Key Takeaways New EU rules take effect June 20, 2025, requiring mobile phones and tablets to be easier and cheaper to repair, including mandatory access to spare parts for up to seven years. Devices must meet durability standards, such as resistance to drops, water, and dust, and batteries must retain 80% capacity after 800 cycles. Manufacturers must offer 5 years of software updates and grant repair professionals access to firmware and repair tools to ensure long-term device usability. Energy consumption is expected to drop, with households projected to save €98 over five years due to longer-lasting, more repairable devices. In a win for the right to repair, the European Union (EU) has released its new energy labeling and ecodesign requirements for mobile phones, tablets, and even cordless phones.  Once implemented, this would help extend the product lifetime of these devices by making them cheaper and easier to repair. Up Ahead: Long-Lasting and Easy-to-Repair Devices The new ecodesign and energy labeling regulations will apply to devices sold in the EU effective June 20, 2025. One key area is the availability of spare parts. Once the regulations take effect, critical spare parts for a device must remain available for seven years from the last day the producer stops selling it. These parts must also be available to repairers within 5–10 working days. Mobile phones, cordless phones, and tablets under the ecodesign regulations must also be resistant to scratches and accidental drops, and be protected from dust and water.  The EU also specified that batteries must withstand at least 800 charge and discharge cycles while holding 80% of their initial capacity. These measures could help ensure devices last longer, thus reducing the need to replace them and lowering their cost of ownership. The requirements go beyond just the hardware. On the software side, manufacturers must also provide at least 5 years of operating system upgrades and give professional repairers access to software or firmware.  These would allow devices to continue to run smoothly and protect them from security vulnerabilities. On top of the ecodesign requirements, the EU also specified energy labeling requirements on the applicable devices. The labels would provide information about the device’s repairability score, energy efficiency, battery longevity, dust and water protection, and resistance to accidental drops. Having clear and easy-to-understand labels can help consumers in the EU make better choices when buying phones and tablets. They’ll know at a glance whether a device can withstand drops or if the batteries will remain reliable for as long as possible. The Projected Impact of the Ecodesign and Energy Labeling Requirements With the new requirements, the EU expects a significant reduction in the energy consumption of the applicable devices. Regarding charging time, this should go down from 2.5 to 1.75 hours per day.  Also, the annual electrical consumption of high-end smartphones is projected to decrease by 25%, while it’s 23% for tablets by 2030. Making devices easier to repair is also seen to reduce the acquisition costs of devices by € 98 per household in five years. A Win for the Right to Repair, But Obstacles Lie Ahead While the EU has already made significant progress in the area of the right to repair, many countries remain well behind. In the US, for instance, only a handful of states like California and Colorado have already enacted right-to-repair laws, while most of the country has yet to catch up. Without laws like these, most manufacturers won’t have the incentive to make their devices easier to repair.  A quick look at iFixit’s smartphone repairability scores shows that only Fairphone, which scored a perfect 10, HMD (9), and Nokia (8) had high scores among current phones. At the same time, Apple’s iPhone 16 line received a respectable 7. The Fairphone 5, launched in 2023, is especially notable for its repairability. According to iFixit, it’s easy to disassemble with common tools, the battery can be replaced quickly, and manuals and replacement parts are available to DIYers. As for the others, Ifixit gave Google’s Pixel 9 series a 5 for making it difficult to remove the battery and the USB-C port and requiring a replacement adhesive to secure the screen or back glass instead of using clips or fasteners. Meanwhile, the Samsung Galaxy S23 Ultra received a 4 because the battery is attached with a strong adhesive, and repairing the screen needs a lot of disassembly. All in all, we remain hopeful that with the EU’s lead, manufacturers can finally be encouraged to make repairing their devices easier than ever before. As technology continues to evolve—from the return of 'dumbphones' to faster and sleeker computers—seasoned tech journalist, Cedric Solidon, continues to dedicate himself to writing stories that inform, empower, and connect with readers across all levels of digital literacy. With 20 years of professional writing experience, this University of the Philippines Journalism graduate has carved out a niche as a trusted voice in tech media. Whether he's breaking down the latest advancements in cybersecurity or explaining how silicon-carbon batteries can extend your phone’s battery life, his writing remains rooted in clarity, curiosity, and utility. Long before he was writing for Techreport, HP, Citrix, SAP, Globe Telecom, CyberGhost VPN, and ExpressVPN, Cedric's love for technology began at home courtesy of a Nintendo Family Computer and a stack of tech magazines. Growing up, his days were often filled with sessions of Contra, Bomberman, Red Alert 2, and the criminally underrated Crusader: No Regret. But gaming wasn't his only gateway to tech.  He devoured every T3, PCMag, and PC Gamer issue he could get his hands on, often reading them cover to cover. It wasn’t long before he explored the early web in IRC chatrooms, online forums, and fledgling tech blogs, soaking in every byte of knowledge from the late '90s and early 2000s internet boom. That fascination with tech didn’t just stick. It evolved into a full-blown calling. After graduating with a degree in Journalism, he began his writing career at the dawn of Web 2.0. What started with small editorial roles and freelance gigs soon grew into a full-fledged career. He has since collaborated with global tech leaders, lending his voice to content that bridges technical expertise with everyday usability. He’s also written annual reports for Globe Telecom and consumer-friendly guides for VPN companies like CyberGhost and ExpressVPN, empowering readers to understand the importance of digital privacy. His versatility spans not just tech journalism but also technical writing. He once worked with a local tech company developing web and mobile apps for logistics firms, crafting documentation and communication materials that brought together user-friendliness with deep technical understanding. That experience sharpened his ability to break down dense, often jargon-heavy material into content that speaks clearly to both developers and decision-makers. At the heart of his work lies a simple belief: technology should feel empowering, not intimidating. Even if the likes of smartphones and AI are now commonplace, he understands that there's still a knowledge gap, especially when it comes to hardware or the real-world benefits of new tools. His writing hopes to help close that gap. Cedric’s writing style reflects that mission. It’s friendly without being fluffy and informative without being overwhelming. Whether writing for seasoned IT professionals or casual readers curious about the latest gadgets, he focuses on how a piece of technology can improve our lives, boost our productivity, or make our work more efficient. That human-first approach makes his content feel more like a conversation than a technical manual. As his writing career progresses, his passion for tech journalism remains as strong as ever. With the growing need for accessible, responsible tech communication, he sees his role not just as a journalist but as a guide who helps readers navigate a digital world that’s often as confusing as it is exciting. From reviewing the latest devices to unpacking global tech trends, Cedric isn’t just reporting on the future; he’s helping to write it. View all articles by Cedric Solidon Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

Home Meta and Yandex Spying on Android Users Through Localhost Ports: The Dying State of Online Privacy News Meta and Yandex Spying on Android Users Through Localhost Ports: The Dying State of Online Privacy 7 min read Published: June 4, 2025 Key Takeaways Meta and Yandex have been found guilty of secretly listening to localhost ports and using them to transfer sensitive data from Android devices. The corporations use Meta Pixel and Yandex Metrica scripts to transfer cookies from browsers to local apps. Using incognito mode or a VPN can’t fully protect users against it. A Meta spokesperson has called this a ‘miscommunication,’ which seems to be an attempt to underplay the situation. Wake up, Android folks! A new privacy scandal has hit your area of town. According to a new report led by Radboud University, Meta and Yandex have been listening to localhost ports to link your web browsing data with your identity and collect personal information without your consent. The companies use Meta Pixel and the Yandex Metrica scripts, which are embedded on 5.8 million and 3 million websites, respectively, to connect with their native apps on Android devices through localhost sockets. This creates a communication path between the cookies on your website and the local apps, establishing a channel for transferring personal information from your device. Also, you are mistaken if you think using your browser’s incognito mode or a VPN can protect you. Zuckerberg’s latest method of data harvesting can’t be overcome by tweaking any privacy or cookie settings or by using a VPN or incognito mode. How Does It Work? Here’s the method used by Meta to spy on Android devices: As many as 22% of the top 1 million websites contain Meta Pixel – a tracking code that helps website owners measure ad performance and track user behaviour. When Meta Pixel loads, it creates a special cookie called _fbp, which is supposed to be a first-party cookie. This means no other third party, including Meta apps themselves, should have access to this cookie. The _fbp cookie identifies your browser whenever you visit a website, meaning it can identify which person is accessing which websites. However, Meta, being Meta, went and found a loophole around this. Now, whenever you run Facebook or Instagram on your Android device, they can open up listening ports, specifically a TCP port (12387 or 12388) and a UDP port (the first unoccupied port in 12580-12585), on your phone in the background.  Whenever you load a website on your browser, the Meta Pixel uses WebRTC with SDP Munging, which essentially hides the _fbp cookie value inside the SDP message before being transmitted to your phone’s localhost.  Since Facebook and Instagram are already listening to this port, it receives the _fbp cookie value and can easily tie your identity to the website you’re visiting. Remember, Facebook and Instagram already have your identification details since you’re always logged in on these platforms. The report also says that Meta can link all _fbp received from various websites to your ID. Simply put, Meta knows which person is viewing what set of websites. Yandex also uses a similar method to harvest your personal data. Whenever you open a Yandex app, such as Yandex Maps, Yandex Browser, Yandex Search, or Navigator, it opens up ports like 29009, 30102, 29010, and 30103 on your phone.  When you visit a website that contains the Yandex Metrica Script, Yandex’s version of Meta Pixel, the script sends requests to Yandex servers containing obfuscated parameters.  These parameters are then sent to the local host via HTTP and HTTPS, which contains the IP address 127.0.0.1, or the yandexmetrica.com domain, which secretly points to 127.0.0.1. Now, the Yandex Metrica SDK in the Yandex apps receives these parameters and sends device identifiers, such as an Android Advertising ID, UUIDs, or device fingerprints. This entire message is encrypted to hide what it contains. The Yandex Metrica Script receives this info and sends it back to the Yandex servers. Just like Meta, Yandex can also tie your website activity to the device information shared by the SDK. Meta’s Infamous History with Privacy Norms This is not something new or unthinkable that Meta has done. The Mark Zuckerberg-led social media giant has a history of such privacy violations.  For instance, in 2024, the company was accused of collecting biometric data from Texas users without their express consent. The company settled the lawsuit by paying $1.4B.  Another of the most famous lawsuits was the Cambridge Analytica scandal in 2018, where a political consulting firm accessed private data of 87 million Facebook users without consent. The FTC fined Meta $5B for privacy violations along with a 100M settlement with the US Securities and Exchange Commission.  Meta Pixel has also come under scrutiny before, when it was accused of collecting sensitive health information from hospital websites. In another case dating back to 2012, Meta was accused of tracking users even after they logged out from their Facebook accounts. In this case, Meta paid $90M and promised to delete the collected data.  In 2024, South Korea also fined Meta $15M for inappropriately collecting personal data, such as sexual orientation and political beliefs, of 980K users. In September 2024, Meta was fined $101.6M by the Irish Data Protection Commission for inadvertently storing user passwords in plain text in such a way that employees could search for them. The passwords were not encrypted and were essentially leaked internally. So, the latest scandal isn’t entirely out of character for Meta. It has been finding ways to collect your data ever since its incorporation, and it seems like it will continue to do so, regardless of the regulations and safeguards in place. That said, Meta’s recent tracking method is insanely dangerous because there’s no safeguard around it. Even if you visit websites in incognito mode or use a VPN, Meta Pixel can still track your activities.  The past lawsuits also show a very identifiable pattern: Meta doesn’t fight a lawsuit until the end to try to win it. It either accepts the fine or settles the lawsuit with monetary compensation. This essentially goes to show that it passively accepts and even ‘owns’ the illegitimate tracking methods it has been using for decades. It’s quite possible that the top management views these fines and penalties as a cost of collecting data. Meta’s Timid Response Meta’s response claims that there’s some ‘miscommunication’ regarding Google policies. However, the method used in the aforementioned tracking scandal isn’t something that can simply happen due to ‘faulty design’ or miscommunication.  We are in discussions with Google to address a potential miscommunication regarding the application of their policies – Meta Spokesperson This kind of unethical tracking method has to be deliberately designed by engineers for it to work perfectly on such a large scale. While Meta is still trying to underplay the situation, it has paused the ‘feature’ (yep, that’s what they are calling it) as of now. The report also claims that as of June 3, Facebook and Instagram are not actively listening to the new ports. Here’s what will possibly happen next: A lawsuit may be filed based on the report. An investigating committee might be formed to question the matter. The company will come up with lame excuses, such as misinterpretation or miscommunication of policy guidelines. Meta will eventually settle the lawsuit or bear the fine with pride, like it has always done.  The regulatory authorities are apparently chasing a rat that finds new holes to hide every day. Companies like Meta and Yandex seem to be one step ahead of these regulations and have mastered the art of finding loopholes. More than legislative technicalities, it’s the moral ethics of the company that become clear with incidents like this. The intent of these regulations is to protect personal information, and the fact that Meta and Yandex blatantly circumvent these regulations in their spirit shows the absolutely horrific state of capitalism these corporations are in. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. More from News View all View all

Key Takeaways Dell and Nvidia will together provide architecture for the next set of supercomputers, named Doudna, for the US Department of Energy. Dell will focus more on sustainable hardware and cooling systems, while Nvidia will provide its AI architecture, including the Vera Rubin AI chips. The US Department of Energy wants to focus more on the sustainable development of AI, hence the choice of environment-conscious companies. The US Department of Energy said that Dell’s next batch of supercomputers will be delivered with Nvidia’s ‘Vera Rubin’ AI chips, marking the beginning of a new era of AI dominance in research. The said systems are expected to be 10x faster than the current batch of supercomputers, which HP provided. The supercomputer will be named ‘Doudna,’ after Jennifer Doudna, a Nobel Prize winner who made key contributions in CRISPR gene-editing. Supercomputers have been instrumental in key scientific discoveries in the last few decades and also played a big role in the design and maintenance of the U.S. nuclear weapons arsenal. And now, with the introduction of artificial intelligence, we’re heading towards a new decade of faster and more efficient scientific research. It (supercomputers) is the foundation of scientific discovery for our country. It is also a foundation for economic and technological leadership. And with that, national security – Nvidia CEO, Jensen Huang Dell Going All in on AI This isn’t the first time Dell and Nvidia have come together to develop newer AI solutions. Back in March 2024, Dell announced the Dell AI Factory with NVIDIA, an end-to-end enterprise AI solution designed for businesses.  This joint venture used Dell’s infrastructure, such as servers, storage, and networking, combined with NVIDIA’s AI architecture and technologies such as GPUs, DPUs, and AI software. Image Credit – Dell For instance, the Dell PowerEdge server uses NVIDIA’s full AI stack to provide enterprises with solutions required for a wide range of AI applications, including speech recognition, cybersecurity, recommendation systems, and language-based services. The demand for Dell’s AI servers has also increased, reaching $12.1B in the first quarter of 2025, with a total backlog of $14.4B, which suggests a strong future demand and order book. The company has set a bold profit forecast between $28.5B and $29.5B, as against the analysts’ prediction of $ 25.05 B. With Doudna, Dell is well-positioned to lead the next generation of supercomputers in AI research, invention, and discoveries. Focus on Energy Efficiency Seagate has warned about the unprecedented increase in demand for AI data storage in the coming few years, which is a significant challenge to the sustainability of AI data centers. Global data volume is expected to increase threefold by 2028.  Image Credit – DIGITIMES Asian The data storage industry currently produces only 1-2 zettabytes (1 zettabyte equals 1 trillion gigabytes) of storage annually, which is much lower than what would be required in the next 4-5 years. At the same time, Goldman Sachs predicts that power requirements will also go up by 165% by 2030 due to increasing demand for AI data centers. This calls for a more sustainable approach for the supercomputing industry as well.  Dell will use its proprietary technologies, such as Direct Liquid Cooling, the PowerCool eRDHx, and Smart Flow design in the Doudna, ensuring energy efficiency. Direct Liquid Cooling (DLC) increased computing density by supporting more cores per rack, which reduces cooling costs by as much as 45%. Dell’s PowerCool eRDHx is a self-contained airflow design that can capture 100% of the heat generated by IT systems. This reduces the dependency on expensive chillers, as eRDHx can work in usual temperatures of 32 and 36 degrees Celsius, leading to 60% savings in cooling energy costs. Lastly, the Dell Smart Flow design improves airflow within IT components and reduces the fan power by 52%. This leads to better performance with fewer cooling requirements. Besides this, Dell plans to incorporate Leak Sense Technology. If a coolant leak occurs, the system’s leak sensor will log an alert in the iDRAC system so that swift action can be taken. As per a report titled ‘Energy and AI’ by the IEA, the data center electricity demand will increase to 945 terawatt-hours (TWh) by 2030. For comparison, this is more than the total electricity consumption of Japan today. The US alone will consume more electricity in 2030 for processing data than for manufacturing all energy-intensive goods combined, including aluminum, steel, cement, and chemicals. Therefore, the need to develop sustainable AI data centers and supercomputers cannot be highlighted enough. Dell’s technology-focused, sustainable approach can be a pivotal point in how efficiently we use AI in the next decade. The US Department of Energy’s choice of Dell also seems to be a conscious move to shift towards companies that give importance to sustainability and can vouch for the long-term viability of research-intensive AI setups. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

The housing catalogue includes 50 low-rise home designs, including for garden suites, duplexes, four-plexes and six-plexes. Each design was developed by local architecture and engineering teams with the intent of aligning with regional building codes, planning rules, climate zones, construction methods and materials. TEXT John Lorinc RENDERINGS Office In Search Of During the spring election, the Liberals leaned into messaging that evoked a historic moment from the late 1940s, when Ottawa succeeded in confronting a severe housing crisis.  “We used to build things in this country,” begins Prime Minister Mark Carney in a nostalgic ad filled with archival images of streets lined with brand new post-World War II “strawberry box” bungalows, built for returning Canadian soldiers and their young families.  The video also includes montages from the now-iconic design “catalogues,” published by Canada Mortgage and Housing Corporation (CMHC). These supplied floor plans and unlocked cheap mortgages for tens of thousands of simple suburban houses found in communities across the country. “The government built prefabricated homes that were easy to assemble and inexpensive,” Carney said in the voice-over. “And those homes are still here.”  Over the past year, CMHC has initiated a 21st century re-do of that design catalogue, and the first tranche of 50 plans—for garden suites, duplexes, four-plexes and six-plexes—went live in early March. A second tranche, with plans for small apartments, is under development.  Unlike the postwar versions, these focus on infill sites, not green fields. One of CMHC’s goals is to promote so-called gentle density to residential properties with easily constructed plans that reflect regional variations, local zoning and building-code regulations, accessibility features and low-carbon design. As with those postwar catalogues, CMHC’s other goal was to tamp down on soft costs for homeowners or small builders looking to develop these kinds of housing by providing no-cost designs that were effectively permit sets. The early reviews are generally positive. “I find the design really very compelling in a kind of understated way,” says SvN principal Sam Dufaux. By making available vetted plans that can be either pre-approved or approved as of right, CMHC will remove some of the friction that impedes this scale of housing. “One of the elements of the housing crisis has to do with how do we approve these kinds of projects,” Dufaux adds. “I’m hoping it is a bit of a new beginning.” Yet other observers offer cautions about the extent to which the CMHC program can blunt the housing crisis. “It’s a small piece and a positive one,” says missing middle advocate and economist Mike Moffatt, who is executive in residence at the Smart Prosperity Institute and an assistant professor at Western’s Ivey Business School. “But [it’s] one that probably captures a disproportionate amount of attention because it’s something people can visualize in a way that they can’t with an apartment tax credit.” This kind of new-build infill is unlikely to provide much in the way of affordable or deeply affordable housing, adds Carolyn Whitzman, housing and social policy researcher, and author of Home Truths: Fixing Canada’s Housing Crisis (UBC Press, 2024). She estimates Canada needs about three million new dwellings that can be rented for $1,000 per month or less. The policies that will enable new housing at that scale, she says, involve financing subsidies, publicly owned land, and construction innovation, e.g., prefabricated or factory-built components, as well as “consistent and permissive zoning and consistent and permissive building codes.”  Indeed, the make-or-break question hovering over CMHC’s design catalogue is whether municipalities will green-light these plans or simply find new ways to hold up approvals.   An axonometric of a rowhouse development from the Housing Catalogue, designed for Alberta. A team effort Janna Levitt, partner at LGA Architectural Partners, says that when CMHC issued an RFP for the design catalogue, her firm decided to pitch a team of architects and peer reviewers from across Canada, with LGA serving as project manager. After they were selected, Levitt says they had to quickly clarify a key detail, which was the assumption that the program could deliver pre-approved, permit-ready plans absent a piece of property to build on. “Even in 1947,” she says, “it wasn’t a permit set until you had a site.” LGA’s team and CMHC agreed to expand the scope of the assignment so that the finished product wasn’t just a catalogue of plans but also included details about local regulations and typical lot sizes. Re-Housing co-founder Michael Piper, an associate professor at U of T’s John H. Daniels Faculty of Architecture, Landscape, and Design, came on board to carry out research on similar programs, and found initiatives in places like Georgia, Indiana and Texas. “I have not found any that moved forward,” he says. “Canada’s national design catalogue is pretty novel in that regard, which is exciting.” The noteworthy exceptions are California, which has made significant advances in recent years in pre-approving ADUs across the state, and British Columbia, which last fall released its own standardized design catalogue.  He also carried out a scan of land use and zoning rules in Ontario for 15 to 20 municipalities. “We looked to see [what] their zoning permitted and what the rules were, and as you might expect, they’re all over the place,” he says. “Hence the challenge with the standardized design.” At present, high-level overviews for the 50 designs are available, including basic floor plans, 3D axonometrics, and building dimensions. Full architectural design packages are expected to be released later this year. Levitt says the architects on the team set out to come up with designs that used wood frame construction, had no basements (to save on cost and reduce embodied carbon), and drew on vernacular architectural styles. They researched representative lot sizes in the various regions, and configured designs to suit small, medium and large properties. Some versions have accessibility features—CMHC’s remit included both accessible units and aging-in-place as objectives—or can be adapted later on.  As for climate and energy efficiency considerations, the recommended materials include low-carbon components and cladding. The designs do reflect geographical variations, but Levitt says there’s only so much her team could do in terms of energy modelling. “How do you do heat energy calculations when you don’t have a site? You don’t have north, south, east, west [orientations] and you don’t have what zone are you in. In B.C. and Ontario, there are seven climatic regions. There was a lot of working through those kinds of very practical requirements, which were very complicated and actually fed into the design work quite significantly.” As Levitt adds, “in 1947, there were no heat loss models because the world wasn’t like that.” LGA provided the architects on the team with templates for interior elements, such as bathrooms, as well as standards for features such as bedroom sizes, dining areas, storage sufficient to hold strollers, and access to outdoor space, either at grade or via a balcony. “We gathered together these ideas about the quality of life that we wanted baked into each of the designs, so that [they] expressed a really good quality of life—modest but good quality,” she says. “It’s not about the finishes. People had to be able to live there and live there well.” “This isn’t a boutique home solution,” Whitzman says. “This is a cheap and mass-produced solution. And compared to other cheap and mass-produced solutions, whether they be condos or suburban subdivisions, [the catalogue designs] look fine to my untrained eye.” A selection of Housing Catalogue designs for the Atlantic region. Will it succeed?  With the plans now public, the other important variables, besides their conformity with local bylaws, have to do with cost and visibility to potential users, including homeowners, contractors and developers specializing in smaller-scale projects.  On the costing side, N. Barry Lyons Consultants (NBLC) has been retained by CMHC to develop models to accompany the design catalogue, but those figures have yet to be released. While pricing is inevitably dynamic, the calculus behind the entire exercise turns on whether the savings on design outlays and the use of prefabricated components will make such small-scale projects pencil, particularly at a time when there are live concerns about tariffs, skilled labour shortages, and supply chain interruptions on building materials.  Finally, there’s the horse-to-water problem. While the design catalogue has received a reasonable amount of media attention since it launched, does CMHC need to find ways to market it more aggressively? “From my experience,” says Levitt, “they are extremely proactive, and have assembled a kind of dream team with a huge range of experience and expertise. They are doing very concerted and deep work with municipalities across the country.” Proper promotion, observes Moffatt, “is going to be important in particular, just for political reasons. The prime minister has made a lot of bold promises about [adding] 500,000 homes.” Carney’s pledge to get Canada back into building will take time to ramp up, he adds. “I do think the federal government needs to visibly show progress, and if they can’t point to a [new] building across the road, they could at least [say], `We’ve got this design catalogue. Here’s how it works. We’ve already got so many builders and developers looking at this.’”  While it’s far too soon to draw conclusions about the success of this ambitious program, Levitt is well aware of the long and rich legacy of the predecessor CMHC catalogues from the late 40s and the 1950s, all of which gave many young Canadian architects their earliest commissions and then left an enduring aesthetic on countless communities across Canada.   She hopes the updated 21st-century catalogue—fitted out as it is for 21st-century concerns about carbon, resilience and urban density—will acquire a similar cachet.  “These are architecturally designed houses for a group of people across the country who will have never lived in an architecturally designed house,” she muses. “I would love it if, 80 years from now, the consistent feedback [from occupants] was that they were able to live generously and well in those houses, and that everything was where it should be.” ARCHITECTURE FIRM COLLABORATORS Michael Green Architecture, Dub Architects, 5468796 Architecture Inc, Oxbow Architecture, LGA Architectural Partners, KANVA Architecture, Abbott Brown Architects, Taylor Architecture Group  As appeared in the June 2025 issue of Canadian Architect magazine  The post A housing design catalogue for the 21st century appeared first on Canadian Architect.

Key Takeaways AMD’s RX 9060 XT is set to launch on June 5th, 2025 in both 8GB and 16GB versions under the same name, creating confusion and backlash. Reviewers and gamers say 8GB of VRAM isn’t enough for modern gaming, especially at 1440p. AMD’s decision to showcase only the 16GB model in benchmarks raised concerns about transparency. This move mirrors Nvidia’s controversial RTX 4060 Ti rollout, suggesting an industry trend of misleading GPU marketing. It all started with a new GPU announcement. The AMD Radeon RX 9060 XT is set to launch, and on paper, it looks like a solid move. A $349 graphics card with 16GB of VRAM? Not bad. That’s more memory than some RTX 4070 cards. Sounds like AMD might finally be delivering some value again, right?  Well, yes and no.  Because right alongside that 16GB version, AMD is also releasing an 8GB version for $299. Same name, same chip, half the memory. And that’s where the internet lost it.  Déjà Vu: We’ve Seen This Trick Before If this sounds familiar, it’s because Nvidia pulled the same move with the RTX 4060 Ti.  They sold both 8GB and 16GB versions with the same branding, but a $100 price difference. The RTX 4060 Ti 8GB launched in May 2023, and the 16GB variant followed in July.  Source: Nvidia Gamers hated the confusion. Reviewers criticized the 8GB version’s lack of performance, especially in memory-heavy games, and the way Nvidia tried to sweep the difference under the rug.  Performance dipped significantly at 1440p, and stuttering was a problem even in some 1080p titles. The backlash was swift. Tech media slammed Nvidia for deceptive marketing, and buyers were left second-guessing which version they were getting.  We’ve seen this pattern before in Nvidia’s review restrictions around the RTX 5060, where early coverage was shaped by what reviewers were allowed to test – and what they weren’t.  It led to a mess of misinformation, bad value perceptions, and a very clear message: don’t confuse your customers. So naturally, AMD did it too.  It’s like watching two billion-dollar companies playing a game of ‘Who Can Confuse the Customer More.’ It’s not just about the money. It’s about trust, and AMD just dumped a bunch of it off a cliff.  Frank Azor Lights the Fuse on X The backlash started when AMD’s Director of Gaming Marketing, Frank Azor, took to X to defend the 8GB card.  He said that most gamers don’t need more than 8GB of VRAM and that the cheaper card still serves the mainstream crowd just fine.  It’s the same reasoning Nvidia used last year with the RTX 4060 Ti. That didn’t work then, and it isn’t working now.  Because when Steve from Hardware Unboxed sees a bad take like that, you know a flamethrower video is coming. And oh boy, did it come.  Hardware Unboxed Fires Back The backlash against AMD’s 8GB RX 9060 XT took off after a post from Hardware Unboxed on X called out the company’s defense of limited VRAM.  In response to AMD’s claim that most gamers don’t need more than 8GB of memory, Hardware Unboxed accused them of misleading buyers and building weaker products just to hit certain price points. The criticism gained traction fast. Tech YouTuber Vex picked up the story and added fuel to the fire by showing side-by-side gameplay comparisons.  In multiple games, the 8GB RX 9060 XT showed serious performance issues – stuttering, frame drops, and VRAM bottlenecks – while the 16GB version handled the same titles smoothly.  And yet, during the GPU’s official reveal, AMD only showed performance data for the 16GB card. There were no benchmarks for the 8GB version – not a single chart. That omission wasn’t lost on anyone. If AMD truly believed the 8GB model held up under modern gaming loads, they would have shown it. The silence speaks volumes.  Why This Actually Matters You might be thinking: ‘So what? Some games still run fine on 8GB. I only play Valorant.’ Sure. But the problem is bigger than that. Source: AMD Games are getting heavier. Even titles like Cyberpunk 2077, released in 2020, can eat up more than 8GB of VRAM. And with GTA 6 (still) on the horizon, do you really think game developers are going to keep optimizing for 8GB cards in 2025? That’s not how game development works. Developers target the most common setups, yes. But hardware also shapes software.  If everyone’s stuck with 8GB, games will be designed around that limit. That holds back progress for everyone.  It’s like trying to make a movie with a flip phone because some people still own one. Same Name, Different Game Another big issue is how these cards are named and sold.  The RX 9060 XT 16GB and RX 9060 XT 8GB are not clearly labeled as different products. They’re just two versions of the same GPU.  But that extra memory makes a huge difference.  In some games, the 8GB card performs dramatically worse. And yet, unless you know what to look for, you might walk into a store and buy the 8GB version thinking you’re getting the same performance.  You’re not. You’re getting a watered-down version with the same name and a silent asterisk. This isn’t just AMD’s Problem Nvidia started this mess with the 4060 Ti naming confusion. AMD just saw the outrage and decided to walk straight into the same buzzsaw.  It’s hard not to feel like both companies are treating consumers like they’re too dumb to notice. Spoiler: they noticed. And this whole ‘VRAM doesn’t matter’ argument? It’s already been debunked by dozens of reviewers.  If you’re spending over $300 on a graphics card in 2025, it needs to last more than a year or two. 8GB cards are already struggling. Buying one now is like buying a smartphone in 2025 with 64GB of storage. Sure, it works. Until it doesn’t. Steam Data Doesn’t Help AMD’s Case AMD and Nvidia both love to point at the Steam Hardware Survey. They say, ‘See? Most people still play at 1080p.’ And that’s true – for now. Source: Nvidia But what they leave out is that 1440p gaming is growing fast. More gamers are upgrading their setups because 1440p monitors are getting a lot more affordable.  Take the Pixio PXC277 Advanced, for instance – a 27-inch curved 1440p monitor with a 165Hz refresh rate and 1ms response time, all for $219.99. A few years ago, a screen like that would’ve cost you double. Now it’s entry-level. Gamers are ready to step up their experience. The only thing holding them back is GPU hardware that’s still stuck in 2020.  Planned Obsolescence in Disguise Here’s the worst part. Companies know full well that 8GB won’t cut it in 2026.  But they still sell it, knowing many gamers will only find out when it’s too late – when the stutters kick in, the textures disappear, or the next big title becomes unplayable. It’s planned obsolescence disguised as ‘choice.’ And while it’s great to have options at different price points, it should be clear which option is built to last – and which one is built to frustrate.  So, Is AMD Actually Screwed?  Not right now. In fact, they’re playing the game better than they used to.  They’ve learned from past pricing disasters and figured out how to get better launch-day headlines – even if it means faking the MSRP and letting street prices run wild.  But this kind of marketing comes at a cost. If AMD keeps making decisions that prioritize short-term wins over long-term trust, they’ll lose the very crowd that once rooted for them.  We don’t need two Nvidias. We need AMD to be different – to be better.  One Name, Two Very Different Cards The RX 9060 XT 16GB might be a good deal. But it’s being overshadowed by the 8GB version’s drama. And the longer AMD keeps playing games with memory and naming, the more it chips away at its hard-earned goodwill.  This whole mess could’ve been avoided with one simple move: name the 8GB card something else. Call it the RX 9055. Call it Lite or whatever. Just don’t make it look like the same card when it isn’t.  Until then, buyers beware. There’s more going on behind the box art than meets the eye.  Anya Zhukova is an in-house tech and crypto writer at Techreport with 10 years of hands-on experience covering cybersecurity, consumer tech, digital privacy, and blockchain. She’s known for turning complex topics into clear, useful advice that regular people can actually understand and use.  Her work has been featured in top-tier digital publications including MakeUseOf, Online Tech Tips, Help Desk Geek, Switching to Mac, and Make Tech Easier. Whether she’s writing about the latest privacy tools or reviewing a new laptop, her goal is always the same: help readers feel confident and in control of the tech they use every day.  Anya holds a BA in English Philology and Translation from Tula State Pedagogical University and also studied Mass Media and Journalism at Minnesota State University, Mankato. That mix of language, media, and tech has given her a unique lens to look at how technology shapes our daily lives.  Over the years, she’s also taken courses and done research in data privacy, digital security, and ethical writing – skills she uses when tackling sensitive topics like PC hardware, system vulnerabilities, and crypto security.  Anya worked directly with brands like Framework, Insta360, Redmagic, Inmotion, Secretlab, Kodak, and Anker, reviewing their products in real-life scenarios. Her testing process involves real-world use cases – whether it's stress-testing laptops for creative workloads, reviewing the battery performance of mobile gaming phones, or evaluating the long-term ergonomics of furniture designed for hybrid workspaces.  In the world of crypto, Anya covers everything from beginner guides to deep dives into hardware wallets, DeFi protocols, and Web3 tools. She helps readers understand how to use multisig wallets, keep their assets safe, and choose the right platforms for their needs.  Her writing often touches on financial freedom and privacy – two things she strongly believes should be in everyone’s hands. Outside of writing, Anya contributes to editorial style guides focused on privacy and inclusivity, and she mentors newer tech writers on how to build subject matter expertise and write responsibly.  She sticks to high editorial standards, only recommends products she’s personally tested, and always aims to give readers the full picture.  You can find her on LinkedIn, where she shares more about her work and projects.  Key Areas of Expertise: Consumer Tech (laptops, phones, wearables, etc.) Cybersecurity and Digital Privacy PC/PC Hardware Blockchain, Crypto Wallets, and DeFi In-Depth Product Reviews and Buying Guides Whether she’s reviewing a new wallet or benchmarking a PC build, Anya brings curiosity, care, and a strong sense of responsibility to everything she writes. Her mission? To make the digital world a little easier – and safer – for everyone.  View all articles by Anya Zhukova Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. Silicon’s mid-life crisis AI has evolved from classical ML to deep learning to generative AI. The most recent chapter, which took AI mainstream, hinges on two phases—training and inference—that are data and energy-intensive in terms of computation, data movement, and cooling. At the same time, Moore’s Law, which determines that the number of transistors on a chip doubles every two years, is reaching a physical and economic plateau. For the last 40 years, silicon chips and digital technology have nudged each other forward—every step ahead in processing capability frees the imagination of innovators to envision new products, which require yet more power to run. That is happening at light speed in the AI age. As models become more readily available, deployment at scale puts the spotlight on inference and the application of trained models for everyday use cases. This transition requires the appropriate hardware to handle inference tasks efficiently. Central processing units (CPUs) have managed general computing tasks for decades, but the broad adoption of ML introduced computational demands that stretched the capabilities of traditional CPUs. This has led to the adoption of graphics processing units (GPUs) and other accelerator chips for training complex neural networks, due to their parallel execution capabilities and high memory bandwidth that allow large-scale mathematical operations to be processed efficiently. But CPUs are already the most widely deployed and can be companions to processors like GPUs and tensor processing units (TPUs). AI developers are also hesitant to adapt software to fit specialized or bespoke hardware, and they favor the consistency and ubiquity of CPUs. Chip designers are unlocking performance gains through optimized software tooling, adding novel processing features and data types specifically to serve ML workloads, integrating specialized units and accelerators, and advancing silicon chip innovations, including custom silicon. AI itself is a helpful aid for chip design, creating a positive feedback loop in which AI helps optimize the chips that it needs to run. These enhancements and strong software support mean modern CPUs are a good choice to handle a range of inference tasks. Beyond silicon-based processors, disruptive technologies are emerging to address growing AI compute and data demands. The unicorn start-up Lightmatter, for instance, introduced photonic computing solutions that use light for data transmission to generate significant improvements in speed and energy efficiency. Quantum computing represents another promising area in AI hardware. While still years or even decades away, the integration of quantum computing with AI could further transform fields like drug discovery and genomics. The developments in ML theories and network architectures have significantly enhanced the efficiency and capabilities of AI models. Today, the industry is moving from monolithic models to agent-based systems characterized by smaller, specialized models that work together to complete tasks more efficiently at the edge—on devices like smartphones or modern vehicles. This allows them to extract increased performance gains, like faster model response times, from the same or even less compute. Researchers have developed techniques, including few-shot learning, to train AI models using smaller datasets and fewer training iterations. AI systems can learn new tasks from a limited number of examples to reduce dependency on large datasets and lower energy demands. Optimization techniques like quantization, which lower the memory requirements by selectively reducing precision, are helping reduce model sizes without sacrificing performance.  New system architectures, like retrieval-augmented generation (RAG), have streamlined data access during both training and inference to reduce computational costs and overhead. The DeepSeek R1, an open source LLM, is a compelling example of how more output can be extracted using the same hardware. By applying reinforcement learning techniques in novel ways, R1 has achieved advanced reasoning capabilities while using far fewer computational resources in some contexts. The integration of heterogeneous computing architectures, which combine various processing units like CPUs, GPUs, and specialized accelerators, has further optimized AI model performance. This approach allows for the efficient distribution of workloads across different hardware components to optimize computational throughput and energy efficiency based on the use case. As AI becomes an ambient capability humming in the background of many tasks and workflows, agents are taking charge and making decisions in real-world scenarios. These range from customer support to edge use cases, where multiple agents coordinate and handle localized tasks across devices. With AI increasingly used in daily life, the role of user experiences becomes critical for mass adoption. Features like predictive text in touch keyboards, and adaptive gearboxes in vehicles, offer glimpses of AI as a vital enabler to improve technology interactions for users. Edge processing is also accelerating the diffusion of AI into everyday applications, bringing computational capabilities closer to the source of data generation. Smart cameras, autonomous vehicles, and wearable technology now process information locally to reduce latency and improve efficiency. Advances in CPU design and energy-efficient chips have made it feasible to perform complex AI tasks on devices with limited power resources. This shift toward heterogeneous compute enhances the development of ambient intelligence, where interconnected devices create responsive environments that adapt to user needs. Seamless AI naturally requires common standards, frameworks, and platforms to bring the industry together. Contemporary AI brings new risks. For instance, by adding more complex software and personalized experiences to consumer devices, it expands the attack surface for hackers, requiring stronger security at both the software and silicon levels, including cryptographic safeguards and transforming the trust model of compute environments. More than 70% of respondents to a 2024 DarkTrace survey reported that AI-powered cyber threats significantly impact their organizations, while 60% say their organizations are not adequately prepared to defend against AI-powered attacks. Collaboration is essential to forging common frameworks. Universities contribute foundational research, companies apply findings to develop practical solutions, and governments establish policies for ethical and responsible deployment. Organizations like Anthropic are setting industry standards by introducing frameworks, such as the Model Context Protocol, to unify the way developers connect AI systems with data. Arm is another leader in driving standards-based and open source initiatives, including ecosystem development to accelerate and harmonize the chiplet market, where chips are stacked together through common frameworks and standards. Arm also helps optimize open source AI frameworks and models for inference on the Arm compute platform, without needing customized tuning.  How far AI goes to becoming a general-purpose technology, like electricity or semiconductors, is being shaped by technical decisions taken today. Hardware-agnostic platforms, standards-based approaches, and continued incremental improvements to critical workhorses like CPUs, all help deliver the promise of AI as a seamless and silent capability for individuals and businesses alike. Open source contributions are also helpful in allowing a broader range of stakeholders to participate in AI advances. By sharing tools and knowledge, the community can cultivate innovation and help ensure that the benefits of AI are accessible to everyone, everywhere. Learn more about Arm’s approach to enabling AI everywhere. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. Silicon’s mid-life crisis AI has evolved from classical ML to deep learning to generative AI. The most recent chapter, which took AI mainstream, hinges on two phases—training and inference—that are data and energy-intensive in terms of computation, data movement, and cooling. At the same time, Moore’s Law, which determines that the number of transistors on a chip doubles every two years, is reaching a physical and economic plateau. For the last 40 years, silicon chips and digital technology have nudged each other forward—every step ahead in processing capability frees the imagination of innovators to envision new products, which require yet more power to run. That is happening at light speed in the AI age. As models become more readily available, deployment at scale puts the spotlight on inference and the application of trained models for everyday use cases. This transition requires the appropriate hardware to handle inference tasks efficiently. Central processing units (CPUs) have managed general computing tasks for decades, but the broad adoption of ML introduced computational demands that stretched the capabilities of traditional CPUs. This has led to the adoption of graphics processing units (GPUs) and other accelerator chips for training complex neural networks, due to their parallel execution capabilities and high memory bandwidth that allow large-scale mathematical operations to be processed efficiently. But CPUs are already the most widely deployed and can be companions to processors like GPUs and tensor processing units (TPUs). AI developers are also hesitant to adapt software to fit specialized or bespoke hardware, and they favor the consistency and ubiquity of CPUs. Chip designers are unlocking performance gains through optimized software tooling, adding novel processing features and data types specifically to serve ML workloads, integrating specialized units and accelerators, and advancing silicon chip innovations, including custom silicon. AI itself is a helpful aid for chip design, creating a positive feedback loop in which AI helps optimize the chips that it needs to run. These enhancements and strong software support mean modern CPUs are a good choice to handle a range of inference tasks. Beyond silicon-based processors, disruptive technologies are emerging to address growing AI compute and data demands. The unicorn start-up Lightmatter, for instance, introduced photonic computing solutions that use light for data transmission to generate significant improvements in speed and energy efficiency. Quantum computing represents another promising area in AI hardware. While still years or even decades away, the integration of quantum computing with AI could further transform fields like drug discovery and genomics. The developments in ML theories and network architectures have significantly enhanced the efficiency and capabilities of AI models. Today, the industry is moving from monolithic models to agent-based systems characterized by smaller, specialized models that work together to complete tasks more efficiently at the edge—on devices like smartphones or modern vehicles. This allows them to extract increased performance gains, like faster model response times, from the same or even less compute. Researchers have developed techniques, including few-shot learning, to train AI models using smaller datasets and fewer training iterations. AI systems can learn new tasks from a limited number of examples to reduce dependency on large datasets and lower energy demands. Optimization techniques like quantization, which lower the memory requirements by selectively reducing precision, are helping reduce model sizes without sacrificing performance.  New system architectures, like retrieval-augmented generation (RAG), have streamlined data access during both training and inference to reduce computational costs and overhead. The DeepSeek R1, an open source LLM, is a compelling example of how more output can be extracted using the same hardware. By applying reinforcement learning techniques in novel ways, R1 has achieved advanced reasoning capabilities while using far fewer computational resources in some contexts. The integration of heterogeneous computing architectures, which combine various processing units like CPUs, GPUs, and specialized accelerators, has further optimized AI model performance. This approach allows for the efficient distribution of workloads across different hardware components to optimize computational throughput and energy efficiency based on the use case. As AI becomes an ambient capability humming in the background of many tasks and workflows, agents are taking charge and making decisions in real-world scenarios. These range from customer support to edge use cases, where multiple agents coordinate and handle localized tasks across devices. With AI increasingly used in daily life, the role of user experiences becomes critical for mass adoption. Features like predictive text in touch keyboards, and adaptive gearboxes in vehicles, offer glimpses of AI as a vital enabler to improve technology interactions for users. Edge processing is also accelerating the diffusion of AI into everyday applications, bringing computational capabilities closer to the source of data generation. Smart cameras, autonomous vehicles, and wearable technology now process information locally to reduce latency and improve efficiency. Advances in CPU design and energy-efficient chips have made it feasible to perform complex AI tasks on devices with limited power resources. This shift toward heterogeneous compute enhances the development of ambient intelligence, where interconnected devices create responsive environments that adapt to user needs. Seamless AI naturally requires common standards, frameworks, and platforms to bring the industry together. Contemporary AI brings new risks. For instance, by adding more complex software and personalized experiences to consumer devices, it expands the attack surface for hackers, requiring stronger security at both the software and silicon levels, including cryptographic safeguards and transforming the trust model of compute environments. More than 70% of respondents to a 2024 DarkTrace survey reported that AI-powered cyber threats significantly impact their organizations, while 60% say their organizations are not adequately prepared to defend against AI-powered attacks. Collaboration is essential to forging common frameworks. Universities contribute foundational research, companies apply findings to develop practical solutions, and governments establish policies for ethical and responsible deployment. Organizations like Anthropic are setting industry standards by introducing frameworks, such as the Model Context Protocol, to unify the way developers connect AI systems with data. Arm is another leader in driving standards-based and open source initiatives, including ecosystem development to accelerate and harmonize the chiplet market, where chips are stacked together through common frameworks and standards. Arm also helps optimize open source AI frameworks and models for inference on the Arm compute platform, without needing customized tuning.  How far AI goes to becoming a general-purpose technology, like electricity or semiconductors, is being shaped by technical decisions taken today. Hardware-agnostic platforms, standards-based approaches, and continued incremental improvements to critical workhorses like CPUs, all help deliver the promise of AI as a seamless and silent capability for individuals and businesses alike. Open source contributions are also helpful in allowing a broader range of stakeholders to participate in AI advances. By sharing tools and knowledge, the community can cultivate innovation and help ensure that the benefits of AI are accessible to everyone, everywhere. Learn more about Arm’s approach to enabling AI everywhere. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. First, technical progress in hardware and silicon design is critical to pushing the boundaries of compute. Second, advances in machine learning (ML) allow AI systems to achieve increased efficiency with smaller computational demands. Finally, the integration, orchestration, and adoption of AI into applications, devices, and systems is crucial to delivering tangible impact and value. Silicon’s mid-life crisis AI has evolved from classical ML to deep learning to generative AI. The most recent chapter, which took AI mainstream, hinges on two phases—training and inference—that are data and energy-intensive in terms of computation, data movement, and cooling. At the same time, Moore’s Law, which determines that the number of transistors on a chip doubles every two years, is reaching a physical and economic plateau. For the last 40 years, silicon chips and digital technology have nudged each other forward—every step ahead in processing capability frees the imagination of innovators to envision new products, which require yet more power to run. That is happening at light speed in the AI age. As models become more readily available, deployment at scale puts the spotlight on inference and the application of trained models for everyday use cases. This transition requires the appropriate hardware to handle inference tasks efficiently. Central processing units (CPUs) have managed general computing tasks for decades, but the broad adoption of ML introduced computational demands that stretched the capabilities of traditional CPUs. This has led to the adoption of graphics processing units (GPUs) and other accelerator chips for training complex neural networks, due to their parallel execution capabilities and high memory bandwidth that allow large-scale mathematical operations to be processed efficiently. But CPUs are already the most widely deployed and can be companions to processors like GPUs and tensor processing units (TPUs). AI developers are also hesitant to adapt software to fit specialized or bespoke hardware, and they favor the consistency and ubiquity of CPUs. Chip designers are unlocking performance gains through optimized software tooling, adding novel processing features and data types specifically to serve ML workloads, integrating specialized units and accelerators, and advancing silicon chip innovations, including custom silicon. AI itself is a helpful aid for chip design, creating a positive feedback loop in which AI helps optimize the chips that it needs to run. These enhancements and strong software support mean modern CPUs are a good choice to handle a range of inference tasks. Beyond silicon-based processors, disruptive technologies are emerging to address growing AI compute and data demands. The unicorn start-up Lightmatter, for instance, introduced photonic computing solutions that use light for data transmission to generate significant improvements in speed and energy efficiency. Quantum computing represents another promising area in AI hardware. While still years or even decades away, the integration of quantum computing with AI could further transform fields like drug discovery and genomics. Understanding models and paradigms The developments in ML theories and network architectures have significantly enhanced the efficiency and capabilities of AI models. Today, the industry is moving from monolithic models to agent-based systems characterized by smaller, specialized models that work together to complete tasks more efficiently at the edge—on devices like smartphones or modern vehicles. This allows them to extract increased performance gains, like faster model response times, from the same or even less compute. Researchers have developed techniques, including few-shot learning, to train AI models using smaller datasets and fewer training iterations. AI systems can learn new tasks from a limited number of examples to reduce dependency on large datasets and lower energy demands. Optimization techniques like quantization, which lower the memory requirements by selectively reducing precision, are helping reduce model sizes without sacrificing performance.  New system architectures, like retrieval-augmented generation (RAG), have streamlined data access during both training and inference to reduce computational costs and overhead. The DeepSeek R1, an open source LLM, is a compelling example of how more output can be extracted using the same hardware. By applying reinforcement learning techniques in novel ways, R1 has achieved advanced reasoning capabilities while using far fewer computational resources in some contexts. The integration of heterogeneous computing architectures, which combine various processing units like CPUs, GPUs, and specialized accelerators, has further optimized AI model performance. This approach allows for the efficient distribution of workloads across different hardware components to optimize computational throughput and energy efficiency based on the use case. Orchestrating AI As AI becomes an ambient capability humming in the background of many tasks and workflows, agents are taking charge and making decisions in real-world scenarios. These range from customer support to edge use cases, where multiple agents coordinate and handle localized tasks across devices. With AI increasingly used in daily life, the role of user experiences becomes critical for mass adoption. Features like predictive text in touch keyboards, and adaptive gearboxes in vehicles, offer glimpses of AI as a vital enabler to improve technology interactions for users. Edge processing is also accelerating the diffusion of AI into everyday applications, bringing computational capabilities closer to the source of data generation. Smart cameras, autonomous vehicles, and wearable technology now process information locally to reduce latency and improve efficiency. Advances in CPU design and energy-efficient chips have made it feasible to perform complex AI tasks on devices with limited power resources. This shift toward heterogeneous compute enhances the development of ambient intelligence, where interconnected devices create responsive environments that adapt to user needs. Seamless AI naturally requires common standards, frameworks, and platforms to bring the industry together. Contemporary AI brings new risks. For instance, by adding more complex software and personalized experiences to consumer devices, it expands the attack surface for hackers, requiring stronger security at both the software and silicon levels, including cryptographic safeguards and transforming the trust model of compute environments. More than 70% of respondents to a 2024 DarkTrace survey reported that AI-powered cyber threats significantly impact their organizations, while 60% say their organizations are not adequately prepared to defend against AI-powered attacks. Collaboration is essential to forging common frameworks. Universities contribute foundational research, companies apply findings to develop practical solutions, and governments establish policies for ethical and responsible deployment. Organizations like Anthropic are setting industry standards by introducing frameworks, such as the Model Context Protocol, to unify the way developers connect AI systems with data. Arm is another leader in driving standards-based and open source initiatives, including ecosystem development to accelerate and harmonize the chiplet market, where chips are stacked together through common frameworks and standards. Arm also helps optimize open source AI frameworks and models for inference on the Arm compute platform, without needing customized tuning.  How far AI goes to becoming a general-purpose technology, like electricity or semiconductors, is being shaped by technical decisions taken today. Hardware-agnostic platforms, standards-based approaches, and continued incremental improvements to critical workhorses like CPUs, all help deliver the promise of AI as a seamless and silent capability for individuals and businesses alike. Open source contributions are also helpful in allowing a broader range of stakeholders to participate in AI advances. By sharing tools and knowledge, the community can cultivate innovation and help ensure that the benefits of AI are accessible to everyone, everywhere. Learn more about Arm’s approach to enabling AI everywhere. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Home OpenAI Academy – A New Beginning in AI Learning News OpenAI Academy – A New Beginning in AI Learning 5 min read Published: May 29, 2025 Key Takeaways The OpenAI Academy is a collection of tutorials and live webinars that educate users about OpenAI products and LLMs. The main aim of the Academy is to enable users to understand the full capabilities of these AI tools in various sectors, such as education, business, and everyday work. With AI advancing rapidly, we might soon see artificial intelligence being offered as professional courses from recognized universities. OpenAI has launched the OpenAI Academy – a one-stop destination for you to learn everything about OpenAI LLM products, such as ChatGPT, Sora, Deep Research, and more. The portal provides extensive video demonstrations on using various features of these AI models and optimizing inputs to get the best results. Here’s a quick look at what you can learn at the OpenAI Academy: ChatGPT on Campus: Meant for students, specifically those in higher education courses. This includes tutorials on researching and writing, managing time effectively, designing resumes, and preparing for interviews. AI for K-12 Educators: Designed for educators, this module focuses on how teachers and support staff can leverage AI tools to streamline school operations and enhance communications. With detailed videos on lesson planning and student support, educators can improve education from the bottom up. It also includes detailed tutorials on using ChatGPT Edu – OpenAI’s specialized chatbot version for educators. ChatGPT at Work: This module will help you with prompt engineering, coding, introduction to ChatGPT projects, ChatGPT Search, data analytics, and Deep Research. Sora Tutorials: Sora is OpenAI’s very own video generator. It can render videos from text, images, and video prompts. Sora tutorials on OpenAI Academy teach you how to build a storyboard, recut, blend, and remix videos with the AI tool. OpenAI for Business: This module is all about personalizing ChatGPT to make it more relevant to your day-to-day work. You can learn more about ChatGPT’s Deep Research integration with GitHub, connecting ChatGPT to Google Drive to tap into internal knowledge, run multi-step workflows, and do extensive market research. In addition to these pre-recorded tutorials, videos, and ‘how-to’ articles, the OpenAI Academy hosts many live events and webinars. These sessions are quite exhaustive, from the introduction to ChatGPT to practical use cases for career development and education. There’s no debating that ChatGPT has endless real-world applications, whether in business or education. However, a vast majority of people are simply unaware. The OpenAI Academy looks to eliminate the lack of awareness around its products and show the world their capabilities. The Art of Prompts In a nutshell, the art of using AI is essentially your ability to feed these LLM models proper and effective prompts. For example, if there’s an image to be generated, an average person’s prompt might be as simple as ‘generate an image of a kid playing football.’ However, those who know what the AI is truly capable of will feed more specific prompts, such as ‘Generate a portrait image in HD showing a kid aged roughly six years old, wearing a Chelsea jersey and shorts with Nike boots playing football on a grassy ground. Keep the lighting in the image close to what we see during sunset in August.’ Here’s how the two results would vary: Interestingly, both these images have been generated using the same model, i.e., ChatGPT, with the only difference being the prompt. This goes to show that people who have fully wrapped their heads around what AI can do will be able to use it to its full potential. It’s worth noting that you can extrapolate this example to endless scenarios. For example, if you’re too lazy to read a book, you can simply ask ChatGPT to generate a mind map of the entire book. This will help you understand the text better and remember the book’s content for a long time. However, you must have professional AI training to do so. Alternatively, ChatGPT can serve up some work emails or sort numerical data. However, experienced users can also use it for tasks like strategic planning, breaking down trends and competitions, finding new strategic leads and opportunities, and generating an actionable market strategy using accurate prompts. AI Isn’t Just a Feature Anymore Gone are the days when AI was just a new, cool feature for us to try. Using ChatGPT or Gemini to write a work email, complete an assignment, or generate an AI image are just the tip of the iceberg. Using AI tools now requires real skills and comes with a learning curve and even a curriculum. If you give an average person a blank canvas and a paintbrush, he may draw a couple of houses, a river, the sun, and other basic elements. However, put the same canvas and paintbrush in the hands of a trained artist, and he’ll make you a masterpiece. Today’s AI tools are essentially the canvas and the paintbrush. In the hands of the average user, it’s hardly anything more than a text or image generator. However, those aware of their potential can design entire business plans, write and run complex codes, and learn new things almost every day. In its initial days, coding, too, was just a soft skill or an extracurricular activity. However, with time, coding has developed into a full-fledged education stream with postgraduate and PhD certifications. Something similar might happen with AI as well. As these tools become more sophisticated, the need for a structured AI education will also rise. The day isn’t far when we might see recognized universities offering professional degrees in artificial intelligence, focusing not only on designing the systems but also harnessing the most out of AI. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. More from News View all View all

Home You Can Now Make AI Videos with Audio Thanks to Veo3: A New Era of Scams Await News You Can Now Make AI Videos with Audio Thanks to Veo3: A New Era of Scams Await 6 min read Published: May 27, 2025 Key Takeaways With Google’s Veo3, you can now render AI videos, audio, and background sounds. This would also make it easy for scammers to design deepfake scams to defraud innocent citizens. Users need to exercise self-vigilance to protect themselves. Developers’ responsibilities and government regulations will also play a key part. Google recently launched Veo3, an AI tool that lets you create videos with audio, including background tracks and various sound effects. Until recently, you could either use voice cloning apps to build AI voices or video rendering apps to generate AI videos. However, thanks to Veo3, folks can now create entire videos with audio. While this is an exciting development, we can’t help but think how easy it would be for scammers and swindlers to use Veo3’s videos to scam people. A video posted by a user on Threads shows a TV anchor breaking the news that ‘Secretary of Defence Pete Hegseth has died after drinking an entire litre of vodka on a dare by RFK.’ At first glance, the video is extremely convincing, and chances are that quite a few people might have believed it. After all, the quality is that of a professional news studio with a background of the Pentagon. Another user named Ari Kuschnir posted a 1-minute 16-second video on Reddit showing various characters in different settings talking to each other in various accents. The facial expressions are very close to those of a real human. A user commented, ‘Wow. The things that are coming. Gonna be wild!’ The ‘wild’ part is that the gap between reality and AI-generated content is closing daily. And remember, this is only the first version of this brand-new technology – things will only get worse (worse) from here. New AI Age for Scammers With the development of generative AI, we have already seen countless examples of people losing millions to such scams.  For example, in January 2024, an employee of a Hong Kong firm sent $25M to fraudsters who convinced the employee that she was talking to the CFO of the firm on a video call. Deloitte’s Center for Financial Services has predicted that generative AI could lead to a loss of $40B in the US alone by 2027, growing at a CAGR of 32%. Until now, scammers also had to take the effort of generating audio and video separately and syncing them to compile a ‘believable’ video. However, advanced AI tools like Veo3 make it easier for bad actors to catch innocent people off guard. In what is called the internet’s biggest scam so far, an 82-year-old retiree, Steve Beauchamp, lost $690,000 after he invested his retirement savings in an investment scheme. The AI-generated video showed Elon Musk talking about this investment and how everyone looking to make money should invest in the scheme. In January 2024, sexually explicit images of Taylor Swift were spread on social media, drawing a lot of legislative attention to the matter. Now, imagine what these scammers can do with Veo3-like technology. Making deepfake porn would become easier and faster, leading to a lot of extortion cases. It’s worth noting, though, that we’re not saying that Veo3 specifically will be used for such criminal activities because they have several safeguards in place. However, now that Veo3 has shown the path, other similar products might be developed for malicious use cases. How to Protect Yourself Protection against AI-generated content is a multifaceted approach involving three key pillars: self-vigilance, developers’ responsibilities, and government regulations. Self Vigilance Well, it’s not entirely impossible to figure out which video is made via AI and which is genuine. Sure, AI has grown leaps and bounds in the last two years, and we have something as advanced as Veo3. However, there are still a few telltale signs of an AI-generated video.  The biggest giveaway is the lip sync. If you see a video of someone speaking, pay close attention to their lips. The audio in most cases will be out of sync by a few milliseconds. The voice, in most cases, will also sound robotic or flat. The tone and pitch might be inconsistent without any natural breathing sounds. We also recommend that you only trust official sources of information and not any random video you find while scrolling Instagram, YouTube, or TikTok. For example, if you see Elon Musk promoting an investment scheme, look for the official page or website of that scheme and dig deeper to find out who the actual promoters are.  You will not find anything reliable or trustworthy in the process. This exercise takes only a couple of minutes but can end up saving thousands of dollars. Developer’s Responsibilities AI developers are also responsible for ensuring their products cannot be misused for scams, extortion, and misinformation. For example, Veo3 blocks prompts that violate responsible AI guidelines, such as those involving politicians or violent acts.  Google has also developed its SynthID watermarking system, which watermarks content generated using Google’s AI tools. People can use the SynthID Detector to verify if a particular content was generated using AI. However, these safeguards are currently limited to Google’s products as of now. There’s a need for similar, if not better, prevention systems moving forward. Government Regulations Lastly, the government needs to play a crucial role in regulating the use of artificial intelligence. For example, the EU has already passed the AI Act, with enforcement beginning in 2025. Under this, companies must undergo stringent documentation, transparency, and oversight standards for all high-risk AI systems.  Even in the US, several laws are under proposal. For instance, the DEEPFAKES Accountability Act would require AI-generated content that shows any person to include a clear disclaimer stating that it is a deepfake. The bill was introduced in the House of Representatives in September 2023 and is currently under consideration.  Similarly, the REAL Political Advertisements Act would require political ads that contain AI content to include a similar disclaimer. That said, we are still only in the early stages of formulating legislation to regulate AI content. With time, as more sophisticated and advanced artificial intelligence tools develop, lawmakers must also be proactive in ensuring digital safety. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. More from News View all News OpenAI Academy – A New Beginning in AI Learning Krishi Chowdhary 44 minutes ago View all