Technology News

Could the GTA 6 launch break the Australian internet? We asked NBN Co to find out

admin — Wed, 04 Nov 2026 10:43:11 +0000

NBN Co says Australia’s broadband network is ready for the expected surge in traffic when GTA VI launches
A previous major game release pushed NBN traffic to a record 32.91Tbps, around 30% above normal levels
GTA VI’s week-long preload period should help prevent a massive spike in network traffic on launch day
NBN Co believes GTA VI could set a new record for total download volume across its 7–8-day launch period

Grand Theft Auto VI is shaping up to be one of the biggest video game launches in history, and with millions of gamers expected to download Rockstar’s long-awaited sequel, it’s fair to wonder whether Australia’s internet infrastructure can cope with the demand.

After all, GTA VI is confirmed to require a hefty 212GB download, meaning even a single copy will consume a considerable amount of bandwidth. Multiply that by potentially hundreds of thousands of Australian gamers all wanting to get their hands on the game at once, and it’s easy to imagine the strain it could place on the country’s broadband network.

Meta is ending support for the Quest 2 and Quest 2 Pro VR headsets — as it promises its VR Glasses will be ‘another leap forward’ for users

admin — Wed, 04 Nov 2026 05:18:48 +0000

Meta has announced the end of support for two of its popular VR headsets
The devices affected are the Quest 2 and the Quest 2 Pro
Support will be discontinued at the end of 2027

If you own a Meta Quest 2 or a Meta Quest Pro virtual reality headset, we’ve got some bad news: Meta has announced it is planning to wind down support for these devices next year, leaving large numbers of users without support for their VR products.

An announcement on the Meta blog aimed at developers details how things stand for creators of apps and services made for these headsets. With the two devices “reaching the end of feature development⁠,” big changes are on the way.

For one thing, new app submissions for these devices will be closed in December 2027. For another, version 2.13 of the Quest’s operating system (due out in March 2027) will be the last one to work with these headsets. Quarterly security patches and critical bug fixes will continue “through the end of 2027,” Meta said.

Time to upgrade?

(Image credit: Shutterstock / Boumen Japet)

The Quest 2 launched in 2020, making it six years old at this point. Although we scored it highly in our review, it’s since been succeeded by the Quest 3, which is our pick for the best VR headset around. Evidently, Meta feels it’s now time to retire the Quest 2 and its Quest 2 Pro sibling, with the Quest Pro line having allegedly been phased out entirely in 2024.

Meta framed the change as one that’s paving the way for its upcoming VR Glasses. The company said that the Quest 3 and Quest 3S are “the baseline for Meta VR gamers” with its VR Glasses due to bring “another leap forward” for the industry. In that world, there’s apparently no more room for the Quest 2 and Quest 2 Pro.

Continuing app development for the Quest 2 and Quest 2 Pro might prove to be tricky, as Meta says it will not test new operating system and SDK releases on these products. Instead, it’ll be up to developers to ensure their apps function as intended.

Meta urged developers to assess how much of their audience is using Quest 2 and Quest 2 Pro devices and to consider splitting their releases to accommodate those on older headsets. If developers want to end support for these products, they should give users at least 180 days’ notice if customers could end up losing access to content or services as a result.

If you’ve got a Meta Quest 2 or Meta Quest 2 Pro at home, you’ll need to think about what you want to do going forward. While you should be able to continue using your headset, the lack of critical bug fixes after the end of 2027 could prove to be a problem. In that case, it might finally be time to upgrade to one of the best VR headsets to ensure you don’t miss out on anything.

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Steelseries Rival Pro review: peak performance with endless playtime

admin — Wed, 04 Nov 2026 01:42:39 +0000

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SteelSeries Rival Pro review

The SteelSeries Rival Pro feels as if it’s really pushing the boundaries of what a gaming mouse can do. Even when I glanced at the specs sheet, I was mightily impressed with the features on offer: interference-free ‘true’ 8K polling, hot-swappable batteries for endless play, and 42,000 DPI sensitivity.

But it’s one thing having a good set of specs, and another performing well in practice. So just how good is the SteelSeries Rival Pro? And is it worth its asking price of $179.99 / £169.99 (about AU$250)? Here’s my verdict after hours of testing.

First of all, this mouse feels incredibly comfortable to use — in fact, it’s up there with some of the best mice as far as ergonomics are concerned.

SteelSeries Rival Pro review: price & release date

Priced at $179.99 / £169.99 (about AU$250)
Released in October 2026

The SteelSeries Rival Pro was released in October 2026, alongside three other models: the SteelSeries Rival Pro Mini, as well as the SteelSeries Sensei Pro and Sensei Pro Mini. The Rival Pro is priced at $179.99 / £169.99 (about AU$250). That lands it firmly in the premium category, with rivals including the Logitech G Pro X2 Superstrike and the Razer DeathAdder V4 Pro.

SteelSeries Rival Pro review: specs

Swipe to scroll horizontally

Interface	Ultra Wideband Wireless (6GHz and above)
Battery life	Up to 60 hours 1K / 16 hours 8K
Sensor	TrueMove 42K Pro Sensor
Polling rate	8KHz (with 9KHz overpolling)
Buttons	6
DPI	42,000
Switches	Optical
Dimensions	4.95 x 2.69 x 1.66 inches / 125.7 x 68.2 x 42.2mm
Weight	1.9oz / 53g

(Image credit: Future)

Should I buy the SteelSeries Rival Pro?

Swipe to scroll horizontally

Attribute	Notes	Score
Design	Excellent ergonomics, lightweight, great build quality, a little prone to fingerprints.	4.5/5
Performance	True 8K polling with SuperSync, 42K DPI sensitivity, essentially endless battery life.	5/5
Value	Undoubtedly pricey, but has the performance to back it up.	4/5

Buy it if…

Don’t buy it if…

SteelSeries Rival Pro review: also consider

(Image credit: Future)

How I tested the SteelSeries Rival Pro

Tested intensively for hours
Used for gaming and general productivity
Made use of all features in the GG web app

I spent many hours testing the SteelSeries Rival Pro, during which time I used it for gaming and general productivity.

When gaming, I paired the mouse with the LG UltraGear 25G590B 1000Hz gaming monitor to get the best performance possible. I made sure to play a range of games, but mainly tested the mouse with the PC version of Cyberpunk 2077. On top of this, I made sure to try out all of the mouse’s features and customization options via the GG web app.

More generally, I’ve spent years testing gadgets here at TechRadar, where I serve as a Senior Reviews Writer. I’ve reviewed everything from premium Dolby Atmos soundbars to gaming software, and have tested a number of mice, including the recently released Logitech Mobi Fold.

Cybercriminals are stealing AI API keys and cloud credentials to hijack expensive LLM services and infrastructure
Stolen access is used to run AI workloads, create proxy services, and rack up massive bills for victims
Google warns “LLMjacking” is evolving into a specialized cybercrime ecosystem with dedicated monetization operators

Threat actors are using large language models (LLM) to support almost every stage of a cyberattack, from researching potential victims to developing malware and automating various attacks. However, using AI is not cheap, and for those eager to use frontier models, costs can stack up relatively quickly.

But cybercriminals are used to stealing things, so why not steal someone else’s tokens, too?

Everyone should be building with AI, and everyone should know its cost

admin — Mon, 02 Nov 2026 17:14:52 +0000

Generic AI tools promise to solve every problem under the sun, from running enterprise CRM to analysing why your cat isn’t drinking enough water during a heatwave. And when you try to use one tool with no defined role for every task, results are disappointing and erode trust in technology.

Multiply that pattern across hundreds of employees, and you get tokenmaxxing: prompt after prompt, refining AI output until something usable comes out.

Eric Simons

CEO and co-founder of StackBlitz, the parent company of Bolt.new.

Some organizations, by instinct, default to blaming the employees for tokenmaxxing, or double down on training on ‘responsible use of AI’.

Locking it down is not the solution

Traditional procurement systems were designed for traditional business software cycles. If building an enterprise system takes years, it’s not a big deal if procuring the right vendor takes years, too.

Routing AI through procurement to evaluate all options and keep the spend predictable initially felt to organizations like a safe and responsible choice. But software creation is very different today than it was even 5 years ago. Standard IT procurement still takes months, while opening a public LLM chatbot and asking it to generate a landing page is a matter of five seconds.

Policies tend to live their happy lives somewhere in the corporate portals, unbothered and forgotten unless an incident calls for a formal investigation. And when the approved systems are slow or restrictive, employees find workarounds. They turn to unvetted shadow AI tools for daily tasks and put proprietary corporate data into public models.

Look at the app prototypes that come out of it: the bad code is one problem, but the interfaces are off-brand, ignore corporate design systems and WCAG 2.2 accessibility rules, and lack basic authentication patterns.

Restricting AI use for employees in reality means they pay the £18 monthly subscription out of pocket and still use it. It moves the spend somewhere finance can’t see it, at least not immediately and not until it’s too late, adding a data governance problem on top.

The other consequence is harder to put on a balance sheet. A company where only engineers can create software moves at the speed of engineering’s queue, but opening options up to a wider group – product management, analysts, marketers – means a company can speed up innovation while having greater visibility into it.

The cost of ungoverned building

The pressure is already on. Teneo’s Vision 2026 survey of more than 350 public company CEOs and 400 institutional investors found that 53% of investors expect to see a positive ROI on new AI initiatives in six months or less, while only 16% of large-cap CEOs believe they can deliver on that timeline. The same CEOs report that fewer than half of their current AI projects are ROI-positive. Investors want returns on a clock that executives don’t think they can meet.

Volume is not the problem. Goldman Sachs Research forecasts that token consumption will multiply 24 times, to 120 quadrillion tokens per month, between 2026 and 2030, while semiconductor providers are already delivering 60% to 70% annual declines in cost per token. Tokens are getting cheaper, and we are going to use far more of them.

But who – or what – uses tokens is going to change. Organizations are building and deploying AI agents who act on their own. And agentic AI burns far more tokens than good old human prompting, because the queries repeat in sequence: a chatbot request multiplied by 20, or even 50, running unattended. Send an agent to work on an unscoped problem, and it will keep going long after a human would have stopped to ask whether this was worth it.

A lot of those tokens go into prototypes that never get to see the real world. Prototyping and sifting through ideas is how software evolves, but building with no eye on production drains resources. Gartner found that at least 50% of generative AI projects were abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, or unclear business value. If a prototype works in isolation but has to be scrapped because engineering would need to rebuild it from scratch, that’s pure waste.

Then organizations have to pay the bill twice – for inflated tokens and lost engineering hours. When teams build with generic LLMs and no guardrails, senior engineers spend hours cleaning up the output. That deepens the trust gap in technical teams. Stack Overflow’s 2025 developer survey found that 46% of developers distrust the accuracy of AI output. “Almost right” (but not quite) AI answers are the biggest frustration for 66% of developers, while 45% report that debugging AI-generated code is more time-consuming.

Access for everyone, accountability from everyone

The speed of code generation isn’t the problem. Lowering the technology barrier for non-engineers produces better software, software shaped by the people who understand the business need. AI building is an edge, and an edge works best when everyone has it.

But alongside democratized access to technology, we need new ways to think about accountability for how you use it and the associated costs. Modern corporate culture can promote unnecessary complexity in how we think and speak about our work. Being able to explain in simple words how you spent your day is a useful exercise, and we should apply the same thinking to AI spend.

So, stated plainly: tokens should be used for a job somebody actually needed. In turn, the user needs to be able to explain, beyond the buzzwords, what the purpose was behind a particular token spend.

To work, rather than become another source of corporate frustration, this accountability needs to be part of the environment offered. One way to start is to retire the do-everything chat box as the default. Instead, organizations should start thinking about how to give people task-specific agent workflows tailored to their roles, and build execution limits into the software environment.

This way, non-technical staff get brand guidelines, fonts, security rules, and components pre-coded into the environment, so the apps they build are compliant and production-ready by default. They don’t need to file separate requests because the shape of the environment already includes the right guardrails on security and spend.

What it looks like when it works

Bounded workflows pay off in ways you can measure. In a recent mid-sized company implementation, a 3-person data analytics team built an agent that analyses data across all their systems; it saved each of them 12 to 13 hours of manual work a week.

Three analysts now produce what once took a team ten times their size, and their daily job also got way more interesting. They spend their time on high-value strategy instead of gathering data by hand.

That agent has a clearly scoped job and executes it in line with pre-defined parameters – it is not a chat box with a company credit card.

Bringing together abundance and accounting

Uncontrolled AI costs and falling trust in technology have the same solution, and it is not rationing of tokens. Tokens will be getting ever cheaper, and their abundance is the point of this technology.

The answer is in defining and understanding the scope of AI. Give everyone the tool but make it purpose-specific from the start rather than a generic catch-all.

Every company I talk to can tell me what it spent on AI last quarter, but when I ask what they wanted to get in return, the answer is murky.

This lack of understanding of what job AI should do for an organization is the problem, and the surprising bill is only its symptom. The first step is for the organization to articulate, in the plainest words possible, what the tools are for before the tokens start moving.

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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

Windows 11’s new internet speed test was built in a flash by agentic AI — which is both impressive and worrying

admin — Mon, 02 Nov 2026 13:38:48 +0000

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Windows 11 just got a revamped internet speed test in preview

This feature was shuttled swiftly through development using agentic AI in combination with human oversight

Microsoft boasts that this is about: “Getting from idea to customer value faster. Without AI slop.” Others are more cynical, though

Windows 11 just got a useful change in preview, with its built-in internet speed test being revamped, and the new version was produced using AI (in conjunction with human oversight, I should add).

Windows Latest spotted that Marcus Ash, Microsoft‘s VP of Windows Design and Research, posted on X to explain that the “team moved pretty fast on this one”, adding that following feedback asking for the speed test to be improved, “within a day, Diego had a working prototype.” (Diego Baca is VP of Design for Windows).

Ash tells us: “From there, our connectivity team worked with partners to bring the experience into Windows. The design and build work was driven by an agentic AI workflow, with a person guiding the process in their spare time.”

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The Microsoft exec further explains that this is where he sees “a lot of potential in AI” and using agentic workflows, in terms of “Getting from idea to customer value faster. Without AI slop. That is the way.”

Analysis: AI, oversight, bugs, and jobs

(Image credit: Microsoft)

Of course, this is a controversial topic. Mention AI being involved in the coding of Windows 11 and some tempers will flare online, even though we know this is happening anyway. Microsoft has made no secret of that fact.

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Indeed, among the AI hate and flames on social media, a lot of people are point out that these days pretty much all software is being developed in conjunction with Large Language Models (LLM agents) — even Linux.

As one Redditor makes clear: “Newsflash — every single piece of software you use, except for open source projects that are making a statement, you should expect to have AI written code in it. It is pervasive, it is everywhere.”

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The key aspect here is exactly how the AI agent in question is employed.

It’s one thing for such an agent to be given a task to write a piece of code for a certain feature, and then just to slap that into your software or operating system as it is (or maybe after a cursory glance through the work). It’s another thing to have a human programmer working on a feature and using an agent to perform grunt-work (easy but perhaps time-consuming) coding therein — and then to properly check the AI’s output.

Theoretically, there’s nothing wrong with the latter approach, at least in terms of the soundness of the coding work. And as Microsoft’s Ash points out, this is really accelerating development times — meaning Windows 11 users will get new features (hopefully capabilities they want, like this improved internet speed test) much more swiftly. Which is exactly why almost every developer is now utilizing this tech.

What to read next

There are problems here, though, and they’re bound up in the public perception of Microsoft. Do Windows 11 users trust the company to be using AI in the right — i.e., properly checked and vetted — way? Not really, in many cases, as there remains a lot of doubt over the overall quality of Windows 11 code, and rightly so given the regularity, and severity, of the bugs we’ve seen (with more prevalence since 2024, and that 24H2 update).

As another Redditor observes: “People aren’t calling Windows slop because it’s being made using AI these days. They’re calling it slop because it’s being made using AI with woefully insufficient human oversight and even more woefully insufficient QA, leading to repeated disastrous bugs making it to production and end users being treated as beta testers. You want people to stop calling your product slop? Stop shipping updates that take down people’s computers or break existing features.”

The other issue here is that of AI stealing coding jobs from people. Agents may make things happen a lot more quickly, but as Ash notes, this revamped speed test was implemented mainly by an agent — including its design — with the software engineer “guiding the process in their spare time”, making it sound like a rather casual amount of effort on the human side of the equation.

Is it a particularly sensitive approach to be touting the benefits of AI — as real as they may be for an operating system’s ongoing development and maintenance — in this way at a time when Microsoft has been swinging the axe with various job cuts and pushing staff towards voluntary redundancy in a big way? I’d suggest it isn’t, and the timing of this particular push of the value of agentic AI is dubious in that respect.

At least in this case Microsoft is being upfront and transparent about what it’s doing — and we can expect a good deal more AI acceleration of Windows 11’s development going forward.

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AI isn’t enough: Why store infrastructure will determine which retailers scale successfully

admin — Sun, 01 Nov 2026 23:12:35 +0000

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The hardest part of retail AI may soon be the part nobody puts in the demo.

A model can recommend a markdown, predict a stockout, adjust a picking route or identify a pattern in store traffic in milliseconds. But a retailer only gets value when that decision becomes an accurate action in the physical store. Whether it’s a price change or a sensor reading, it has to arrive promptly for it to be useful.

Stefan Völkel

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There is already a striking gap between AI ambition and execution. Deloitte’s 2026 survey of 200 retail and consumer products executives found that 75% consider AI a top strategic priority, yet only 16.5% can quantify the return they are getting from it. Clearly, retailers believe in AI. The harder problem is turning isolated capabilities into something that works consistently across the business.

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For physical retailers, the challenge does not end with models, data or software. AI-driven decisions have to pass through stores containing thousands of devices, existing networks and years of installed technology. In complex environments, the building itself can affect connectivity. As the number of connected endpoints grows, so does the importance of the wireless foundation carrying information across the store.

For CIOs and CTOs, that means the next constraint on AI may be something that has been underappreciated: whether the store estate underneath it can execute reliably at scale.

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A store is not a cloud workload

Physical retail introduces conditions that software teams cannot abstract away. Stores have walls, metal shelving, refrigeration, back rooms, loading areas and legacy systems installed. They also have trading hours. Infrastructure changes that look simple on an architecture diagram can become expensive or disruptive when repeated across hundreds of sites.

Take inventory, for example. A forecasting model can improve a replenishment decision, but store-level execution may depend on shelf sensors, handheld devices, picking tools and accurate product information all exchanging data.

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Computer vision, digital displays and environmental monitoring add further endpoints and a wider mix of data, from routine device updates and sensor readings to visual content and real-time operational data. These workloads place very different demands on the network.

Some require high bandwidth or low latency. Others involve large populations of battery-powered devices sending regular updates across difficult environments. Individually, none is necessarily a network problem. Collectively, they can become one.

If a pricing decision cannot reach every store quickly, a replenishment signal arrives too late or each new application requires another costly infrastructure layer, the retailer loses part of the value the technology was supposed to create.

What to read next

One response is to add another gateway, controller or application layer for the next project. That can work for a pilot. Repeated often enough, though, every new device gains a separate integration path, security owner, management console and failure mode.

In difficult store environments, adding project-specific infrastructure to overcome coverage gaps can also change deployment economics. The technology works, but operating it becomes progressively harder.

The scaling problem appears after the pilot works

Retail technology teams are used to asking whether a new application meets its business case. AI adds another question: what happens to the infrastructure if the application succeeds?

A pilot may involve one store and a limited number of endpoints. Scale changes the economics. Coverage has to work throughout the building, updates must remain reliable during normal trading and security teams need a consistent way to onboard and manage devices. Operations teams also need clear ownership when something fails.

Connectivity should therefore be treated as an architectural choice, not a specification buried inside each procurement. The objective is not to replace every existing network or create one for every application, but to match workloads to the wireless layer best suited to their requirements while keeping the estate manageable.

Current designs may be adequate in some stores. In large or difficult formats, or where suitable retail IoT endpoints are growing quickly, an additional purpose-built wireless layer may be justified. The decision should be deliberate rather than inherited one project at a time.

Different workloads need different wireless foundations

The future-ready store is unlikely to depend on one network doing everything. It will more likely use a combination of connectivity layers, each supporting the workloads it suits best. Now, retailers should consider whether enterprise Wi-Fi should remain the de facto transport layer for every new category of connected device.

As electronic shelf label estates expand and retailers introduce more sensors and retail IoT applications, suitable workloads may be better served by a purpose-built wireless layer alongside the enterprise network.

This should not mean an isolated network for each use case, but a scalable, reusable connectivity layer able to support large device populations over time. Security belongs in the same conversation. Every endpoint needs consistent management, from trusted onboarding and credentials through to decommissioning.

Open standards remain important because they provide a more consistent foundation for future devices, applications and platforms. But openness alone is not the architecture. The decision is which workloads belong on which connectivity layer; standards help those layers remain manageable and adaptable.

The most successful retail AI programs will be judged by more than the quality of their predictions. As connected estates grow the test will be whether organizations can turn those predictions into dependable action across real stores.

Before approving the next AI pilot, retailers should ask what will their estate need if it succeeds, which workloads will grow, and should they all continue to be carried in the same way?

If success means more devices, richer data, more frequent updates or deployment across a much larger estate, the wireless architecture supporting those workloads has to be a central part of the AI strategy.

Retailers are not investing in AI tools or connectivity for their own sake. They want to protect margin, improve availability and make stores easier to operate. AI capability and AI scalability are different problems.

The objective should be an architecture in which new workloads can use the wireless layer best suited to them without forcing the estate to be rebuilt each time. The retailers that design for both will be in a much stronger position to turn ambition into reliable, repeatable execution.

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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

HubSpot AEO review 2026 | TechRadar

admin — Sun, 01 Nov 2026 14:11:29 +0000

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I review tech for a living, so asking AI for the “best website for tech reviews” came with a small risk of bruised pride. Would TechRadar get a mention, or would the answer send readers straight to the competition? That was one of the questions I put to HubSpot AEO.

HubSpot’s answer engine optimization tool tracks selected questions across ChatGPT, Gemini and Perplexity. It shows which brands appear, which pages the answers cite, and suggests content work based on those findings. What interested me was being able to read the answers behind the score, then check the sources for myself.

I used the free trial to set up TechRadar and track 11 of the 25 available prompts. My hands-on review covers setup and reporting, with product demonstrations and documentation filling in the workflows I didn’t try. One day of results can’t prove long-term gains, but it gave me enough to judge whether the tool helps make sense of AI search or simply adds another chart to the working day.

HubSpot AEO: Plans and pricing

(Image credit: HubSpot)

The standalone price for using HubSpot’s AEO tool is $50 a month, or $45 a month billed annually, which comes to $540 a year before tax. You also get a 28-day free trial that requires no credit card; there’s no need to buy another paid HubSpot product first.

Swipe to scroll horizontally

Access

Starting price (USD)

Tracked prompts

Monthly answers

AEO trial

Free for 28 days

25

2,500

Standalone AEO

$50 monthly or $540 yearly

25

2,500

Marketing Hub Professional

$800/month, paid annually

25

2,500

Marketing Hub Enterprise

$3,600/month

50

5,000

HubSpot for Marketers Professional

$900/month, paid annually

25

2,500

HubSpot for Marketers Enterprise

From $3,800/month

50

5,000

The AEO features are also included in Marketing Hub Professional and Enterprise plans. And, the HubSpot for Marketers option bundles Marketing Hub with Content Hub and include AEO too. Those prices cover the wider software package, and not an AEO surcharge.

Now, if you’re just on the AEO plan and want more credits, it costs $20 a month for another 10 prompts and 1,000 monthly answers. A standalone account tracking 45 prompts therefore costs $90 a month on monthly billing. Not to forget, tracking another brand requires a separate AEO instance.

Content generation has a separate cost too. Generating a piece from an AEO recommendation requires Marketing Hub Professional or Enterprise and uses 1,000 HubSpot Credits. Standard Professional plans include 3,000 monthly credits, while Enterprise includes 5,000. Extra credits cost $10 per 1,000, and unused monthly credits expire. CRM-based prompt suggestions also require Marketing Hub Professional or Enterprise.

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There’s one pricing wrinkle, though. Accounts enrolled in HubSpot’s flexible seats-and-credits program have separate terms, including two credits per AEO answer. That is a different model from the standard prompt allowances above. So, do check which model your account uses before budgeting.

HubSpot AEO: Setup

I signed up with my email and answered a question about my role. From there, HubSpot took me through the AEO setup one step at a time.

(Image credit: HubSpot)

Next, I entered TechRadar’s name and website address. That led to the competitor and audience settings, where I could narrow down what I wanted to track.

(Image credit: HubSpot)

I reviewed the suggested competitors and chose TechRadar Streaming, focusing on deal-seeking shoppers in the United States. I wasn’t convinced by every competitor suggestion, though. Some were publishing companies rather than the review sites readers might compare.

(Image credit: HubSpot)

HubSpot used those choices to suggest questions to track. I could edit or remove prompts before continuing. Several asked much the same thing, so I found the suggestions useful for getting started, but too narrow to cover different reader needs on their own.

(Image credit: HubSpot)

I then started the checks and followed HubSpot’s progress as it worked through the prompts. From there, I could open the dashboard and start exploring the answers, brand mentions and cited sources.

(Image credit: HubSpot)

HubSpot AEO: Features and specs

Prompt tracking

(Image credit: HubSpot)

The panel also separates Answers, Prompt Analysis and Recommendations. I like that structure because a weak score has somewhere to lead like you can inspect the answer, compare the sources, then consider a change. These are repeatable checks of chosen questions, not a log of what every buyer asks.

Brand visibility and competitor analysis

The earlier dashboard screenshot showed 6.67% brand visibility while the account held 10 prompts. Its date range ran from October 27 to October 27, with a single point on the trend chart. That was a baseline, not enough history to judge progress.

(Image credit: HubSpot)

The broader review-site question above comes from the later 11-prompt view. Comparing that single question with the earlier average would be misleading. The contrast shows how much the choice of questions matters, not that visibility suddenly improved.

Visibility measures how often the brand appears in the tracked answers. Share of voice compares its mentions with the selected competitors. Filters for engine, product, audience and buying stage help explain where a gap sits. And, Sentiment adds context about how the brand is described, rather than just whether it appears.

For TechRadar, I also need to separate being named as a recommended review site from having a guide cited in an answer. Both can be useful, but a mention score alone doesn’t capture that difference.

Citation analysis

This is the report I find most useful for planning content. A mention tells me the brand appeared. Citation analysis shows the pages and domains used as sources, including sources owned by other businesses.

My Citations screen splits the results by content type and channel. The first view concerns formats, such as blogs, comparisons, and documentation. The second concerns where the material comes from, with categories including owned, earned, peer, review-site, and user-generated sources.

(Image credit: HubSpot)

Earned and review-site sources occupied large parts of my channel chart, while owned content had a thin slice. I read that as a reason to inspect outside sources before assuming another page on our own site is the answer. These are sources across the tracked prompts, though. They don’t all necessarily mention TechRadar.

The wider report lets you inspect top domains and individual URLs, and compare owned-domain citations with citations whose source pages mention your brand. That helps distinguish if you should improve your own pages or earn coverage elsewhere. Neither count proves that someone clicked through or bought anything.

Content recommendations

HubSpot uses the prompts and cited sources to suggest workarounds for owned content, social amplification, and outreach. Each recommendation has a content type, channel, and priority. Opening it adds a suggested title, summary, audience, and keywords, with the prompts and citations behind the idea.

Suppose a tracked question keeps citing third-party comparisons. Here, updating a relevant comparison page or seeking inclusion in one could be more useful than publishing another broad guide. This is exactly where the source list helps make that choice.

The recommendation can then be marked complete. I see this as a useful brief for a writer or outreach lead, rather than a finished strategy. Someone still needs to check the cited pages, decide whether the suggestion fits the business, and do the work.

Content generation and impact tracking

For eligible Marketing Hub accounts, the Generate asset feature takes an owned-content or social recommendation into Content Agent. It uses the brief alongside brand voice, audience information, and cited pages. The draft appears in the recommendation’s Content tab, ready to open in the relevant editor.

I like the direct handoff because the source research and writing brief stay connected. It still needs an editor. That’s because a draft can follow a useful outline and still remain thin on facts, examples, or a reason to choose that business.

After publication, the Impact tab accepts the page URL and tracks whether it earns citations for the target prompts. This gives the recommendation a follow-up check instead of ending at “published”. It doesn’t prove that the tool caused an increase, and it isn’t sales attribution.

Is HubSpot AEO worth it?

I recommend starting with the trial for one clear product, audience, and set of questions. My setup exposed the main risk early: a tidy dashboard can still rest on repetitive prompts and a questionable competitor list. Fix those inputs before spending time on the score.

For a small team, the standalone plan is a reasonable way to replace repeated manual checks and find useful source pages. Existing Marketing Hub Professional and Enterprise users get more value from the connected content tools. That’s why I don’t see a case for buying the wider suite solely for AEO.

The buying decision comes down to what the reports change. Find a worthwhile page to improve, a source to approach, or a buyer question you’ve missed. Without that next step, more daily data won’t earn its monthly fee.

FAQs

What is HubSpot AEO tool?

HubSpot AEO is an answer engine optimization tool that tracks how your brand appears in answers from different AI tools. It monitors selected questions, compares brand mentions with competitors, and shows which pages get cited. It also suggests content updates and outreach opportunities based on those findings.

Is HubSpot AEO free?

No, HubSpot AEO is not free, but it offers a 28-day free trial with 25 tracked prompts and no credit card required. Standalone access then costs $50 a month, or $45 a month billed annually. AEO is also included with paid Marketing Hub Professional and Enterprise plans. HubSpot’s free AI Search Grader is a separate snapshot tool.

Is AEO different from SEO?

Yes, AEO differs from SEO in its main goal, though the two overlap. SEO focuses on visibility in search results, whereas AEO focuses on getting your brand mentioned or your content cited in AI-generated answers. Both depend on helpful content and pages that search engines can access. I see AEO as an extension of SEO, not a reason to abandon it.

How to do AEO optimization?

AEO optimization starts with checking how your brand appears in answers to questions your customers ask. I recommend reviewing the brands and sources cited, then improving pages that leave those questions unanswered. Make your content accessible to crawlers and seek relevant coverage on trusted websites. Track the same questions over time to see whether mentions and citations change.

How to write content for AEO?

To write content for AEO, answer a specific question first, then add evidence, examples, and context. I keep each section focused on one topic, with clear headings and tables or lists where they help. Each passage should make sense on its own. Original research, expert insights, and useful FAQs give the page more substance than a rewrite of existing advice.

A criminal investigation is now underway into Google’s AI data center project in Finland, where 330 hectares of forest were torn down

admin — Sun, 01 Nov 2026 10:35:06 +0000

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Google’s subsidiary Tuike Finland cleared and drained 330 hectares of forest at Muhos for a data center site

The company failed to get the necessary environmental impact assessments completed before carrying out the felling

A preliminary police investigation is now taking place, based on ‘ambiguities and ‌shortcomings in the drainage and water management’, although the results of this won’t be known until 2027

A preliminary police investigation is now underway into Google’s clearing of a forest in Finland to make space for an AI data center.

As The Guardian reports, the allegation is that Google‘s subsidiary Tuike Finland cleared and drained 330 hectares of forest at the site in Muhos before it had ensured the necessary environmental impact assessments were completed.

Yle (Finland’s public broadcasting company) further reports that the police have said that the investigation is into suspected environmental damage, but the other possible charges could be added depending on its ongoing findings.

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Finnish police said: “Based on preliminary information, there have been ambiguities and ‌shortcomings in the drainage and water management at the construction site of Tuike Finland Oy’s datacentre project.”

The investigation will be far from a short process, though, and won’t be completed until some point in 2027.

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Construction work was stopped on the data center in Muhos, and also at a second data center site at Kajaani-Otanmäki, on October 14. In both cases, the environmental assessments were apparently incomplete.

Antti Järvinen, who heads up Google Finland as country manager, apologized for the forest clearances, but said that Google had acted according to the “best understanding we had of the legislation at the time.”

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Analysis: the rush for AI data centers

(Image credit: Microsoft)

Some 400,000 trees have reportedly been felled across both sites (250,000 of those at Muhos). As The Guardian makes clear in a separate report from last week (via Tom’s Hardware), there are a lot of very unhappy Finnish people, as you might imagine.

Many of them question the building of data centers at all, and the AI gold rush that’s the driving force behind this activity. There’s apparently little direct benefit for the people of the country where these projects are underway, with only a small number of local jobs created. As a professor of computer science at the University of Helsinki, Jussi Kangasharju points out, “Really, for Finland, there are no benefits. They are international companies serving international customers. Datacentres ultimately have little benefit for others except the datacentre operator.”

Others obviously worry about the impact on wildlife. Finnish conservationists inform us that the three-toed woodpeckers in the forest have been displaced, and The Guardian highlighted that in Otanmäki, wolves that had nowhere to go when the trees were felled ended up coming into a local village.

It will be some time before the criminal investigation is completed, as noted, but you can bet that the results are going to be closely watched.

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The AI Act’s longer runway is an opportunity to build governance that lasts

admin — Sun, 01 Nov 2026 05:10:45 +0000

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Transparency duties under Article 50 have begun to apply, even as the EU AI Omnibus implementation for many high-risk systems has moved to December 2027 and, for certain AI-enabled products, to August 2028. For organizations building and deploying AI in Europe, the message is clear: regulators extended the timeline on complexity, but not on transparency.

While some organizations may view the extension as welcome breathing room, the time would be far better used preparing for the future of AI. To do that, we need to frame the challenge around creating the conditions for long-term innovation, rather than around compliance deadlines.

Ivana Bartoletti

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Global Chief Privacy and AI Governance Officer at Wipro.

As AI becomes embedded in products, services and decisions that affect people’s lives, organizations need confidence in the systems they are deploying, and users need confidence in the organizations deploying them. That work cannot begin in 2027. It needs to start now.

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Compliance is achieved at a point in time; governance is built as a capability. A deadline can be moved, but an institutional capability cannot simply be created at short notice.

Start with the AI already in use

The starting point for every organization should be an honest assessment of whether it has a reliable picture of the tools, outputs and interactions that Article 50 covers. As we have seen in recent years, AI systems can quickly become more deeply embedded in business over time as employees explore new tools and suppliers innovate and ship new updates.

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This natural expansion is a normal part of adopting a new technology, but it can result in AI becoming a core part of business processes before the proper governance is in place. Governance is most effective when it shapes those decisions from the beginning.

Attempting to retrofit governance after AI has become embedded is significantly more difficult, more expensive and ultimately less effective, and the resulting loss of trust can be more costly than the upfront work required to prevent it.

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Extend the controls already in place

The good news is that many of the disciplines required by the legislation should already be familiar. They build on existing practices around privacy, security, risk management, accountability and operational governance, and much of the challenge is around applying established governance disciplines to a rapidly evolving technology landscape.

Organizations that have taken data protection regulation seriously, and already understand impact assessments and effective governance processes, are well positioned to meet many of these requirements – there is considerable overlap.

The task is to extend them for AI, not replace them. A privacy impact assessment can identify data risks, but it cannot on its own establish whether a model is dependable in a particular context, whether someone can meaningfully question its output or whether an automated process disadvantages a group.

What to read next

Those questions should be built into existing workflows, particularly when AI is bought rather than built. Before a capability is embedded in software or a critical process, organizations need clarity on data treatment, service and model changes, incident response, support for transparency duties and the evidence a provider can supply. Even where the underlying model comes from a third party, organizations still need to govern how it is selected, configured and used.

The sovereignty question

Alongside the AI Act, the other crucial question impacting the way we think about governance in Europe is sovereignty. As the debate gains momentum across Europe, the practical issues raised are becoming clearer. Where is data stored? Which providers do we depend on? How resilient are our technology choices? What happens if commercial, legal or geopolitical circumstances change?

It is worth being precise about what sovereignty can realistically mean for enterprises. Full compute sovereignty, including control over chips, infrastructure and supply chains, is far beyond the reach of most organizations. The more practical goal is compute resilience: understanding where critical dependencies sit, ensuring no single vendor, cloud provider or model becomes a point of leverage, and retaining control over the layers of the AI stack that remain within the organization’s influence.

Open source can support this by providing greater transparency and control over code, but it does not remove infrastructure dependency on its own. An organization may use an open model and still rely on external compute or cloud capacity to operate it.

Organizations should adopt a sovereign-by-design approach to innovation. Sovereignty should not be considered after technology decisions have been made, it needs to be embedded into decisions on data architecture, vendor selection and risk management from the outset, with resilience, not full independence, as the realistic target.

It is also important to note that not every dataset, process or workload carries the same level of risk. Organizations should take a tiered approach, applying the strongest sovereignty requirements where data sensitivity, regulatory exposure or operational importance demand it.

These are not abstract concerns. Viewing AI governance and sovereignty as interconnected challenges creates an opportunity to build a more coherent approach. This is not a case for technological isolation or for stepping back from global technology ecosystems, the aim is narrower than that. It is about ensuring organizations retain meaningful freedom of action in an increasingly complex and uncertain environment.

The real opportunity

The debate about innovation, regulation and sovereignty will continue. Europe will keep refining its approach to AI, and organizations will keep adapting to new technologies, new providers and new risks. The more important question is whether organizations use this window to build the capabilities they will need long term, rather than treating each regulatory milestone as a separate deadline.

Governance should not be viewed as a compliance exercise that slows innovation. It is an institutional capability that enables organizations to innovate sustainably, adopt new technologies with confidence, and remain resilient as markets, regulations and geopolitical conditions evolve.

The ultimate goal is innovation built on trust, and we have the ability to deliver that regardless of regulatory deadlines. The real test is not whether an organization built every part of the system it relies on, but whether it understands its dependencies well enough to change course if circumstances demand it.

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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

Access	Starting price (USD)	Tracked prompts	Monthly answers
AEO trial	Free for 28 days	25	2,500
Standalone AEO	$50 monthly or $540 yearly	25	2,500
Marketing Hub Professional	$800/month, paid annually	25	2,500
Marketing Hub Enterprise	$3,600/month	50	5,000
HubSpot for Marketers Professional	$900/month, paid annually	25	2,500
HubSpot for Marketers Enterprise	From $3,800/month	50	5,000

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HubSpot AEO review 2026 | TechRadar

A criminal investigation is now underway into Google’s AI data center project in Finland, where 330 hectares of forest were torn down

The AI Act’s longer runway is an opportunity to build governance that lasts