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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 thinkRecently, 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 ignoreThe 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 misallocatedHere 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 wrongThere 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 multiplyThere 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 teamsThe 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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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
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.
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 infrastructureToday, 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 layersTraditionally, 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 practiceA 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 specializationThis 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 biggerIt’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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- 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 storyThe 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.
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 modelHistorically, 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 pressureIt’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 upgradabilityAs 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 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
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
- Capable everyday performance
- Light gaming possible
- Keys are a little heavy
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
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
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.
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.
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.
- 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