, Canon MegaTank, and Brother INKvestment range, we strongly recommend ink tank printers over traditional inkjet models that use cartridges.

The pitch for tank printers like the HP Smart Tank 5101 is straightforward: instead of buying ink cartridges that run out every few hundred pages, you refill four visible ink tanks from bottles, and each bottle lasts dramatically longer. HP estimates the included set of ink bottles is good for roughly 6000 pages.

Setup leans on HP's Smart app, which walks through Wi-Fi connection and initial configuration with guided, step-by-step prompts rather than dropping you into a printer's typically clunky onboard menu system. Once connected, illuminated smart buttons on the printer itself are meant to guide you through common tasks like printing, scanning, and copying.

Print speed is modest rather than fast — HP rates this around 12 pages per minute for black-and-white and 5 pages per minute for color in normal mode, with the higher-quality "best" setting slower still. That's typical for tank printers in this price range, and fine for everyday home printing, but not the machine to reach for if you regularly print large batches of documents quickly.

Price Context & Historical Value

This isn't the cheapest the 5101 has ever been - back in 2024, it dropped to an all-time low of $140 direct from Amazon. However, it's cheaper now than any third-party seller has sold it before brand-new ($180 was the price back in April 2025). Typically, we see it selling at around the $190 to $250 mark when not on sale. The highest price it's been sold for is $280. Right now, it's also available for $170 at Best Buy, too.

The Catch: What to know before you buy

A few honest caveats worth flagging: some reviewers have reported software quirks and occasional paper jams with this model, and if you print only occasionally rather than regularly, the ink in an inkjet's print head can dry out and clog between uses. This is best suited to households or small offices that print often enough to make the low cost-per-page actually pay off.

For more options, see our guides to the best home printers and best ink tank printers we've personally tested.

• See all printer deals at Best Buy.

• Someone has made a custom version of the Companion Cube case for the Steam Machine
• This DIY effort is on Printables, and can be 3D-printed for those with the hardware and necessary skills
• There are a growing number of custom projects around the Steam Machine, from faceplates through to full-on alternative takes on the gaming PC

Remember the ill-fated Companion Cube case for the Steam Machine? It had the plug pulled on it by Valve a few weeks ago, after maker Dbrand failed to get official permission to make its hard-shell case for the gaming PC, but now the concept has returned, in DIY form.

Tom's Hardware reports that a user on Printables, Jaron, has provided files that allow you to 3D-print your own version of the Dbrand Companion Cube (as flagged on X). You can print the pieces, and a full set of assembly instructions (including smart video illustrations) and extra necessary parts (screws and so forth) are supplied.

As made clear on X, this is a fan-made creation implemented "from scratch" and it's based on images of Dbrand's Companion Cube. Whether it'll attract the attention of Dbrand's own lawyers, well, we'll just have to see, but you'd imagine that some leeway might be granted here given the situation and what happened with Valve.

The creator makes it clear that the case isn't an official product, is nothing to do with Valve, and that it doesn't use "any official assets from any game".

An additional caveat is that Jaron notes: "I have not done any extensive thermal tests but no airflow or venting is blocked, and the additional vent provided by the front face plate aperture logo can help provide a little extra airflow. So I don't believe there will be any cooling issues caused by this case."

My main concerns about the original Dbrand Companion Cube were bound up in cooling issues — especially on warm days, in confined spaces like a TV cabinet — and that remains true with this creation.

Still, if you were disappointed that Dbrand's case was cancelled, and you have a 3D printer and want to have a crack at making your own — and you're pretty decent with a soldering iron — you can now do just that.

Other Steam Machine companions

(Image credit: Goldboat90 on Reddit)

Since the Steam Machine came out, there's inevitably been a lot of interest around the device, despite the high price. So, as well as this clone of the abandoned Companion Cube case, what else is out there in terms of Steam Machine customization?

New faceplates (front panels) are popular, and a much easier project than the above case, such as this example on Reddit (see the image above). I really like the slat effort here, and I can see why it's gone down very well with other Redditors (although I'm not so keen on the blue one).

(Image credit: PoisonISSweet666 on Reddit)

What's also quite common is the number of custom small form-factor (SFF) PCs which are DIY takes on the Steam Machine, generally featuring beefier components (and at the same time, inevitably being larger than Valve's PC). 'The Orange Box' as shared on Reddit is a particularly smart example of one such effort with a strong Valve theme, built in a SilverStone SG05BB-Lite case. It has a Ryzen 7 5800X processor inside and an RX 9060 XT graphics card to give this considerably more pep than Valve's gaming PC.

There are other custom SFF PCs on Printables and this one from 3DCatt, which looks like a sized-up Steam Machine, has very similar aesthetics to fit your living room nicely, and a finished version someone has completed went very well indeed by all accounts.

Of course, there are issues around these alternatives to the Steam Machine as I explored in detail recently, and downsides to these custom SFF builds – albeit definite positives too, better performance being the main one. A PC that looks as good as The Orange Box doesn't hurt, either, and I'm sure we'll see more of these kind of SFF spins on the Steam Machine going forward.

• Only 34% of organizations say they trust their agents' actions
• 77% of businesses in 'agentic chaos' have deployed agents
• Those organizations are focus on the wrong thing (models and tools)

New Boomi data has claimed while 86% of enterprises have evolved from agentic AI pilots to actual production-level deployment, only 34% trust the actions their agents are taking.

However those trust issues don't stem from model intelligence – Boomi argues that data quality, integrations, governance and controls could be to blame.

The research splits respondents into distinct categories, with the bottom quartile for readiness referred to as experiencing 'agentic chaos'. Among those in agentic chaos, as many as 77% are still moving AI agents into production, highlighting a worrying gap.

AI trust is entirely in the hands of enterprises

With more than three in four of the enterprises in agentic chaos still pushing ahead with their plans, Boomi warns they could face unexpected costs from compliance fines, lost customers and operational downtime.

On the flip side, the top quartile was categorized as being in 'agentic control', and more than half (55%) of them said they're highly confident in their agents' decisions and actions.

Additionally, the report criticizes those in agentic chaos for focusing on the wrong thing, with around half (51%) prioritizing AI model or tool maturity instead of the true causes of distrust.

"Agents can only be trusted to act on data that's been properly activated, connected, and governed, and most companies deployed agents before they did that work," CEO Steve Lucas wrote.

At the end of the day, Boomi's data implies that pressure to demonstrate AI value and ROI could actually be leading to premature deployment before the foundations are in place, leaving organizations to play catch-up in a far more inefficient way.

Robot lawn mowers are growing in popularity, and with good reason. They're getting more advanced in features, easier to set up and use, and can be a huge time- and effort-saver... provided you pick the right model. There are plenty of options to choose from, spanning a range of prices and capabilities, and if you're new to the lawnbot world then it can be tricky to figure out which one you need.

This article is here to guide you through that process. I've pulled together five big questions to help you narrow down your options. And for added expertise, I've enlisted the help of KK Yin, Marketing Director at leading lawnbot brand Mammotion, whose insights you'll find throughout the piece.

#1. Do I need all-wheel drive?

"Everyone has a different environment for their lawn. So the first thing a customer should do is choose which kind of power system they need — for example, all-wheel drive or rear-wheel drive, or some the brands have front-wheel drive," says KK.

Rear-wheel drive will be perfectly fine if you have a relatively even and flat lawn, and will have no issue scaling relatively gentle inclines. However, if you have a very steep or uneven lawn, all-wheel drive (AWD) will help ensure the lawnbot maintains its grip without churning up your grass. Front-wheel drive is really only for flat lawns, and is a less common power system.

AWD tends to be reserved for larger, more premium robot mowers. However, at the start of this year Segway Navimow launched compact AWD model, with the suggestion that it would treat your turf more gently.

All-wheel drive improves a lawnbot's climbing ability (Image credit: Future)#2. Does my lawn have shaded areas?

Next, KK suggests figuring out which navigation method is best suited to the environment. "A customer should look at if they have shaded areas, the house is tall or they have trees in the garden. If they do, I suggest they don't choose an RTK positioning system. They should maybe choose a LiDAR model," says KK.

There are two major navigation systems for robot mowers. RTK, which uses satellites, can work very well if the lawnbot has a wide, uninterrupted view of the sky. However, if there are obstructions — such as those listed by KK — in the way, these can block the signal and lead to the lawnbot getting lost.

LiDAR is the system used by robot vacuums, and is relatively new in the robot mower market. LiDAR builds a picture of its surroundings by bouncing light off objects, so it's not well suited to wide-open lawns with no objects in them.

Broadly speaking, RTK will be a good choice in big, open yards, whereas LiDAR might be a better fit if you have a smaller, more cluttered yard. LiDAR models are often pricier, so if you can get away with an RTK bot, you might prefer to go with that option.

Overhead obstructions can cause issues if the robot relies on satellites for navigation (Image credit: Future)#3. How big is my lawn?

Many lawnbot models are available in a few different iterations aimed at different-sized lawns (typically indicated by a number after the product name). The difference is usually the battery capacity — a bot designed for a larger area will have a bigger battery, enabling it to cover more ground before having to return to its dock to recharge, thus making it more efficient.

"I think most users figure out that in one week they will mow maybe two or three times. So they need to work out how long it will take a product to mow their lawn once," suggests KK. With that information, you can work out if your mower will be efficient enough to keep up with your grass-trimming demands.

#4. Do I have more than one mowing zone?

"The next point, I think, is about multi-zone management," says KK. "Not all people have one big lawn — it's divided by the house, or the garden is split into different areas. That means they need to manage more than one mowing zone."

Pretty much every lawnbot will be able to mow multiple separate lawns, with the ability to create different maps in your app. However, some have more advanced multi-zone capabilities than others. One bot might be intelligent enough to handle a schedule where the lawnbot spends the morning in Zone A and moves to Zone B in the evening, and it might even be able to work out the optimal mowing patterns and cadence.

#5. How much setup do I want to do?

If you end up with a satellite-based lawnbot (see point #2), you might need to decide between a bot that comes with a separate antenna and one that does not. This RTK station is required to make the satellite information more accurate — so satellite positioning on its own might tell you where a bot is to within a meter or two, but add in a separate RTK receiver and it's more like centimeters.

Traditionally, you'd have this receiver in your own back yard. This adds an extra level of complexity to setup — the reciever needs to sit high up, with direct line of sight to satellites in the sky, and it also needs a power source. However, increasingly, we're seeing lawnbots that use a big, centralized RTK receiver that covers a large area. Mammotion calls this NetRTK. It's definitely a more low-effort option.

Some RTK lawnbots require you to install a separate receiver in your yard (Image credit: Future)

There are further features to look out for if you want the most straightforward setup. Some modern robot mowers have cameras and AI features that enable them to instantly create a map of their location, so you can just pop it on the floor and it'll be able to start mowing pretty much instantly. KK suggests these kinds of features are more of a nice-to-have, and advises focusing on the first four points first.

Reviews round-up

We've tested plenty of excellent lawnbots at TechRadar. Here's a taster of some of our favorites — tap the 'View details' button for more information, plus a link to our full reviews.

Mammotion LUBA 3 AWD 3000

Read our full review

Mammotion Yuka Mini Robot Lawn Mower

Read our full review

Mammotion YUKA Mini 2 1000 Robot Lawn Mower with LiDAR

Segway Navimow i210E LiDAR Pro

Read our full review

Segway Navimow X3 Series

Read our full review

TerraMow V1000 Robot Lawn Mower

Read our full review

ANTHBOT Genie Smart AI Robot Lawn Mower

Read our full review

Eufy E15 Robot Lawn Mower

Read our full review

Mammotion LUBA 2 AWD

Read our full review

As part of Best Buy's Black Friday in July sale, the price of the Apple MacBook Air 13-inch (M5) has dropped to $1199 (was $1299).

Powered by Apple's M5 chip, it combines a 10-core CPU, 16GB of memory, and a 512GB SSD in a lightweight design. For my money, with the extra RAM and storage this makes it to model to choose if you're buying a MacBook Air.

It's an excellent choice for students, professionals, and anyone looking for a premium everyday laptop that's lightweight and loaded with long-term value - especially given Apple recently announced price increases across all its laptops and tablets.

Today's best MacBook Air deal

13.6-inch 2560 x 1664 display, Apple M5 processor with 10-core CPU and 8-core GPU, 16GB memory, 512GB SSD, two Thunderbolt 4 ports, backlit keyboard.View Deal

Should you buy it?

✅ Buy this deal if...

You want a lightweight premium laptop with strong everyday performance, and enough memory and storage for years of work, study, and creative tasks. Battery life impressed us during testing, lasting around 15.5 hours of web use and comfortably getting through a full working day on a single charge.

❌ Skip this deal if...

You need a larger display, more ports than Thunderbolt 4 provides, or dedicated graphics for demanding professional workloads and modern PC gaming. In that case, check out the MacBook Pro instead.

Why we recommend it

In his review, our laptop expert Lance said the latest MacBook Air was "the best ultraportable I've ever used". He noted it delivers an outstanding balance of performance, battery life, and portability, handling everything from everyday productivity to photo editing, 8K video editing, and even gaming without feeling out of its depth.

Despite its slim, fanless design, he found it surprisingly difficult to push the M5 chip to its limits, even with demanding creative applications and heavy multitasking.

The 10-core CPU, 8-core GPU, and 16GB of memory provide plenty of performance for work, creative projects, and everyday computing, while the upgraded 512GB SSD delivers faster storage than previous entry-level MacBook Air models, helping applications launch quickly and improving file transfers.

At just 2.7 pounds, it's easy to carry every day, while the premium aluminum chassis feels exceptionally solid despite its lightweight design.

Price Context & Historical Value

Although the $100 discount isn't huge, deals on Apple's newest MacBook Air models are relatively rare - especially with Apple recently hiking prices across all its devices. With the upgraded 512GB base storage and M5 processor, Apple's latest ultraportable is a must-have.

The Catch: What to know before you buy

This configuration offers excellent everyday performance, but it isn't designed for intensive 3D rendering, high-end gaming, or workloads that benefit from dedicated graphics.

Connectivity is also limited to two Thunderbolt 4 ports, so you may need a hub or dock if you regularly connect multiple wired accessories.

• Shop all MacBooks at Best Buy

• Software support for the Pixel 6 and Pixel 6 Pro ends soon
• The Android QPR1 will be the final update for these phones
• They were first launched by Google back in October 2021

If you're rocking a Google Pixel 6 or Pixel 6 Pro, launched in October 2021, then your five years of software updates are coming to an end: Google has confirmed that these devices aren't going to get access to the latest Android QPR2 Beta 1.

"Pixel 6 and 6 Pro users will not be getting this OTA since the device will reach End of Life and Android 17 QPR1 beta series will be the last for these devices," says Google in the release notes of the latest beta, via Android Police.

That means no more beta updates for the Pixel 6 and 6 Pro, with the final stable software release officially Android 17 QPR1, set to be part of the September Pixel Drop. That should land just before the five-year anniversary of these phones in October 2026.

As with every end-of-life situation like this, your handset isn't suddenly going to stop working. However, it will no longer get new features or security updates — the more time passes, the more vulnerable it will be to bugs and malware attacks.

Apps may start slowing down and misbehaving, and the recommended course of action is to upgrade as soon as possible. Google is all set to unveil the new Pixel 11 series on Wednesday, August 12, so maybe you could treat yourself to one of those handsets.

There's life in the old phone yet

The Pixel 6 Pro (Image credit: TechRadar)

You've got options for what to do with your old phone. Your network operator may recycle it for you and even give you some cash back (check the website for details), but if not, you can find numerous third-party services that will — just run a web search for them.

You can also repurpose your old Pixel 6 or Pixel 6 Pro and turn it into something else. These ideas will all work without needing regular Android software updates:

• Dedicated e-reader — get the Kindle app (or something similar) up on your old phone and you can keep it exclusively for getting through your e-books.
• Car dashcam — get your old phone mounted on your car dashboard and plugged in, install an app such as Droid Dashcam, and you've got yourself a dashcam.
• Alternative webcam — sticking with cameras, your old phone probably offers a higher resolution and better angles than your current webcam. Here's how to set it up.
• Portable power bank — both the Pixel 6 and 6 Pro can work as chargers for other phones when connected via USB-C (they can also charge smaller devices wirelessly).
• Wi-Fi extender — if part of your home can't get Wi-Fi, use your old Pixel as a Wi-Fi extender for your router via the hotspot option (under Network and internet in Settings).
• PC console — with an app like Touch Portal installed, you can get a Stream Deck-like experience for gaming and streaming with your old Android phone.

There's no excuse for leaving it gathering dust in a drawer, or just throwing it in the trash (and adding to our growing e-waste problem). And you've still got a couple of months to decide what you'd like to do.

• Philips is rumored to be launching a new camera-based Hue Sync device for its TV backlights
• It removes the need for a pricey Hue Play HDMI Sync Box
• The 'Sync Screen' would be the first camera for the Philips Hue Play system

Philips Hue could be working on a new device for syncing your TV’s color palette to your backlights, giving you another option for illuminating your entertainment setup

Fabian of Hue Blog attended an online presentation hosted by Philips, and spotted a new Hue Sync device with an integrated camera that looks strikingly similar to rival devices like the Nanoleaf 4D.

The new gadget could be called ‘Sync Screen’, and may be unveiled at IFA 2026 in September. We don't know how much it will cost yet, but considering Philips Hue is a premium brand, it's likely the new device will sit in a higher price bracket than its competitors.

Just like other camera-operated TV LED lights, Philips’ version mounts to the top of a TV or monitor, and captures the color movements of the movie, show, or game on the screen. This movement is then processed and synced with backlights mounted behind the display.

From the image shared by Hue Blog, it seems that the Philips Sync Screen camera will only have one lens, but will likely be a fish-eye one to capture your entire TV screen — just like those used by rival devices.

You will almost certainly still need a Philips Hue Bridge to link the lights and camera to one another, but because the system camera-operated, you won't need a Philips Hue Play HDMI Sync Box or Hue Sync TV app subscription, which could make it a more tempting proposition.

The Nanoleaf 4D TV is just one of the Philips rivals that use cameras to sync TV content with smart backlights (Image credit: Nanoleaf)

As mentioned, Philips isn’t the first smart home giant to offer TV lighting gadgets like this, which aim to make your TV and gaming experiences more immersive by extending the colors from the screen onto the surrounding walls.

In addition to the Nanoleaf 4D light strips, the Govee StarPal Pro and the more recent Govee TV Backlight 3 Pro models are all camera-operated — the latter having a dual-camera system to capture more of your TV screen or monitor.

Philips has had quite an eventful 2026 already. In March the company unveiled its new OLED TVs, which are also the first native models for Dolby Vision 2, as well as its beautiful yet expensive ceiling light that mimics natural daylight. We’re just a mere two months away from IFA 2026, and I think the best is yet to come.

The conversation about AI in space keeps arriving at the same image: a floating supercomputer processing the world's information from 400 miles up. It’s a compelling narrative, but there’s a gap between what companies are hoping to build and what is being built today.

Today, satellites run on fixed power budgets measured in watts with strict constraints. Bandwidth is scarce enough that every byte reaching the ground has to earn its place.

Those limits push the field toward architecture that looks more like a nervous system, than a data center – lightweight models running onboard that interpret sensor data in near real time and convert observations into structured events. The event reaches the ground.

The raw pixel doesn’t.

From hours to minutes

In a conventional Earth observation pipeline, a satellite captures an image, downlinks it to a ground station, ground systems process the raw data, and the result reaches whoever needs it. Best case that happens in hours, but often it’s closer to a day.

For a disaster response team managing a flood in a low-lying river delta, or a conservation authority trying to locate the origin point of a wildfire in a remote national park, that day comes at a cost.

A satellite running inference onboard changes the math. Detection happens in seconds. What gets downlinked is a position, timestamp, or risk score. The bottleneck shifts from the space segment to the ground distribution, which is a comparatively manageable problem.

The use cases where this matters most are not the visible disasters people are watching. They’re the methane leak on a pipeline with no weekly inspection schedule, an oil spill beyond the reach of coastal patrols, a wildfire that began in a remote area before anyone had reported smoke. Onboard inference turns a passive imaging asset into an early warning system.

What orbital constraints teach edge architects

The tradeoffs being resolved in orbit are an extreme version of the same constraints facing any organization deploying AI outside a well-provisioned data center.

Cloud-native AI development often has a back up plan: when the model is too large add compute; when bandwidth is constrained, increase it; when latency is a problem, move the processing closer. In orbit, none of these options exist. You build within the envelope, or the system doesn’t function.

The result is a forcing function that enterprise architects rarely encounter at the same level. Industrial IoT deployments face intermittent connectivity. Autonomous systems can’t afford round-trip latency to a central server at decision time.

The shift from 'send everything, process centrally' to 'process locally, transmit what matters' is happening across multiple industries. Space is where that shift ran without a safety net.

The bandwidth math

The data reduction numbers transmitted in real time is not just 80-90 percent. Once processing happens on the spacecraft, the reduction for the real-time layer exceeds 99 percent. This is semantic compression. The satellite sends the meaning of what it saw, not the measurement it produced.

A conventional operator downlinking hundreds of terabytes of raw imagery daily is paying bandwidth cost for data that largely contains nothing of interest. With onboard inference, what's transmitted in real time is a structured detection event: a position, a timestamp, a risk score, and perhaps a small compressed image. That is hundreds of kilobytes, not terabytes.

An operator downlinks only what warrants examination, rather than blindly dumping the full data stream.

Architecture decisions that preview what's next

A model making decisions before a human is in the loop carries different requirements than one generating recommendations for human review. Ambiguity tolerance is lower. Inference behavior needs tighter scoping. This is the same conversation that medicine and finance have been having for years.

AI-assisted diagnostics and accountability distributed across platform, model, training data, and end customer rather than concentrated in a single layer. The space industry is joining a conversation other sectors have already been having for years.

In a cloud environment, model size and computational efficiency are optimization targets. Meaning they are important, but secondary to capability. In a constrained orbital environment, they are the primary design constraint from which everything else follows. A model that cannot run within the available compute envelope is not a model that gets deployed. There is no option to add a larger instance.

A maritime patrol aircraft that previously ran random vessel inspections now works from a ranked list of targets with risk scores attached. Some alerts will be false positives which is a physical reality of any probabilistic system. But the aircraft's operational effectiveness improves substantially compared to random patrolling or no monitoring at all. The AI narrows the search.

The scaling problem is familiar

One satellite running an onboard model is a proof of concept. A constellation of hundreds running distributed inference is a different infrastructure problem as orbital AI scales.

Centralized orchestration becomes the bottleneck when constellations grow. Every decision can’t route through a ground station. Distributed inference is a requirement. Enterprise architects hit the same wall when a pilot deployment expands to thousands of edge nodes. The centralized model that worked in development becomes the thing that breaks in production.

The cloud infrastructure analogy supports this. Nobody builds a data center before launching an application. The pattern is shared infrastructure, with control at the model, mission logic, and decision layer. Those can be sovereign regardless of who owns the underlying compute.

A design principle worth carrying

It’s hard to develop constraint-based thinking in environments where adding compute is always on the table. The organizations that have built it tend to have faced conditions where it wasn’t.

The strategic advantage in edge AI over the next decade will not just be measured in the amount of compute available. It will also be measured in code deployed to the right place in the stack. Satellites are running that experiment first.

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The TobenONE 11-in-1 USB-C Docking Station is on sale for $83 (was $130) at Amazon, a saving of $47.20.

Thanks to its programmable shortcut keys, dual 4K display support, and a wide selection of ports, it will be especially appealing to productivity-focused Windows users.

This docking station supports dual 4K 60Hz displays, 100W USB-C Power Delivery, Gigabit Ethernet, three 5Gbps USB ports, SD and microSD readers, programmable shortcut controls, audio jack, and dual HDMI connectivity for Windows laptops.

Dual HDMI 4K@60Hz, programmable shortcut keys and volume knob (Windows), 100W USB-C Power Delivery input, Gigabit Ethernet, USB-C and USB-A 5Gbps ports, SD/microSD card readers, 3.5mm audioView Deal

Should you buy it?

✅ Buy this deal if...

You want a single USB-C dock that connects dual 4K monitors, wired networking, storage devices, and accessories while adding programmable shortcut controls to speed up repetitive Windows tasks.

❌ Skip this deal if...

You primarily use a Mac and need two independently extended external displays. The customizable shortcut features are also designed specifically for Windows systems.

Why we recommend it

In his review, our expert Mark said: "This is a clever concept, and for those with a video or photo editing workflow, it's genuinely useful."

Most budget USB-C docks follow a familiar formula, but this model takes a different approach by integrating four programmable shortcut keys and a multifunction rotary dial alongside its standard connectivity.

The controls can be configured via the TobenONE software to launch applications, take screenshots, control media playback, mute audio, or trigger custom key combinations.

Dual HDMI outputs support two independent 4K 60Hz displays on compatible Windows laptops with DisplayPort 1.4 MST support, making it well suited to coding, design, trading, and other multi-monitor workloads.

Gigabit Ethernet provides a reliable wired connection, while three 5Gbps USB ports, SD and microSD card readers, and a USB-C Power Delivery input supporting up to 100W charging complete a versatile desktop setup with a single cable.

Price Context & Historical Value

Amazon has reduced the TobenONE from $129.99 to $82.79, a 36% discount that will save you $47.20. This is as low as the dock has ever been sold, so if you've been planning to add a docking station to a home office or hybrid work setup, this is a great time to buy.

The Catch: What to know before you buy

The programmable buttons and smart control knob require the TobenONE software and are available only on Windows. Mac users can still use the dock's core functions, but customization is limited, and macOS mirrors content across two external HDMI displays rather than supporting two independently extended screens.

Our biggest criticism was bandwidth. Although compatible with USB4 and Thunderbolt hosts, the dock limits its USB data ports to 5Gbps, so fast external SSDs won't reach their full performance. It also ships without a power adapter, meaning you'll need to provide your own if you want laptop charging.

• Shop all docking stations at Amazon

The 2026 World Cup is already under way. Billions of eyes are on the pitch. The story that matters to anyone running a supply chain, though, is playing out in warehouses, customs terminals and distribution centers spread across three countries.

For the first time, the tournament spans three host nations: the United States, Canada and Mexico. That means millions of product lines, thousands of supplier handoffs and cross-border compliance requirements across three distinct regulatory environments, all compressed into a window with zero tolerance for error.

The operational scale of this tournament is without precedent.

Now that the group stages are live, the pressure on supply chains is real and immediate. The lessons surfacing are worth paying attention to, because they apply well beyond sport.

A live packaging stress test

Official merchandise for an event of this scale moves across multiple countries, customs jurisdictions and retail channels simultaneously. Product identification has to work at every stage of that journey, from the manufacturer's floor to the stadium vendor's shelf. The label that cleared customs in Los Angeles may face entirely different requirements in Toronto.

The deeper problem is structural. Today's supply chains still largely operate as disconnected islands. Each site, supplier, co-packer and carrier maintains its own systems and repeatedly re-enters the same product and compliance data. That fragmentation creates built-in waste at every handoff: redundant setup, duplicate records and inconsistent label versions. Under normal conditions, these inefficiencies are costly but can be masked by day-to-day operations. Under the pressure of a live global tournament, they become critical.

Demand shifts are happening in real time. A host city that reaches the knockout stages sees fan merchandise demand surge overnight. Supply chains built on static, batch-processed labelling data are finding they cannot respond at that pace.

The speed of these shifts can be surprisingly tangible. In Atlanta, shortly after the Spain-Cape Verde match, I walked through the airport and was struck by how many people were wearing Cape Verde jerseys. In the space of a few hours, merchandise that had been relatively low-profile had become highly visible, underscoring how quickly demand signals can emerge and spread during a global event.

From labels to live data

What the World Cup is making visible in real time is a shift that has been under way for several years. Product identification is no longer a print-and-forget exercise. It is a live data problem.

The industry is moving from fragmented, internal systems to connected, multi-partner ecosystems. The organizations managing the tournament's supply chain most effectively are those that have made this shift: rather than each stakeholder operating in isolation and recreating the same product and compliance data from scratch, they are working within a shared, real-time environment where information flows seamlessly across systems, suppliers, customers and geographies.

The benefits of this approach are measurable. Organizations that can operate with real-time visibility and trusted data across their extended value chain can reduce delays, prevent errors at source and respond faster when disruption hits. In sectors where production downtime can exceed $1-2 million per hour, that responsiveness is not a nice-to-have but operationally critical.

The cost of disconnected systems

The consequences of siloed product data are well understood in theory. A tournament of this scale is making them visible in practice.

When product data does not flow seamlessly across sites and trading partners, the failure surfaces in predictable ways. Rejected shipments at customs. Compliance failures that stall distribution. Production downtime while teams manually reconcile data across systems. Against the backdrop of a global event with fixed deadlines, those failures are not recoverable.

The organizations absorbing those costs right now are those still operating inside the organization perimeter - managing product identification as an internal function rather than a network-level capability. The distinction matters. Supply chain resilience increasingly depends on the ability to coordinate accurate product data across every site, trading partner, and customer - creating a connected ecosystem in which product identity can be shared, trusted, and acted upon seamlessly.

What happens after the final whistle

Connected, network-driven approaches to product identification are no longer a future aspiration. The World Cup is demonstrating their value in real time, at a scale most supply chains will never encounter but from which every supply chain can learn.

The direction of travel is clear. Organizations that can rapidly adapt labelling requirements across plants and partners, share trusted product data in real time and eliminate the manual rework that comes with disconnected systems will outperform those that cannot. That is as true in retail, pharma and automotive as it is in a stadium in Los Angeles.

The World Cup will be over in a matter of weeks. The infrastructure challenges it is exposing will still be there when it ends. The organizations that use this moment to address those fundamentals will be better placed for whatever high-pressure deadline comes next.

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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.

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Minisforum has two slim mini PCs discounted right now, and which one to buy comes down to how much CPU you actually need.

As part the the Black Friday in July sale, the Minisforum UM760 Slim is $600 (was $680) at Best Buy, while the beefier Minisforum UM870 Slim is $700 (was $800).

Today's top Minisforum mini PC deals

AMD Ryzen 5 7640HS (6-core/12-thread, up to 5.0GHz) with Radeon 760M graphics. 16GB RAM and a 512GB SSD, with dual M.2 slots for expansion. HDMI 2.1 and USB4 output supporting up to 8K@60Hz, Wi-Fi 6E, and Bluetooth 5.3.View Deal

AMD Ryzen 7 8745H (8-core/16-thread, up to 4.9GHz) with Radeon 780M graphics. 16GB RAM and a 512GB SSD, expandable to 96GB RAM and up to 4TB storage across dual M.2 slots. Triple display support via HDMI, DisplayPort, and USB4, dual 2.5G LAN, Wi-Fi 6E, and Bluetooth 5.3.View Deal

Should I buy it

Which to choose

✅ Choose the UM760 Slim if...

✅ Choose the UM870 if...

Workflow

Your workload is mostly everyday desktop use

You want real multi-core headroom and better integrated graphics

Connectivity

You only need to connect via HDMI and USB4

You prefer wider connectivity including dual LAN

Why we recommend these mini PC deals

Both machines share the same slim Minisforum chassis and general design philosophy, but the CPU gap between them is real rather than cosmetic.

The Ryzen 5 7640HS in the UM760 is a capable 6-core, 12-thread chip that handles everyday desktop work, browsing, and light multitasking comfortably.

The Ryzen 7 8745H in the UM870 steps up to 8 cores and 16 threads with a newer Radeon 780M GPU, and independent benchmarking has shown its multi-core performance landing in the upper third of current mini PCs, without thermal throttling under sustained load.

That extra core count and the RDNA 3-based Radeon 780M make the UM870 the meaningfully better pick for anyone doing content creation, code compiling, or casual 1080p gaming — reviewers have measured the 780M hitting 75-123 FPS in popular titles at 1080p, a tier above what the 760M in the UM760 typically manages.

Connectivity is another real difference, not just a spec-sheet footnote: the UM870 adds dual 2.5G Ethernet ports and triple display output, compared to the UM760's single display path over HDMI/USB4.

If you're setting either of these up as a home server or a multi-monitor workstation, that difference in networking and display support is worth the extra $100 on its own.

For more top picks, see our guide to the best mini PCs.

What to know before you buy

Something worth flagging on both: the blue status LED on Minisforum's UM-series machines has drawn complaints from some owners as distractingly bright in a dark room.

And if you plan to run Linux on either, the bundled Wi-Fi cards have had driver support issues reported by some reviewers — a wired Ethernet connection sidesteps that entirely.

More mini PC deals

Powered by AMD Ryzen AI 9 HX 370, this Geekom mini PC combines 32GB DDR5 memory, a 1TB SSD, WiFi 7, USB4, 8K output, and 80 TOPS AI performance for demanding workloads.

Read our full reviewView Deal

The GMKtec K16 mini PC delivers Ryzen 7 7735HS performance with 32GB LPDDR5 RAM and a 1TB SSD. Featuring OCuLink eGPU support, triple-display output, dual 2.5GbE LAN, Wi-Fi 6E, and USB4, it’s a versatile compact system for gaming and creative workloads.

Read our full reviewView Deal

• See the full Black Friday in July sale at Best Buy.

If you’ve done any shopping for marketing automation tools these days, you’ve probably noticed they all claim to be “powered by AI.” Apologies for splitting hairs, but that’s just not true.

A significant portion still rely primarily on rule-based automation, work identically to the platforms they replaced, triggering if/then workflows created by human engineers years ago. Give their systems an edge case to parse and you’ll soon see them send an inappropriate email, crash, or output some stale nonsense that wouldn’t matter to any living person.

Same label. Same old architecture. The problem? A rule bottleneck.

Traditional marketing automation relies on knowing rules ahead of time. A lead hits a score? Send an email. A prospect completed behaviors A, B, and C? Trigger sequence Y. Wait Z days, send the follow-up email.

A rules engine can execute these commands flawlessly – but it can only execute what it knows. When a situation arises that doesn’t fit the rules, what do you do? You update the rules.

Why this matters

Marketing is messy. Prospects take unpredictable journeys, trends come and go overnight, and audiences who loved your message last week don’t care about it this week. But rule-based systems can only improve when given new rules to fire. Engineers can’t possibly keep writing rules faster than the world changes.

The industry has been papering over this problem with AI buzzwords. Sprinkle some Neural Network magic on a rule engine, and suddenly you’ve got yourself an “AI platform.” Engineers who look past the updated sales brochures still find the same good old-fashioned if/then statements, patched up with trendy new nomenclature for the latest round of funding.

The difference between legacy automation and true agentic AI is that true agentic AI won’t just patch up the last generation of marketing automation tools – it will replace them. Agentic AI isn’t defined by capabilities so much as by the way decisions are made.

Rules engines ask, “what rule should fire next, given this input?” Agents ask, “what action should I take to get closer to my goal?” This is subtle but critical. Agent theory holds that the system knows its goal, its current context, and a list of available actions it can take.

Based on those three pieces of information, it can reason as to which action will bring it closer to accomplishing its overall objective. This extends far beyond executing canned responses - it’s deciding what to do.

Agentic systems maintain goals, reason over available actions, invoke tools, evaluate intermediate results, and adapt their plans as new information becomes available. The architecture is fundamentally iterative rather than purely reactive.

You know where this is going.

An agent can adapt if a campaign stops performing. It can coordinate with other agents who manage different subsets of that workflow. And it can do so without a human engineer going back into the system to rewrite the rules every time the world changes. The system manages its goals.

Why specialization matters

One important architectural decision that separates good agent implementations from the rest is specialization. Should you build one big generalist AI system to handle everything or many specialized agents, each performing their own task?

Specialization comes up often in discussions around AI, from medical doctors to Renaissance men. There is broad utility in generalization, but singular accuracy in specialization. The family doctor can handle any symptoms you throw at them. But when you need to be absolutely certain about your diagnosis, you see a specialist.

That’s because specialists aren’t smarter than the generalist – they’re just trained on narrower data. Likewise, generalist AI models aren’t going to produce great results for highly specific use cases. OpenAI’s models can write you a marketing strategy. They can craft creative assets. But they can’t produce marketing assets that:

• Fit the pixel ratio requirements of a given publisher
• Match your brand’s color palette
• Align with your target audience’s emotional affinity profile
• Incorporate mentions of trending topics from the previous day

They can’t do all of those at once, either. And you shouldn’t expect them to. For hard problems with specific solutions, you should build specialized agents (sometimes called “agent crews”) that own a narrow subset of your workflow.

One crew might specialize in strategy generation, while another focuses on creative writing. One might select publishers while another analyzes performance. Separately, these crews create atomic workflows that a generalist system would struggle to manage.

How not hosting your models affects data privacy

There’s another argument for specialized, privately hosted models that isn’t made enough: data privacy.

Whenever you use a public large language model (LLM) to write marketing copy, your data is being uploaded to someone else’s infrastructure. “We don’t use customer data for training” is easy to say but barely offers any assurance. Inputs are still being ingested, processed, stored, and handled according to what that provider’s internal policies dictate.

And those policies can change… most corporate lawyers have never looked at the data use section of public AI providers Terms of Service, let alone dissected it line-by-line.

But what about controls your organization can enforce? Do your developers scrub data for PII before generating content with an LLM? That only works if everyone in your company memorizes your data policies and uses tools responsibly. One rogue employee attaching a spreadsheet full of internal pricing to a prompt breaks your compliance.

But if the model itself is hosted privately, that’s one major source of exposure that goes away. Your data never leaves your infrastructure. There’s no ingestion point to transmit it to a third-party, no training feedback loop that will process it, and no agreement to parse about how that company will handle your data “moving forward.”

Governance is a system property

Because AI in the enterprise has reached a maturity level where governance is a legitimate concern, many teams treat it as a bulk edit at the end of AI-generated content. Have humans review and approve. That’s fine, and many teams require this today. But governance should be built into the system at a fundamental level.

Well-built agents have guardrails at every stage of the decision-making process. That means models that make predictions within set bounds. That means observability that can trace every word generated back to its origin.

That means third-party benchmarking to prove your models perform well against industry standards, not just internal testing. Governance shouldn’t just be applied to outputs – it should be inherent in the architecture.

What enterprise buyers should actually be asking about

Buying criteria for agentic AI will vary by company, but as requests for proposal accelerate to keep pace with innovation in the industry, here are a few considerations every enterprise buyer should ask about:

• Goals vs. rules - Is this system actually agentic? Or is it just automating workflows with AI tools bolted on? The first step is asking vendors point blank what their system does when it encounters data it doesn’t know how to parse. Rules engines will point to specific fallback rules that execute. Agents will talk about reassessing their goal and weighing their available actions until they decide on the next best step.
• Models and hosting - Where are the models hosted? Are they specialized and trained on domain-specific data? This answers two questions at once – vendor capability, as well as data privacy concerns.
• Long-term memory and context - Enterprise agents become dramatically more useful when they retain organizational context over time. Rather than treating every interaction as a new conversation, they can accumulate institutional knowledge, remember previous decisions, and personalize future actions while remaining within governance boundaries. Persistent memory allows agentic systems to improve continuously without requiring engineers to encode new rules after every edge case.
• Hallucination - No current LLM is immune to hallucinations. The important architectural question is how the system detects, bounds, and mitigates them before they affect downstream business processes. Specialists hallucinate less in their domain of expertise. Prediction window guardrails limit how far an AI system can go “outside the data.” Human approval gates before sending anything live catch anything that slips through.
• Governance / auditability - Can the system provide traceability for every output it generates? Is the system’s accuracy benchmarked against a third-party, or just internally verified?

The economic case for getting this right

There's an additional argument that often gets overlooked in discussions focused on capability: cost structure.

Token-based pricing from large model providers creates a fundamentally unpredictable cost model for enterprise deployments. Every question, every generation, every iteration costs tokens — and iterating toward an acceptable output for a complex campaign task can consume a significant volume of them.

Enterprise subscriptions impose usage caps that create their own operational friction. The more AI-dependent your workflows become, the more acute this pressure grows.

Organizations that own and host their own specialized models are not subject to this dynamic. There is no token meter running. The economic relationship is closer to infrastructure than to a metered service - you bear the cost of building and maintaining the system, and in return you have predictable marginal cost. For organizations at scale, that math changes substantially.

Don’t fall victim to marketing speak

AI marketing platforms will continue to flood the market with AI-sounding languages attached to rules engines. But for enterprises who truly want to deploy agentic AI, there’s a far better solution. Domain specific, privately hosted agents that don’t leave your organization exposing itself to risk.

As agentic systems mature, the organizations that differentiate between genuine autonomous architectures and AI-enhanced workflow engines will be better positioned to capture sustainable competitive advantage.

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Lenovo Yoga Slim 7x Gen 11: Two-minute review

The Lenovo Yoga Slim 7x Gen 11 is an ultra-thin laptop with a strong spec that makes it more than capable for workday use.

It looks smart, thanks to its minimalist form. All sides are flat, and the Cosmic Blue colorway is subtle yet helps to distinguish it in this largely monochromatic sector.

The build quality is excellent. The body is as premium as it comes, feeling very solid but also light and smooth to the touch. In fact, it could probably lay claim to having one of the best laptop constructions around right now. Allied to that lightness is its thinness, which is quite remarkable, although the thick rear foot underneath somewhat negates this aspect, which is a shame. What’s more, these truncated dimensions mean that you get three ports, all of which are USB-C.

(Image credit: Future)

With the new Snapdragon X2 Elite chip equipped, the Yoga Slim 7x Gen 11 is a capable all-round performer. It handles multiple browser tabs and 4K streaming with ease, as well as basic productivity. While it's not necessarily aimed at gamers and lacks a dedicated GPU, it's even capable of some light gaming; it managed to run Cyberpunk 2077 in a playable state, although don't expect top-tier performance. Heat and fan noise are generated when such workloads are conducted, but neither is too disruptive.

Another advantage of the X2 Elite chip is the improved efficiency. This is evident in the battery life of the Yoga Slim 7x Gen 11, which is one of its most impressive aspects. It lasted close to a full day when I ran a movie on a continuous loop, which ranks among the best in class. It’s also quick to charge, taking about two hours to do so.

The 2.8K display in my review unit was sharp, bright, and vivid, although it was prone to showing reflections at times. For the most part, though, these were only apparent in the most unfavorable of lighting conditions.

The keyboard is fairly easy to use, thanks to the ergonomic keycaps and their spacing. However, presses are a little heavier than you might expect, so this can take some getting used to if you’ve come from a board that requires a light touch. The touchpad is large and smooth, which makes for easy navigation, while taps and clicks are responsive and provide good feedback.

The Yoga Slim 7x Gen 11 commands a reasonably high price tag, but considering its spec and quality, it makes for good value compared to some of its less premium-feeling rivals. It might lack the ports and graphical performance of other laptops, but it’s a fine choice if portability and build quality are among your top priorities.

Lenovo Yoga Slim 7x Gen 11 review: Price & availability

(Image credit: Future)

• Starts from $999.99 / £945 / AU$1,999
• Available now in one colorway
• Reasonably expensive for the spec

The Yoga Slim 7x Gen 11 starts from $999.99 / £945 / AU$1,999 and is available now in one color: Cosmic Blue. This base spec features a Snapdragon X2 Plus X2P-42-100, 16GB of RAM, 512GB of storage, and WUXGA display. This isn't exactly cheap, and costs escalate quickly as you move up the model range. The top-spec unit, which features 32GB of RAM, 1TB of storage, and a 2.8K display, costs $2,299.99 (about £1,700 / AU$3,300).

If you’re looking for another thin and light laptop, then the HP OmniBook 7 is a very strong alternative. I was very impressed with its performance when I reviewed it, and its only missteps are the port placements and slightly harsh keyboard. It's base model is slightly cheaper than the Yoga Slim 7x Gen 11's in the US, and significantly so in the UK.

There’s also the Asus Zenbook A14, which is incredibly thin and light, and has one of the best battery lifespans around. However, it lacks the build quality and display brightness of the Yoga Slim 7x Gen 11, yet it’s significantly more expensive when comparing like-for-like models.

• Value score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Specs

Base spec

Max spec

Price

$999.99 / £945 / AU$1,999

$2,299.99 (about £1,700 / AU$3,300)

CPU

Snapdragon X2 Plus X2P-42-100 Processor (4.04 GHz)

Snapdragon X2 Elite X2E-80-100 Processor (4.70 GHz)

Graphics

Integrated

Integrated

RAM

16GB LPDDR5X

32GB LPDDR5X

Display

14-inch WUXGA (1920 x 1200), OLED, Glare, HDR 500 True Black, 100% DCI-P3, 400 nits, 60Hz

14-inch 2.8K WQXGA+ (2880 x 1800), OLED, Glare, HDR 1000 True Black, 100% DCI-P3, 500 nits, PureSight Pro, 120Hz

Storage

512GB SSD M.2 PCIe Gen4

1TB SSD M.2 PCIe Gen4

Ports and Connectivity

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

Battery

70Wh

70Wh

Weight

2.6lbs (1.2kg)

2.6lbs (1.2kg)

Dimensions

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

Lenovo Yoga Slim 7x Gen 11 review: Design

(Image credit: Future)

• Extremely thin
• Premium materials
• Lacks ports

The minimalist design of the Yoga Slim 7x Gen 11 is appealing, while the dark Cosmic Blue colorway rescues the unit from looking dull. The rounded sides and corners help to soften its appearance. Most sides are flat, with no extraneous bulges, save for the central camera bezel that extends beyond the rest of the lid. However, the overhang this creates actually helps you to open the lid, which is a thoughtful touch.

The body is one of the best I’ve seen in a laptop. It looks and feels premium, rivaling what the best MacBooks have to offer. It’s incredibly smooth and seems very durable. There’s some flex to the base and the lid, but they’re remarkably solid when you consider just how light the whole unit is. The lid is also very stable when open, and adjustments are smooth and easy to make.

(Image credit: Future)

Just as impressive is the thinness of the Yoga Slim 7x Gen 11. It’s certainly one of the thinnest 14-inch laptops I’ve seen, although it’s a shame the bulky bar underneath, which acts as the rear foot, somewhat undermines this aspect. This is no doubt to improve airflow from the underside vent, which is more exposed than many others.

To achieve this thinness, port selection has been compromised. There are only three ports, all of which are USB-C, which means you’ll need plenty of adapters (or a hub) if you’re going to be connecting lots of peripherals. The saving grace is that all ports have 40Gbps transfer rates, support the Power Delivery standard, and can be used to charge the laptop itself. They’re also split across both sides of the unit, which improves practicality.

On the right you’ll also find the power button and a button for disabling the webcam, which might disappoint those hoping for a physical privacy shutter.

• Design score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Performance

(Image credit: Future)

• Capable everyday performance
• Light gaming possible
• Keys are a little heavy

Lenovo Yoga Slim 7x Gen 11 benchmarks

3DMark: Night Raid: 40,637; Fire Strike: 7,911; Steel Nomad: 1,051; Solar Bay: 21,754; Solar Bay Unlimited: 22,518; Solar Bay Extreme: 2,491; Solar Bay Extreme Unlimited: 2,526
Geekbench 6.5: Multicore: 20,362; Single-core: 3,815
Cinebench R23: Multi Core: 13,071; Cinebench R24: Single Core: 90; Multi Core: 723
Crossmark: Overall: 1,896; Productivity: 1,703; Creativity: 2,171; Responsiveness: 1,742
Passmark Overall: 7,821.2; CPU: 30,341.4; 2D Graphics: 594.2; 3D Graphics: 6,632.9; Memory: 3,429.5; Disk: 51,620.7
BlackMagicDisk: Read: 5,214MB/s; Write: 4,947MB/s
HandBrake 4K to 1080p: 63.68fps
Total War: Warhammer III: 1080p, Medium: 43fps
Total War: Warhammer III: 1800p, Ultra: 11fps
Battery Life (TechRadar movie test): 23 hours and 30 minutes

For everyday use, the Yoga Slim 7x Gen 11 is certainly fast enough. It handled many of the tasks I threw at it with aplomb, from basic productivity to 4K streaming. With the 32GB of RAM in my unit, it also handled multi-tab web browsing with ease.

No doubt a lot of this performance comes from the new Snapdragon X2 Elite chip inside the Yoga Slim 7x Gen 11. Qualcomm claims this chip is a stronger performer than Intel equivalents, and based on our testing this appears to be true. For instance, it beat the Geekbench Single Core benchmark score of the HP OmniBook 7, with its Intel Core 5 220H, by over a thousand points, and doubled its Multi Core score.

When it comes to gaming, the Yoga Slim 7x Gen 11 was less competent, but that's hardly a surprise — it's not really aimed at wooing gamers, given it doesn’t have a dedicated GPU. However, I still managed to run Cyberpunk 2077 in a playable state, albeit with ray tracing disabled and resolution scaling set to Balanced. It wasn’t exactly a smooth experience, so it's not likely to be for you if you're in the market for a dedicated gaming laptop, but if you want a productivity powerhouse with just a little casual gaming on the side, it should suffice.

As expected, the fans can be heard whirring away in such cases, but the noise isn’t too disruptive. Heat is also generated, and temperatures can get quite high in places, although thankfully these hotspots are confined to the top and underside of the base.

(Image credit: Future)

The display in my review unit was crisp, thanks to its 2880 x 1800 resolution, and the high levels of brightness ensure content is viewable in most conditions. There were times when reflections showed on screen, but only when the lighting in my environment was particularly unsuitable.

The keys in the Yoga Slim 7x Gen 11 adopt Lenovo’s trademark shape, which are very ergonomic. The generous spacing between them also makes for a forgiving typing experience. However, they’re a little heavier and travel further than you might expect, which might throw off those who prefer a light and snappy response. The touchpad is suitably large and incredibly smooth, making it conducive to cursor navigation. Taps are responsive without being overly sensitive, and clicks are easy to perform and provide plenty of feedback.

• Performance score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Battery life

(Image credit: Future)

The battery life of the Yoga Slim 7x Gen 11 is very impressive. No doubt that aforementioned new Snapdragon chip plays a major part here, due to its improved efficiency. It managed to last just under 24 hours in our movie playback test, putting it ahead of many other laptops in this sector. This may be partly due to that aforementioned chipset, which Snapdragon claims brings improved efficiency. Charging is a quick affair, too, as it took about two hours to fully replenish.

However, there are those that can match and even outlast it, such as the HP OmniBook 7, which managed 26 hours in the same test. And then there’s the Asus Zenbook A14, which managed a staggering 28 hours and 25 minutes.

• Battery life score: 4.5 / 5

Should I buy the Lenovo Yoga Slim 7x Gen 11?Scorecard

Attributes

Notes

Rating

Value

It seems expensive, but when you consider its design and spec, it’s reasonable.

4 / 5

Design

I couldn’t help but marvel at its thin and premium design. The port selection is disappointing, though.

4 / 5

Performance

Great for everyday tasks, and it can even handle light gaming. The display is vivid if a little reflective, and it can get hot under load.

4 / 5

Battery life

Among the best in class, it really can last a full day. It’s quick to charge, too.

4.5 / 5

Total Score

The Yoga Slim 7x Gen 11 is great if you need a thin yet powerful Windows machine that lasts all day unplugged. A few areas are less impressive, but it’s still a good value proposition.

4 / 5

Buy it if…

You want one of the thinnest laptops around
That bulky foot underneath might spoil things a little, but the Yoga Slim 7x Gen 11 is still incredibly thin.

You want a premium design
It looks premium, and the body feels exquisite and hardwearing. It’s also quite light.

Don't buy it if…

You want lots of ports
There are only three ports on board, and all are USB-C, so you'll need a hub if you have lots of connections to make.

You want light keys
Although they’re comfortable to use, the keys on the Yoga Slim 7x Gen 11 are a little heavier than on many other laptops, which might take some getting used to.

Lenovo Yoga Slim 7x Gen 11 review: Also consider

HP OmniBook 7 14-inch (2025)
Like the Yoga Slim 7x Gen 11, the OmniBook 7 is an incredibly thin and light laptop. Not only that, I was very impressed with how well it handled all kinds of workloads, and it has a superb battery life to boot. Read our full HP OmniBook 7 14-inch (2025) review.

Asus Zenbook A14
Another portable hero, the A14 is even lighter than the Yoga Slim 7x Gen 11. However, its battery life is even better; in fact, it’s the most enduring laptop I’ve ever tested, lasting a staggering 28 hours in our movie playback test. Its build isn’t as premium as the Yoga Slim 7x Gen 11’s, and its display isn’t as bright, but it’s still a capable machine. Read our full Asus Zenbook A14 review.

(Image credit: Future)How I tested the Lenovo Yoga Slim 7x Gen 11

• Tested for several days
• Used for a variety of tasks
• Plentiful laptop experience

I tested the Yoga Slim 7x Gen 11 for several days, during which time I used it for a variety of tasks, from productivity and browsing to streaming and gaming.

I also ran our series of benchmark tests, designed to assess every aspect of a laptop's performance. I also ran a movie on a continuous loop to test the battery life.

I've been using laptops for decades, and have reviewed a large number of them, from small budget models to large gaming machines.

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

Data center design has been shaped by a familiar set of priorities for years: keep systems available, resilient and predictable in any condition. Just like the electrical grid that powers these sites, they have been engineered to provide a highly consistent service regardless of what happens, even when individual components fail.

This has meant operators build layers of redundancy into power, cooling and network infrastructure.

However, artificial intelligence has changed the story. While organizations are racing to roll out AI tools at scale, the data center industry is discovering that not all workloads have the same infrastructure requirements.

For instance, training a large language model, running real-time inference, supporting enterprise applications and processing business-critical transactions each place very different demands on the underlying infrastructure.

Today, one data center doesn’t need to serve every purpose equally and we’re increasingly seeing that facilities can be both flexible and tailored to specific workload requirements.

The end of the traditional model

Historically, 99.999% uptime was non-negotiable. Data centers have traditionally powered systems like banks, emergency networks and customer-facing digital services, requiring continuous availability.

In these types of environments, where outages could have an extreme impact (from high financial losses to putting real lives at risk) this approach makes sense. Since operators couldn’t always predict which applications would be truly mission-critical, many facilities were built to the highest resilience standards by default.

But AI has changed this. “One-size-fits-all” redundancy isn’t necessary anymore. Different models, training and inference processes each require totally different service levels. For example, facilities for AI training workloads are being designed without backup generators, complex redundancy systems or high-tier architecture.

The good news is that there is a growing understanding of the distinction between environments needed for AI training and AI inference. Training facilities are increasingly being located wherever power is available.

The primary constraints are energy supply, cooling capacity and speed of deployment. In many cases, maximizing compute density and accelerating delivery timelines are more important than achieving the highest possible redundancy levels.

Inference infrastructure presents a different set of priorities. These workloads are often deployed closer to users and support services that people interact with daily. In these scenarios, latency, availability and customer experience become notably more important, creating a stronger case for resilient infrastructure and geographically distributed architectures.

Precision resilience to support an industry under pressure

It’s clear, therefore, that reliability still matters. However, infrastructure requirements vary significantly depending on the service being supported. In today’s age of AI, ‘precision resilience’ should be the focus, e.g., redundancy matching how workloads actually behave, rather than relying on legacy design assumptions.

The key challenge here for operators is determining where resilience delivers genuine business value and where it simply adds cost and complexity.

In a time when developers are facing a huge amount of pressure amid labor shortages, with demand outpacing supply, defaulting to ultra-resilient, high-tier designs for every AI deployment only intensifies challenges.

The industry is also expected to deliver capacity faster than ever before, while battling an ongoing power gap, meaning large-scale developments are increasingly difficult to execute. In this landscape, overengineering infrastructure can have unintended consequences.

Every additional layer of redundancy consumes capital and increases complexity. This is triggering an increased focus on efficiency, not just in terms of energy consumption, but in how capital is allocated throughout a project. Operators are looking to design infrastructure that maximizes the value generated by every watt of available power.

The role of upgradability

As operators move away from this one-size-fits-all redundancy to optimize their bottom line, it’s crucial that their facilities can adapt as workload requirements change.

While inference is expected to account for a growing share of AI demand, the landscape continues to evolve and it’s difficult to predict which workloads, densities and cooling requirements will dominate in the future. Infrastructure that can accommodate changes in compute technologies will be better positioned to support the next generation of AI applications.

Flexibility and fungibility are therefore the new non-negotiables in data center design. How is this made possible? Increasingly, developers are using ‘building blocks’ constructed off-site in factory environments, and then later assembling them on site to create an adaptable facility that can forever evolve, grow and shift.

This approach reduces the need to make every resilience decision upfront and builds with tomorrow’s changes in mind. In the coming years, we will see a shift towards multiple types of facilities, each developed for a different purpose.

These will range from energy-optimized training campuses built close to power sources, to distributed inference sites where uptime and latency directly affect user experience, alongside hybrid environments supporting both AI and traditional workloads. Yet they should all be built with flexibility front of mind to ensure they can evolve as requirements change.

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• The number of companies using AI is up, but the number of tools per company hasn't grown much
• ONS data also confirms that AI doesn't seem to be having a major impact on headcount
• SMBs could be lacking a clear business use case for AI

New ONS data has claimed that while AI adoption has expanded rapidly across UK businesses, the actual depth of adoption is still limited, with most firms using just a small number of tools to improve existing processes rather than totally overhauling their businesses.

Among the businesses surveyed that had adopted AI, the number of tools they used only increased marginally from 1.4 to 1.6 in the three years leading up to 2026.

The ONS also revealed that, while 60% of larger businesses have used AI to improve business operations, no negative impacts on headcount have been observed.

AI adoption might be growing, but that's not the full story

The data mostly implies experimentation rather than widespread transformation, with just 17% of SMBs (0-9 employees) reporting extensive AI use, however the report warns that many small businesses are merely trying general-purpose AI tools rather than making substantial investments into tech that can truly unlock new levels of productivity.

What's less clear is why SMBs are falling behind their enterprise counterparts, because while 7-14% of all business sizes cited cost as a barrier, 41% of SMBs with 10+ employees said they didn't have any major barrier concerns.

Rather than being held back by costs, infrastructure or capability, the ONS data actually implies that SMBs might actually be lacking compelling business cases to use AI altogether.

All in all, the most recent data confirms that while SMBs haven't exactly ignored AI, they're still stuck in the experimentation stage. The readiness to use automation tech is very much there, but ability to identify a genuine use case is potentially holding back deeper deployment.

AI is now part of the operating system of SaaS. It shapes how products are built, how teams collaborate, and how quickly ideas move from concept to launch.

But speed isn’t the most important shift. The real change is that AI is reducing the coordination cost inside organizations.

Work that once required multiple layers of approvals, handoffs, and alignment can now move more directly between the people closest to the problem. And as that friction drops, something more fundamental starts to change: how companies are structured.

Projects that once demanded large teams, heavy investment, and long development timelines can now be delivered by smaller groups using AI tools to accelerate execution.

The rise of micro-SaaS businesses is one clear example, with small teams able to build and scale products with a level of speed that would have been difficult to imagine a few years ago.

This isn’t just about building faster. It’s changing what scale actually looks like.

From experimentation to infrastructure

Today, AI is embedded directly into product development, engineering, growth, and support. It’s no longer something teams experiment with on the side.

This has brought about a fundamental change: individual contributors can move faster, make decisions earlier, and deliver more on their own.

That has a direct impact on how teams scale.

And increasingly, the companies with an edge are not the ones with the biggest teams, but the ones that can remove friction and make better decisions faster.

Why scale no longer means more layers

Traditionally, growth came with added complexity. More customers meant more people. More people meant more managers, more processes, and more coordination.

At a certain point, coordination becomes a job in itself.

AI starts to break that pattern and bottleneck, freeing up time for quality decisions. When a product manager can analyze user feedback, draft a roadmap, and collaborate more directly with engineering using AI tools, you reduce the need for multiple handoffs. When a growth team can produce, test, and iterate on campaigns faster, execution accelerates without increasing headcount at the same pace.

It doesn’t remove the need for structure. But it does reduce the need for layers whose main role is coordination.

And that opens the door to a different model of scaling: one that is lighter, more direct, and more focused on making the right decisions, not just executing faster.

The return of the “contribution era”

What we’re starting to see is a shift back toward what could be called a contribution-led model. For a long time, SaaS organizations leaned heavily into management structures. That made sense when scaling meant handling more complexity across teams, regions, and products.

Now, as AI lowers the cost of execution, the balance starts to shift again: the biggest advantage AI creates is not productivity but organizational simplification.

Strong individual contributors who can own a problem and drive it to completion become even more valuable. They don’t need to wait for as much coordination. They can test, build, and iterate independently. And they can do it while staying closely connected to the outcome. Importantly, this isn’t about removing managers. It’s about rebalancing the system.

What builder-led really looks like in practice

A builder-led model doesn’t mean everyone is an engineer, and it doesn’t mean structure disappears.

It means the people closest to the work have more autonomy to move it forward.

You see this already across teams:

• Product teams prototyping faster using AI-assisted tools
• Growth teams running more experiments with shorter feedback cycles
• Support teams handling higher volumes while focusing human attention where it matters most

In each case, AI is not replacing people. It’s increasing their speed and range.

And when that happens consistently, the bottleneck shifts. It’s no longer capacity. It’s clarity and decision quality: knowing what to work on, what to prioritize, and where to invest time.

This is where leadership becomes even more important, not less.

Instead of focusing on overseeing activity or managing layers of communication, leaders have to focus on creating the right conditions for execution.

In practice, it often looks like:

• Fewer approval steps
• More direct communication between teams
• More emphasis on outcomes rather than process

Leaders still set the direction and make the hard decisions. But they rely more on capable contributors to carry things forward.

In many cases, the most effective leaders are those who can still contribute when needed, not just coordinate others.

Hiring for ownership, not just specialization

This shift also changes how companies think about hiring.

Specialists remain essential. But, if smaller teams can deliver more, the focus moves toward people who combine expertise with ownership, and have a strong ability to make good decisions in fast-moving environments. There’s growing value in hiring people who can operate with autonomy, make decisions, and adapt as things change.

In a builder-led environment, the question is less “what is your lane?” and more “how effectively can you solve the problems in front of you?”

That doesn’t mean everyone needs to do everything. It means teams benefit from individuals who can connect dots, move across boundaries, and take responsibility for outcomes.

Building smarter, not just bigger

It’s important to stay grounded in how this shift plays out. AI won’t fix weak strategy or unclear thinking, and layering it onto already complex processes can sometimes create new friction rather than remove it.

At Weglot, we've seen teams ship projects with significantly fewer handoffs than two years ago. Marketing can prototype ideas faster, product teams can validate concepts earlier, and engineers spend less time on repetitive tasks.

Our support team is another good example. Over time, they've built a suite of AI-powered tools including a case summarizer, customer profiler, drafting assistant, internal copilot, knowledge base, and AI chatbots. Together, these tools help agents access context faster, learn from previous cases, and resolve more requests independently.

The biggest change isn't speed itself. It's the reduction in coordination overhead, which is ultimately a more sustainable way of scaling.

Smaller, highly capable teams with clear ownership tend to stay closer to the product and the customer and can adapt more quickly when things change. We’re already seeing that in micro-SaaS businesses, but the same thinking applies more broadly.

AI will continue to evolve, but one direction is becoming clear. The companies that will stand out are not necessarily the ones that grow headcount fastest. They’re the ones that stay focused, reduce friction, and make it easier for their best people to build and deliver impact.

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What do you mean Heartstopper Forever hasn't actually ended? Alongside the fact that the new movie is now streaming on Netflix, the streaming service has slyly announced a "companion piece" — Heartstopper: Ending on a Hi.

Obviously, we learned exactly what would happen to the gang once the cameras stopped rolling, and were left happy in the belief that Nick (Kit Connor) and Charlie (Joe Locke) definitely weren't breaking up, despite how much their terrible communication threatened to do just that.

So... what is actually coming out way? And when does Heartstopper: Ending on a Hi arrive on Netflix?

What time can I watch Heartstopper: Ending on a Hi on Netflix?

(Image credit: Netflix)

Heartstopper Forever drops on Netflix on July 24, 2026.

As for the exact time, it should be the standard 12am PT release that we saw for the movie.

For global regions, here's when you need to be prepared:

• US – 12am PT / 3am ET
• Canada – 12am PT / 3am ET
• UK – 8am BST
• India – 12:30pm IST
• Singapore – 3pm SGT
• Australia – 6pm AEST
• New Zealand – 8pm NZDT

What exactly is Heartstopper: Ending on a Hi?

(Image credit: Netflix)

Heartstopper: Ending on a Hi is an official 35-minute behind-the-scenes documentary special.

Netflix describes it as a "love letter to the franchise, featuring unseen archival footage, cast interviews, and fan reactions following the final Heartstopper: Forever film."

Don't take the title as a sign of something more to come, though... it's a reference to the very first time Nick and Charlie met in season 1.

Every enterprise I talk to right now has the same complaint dressed up in different language.

Their AI bills are climbing faster than anyone budgeted. Their model invoices look reasonable when viewed on their own.

But somewhere between boardroom approvals and the monthly cloud statements, money is disappearing in ways that nobody can fully explain.

Here’s the central issue that people struggle to understand: the most expensive part of AI isn't always the model itself. It's the infrastructure, orchestration, retries, idle GPUs, oversized context windows, and inefficient routing decisions that sit between a user's prompt and the final response.

This is something I call the hidden tax on AI adoption, and it's growing faster than most organizations realize.

Three numbers that should change how you think

Recently, at FinOps X in San Diego, a Goldman Sachs projection appeared on the main stage. Current enterprise token consumption globally sits at around six quadrillion tokens. The three-year projection: 120 quadrillion. That is not a rounding error… it is a 20x expansion, and it is arriving faster than the governance frameworks to manage it.

The same conference saw our launch of the Tokenomics Foundation (I’m fortunate to be a governing board member). It’s a vendor-neutral body inside the Linux Foundation dedicated specifically to the economics of AI token consumption.

The FinOps community, practitioners who have spent the better part of a decade building discipline around cloud computing spend, recognized that tokens represent a fundamentally different problem. Not a harder version of cloud cost optimization. A different one.

Here is why. When your organization runs a cloud workload, the cost is relatively legible. You provision compute, it runs, and you receive a bill. The relationship between action and expense is traceable. With AI tokens, that relationship fractures across three layers, and most organizations have visibility into only one.

Production → consumption → value: the three layers most teams ignore

The first layer is production. Before any AI model responds to any prompt, your infrastructure has to manufacture the tokens. GPU clusters, inference nodes, autoscaling policies, Kubernetes configurations: these are your token factories. Their efficiency, or lack of it, determines the base cost of everything that follows. A GPU node at 30% utilization is an expensive factory running at a third of capacity.

The second layer is consumption. This is where counterintuitive economics live, and it is what I spend most of my time thinking about. Two organizations can send identical prompts to different coding agents and arrive at radically different costs depending on how they manage context, caching, retries, routing, and the infrastructure supporting inference.

Prompt length, context window usage, caching strategy, and model routing decisions all compound. The common assumption that routing a task to a cheaper model always saves token cost, turns out to be wrong often enough to matter. A routing decision that invalidates a warm cache can make a "cheaper" model call more expensive than the frontier option it was meant to replace. These are second-order effects. They do not appear in standard dashboards but they appear in your monthly bill.

The third layer is value. This is the one that FinOps teams are comfortable with, and the one that matters least until you have the first two under control. Mapping token spend to business outcomes is a legitimate and important discipline. But you cannot govern at the value layer without instrumentation at the production and consumption layers. You are doing math with incomplete inputs.

Why 85% of your AI spend is probably misallocated

Here is a pattern I see consistently. Organizations treat frontier AI models, the most capable, most expensive models available, as their default infrastructure. Every task goes to the same model. Every prompt is constructed the same way. There is no routing logic, no tiering, no architectural distinction between work that genuinely requires the full capability of a frontier model and work that does not.

Based on my observations across organizations deploying AI at scale, roughly 15% of software development tasks actually require frontier model capabilities. The remaining 85% of routine coding, summarization, classification, and retrieval work can be handled by smaller, faster, and less expensive models, if you have the infrastructure to make those decisions intelligently and automatically.

The unlock is not picking better models manually. Manual model selection does not scale and degrades the developer experience by introducing friction at the moment when a developer needs to move fast. The unlock is building infrastructure that makes routing decisions for you: one that understands the task, routes it to the appropriate model tier, evaluates output quality, and escalates if needed. You specify the outcome you need. The system handles the economics of achieving it.

This is the direction the industry is moving, and the organizations that build this capability first will have a structural cost advantage that compounds over time.

The invoice arrives last. And you realize something has gone horribly wrong

There is a phrase I have started using with customers that captures the core problem: the invoice arrives last.

By the time you see the model provider bill, the cost decisions were made weeks ago in infrastructure configurations, autoscaling policies, and prompt architectures that nobody has reviewed since the initial deployment.

The retry logic runs silently when an upstream service slows down. The GPU nodes were reserved for peak traffic that never came. The agentic workflow, where a single user request fans out into dozens of model calls beneath it, each billed separately, none visible in the tool that generated the original request.

These costs do not live in the model invoice. They live in the infrastructure layer, in the consumption layer, and in the gap between how teams think their AI systems work and how they actually behave in production. You cannot govern what you cannot see. And right now, most teams are looking at one layer of a three-layer problem.

When AI goes from copilot to coworker, the stakes multiply

There is a shift underway that makes all of this more urgent. The AI deployments most enterprises built over the last two years were assistants, tools that accelerated individual work by handling the first draft, the next suggestion, and the boilerplate. A human remained in the loop at every consequential step. The economics were bound by how many people were using the tool and how often.

Autonomous agents change the economic profile entirely. When an AI system can receive a goal, build a plan, execute multi-step work, evaluate its own outputs, and iterate to completion without human intervention at each stage, you are no longer running an assistant. You are running something closer to a coworker, one that operates continuously, scales horizontally, and generates token consumption at rates that individual user interactions never approached.

The transition from copilot to coworker has already happened. And the governance implications are significantly more serious. A copilot with poor token economics costs you some efficiency. An autonomous agent with poor token economics runs that inefficiency at scale, continuously, without generating the natural friction that would cause a human user to pause or change approach. Infrastructure discipline and token optimization need to be in place before autonomous workloads scale rather than retrofitted afterward when the bill arrives.

The mandate for infrastructure teams

The right answer to this problem is not more dashboards. More visibility into a system you cannot control is just a more detailed invoice; it arrives with the same lag, and it changes nothing about the decisions that were already made upstream.

What infrastructure teams actually need is control that operates at the layer where costs are determined, not where they are reported. That means autonomous management of GPU and inference workloads, continuously rightsizing to match actual demand rather than peak assumptions, absorbing the bursty consumption patterns that agentic jobs produce, and moving compute capacity across providers when one environment becomes the bottleneck.

This is especially urgent now, because the transition from copilot to coworker does not give you a grace period to retrofit discipline. Autonomous agents do not pause. They do not get frustrated and choose a different approach. They run the inefficiency you built into them at scale, continuously, until something external stops them.

So the next phase of enterprise AI is defined by who can deploy models efficiently. As AI systems become more autonomous and token consumption accelerates, competitive advantage comes from understanding the full economics of AI, and not just the price of a model call.

Because by the time the invoice arrives, the decisions that shaped it have already been made.

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