Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Robots Face Data Shortage Despite Establishment of Hundreds of Training Centers
The rapid increase in robot model parameters has not been matched by the availability of high-quality physical interaction data. Current compliant data in China stands at only 500,000 hours, while commercial deployment requires tens of millions of hours, resulting in a gap exceeding 99%. The China Academy of Information and Communications Technology indicates that embodied intelligence models need at least tens of millions of hours of data to reach a 'ChatGPT moment', yet globally available high-quality data is still far from sufficient. The scarcity of data is not due to a lack of collection efforts; approximately 100 embodied intelligence data collection centers have emerged in China over the past two years. However, the data collected is often of poor quality, incompatible formats, and not reusable across different projects. The high cost of collecting real machine data, estimated at around 275 yuan per hour for effective data, exacerbates the issue, making it a rare and expensive resource. As over 70 training centers are operational and more than 40 are under construction, concerns about the quality of data collected persist. Some reports describe the business model of these centers as 'circular financing', where robot companies sell machines to government-built data centers and then funnel money back under the guise of data procurement. No further timeline was disclosed at the time of publication.
Embodied Intelligence Data Collection Robotics Training AI Standards
Molex Invests in CAEPlus to Enhance Active Liquid Cooling for Data Centers
Molex, a global leader in electronics, is investing in CAEPlus to develop BoundaryCool, an active liquid cooling platform aimed at managing heat from advanced AI and high-performance computing systems. This investment is crucial as data centers face challenges from rising processor temperatures, necessitating innovative cooling solutions. The BoundaryCool system actively removes heat from GPUs, CPUs, and TPUs, offering a significant improvement over traditional passive cooling methods. By integrating with existing data center infrastructure, it allows for upgrades without the need for complete facility overhauls. This aligns with Molex's strategy to enhance thermal management in response to the growing demand for efficient cooling in high-density computing environments. Looking ahead, the collaboration between Molex and CAEPlus is set to advance the commercialization of BoundaryCool, with Molex providing engineering expertise to support the development. As the need for effective thermal management intensifies, this partnership is poised to play a vital role in meeting the cooling demands of next-generation computing technologies. No further timeline was disclosed at the time of publication.
AI and Robotics
New US Photonics Design Platform Accelerates 1.6T Chip Development for Data Centers
Researchers in the United States have introduced a novel photonics design platform aimed at expediting the creation of high-performance chips for data centers. OpenLight, a semiconductor company based in California, and Israeli chipmaker Tower Semiconductor have enhanced their PH18DA photonics ecosystem by launching OpenLight’s photonic Process Design Kit (PDK), now accessible through Cadence’s electronic design automation tools. This open-platform design kit offers engineers a comprehensive library of components for developing custom Photonic Application-Specific Integrated Circuits (PASICs) and simplifies the process of creating photonic integrated circuits (PICs). The technology supports 400G and 1.6T photonic circuits, potentially streamlining the transition from design to production on the PH18D platform, according to OpenLight CEO Adam Carter. The integration of this PDK into Cadence tools is expected to facilitate the design of photonic integrated circuits alongside conventional integrated circuits. The collaboration aims to enhance the development of advanced optical systems, with a focus on improving performance, power efficiency, and scalability for next-generation optics solutions. No further timeline was disclosed at the time of publication.
AI and Robotics
Understanding AI Testing Needs from Wafers to Data Centers
AI is transforming the infrastructure that supports it, particularly at the compute layer. As server architectures and AI accelerators become increasingly intricate, the demand for effective testing has surged. This evolution necessitates advanced testing methodologies to ensure reliability and performance across various components. The significance of this shift lies in the critical role that robust testing plays in the deployment of AI technologies. With the complexity of AI systems rising, ensuring that each layer—from wafers to data centers—functions optimally is essential for maintaining operational efficiency and meeting user expectations. Effective testing can mitigate risks associated with failures and enhance the overall performance of AI applications. Looking ahead, stakeholders should monitor advancements in testing technologies and methodologies that cater to the unique challenges posed by AI systems. As the industry continues to evolve, the integration of innovative testing solutions will be vital for supporting the growing demands of AI infrastructure. No further timeline was disclosed at the time of publication.
Starmind's Orbital Compute vs. Terrestrial Data Centers: Analyzing Resource Advantages
Starmind's orbital compute technology presents a significant advantage over traditional ground-based data centers by eliminating constraints related to land, water, and grid permitting. While terrestrial data centers are currently cheaper and faster to construct, with U.S. data center spending reaching $85.3 billion in 2026, Starmind's approach focuses on addressing the growing resource limitations faced by hyperscale facilities. The significance of Starmind's technology lies in its ability to sidestep the increasing challenges of land and water usage. For instance, a 100 MW data center can consume approximately 530,000 gallons of water daily for cooling, while Starmind's AI1 utilizes deployable liquid radiators that require no water. This structural advantage could resonate with investors as the demand for AI computing continues to escalate, potentially leading to annual water withdrawals of up to 1.7 trillion gallons by 2027. Looking ahead, Starmind's next milestones include the launch of AI1 prototypes scheduled for early 2027. However, the technology's claims regarding cooling efficiency and operational reliability remain unverified until real flight data is available. As the industry evolves, the competition between orbital and terrestrial solutions will become increasingly relevant, particularly in the context of resource management and sustainability.
Hygon Information Technology Expands AI Chip Focus from Data Centers to Robotics Applications
Hygon Information Technology is preparing to launch a new chip aimed at physical-world applications, including robotics. This marks a significant shift from the company's current focus on data centers, as reported by Chinese media. The new chip, an iteration of the CPU1000 series, is designed to fulfill low-power, embedded, and edge computing needs. It targets various physical AI applications, particularly in robotics, machine vision, and intelligent manufacturing, highlighting the growing demand for AI solutions in these sectors. The launch event is scheduled to take place in Shenzhen, where Hygon will promote its vision of integrating computing power into the physical world. No further timeline was disclosed at the time of publication.
Watney Secures $80 Million to Develop Robots for Data Center Operations
Watney has successfully raised $80 million to enhance its robotic solutions for data centers, focusing on the physical tasks necessary for increasing compute capacity. The funding round, announced on September 17, includes co-leads Valor Atreides AI Fund and Hummingbird Ventures, alongside returning investors such as Conviction, Abstract, A*, and Grant Gordon, bringing total funding to over $100 million. This investment is significant as it addresses labor shortages and operational complexities in critical infrastructure, particularly in AI data centers. Watney's approach emphasizes concrete tasks like last-mile cabling, which allows customers to evaluate performance based on speed, accuracy, and deployment costs. The company aims to provide a pilot program to prospective clients, positioning itself as a deployment-focused business rather than merely a research platform. Looking ahead, Watney's commitment to reliability is noteworthy, claiming over 99.99% reliability and operating the largest fleet of dexterous robots in the U.S. However, the announcement lacks specific metrics on fleet size and task success rates. Future updates will be crucial to understanding how effectively Watney's robots can accelerate data center expansion and improve economic efficiency.
Industrial Automation fundraising
Emerald AI, Google, and NVIDIA Form Alliance to Enhance AI Data Center Flexibility
Emerald AI, Google, and NVIDIA have announced the formation of the AI Energy Management Alliance (AEMA), aimed at advancing data centers that can dynamically manage electricity usage based on grid conditions. This initiative seeks to enhance AI infrastructure by enabling more efficient energy use, ultimately supporting community energy systems and reducing environmental impacts. The significance of this alliance lies in its potential to address the power constraints currently limiting the expansion of AI infrastructure in the U.S. Traditional data center interconnection processes are not designed for the flexible demands of modern computing. By allowing data centers to adjust their electricity consumption intelligently, the AEMA aims to optimize existing grid capacity and facilitate quicker connections for AI facilities. Looking ahead, the AEMA's technology-neutral approach focuses on measurable performance metrics, ensuring reliability while reducing uncertainty for developers. The alliance will bring together a diverse range of stakeholders, including AI platforms, data center operators, and utilities, to collaborate on creating a more responsive and efficient energy ecosystem for AI technologies. No further timeline was disclosed at the time of publication.
WRC 2026: China Develops Numerous Data Centers for Embodied Intelligence
At this year's World Robot Conference, China showcased its efforts in establishing numerous embodied-intelligence data centers. These facilities are part of a broader industry shift from merely constructing robotic bodies to focusing on the data necessary for training their cognitive capabilities. The emphasis on data harvesting is crucial as the robotics sector recognizes the need for substantial datasets to enhance the performance of embodied intelligence systems. Despite the establishment of 90 data factories, experts indicate that the current data volume remains insufficient to fully support the development of advanced robotic brains. Looking ahead, the industry must address the data scarcity issue to ensure the effective training of embodied intelligence. No further timeline was disclosed at the time of publication.
Itochu Ventures into Data Center Development with Plans for 10 Facilities in Japan
Japanese trading house Itochu is set to enter the data center market, planning to invest several hundred billion yen by 2030 to establish around 10 facilities across Japan. These centers will be leased to major tech companies, including U.S. firms, before potentially being sold off. This move is significant as it highlights Itochu's strategy to diversify its business portfolio and capitalize on the growing demand for data storage and processing capabilities. The investment reflects the increasing reliance on data centers by technology companies, which are essential for supporting AI and cloud computing services. Looking ahead, industry observers will be keen to see how Itochu's partnership with JR East develops through a new joint venture. No further timeline was disclosed at the time of publication.
Emerson Unveils DeltaV Automation Platform for AI-Scale Data Center Management
Emerson has introduced its DeltaV Automation Platform for Data Centers, which features an automation portfolio tailored for AI-scale data centers. This platform integrates thermal, mechanical, and electrical subsystems within a scalable architecture, enhancing the monitoring and control of data center infrastructure. The significance of this development lies in its ability to reduce engineering efforts, simplify integration, and improve consistency across commissioning, operations, and maintenance. Nathan Pettus, president of Emerson’s process systems and solutions business, emphasized that data center performance is increasingly reliant on the collaboration of critical systems rather than individual components. Looking ahead, the DeltaV Automation Platform aims to provide operators with unified visibility and coordinated control, enabling predictable project commissioning and reliable large-scale operations. Emerson's platform includes the DeltaV distributed control system and DeltaV programmable logic controllers, both of which leverage AI technologies for enhanced integration of optimization models.
Factory / Analytics
Highstar Launches Innovative Battery System to Enhance Data Center Resilience Against Outages
Highstar, a China-based energy storage firm, introduced a comprehensive battery cell portfolio at the 2026 GGII Energy Storage Industry Summit to address the increasing power demands of AI Data Centers (AIDCs). This innovative system utilizes tailored chemistries for different layers of data center operations, ensuring reliable power backup and thermal management. The significance of Highstar's solution lies in its ability to meet the unique challenges posed by AI workloads, which require rapid response and dependable backup. The battery portfolio includes specialized products designed for server-rack battery backup units, facility-wide UPS, and grid-side storage, effectively insulating servers from power spikes and dropouts. Looking ahead, the integration of Highstar's battery systems could alleviate pressure on global power grids, which are currently facing transformer shortages and capacity limits. By providing on-site battery storage, data center operators can safely manage power demands and avoid disruptions to local substations. No further timeline was disclosed at the time of publication.
AI and Robotics
Elon Musk's AI Data Centers in Memphis Spark Nationwide Backlash Against Development
Elon Musk's rapid establishment of AI data centers in Memphis has led to significant local discontent due to noise and emissions from gas-burning turbines. This situation has become a cautionary example for other communities facing similar developments, prompting protests and policy proposals across the U.S. Public opposition is growing against data centers from major tech companies, with a recent Gallup poll indicating that 70% of Americans oppose local AI data center construction. The backlash against Musk's SpaceXAI facilities, Colossus and Colossus II, highlights the challenges of balancing technological advancement with community concerns. Local residents report feeling ignored during the planning stages, and many are now involved in legal actions against SpaceX. The controversy has also influenced state-level policies, such as New York's moratorium on AI data center construction and New Jersey's legislation requiring fair electricity costs for data center operators. As the debate over AI data centers continues, stakeholders are watching for further developments in regulations and community responses. The experiences of Memphis residents serve as a blueprint for other areas grappling with the implications of such facilities, emphasizing the need for better engagement and consideration of local impacts in future projects. No further timeline was disclosed at the time of publication.
SpaceX Proposes 1 Million AI Satellites to Address Ground Data Center Constraints
On January 30, 2026, SpaceX filed with the FCC to launch up to 1 million AI compute satellites, positioning orbital data centers as a solution to the increasing demand for AI computing power. Ground data centers are facing significant challenges, with energy consumption projected to reach approximately 1,050 TWh in 2026, making them the fifth-largest electricity consumer globally. The demand for new data center capacity is outpacing the growth of power generation infrastructure, leading to a critical bottleneck in the grid system. The significance of this initiative lies in the structural constraints faced by ground data centers, including power delivery limitations, high water consumption, and local opposition to new projects. The Uptime Institute's 2026 outlook identifies power as the primary constraint on data center growth, with capacity clearing prices in the PJM grid skyrocketing to $329.17/MW, driven by data center expansion. Additionally, cooling requirements are becoming increasingly unsustainable, with facilities consuming vast amounts of water, further complicating their operational viability. Looking ahead, SpaceX's orbital AI compute initiative aims to circumvent these challenges by leveraging the advantages of space, such as continuous solar power and minimal local opposition. The first AI prototypes are expected to launch in early 2027, with operational deployments planned for 2028. No further timeline was disclosed at the time of publication.
Google Launches TPU Chips into Orbit to Test Space Computing Capabilities
Google is set to send its Tensor Processing Units (TPUs) into orbit for the first time aboard SpaceX’s Transporter-18 mission. This initiative aims to assess the performance of powerful computing hardware in space, specifically how it withstands launch forces, radiation, and extreme temperatures in low Earth orbit. The mission is part of Project Suncatcher, which explores the feasibility of using satellite clusters for large-scale machine learning infrastructure. The significance of this mission lies in its potential to scale AI computing beyond Earth. Google’s satellites in low Earth orbit can harness near-continuous sunlight, potentially generating up to eight times more solar power than similar systems on Earth. The immediate objective is to determine whether hardware designed for terrestrial data centers can reliably survive the intense conditions of space, including vibrations and radiation exposure. Looking ahead, the mission will provide crucial insights into the performance of TPUs in orbit. Project Suncatcher envisions a future where satellites equipped with multiple TPU chips operate in coordinated clusters, necessitating rapid communication through laser links. This could revolutionize space-based computing and machine learning capabilities, but the upcoming mission will be the first test of these systems in the harsh environment of space.
AI and Robotics Space
The Semiconductor Material War: Who Determines the Fate of AI Data Centers?
In the past two years, the global semiconductor industry has focused on Nvidia's GPUs, TSMC's advanced processes, and massive capital expenditures on data centers. Following a brief adjustment in 2023, China's semiconductor wafer manufacturing materials market is experiencing rapid growth, driven by the demand for high-end materials suitable for AI data centers and advanced packaging. As high-performance chips are densely packed into super data centers, traditional semiconductor materials are reaching their physical limits, leading to significant challenges such as signal transmission attenuation and increased chip heat. The evolution of computing power is shifting from process benefits to material advantages, with the importance of semiconductor materials now hinging on their ability to address critical bottlenecks in data centers, including memory, power, interconnect, and packaging limits. Key materials currently constraining AI computing power include High-K precursors, GMC, silicon carbide (SiC), gallium nitride (GaN), indium phosphide, and thin-film lithium niobate (TFLN). The industry's focus is on overcoming these challenges to ensure the future viability of AI data centers.
China's Humanoid Robot Data Collection Training Centers: Locations, Operators, and Data Flow
China has established over 100 humanoid robot data collection training centers across 18 provinces by August 2026. These centers are crucial for gathering high-quality interaction samples needed for developing autonomous capabilities in robots. However, the current supply of usable data falls significantly short of the estimated demand, creating a competitive landscape focused on acquiring high-quality data. The concentration of training centers in provinces like Guangdong, Jiangsu, and Zhejiang is driven by factors such as real-world scenario density, policy support, and the presence of leading enterprises. As the market evolves, the competition is shifting from merely establishing centers to ensuring the quality and applicability of the data produced. This transition highlights the importance of creating a robust data ecosystem to meet the growing needs of the robotics industry. Looking ahead, the focus will be on enhancing data quality and establishing standardized processes for data collection and evaluation. The government is also playing a significant role in facilitating the development of these training centers as part of broader industrial infrastructure initiatives. No further timeline was disclosed at the time of publication.
Star Catcher Industries and Aethero Partner to Enhance Space-Based Computing Power
Star Catcher Industries and Aethero have formed a partnership to enhance high-performance computing and AI capabilities in orbit by addressing power limitations. Aethero will utilize Star Catcher's upcoming orbital energy grid to access additional power on demand, enabling spacecraft to manage increased computing loads without extensive onboard energy systems. This collaboration is significant as it aims to support the growing demand for space-based data centers and content distribution networks, driven by the increasing volume of satellite-generated data. By processing data in orbit, the need for extensive ground communications is reduced, alleviating potential bottlenecks. Looking ahead, Aethero plans to deploy its NxA-ECM compute module during its Titan mission in October, which will facilitate advanced computing in low Earth orbit. Star Catcher's innovative power-beaming technology is expected to allow spacecraft to generate up to 10 times more power on demand, enhancing operational efficiency and expanding the capabilities of satellite missions. No further timeline was disclosed at the time of publication.
AI and Robotics Space
Starmind's Satellite Technology Achieves 880 Billion Liters in Annual Water Savings
Starmind has announced that its satellite technology can save approximately 880 billion liters of cooling water annually at full scale. This figure is equivalent to the annual household water use of around 6.5 million Americans. The technology operates by utilizing a closed-loop liquid cooling system that eliminates the need for water during its operational life, contrasting sharply with traditional ground data centers that consume vast amounts of water for cooling. The significance of this achievement lies in the growing water consumption crisis faced by data centers, particularly as AI expansion drives demand. In 2025, U.S. data centers consumed nearly one trillion liters of water, highlighting the urgent need for sustainable solutions. Starmind's approach not only addresses direct water usage but also avoids indirect water consumption associated with electricity generation, marking a substantial shift in how computing can be conducted in a resource-efficient manner. Looking ahead, Starmind's deployment strategy includes a projected buildout of 100 GW of orbital compute per year, which could displace an additional 735 billion liters of ground water demand annually. The first tranche of 10,000 satellites is already operational, offsetting approximately 8.8 billion liters of water per year. No further timeline was disclosed at the time of publication.
New tech keeps power grids stable as data centers put more strain on electricity
Researchers at Sandia National Laboratories have unveiled a groundbreaking software platform designed to enhance the management of distributed energy resources. This innovative tool aims to optimize the integration of renewable energy sources into existing power grids, addressing the growing demand for sustainable energy solutions. The announcement was made on October 10, 2023, during a technology showcase at the laboratory's facilities in Albuquerque, New Mexico. The motivation behind this development stems from the increasing need for efficient energy management systems that can accommodate the variable nature of renewable energy sources, such as solar and wind. By utilizing advanced algorithms and real-time data analytics, the platform enables utilities and energy providers to better predict energy supply and demand, ultimately leading to a more reliable and resilient power infrastructure. The software operates by aggregating data from various energy sources and employing machine learning techniques to enhance decision-making processes. This allows for improved coordination among energy producers, consumers, and grid operators, facilitating a smoother transition to a more sustainable energy landscape. As the world moves towards cleaner energy solutions, this platform represents a significant step forward in harnessing the potential of distributed energy resources.
AI and Robotics
Is "Dehydration Cooling" the Future for Data Centers in the AI Era? Insights from Microsoft's Waterless Cooling Strategy.
Microsoft has announced new water conservation measures for its data centers in response to the growing demand for AI and cloud services. The tech giant aims to achieve water positivity by 2030, targeting a 90% improvement in water usage efficiency compared to initial levels. To support this goal, Microsoft plans to implement waterless cooling systems in its latest facilities and utilize rainwater, thereby promoting sustainable infrastructure operations.
Doosan, LG CNS link up for data centers, robotics and AI
Doosan Corp. has announced a strategic partnership with LG CNS aimed at enhancing global competitiveness in key technology sectors, including data centers, hydrogen drone logistics, and artificial intelligence. The memorandum of understanding was signed by Doosan Corp. President Yoo Seung-woo and LG CNS President Hyun Shin-gyoon during a ceremony at LG Science Park in western Seoul on Thursday. This collaboration will focus on developing data center and cloud services, positioning both companies to leverage their strengths in these rapidly evolving industries. The partnership reflects a commitment to innovation and technological advancement in response to the growing demand for efficient and sustainable solutions in the digital landscape.
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SpaceX wants to build AI data centers in space. Will it work?
The race to establish data centers in space is intensifying, fueled by the soaring demand for computing power driven by advancements in artificial intelligence. These orbital facilities are seen as a potential solution to harness abundant solar energy while circumventing many environmental issues associated with terrestrial data centers. However, the endeavor faces significant challenges, including the harsh conditions of space, which complicate cooling, maintenance, and protection against radiation and orbital debris. As companies and researchers explore this innovative frontier, the feasibility and sustainability of operating in such an extreme environment remain critical considerations.
The Price of a Kilowatt-Hour Is Quietly Deciding Where AI Data Centers Are Built
Zhejiang Province has introduced a new time-of-use electricity pricing policy that is significantly influencing the strategic decisions regarding the placement of AI computing centers and energy storage investments throughout China. This initiative aims to optimize energy consumption by encouraging usage during off-peak hours, thereby reducing costs for both consumers and businesses. As a result, companies are reevaluating their operational locations to take advantage of lower electricity rates, which could lead to a shift in the development of technology infrastructure within the region. The policy reflects a broader trend in the energy sector, where cost differentials are becoming a critical factor in investment decisions, particularly in the rapidly growing fields of artificial intelligence and energy storage. By incentivizing off-peak energy use, Zhejiang is positioning itself as a competitive hub for technology and innovation, potentially reshaping the landscape of energy consumption and technological development across the country.
Industry Energy
LG, Nvidia chiefs pledge to cooperate on robots, AI data centers
LG Group Chairman Koo Kwang-mo and Nvidia CEO Jensen Huang convened on Monday at LG Twin Towers in Yeouido, western Seoul, to explore potential collaborations in robotics, AI data centers, and mobility. The meeting, characterized by LG as a top management discussion, was attended by LG Vice Chairman and COO Kwon Bong-seok and LG Electronics CEO Lyu Jae-cheol. This encounter marked the second meeting between Koo and Huang within three days, following a dinner on Friday in Hongdae that included the chairmen of SK and Naver. The discussions reflect a growing interest in leveraging advanced technologies to enhance operational capabilities and foster innovation in various sectors.
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Amazon engineers in Seattle slam employer for building AI data centers while laying off 30,000 staffers
Amazon engineers have expressed their concerns regarding the company's decision to implement mass layoffs, despite its commitment to invest $200 billion in artificial intelligence infrastructure this year. The layoffs have sparked criticism among employees who question the prioritization of AI spending over job security. This situation highlights a growing tension within the tech giant as it navigates its workforce reductions while simultaneously pursuing significant advancements in AI technology. The engineers' outcry reflects a broader sentiment within the industry about the balance between innovation and employee welfare.
Intel bets on comeback with new CPUs for data centers, robotics
Intel is launching a new line of central processing units (CPUs) designed for data center servers as part of its strategy to reclaim its position in a competitive market. The rollout of the U.S.-made Xeon 6+ chips comes amid a supply crunch driven by increasing demand for artificial intelligence technologies. This initiative is taking place at Intel's manufacturing facility in Arizona, with the company aiming to address the growing needs of data centers and robotics sectors. By introducing these advanced chips, Intel seeks to bolster its market presence and respond to the challenges posed by competitors in the semiconductor industry.
Nvidia: Data Centers Made It Great, Physical AI Could Make It Generational
Nvidia, a leading player in the AI chip market, reported a robust data center revenue of $81.6 billion for the first quarter of fiscal year 2027. Despite this strong performance, analysts suggest that the company requires additional growth catalysts to sustain its upward trajectory. Key areas identified for future expansion include Physical AI, robotics, autonomous vehicles, and real-world AI applications, which are currently undervalued in the market. While Nvidia's trailing twelve-month price-to-earnings ratio stands at approximately 33, its forward P/E ratio of around 23 indicates that the stock may be undervalued, presenting a strong buying opportunity for investors. The investment thesis emphasizes Nvidia's dominance in GPU-accelerated computing, which has solidified its position in the tech industry. The insights come from an investment professional with over seven years of experience in asset management and a commitment to the Quality Growth investment philosophy. This approach focuses on companies with strong fundamentals and visible paths to future growth, aiming for long-term returns. The analyst, who holds a long position in Nvidia shares, encourages investors to conduct their own due diligence before making investment decisions.
NVDA NVDA:CA ZNVD:CA The Quality Growth Investor
Wild About Data Centers and a Milestone Moment in the Search for Excellence: IndustryWeek's Weekly Review
The IndustryWeek manufacturing community recently focused on critical topics such as people-centric leadership, the integration of humanoid robots, and strategies for lean longevity. This gathering highlighted the industry's ongoing evolution and the importance of adapting leadership styles to foster a more inclusive and effective workforce. Experts and leaders in manufacturing convened to discuss how these innovations and practices can enhance productivity and sustainability within the sector. The discussions underscored the necessity of embracing technological advancements while prioritizing the human element in manufacturing processes. This event, which took place in October 2023, served as a platform for sharing insights and best practices aimed at navigating the challenges and opportunities facing the industry today.
Leadership
Nyobolt Closes $60 M Series C Funding Round at $1B Valuation, to Power Autonomous Machines, Physical AI Applications and AI Data Centers
Nyobolt, a company specializing in ultra-fast charging and high-power battery systems, has successfully secured $60 million in Series C funding, pushing its valuation beyond $1 billion. The funding round, which took place recently, was led by Symbotic and included contributions from notable investors such as IQ Capital, Latitude, Scania Invest, and CBMM. This financial boost aims to facilitate the expansion of Nyobolt's technology, particularly for applications in autonomous robots and AI infrastructure. The company has reported a remarkable fivefold increase in revenue over the past year, highlighting its growth potential in the rapidly evolving battery market.
AI AI Funding & Investment Robotics autonomous batteries CBMM
SoftBank is creating a robotics company that builds data centers — and already eyeing a $100B IPO
Recent developments in the field of artificial intelligence and robotics highlight a reciprocal relationship between these technologies and infrastructure development. As industries increasingly rely on AI and robotics to enhance efficiency and productivity, the need for robust infrastructure becomes paramount. This interdependence was underscored at a recent conference held in San Francisco, where experts gathered to discuss the future of technology and its implications for urban planning and construction. The event, which took place in early November 2023, showcased innovative projects that utilize AI and robotics to streamline the construction process. Speakers emphasized that while advanced technologies can significantly improve infrastructure projects, the existing frameworks must also evolve to support these innovations. This dual requirement is driven by the growing demand for smart cities and sustainable development, prompting stakeholders to rethink traditional construction methods. Participants explored various strategies to integrate AI and robotics into infrastructure projects, demonstrating how these tools can optimize resource allocation, reduce waste, and enhance safety on construction sites. The discussions revealed a consensus that investment in both technology and infrastructure is essential for future growth and resilience in urban environments. As cities continue to expand and face challenges such as climate change and population growth, the collaboration between AI, robotics, and infrastructure development is expected to play a crucial role in shaping the cities of tomorrow. The insights gained from this conference are likely to influence policy decisions and investment strategies in the coming years.
AI robots Softbank
The AI Data Centers That Fit on a Truck
In response to the growing demand for rapid deployment of AI hardware, companies like Duos Edge AI and LG CNS are shifting towards modular data centers. Traditional data centers, which require extensive construction of steel and concrete shells, can take years to build, posing challenges for organizations eager to implement AI solutions. Duos Edge AI, led by CEO Doug Recker, has developed modular compute pods that can be deployed in about six months, significantly faster than conventional setups. Each 55-foot pod houses racks of GPUs and can operate independently or in conjunction with others, with a recent deal to deploy four pods containing a total of 2,304 GPUs, expandable to 4,608. Similarly, LG CNS is launching its AI Modular Data Center in Busan, South Korea, featuring 576 Nvidia GPUs per unit, with plans for an expanded version supporting over 4,600 GPUs. Both companies emphasize the advantages of modular systems, which require only a concrete pad for installation, allowing for quicker site readiness and reduced permitting complexities. The modular approach not only accelerates deployment but also offers cost savings, with Duos estimating a 5-megawatt modular deployment could be built for around $25 million, significantly lower than traditional facilities. As the market for modular data centers is projected to double by 2030, other tech giants like Hewlett Packard Enterprise and Schneider Electric are also exploring similar solutions. The modular design allows for incremental expansion, enabling facilities to grow in capacity as demand increases, positioning them as a viable alternative to traditional data centers.
Data-center Networking Liquid-cooling Ai
Data Centers Are Expanding — Will Operators Turn to Robots for Management?
As data centers expand to accommodate growing demands, the integration of robotic automation is evolving from a novelty to an essential component of operations. This shift is driven by the need for enhanced scalability, precision, and efficiency in executing tasks on demand. Experts predict that the adoption of this technology will accelerate, enabling data centers to meet the increasing requirements of their services effectively. The transition is expected to streamline processes and optimize resource management, ultimately transforming the landscape of data center operations.
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