Despite the gap, the research found that almost 70pc of Irish executives have formal technology transformation strategies in place.

New research from the latest Forvis Mazars C-suite Barometer: Outlook 2026 report has found that while Irish organisations prioritise the use of artificial intelligence (AI) in long-term growth plans, in terms of investment, companies are lagging behind their global counterparts. 

Professional services firm Forvis Mazars’ research examines the views, challenges and strategic priorities of today’s C-suite leaders globally. For the purpose of the study, data was collected from 3012 C-suite leaders, including from Ireland, between October and November of 2025. 

What was discovered is that Irish businesses are “investing significantly less in AI than their global counterparts, even as they acknowledge it as critical to competitive advantage, indicating a potential competitive vulnerability”. 

Of contributing Irish executives, 68pc have technology transformation plans in place, with AI of key importance; however, only 10pc said they allocate more than 20pc of their tech budget to AI. This falls short compared to the 15pc globally who allocate more. The research suggests this raises “important questions about whether Irish businesses can sustain competitive advantage without increasing investment”. 

Commenting on the findings of the report, Liam McKenna, a partner at Forvis Mazars in Ireland, said: “Irish business leaders are convinced of AI’s importance and are moving fast to implement it. What is concerning is the investment gap. 

“While they express the highest confidence in AI ROI among all technology investments, their budget allocation doesn’t live up to that. With Irish businesses investing at lower rates than global peers, they risk missing the opportunity AI brings and competitive vulnerability. Now is the time for boards to align their investment with their strategy.”

Generating jobs

Forvis Mazars’ data also highlighted the potential of AI to create future career opportunities for professionals in Ireland, with 44pc of participants reporting the creation of new roles around AI. Almost a quarter of leaders, however, did report job displacement. “This suggests a workforce in transition with skills evolving rather than disappearing, though it raises questions about reskilling, talent development and education pipeline readiness,” stated the research. 

While three-quarters of participating Ireland-based leaders expressed their ethical and societal concerns around AI, they were found to still be open to the adoption of advanced technology. The report suggests that Irish businesses are grappling with responsible AI deployment, as they try to harness a competitive advantage while managing social and governance risks.

McKenna said: “The organisations that win in the next three to five years will be those that move decisively on AI investment while managing risk and ethical and societal concerns in parallel.

“Irish businesses must bridge the investment gap while building the infrastructure, skills and governance frameworks to support responsible AI adoption. This means stronger collaboration between business, education and government to unlock the full potential of AI as a competitive advantage.”

Last week (19 February) Irish-owned global professional services company Morgan McKinley published the findings of the 2026 Morgan McKinley Irish Salary Guide. As part of its research, the organisation highlighted how, while Ireland’s labour market is active, it is becoming far more disciplined in how it hires – that is to say that hiring and improved salaries are often being reserved for those with skills considered to be critical to delivery or risk management.

In the technology ecosystem, for example, the most in-demand roles were found to be positions in data engineering, cybersecurity analytics and risk specialisation, machine learning engineering and data science, AI auditing and AI ethics, automation and dev-ops. The report also said that new roles for AI auditors and ethicists have emerged as a response to regulatory frameworks. 

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Can Meze’s $799 STRADA closed-back headphones stand out in a segment that eats its own? That is the real question. The $500 to $1,000 headphone market in 2026 is not polite competition. It is a knife fight with good lighting. Grado, Focal, Denon, HiFiMAN, Dan Clark Audio, Beyerdynamic, Audeze, and Sennheiser are all circling the same buyers, the same review cycles, and the same limited attention span. Nobody is short on options. Everyone claims “balanced.” We know that not to be true.

Balanced has become shorthand for “inoffensive.” Safe. Smoothed over. Tuned to offend no one and excite even fewer. In this price bracket, you are not just competing on frequency response. You are competing on durability, comfort, brand loyalty, and whether someone already owns two other pairs that do roughly the same thing. That is where the STRADA enters the room.

The harder question is this: can a brand outsmart itself? Does the market actually need another $800 headphone from Meze when the company already has a strong sub-$1,000 lineup that includes the underrated 105 AER, crowd pleasing 99 Classics Second Generation, 105 Silva, and the excellent 109 Pro?

The reality is that each of those sub-$1,000 Meze models comes with its own pros and cons, but the market has generally responded well to them. The 109 Pro are the strongest of the bunch. They strike a rare balance between openness, tonal coherence, and comfort that makes them very easy to recommend.

The 99 Classics 2nd generation are not just a cosmetic refresh of the original that launched Meze into the mainstream. They are better in almost every meaningful way: tighter low end, improved balance and clarity, refined build, and still extremely easy to drive. They remain one of the most accessible entry points into the brand and continue to make sense for those who are considering a pair of wired high-end headphones for the first time.

Meze Audio has also proven beyond any doubt that it knows how to build reference-caliber headphones. The LIRIC II, Empyrean II, and ELITE are not accidents. I spend more time listening to the Empyrean II than most of my loudspeakers. Why? It is simply more enjoyable, more immersive, and less complicated. And I am not convinced I have even reached the ceiling of what it can do in the right chain.

So where does the STRADA fit? Closed-back headphones almost always trade some openness and spatial air for isolation and control. They can deliver greater density and impact down low, but that often comes at the expense of natural space and a more effortless presentation.

Did Meze strike gold with the STRADA, or are we looking at silver and a stuffed animal on the flight home?

What You Actually Get in the Box with the Meze STRADA

Meze keeps the package practical. The STRADA arrives in a hard EVA carrying case with a soft velvet lining that actually protects the finish instead of just looking good in photos. Inside, you get a separate PU leather pouch for the cables, which keeps things organized and avoids the usual tangle.

Two 1.8 meter dual-twisted Kevlar-wrapped OFC cables are included: one terminating in 3.5 mm and another in 4.4 mm balanced. A gold-plated 3.5 mm to 6.3 mm adapter rounds it out for desktop use.

Premium Build and Green with Envy?

From a build standpoint, Meze stayed consistent with its design language but adapted it for a closed-back dynamic platform. The STRADA borrows the core structural thinking of the LIRIC, but instead of planar magnetic loading, this chassis is optimized around a dynamic driver. The focus is on low mass, rigidity, and comfort over long sessions which are likely to be key selling points for potential buyers.

At roughly 330 grams, the STRADA sits toward the lighter end of the closed-back category, and that matters in practice. The magnesium frame keeps the structure rigid without adding unnecessary weight, and the distribution of that weight across the top of my head was very effective.

I have a larger than average head and very little tolerance for excessive clamping force. The STRADA avoids that trap. The pressure is firm enough to maintain a proper seal, which closed-backs absolutely require, but it never crosses into that vise-like squeeze that ruins longer sessions.

The deep green finish has divided opinion, and not quietly. It’s a saturated, metallic tone that leans closer to vintage performance car than conventional hi-fi aesthetic. I wasn’t convinced at first glance. In product photos it felt aggressive. In person, under normal light, it reads more controlled and far more refined. The finish has depth, and the execution is clean. Whether someone likes the color is subjective. Whether it’s well done is not. It is.

The ear pads attach magnetically. Simple idea, but it works. They line up the same way every time, and that matters with a closed-back. If the seal is off, the bass changes. Here, the seal is consistent without cranking up the clamping force. Isolation is strong for a passive closed-back. It blocks more outside noise than the 99 Classics 2nd Generation, and that difference shows up in normal use. HVAC hum fades into the background. Street noise softens. It’s not noise cancelling magic, but it’s enough to stay focused while listening.

I wore them during the Men’s Olympic Ice Hockey Gold Medal Game, streaming through Peacock on my iPad Pro using a dongle DAC. Between the seal and the volume headroom, I was fully locked in. I could barely hear the shouting from the rest of my family demanding to know whether I was having some kind of episode after Jack Hughes buried the golden goal. I wasn’t. I was celebrating properly. The STRADA let me stay in the moment without cranking the volume into dangerous territory. That’s the point.

The ear pads use soft PU leather on the outer contact surface, which helps maintain a consistent seal which is important for bass response and isolation. Inside, the Alcantara lining does a good job of keeping things from getting overly warm during longer sessions. They don’t turn into sweat traps after an hour, which is not always a given with closed-back designs and a situation that has turned me off from a number of high-end designs.

The Macassar ebony cups look expensive and they should for the $799 asking price. The finish and grain are very much in line with Meze’s house aesthetic; polished, tactile, and clearly meant to signal that this is not an entry-level afterthought. It is a dense, stable wood and makes sense for a closed back design, but let’s not pretend this is rustic minimalism. This is deliberate styling.

Each pair has slightly different grain, which gives them some individuality without screaming for attention. It feels well made and intentional, not decorative for the sake of it. Do I like the look as much as the 109 Pro? Not quite. That one clicked immediately. This feels more like an acquired taste.

Overall, the STRADA is built to a high standard, but the fit is not identical to what long-time Meze owners might expect. The suspension and headband system are different from the floating designs used on models like the 109 Pro or Empyrean II. Those tend to settle onto your head and disappear. The STRADA doesn’t quite do that. It feels more structured. More fixed.

The gimbals and adjustment rods also feel different. They’re solid, but the range adjustment is smooth rather than notched. I would have preferred small detents to make it easier to return to the exact same setting every time. As it stands, getting both sides perfectly matched takes a bit more attention than it should.

Technically, the STRADA runs a 50 mm dynamic driver rated from 5 Hz to 30 kHz, with distortion listed at under 0.1 percent at 1 kHz. On paper, that tells you two things: there’s plenty of bandwidth, and Meze is trying to keep things clean at normal listening levels.

This is the same basic dynamic platform first used in the 109 Pro, but it’s been adjusted for a closed-back enclosure. That’s not a small detail. Closed designs deal with internal pressure, reflections, and damping in ways open-backs don’t. You can’t just drop the same driver into a sealed cup and call it a day. It has to be tuned differently.

The diaphragm is a carbon fiber reinforced cellulose composite, designed to stay light but controlled when the music gets demanding. Around it sits a polymer ring with a thin beryllium coating applied through a vapor process to increase stiffness and improve how quickly the driver starts and stops. The surface grooves help manage resonance, and the copper-zinc stabilizer ring works to damp small vibrations before they smear detail.

At 40 ohms and 111 dB sensitivity, the STRADA is easy to drive. You don’t need a desktop amp the size of a shoebox to make it work. A good dongle DAC, portable player, or modest desktop setup will get it to proper listening levels without running out of headroom.

Listening

I rotated the STRADA through a fairly typical mix of sources: my iMac and a MacBook Air at the desk, an iPad Pro for casual listening, and a range of DACs and amplifiers including units from Topping, FiiO, the Apos x Community Gremlin, and a Schiit Magni from Schiit Audio. I also spent time with a few dongle DACs with an iPhone 14 because that’s how a lot of people will actually use a 40 ohm, 111 dB headphone.

In other words, the STRADA had every opportunity to show what it could do — or to miss an empty net with less than ten minutes left in the third period.

Right off the bat, the iFi GO blu Air that worked well with the 99 Classics 2nd Gen and Beyerdynamic DT 770 PRO X wasn’t the best match here. Even running balanced, with 262mW into 32 ohms on tap, the STRADA felt slightly underfed. The sub bass lacked grip. Notes had weight, but not the kind of control that keeps things from spilling over the edge.

That matters because the STRADA has real low-end presence. The bass is full and carries impact well into the upper bass. This is not a polite, neutral closed-back. It pushes air. And when the amplifier doesn’t have enough authority, that energy can blur instead of punch.

With better amplification, the bass tightens and the overall presentation settles down. The midrange and treble benefit from that control as well. This is one of those headphones where system matching counts more than usual. If you’re looking for strong bass impact with definition, the STRADA can deliver it. But you need the right partner on the other end of the cable.

Switching over to the iFi GO bar Kensei was a different story. With 477 mW available from the 4.4 mm balanced output, the STRADA woke up. The bass tightened, the sub bass gained shape, and the upper bass stopped bleeding into the lower mids.

Cue up Daft Punk, deadmau5, Kraftwerk, Brian Eno, Aphex Twin, or The Orb, and the difference was obvious. The low end stopped coloring outside the lines. It stayed full, but it was more disciplined. Kick drums had impact without smearing. Synth bass lines hit hard and then got out of the way.

If you live on electronic music, there’s a lot to like here. The STRADA can move air. With the right power behind it, the bass is deep, defined, quick, and punchy. Without it, things get a little loose. This is not a set-it-and-forget-it headphone. Feed it properly and it rewards you.

So far, so good. Then I moved into the midrange and things got more complicated.

Hit the One in the Middle

Instruments and male vocals came through clean and detailed, but they sat a touch behind the bass. Not buried. Not three steps back into the drum kit starting a studio argument. Just slightly set back. Enough to notice.

I listen to a lot of piano, bass guitar, acoustic guitar, and male vocals; Nick Cave, Sam Cooke, Jason Isbell, John Prine, Elvis Presley, David Bowie, and David Byrne — so that region matters to me. When the vocal center image shifts even slightly, I hear it.

I started rotating through my solid state desktop gear from Topping, FiiO, and Schiit Audio to see how much of it was source dependent. The more linear, slightly more forward presentation from the Topping and Schiit gear helped push the midrange forward a bit. It didn’t completely change the tuning, but it narrowed the gap between bass and vocal presence.

The warmer FiiO setup, though, with its thicker midband, smoothed everything out a little too much. It was like ordering medium smoked meat at Lester’s in Montreal and getting the fatty cut. Satisfying at first, but after a while, everything starts to taste the same. Too much weight, not enough edge definition.

The STRADA doesn’t need more warmth in the mids. It needs more texture, presence, and a sharper edge.

Illumination with a Candle or Floodlight?

Strings, upper piano registers, and female vocals fared a bit better, but I’d still describe the STRADA as slightly recessed through the core midrange. Piano didn’t quite have the weight or sustain I expect without a small EQ nudge. Notes were clean, but they didn’t bloom or hang in the air the way they can on a more linear tuning. Strings, too, lost a bit of bite. The leading edge was softened, and without adjustment, they didn’t always sound as natural as they should.

Female vocals, as the response climbed into the upper mids and lower treble, had more presence than the male vocals. Amy Winehouse, Ella Fitzgerald, Björk, Tori Amos, Nina Simone, and Brandi Carlile all had more immediacy and projection. There was more pep in their step compared to the men.

That same region is also where I began to notice some treble peaks. Not constant glare, but moments where energy jumped forward. Push the volume with a hot or poorly mastered track and the STRADA will let you know. I wouldn’t call it bright overall, but there is more energy and detail in the extreme upper mids and lower treble than in the center midrange. It’s a tuning choice. And yes, it’s fixable with EQ. How much you choose to correct it, or whether you feel compelled to, depends on your tolerance and your music library.

Soundstage is good for a closed-back. Not cavernous. Not claustrophobic either. There’s some depth and a bit of vertical space, but it doesn’t approach the openness of the 109 Pro or Empyrean II. That’s the tradeoff with a sealed design. You gain isolation, you give up some air.

Imaging is solid, just not laser etched. Instruments are placed clearly enough, but nothing hangs in space with surgical precision. You’re not getting that razor sharp, walk around it kind of presentation. It’s more cohesive than pinpoint.

Stereo separation is respectable. Left and right channels are distinct, and layering holds together, especially with well recorded material. For a closed-back at this price, it performs about where it should. Competent. Controlled. Not class leading in spatial tricks, but not a weakness either.

The Bottom Line

The STRADA gets a lot right. Build quality is excellent. Comfort is good once you dial in the fit. Isolation is strong for a passive closed-back. The bass is the headline feature; deep, impactful, and capable of real authority when properly amplified.

The midrange is clean and detailed but sits slightly behind the low end. It’s not hollow, just not as forward as some listeners might prefer. The treble is not etched or aggressively bright in the way some older Beyerdynamic models could be and to be fair, their newer releases have improved in that regard — but there are noticeable peaks. Poor recordings and high volume will expose them.

Overall, the STRADA is resolving and engaging, but it benefits from careful system matching and, in my case, some light EQ. Once you sort that out, it becomes a very enjoyable closed-back option.

That said, I still prefer the 109 Pro for its cohesiveness, openness, and more natural balance. The STRADA is a strong entry in Meze’s lineup, but it doesn’t unseat my favorites. It’s a very good headphone that rewards attention — especially on the amplification and tuning side.

Pros:

• Strong, impactful bass with good depth when properly amplified
• Clean, detailed overall presentation
• Solid passive isolation for a closed-back design
• Excellent build quality with premium materials
• Easy to drive on paper and works well with portable and desktop gear

Cons:

• Midrange sits slightly behind the bass and upper mids
• Noticeable treble peaks with certain recordings or higher volume
• Benefits from careful amp pairing and often some EQ
• Headband adjustment system lacks notches and takes dialing in
• Not as cohesive or spacious as the 109 Pro

Where to buy:

Related Reading:

The team behind the beloved Dark Sky weather app has a new iPhone app called Acme Weather. The release comes after Apple’s 2020 , which it ultimately in 2022 after integrating much of its tech into the native iOS Weather app.

Acme Weather is primarily designed to address the uncertainty inherent in most forecasts, as different models yield disparate results and no two weather apps seem to report the same thing. Acme’s answer to this issue is “Alternate Predictions,” which shows users a range of possible outcomes alongside the app’s core forecast line throughout the day. If the lines are arranged together tightly, it means the app has high confidence in the forecast at that time. When those lines start to diverge, the app is signaling lower confidence while showing users alternate predictions for that time of day.

The app also supports community reporting, seeking to do for weather what Waymo did for traffic. Users can share real-time conditions in their area using icons or emojis, helping increase accuracy when conditions are changing quickly. Like most weather apps, there is also a map component with layers for radar, lightning, rain and snow totals, wind and more.

Acme leverages notifications to help make sure you don’t miss important changes to the forecast or weather alerts. Grossman says they are comprehensive and should help you avoid getting caught in the rain unawares. Notifications also include community reports, government weather alerts and even experimental tools from “Acme Labs” like rainbow and beautiful sunset alerts.

Acme offers a two-week free trial, then costs $25 a year. The iOS version is available now and an Android is forthcoming.

For years, identity has been treated as the foundation of workforce security. If an organization could reliably confirm who a user was, the assumption followed that access could be granted with confidence.

That logic worked when employees accessed corporate networks from corporate devices under predictable conditions. Today, that no longer reflects how access is actually used or abused.

The modern workforce operates across multiple locations, networks, and time zones. Employees routinely switch between corporate laptops, personal devices, and third-party endpoints.

Access is no longer anchored to a single environment or device, yet security teams are expected to support this flexibility without increasing exposure or disrupting productivity, even as the signals used to make access decisions become noisier, more fragmented, and harder to trust on their own.

As a result, identity is being asked to carry responsibility it was never designed to hold alone. Authentication can confirm who a user claims to be, but it does not provide sufficient insight into how risky that access may be once device condition and context are taken into account. In modern environments, the core issue is not identity failure, but the over-reliance on identity as a proxy for trust.

Identity tells us who, not how risky the access is

A legitimate user accessing systems from a secure, compliant device represents a fundamentally different risk from the same user connecting from an outdated, unmanaged, or compromised endpoint. Yet many access models continue to treat these scenarios as equivalent, granting access primarily on identity while device condition remains secondary or static.

This approach fails to account for how quickly device risk changes after authentication. Endpoints regularly shift state as configurations drift, security controls are disabled, or updates are delayed, often long after access has already been granted.

When access decisions remain tied to the conditions present at login, trust persists even as the underlying risk profile degrades.

These gaps are most visible across access paths that fall outside modern conditional access coverage, including legacy protocols, remote access tools, and non-browser-based workflows. In these cases, access decisions are often made with limited context, and trust is extended beyond the point where it is justified.

Attackers are increasingly exploiting these blind spots by reusing misplaced trust rather than breaking authentication, stealing session tokens, abusing compromised endpoints, or working around multi-factor authentication.

After all, it’s easier to log in than break in. A valid identity presented from the wrong device remains one of the most reliable ways to bypass modern controls and fly under the radar.

Why Zero Trust often falls short

Zero Trust is widely accepted as a security principle, but far less consistently applied across workforce access. While identity controls have matured, progress frequently stalls at the device layer, particularly across access paths outside browser-based or modern conditional access frameworks that inherit trust by default.

Establishing device trust introduces complexity that identity alone cannot address. Unmanaged and personal devices are difficult to assess consistently, compliance checks are often static rather than continuous, and enforcement varies depending on how access is initiated.

These challenges are compounded when identity and endpoint signals are handled by separate tools that were never designed to work together. The result is fragmented visibility and inconsistent decisions.

Over time, access policies can harden and become static, creating more opportunities for identity abuse. When access is granted without ongoing checks, traditional controls are slow to detect and respond to malicious behavior.

From identity checks to continuous access verification

Addressing static, identity-centric access controls requires mechanisms that remain effective after authentication and adapt as conditions change.

Solutions such as Infinipoint operationalize this model by extending trust decisions beyond identity and maintaining enforcement as conditions evolve.

The following measures focus on closing the most common access failure points without disrupting how people work.

• Verify both user and device continuously: This approach reduces the effectiveness of stolen credentials, session tokens, and multi-factor authentication bypass techniques by ensuring access is tied to a trusted endpoint rather than granted on identity alone.

• Apply device-based access controls: Device-based access controls make it possible to enroll approved hardware, limit the number and type of devices per user, and differentiate between corporate, personal, and third-party endpoints. This prevents attackers from reusing valid credentials from untrusted devices.

• Enforce security without defaulting to disruption: Proportionate enforcement allows organizations to respond to risk without unnecessarily interrupting legitimate work. This includes conditional restrictions and grace periods that give users time to resolve issues while maintaining security controls.

• Enable self-service remediation to restore trust: Self-guided, one-click remediation for actions such as enabling encryption or updating operating systems allows trust to be restored efficiently, reducing support tickets and demand on IT teams while keeping security standards intact.

Specops, the Identity and Access Management division of Outpost24, delivers these controls through Infinipoint, enabling zero trust workforce access that verifies both users and devices at every access point and continuously throughout each session across Windows, macOS, Linux, and mobile platforms.

Talk to a Specops expert about enforcing device-based Zero Trust access beyond identity.

Sponsored and written by Specops Software.

Anyone who’s scrolled social media lately knows that AI is everywhere. But we aren’t always great at spotting it when we see it. That’s a big problem, and our frustrations with AI are growing.

AI slop has infected every platform, from soulless images to bizarre videos and superficially literate text. The vast majority of US adults who use social media (94%) believe they encounter content that was created or altered by AI, but only 44% of US adults say they’re confident they can tell real photos and videos from AI-generated ones, according to an exclusive CNET survey.

Read more: AI Slop Is Destroying the Internet. These People Are Fighting to Save It.

There are a lot of different ways people are fighting back against AI content. Some solutions are focused on better labels for AI-created content, since it’s harder than ever to trust our eyes. Of the 2,443 respondents who use social media, half (51%) believed we need better AI labels online. Others (21%) believe there should be a total ban on AI-generated content on social media. Only a small group (11%) of respondents say they find AI content useful, informative or entertaining.

AI isn’t going anywhere, and it’s fundamentally reshaping the internet and our relationship with it. Our survey shows that we still have a long way to go to reckon with it.

Key findings

• Most US adults who use social media (94%) believe they encounter AI content on social media, yet far fewer (44%) can confidently distinguish between real and fake images and videos.
• Many US adults (72%) said they take action to determine if an image or video is real, but some don’t do anything, particularly among Boomers (36%) and Gen Xers (29%).
• Half of US adults (51%) believe AI-generated and edited content needs better labeling. 
• One in five (21%) believe AI content should be prohibited on social media, with no exceptions.

Watch this: AI Is Indistinguishable From Reality. How Do We Spot Fake Videos?

03:15

US adults don’t feel they can spot AI media

Seeing is no longer believing in the age of AI. Tools like OpenAI’s Sora video generator and Google’s Nano Banana image model can create hyperrealistic media, with chatbots smoothly assembling swaths of text that sound like a real person wrote them. 

So it’s understandable that a quarter (25%) of US adults say they aren’t confident in their ability to distinguish real images and videos from AI-generated ones. Older generations, including Boomers (40%) and Gen X (28%), are the least confident. If folks don’t have a ton of knowledge or exposure to AI, they’re likely to feel unsure about their ability to accurately spot AI.

People take action to verify content in different ways

AI’s ability to mimic real life makes it even more important to verify what we’re seeing online. Nearly three in four US adults (72%) said they take some form of action to determine whether an image or video is real when it piques their suspicions, with Gen Z being the most likely (84%) of the age groups to do so. The most obvious — and popular — method is closely inspecting the images and videos for visual cues or artifacts. Over half of US adults (60%) do this. 

But AI innovation is a double-edged sword; models have improved rapidly, eliminating the previous errors we used to rely on to spot AI-generated content. The em dash was never a reliable sign of AI, but extra fingers in images and continuity errors in videos were once prominent red flags. Newer AI models usually don’t make those pedestrian mistakes. So we all have to work a little bit harder to determine what’s real and what’s fake.

As visual indicators of AI disappear, other forms of verifying content are increasingly important. The next two most common methods are checking for labels or disclosures (30%) and searching for the content elsewhere online (25%), such as on news sites or through reverse image searches. Only 5% of respondents reported using a deepfake detection tool or website.

But 25% of US adults don’t do anything to determine if the content they’re seeing online is real. That lack of action is highest among Boomers (36%) and those in Gen X (29%). This is worrisome — we’ve already seen that AI is an effective tool for abuse and fraud. Understanding the origins of a post or piece of content is an important first step to navigating the internet, where anything could be falsified.

Half of US adults want better AI labels

Many people are working on solutions to deal with the onslaught of AI slop. Labeling is a major area of opportunity. Labeling relies on social media users to disclose that their post was made with the help of AI. This can also be done behind the scenes by social media platforms, but it’s somewhat difficult, which leads to haphazard results. That’s likely why 51% of US adults believe that we need better labeling on AI content, including deepfakes. Support was strongest among Millennials and Gen Z, at 56% and 55%, respectively.

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Other solutions aim to control the flood of AI content shared on social media. All of the major platforms allow AI-generated content, as long as it doesn’t violate their general content guidelines — nothing illegal or abusive, for example. But some platforms have introduced tools to limit the amount of AI-generated content you see in your feeds; Pinterest rolled out its filters last year, while TikTok is still testing some of its own. The idea is to give every person the ability to permit or exclude AI-generated content from their feeds.

But 21% of respondents believe that AI content should be prohibited on social media altogether, no exceptions allowed. That number is highest among Gen Z at 25%. When asked if they believed AI content should be allowed but strictly regulated, 36% said yes. Those low percentages may be explained by the fact that only 11% find AI content provides meaningful value — that it’s entertaining, informative or useful — and that 28% say it provides little to no value.

How to limit AI content and spot potential deepfakes

Your best defense against being fooled by AI is to be eagle-eyed and trust your gut. If something is too weird, too shiny or too good to be true, it probably is. But there are other steps you can take, like using a deepfake detection tool. There are many options; I recommend starting with the Content Authenticity Initiative‘s tool, since it works with several different file types. 

You can also check out the account that shared the post for red flags. Many times, AI slop is shared by mass slop producers, and you’ll easily be able to see that in their feeds. They’ll be full of weird videos that don’t seem to have any continuity or similarities between them. You can also check to see if anyone you know is following them or if that account isn’t following anyone else (that’s a red flag). Spam posts or scammy links are also indications that the account isn’t legit.

If you want to limit the AI content you see in your social feeds, check out our guides for turning off or muting Meta AI in Instagram and Facebook and filtering out AI posts on Pinterest. If you do encounter slop, you can mark the post as something you’re not interested in, which should indicate to the algorithm that you don’t want to see more like it. Outside of social media, you can disable Apple Intelligence, the AI in Pixel and Galaxy phones and Gemini in Google Search, Gmail and Docs. 

Even if you do all this and still get occasionally fooled by AI, don’t feel too bad about it. There’s only so much we can do as individuals to fight the gushing tide of AI slop. We’re all likely to get it wrong sometimes. Until we have a universal system to effectively detect AI, we have to rely on the tools we have and our ability to educate each other on what we can do now.

Methodology

CNET commissioned YouGov Plc to conduct the survey. All figures, unless otherwise stated, are from YouGov Plc. The total sample size was 2,530 adults, of which 2,443 use social media. Fieldwork was undertaken Feb. 3 to 5, 2026. The survey was carried out online. The figures have been weighted and are representative of all US adults (aged 18 plus).

A 3000W power supply often conjures up images of huge metal boxes with noisy fans pumping air over massive heat sinks, but this Intel reference unit blasts that idea out of the water, or rather keeps it cool, due to a water cooling system that keeps everything nice and clean.

Intel engineers designed this beast of a device for data centers and servers, where squeezing as much power as possible into a small space is the name of the game. It accepts a large 240-volt AC and outputs a reasonable 12 volts DC at up to 250 amps, suitable for 3000 wattage.Water flows through the back of the unit via quick-release fittings, carrying heat away without the need for a noisy internal fan or traditional heat sink fins, and the entire thing remains sealed and silent with no internal moving parts, relying on external coolant to keep things from overheating.

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Inside, things get a little more tricky because the enclosure is a solid black box with no vents to speak of. When the screws are removed, you’ll discover two chunky circuit boards jammed together with a large cold plate crammed in between. The components all push against the cold plate using thermal pads, and the circulating fluid then transports the heat to the outside world. The configuration works well to split chores, with one board handling power factor adjustment on the input side and the other handling the primary DC-DC conversion bit.

Input protection is the first priority, with fuses, surge arrestors, metal oxide varistors, gas discharge tubes, and common-mode chokes all doing their part to keep unpleasant little shocks from the mains supply from causing issues. Following that, bulk capacitors are used to smooth out the rectified line voltage, ensuring that it is not unsteady or unpredictable. The power factor adjustment step, on the other hand, employs a neat technique known as an interleaved totem-pole topology to reduce ripple and enhance efficiency. The GaN transistors in that part are from Texas Instruments and have a reasonable 600 volt rating. They are also fast-switching (which is good) and low-loss, enabling it to achieve the unit’s 80 Plus Platinum certification

The power is subsequently transferred to the phase-shifted full-bridge section on the other board. Silicon carbide MOSFETs are the high-voltage switching components here, paire with a transformer that graciously lowers down the voltage for us. The litz wire on the main side is wound to reduce signal loss as the frequency increases, while synchronous rectifiers on the secondary side work their magic to provide even greater efficiency. The output filtering is then provided by a large inductor and 9000 microfarads of polymer capacitors, all working together to provide a clean 12 volts DC at the output.

Of course, there have to be some control circuits in there somewhere to keep things running smoothly; a Texas Instruments C2000 microcontroller manages the PFC stage, while a PIC24 monitors overall supervision. To keep things nice and safe, there are digital isolators (which are really brilliant, in my opinion) and auxiliary flyback converters that provide standby power as necessary.

Some of the design choices on show here are quite impressive, as the cold plate design allows the unit to chug out a respectable 2500 watts per liter, as that’s a whopping 250 times more power per liter than the average air-cooled supplies, which might only scrape in at around 800 watts per liter, but using advanced semiconductors like GaN and SiC really helps to keep losses to a minimum. However, it’s the water block that still proves essential at full load where losses total around 300 watts.
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In 2026, one of Europe’s most ambitious scientific ventures, Horizon Europe, a seven-year, roughly €93 billion framework dedicated to research and innovation, underwent a quiet but significant transformation. 

What had once been an open invitation to researchers across the globe now carries a more guarded tenor. 

In critical areas such as artificial intelligence, semiconductors, quantum technologies, and biotechnology, organisations based in China are no longer automatically eligible to receive EU funding, a sharp deviation from earlier years when Chinese participation was possible, albeit under evolving conditions. 

This change is neither arbitrary nor purely technical. It reflects the culmination of years of negotiation and strategic signalling in Brussels. 

According to the European Commission’s own international cooperation guidance, cooperation with third countries like China has always been conditional; Chinese researchers may contribute, but they are required to enter as Associated Partners and often must bring their own funding where EU funding does not automatically apply. 

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Yet the updated participation rules go further. 

In late 2025, the Commission codified conditions that essentially block Chinese institutions from receiving core Horizon Europe grants in sensitive clusters of research and innovation. 

In policy terms, the threshold for inclusion has shifted: European partners must now demonstrate that their collaborators are not owned or controlled by Chinese entities, creating de facto barriers for significant portions of bilateral work in cutting-edge fields. 

While cooperation is not extinguished outright, joint work continues in areas like climate science and agriculture under bilateral road-map mechanisms; this recalibration is telling. 

It amounts to Europe drawing boundaries around where it will share its most prized scientific infrastructure and intellectual capital and where it will withhold it. 

The official justifications, as framed by Commission texts, lean heavily on concerns about research security, intellectual property protection, and the perceived risk of unintended transfers of strategic technology where civil and military boundaries blur. 

Viewed in isolation, these adjustments might read as bureaucratic fine-tuning. But in the broader context of EU policy, which straddles an ambition for open scientific cooperation and an emergent emphasis on strategic autonomy, they underscore a fundamental tension. 

Europe still champions collaborative discovery across borders, yet it acknowledges nowadays research ecosystem is intertwined with global power dynamics in once unimaginable ways.

Beyond the sharp edges of eligibility rules lies a deeper question: why does this particular rebalancing matter in practice? 

Over the past decade, China has become increasingly visible in global scientific networks. Its researchers regularly co-authored papers with European counterparts, and its rapidly expanding domestic science base, often supported through state mechanisms, moved from peripheral to central positions in disciplines ranging from materials science to computational biology. 

Yet in the architecture of Horizon Europe that emerges in 2026, participation is no longer synonymous with access to EU funds.

Chinese entities still can contribute to research proposals, but they do so as Associated Partners and typically must bring their own financing, a distinction that subtly but fundamentally changes the incentives and power dynamics of collaboration.

In practical terms, the new rules change how research consortia form and operate. European institutions seeking to work at the frontier of emerging technologies must now factor in eligibility constraints when structuring partnerships. 

Where once multinational consortia could mix researchers from across continents with minimal procedural friction, they now must design collaborations that either exclude certain partners from funding streams or justify their presence through alternative mechanisms. 

This places a renewed premium on legal expertise, consortium management, and alignment with EU strategic priorities, an additional administrative layer that did not exist to the same degree in earlier cooperation frameworks. 

These restrictions could have unintended intellectual or scientific consequences. When large research systems are pushed to the margins, there is a risk that parallel ecosystems evolve, with reduced interoperability between them. In the long term, this could alter citation networks, collaborative norms, and research mobility patterns. 

It could also prompt other powerful actors to adopt similar measures, reshaping the landscape of global science into distinct blocs defined by policy fences rather than open inquiry.

It’s important to emphasise that the EU has not abandoned bilateral scientific engagement outright.

Mechanisms outside Horizon Europe,  including mobility schemes and targeted co-funding instruments designed to support researcher exchanges, continue to exist, and cooperation on transnational challenges such as climate change and biodiversity remains active. 

What has changed is the weight of strategic calculation in decisions about where and how to invest EU funding. As a result, science policy in Europe now sits at the intersection of research excellence, economic sovereignty, and geopolitical strategy.

For Europe’s research community, this presents a complex set of questions. Does tighter control over strategic collaborations strengthen the European innovation base? Or does it risk isolating European science from talent and knowledge flows? 

The answer is unlikely to be binary. 

What is clear, however, is that Horizon Europe,  once known chiefly as a vehicle for excellence and discovery, is now also a mirror of shifting geopolitical realities, showing how science policy has become part of broader efforts to navigate uncertainty in a multipolar world.

In the end, the EU’s decision to redraw the terms of research partnership with China feels less like a closing door and more like a recalibration of Europe’s compass. It acknowledges a world in which scientific discovery and geopolitical currents are no longer parallel tracks but deeply intertwined. 

The Horizon Europe programme, once the grand symbol of open scientific cooperation, now also stands as a marker of strategic foresight,  a space where Europe seeks to balance openness with caution, curiosity with control.

This turning point doesn’t signal a retreat from global engagement. 

What it does reflect is a modern realpolitik of research: where funding decisions are informed not only by scientific merit but by questions of security, reciprocity, and long-term technological sovereignty. 

In a scenery defined by rising competition over frontier technologies, Europe is choosing to hedge its bets, opening some doors wider, while tightening others. The future of scientific collaboration may be neither total isolation nor full openness but a nuanced choreography between cooperation and strategic self-interest.

AI has driven an explosion of new number formats—the ways in which numbers are represented digitally. Engineers are looking at every possible way to save computation time and energy, including shortening the number of bits used to represent data. But what works for AI doesn’t necessarily work for scientific computing, be it for computational physics, biology, fluid dynamics, or engineering simulations. IEEE Spectrum spoke with Laslo Hunhold, who recently joined Barcelona-based Openchip as an AI engineer, about his efforts to develop a bespoke number format for scientific computing.

What makes number formats interesting to you?

Laslo Hunhold: I don’t know another example of a field that so few are interested in but has such a high impact. If you make a number format that’s 10 percent more [energy] efficient, it can translate to all applications being 10 percent more efficient, and you can save a lot of energy.

Why are there so many new number formats?

Hunhold: For decades, computer users had it really easy. They could just buy new systems every few years, and they would have performance benefits for free. But this hasn’t been the case for the last 10 years. In computers, you have a certain number of bits used to represent a single number, and for years the default was 64 bits. And for AI, companies noticed that they don’t need 64 bits for each number. So they had a strong incentive to go down to 16, 8, or even 2 bits [to save energy]. The problem is, the dominating standard for representing numbers in 64 bits is not well designed for lower bit counts. So in the AI field, they came up with new formats which are more tailored toward AI.

Why does AI need different number formats than scientific computing?

Hunhold: Scientific computing needs high dynamic range: You need very large numbers, or very small numbers, and very high accuracy in both cases. The 64-bit standard has an excessive dynamic range, and it is many more bits than you need most of the time. It’s different with AI. The numbers usually follow a specific distribution, and you don’t need as much accuracy.

What makes a number format “good”?

Hunhold: You have infinite numbers but only finite bit representations. So you need to decide how you assign numbers. The most important part is to represent numbers that you’re actually going to use. Because if you represent a number that you don’t use, you’ve wasted a representation. The simplest thing to look at is the dynamic range. The next is distribution: How do you assign your bits to certain values? Do you have a uniform distribution, or something else? There are infinite possibilities.

What motivated you to introduce the takum number format?

Hunhold: Takums are based on posits. In posits, the numbers that get used more frequently can be represented with more density. But posits don’t work for scientific computing, and this is a huge issue. They have a high density for [numbers close to one], which is great for AI, but the density falls off sharply once you look at larger or smaller values. People have been proposing dozens of number formats in the last few years, but takums are the only number format that’s actually tailored for scientific computing. I found the dynamic range of values you use in scientific computations, if you look at all the fields, and designed takums such that when you take away bits, you don’t reduce that dynamic range

This article appears in the March 2026 print issue as “Laslo Hunhold.”