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ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
50.0
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100 = ๊ธ์ ์ฐ์ธ
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Rising incomes, financial discipline and shifting life priorities are changing the marketโeven as affordability and hidden costs remain major barriers.
Desperate Seattle residents putting up steel flower planters as barriers to block off their streets to quell runaway crime have finally drawn the attention of the cityโs socialist mayor. The post Socialist Mayor Responds After Seattle Residents Use Steel Planters to Block Streets from Criminals appeared first on Breitbart.
An open letter signed by the heads of OpenAI, Anthropic, Google DeepMind, and Microsoft AI warns that advances in AI are eroding barriers to biological weapons development
The nationโs largest provider of Medicare Advantage plans is urging policymakers to bring the Make America Healthy Again agenda into an unexpected arena: health insurance. Centene, one of the countryโs largest managed care organizations, recently called on the Centers for Medicare and Medicaid Services to remove regulatory barriers that limit Medicare Advantage plans from covering [โฆ]
Across all 11 U.S. host cities, 80% of hoteliers report bookings below forecast, with visa barriers and immigration enforcement cited as top constraints.
For years, researchers and advocates have documented the barriers students from immigrant families face when pursuing higher education. But the Trump administration's mass deportation campaign has introduced new challenges and fears, even for many immigrants who are legally in the United States. Special correspondent Fred de Sam Lazaro reports from Minnesota for our series Rethinking College.
This article is adapted by the author with permission from Tech Policy Press. Read the original article. South Africa is not just another developing country struggling to govern artificial intelligence; it is the exception with leverage, and the window to act on it is closing. It holds approximately 88 percent of global platinum-group metal reserves, critical inputs to parts of the semiconductor and data-center supply chains that make AI infrastructure possible. It hosts the largest data-center market on the continent. Its existing hyperscaler relationships give it procurement leverage that most African states will never have. And a major geopolitical contest over AI infrastructure is being fought on its soil right now, between Chinese and American technology companies competing for control of the systems that will underpin an entire continentโs public sector. In physics, leverage requires three things: a fulcrum, a lever arm, and the ability to apply force. The Bushveld Complex, the worldโs largest platinum-group metal deposit, is the fulcrum: a mineral endowment that gives South Africa a position in the semiconductor supply chain that no other African state holds. The since-withdrawn draft policy is the lever arm. The unresolved โOPTIONโ provisions in the policy are where force would be applied. Without a policy that specifies what South Africa wants in return for market access, the lever arm sits unused, and the weight of two of the worldโs largest technology ecosystems settles exactly where those ecosystems want it to settle. This makes South Africa a global test case. Not because its proposed means of governance is exemplary, but because it is the one developing country with enough structural leverage to negotiate genuinely different terms, and the one that is choosing, through inaction, not to. The recent announcement of a new panel to update the draft policy is an important opportunity. But the deeper failure is not that an AI policy contained bad references. It is that no verification process caught them before the document entered the public domain. That is a systems problem, not merely a political one. It points to a missing layer in how governments are adopting AI. The contest already underway Last year, Huawei pitched an emerging-product bundle to tech executives across the continent. Huawei was now bundling access to DeepSeekโs large language model with its own cloud and storage infrastructure. The price differential was starkโin some cases by more than 90 percent. At the same time, Microsoft announced plans to spend ZAR 5.4 billion ($300 million) by the end of 2027 on cloud and AI infrastructure in South Africa, building on a prior ZAR 20.4 billion investment. Google, Amazon Web Services, and Oracle already have cloud regions in the country. According to one analysis, the countryโs data-center market was valued at US $2.16 billion in 2024, the largest in Africa. These are not commercially neutral investments. Huaweiโs infrastructure reach has been explicitly linked to Chinese strategic objectives, including a documented track record of providing governments with surveillance infrastructure through its Safe Cities network. U.S. hyperscaler investment comes with its own dependency structure: closed models, pricing set unilaterally, and terms of access that no African government has meaningfully shaped. South Africa is being asked to choose between these dependency models without a policy that specifies what it wants in return. The leverage it has There is a particular irony in South Africaโs position. The country whose mines supply platinum-group metals essential to semiconductor manufacturing, and through them to AI compute, has drafted a policy that treats it as a consumer of AI systems rather than a stakeholder in their governance. South Africa digs up the minerals that make AI possible. It has no say over the AI built from them. The AI triad framework covers algorithms, compute, and data. South Africa has no frontier model development capacity. South Africa holds significant data assets in financial services, health care, and agriculture, with no clear framework for their sovereign management. South Africa possesses PGM (Platinum Group Metals) leverage of global significance on the compute axis, currently being transferred without meaningful condition. It also has exceptionally high solar irradiance and significant renewable-energy potential. A country that can offer both critical mineral inputs and the energy to power the infrastructure those minerals help build occupies a negotiating position of unusual strength. The Draft Policy proposes no minimum terms for hyperscaler investment, no data sovereignty requirements, no technology transfer conditions and no compute visibility mechanism. Multiple provisions are explicitly left unresolved, marked โOPTION,โ including the most consequential choices about how governance will function. Infrastructure decisions made now determine what is renegotiable later, and the answer is: very little. Three futures, one default The three infrastructure futures on offer each create a structurally different form of dependency, and only one creates sovereign capability. The Huawei-hosted DeepSeek integration offers low cost and open-source weights, but with data stored on infrastructure potentially accessible under Chinese legal frameworks, creating surveillance dependency in a pattern already documented across Africa. The second is U.S. closed-model dependency: higher capability, more reliable data protection, but complete API dependency on developers abroad. The third is locally hosted open-weight infrastructure: models governed under South African data-sovereignty rules, on infrastructure subject to minimum terms, developed with South African data. As Nathan Lambert at Interconnects has observed, open-weight models are likely the only realistic way to get sovereign AI off the ground as a real effort, enabling local communities and economies to integrate meaningfully with the technology. But this requires procurement conditions, not goodwill. What binding governance looks like The GovAI โGoverning Through the Cloudโ framework identifies four roles compute providers should accept as conditions of operating at scale: securers (protecting model weights and training data), record keepers (maintaining infrastructure usage logs), verifiers (confirming customer compliance with safety standards) and enforcers (restricting access when violations occur). These are operational requirements, not theoretical categoriesโspecific, enforceable, and well within the bargaining power of a market of South Africaโs size and mineral position. A detailed policy analysis submitted to the Department of Communications and Digital Technologies (DCDT) identifies the specific provisions the final policy must contain: mandatory minimum terms for foreign compute infrastructure investments above ZAR 500 million (~$30 million); a compute reporting threshold; a National AI Safety Institute mandate covering defensive monitoring of AI capability accumulation; and National AI Champion Sector designations to create data assets for domestic model development. Each provision converts a structural advantage into a governance instrument before that advantage is foreclosed by market reality. Just as modern software security increasingly depends on knowing what components are inside a systemโmodel provider, training data, compute environment, evaluation methods, update cadence, human review points, and failure-reporting proceduresโpublic-sector AI governance requires a clear account of the stack before deployment, not after a problem surfaces. A public institution that cannot verify the sources in its own AI policy is unlikely to be ready to verify the AI systems it procures, deploys, or regulates. Why this is the continental test case South Africaโs choices will establish a regional precedent for what is commercially negotiable in AI infrastructure. If South Africa negotiates data-sovereignty guarantees and technology-transfer conditions as requirements for hyperscaler investment, it creates a replicable model. If Microsoftโs $300 million investment and Huaweiโs infrastructure expansion proceed on standard commercial terms, as they are currently, it normalizes extractive AI infrastructure across the continent. The lesson is not specific to Africa. Governments everywhere are producing AI strategies while lacking AI assurance infrastructure. South Africa is an early warning, not an isolated case. The public comment period closed when the policy was withdrawn. But a parallel process remains live: the National Treasuryโs Draft General Public Procurement Regulationsโthe legal instrument that will govern every government AI contractโcloses for comment on June 15. Those regulations contain no AI-specific provisions. South Africa has more AI leverage than any country on the continent. Some argue, with force, that governance requirements risk deterring the infrastructure investment South Africa urgently needs: compute capacity, reliable energy, venture capital, and talent retention. That concern deserves a direct answer. Minimum procurement terms, compute reporting thresholds, and technology transfer conditions are not barriers to investment. They are the conditions under which investment serves the host country rather than extracting from it. Infrastructure built without minimum terms produces dependency. Infrastructure built with them produces leverage. To serve the public interest, its AI policy must use it. When late last month News24 reported AI-hallucinated references in the draft AI policy, Minister of Communications and Digital Technologies Solly Malatsi withdrew the draft policy. That was a mistake that could cost South Africa and the rest of the continent the initiative on this urgent issue. His more recent constitution of an independent panel is a belated step in the right direction, if it can turn South Africaโs leverage into policy. The panelโchaired by Professor Benjamin Rosman of the Wits Machine Intelligence and Neural Discovery Institute, and including Professors Vukosi Marivate and Alison Gillwald of Research ICT Africa and Dr. Jabu Mtsweni of the Council for Scientific and Industrial Researchโhas the technical and governance credibility to produce a stronger document. What it has not yet produced is a timeline. No revised draft has been scheduled. South Africa remains without a formal AI governance framework in the interim.
International graduates say a weak hiring market and changing immigration rules make it harder for them to achieve their American dream of working in the U.S.
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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 wonโt. 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 todayโs robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but thereโs 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 programmedโinstead, 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 โnever trust a YouTube robot video.โ 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 arenโt they also showing up on factory floors yet? And why canโt 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 OpenAIโs ChatGPT and Anthropicโs 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 โspeakโ 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 โdimensionsโ represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good dataโit 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. Itโs 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 donโt. 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 โagentic AIโ 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. Itโs 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. Actuatorsโthat is, the motors and gearsโare a good example of an important part of the robot where what got us here wonโt 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 donโt move in this way. We are far more compliant in how we interact with the world, and weโre 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 donโt 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 โEasyโ Tasks Thereโs a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravecโs 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 toddlerโs 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 Roboticsโ 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 โmessyโ 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 โahaโ 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 โCambrian explosionโ 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.
This sponsored article is brought to you by Applied Materials. At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs โ such as those achieved under the Human Genome Project โ required a new operating paradigm: Concentrate the worldโs best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace. Todayโs AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much โ or more โ energy than compute itself. As a result, reducing energy per bit can extend systemโlevel performance alongside gains in peak compute. The path to energyโefficient AI therefore runs through systemโlevel engineering, spanning three tightly interconnected domains: Logic, where performance per watt depends on efficient transistor switching, lowโloss power, and signal delivery through dense wiring stacks. Memory, where surging bandwidth and capacity demands expose the memory wall, with processor capability advancing faster than memory access. Advanced packaging, where 3D integration, chiplet architectures, and highโdensity interconnects bring compute and memory closer together โ enabling system designs monolithic scaling can no longer sustain. These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both frontโend device fabrication and backโend integration processes. In the angstrom era, the hardest problems arise at the boundaries โ between compute and memory in the package, frontโend and backโend integration, and the tightly coupled process steps needed for precise 3D fabrication. And it is precisely this boundaryโdriven complexity where the traditional innovation model breaks down. The Traditional R&D Workflow Is Too Slow for AngstromโEra AI For decades, the semiconductor industryโs R&D model has resembled a relay race. Capabilities are developed in one part of the ecosystem, handed off downstream through integration and manufacturing, evaluated by chip and system designers, and only then fed back for the next iteration. That model worked when progress was dominated by relatively modular steps that could be scaled independently and simply dropped into the manufacturing flow. But the AI timeline has upended these rules. At angstromโscale dimensions, the physics enforces inescapable coupling across the entire stack: materials choices shape integration schemes; integration defines design rules; design rules dictate power delivery; wiring sets thermal budgets; and thermals ultimately constrain packaging scaling. System architects simply cannot wait 10โ15 years for each major semiconductor technology inflection to mature. Representing a roughly $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. A longโterm perspective is essential to align materials innovation with emerging device architectures โ and to develop the tools and processes required to integrate both with manufacturable precision. At Applied Materials, together with our customers, we are charting a course across the next 3โ4 generations, extending as far as 10 years down the roadmap. The angstrom era demands that we break down silos and bring together the industryโs best minds โ from leading companies to leading academic institutions. If the problem is coupled, the solution must be coupled. If the timeline is compressed, the learning loop must be compressed. Itโs not enough to just innovate โ we must innovate how we innovate. EPIC: A Center and Platform for HighโVelocity CoโInnovation This is the challenge that Applied Materials EPIC Center is designed to solve. Representing a roughly US $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. When it opens in 2026, it will deliver stateโofโtheโart cleanroom capabilities built from the ground up to shorten the path from earlyโstage research to fullโscale manufacturing. But the facilities are only one component of the model. EPIC is also a platform, an operating system for high-velocity coโinnovation that revolutionizes how ideas move from the lab to the fab. EPIC is a platform, an operating system for high-velocity coโinnovation that revolutionizes how ideas move from the lab to the fab.Applied Materials The EPIC model compresses the traditional workflow. Customer engineers work sideโbyโside with Applied technologists from day one โ moving beyond isolated process optimization and downstream handoffs. Within a shared, secure environment, EPIC tightly integrates atomistic modeling, test vehicles, process development, validation, and metrology feedback. Constraints that once surfaced late in development are identified and addressed early. The result is a potentially 2x faster path that benefits the entire ecosystem under one roof: Chipmakers gain earlier access to Appliedโs R&D portfolio, faster learning cycles, and accelerated transfer of nextโgeneration technologies into highโvolume manufacturing. Ecosystem partners gain earlier access to advanced manufacturing technology and collaboration opportunities that expand what is possible through materials innovation. Academic institutions gain opportunities to strengthen the labโtoโfab pipeline and help develop future semiconductor talent. Building on decades of coโdevelopment, we are reinventing the innovation pipeline with our partners across logic, memory, and advanced packaging to deliver the next leap in energyโefficient AI. Accelerating Advanced Logic Logic remains the engine of AI compute. In the angstrom era, however, systemโlevel gains are increasingly constrained by power and energy. Extending AI performance now depends on architectures that deliver more performance per watt โ accelerating the move to 3D devices such as gateโallโaround (GAA) transistors, which boost density within a compact footprint while preserving power efficiency. Architectures that deliver more performance per watt are accelerating the move to 3D devices such as gateโallโaround (GAA) transistors, and further out, complementary FETs (CFETs), which push density scaling even more.Applied Materials These architectural shifts are unfolding at unprecedented scale, with the logic roadmap already extending beyond firstโgeneration GAA toward more advanced designs. One key example is GAA with backside power delivery, which relocates thick power lines to the backside of the wafer, reducing resistive losses and freeing frontโside routing for tighter logic cell integration. Another example brings adjacent GAA PMOS and NMOS transistors closer together while inserting a dielectric isolation wall between them to minimize electrical interference. Further out, complementary FETs (CFETs) push density scaling even more by stacking PMOS and NMOS devices directly atop one another. While these architectures deliver compelling gains in performance per watt and logic density without relying solely on tighter lithography, they significantly raise integration complexity. Manufacturing a single GAA device today can involve more than 2,000 tightly interdependent process steps. At the same time, wiring stacks continue to grow taller and denser to connect these advanced logic devices. Modern leadingโedge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring. Modern leadingโedge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring.Applied Materials At this level of complexity, the process steps used to create these precise 3D devices and wiring stacks cannot be optimized independently. Design and process must evolve in lockstep, and materials innovation and fabrication methods must advance alongside device architecture. EPICโs coโinnovation model is designed to accelerate exactly this convergence โ enabling logic compute to continue advancing the frontiers of AI at the pace the roadmap demands. Powering the Memory Roadmap At the same time, the AI computing era is fundamentally reshaping how data is generated, moved, and processed โ making memory technologies, especially DRAM, central to delivering the energyโefficient performance AI systems require. As models grow larger and more dataโhungry, the DRAM roadmap is shifting toward architectures that deliver higher density, greater bandwidth, and faster access per watt. At the DRAM cell level, AI performance requirements are driving a transition from 6Fยฒ buriedโchannel array transistors (BCAT) to more compact 4Fยฒ, and beyond that, architectures that move past what 2D scaling alone can deliver. Applied Materials At the DRAM cell level, this shift is driving a transition from 6Fยฒ buriedโchannel array transistors (BCAT) to more compact 4Fยฒ architectures, which orient the transistor vertically to boost density and reduce chip area. Looking beyond 4Fยฒ, sustaining gains in performance per watt will require moving past what 2D scaling alone can deliver. The industry is therefore turning to 3D DRAM, stacking memory cells vertically to add capacity within a constrained footprint. As these structures grow taller and aspect ratios intensify, high-mobility materials engineering in three dimensions becomes increasingly critical to performance and reliability. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring. One emerging approach places select periphery functions beneath the DRAM array by bonding two wafers โ one optimized for the DRAM cells and the other for CMOS logic โ using multiple wiring layers. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring.Applied Materials In parallel, DRAM performance is being extended by leveraging logicโproven enhancers in the memory periphery. These include mobility boosters such as embedded silicon germanium and stress films, along with wiring upgrades like improved lowโk dielectrics and advanced copper interconnects. Memory manufacturers are also transitioning periphery transistors from planar devices to FinFET architectures, following the logic roadmap to further improve I/O speed. These valuable inflections are central to EPICโs mission โ where they can be co-developed and rapidly validated for nextโgeneration memory systems. Driving System Scaling With Advanced Packaging As data movement becomes the dominant energy cost in AI systems, advanced packaging has emerged as a critical lever for improving systemโlevel efficiencyโshortening interconnect distances, increasing bandwidth density, and reducing the power required to move data between logic and memory. The rise of 3D packages such as highโbandwidth memory (HBM) underscores why advanced packaging is becoming central to the AI era.Applied Materials Highโbandwidth memory (HBM) marks a major inflection along this path. By stacking DRAM dies โ scaling to 16 layers and beyond โ and placing memory much closer to the processor, HBM enables rapid access to everโlarger working datasets. This delivers stepโfunction gains in both bandwidth and energy efficiency. More broadly, the rise of 3D packages such as HBM underscores why advanced packaging is becoming central to the AI era. Packaging now addresses systemโlevel constraints that logic and memory device scaling alone can no longer overcome. It also enables a move away from monolithic systemsโonโchip toward chipletโbased architectures, as AI workloads increasingly demand flexible designs that combine logic, memory, and specialized accelerators optimized for specific tasks. A vital technology powering this roadmap is hybrid bonding. With interconnect pitches approaching those of onโchip wiring, conventional bumps and microbumps run into fundamental limits in density, power, and signal integrity. Hybrid bonding removes these barriers by allowing dramatically higher interconnect and I/O density, supporting a broad range of chiplet architectures โ from memory stacking to tighter computeโmemory integration. EPIC tackles highโvalue advancedโpackaging challenges through early, parallel coโinnovation across materials, integration, and manufacturing.Applied Materials As bonded structures like HBM stacks grow larger and more complex, warpage control, die placement, stack alignment, and thermal management become firstโorder challenges. EPIC tackles these and other highโvalue advancedโpackaging challenges through early, parallel coโinnovation across materials, integration, and manufacturing. Bringing It All Together Across logic, memory, and advanced packaging, our industry faces an ambitious roadmap that promises significant gains in energy efficiency for AI systems. But realizing that potential demands breakthrough materials innovation at a time when feature sizes are shrinking, interfaces are multiplying, and process interdependencies are escalating. These challenges cannot be solved on 10โ15โyear timelines under the traditional relayโrace model. We must break down silos, align earlier across the ecosystem, and parallelize learning to keep pace with AIโs demands. In the AI era, progress will be defined by the speed at which lightbulb moments turn into manufacturing and commercialization reality. The only viable path forward is a new innovation model โ and EPIC is how we are driving it.