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Businesses invest heavily in attracting, developing and retaining talent. At the same time, many continue to view workplace technology primarily through the lens of procurement cycles, standardization and cost. Yet the technology employees use every day shapes how they experience the business and how effectively they can contribute.

As AI becomes embedded in everyday work, that connection becomes even more important. The organizations that realize the greatest value from AI will not simply introduce new tools. They will ensure employees have the technology, support and confidence to use them effectively.

The employee experience starts with technology

When workplace technology works well, it is largely invisible. When it does not, the effects are felt in everyday moments: a video call that starts late because something will not connect, a laptop that struggles through a day of meetings, or a workspace setup that is inconsistent from one location to the next. Individually, these are minor frustrations. Collectively, they influence productivity, engagement and perceptions of the workplace over time.

Employees do not judge technology by specifications or refresh cycles. Increasingly, they judge employers by the quality of the digital experience they provide and whether they can work without friction. As a result, organizations are increasingly looking beyond individual devices and considering the broader technology environment.

The PC, display, peripherals and collaboration tools all contribute to the employee experience, shaping how effectively people can collaborate, stay productive and adapt to changing business needs.

From access to enablement

This conversation is becoming more significant as AI moves from experimentation into everyday workflows. As these capabilities become more commonplace, organizations are recognizing that successful AI adoption depends on more than simply providing access to new tools. It requires the surrounding systems, processes and technology environments that enable employees to use them effectively.

This challenge is reflected in Microsoft's 2026 Work Trend Index, which highlights a growing "Transformation Paradox": employees are increasingly ready to work with AI tools, while many organizations are still adapting their systems, processes and operating models to fully support them.

While AI is often discussed in terms of applications and platforms, employees experience it through the devices they use every day. For most employees, the PC remains the primary workspace: the place where meetings happen, content is created, decisions are made and work gets done. As AI is embedded in everyday business applications, the role of the PC is evolving.

It is no longer simply a gateway to information or a communication tool. Increasingly, it is becoming a platform through which employees interact with AI-powered capabilities as part of their daily work.

As these capabilities become embedded into everyday workflows, employees can spend less time summarizing meetings, searching for information or completing repetitive administrative tasks and more time on work that requires judgement, creativity and human interaction. The outcome is not simply productivity in the traditional sense. It is a better day-to-day experience of work.

The technology behind workforce readiness

Workforce readiness increasingly depends on whether employees have the tools and support required to make effective use of emerging capabilities. This is where device strategy becomes increasingly important. Ageing technology rarely creates a single catastrophic failure. More often, it limits potential through delays, workarounds and interruptions that make it harder for employees to take full advantage of new ways of working.

At the same time, AI is raising expectations about what employees should be able to accomplish. If organizations want people to embrace new ways of working, they need to consider whether the technology environment supporting them is fit for that purpose. That means looking beyond refresh schedules alone and considering the broader role workplace technology plays in enabling productivity and workforce readiness.

Why device strategy is becoming talent strategy

Organizations do not build successful workplaces through technology alone. Yet it is becoming increasingly difficult to separate workforce outcomes from the technology environment that supports them.

That is why the conversation around AI PCs is expanding beyond IT and procurement to include HR and business leaders. As organizations look for ways to improve employee experience, strengthen productivity and prepare people for AI-enabled work, device strategy is becoming part of a broader talent conversation.

In the past, technology was viewed as infrastructure that supported work. In the AI era, it is increasingly becoming infrastructure that supports talent. Organizations that recognize technology as part of their talent infrastructure will be better positioned to attract, empower and retain the people they need to succeed.

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Russian President Vladimir Putin says he has freed an American, Stephen Hubbard, from a Russian prison following a request from President Trump.

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Psychologist Abraham Maslow once warned: “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” Agentic AI is becoming the enterprise version of that hammer.

Once people understand what agentic AI can do, the list of possible use cases grows quickly.

While that enthusiasm should be applauded, it can lead to strategies that are back to front “If AI is the answer, what’s the question?” By the time it comes to looking at results and whether the investment in AI has been fruitful, it’s hard to identify what’s been achieved and why achieving it was important in the first place.

Start with the value, not the agent

In the rush that followed ChatGPT, many organizations deployed AI tools to their workforces at large with no defined value or end goal. People started working differently almost immediately, but with no baseline captured, there was no way to know what had actually improved. Who knows how long it takes to write a coherent email and who knows what time is saved by having ChatGPT rephrase it for you!

This is what McKinsey calls the “gen AI paradox”. Its 2025 survey found just 39% of organizations reported any enterprise-level financial impact from their AI investment, despite mass adoption.

Organizations need to be precise about what they want to measure. There are three layers to this and most only capture the first.

• Adoption: are people using it?
• Behavior: are people doing their jobs differently?
• Outcome: revenue per salesperson, tickets closed per engineer, cycle time on a review gate.

Adoption and outcome together show a correlation; for example, people who use AI sell more. But it’s the, often missed, behavior layer in the middle that turns that correlation into causation, i.e. they sell more because they are now able to use AI to fill in more client reports, leaving more time to hit the phones.

Know your workflow

If organizations can’t see how work is really being done, two things happen. The baseline measures the wrong process, making any claimed improvement meaningless and an agent given the wrong steps cannot complete the job.

One procurement process at Atos looked like a four-step workflow on paper, but when we looked carefully and asked those doing it, it was closer to 10. People moved between Enterprise Resource Planning (ERP), Outlook and spreadsheets, copying information, waiting for replies and re-entering data manually.

A baseline on four steps would have measured the wrong process and an agent given four steps couldn’t have completed a job that takes ten.

Not every problem needs an agent

Understanding the workflow also shows whether agentic AI is the right technology at all. If tasks follow fixed rules “if this happens, do that” the answer may be Robotic Process Automation (RPA), workflow automation or a process change.

Agentic AI is better suited to workflows where the system needs to interpret context, coordinate across tools, make recommendations or act within defined boundaries. Even then, it needs a clear measure of success and a credible business case. The economics of deploying Agentic AI are still in infancy, but this will act as a natural filter for only the most valuable cases.

Prove the value before building

Value is where the tough work of a clear business case comes in. Proving the value of Agentifying a process before any code is written.

This requires access to good data sets. Evidence on cost and business value before introducing Agentic AI is needed before budget is committed. Where the benefits are measurable and enough open data exists, it’s possible to build something real in a week. Where the data still requires work, it is always worth refining the data you have before attempting to justify a business case.

Work managed upfront will save cost and time and give you evidence of value.

Build a portfolio, not a queue

Once there is a shortlist of use cases, organizations can prioritize against financial impact, feasibility, process impact, technology requirements, human impact and risk. Scoring across multiple factors rather than ranking on ROI alone creates a portfolio instead of a queue. A lower-value use case with fast impact might prove value early and build credibility for agents that follow. A high-value use case with a two-year runway may need to wait.

Budget for a new mortgage

Even once the right case has been selected, many organizations still struggle to see impact because they focus on the build, then under-resource the work needed to make the change land.

Gartner calls this a “transition mortgage”, where AI adoption demands more support than traditional technology, often requiring 25% more training effort and up to 200% additional change management.

In our own organization, we were guilty of building independent tools in different regions and teams to solve the same business problem. We have now established clearer oversight of our use cases. The issue was not the quality of the tools, but duplicated effort, cost and resources.

Not every workflow needs a hammer

C-suite leaders should start with fewer use cases where the value and workflows are clear, the baseline is measurable, the data is available and the role of the agent is well defined. Then they should budget for communications, adoption, measurement and scale.

The value from agentic AI is not measured by the number of agents an organization deploys. It is measured by whether the work changed and whether the organization can prove it. Agentic AI is a powerful hammer, but success lies in understanding which problems are nails and which are not.

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The most fascinating thing about Apple's new iPhone Duo – and others like it – may not be that it folds. It may be the innovation required to keep it cool. Because at a time when performance demands from edge AI continue to increase, we’ve now taken available smartphone real estate and – though not exactly halved it – constrained it more than ever. And that impacts heat. Which impacts performance.

What makes the Duo interesting isn't simply the engineering required to create a folding phone. It's that Apple is confronting the same challenge every edge device manufacturer now faces: AI capability is increasing faster than our ability to manage the heat it generates.

That’s why I believe we’re entering a new phase of device innovation, with thermal design increasingly shaping what’s possible. In the edge AI era, managing heat helps determine what a device can do, how long it can sustain performance, and how small or thin it can be.

The iPhone Duo is just the latest reminder that smartphone manufacturers have run into a stubborn engineering problem: How to keep mobile processors cool when there’s almost nowhere left to put a cooling system.

For its part, with the Duo, Apple has packed its A20 Pro and other heat-generating electronics into an exceptionally thin, foldable design. It’s also created a custom vapor chamber to help dissipate heat and sustain performance in the new form factor.

The latter is more than an engineering footnote. It’s a glimpse at a challenge the edge AI industry is starting to confront. Thermal design and industrial design are inseparable. And new solutions to thermal challenges must be considered.

Foldables Make the Challenge Obvious

Foldable phones make the thermal management challenge easy to perceive. Conventional smartphone design gives engineers a relatively predictable enclosure in which to arrange the processor, battery, cameras, and thermal management system. A foldable must accommodate a hinge and two display sections while remaining thin enough to fold comfortably and fit in a pocket.

At the same time, the processor and other electronics still generate heat – more, even, when we consider how people are starting to use mobile devices. Which means foldable designs necessarily concentrate significant heat-generating components into smaller and thinner portions of the device, precisely where space for thermal hardware is already constrained.

In general, vapor chambers remain one of the most effective ways to manage heat in a compact electronic device. They work by moving heat away from a concentrated source, such as a mobile processor, and spreading it across a larger area. But vapor chambers ultimately spread heat rather than remove it.

Sure, as processors become more powerful, manufacturers can increase the size and effectiveness of the vapor chamber. But eventually that thermal component itself becomes a design constraint, especially in foldable phones.

Agentic AI Makes the Challenge Urgent

In foldables, innovating around thermal management is itself an admirable design goal – if folding were the only technological advancement engineers had to tackle. But they’re pushing the envelope on physical design at the exact moment when smartphones are being asked to do much more. Namely, to process agentic AI workloads.

As agentic AI moves from the cloud to edge devices – AI-capable smartphones, wearables, glasses, and more – thermal management becomes the industry's most important challenge. Imagine a new foldable smartphone with its own specialized thermal management system, designed to accommodate an innovative new form factor. Now imagine asking that foldable to process vast amounts of data differently than before.

Unlike traditional mobile apps that perform discrete tasks, AI agents operate continuously. They sense their environment, preserve context, analyze streams of data, and act in real time.

In my view, the industry is still underestimating how significant this transition will be. Historically, a smartphone's thermal profile was defined by short bursts of activity: launching an app, streaming a video, capturing a photo. Agentic AI at the edge changes that model entirely. Devices are moving toward persistent intelligence, continuously sensing, processing, reasoning, and responding. With these sustained workloads, a device’s ability to manage heat becomes a defining constraint on its performance.

Passive thermal technologies remain indispensable. Vapor chambers, graphite, heat spreaders, and advanced materials will continue to play a critical role in future devices. The challenge is that Edge AI introduces thermal loads that increasingly benefit from more than heat spreading alone. As devices become thinner and workloads become more persistent, designers will need additional ways to not just to move heat away from sensitive components, but to move heat out of the system entirely.

Micro-Cooling Complements the Vapor Chamber

So here we are, with foldable smartphones capable of agentic AI processing. It’s a perfect storm of thermal design challenges. Engineers can continue enlarging or customizing the thermal systems they integrate into new and different form factors. Or they can find other ways to extract more thermal performance from available space.

The latter approach is where active, micro-cooling technology becomes a viable and important solution.

Unlike miniature rotary fans, which exist today and take up precious real estate of their own, MEMS-based piezoelectric actuators, fabricated in silicon, generate airflow inside extremely compact packages. Basically, it’s a fan on a chip, capable of precise active cooling that fits where conventional solutions cannot.

With micro cooling (µCooling), heat isn’t just spread out, it’s removed from the device entirely before it can sink into the case, delaying or eliminating thermal throttling. This hybrid approach to cooling in highly constrained spaces delivers better thermal performance in today’s AI-enabled smart devices.

It also creates greater design flexibility – a must as devices themselves become more physically flexible. If active µCooling can provide additional thermal performance, manufacturers may not need to maximize the physical dimensions of vapor chambers. A more appropriately sized, passive thermal system could be paired with localized active µCooling and thereby free up internal volume for other components and give designers more flexibility in how a device is assembled.

Put simply, with µCooling, thermal hardware wouldn’t have to consume as much of a device’s design envelope.

µCooling As Part of the Compute Architecture

The implications extend beyond folding smartphones. AI glasses, headphones, watches, robots, and other edge devices face the same fundamental problem, often with even tighter constraints on size, weight, and power.

The industry wants to put more intelligence into mobile devices while simultaneously making them smaller and more comfortable. That creates a physical contradiction. More compute generates more heat. Smaller devices provide less room to manage it.

µCooling offers a way to change the equation. Not by eliminating vapor chambers and other passive solutions, but by supplementing them with localized active cooling that provides additional thermal headroom while giving product designers more freedom.

As AI runs more continuously in the background, edge devices will spend more time computing and less time idle. Sustaining performance will matter as much as reaching peak capability. Foldable phones make this challenge acute, but it extends across the edge AI market as more intelligence and local processing place greater demands on compact hardware.

That intelligence has a thermal cost. Over the next decade, progress will depend as much on what physical devices can sustain as on what AI models can do. That’s why I believe thermal management is becoming a foundational technology of the edge AI era. The companies that solve this challenge will help define the next generation of intelligent devices.

Manufacturers already know this, which is why thermal management is so essential to product design. µCooling is the innovation behind the innovation – like smartphones that fold.

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Arrests for more minor offenses, including vagrancy, rose in the U.S., even as violent crime rates dropped to historic lows, according to an analysis from criminal justice reform advocacy group FWD.us

(Image credit: Spencer Platt/Getty Images)

The cost of having a baby is difficult to predict. The amount families pay varies widely. Here are five tips to help you prepare for the often unexpected costs of pregnancy and giving birth.

The "enterprise brain" is a term featuring more and more prominently in the enterprise technology market and at the C-suite level. Technology vendors of all varieties are scrambling to connect internal documents, knowledge bases and structured data systems into a unified architecture.

The compelling goal is to provide AI agents with as deep organizational context as possible so they can reason, automate and assist with scarcely believable accuracy.

But as businesses pour resources into linking their databases, policy documents and CRM entries, a significant blind spot remains.

Efforts to build an enterprise brain often overlook the intelligence contained in the millions of conversations employees are already having with customers, colleagues and candidates across phone calls and messaging channels.

Documents tell you what your policies are and CRM records can tell you what happened, but business conversations tell you why an incident happened.

For any company that is serious about building a true enterprise brain, conversations cannot remain a missing layer of organizational intelligence.

Beyond the confines of structured data

Standard business tools are excellent at tracking outcomes but terrible at preserving nuances. Structured systems may record instances of an enterprise deal stalled, an account at risk of churning or a candidate withdrawing from an interview process. But it of course can’t put its finger on the actual trigger.

A deal doesn’t just stall, there will always be a key factor that changed the buyer's mind during a late-stage call. Similarly a candidate doesn’t just decline an offer, maybe a competitor introduced a work-from-home policy during a quick negotiation chat.

When organizations rely solely on static databases to train AI models or inform strategy, they are feeding their enterprise brain an incomplete diet. They receive the post-mortem without the symptoms, and AI needs the sometimes messy, real-time context found in human dialogue to understand the operational reality of a company.

Actionable context

Artificial intelligence has reached a level of maturity where analysing high-volume voice and chat interactions is no longer a nice to have or a bonus. But just recording every interaction and dumping raw transcripts into a data lake, which effectively hoards unstructured audio, solves absolutely nothing. To make conversational context truly useful, businesses must focus on a few core operational pillars.

First, conversational insights must automatically sync with existing enterprise systems rather than living solely within a VoIP dashboard, allowing real-time data from a support call to enrich the CRM and trigger automated alerts. Second, the extracted intelligence needs to be routed immediately to the teams equipped to act on it. For example, if sales calls routinely show that prospect friction stems from a specific pricing tier, that signal should flow directly to product strategy and marketing rather than remaining trapped in the revenue organization.

Finally, while AI should perform the heavy lifting of summarization, sentiment analysis and pattern recognition, human judgment must always remain the final filter for important decision-making. While this may seem obvious, it is amazing how overly reliant on AI many employees have quickly become, including using it for critical business judgement calls.

Breaking down knowledge silos

Knowledge silos continue to be huge challenges for many organizations. Sales teams learn what buyers want, support teams learn where the product fails, and recruitment learn how the market perceives employer branding. All is highly valuable information, but all too often these groups don’t share a unified intelligence pool.

When conversational context is ingested into the enterprise brain, these walls dissolve across every department. For instance, when a prospect mentions a niche feature gap during a sales demo, that insight can prompt product teams to prioritize the feature while sales updates its outreach messaging.

Similarly, if a customer casually mentions an upcoming corporate restructuring on a support call, account management can initiate proactive outreach to protect the account before contract renewal.

By connecting these conversational dots across departments, organizations shift from reactive firefighting to proactive alignment.

Value over volume

Now this is not to say that trying to capture, transcribe, and analyze every single internal interaction is the way forward. Because it goes without saying, this is borderline impossible. If attempted, it can lead to sky-high compute costs and even legitimate anxiety regarding employee monitoring.

To build a functional, trusted enterprise brain, organizations must prioritize value over volume. The focus should be on high-value workflows where conversation directly drives business outcomes, such as customer-facing revenue calls, critical support interactions or key talent acquisition touchpoints.

Focusing on these targeted areas minimizes data noise while delivering immediate ROI. Organizations must build trust by maintaining clear boundaries. Conversational analysis should focus on market trends, product feedback and operational blockers instead of micromanaging employee tone or enforcing rigid scripts.

The complete enterprise brain

A truly intelligent enterprise cannot run on paperwork and revenue tables alone. It requires the context and nuance that only human interactions provide.

As enterprise AI continues to evolve at rapid pace, having the largest static document libraries will count for nothing. The organizations that thrive will be the ones who successfully pair their structured business data with the rich, dynamic intelligence hidden inside their everyday conversations.

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Trump envoy Steve Witkoff to visit Russia in hopes of ending war, Trump campaigns in North Carolina, Saudi Arabia cancels 'Davos in the Desert' due to air strikes.

India is plunged into political crisis after 130 million names were struck off voter rolls during a massive revision.

(Image credit: Elke Scholiers for NPR)

Britain offers military support as Saudi cancels renowned 'Davos in desert" investor meeting due to strikes from Houthi forces in Yemen.

The Sixers got a first look at the potent lineup they put together this summer in hopes of winning their first title since 1983.

(Image credit: Frank Franklin II)

Nearly a decade after Boeing unveiled its largest 737 Max jet, the company has cleared a major hurdle in its effort to bring the plane to airlines.

(Image credit: Lindsey Wasson)

Ukrainian President Volodymyr Zelenskyy said the change happened after talks with his American counterparts, including a phone call with U.S. Secretary of State Marco Rubio.

(Image credit: Andrii Marienko)

Fresh curfews were being imposed across the city centers of Paris and Lyon on Tuesday after the first quiet night since France's most destructive rioting in years.

(Image credit: Christophe Ena)

The U.S. is the only major economy where planet-warming pollution dramatically increased last year. The United Nations warns the planet is on track for catastrophic warming.

(Image credit: Charlie Riedel)

Election officials in Los Angeles County say their preliminary analysis of the federal government's list of 20,000 "likely" noncitizens registered to vote in California shows the list is "unreliable."

(Image credit: Frederic J. Brown)

• US data center developers have requested 1,066GW of grid connections, but Wood Mackenzie expects utilities to commit to only about 28%
• Utilities are demanding collateral, longer contracts, and study fees; Exelon's high-probability load fell from a whopping 18GW to 11GW once it insisted on the former
• Because grid costs are shared, phantom demand risks landing on household bills, and nobody knows how much of it is real yet

US utilities and grid operators are now demanding higher rates, longer contracts, collateral, and steep engineering study fees from data center developers, in an attempt to weed out what the industry calls "phantom" data centers.

Reporting from The Wall Street Journal claims the reasoning is straightforward: Grid costs are shared, so if a utility builds substations and transmission lines for a project that never shows up, everyone else on the network ends up helping to pay for them.

The numbers back up the nerves in play: developers have requested 1,066GW of grid connections for US data centers, according to a Wood Mackenzie analysis first reported by Bloomberg in August 2026, a figure equal to roughly 83% of the country's entire utility-scale generating capacity at the end of 2025.

A queue full of ghosts as collateral becomes the norm?

Wood Mackenzie expects utilities to commit to serving only about 28% of it: roughly 12% that already has firm service commitments, plus a further 17% that looks likely. The WSJ also cites a separate Wood Mackenzie measure that puts just 23% of disclosed requests in its high-confidence category.

Much of the gap is due to duplication; developers who don't yet know which utility can deliver power fastest file the same project with several of them and keep every option open until one pans out.

Others are simply speculators: commentary in POWER Magazine argued that private equity funds, land brokers and shell companies are claiming queue positions without site control or a signed customer, betting that land with a grid connection attached will find a buyer.

The phenomenon isn't new. Utility Dive was writing about phantom data centers in 2025, and TR reported in July that 67% of electricity executives surveyed by Capgemini were seeing speculative applications for capacity. The change is that utilities are now pricing the problem, and it is causing many adverse effects.

Exelon's attempts to lock down potential consumers

Exelon, which runs utilities across the Mid-Atlantic and Midwest, offers the clearest look at what happens when developers are asked to put money down.

After it began requiring transmission security agreements (TSAs), contracts that bind customers to credit obligations, committed revenue contributions, and shortfall payments, its high-probability data center load fell nearly 40%, from 18GW at the end of 2025 to about 11GW in the second quarter. Its wider pipeline of projects under study dropped from about 43GW to 25GW.

Jeanne Jones, Exelon's CFO, told the WSJ that typical requests used to be around 50MW and now often run to 1,000MW or more. The question she kept fielding, internally and externally, was "How do you know what's real?"

Exelon, however, also told analysts its $41 billion capital plan through 2029 is unchanged, so a 40% cut in expected data center load has not, so far, shrunk the spending plan.

None of this would matter much if grid capacity were cheap, but it simply isn't. PJM, the largest US grid operator, saw its capacity auction clear at the price cap for the third time in a row this summer while missing its reliability target by 6.8GW.

In September, its independent market monitor told federal regulators that 4,017MW of forecast data center load, 42% of the growth PJM had assumed, had already been canceled or delayed.

Households are feeling the cost increase; reports in July indicated that Henrico County, Virginia, asked staff and schools to cut electricity use after a 24.9% rate increase. Virginia has also moved to stop utilities passing data center costs onto everyday users, and Texas bills are rising faster than inflation. Lindsey Short of the Ohio Manufacturers' Association warned against building the grid around "the most optimistic number on somebody's spreadsheet."

Underbuilding carries its own risks, though, especially when transformers can take years to arrive, as a recent deep dive on grid bottlenecks laid out. That is why utilities are locking in the customers they do accept. Average minimum contract lengths for large customers have risen to about 10.8 years from 4.6 before 2024, according to filings aggregated by Halcyon and cited by the Journal, and a separate research brief built on Halcyon's tracker found a similar jump.

What nobody can yet say is how much of the AI power rush is real, and that poses problems for planners, stakeholders, and households alike. Until it does, utilities are doing what any lender would: asking to see the money first; households just happen to be roadkill if things don't pan out.

• Trump administration officials asked CISA to hack a voting machine just months into his second term
• CISA was told not to disclose any vulnerabilities to the voting machine manufacturer
• CISA has been gutted by job cuts and a lack of funding, with many state officials hesitant to trust the agency with securing the midterm elections

Towards the start of President Trump’s second term, allies in the Department of Homeland Security tasked the Cybersecurity and Infrastructure Security Agency (CISA) with secretly acquiring a voting machine with instructions to hack it and not tell anyone except the White House about the vulnerabilities they found.

ProPublica, who spoke to two former administration officials with knowledge of the request, noted that the standard practices of CISA require the agency to let manufacturers know of vulnerabilities in their technology, but the political appointees explicitly asked CISA not to.

“My thought was they could keep it in their back pocket to invalidate any [election] results they don’t like,” one of the former officials told ProPublica. “This goes against everything CISA represents.”

CISA asked to hack voting machine

CISA has been a relentless target of the Trump Administration, with the agency’s jobs being cut by one third and the majority of its senior leadership resigning or being fired.

In total, CISA has cut 1,000 jobs since the start of Trump’s second term - largely down to Elon Musk’s Department of Government Efficiency - with additional cuts to funding in 2027 likely to result in a further 900 jobs being lost.

As a result of these job cuts, remaining CISA employees told the political appointees that the request to hack a voting machine couldn’t be fulfilled.

Since 2018, CISA has been responsible for helping to protect election infrastructure from cyber attacks and interference, but has had to cut back on much of its work in these areas due to a lack of funding and staff. Many state officials have been unable to seek election support ahead of the November midterm elections.

As a result of Trump’s cuts to CISA’s jobs and funding, many state officials fear the agency has become a shell occupied by Trump loyalists. “They’ve violated the entire premise of that information-sharing relationship,” a former high-ranking federal cybersecurity official told ProPublica. “There’s no fucking way they’re ever going to give any information to CISA. And it’s unclear if they ever will in the future.”

In response to the claims, White House spokeswoman Lauren Bis said, “President Trump has done more than any other President to ensure we have free and fair elections.”

“I don’t pay attention to what the president says. I pay attention to what his administration does,” said Arizona Secretary of State Adrian Fontes. “And what they’ve done is basically cut us off and left us on our own.”

Voting difficulties start early

Early voting for the midterms has already started in some states, but early polling sites at North Carolina Agricultural and Technical State University, the University of North Carolina Greensboro, and Western Carolina University have been shut down, with Guilford County board members saying the resources could be better allocated elsewhere.

Shutting the polling sites makes it significantly harder for the collective 40,000 students to vote, with university policy preventing some students from owning a car during their first year of study.

Shia Rozier, a senior at North Carolina A&T and cofounder of student organization Protect Ours NC told NPR, “In order to get to the polls, they now have to get a Lyft or get a Uber or get somebody to take them at a time where gas prices are extremely high, creating an additional barrier to the poll.”

In response, students organized a march to the nearest polling site over 45 minutes away.

• Amazon Prime subscriptions will increase to AU$12.99 per month or AU$99 per year, up from AU$9.99 and AU$79, respectively
• These prices are effective immediately for new subscribers, while existing members will be charged starting from November 25
• Inclusions and perks like Prime Video, Music Prime and sale events remain unchanged

Amazon Australia has revealed its monthly Prime membership fee has increased from AU$9.99 to AU$12.99, effective immediately for new subscribers — a significant 30% hike from the previous cost. The annual membership price also increases from AU$79 to AU$99, a 25% rise.

In emails sent to Prime members, Amazon said the new pricing reflects the growing range of products and benefits available through the service, although the inclusions remain the same, including free and expedited delivery, Prime Video, Prime Music, Prime Reading, video games with Amazon Luna, and member-only events such as Prime Day and Prime Big Deal Days.

Existing customers will continue to pay the usual AU$9.99 per month and the price increase will apply from their first membership renewal date on or after November 25, 2026.

In a statement, an Amazon spokesperson told TechRadar that this price change is specific to Australia and attributes it largely to the expanded delivery footprint, new Prime Video content and access to Alexa+.

"We are committed to growing the value of Prime for members, and in just the last six months we have brought Prime Free overnight delivery before 8am on hundreds of thousands of products to four cities, expanded Prime Free Same-Day Delivery available now in nine regions across Australia, added new TV series and movies to Prime Video plus live sport, given free access to Alexa+, provided exclusive deals through events like Prime Day, and more," the spokesperson said.

"With the continued expansion of Prime member benefits, and so we can continue growing the offering, we will change the price of Prime to $12.99 per month or $99 per year. Prime has never offered as much value for members as it does today."

This price hike comes just in time before Amazon’s own Black Friday sale in Australia, which we expect to start a few days before the actual Black Friday date of November 27, and will run until Cyber Monday on November 30.

While the US has seen multiple price increases recently, this is Amazon’s second round of hikes in Australia since launching in 2018. Initially just AU$6.99 per month or AU$59 per year at launch, Amazon later bumped that up to AU$9.99 per month (43%) or AU$79 per year (34%) in May 2023 as more inclusions were added. As mentioned, the price doesn’t reflect better inclusions, which is surely going to factor into household costs for many regular Amazon shoppers.

Earlier this month, Amazon opened its third Sydney fulfilment centre located in western suburb of Horsley Park which is “a 12-hour drive of 80% of Australia”, meaning even more shoppers will benefit from same-day delivery. Perhaps this goes some way in justifying a price increase, along with the fact that Amazon typically offers the best prices on thousands of items even outside of major sales. However, with an additional AU$2 monthly payment to get rid of ads on Prime Video, this news is sure to sting some customers.

While the Amazon Black Friday sale in Australia isn’t a Prime-exclusive event, the best prices will be for subscribers, so it does still pay to have a subscription. Thankfully, Amazon also makes it easy to cancel your subscription any time, although you won’t be eligible for the usual 30-day free trial if you’re a returning customer.

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