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Bulls declare victory in AI debate, but two classic signs of a market top are looming
The trend of ‘capex recycling’ is considered a problem by a TS Lombard analyst
🇺🇸 미국 · IT/기술 · "LOOM" · 총 12건
필터 보기현재 지수
50.0
0 = 부정 우세
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100 = 긍정 우세
최근 7일 기준 12,164건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 12,162건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.4(중도 균형)입니다.
The trend of ‘capex recycling’ is considered a problem by a TS Lombard analyst
Taiwan Semiconductor Manufacturing Co. - the world's biggest semiconductor-maker - is struggling to meet demands from American customers even with its factory buildout in the US, according to reports from Reuters and Bloomberg. "Customer demand is so high, and we can only support so much," TSMC CEO C.C. Wei said after a shareholder meeting on […]
Over the past several years, Microsoft has largely managed to withstand populist calls to break up Big Tech while peers faced sweeping lawsuits. But a probe by the Federal Trade Commission suggests that grace period could be nearing an end. Earlier this year, Bloomberg outlined the contents of civil investigative demands (CIDs) - similar to […]
Apple isn't just looking to take on Meta in the smart glasses market; it's looking to upend eyewear as a whole, according to Bloomberg's Mark Gurman. When the Apple Watch launched, it wasn't simply competing against the Pebbles and the Motorolas of the world. The company also had Swatch, Fossil, and Seiko in its crosshairs. […]
Apple's long-awaited Siri overhaul, expected to arrive in iOS 27, might look a lot like ChatGPT with a splash of Liquid Glass. Renders from Bloomberg offer a preview of iOS 27, including the new app and chat interface for Siri. The renders are "based on information viewed by Bloomberg and people with knowledge of [Apple's] […]
After announcing tests of premium subscriptions for Facebook, Instagram, WhatsApp earlier this year, TechCrunch and Bloomberg report that Meta is launching a global rollout over the next few weeks and is also starting to test subscriptions for Meta AI. With the new offerings, Meta joins many other tech companies in changing up its subscription plans […]
Two of the leading figures in the AI industry, Sam Altman and Dario Amodei, have walked back dire predictions about AI eliminating white-collar jobs, acknowledging their earlier forecasts were incorrect as both firms prepare for potential IPOs. The post OpenAI’s Sam Altman and Anthropic’s Dario Amodei Reverse Predictions on AI Job Displacement as IPOs Loom appeared first on Breitbart.
As Americans stew over the looming risk of job-stealing AI and data centers in their back yards, the feds are raising the alarm about a new category of threat, documents obtained by WIRED show.
Artificial intelligence has not so far produced a clean story of mass unemployment. Aggregate employment in developed countries remains broadly stable, and recent assessments have found limited evidence that AI has shifted the headline numbers. But a troubling change may be hiding beneath the surface: the quiet weakening of the first rung of the career…
Editor’s note: If you’d like to pinpoint the instant when the world entered the nuclear age, 5:29:45 a.m. Mountain War Time on 16 July 1945, is an excellent choice. That was the moment when human beings first unleashed the power of the nucleus in an immense, blinding ball of fire above a gloomy stretch of desert in the Jornada del Muerto basin in New Mexico. Emily Seyl’s Trinity: An Illustrated History of the World’s First Atomic Test (The University of Chicago Press) offers hundreds of startlingly vivid photographs of the Manhattan Project that emerged from a 20-year restoration effort. This excerpt and the accompanying photos record the massive effort to capture the awesome detonation of “the Gadget.” aspect_ratioReprinted with permission from Trinity: An Illustrated History of the World’s First Atomic Test by Emily Seyl with contributions by Alan B. Carr, published by The University of Chicago Press. © 2026 by The University of Chicago. All rights reserved. In the North 10,000 photography bunker, Berlyn Brixner was listening to the countdown on a loudspeaker, his head inside a turret loaded with cameras and film. He was one of the only people instructed to look toward the blast—through his welder’s glasses—ready to follow the path of the fireball as it launched into the sky. The two Mitchell movie cameras at his station would deliver the best footage to come of the Trinity test, used by Los Alamos scientists to make some of the first measurements of the effects of a nuclear explosion. Related: New Trinity Book Uncovers Images of the First Atomic Test When the detonators fired, the cameras captured what Brixner could not have seen—the very first light of a violent, silent sea of energy unfurling into the basin. As 32 blocks of high explosives erupted all together, their incredible force surged inward toward the sleeping plutonium core, compressing the dense sphere of metal instantaneously from all sides and bringing its atoms impossibly close together. A carefully timed burst of neutrons sowed momentary, uncontrolled chaos, and then, as quickly as it began, the fission chain reaction ended. Footage from a high-speed Fastax camera in Brixner’s bunker, shot through a thick glass porthole, shows a translucent orb bursting through the darkness less than a hundredth of a second after detonation, as a rush of heat, light, and matter blew apart the Gadget. When the brightness faded enough for witnesses to make out ground zero, they saw a wall of dust rise up around a brilliant, shape-shifting, multicolored ball of flames—forming a fiery cloud that shot into the sky atop a twisting stream of debris. The camera footage tells a story no less dramatic but hundreds of times more intricate, preserving the moment for scientists to return to again and again to measure and describe the behavior of the fireball and other visible effects with exacting detail. On balance, the photography effort was a huge success, despite only 11 of the 52 cameras producing satisfactory images. By arranging those cameras at intentionally staggered distances, complementary angles, and with a broad spectrum of frame rates and focal lengths, the Spectrographic and Photographic Measurements Group was able to piece together a remarkably complete picture of their subject. On 12 July 1945, Herbert Lehr, a U.S. Army sergeant and electrical engineer assigned to Los Alamos, delivered the plutonium core to the McDonald ranch house, where the bomb was assembled. Los Alamos National Laboratory According to the group’s leader, Julian Mack, the more than 100,000 frames that were captured still “give no idea of the brightness, or of time and space scales.” Mack attributed fortune, as much as foresight, to the photographic record that was made, especially during the earliest phase of the blast. Indeed, the explosion was several times more powerful than predicted, and the intensity of its effects overwhelmed many of the cameras and diagnostic instruments. The human observers were similarly overcome. “The shot was truly awe-inspiring,” said Norris Bradbury, the physicist who would succeed Robert Oppenheimer as director of Los Alamos. “Most experiences in life can be comprehended by prior experiences, but the atom bomb did not fit into any preconception possessed by anybody. The most startling feature was the intense light.” Norris Bradbury, the physicist responsible for the final assembly of the Gadget, stands next to the partially assembled bomb at the top of the shot tower. The cables on the outside of the bomb would transmit the signals to trigger the synchronized detonations of conventional explosives, which would then create the inward-directed shock wave that would compress the bomb’s plutonium core. Bradbury would go on to succeed Robert Oppenheimer as director of Los Alamos on 17 October 1945.Los Alamos National Laboratory It is a common sentiment that words and even pictures pale in comparison to the experience of the explosion. Even so, soldiers, scientists, and many other witnesses have added their firsthand accounts—often absorbing and poetic—to complement the trove of hard data collected during the test shot. They describe an intense and blinding brightness that filled the basin with daytime; an ominous, darkening cloud rearing its head in eerie silence; the wait for the invisible wave rushing out from the heart of the Gadget; and the mighty roar that arrived at last, in a thunder, and seemed never to leave. Physicist Isidor Isaac Rabi, watching from 20 miles away, remembered, “It blasted; it pounced; it bored its way right through you.” James Chadwick, head of the British contingent of scientists who joined the Manhattan Project, later said, “Although I had lived through this moment in my imagination many times during the past few years and everything happened almost as I had pictured it, the reality was shattering.” The blast, captured with an assortment of high-speed and motion-picture cameras, shows the fireball expanding between 25 milliseconds and 60 seconds, by which time the mushroom cloud is over 3 kilometers high.Los Alamos National Laboratory And physicist George Kistiakowsky found himself certain that “at the end of the world—in the last millisecond of the Earth’s existence—the last human will see what we saw.”
When Ana Inês Inácio goes to work at the Netherlands Organization for Applied Scientific Research (TNO) in The Hague, she thinks about signals most people never notice: radio waves moving between satellites, sensors, and future wireless networks. The integrated circuits the research scientist designs lay the foundation for next-generation RF sensor systems critical to advancing radar technologies. Ana Inês Inácio EMPLOYER Netherlands Organization for Applied Scientific Research, TNO TITLE Scientist IEEE MEMBER GRADE Senior member ALMA MATER University of Aveiro, in Portugal Those invisible RF signals are only part of what earned the IEEE senior member her global recognition. Inácio recently received the IEEE–Eta Kappa Nu Outstanding Young Professional Award for “leadership in IEEE Young Professionals, fostering innovation and inclusivity, and pioneering advancements in RF sensor systems, bridging technical excellence with impactful community engagement.” The recognition from IEEE’s honor society reflects a career built along two parallel paths: advancing RF circuit design while helping engineers worldwide build professional communities. “I’ve always liked building things,” Inácio says. “Sometimes that means circuits; sometimes it means helping people connect and grow together.” That blend of technical innovation and global leadership gives her work impact far beyond the laboratory. EE lessons at the kitchen table Inácio grew up in Vales do Rio, a rural village near Covilhã in central Portugal. The region was known for farming and textiles, she says. Many residents worked in the textile industry, including her grandfather, who repaired machinery such as industrial looms. He became her first engineering teacher without ever holding the formal title. Through correspondence courses delivered by mail, he taught himself electrical systems. At home, he explained electricity to his granddaughter while he repaired the household’s appliances and wiring. “He would show me why something broke and how we could fix it,” she recalls. It sparked her curiosity. Her mother was a tailor who later managed other tailors. Her father left his factory job to attend culinary school and now cooks at an elder-care facility. Curiosity was a trait that ran through the family. By high school, Inácio was drawn equally to mathematics and physics and to biology and geology, she says. Encouragement from teachers and an uncle, an engineer, ultimately steered her toward electronics engineering. Conducting research on integrated circuits In 2008 she enrolled in an integrated master’s degree program in electrical and telecommunications engineering at the Universidade de Aveiro in Portugal, a five-year degree that combined undergraduate and graduate studies. An opportunity to study abroad changed her path. In 2012 she moved to the Netherlands to study at Eindhoven University of Technology (TU/e) through a six-month European exchange program with UAveiro. A professor encouraged her to stay on, so she completed her final year of masters in the Netherlands. She focused on techniques to improve the linearization of RF power amplifiers at Thales. The company, based in Hengelo, Netherlands, designs and produces electronics for defense and security. She earned her master’s degree from UAveiro in 2013. After graduating, she joined the integrated circuit design group at the University of Twente, in The Netherlands, conducting collaborative research as part of a nationally funded program on linearization techniques for RF front-end systems. The experience introduced her to international research culture and persuaded her to pursue a career abroad, she says. Engineering the future of wireless Inácio joined TNO in 2018 as a junior scientist and innovator: her first professional industry job. Today she designs integrated RF front-end systems—the circuits that allow devices to transmit and receive wireless signals. The components sit at the core of modern communications, enabling sensor networks, satellite links, and emerging 6G technologies. Her work aims to tackle a central challenge: getting greater performance from smaller chips. “As communication evolves, we need more bandwidth to transfer more data at higher speeds,” she says. “The question is how much complexity you can integrate into one system while keeping it efficient.” Unlike commercial lab environments, which reuse established designs, research projects often start from scratch. Each transmit-receive chain—the signal path that converts digital data to radio waves and back again—is tailored to specific requirements. Her work focuses on improving key circuit characteristics including linearity (ensuring that the signals that go out of the antenna are not distorted) as well as noise reduction (so design blocks can be optimized). Advanced design techniques help devices communicate more reliably while consuming less energy, a critical need for large sensor networks such as the Internet of Things, she says. Artificial intelligence is beginning to influence her field, she says: “AI is already helping us work faster. The real challenge is learning how to use it to make better designs, not just quicker ones.” A parallel vocation with IEEE While her technical career flourished in research labs, an additional journey unfolded through IEEE. Inácio joined the organization in 2009 as a student after discovering UAveiro’s student branch. What began as curiosity evolved into a long-term leadership path. She advanced through roles within Region 8—covering Europe, Africa, and the Middle East—one of the organization’s most culturally diverse regions. She was the student branch’s vice chair, and the region’s student representative for more than 22,000 IEEE members. She also served as the Young Professionals Affinity Group chair for the IEEE Benelux Section, which encompasses Belgium, the Netherlands, and Luxembourg. Currently, she serves as the immediate past chair of the Region 8 Young Professionals Committee, and vice chair and IEEE Member and Geographical Activities representative on the IEEE Young Professionals Committee. In those roles, she represents close to 135,000 IEEE members. In addition, she is an active member of the IEEE Microwave Theory and Technology Society, currently serving as its Young Professionals liaison. Her involvement with IEEE has boosted her professional confidence, she says. “IEEE didn’t directly give me promotions at my day job, but it gave me leadership skills, networking opportunities, and the ability to work with people from everywhere,” she says. Those experiences now shape her collaborations at TNO, where international teamwork is essential. The IEEE-HKN Outstanding Young Professional Award recognizes that combination of technical excellence and community impact, she says. Looking back, Inácio sees a clear thread connecting her childhood curiosity, her international career, and her IEEE leadership: Engineering, she says, is ultimately about people as much as it is about technology.
The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process. Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version. But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform. How is AI currently being used to design the next generation of chips? Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider. Heather GorrMathWorks Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI. What are the benefits of using AI for chip design? Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design. So it’s like having a digital twin in a sense? Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end. So, it’s going to be more efficient and, as you said, cheaper? Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering. We’ve talked about the benefits. How about the drawbacks? Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years. Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together. One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge. How can engineers use AI to better prepare and extract insights from hardware or sensor data? Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start. One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI. What should engineers and designers consider when using AI for chip design? Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team. How do you think AI will affect chip designers’ jobs? Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip. How do you envision the future of AI and chip design? Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.