MarketWatch
중도 성향
IT/기술
First Google, now Meta? Big Tech may increasingly sell stock to bankroll $820 billion AI boom.
Stock investors may not love it — but bond investors already heavily engaged in funding the AI buildout are pleased.
🇺🇸 미국 · IT/기술 · "HEAVILY" · 총 7건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 11,397건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 11,395건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.3(중도 균형)입니다.
Stock investors may not love it — but bond investors already heavily engaged in funding the AI buildout are pleased.
Amazon's gaming strategy has never really been clear. It's been very active in the space: acquiring Twitch, launching its Luna cloud gaming service nearly six years ago, investing heavily in MMOs during the peak of live-service wave, and having access to a huge slate of franchises through Prime Video and the MGM Studios library. Late […]
Carvana was granted a warrant to buy shares in Slate last year, according to documents obtained by TechCrunch. Guggenheim Partners CEO Mark Walter is heavily invested in both companies.
AI firms heavily use news and other creative content to provide answers — but New York Times Publisher A.G. Sulzberger says their resistance to pay for it amounts to a repackaging of “stolen goods.”
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations. Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset. Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designers—but if you’re coming from an academic background as I did, there are a few things you’ll need to know. Different goals lead to different strategies The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule. This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. “Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.” This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.’s University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. What the industry-academia split means in practice Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability. Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing. The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative. In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. Design scope, verification, and time horizons With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield. In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face. Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain. In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape. This article appears in the June 2026 print issue.
DuckDuckGo, which positions itself as a privacy-focused alternative to Google, has experienced a significant increase in user adoption following the search giant's announcement of major changes to its search platform that heavily incorporate AI. The post DuckDuckGo Installs Surge 30% as Consumers Reject Google ‘Force-Feeding’ AI with Search Engine appeared first on Breitbart.
Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value. In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality. The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe. The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough. As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics. 1. The YouTube-to-Reality Gap Is Real For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon. Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work. 2. Data Is An Unsolved Challenge Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training. The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world. Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level. 3. There Will Be No Single Robot AI We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us. General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time. We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models. AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies. 4. Hardware Is Still Very Hard Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators. Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things. Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people. 5. Real Value Comes From “Easy” Tasks There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots. Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control. Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models. This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience. AI Robots Are Coming, One Step at a Time As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.