Technology

When a competitor appears in an AI answer and your brand doesn't, the obvious reaction is to publish more content. But that skips the useful questions like:

Which answer?

What was the person asking?

And did the assistant reference your competitor's website, an independent review, or neither?

Those questions shaped this review. To test the platform, I compared Saily eSIM with two of its close competitors, Airalo and Holafly. The aim was to find something worth investigating and see whether the Brand Radar data holds up to the hype.

My hands-on work focused on its existing AI Visibility Index, moving from combined-platform reports to ChatGPT-specific checks using recent data. Here’s how it went.

Ahrefs Brand Radar: Plans and pricing

First, separate the two purchases. The AI Visibility Index lets you search data Ahrefs has already collected, and the Custom Prompts run the questions you choose from the point you set up tracking. Now, Brand Radar is sold separately, so you don't need to buy the wider Ahrefs SEO suite first.

Ahrefs pricing breaks the options down as follows. These are monthly US-dollar prices, accurate as of October 15, 2026.

Purchase

Monthly price

What it covers

One AI index

$199

Existing data for one AI platform

All AI indexes

$699

All available AI indexes and 2,500 custom checks

Custom Prompts: Basic

$50

2,500 additional monthly checks

Custom Prompts: Growth

$100

7,000 additional monthly checks

Custom Prompts: Scale

$250

25,000 additional monthly checks

Lite, Standard, and Advanced subscribers already receive 150, 300, and 600 custom checks per month, respectively. That gives existing customers a small starting allowance without buying index access.

The catch is that checks aren't interchangeable with prompts. Each platform, location, and scheduled run consumes allowance. At the standard one-check rate, 20 prompts checked daily across three platforms and two countries use 3,600 checks over 30 days. By the way, Claude consumes eight checks per execution, so its allowance disappears faster.

For an agency researching different brands every week, the index has a clear purpose. But for a small, growing business following ten buying questions, paying for the entire archive before using those smaller allowances feels premature to me.

Ahrefs Brand Radar: Features and specsAI Visibility Index

I entered Saily, an eSIM provider, along with its competitors (Airalo and Holafly), and opened the overview.

Mentions, citations, estimated impressions, and AI Share of Voice sit together, with platform comparisons underneath. The index already includes historical data to explore, which is what makes it attractive. That means you can investigate a competitor before deciding which questions deserve ongoing tracking.

My starting point was a comparison of Saily, Airalo, and Holafly across the selected AI platforms. (Image credit: Ahrefs) (Image credit: Ahrefs)

Saily's smaller mention footprint gave the investigation a direction. Scrolling down brought its own performance history and associated topics into view, making it easier to move from a broad comparison to a specific question.

Performance trends and topic breakdowns help narrow a broad brand comparison. (Image credit: Ahrefs) (Image credit: Ahrefs)

You still need to understand the sample here. Ahrefs' methodology combines search-informed questions, including Google's People Also Ask, with semantic expansion. These aren't private conversations gathered from ChatGPT users. Its newer AI-adjusted volume applies platform-specific usage estimates to a parent keyword's Google search volume.

I prefer that qualification to treating Google demand as AI demand, but it remains a model. A rising line isn't proof that more customers saw your brand, and dataset changes can affect comparisons. AI Share of Voice also depends on the brands and filters you selected.

Competitor gap analysis

The comparison became more useful when I isolated ChatGPT answers mentioning competitors but excluding Saily. This turns an intimidating report into a reading list. You can investigate the questions behind the gap instead of guessing what another brand is doing right.

This filtered ChatGPT view excludes Saily. Its zero line reflects the selection, not Saily's overall visibility. (Image credit: Ahrefs) (Image credit: Ahrefs)

Opening “What's the best eSIM in the UK?” revealed a stored response dated October 9. Brand Radar's tracked-brand panel listed Airalo and Holafly, with no Saily. The question also mixed domestic mobile service with travel data, a useful reminder that apparently relevant prompts can cover different needs.

The individual response separates tracked-brand mentions from cited and uncited sources. (Image credit: Ahrefs) (Image credit: Ahrefs)

That response exposed some friction too. Parts of the answer displayed raw markup, which made it harder to read. The sidebar reported no citations while listing pages under “Found but not cited.” Keeping those categories separate was helpful, even if the presentation needed tidying.

This is where your judgment matters. A missing brand is worth investigating when the question fits its product. It isn't automatically an instruction to write an article, and a brand mention doesn't necessarily amount to a recommendation.

AI citation analysis

The Cited pages report was the natural next stop. Its page-type view groups results into formats such as articles, landing pages, and reviews. That gives you a starting hypothesis about the material appearing around your category before you inspect individual URLs.

Page-type groupings provide a starting point for investigating the source material. (Image credit: Ahrefs) (Image credit: Ahrefs)

Switching to individual pages made the distinction between discovery and citation much clearer. “Found in” includes pages retrieved while producing an answer. “Cited in” records answers that actually cite the page. A source can appear frequently in the first column without receiving much visible credit in the second.

Individual URLs expose the difference between pages retrieved and pages cited in this filtered comparison. (Image credit: Ahrefs) (Image credit: Ahrefs)

TechRadar's own best eSIMs guide appeared alongside review website listings and provider-owned pages. More interestingly, the guide's “Mentions on page” indicators included Saily even though this investigation had excluded Saily from the AI answers. A roundup can mention your product without an assistant carrying that mention into its response.

That is a better research lead than a score alone. You can inspect the source's coverage and ask what information an answer used or omitted. However, “Other” wasn't a ready-made outreach list because Holafly-owned URLs were present too. So, always check actual domains and your brand setup before treating a result as independent coverage. Not to forget, getting included on a cited page also doesn't guarantee a future AI recommendation.

Brand sentiment analysis

Ahrefs initially described sentiment analysis as a work in progress, but a native Sentiment report was available in my review account. In simple terms, this report adds a useful second question to the comparison, i.e., when AI mentions a brand, how does it describe it?

The Sentiment report adds a view of how the selected brands are described. (Image credit: Ahrefs) (Image credit: Ahrefs)

Saily's lower mention volume sat alongside a higher positive-response share than the two competitors. I found that contrast useful because, in my opinion, being discussed less often and being described less favorably are different problems. The former might prompt discovery research, while the latter calls for examining what the answers say.

I didn't independently audit the classifications, so this isn't a verdict on sentiment accuracy or customer satisfaction. So, use the report to identify answers worth reading, then check their wording.

Custom prompt tracking

The existing index is convenient until the question you care about isn't there. Custom Prompts let you add it, choose platforms and locations, organize questions with tags, and schedule daily, weekly, or monthly checks. Its history starts with collection, not arriving pre-populated.

For ChatGPT and Perplexity, Brand Radar also exposes Fan-out queries (extra searches an assistant generates while working through a prompt). They suggest useful supporting topics, although Ahrefs only shows the queries returned with the response, not necessarily every search the model ran.

The public indexes cover ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, and AI Mode. However, platform coverage needs a closer look than a row of logos. Plus, Claude is available for Custom Prompts rather than in the public index. Grok collection is paused, with historical data retained. Such distinctions are pretty vital in case either platform is central to your audience.

AI research tools

Brand Radar also offers search-demand and web-visibility reports. YouTube research covers titles, descriptions, and transcripts, while Reddit visibility focuses on threads appearing in Google results. TikTok has its own beta report.

These surrounding reports have separate Ahrefs-plan and beta conditions, so don't assume an index purchase includes every sidebar item. I see these as ways to investigate surrounding coverage, not proof that every mention influences an AI answer.

There is more assistance than the old “research only” description suggests. “Ask Ahrefs” is built into the interface, and the agent previously called Agent A now lives under Letaido, which offers additional analysis and automation.

Is Ahrefs Brand Radar worth it?

The strongest reason to buy Brand Radar is the ability to move between a competitive overview, an individual answer, and its source material. My eSIM investigation showed why that matters. Having lower visibility, positive sentiment, source-page mentions, and actual AI recommendations can tell different stories.

For agencies and in-house teams investigating several competitors, that depth can justify paying for research access. Entity matching, which distinguishes a company from unrelated uses of its name, is another useful safeguard when your brand isn't as distinctive as Saily.

For a small business with a short list of questions, the smaller Custom Prompts allowance or a limited manual check is a more proportionate start. The expensive part shouldn't arrive before you have a plan for using the findings.

I came away with useful leads and a clearer understanding of their limits. Brand Radar makes the evidence easier to inspect. Whether that evidence becomes better content, corrected information, or relevant coverage still depends on what you do next.

FAQsWhat does Ahrefs Brand Radar do?

Ahrefs Brand Radar researches brand mentions, citations, and estimated visibility across supported AI platforms. You can compare competitors, inspect stored answers and source pages, or set up tracking for questions you choose.

Is Brand Radar included in an Ahrefs subscription?

Brand Radar is included in Ahrefs Lite and higher plans with a limited Custom Prompts allowance. If you want full AI Visibility Index access, then that’s a separate purchase. Keep in mind, an included tracking allowance doesn't give you unrestricted access to the existing index.

What is the difference between the index and Custom Prompts?

The index is an existing research database that you can search immediately. Custom Prompts collects answers to your selected questions on a schedule, with tracking history building from setup onward.

Does Brand Radar track Claude and Grok?

Claude is available for Custom Prompts and uses eight checks per execution. However, it isn't part of the public AI Visibility Index. The new Grok collection is currently paused, although historical results remain available.

Does AI Share of Voice measure traffic?

No. It compares a brand's share of modeled impressions with the other tracked brands. It isn't a count of visits, clicks, or conversions. Keep referral and conversion reporting separate when evaluating commercial results.

Can a brand be mentioned without its website being cited?

Yes. An answer can name a brand without linking to its website. It can also cite a third-party page that discusses several brands. Check the answer's wording rather than assuming every source-page mention reached the reader.

How often does Brand Radar update its data?

Ahrefs describes monthly chatbot question-set refreshes using a 90-day reporting window. Google's AI search indexes follow different refresh cycles. Custom Prompts has its own daily, weekly, or monthly schedule, so a daily chart doesn't mean every archived answer was refreshed that day.

Which prompts should an SEO team track first?

Start with buying decisions like comparisons, pricing, suitability, alternatives, and specific customer requirements. Include unbranded questions as well as questions naming your business. Keep the set small enough that someone can review and act on it.

Will Brand Radar improve my AI citations automatically?

The reports identify evidence and opportunities, not guaranteed improvements. You still need to decide what to correct, publish, or pursue. Connected automation can assist with work, but this review didn't test a resulting citation uplift.

Is Brand Radar worth it for a small business?

Potentially, but start with the workload. A few recurring questions rarely justify the full index on their own. Broader access becomes more persuasive when you regularly investigate new topics, competitors, and cited sources.

• CMA investigation finds that Microsoft has "a strong position" across five separate areas
• An ongoing investigation is also considering whether Copilot has an advantage
• Microsoft could have to make changes to licensing and improve interoperability

The UK's Competition and Markets Authority (CMA) is proposing to give Microsoft a Strategic Market Status (SMS) designation in the business software market as a result of an earlier investigation into its software practices and impact on competition.

Core to the investigation was the company's extensive software ecosystem, which customers reportedly find difficult to leave to switch providers.

According to the data, around 20 million people use Microsoft's software in the UK every month, including private businesses and public sector organizations.

CMA proposes labelling Microsoft with SMS in the UK

"Microsoft has a strong position across each of the 5 areas of digital activity that the CMA investigated," the CMA wrote. Those five areas include operating systems, productivity software, security software, server operating systems and database management systems.

The report slates Microsoft for using barriers to prevent customers from switching as well as other "commercial practices and integration advantages" that keep users tied to the broader ecosystem.

If Microsoft is deemed to have Strategic Market Status, the CMA would likely want the company to make a series of changes, including changes to its bundling and licensing practices. The Authority would also likely want to see improvements to interoperability so that customers could mix and match software.

Copilot is another integral part to the investigation, and the CMA is working to determine whether Microsoft's existing market power could also give an unfair advantage in favor of its Copilot AI.

"Organisations should be able to choose the products that work best for them, switch provider when they want to and combine services from different suppliers," CMA CEO Sarah Cardell wrote.

The CMA has also been working with Microsoft outside of the formal designation process to reach a resolution.

"All our products - including Copilot - are open and interoperable, giving customers the ability to use them alongside competing services and choose solutions that work best for them at attractive prices," a Microsoft spokesperson said.

"We will continue engaging constructively to ensure UK customers and businesses carry on benefiting from robust competition."

There’s never been so much change happening across an IT estate. And it’s constant. On a day-to-day basis, IT teams might be creating and monitoring user permissions, deploying any scheduled operating system (OS) updates or integrating new third-party software tools. This is not to mention all the change carried out by external providers, outsourced teams and employees.

Change itself is not a problem. Indeed, it’s essential for ongoing security, resilience and modernization. Change that isn’t actively managed, however, is a different story. This could be change that goes unnoticed, like an external patch that automatically updates a system without IT oversight, or change that is forced, like rushing an OS upgrade to meet an impending end-of-life deadline.

The trouble is many organizations have to monitor incredibly large and complex IT estates that can contain hundreds or thousands of business-critical endpoints. And so much change is now automated or carried out by external parties. Consequently, without the right software and processes, this means that IT teams can have limited visibility into many changes taking place.

These changes might seem small or innocuous on their own. But left unmanaged, they mean systems are drifting away from their intended baseline, which adds risk to each subsequent change – and that’s when systems can fail and damaging outages occur.

Change that is not managed effectively also increases vulnerability to cyber attacks. Misconfigurations or gaps in security coverage materially enabled more than 90% of cyber incidents investigated in recent research, showing how seemingly minor changes or lapses in oversight can create openings for attackers.

So, what are some of the ways IT teams can effectively control change across their estate?

Here are three key steps:

1. Understand

The first step is to gain greater visibility into change across the IT estate, so teams can identify potential risks and understand their impact before they cause disruption. Achieving this requires the use of automation tools that enable continuous drift detection. With this in place, IT teams can monitor their entire estate – servers, end-user computing, networks, on-prem and cloud – for configuration change in real time.

To do this, the technology needs to scan every layer in the infrastructure to establish a known-good configuration baseline to use. The platform can then flag any time a change causes a device to drift from this intended baseline, while also allowing teams to gain visibility on the gap between the change that was expected and approved and what is actually running in the estate. This means a manual fix or external patch gets flagged, rather than flying under the radar until it triggers an outage or failure.

2. Respond swiftly

One of the key challenges IT teams face is reconciling and approving change. Inevitably, with so much change taking place, trying to manually perform this process leaves room for errors and can delay approvals. So, to reduce manual effort, the next step is to automate change approval processes, using software to automatically reconcile every detected change against approved baselines and validate what steps were taken against those planned in the change request.

With this, teams will then be able to rapidly respond to unapproved changes and use their time to take action on issues before they escalate and impact operations. More broadly, this also speeds up the delivery of new upgrades or projects and gives IT teams greater freedom to innovate safely.

3. Protect

There’s always a risk of a cyber incident taking place. But when teams understand what’s happening in their estate and accelerate their approval processes, they can also detect and remediate issues faster and reduce their mean time to resolution. This can help to mitigate a failure escalating into an outage, or minimize the cost of an incident, while also strengthening overall resilience.

Protecting systems also means ensuring they are compliant and that teams can export defensible evidence on demand whenever a regulator requests to see it. This also highlights why continuous compliance that generates clear and auditable trails is so important. Otherwise, audits are often accompanied by a time-consuming, manual search through spreadsheets and screenshots for evidence, and this also means information can be out-of-date by the time it’s collected.

Controlled change unlocks innovation

IT change is constant, and with the rapid development of AI and cloud tools, it’s also only going to increase. This provides more opportunities for companies to modernize, but it also creates more risk when it happens without control.

To maintain control and prevent damaging incidents from taking place, IT teams need to understand when configuration change happens across their estate, accelerate the rate at which change can be approved and reconciled, and protect their systems through faster remediation and establishing a real-time audit trail.

While it can be easy to put off resilience projects in favor of innovation, the threats facing IT are becoming stronger and more varied, so controlling change has to be a key priority. If anything, it provides the foundation to innovate successfully and safely.

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

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

• Three in five workers report physical discomfort with their desk setups
• Better tech means higher productivity and better wellbeing, Logitech says
• Hardware is more important than it was five years ago, especially among heavy AI users

A new Logitech study has argued that something as simple as unsuitable or dated tech could be impacting workers both at work and at home, leading to an overall downward trend in productivity.

According to the analysis of more than 9,400 respondents and 1,350 IT decision-makers (ITDMs), four in five (81%) believe having the right technology could make the difference between achieving their goals and not.

But Logitech's biggest findings reveal technology's impacts on workers' general lives, with over half reporting improvements in physical or mental wellbeing (54%) and higher optimism about their future (58%) just with a bit more control over the tech they use.

Logitech says the right tech boost productivity and morale – but it would say that

On the flip side, nearly two-thirds (64%) reported feeling frustrated, anxious or angry due to technology problems, as three in five (58%) even reported physical discomfort like wrist pain, eye strain and neck fatigue from their desk setup.

The reality is that only a quarter (24%) of workers say they have control over the technology in their office, yet just one-fifth would keep their existing setup if they were given the choice, revealing a widespread appetite for change.

In its report, Logitech declares that "hardware is the human layer," to the point that 42% of respondents rely on hardware more now than they did five years ago – a figure that climbs to 76% among heavy AI users, implying that despite automation's purported productivity gains, human-machine interaction isn't going anywhere.

Logitech's report calls for employers to consider employee experience during procurement processes – not just cost, security and technical specifications. It also covers the impact of hybrid working, and how employers should bear that in mind when allocating hardware.

Not everyone owns a Ferrari, but I’m sure those who do rarely use it to pick their children up from school. It is less energy-efficient, not as safe or spacious and may garner unwanted attention. The analogy is simple: in AI, you do not always need the most powerful tool for everyday tasks.

It is a trap many businesses have fallen into since the first wave of AI, when early adopters were trying to prove they could access the cutting edge of the technology. Everyone wanted to have the most powerful AI model, the most sought-after accelerators and the fastest route from the pilot stage to implementation.

But as AI moves from pilot into production, new costs are emerging, largely associated with usage. A proof-of-concept used by a handful of people can look cheap, but running the same application thousands of times a day, or by agents calling upon multiple models for one task, is an entirely different proposition.

The mistake is not owning a “Ferrari”, but using the same model, depending on the same hardware choices or taking the same deployment path for every project. That is where the next phase of enterprise AI is getting interesting, with new efficiencies coming from matching each workload to the most suitable model and accelerator, rather than defaulting to your biggest “engine”.

Every model has a different role to play

There is a popular idea among some enterprises that AI is a ranking exercise, in which you should standardize as much as possible on the smartest model available. In theory it sounds tidy, but it ignores the complex differences between each AI task and workload.

For instance, a customer service copilot needs to respond quickly enough that the user never notices the machinery behind it. A large document summarization job can wait longer, with the user expecting the AI to “think” much more carefully. Government applications of AI are likely to instead prioritize sovereignty and ownership, making where and how workloads run just as important as performance.

This logic applies from the size of the model to the infrastructure underneath it. Smaller models can often handle routine classification, extraction and routing tasks quicker and at lower cost, while mid-sized models support a great deal of everyday reasoning work. The largest, most powerful models are important but may be used more selectively, when their capability and cost are justified – for example, in complex reasoning, deep strategic planning and high-stakes tasks where accuracy and nuance are critical.

Equally, to maximize token efficiency, enterprises should consider how different workloads behave and why they do not all require the same type of accelerator. With a diversity of silicon to draw on, different combinations of models and hardware can be selected to suit different requirements, whether the priority is speed, throughput, cost or control.

As such, rather than fitting every workload around an assumed preference, enterprise teams should start with the desired outcome. A retailer running a customer service assistant will have very different priorities from a government department handling sensitive information.

The former is likely to prioritize response time, user experience and cost at scale, while the latter places greater weight on sovereignty, security and where the workload runs. The infrastructure should leave enough choice to match different workloads and changing requirements without being locked into one route.

Building on a diversity of silicon

If an organization invests heavily in one type of accelerator, it can be tempting to run every workload through it. This is where the wrong assumptions, made at enterprise scale, can increase costs rapidly. Using the same piece of hardware for every task can mean overpaying for performance with minimal or no benefit to the user.

Low-latency applications need hardware optimized for responsiveness, while high-volume workloads may benefit more from throughput and cost efficiency. Running both on the same infrastructure can mean paying for performance that one of them never uses.

In most cases, building on a diversity of silicon – heterogeneous accelerators that can be aligned to each workload – will enable enterprises to make smarter infrastructure decisions. In some scenarios, organizations may even prefer to combine hardware from different providers that complement each other. NVIDIA GPUs will make sense for many enterprise scenarios, but choice matters.

Being able to select across providers such as AMD, Qualcomm, Microsoft, Cerebras and others lets enterprises align infrastructure to their specific needs and improve efficiency. Cerebras, for example, has run OpenAI's gpt-oss-120b at around 3,000 tokens per second – a level of speed that is especially valuable for applications where response time is critical.

The deployment environment also matters, particularly in large-scale enterprises or at the government level, where a great deal of sensitive information needs to stay within a specific jurisdiction. In Europe, there is strong demand for infrastructure developed in-market and operated around local requirements.

For enterprises operating across markets, that means infrastructure choices cannot be separated from questions of data residency and local control. Increasingly, the answer is infrastructure that is sovereign enabled – with in-country accelerators, consumption-based pricing and no upfront capital commitment – rather than a single hardware decision made once and lived with for years. Being locked into one route leaves little room to respond when those requirements change.

Understanding AI tokens and efficiency

Another consideration is that AI spending behaves differently from traditional enterprise software, where a license has a relatively predictable cost. Businesses will face a larger AI bill when an agent takes more steps, or a powerful model is pulled into more tasks than necessary, which makes usage itself a poor proxy for value.

Enterprises are already starting to rethink how freely employees can consume tokens, particularly in coding roles. Uber, for example, reportedly used its entire 2026 AI budget in four months, largely on Claude Code. The lesson from this type of example is that giving more people more tokens does not necessarily equate to greater value.

Agentic AI provides another example. Unlike an employee who occasionally asks a routine standalone question, an AI agent can work through a long sequence of steps, read files, generate code, test it and review the output for mistakes. Agentic AI, especially in coding, can be uniquely expensive compared to reasoning and chat, with highly variable token usage where increased consumption does not always improve accuracy.

As counting tokens does not provide the complete picture, enterprises should consider measuring success on outcomes rather than on performance or capability alone. It can be useful to use faster AI models, more powerful hardware or longer-running agents, but only when the outcome justifies what they consume.

For a customer service agent that could be the cost of resolving a case or, for an employee copilot, whether human employees are making faster or more complete progress on meaningful work.

However, the aim should not be to cut token use altogether. IT leaders should seek to understand exactly what they’re paying for and whether it has a clear business case. That is the definition of AI efficiency we should be moving towards. Rather than seeking maximum usage or minimum cost, CIOs and their teams should ensure they return enough value from each workload to justify the investment that goes into running it.

Saving the Ferrari for the racetrack

There will always be AI workloads that require the largest, most powerful model, the fastest accelerator or the most expensive deployment option. The point is to stop making those choices by default. The next phase of widespread AI production requires a more deliberate approach to every workload.

Start with the task, choose a model that’s capable enough to do it well, run it on the accelerator suited to its performance needs, and make sure the deployment environment meets the security and sovereignty requirements around it. Then measure whether the result is worth what it costs.

This approach may be less headline-grabbing, but it is what will separate resilient, production-scale enterprise and government deployments from experimentation. As AI becomes part of everyday workflows, efficiency will increasingly stem from making smarter choices about where each capability is needed – and from having the diversity of models, silicon and deployment options to act on them.

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For eight years, Surfer had one job, and that was to help you rank on Google.

You wrote your content, Surfer told you what to fix, and your page climbed. Then the whole AI wave showed up. People started asking ChatGPT and Perplexity for recommendations instead of scrolling through blue links, and ranking first stopped meaning what it used to.

So Surfer built its own AI Tracker. It's a paid add-on that watches whether AI engines mention your brand when people ask about your industry, and shows you who gets named when you don't.

That's a big promise, so I put it to the test. I tracked a well-renowned brand across all five engines Surfer covers. I checked its numbers against what I saw by asking ChatGPT myself. I dug into where the data holds up, where it wobbles, and where the tool quietly stops being useful.

Some of it genuinely impressed me. Some of it didn't. Here's everything I found.

What is Surfer AI Tracker?

Surfer AI Tracker is a paid add-on inside Surfer that shows you how often AI engines mention your brand when people ask about your industry, and which sources they cite instead of you.

Surfer itself is an SEO tool that has helped writers rank on Google since 2017. The AI Tracker, launched in 2025, is its answer to a new problem, i.e., buyers now ask ChatGPT and Perplexity for recommendations and not just Google.

Here is what it tracks and how.

What it tracks:

• Whether your brand appears in AI answers, and how often (Mention Rate)
• How prominently you show up when you do appear (Average Position)
• An overall Visibility Score across engines
• Which websites AI pulls from to build its answers (Top Sources)
• The sentiment of each mention, positive or negative
• The same data for your competitors, side by side

How it tracks it:

• You give it real prompts your buyers type, like "best CRM for small teams"
• It runs those prompts across ChatGPT, Perplexity, Gemini, Google's AI Overviews, and Google AI Mode
• It scrapes the actual answer a real user sees, not a stripped-down API response
• It re-runs them on a schedule (daily on Pro) so you can watch the trend

How I tested Surfer AI Tracker

Before I trusted a single chart, I wanted to know whether the numbers on my screen matched what a real person would actually see if they asked ChatGPT the same thing.

That's the lens I judged it through. These are the things I cared about, and why.

• Is the data real? Surfer's big claim is that it scrapes the actual answer a user sees, citations and all, not a stripped-down version pulled from an API. That's the entire foundation. If it's off, nothing else matters. So this got the hardest look.
• Does it hold still? AI answers shift. Ask twice, get two replies. I wanted to know if the tool's readings wobble so much that any single number is meaningless.
• Do you need every engine? The cheaper plan tracks ChatGPT only. The pricier one adds Gemini, Perplexity, Google's AI Overviews, and Google AI Mode. I wanted to see if those engines actually tell different stories, or if you're paying extra to hear the same thing four times.
• Does it tell you what to do? A gap you can't act on is just anxiety with a dashboard. Surfer's mention-gap and fan-out reports promise a to-do list. I wanted to know if that's real guidance or a prettier version of the problem.

Then I actually used it. Here's what that looked like.

• I picked a brand I know inside out (I already knew its real rivals and roughly how often it gets talked about), and let Surfer build the competitor set from the brand name, so it had something to measure against.
• I loaded 40 prompts real buyers type into AI, like "best [category] tool for [use case]". Not vanity searches for the brand name. The questions that actually decide who gets picked.
• I let it run a full pass across all five engines, then ran the same prompts by hand myself and compared what I saw to what Surfer captured.
• I watched how much the numbers moved day to day, checked how the story changed engine to engine, and dug into the Sources view, clicking different gaps to see if the advice ever actually changed.

Setting up Surfer AI Tracker

The AI Tracker sits in its own AI Visibility workspace, apart from Surfer's SEO side. You're tracking how a brand shows up in AI answers, not auditing your own pages, so there's nothing to plug in.

Even better, I barely had to type anything. I entered the brand domain name of "Zoho," and Surfer built the rest itself.

(Image credit: Surfer)

Business type, industry, a paragraph on what Zoho sells, its customer base, a list of competitors, and the topics worth tracking. It knew Zoho was a B2B SaaS suite for small and mid-sized teams; it knew privacy and all-in-one bundling were part of the pitch, and it named real rivals like Salesforce and HubSpot.

I find this smarter than most trackers bother to be. The usual tool asks for a brand name and stops there. Surfer uses that name to understand who you actually are, then uses that understanding to judge whether a mention is really about you later. The catch is that you should still read what it generates. It's a starting point, not gospel, and a wrong detail here quietly skews every chart down the line.

Then come the prompts, and this is where the tool genuinely impressed me.

(Image credit: Surfer)

Surfer generated 40 prompts on its own, sorted into eight topics, five prompts each, from accounting and CRM to collaboration and payroll. And they weren't lazy.

These are the exact questions a real buyer types into AI: "Which accounting software is the best for small businesses in the USA and Canada," "Best project management software for a one-man team," "What is the best collaboration software with built-in VOIP for a small business?" You get 50 prompt slots on Pro, and Surfer filled 40 of them for me before I'd done anything.

You can still edit, delete, or add your own, and you can pull your top queries from Google Search Console if you'd rather. But the fact that a single brand name produced 40 usable prompts is a real time-saver, especially if you don't already know what your buyers ask AI.

The last piece is competitors, and here's how it pulls up.

(Image credit: Surfer)

The competitors Surfer tracked weren't only the ones it listed at setup. When the data came back, the table showed brands I never entered, like QuickBooks, Xero, Pipedrive, and Gusto. Surfer surfaces whoever actually shows up in the AI answers next to Zoho. That's the honest way to do it, because your real competition in AI search is whoever the model names, not whoever you think you compete with.

It's also the first hint of a theme you'll see throughout this review, i.e., the tool is very good at showing you what's happening, and Zoho landing second here, behind HubSpot, is exactly the kind of thing you'd want to know.

Then you let it run. Surfer sends every prompt to the engines, records what each one says, and builds your baseline. Load your prompts early. The sooner they're in, the sooner your trends are worth reading.

Surfer AI Tracker dashboard

The dashboard runs on four numbers– Visibility Score, Mention Rate, Average Position, and Top Sources. Once you know what each is telling you, the tool clicks into place.

Start with the Visibility Score. Zoho landed at 51. On its own, that means nothing. It's a blended score out of 100 that mixes how often you're mentioned with how high up you appear. It only starts to mean something when you see it against your rivals, like in the chart below.

(Image credit: Surfer)

It tells you if you're winning, never why. For why, you need the other three.

Mention Rate is the honest one. Zoho showed up in 33.4% of answers. Plain and concrete, and the number I'd track week to week, because when it moves, something real moved.

Average Position was 4, and it comes with a trap. It only counts the times you were actually mentioned, so a brand can post a great position and still be nearly invisible if it shows up once and vanishes everywhere else. Read it alongside Mention Rate, never alone.

Adding to this, you can also go through the Topic breakdown.

(Image credit: Surfer)

Surfer splits your score by topic, and that's where the real story hides. Zoho scored 86 on SMB CRM selection; it's home turf. But on Collaboration it scored 1%, and on Payroll and HR just 10%, both markets Zoho actually sells into.

AI is barely naming it there. That gap is worth more than any headline number, because it shows you exactly where you're missing from conversations you should be winning.

So which numbers are signal and which are vanity? Mention Rate and the topic breakdown are signal. Visibility Score is a summary, useful at a glance, shallow up close. Average Position only means something when read beside Mention Rate. Look past the big number on top, and the dashboard gets a lot more honest.

Surfer AI Tracker accuracy

I checked Surfer's captured answers against what I got by asking ChatGPT the same questions myself, and on both prompts the tool matched reality.

I picked two prompts, opened ChatGPT, ran them, and lined my answers up against what Surfer had captured for the same engine.

I chose the two on purpose i.e. one where Surfer said Zoho shows up, and one where Surfer said Zoho was invisible. If the tool is accurate, it has to get both right.

Start with the accounting prompt, "Which accounting software is the best for small businesses in the USA and Canada?" Surfer scored this one high for Zoho, a 71.4% mention rate over the week.

(Image credit: Surfer)

My own run backed it up. ChatGPT crowned QuickBooks Online as the overall winner, then ran through a table of alternatives, and there was Zoho Books, sitting in the "best value" row. Not the top pick, but clearly named, exactly as Surfer reported.

(Image credit: Future)

Next, I tested the project management prompt, "Best project management software for a one-man team." Surfer scored this one a flat zero. According to the tool, Zoho is never mentioned at all.

(Image credit: Surfer)

I ran it myself, half-expecting to catch the tool missing a mention. I didn't. ChatGPT named Todoist, Trello, Notion, and ClickUp.

(Image credit: Future)

So does the data match? On both prompts, yes. But there's one honest wrinkle you should carry with you, and it's visible right inside Surfer. Look at the trend line on that accounting prompt: over a single week, it swung from near the top down to almost nothing and back up again.

That's not Surfer being unreliable. That's AI answers being inconsistent. The models reshuffle their picks day to day, so any single reading is a snapshot of a moving target.

Sources, gaps, and recommendations

Here's where Surfer is brilliant and frustrating in the same breath. It shows you exactly where you're losing, down to the specific web pages, and then mostly leaves you to figure out what to do about it.

Let me walk you through it, because the diagnosis really is good.

The Source section tells you which websites AI is actually reading to build these answers. For Zoho, it had pulled from 367 domains, 797 URLs, and over 6,000 references, and that pile keeps growing the longer you track. Each source shows whether your brand was mentioned, how often it was shown, and a confidence rating.

The pages themselves are exactly what you'd expect AI to lean on, like Forbes, TechRadar, and big software roundups. These listicles are what the models quote, which is the whole reason visibility in them matters.

Another interesting metric here is the Mention Gap analysis.

(Image credit: Surfer)

Each card pits you against one competitor and splits the sources three ways.

Shared are the pages that mention both of you. Gap is the pages that mention them and not you. Against HubSpot, Zoho had 101 gap sources. Against QuickBooks, 93. It's a literal list of the exact pages where your rival gets named, and you don't.

There's also a Fanout Queries report, which shows the follow-up questions AI asks itself while researching your topic.

(Image credit: Surfer)

So a prompt like "best CRM" quietly branches into "best CRMs integrating with Google Workspace and QuickBooks," which alone spun off ten separate fan-out queries. Those branches are basically a content to-do list the AI handed you for free.

Pricing and plans

(Image credit: Surfer)

If you only want AI tracking and none of the writing tools, there's a tracker-only plan, AI Search Analytics, at $95/month. It covers 50 prompts daily across every engine.

The cheapest plan with content tools, Standard, is $119/month. But, it only tracks ChatGPT, and refreshes weekly. If ChatGPT is all you care about, fine. If you want the full picture of where buyers actually search, you're paying to see a quarter of it.

Pro, at $219/month, is where the tracker gets serious. 50 prompts, refreshed daily, across all five engines. This is the plan I tested, and the one I'd point most people toward.

Above that sits Peace of Mind at $359/month, adding unlimited workspaces, API access for agencies running many brands, and 100 daily prompts.

I’d suggest skipping the Standard tier unless ChatGPT-only genuinely covers you. Pro is the real tool, and everything in this review runs on it.

Who should buy it, and who should skip it

Buy it if you

Skip it if you

Already pay for Surfer's content tools

Only need AI tracking, nothing else

Want tracking and fixing in one place

Want deep, standalone AEO analysis

Track one or a few brands

Manage dozens of brands at scale

Are an SEO who knows how to act on gaps

Need the tool to tell you what to do next

Care most about ChatGPT and Google

Need Claude, Copilot, or Grok tracked

Want fast setup from a brand name

Want prompts and rivals fully hand-built

FAQWhat does Surfer AI Tracker do?

Surfer AI Tracker tracks how often AI engines mention your brand when people ask about your industry, and shows which sources they cite instead of you. You feed it prompts your buyers actually type, and it reports your mention rate, position, and visibility across engines.

Which AI engines does Surfer AI Tracker cover?

Surfer AI Tracker covers ChatGPT, Perplexity, Gemini, Google AI Overviews, and Google AI Mode. It does not track Claude, Copilot, Grok, or Meta AI. On the entry Standard plan, it only tracks ChatGPT.

How much does Surfer AI Tracker cost?

Surfer AI Tracker’s cheapest plan with tracking, Standard, is $119/ month, but it only watches ChatGPT weekly. Pro, at $219/ month, tracks all five engines daily. There's also a tracker-only plan at $95/ month.

Is Surfer AI Tracker accurate?

In my testing, yes. It scrapes the real answer a user sees, not an API response, and its data matched what I got asking ChatGPT the same prompts myself. Just remember AI answers shift daily, so treat any single reading as a snapshot.

Is Surfer AI Tracker worth it?

Surfer AI Tracker is worth it if you already use Surfer's content tools, since tracking and fixing live in one place. If you only need AI visibility tracking, a dedicated tool like Profound gives you more depth for the money.

What are the best Surfer AI Tracker alternatives?

The best Surfer AI Tracker alternatives are Profound for enterprise depth, Peec AI for clean browser-based tracking, and Ahrefs Brand Radar if you want visibility tied to real search-backed prompts.

• Reports claim VMO2 could plan return for older brands
• Virgin Mobile and O2 Broadband could be set for a return
• VMO2 has struggled in recent months, so a refresh could rejuvenate its business

Virgin Media O2 (VMO2) is reportedly considering bringing back defunct brands including Virgin Mobile and O2 Broadband in a bid to rejuvenate its business.

A report from The Times (paywall) claims the move could help the business win back customers as competition in the UK market intensifies.

The site claims VMO2 is keen to stop recent customer losses and build investor support amid reports of a backdrop of falling bond prices and a need to cut millions in costs.

Back from the dead?

One of the most memorable brands in the UK mobile market, Virgin Mobile disappeared from view in 2023 following the merger between Virgin Media and O2 two years earlier.

As part of the deal, Virgin Mobile's three million customers were migrated over to O2's network - this coming a year after its own MVNO had been moved off Vodafone's network.

VMO2, which is co-owned by US investment group Liberty Global and Spain’s Telefónica, recently hired former Pets at Home boss Lyssa McGowan to lead its troubled consumer division.

The Times notes that in recent weeks, McGowan, who previously held the post chief consumer officer at Sky, has appointed two of her former colleagues to lead the O2 and Virgin Media brands respectively.

VMO2 already owns the GiffGaff brand, which targets lower-cost mobile subscriptions, so a move to re-launch another mobile brand may cause some competition concerns - or lead GiffGaff to be rebranded.

Intriuingly, telecoms industry site ISPreview reported spotting a new O2 Broadband page on O2’s website in June 2026 advertising a range of cheaper full fibre plans. After publishing its story, the page was quickly removed.

TechRadar Pro has contacted Virgin Media O2 for comment.

• Successful Kickstarter Trettitre Retro Ambient Audio System is now shipping
• Combines several retro physical media sources in a fun configuration
• Sits on your wall (components can be moved), with multiple extra novelties

You can rely on Kickstarter to surface some of the stranger audio products out there, and it's true to form today. A successful campaign from a company called Trettitre has brought us one such device, and it's shipping now.

This is called the Retro Ambient Audio System, a title that's both uncatchy but accurate. It made over $2 million on a goal of $3,000, in just 15 days of funding (this was back in March and April), but you can still make late pledges of $399 (or a conversion for the equivalent in your country; it's about £300 or AU$600) to receive the full bundle.

According to Trettitre, the full MSRP of the device is $899. Since you can order the bundle for $500 less post-campaign, though, it's not clear if it'll ever actually sell for this price.

So what is the RAAS? Well, as you can see in the images, it's an all-in-one physical media machine. It's a combined record player, CD player and cassette player, which is designed to let you listen to any kind of physical media at home.

You can buy the components separately if you like, but the fun is in the setup with the magnetic wall-rack. Trettitre's marketing videos show users removing the record player from the wall, carrying it via a handle, plugging in some IEMs and listening to vinyl on their kitchen table, because this turntable has an onboard battery that charges when you put it back on its wall-mount.

There's one thing missing though: a speaker. You can buy this separately, with Trettitre selling loads of extras including multiple speakers, but know it's not included in the box.

Lights, colors, buttons

(Image credit: Trettitre)

Perhaps the first novelty of the Trettitre RAAS is that it's all wall-mounted. Following in the footsteps of Ultrasonic Audio's gorgeous wall-mounted speakers and Fuse Audio's vertical turntable, the company is embracing vertical audio, which is popular among fashionistas (and people with smaller apartments) but decried by audiophiles.

The gadgets all have built-in batteries too, so you can remove them from the wall and use them on the go. The battery capacity is unclear (although six hours for the turntable is listed, or three if you're using it solely as a light source), but you should get a few hours of listening before you need to clip them back onto the wall.

Another notch in the toolbelt? The record player, called the TTT-LP3, has a built-in lighting panel which you can change the color of, like a giant RGB record player. The purpose, I glean, is to make the whole system feel like a part of your home decor. It's not the first time we've seen light-up decks, of course — FiiO's done it, Audio-Technica's gone all glowy with a vinyl spinner and Brian Eno's neon version was so popular, it's had a second iteration.

The LP3 has an Audio-Technica AT-3600LA cartridge, and plays at 33.3 or 45rpm. The next device is the TTT-DP3, the CD player which supports CD, CD-R or CD-RW, and seems to have a neat sliding release system which lets you glide the cover off. Finally, there's the TTT-CP3, a cassette player with some nice chunky light-up buttons and a clean, modernist look.

The RAAS is funky to look at, and offers lots of flexibility for physical media users, but might not be best suited for die-hard audiophiles. While each gadget has a 3.5mm jack. it's clear that they're designed to be used by Bluetooth speakers (unless you're going to have a long 3.5mm wire trailing up to that wall rack).

It's also suspiciously cheap for such a complex system; the best turntables alone cost more than the cost, without other gadgets and a wall mount system. Admittedly you'll need to buy a speaker separately, and Trettitre is pushing its add-ons, but it still raises eyebrows. So it might be worth waiting until the units have shipped, to see what users say, before you get too excited.

AI coding assistants are no longer experimental. In organizations that have adopted them, AI is responsible for roughly half of committed code. The question is, therefore, no longer whether developers will use AI, but whether security programs can govern what those systems produce. AI-generated code is remarkably good at producing syntactically correct code, with a 99.9% pass rate. In contrast, security performance tells a very different story.

As it stands, the average security pass rate is only 56%, meaning roughly 44% of tested code-generation tasks introduce a known vulnerability. As the volume of AI-generated code increases while security performance remains largely unchanged, organizations risk introducing more vulnerable code into their development environments. As a result, software vulnerabilities are now the top breach entry point at 31%, overtaking stolen credentials.

Closing that gap means rethinking how security fits into AI-assisted development. Organizations need to make security part of the development process from the start, choose models with risk in mind and verify code in context before it reaches production. The goal is not to slow developers down; it’s to make sure security can operate at the same speed and scale as the software being created.

Model choice is a security decision

AI models learn from enormous volumes of historical and publicly available code, including examples from open-source projects and developer communities. That training data contains secure implementations, but it also contains outdated libraries, weak validation and insecure coding patterns. While models are designed to produce useful, functional code, without the right controls they can reproduce those insecure patterns at scale.

Organizations cannot control every source used to train a model, but they can control which models enter development. The strongest model tested achieved a 68% security pass rate, while six of the 11 models clustered between 50% and 53%. Model selection is, therefore, not simply a productivity decision but a risk consideration.

Choosing a stronger model can reduce exposure, but it does not make the output trustworthy by default. AI-generated code should still be tested throughout the software development lifecycle using capabilities like static analysis and enforceable security policies. Security must be part of the delivery process itself.

Security needs application context

AI-generated code can appear to work as intended, compiling successfully and passing functional tests while still containing security vulnerabilities. Under deadline pressure, that can create misplaced confidence, particularly when developers accept output without fully understanding how it behaves inside the wider application.

The difference in security performance across vulnerability types shows why context matters. While models achieved pass rates of 83% for SQL injection and 87% for insecure cryptographic algorithms, performance fell to 15% for cross-site scripting and 12% for log injection. Some weaknesses can be identified through a recognizable coding pattern, but others often depend on data flows, authorization, trust boundaries, and how different parts of an application interact.

That’s why verification needs to happen in the workflow and in the context of the application. AI-native analysis can add contextual risk detection alongside established static analysis, surfacing issues such as logic flaws and broken authentication directly in pull requests. The key is to combine contextual analysis with independent testing before code is merged and risk has the potential to spread.

Prioritize risk, not just findings

As AI increases the volume and speed of software development, it also increases the amount of risk security teams have to assess, prioritize and remediate. That adds additional pressure to organizations who are already carrying significant backlogs of unresolved vulnerabilities.

That backlog enables security debt to build as flaws are identified but remain unfixed, meaning known risk sits in applications for longer. The more that debt accumulates, the harder and more expensive it becomes to reduce. Today, security debt affects 82% of organizations, while critical security debt affects 60%.

Those figures point to a growing capacity problem. Organizations are generating and discovering risk faster than they can reduce it. Treating every finding as equally urgent will only make that problem worse. Teams, therefore, need to focus on exploitability, application criticality and business impact so they can direct limited remediation capacity towards the vulnerabilities most likely to cause real harm.

AI-powered remediation can help close the gap between discovery and action by proposing fixes inside the developer workflow and reducing repetitive manual work. But remediation still needs to be governed. A generated fix must be tested, validated and checked for regressions. The objective should be to reduce meaningful exposure, not simply increase the number of findings or fixes moving through the system.

What leaders need to do now

AI-assisted code has become embedded into software development. As a result, leaders should treat its security implications as a change to the software operating model.

That means knowing where AI-generated code enters the organization, evaluating models against relevant security requirements, building verification into developer workflows, and focusing remediation on exploitable business risk. It also means retaining evidence of what was tested, what was fixed, and what risk was knowingly accepted.

The organizations that succeed will not be those that slow AI adoption, but those that evolve their development practices so security can keep pace with software creation. AI is making it easier to generate working code, but the real test is whether businesses can prove it is secure before it reaches production.

We've featured the best endpoint protection software.

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

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

If an older laptop or desktop takes minutes to boot, swapping its hard drive for the SATA drive is one of the cheapest ways to make it feel quick again. Especially now the Lexar NS100 256GB SATA SSD is now £35 (was £72) at Amazon.

Lexar has made memory cards and storage for decades, and the NS100 is a no-fuss drive for a straightforward hard drive replacement: it fits in the same 2.5in bay, and owners report noticeably faster boot times on machines that had been struggling.

• Best for: Anyone reviving an aging laptop or PC, or adding a fast boot drive to a budget build.

• Why we like it: up to 520MB/s read speeds, a standard 2.5in SATA III fit for most older machines, and a three-year warranty from an established storage brand.

• What you need to know: This drive is designed for older machines that don't have an M.2 NVMe interface.

NS100 256GB SATA SSD: was £71.99 now £34.99
A 2.5in SATA III (6Gb/s) solid-state drive with read speeds of up to 520MB/s, no moving parts, shock and vibration resistance, Lexar’s SSD Dash monitoring software, and a three-year limited warranty.View Deal

NS100 512GB SATA SSD: was £82.99 now £69.98
If you need more memory, the 512GB version also has a discount right now. View Deal

Price Context & Historical Value

This isn't a record low for either drive - although SSD prices have climbed considerably since the lowest prices of £14 for the 256GB model back in November 2023, and £23 in May of the same year. It’s a fair price for a branded drive today, but treat it as a solid buy rather than a once-a-year bargain.

Should you buy it?

✅ Buy the Lexar NS100 SSD if...

You want to replace a slow hard drive in an older laptop or desktop, need a simple 2.5in SATA drive for an operating system and core apps from a known storage brand.

❌ Skip the Lexar NS100 SSD if...

Your machine has an M.2 NVMe slot that would give you much faster speeds, or the sub-TB storage capacity won’t hold your games or media library.

• See all internal SSDs at Amazon.

• Remote Reaper reaches version 1.0 after its Early Access period
• Armored vehicles feature vulnerable areas that affect simulated damage outcomes
• Players can customize drone frames, motors, propellers, cameras, and payloads

Remote Reaper: FPV Combat Drone Simulator has reached its first full release on Steam, ending an Early Access period that began during development.

Created by Ukrainian developer Vladyslav Fomenko alongside military pilots, the simulator recreates combat scenarios involving remotely controlled aircraft and battlefield electronic interference.

Version 1.0 adds racing courses, expanded missions, customizable aircraft and vehicle damage mechanics, alongside environmental settings that affect visibility and flight behaviour.

Electronic warfare complicates drone operations

The simulation includes electronic warfare systems that interfere with video transmission and control signals, potentially disrupting communication during critical moments of flight.

Terrain and physical obstacles can also weaken connections between operators and their aircraft, adding further complications to the simulated operating environment.

Different interference patterns affect control and video channels, meaning operators must contend with disruptions rather than relying on uninterrupted transmission throughout each mission.

Armored vehicles feature different protection levels, including additional screens, while vulnerable areas determine whether simulated impacts produce decisive damage or fail to disable their targets.

The game also includes infantry positions and airborne threats, requiring players to select different approaches according to the scenario and equipment available.

Its aircraft editor supports 7 inch and 10 inch frames, allowing users to configure motors, propellers, cameras, antennas and other components.

Players can select between shaped charges, high explosive fragmentation and explosively formed penetrators, while coaxial motor arrangements provide another configuration option.

These choices affect aircraft weight, thrust and endurance, requiring users to consider flight characteristics when assembling different virtual configurations.

Weather conditions include rain, fog and wind, while changing daylight conditions affect visibility and how clearly players can identify objects during missions.

Version 1.0 expands missions and controller support

The full release adds racing activities across three courses, alongside additional combat scenarios involving airborne threats and ground-based targets.

A built-in mission editor allows players to create scenarios by placing targets, configuring enemy defenses and adjusting electronic warfare conditions.

Online collaborative play also allows several participants to join missions together, coordinate activities and enter sessions that are already underway.

The game supports a wide range of controllers including RadioMaster, DJI FPV, TBS and BetaFPV equipment, alongside Xbox and PlayStation gamepads and more.

Realistic control mode uses the game's flight physics with radio controllers or gamepads, while a simplified option supports keyboard and mouse operation.

The simplified configuration maintains aircraft altitude automatically and includes a speed boost, making the controls more accessible to players unfamiliar with conventional FPV equipment.

The game requires Windows 11 and at least 16GB of memory, a six-core processor running at 2.4GHz and an NVIDIA GTX 2060 graphics card.

Recommended specifications include 32GB of memory, an NVIDIA RTX 3060 GPU and either an Intel Core i7 6600K or AMD Ryzen 5 5600 processor.

Both configurations require 10GB of available storage and DirectX 12, while the recommended setup additionally specifies broadband internet access.

Looking for a different day?

A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Sunday's puzzle instead then click here: Quordle hints and answers for Sunday, October 18 (game #1728).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,500 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today — or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc's Wordle today column covers the original viral word game.

SPOILER WARNING: Information about Quordle today is below, so don't read on if you don't want to know the answers.

Quordle today (game #1729) — hint #1 — VowelsHow many different vowels are in Quordle today?

• The number of different vowels in Quordle today is 4*.

* Note that by vowel we mean the five standard vowels (A, E, I, O, U), not Y (which is sometimes counted as a vowel too).

Quordle today (game #1729) — hint #2 — repeated lettersDo any of today's Quordle answers contain repeated letters?

• The number of Quordle answers containing a repeated letter today is 1.

Quordle today (game #1729) — hint #3 — uncommon lettersDo the letters Q, Z, X or J appear in Quordle today?

• Yes. One of Q, Z, X or J appears among today's Quordle answers.

Quordle today (game #1729) — hint #4 — starting letters (1)Do any of today's Quordle puzzles start with the same letter?

• The number of today's Quordle answers starting with the same letter is 0.

If you just want to know the answers at this stage, simply scroll down. If you're not ready yet then here's one more clue to make things a lot easier:

Quordle today (game #1729) — hint #5 — starting letters (2)What letters do today's Quordle answers start with?

• E

• D

• H

• F

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

Quordle today (game #1729) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle, game #1729, are…

• EXIST
• DECOY
• HITCH
• FLUID

Somehow I preserved my win streak by risking a rare letter with my final guess.

Today’s issue was the multiple words that end with I-T-C-H. Thankfully, I only exhausted a couple of them.

Daily Sequence today (game #1729) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle Daily Sequence, game #1729, are…

• RUGBY
• SLICK
• OLDEN
• RANCH

Quordle answers: The past 20

• Quordle #1728, Sunday, 18 October: FLINT, FUROR, WEDGE, RODEO
• Quordle #1727, Saturday, 17 October: QUARK, ENEMY, SPASM, BLOCK
• Quordle #1726, Friday, 16 October: SHORT, WORTH, JOIST, SLUNK
• Quordle #1725, Thursday, 15 October: STALE, EPOCH, PIANO, CORNY
• Quordle #1724, Wednesday, 14 October: TIMID, GROUP, GRILL, EVERY
• Quordle #1723, Tuesday, 13 October: FLUFF, DEBUT, TEETH, STEIN
• Quordle #1722, Monday, 12 October: HELLO, BELOW, RIGHT, SLANT
• Quordle #1721, Sunday, 11 October: LILAC, INLET, TRIAD, SHAPE
• Quordle #1720, Saturday, 10 October: SWEAT, THIRD, RURAL, CLASP
• Quordle #1719, Friday, 9 October: OLDEN, DUNCE, SWATH, HILLY
• Quordle #1718, Thursday, 8 October: MINTY, ROWER, BROOD, RESET
• Quordle #1717, Wednesday, 7 October: PALER, FROTH, DOZEN, ERASE
• Quordle #1716, Tuesday, 6 October: FRESH, SKULK, BATCH, ENSUE
• Quordle #1715, Monday, 5 October: SIXTH, SPEAK, SEDAN, HEIST
• Quordle #1714, Sunday, 4 October: INFER, RAINY, THING, ARENA
• Quordle #1713, Saturday, 3 October: CHORE, FLYER, STICK, PURSE
• Quordle #1711, Thursday, 1 October: NAVEL, SHEER, BIRTH, TACKY
• Quordle #1710, Wednesday, 30 September: CHOKE, BATCH, MOSSY, EAGER
• Quordle #1709, Tuesday, 29 September: BILGE, MEDAL, LODGE, PORCH

White dwarf stars, it turns out, come in a variety of hues. Says one astronomer: “Some of them genuinely surprised me.”

Clip it to the back of your phone, and it’ll convert your voice on the fly for fun, privacy and accessibility.

Nvidia CEO Jensen Huang has sung the praises of Microsoft's new Surface Laptop Ultra, describing it as "so beautiful" and "incredible".

Speaking at a developer event in London, Huang outlined the development process that went into building the hardware powering the new product, which is the first Microsoft device powered by an Nvidia chip.

“This computer is going to be incredible - it took us four years to build it," Huang told attendees including TechRadar Pro, "this project is really a work of love - we had several thousand engineering years dedicated to it, we build a brand new revolutionary chip, a whole new line of PCs, it’s so beautiful - you’re going to love it.”

The end of the traditional PC?

Revealed in September 2026, the Surface Laptop Ultra is targeted towards developer and AI usage, powered by Nvidia’s RTX Spark platform with an N1X processor and RTX Blackwell graphics, supporting up to 128GB unified memory

This is alongside 24GB RAM and 512GB storage, while the next version has 32GB RAM and 1TB storage, making it ideal for creators, AI specialists, engineers, scientists, developers, and other users running demanding applications.

But as Huang tells it, the creation of the device, set to launch on October 16 2026, was a long and arduous process.

(Image credit: Microsoft)

“It took four years to do this," Huang said of the hardware development process. "We were thinking about agentic systems, AI and autonomous systems, and how they’ll be able to access your files, and be grounded on information, and be able to reason about it and use tools - but the ultimate tool is your personal computer, right?"

"So wouldn’t it be cool if we could create a personal computer, a PC that actually runs an agent locally - so it can go off and just work on things for you - and not only could it just run locally, it would be smart enough to use the tools."

Huang explained his vision of "texting" your PC to ask for help, to get it to do tasks like rendering images and updating slides, then delivering them straight to you.

"Wouldn’t it be awesome if you could just text your computer - you don’t have to bring it with you anymore - just leave it at your desk!”

“So we created this thing - it took us four years to do it, so I called Satya (Nadella, Microsoft CEO), and the two of us talked about it…and so we had to create a brand new chip, we had to wait until Blackwell was done - and this thing has 1 petaflops of computing, and because we knew the models were going to be big, we had to redesign how the GPU uses memory - so this is the first Nvidia GPU with shared memory, one petaflops, Blackwell architecture - and also it has to run everything a PC doesn’t.”

“There’s three types of PCs, right," Huang outlined, "there’s the PC that basically you use to type in - that’s called a PC, there’s the Nvidia gaming PC, you’ve got GeForce in that, and you’ve got the Nvidia workstations that runs all kinds of applications…so we figured that if you’re going to have an AI go use the tools, it should be able to use every single tool…it runs everything, it runs every AI framework, it runs any AI model as well."

We're about to enter the final stretch of 2026, and with the Black Friday sales and the festive season looming on the horizon, it felt like as good a time as any to take stock of the headphones we've tested this year, and what has impressed us most. Because we've tested just so many great headphones so far this year, in this article I've narrowed things down to just the earbuds. Below, you can see all the pairs that impressed us enough to score four stars or higher in our tough tests.

Sennheiser has produced standouts at both ends of the price spectrum. For those feeling flush, the Momentum True Wireless 5 offer "superb" sound quality with a side order of sustainability, thanks to user-replaceable batteries in both the earbuds themselves and the charge case. Anyone on a tighter budget should take a look at the Sennheiser CX 80U — these wired earbuds deliver stellar sound (our tester noted "clean and agile bass, clear mids, and vivid highs") alongside solid noise isolation, for a price tag that'll give you change from $40 / £30.

Another big winner was the Samsung Galaxy Buds 4 Pro. Our reviewed noted: "they sound incredible, the noise cancellation really works, they look classy yet understated, and knock it out the park with the array of features offered in the app". There are a few little caveats, though: the battery life could be better, and you need a Samsung phone to really get the best from them.

We've also tested plenty of impressive open-ear buds this year. We found the Shokz OpenDots 2 ultra-comfortable and long-lasting, but a little prone to being knocked out of the ear. The affordable Baseus Bowie MC2 also delivered on the comfort front, and are a very solid choice if you're on a tighter budget.

Scroll down to take a look at the earbuds we've been most impressed with this year so far — hit the View details button to see each model's overall score, a rundown of the main pros and cons we noted in testing, and a link to read the full review. Note: you'll only be able to see product cards for the earbuds available in your region.

Earbud excellence Huawei FreeBuds Pro 5

Read our full review

Pros

• Excellent ANC
• Great-looking, comfortable design
• Packed with features

Cons

• Lossless only for Huawei phones
• Slightly chunkier than rivals
• No foam eartips

Sennheiser Momentum True Wireless 5

Read our full review

Pros

• Superb sound quality plus Dolby Atmos spatial audio
• Comfortable and premium design – and finally, user-replaceable batteries
• Lots of future-proof features

Cons

• Touch control area is a little small
• Ever-so-slightly bulkier than rivals
• ANC still lags a bit behind Apple, Sony and Bose

Samsung Galaxy Buds 4 Pro

Read our full review

Pros

• Fantastic sound
• Svelte look
• Wonderful noise cancellation

Cons

• Insecure fit
• Battery life could be much better
• Hard-to-replace ear-tips

Shokz OpenFit Pro

Read our full review

Pros

• Impressive noise reduction
• Dolby Atmos with head tracking for true-to-life listening
• Unbelievably comfortable

Cons

• On the expensive side
• Treble a little over eager

Cambridge A100 SE

Read our full review

Pros

• Spacious, informative and disciplined sound
• Dynamic and punchy presentation
• Impressive specification

Cons

• Not the most accomplished when it comes to rhythmic expression
• Don’t look or feel in any way special
• Touch controls could be more reliable

Sennheiser CX 80U

Read our full review

Pros

• Great sound quality with impressive detail
• Classy design at a low price
• Surprisingly good noise isolation

Cons

• No volume controls
• Some noticeable static when using mic
• Lacks the mid-range finesse of pricier rivals

Shokz OpenDots 2

Read our full review

Pros

• Powerful, bassy sound
• Good battery life
• Stable enough for runners
• Customizable EQ modes

Cons

• Prone to dislodging on contact
• Expensive
• Not suitable for helix piercings

JBL Live Flex 4

Read our full review

Pros

• Smart charging case is feature-rich and fun
• ANC surprisingly effective for open-fit earbuds
• Relaxed, comfortable fit
• Strong overall sound quality…

Cons

• Open-fit designs have drawbacks, sonically speaking
• Spatial audio could be better
• Not the most premium-looking buds

JBL Live Buds 4

Read our full review

Pros

• Fantastic, detailed audio
• Feature-packed, handy smart charging case
• Comfortable, secure in-ear fit

Cons

• Middling ANC
• Call quality not as clear as expected
• Spatial audio in need of improvement

JBL Live Beam 4

Read our full review

Pros

• Excellent audio quality with impactful bass
• Comfortable, secure fit
• Smart case is well-built and fun, even if it’s a bit gimmicky

Cons

• Disappointing call quality
• ANC isn’t quite top-tier
• Poor spatial audio setting

Shokz OpenDots Air

Read our full review

Pros

• Secure, reliable fit
• Svelte carry case
• Decent battery life

Cons

• Audio quality can be beaten
• Can occasionally pinch your ear
• Touch controls barely work

House of Marley Smile Jamaica USB-C

Read our full review

Pros

• Incredibly low price
• Sustainable construction
• Tangle-resistant cable

Cons

• Bass not the most refined
• No volume controls
• Could include more eartips

Baseus Bowie MC2

Read our full review

Pros

• Affordable price point
• Refreshingly bass-heavy sound
• Comfortable to wear

Cons

• Some charging case problems
• Not for audiophiles
• Hard-to-use touch controls

Earfun Clip 2

Read our full review

Pros

• Very affordable for open buds
• Comfortable and light
• AI translation is a cool feature

Cons

• Minor connectivity issues
• Budget build and fit won't suit everyone
• Sound needs EQ tweaking

Sony WF-1000XM6

Read our full review

Anker Soundcore AeroClip 2

Read our full review

Pros

• Ultra-comfortable yet secure fit
• Lightweight with a more compact case
• Solid sound quality for open-ears
• Great features with personalized EQ

Cons

• Pricier than their predecessors
• Open-ear architecture has its sonic limitations
• Sound a bit unexciting out of the box

Nothing Ear (3a)

Read our full review

Pros

• Fun, upfront audio quality
• Slight ANC uptick (and it was no slouch before)
• Curvier case and useful LED lights

Cons

• Still no wireless charging support or flagship hearing tests
• Battery life has taken a hit
• We need to talk about Call Recording

• For now you need to build the feature yourself on a Mac
• Native Player delivers similar audio but isn't open source
• Proposed as an extension to the Nuvio media player

A clever developer has come up with a way to deliver lossless Dolby TrueHD movie soundtracks, complete with Dolby Atmos effects, to home theater speakers from the third-gen Apple TV 4K.

The Apple TV only officially supports Dolby Digital Plus, which is the more compressed Dolby audio codec used by streaming services to reduce the amount of data used, while Dolby TrueHD is used on Blu-rays where bandwidth isn't a problem. So it makes sense for the Apple TV 4K to only use Dolby Digital Plus, since it's a streaming device — but that doesn't stop the enthusiasts from wanting the flexibility of playing files with TrueHD audio on their devices.

The system that Reddit user Agathorpt used to deliver Dolby TrueHD through the Apple TV 4K requires not actually delivering it in TrueHD at all, but converting it to other formats, converting the Dolby Atmos positional data into other formats too, then recombining them in a format that the Apple TV 4K can deliver, but in a higher quality.

How they got TrueHD Atmos on the Apple TV 4K

As mentioned above, the audio takes a bit of a journey here. The path is:

TrueHD Atmos → decoded audio + timed object metadata → fixed 9.1.6 speaker render → APAC → native tvOS playback.

APAC is Apple's Positional Audio Codec, and while it's not truly lossless (the source audio, TrueHD, is a lossless format but APAC is not) the consensus is that the results here are really impressive.

Basically, the system decodes the audio and the 3D Atmos positioning data separately, creates a new audio system where the Atmos effects are mapped to a 9.1.6-channel speaker configuration, then converts that to Apple's own proprietary spatial audio codec, which can then be played by the Apple TV 4K.

The goal was to replicate the audio functionality of a closed-source app called Native Player with an open-source alternative because "keeping the implementation closed… leaves everyone dependent on that vendor for fixes and future support," Agathorpt says. "Other players can't reuse or improve that code, and the community can't independently inspect how it handles the audio."

Apps such as Native Player support TrueHD Atmos but they aren't open source (Image credit: Native Development)

"The spatial audio has been working amazingly well for me," Agathorpt says. "It sounds excellent on my setup." Other redditors have tried the same software and are reporting great results too.

In order to copy what Agathorpt has created, you'd need several things: a Mac; Apple's Xcode development software; and your own Apple development account to sign an app: the full steps they went through are detailed here. The Apple TV 4K needs to be running tvOS 26 or later, and of course you'll also need movie files with 48kHz TrueHD Atmos audio.

There is hope from home theater enthusiasts that Agathorpt's project will be implemented in the Nuvio player for everyone and in other tvOS apps too, but for now you'll need to build it yourself. It doesn't seem too difficult to do, but if you're not the technical type you might want to wait for it to roll out as part of the app. It's been proposed for addition so hopefully that won't take too long.

Agathorpt is at pains to point out that they didn't write the software projects used in this process. It builds on the work of others, especially Superuser404notfound's work on the AetherEngine media engine and LibTrueHD object audio decoder.

One assumes that this will work with the new Apple TV 4K (2026) that Apple just announced too, though its price hike and lack of big new AV features means fans aren't particularly enamored.

Thinking of buying a new TV?

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• WindBorne's weather balloon fleet secures a potential $49 million Air Force contract
• The military has ordered $1.7 million in data so far
• WindBorne operates about 700 balloons collecting atmospheric measurements worldwide

WindBorne Systems has secured a potential $49 million US Air Force agreement, allowing the military to purchase atmospheric measurements gathered by autonomous balloons worldwide.

The California startup operates about 700 balloons, supplying readings to paying customers that use the data to develop AI systems capable of forecasting weather conditions.

Unlike conventional aircraft, WindBorne's balloons change altitude to encounter winds carrying them toward particular locations, adjusting buoyancy through air release and sand ballast.

Military spending supports WindBorne's expanding balloon network

The company uses atmospheric models to guide these movements, combining predictive software with an extensive fleet to place balloons where useful measurements can be collected.

Back in August 2026, WindBorne became the first company to guide an unpowered flight system into the typhoon's eye, collecting data from the typhoon.

“That’s a pretty novel step, and the two ingredients for that are one, having good wind and weather models, and knowing how to navigate them; and two, having a large enough constellation that you have balloons in the right places,” said Andrey Sushko, co-founder and CTO of WindBorne Systems.

“It’s one of the first times that a deep-learning model was used for actual operational decisions.”

The military agreement allows the Air Force to request measurements for particular locations and periods instead of purchasing a fixed quantity of information upfront.

Although the deal carries a maximum value of $49 million, actual spending depends on military needs and so far, the military has placed orders worth about $1.7 million.

“This is largely a vehicle through which continued growth of the constellation can be supported, as well as some work on research and development to continue to improve the hardware and performance of the balloons,” Sushko told TechCrunch.

The contract was awarded without competing bids, and Sushko said the government determined that “for this capability of collecting global data, we were the only company able to provide it in the way that met their needs for these operations.”

AI forecasting gives balloon measurements additional value

WindBorne also incorporates balloon measurements into WeatherMesh 6, its forecasting system, which the company claims performs better than the European Centre for Medium-Range Weather Forecasts' traditional approach.

The European Centre relies on atmospheric physics simulations, while machine learning systems derive predictive relationships from historical observations and generate forecasts using learned patterns.

Neither approach eliminates the need for reliable measurements, because forecasting systems depend on atmospheric information to estimate developing conditions and assess possible future changes.

AI forecasting has advanced alongside transformer architectures and access to large historical weather datasets, enabling researchers to develop models without repeatedly solving conventional atmospheric equations.

The company's claimed forecasting advantage remains its own assessment, and independent comparisons are necessary to establish how consistently WeatherMesh 6 outperforms established systems across different weather conditions.

Programming issues and software updates have been blamed for delays in getting hundreds of thousands of people registered to vote.

It's been a theory for quite some time now that Nvidia is — what's the polite way to put this? — let's say deprioritizing PC gamers, perhaps, or more specifically, its gaming graphics cards. But this week has added more evidence to the collective weight that GeForce GPUs are increasingly at risk of being sidelined further by Team Green.

I'm talking first about the story that emerged a few days ago, where a leaker claimed Nvidia is diverting all GB202 (Blackwell) chips to make AI (RTX Pro) graphics cards. That's the graphics processor used in the RTX 5090, but apparently not anymore; it's for AI boards only now, we're told.

That makes some sense as the RTX 5090 is already disappearing from shelves, certainly in the US, where you can't buy one for less than the best part of 10 grand (from gouging third-party sellers, with no major retailers actually having stock anymore). So arguably this effective discontinuation of the RTX 5090 is already happening.

On top of that, the latest developments are that Micro Center is reportedly listing the Blackwell flagship as 'not carried', and Nvidia itself has removed all mention of the RTX 5090 from its own GPU marketplace (though it is still mentioned elsewhere on the site). Search for the RTX 5090 on that marketplace, and you won't find it any longer — the highest-end graphics card listed is now the RTX 5080 (hat tip to Wccftech for spotting this being flagged up on Reddit).

Aside from an actual announcement from Nvidia (which probably wouldn't ever happen anyway), that seems like about as clear an indication you can get that the RTX 5090 has been shoved out of the exit door, never to return, because Nvidia needs those GB202 chips where they are most profitable. RTX Pro GPUs offer a much heftier premium that Team Green can heap on top.

A new Blackwell flagship?

(Image credit: Future)

So, what does this mean? For starters, if it is true that the RTX 5090 has been scrapped, the RTX 5080 becomes the flagship Nvidia GeForce GPU. Once the existing stock of RTX 5090s has been depleted, this could mean the price of the RTX 5080 is inflated. When I say inflated, I mean jacked up even more, because it has already seen some nasty price hikes. Currently, the RTX 5080 is at around $1,800 in the US on Newegg, or £1,450 on Overclockers in the UK – and those are the lowest prices; it goes up from there. It's a very similar story in many other regions.

This is clearly a concern for gamers as you can see from the Reddit thread I pointed out above, with comments such as: "If you want a 5080 better buy one while you can still afford one." Really, it's debatable if the card is anything like 'affordable' relative to its graphical frame rate muscle even now.

Here's where another rumor comes into play. Elsewhere on the grapevine, there's talk that Nvidia may actually release the RTX 5080 Super variant which has long been speculated about. This would be a 24GB graphics card, and it'd essentially replace the discontinued RTX 5090 – with the theory being that Nvidia would also ditch the vanilla RTX 5080 (16GB). This would leave the rumored RTX 5080 Super as the sole high-end GeForce graphics card, quite some distance above the RTX 5070 Ti.

If that happens, imagine the pricing concerns here – what MSRP would Nvidia attach to the RTX 5080 Super? And then consider the cost inflation that will be added when you take into account that this card will also be sought after for AI usage (as seen with the RTX 5090).

If an RTX 5080 Super is indeed inbound for early 2027, that would also suggest that the next-gen RTX 6000 family is further away than some rumors believe – as this Super version would be a stopgap GPU to keep the Blackwell range ticking over. The RAM crisis may have a fair bit to do with this strategy also, given that most predictions foresee it getting worse next year (and the misery continuing into 2028, and maybe beyond). Of course, launching a new GeForce range into such memory headwinds wouldn't be easy.

Software focus and streaming dreams

(Image credit: Nvidia)

All in all, it feels to me like there's a stream of subtle – and not so subtle – hints and rumors that Nvidia is very much slowing down the development of gaming GPUs (while not wanting to give that impression, naturally). The other part of the equation here is the increased focus on the software side, of course, rather than the hardware. DLSS 5 is out and Nvidia is already pushing hard with honing and refining that neural rendering tech, and something else that happened earlier this week was that Team Green highlighted some big leaps in performance here. Pretty huge strides forward, in fact.

Of course, you could argue that all the development resources being ploughed into DLSS – including frame generation, which got a hefty boost alongside DLSS 5 – shows Nvidia isn't abandoning gaming GPUs for PC gamers. And I concede that's true to an extent, but there is very much a vibe of a major slowdown leading towards the feared deprioritization of GeForce GPUs – and there's another angle to consider here too: cloud gaming.

Don't forget that GeForce Now also benefits from DLSS. Just this week, Nvidia added DLSS 4.5 override controls to the streaming service to help push up frame rates – those on the RTX 5080 (Ultimate) plan can use Dynamic Multi Frame Generation of up to 4x now. Ray Reconstruction for bolstering ray-traced visuals is available, too, among other options.

Would Nvidia rather have you subscribed to its cloud gaming service, making monthly payments, than buying a GeForce GPU for a one-off sum? It seems an obvious direction for the company to be heading in, and with increasing warning signs around Team Green's gaming GPUs, I'm becoming more convinced that this is where Nvidia's priorities are shifting for the future of PC gaming.

This isn't to say GeForce graphics cards are definitely on their way out, or that an RTX 6000 line-up won't happen – it's already well underway after all – but beyond the next generation, I'm increasingly dubious about where GeForce GPUs might fit into the big PC gaming picture. Between AI demand and cloud gaming (plus increasingly faster internet connections), there's a squeeze underway that could prove painful for the GeForce line-up – and the ongoing RAM crisis is hardly helping, either.