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The DOGE Boys Get VC Funding to Support Their Latest Enterprise
Former DOGE members and Elon Musk allies are backing a startup aimed at using AI to apply "learnings" from DOGE to the private sector.
🇺🇸 미국 · IT/기술 · "SECTOR" · 총 16건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 11,993건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 11,991건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 18.8(중도 균형)입니다.
Former DOGE members and Elon Musk allies are backing a startup aimed at using AI to apply "learnings" from DOGE to the private sector.
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OpenAI CEO and co-founder Sam Altman tried to distance himself from the artificial intelligence industry's massive lobbying efforts this cycle as scrutiny mounts over the millions of dollars the sector is throwing into the midterms. Shortly after meeting with lawmakers on Capitol Hill on Wednesday, Altman was asked about OpenAI's involvement in primaries across the...
Mo Gawdat, who predicts that AI would erase 30% of certain job sectors will be gone by 2028, said he has tips for job seekers to survive the AI era.
The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…
Children born after 2013 are the first generation to grow up fully immersed in digital systems, which weren’t designed with them in mind. One‑third of the world’s Internet users are younger than 18, according to UNICEF, yet these systems shaping their daily lives were built for adults. They were optimized for engagement and designed long before people understood how profoundly digital environments influence children. For engineers and technical professionals, online safety is not an abstract policy debate. It is a design challenge that demands rigor, systems thinking, and ethical foresight. Governments around the world are also beginning to recognize the problem. Policymakers from across Australia, Brazil, the European Union, Indonesia, and the United States are responding to risks engineers have long understood: Addictive features, inappropriate content, opaque data practices, and algorithmic systems shape user behavior in ways that their creators did not fully predict. For years, technology moved faster than governance. Now governance is trying to catch up. Global Shift Toward Design Reform Supporting National Digital Ambitions In Athens this year I met with senior leaders of Greek government agencies and key national research institutions. Greece is moving quickly on digital transformation and responsible technology governance, and our discussions reinforced IEEE’s role as a trusted, neutral collaborator. We focused on supporting Greece’s ambitions in digital modernization and public‑sector innovation. We also discussed responsible AI and age-appropriate digital design in Europe and elsewhere. These engagements, grounded in shared values and long‑term commitment, strengthened IEEE’s presence within the European ecosystem and opened new pathways for collaboration on trustworthy AI and child‑focused digital well‑being. The European Union and the United Kingdom have been among the first to act, embedding age‑appropriate digital design into their broader children’s rights agenda. Drawing on IEEE expertise and global best practices, Indonesia is the first country in Asia, and Brazil is the first country in Latin America, to adopt age-appropriate design regulation. Australia is aiming to limit access to harmful content and addictive design features through age restrictions on certain platforms. And in the United States, in addition to federal efforts, states including California, New York, and Utah are enacting approaches including age-appropriate design principles. Across these efforts, a shared realization is emerging. Protecting children online is not simply about filtering content or adding parental controls. It requires rethinking the architecture of digital systems regarding how data is collected, how algorithms make decisions, how interfaces influence attention, and how AI interacts with the developing minds of young users. Engineers and technical professionals understand that design choices are never neutral. They encode values, incentives, and assumptions. When the user is a child, those choices carry greater weight. This is where IEEE’s work becomes more essential. Protecting Children Online For more than a decade, IEEE has been building technical and ethical foundations for safer digital experiences. The first IEEE standard on age-appropriate design in 2021 marked a turning point. It offers a structured, principled approach to designing with children’s rights in mind. The Institute’s 2022 article “Use a New IEEE Standard to Design a Safer Digital World for Kids” highlights how the standard helps translate those principles into engineering practice. Today the IEEE Standards Association’s (SA) Trustworthy Digital Experiences portfolio provides a practical, technically grounded framework for governments and industry. Spanning ethical design, data governance, algorithmic transparency, and child‑focused digital well‑being, it has already initiated discussions with government stakeholders around the world. This work helps bridge the gap between engineering realities and policy ambitions. No single country can solve these challenges alone. Many policymakers lack access to the combined expertise in technology, governance, and children’s rights needed to act quickly and effectively. This collaborative effort helps close that gap. The stakes are high. Without coordinated action, public policy will continue to lag behind technology, leaving children exposed to risks that could have been mitigated through thoughtful design. But with the right frameworks, governments can ensure digital systems respect children’s rights, support healthy development, and promote well‑being. IEEE’s emerging standards and collaborative technology policy work offer a path forward. By grounding national efforts in evidence‑based, rights-aligned design principles, IEEE is helping governments move from reactive regulation to proactive, coherent, and globally informed strategies for protecting children online. Safeguarding childhood in the digital age is both a moral imperative and an engineering challenge. And IEEE is helping to lead the way. —Mary Ellen Randall IEEE president and CEO Please share your thoughts with me: president@ieee.org. This article appears in the June 2026 print issue.
SXSW London kicks off with near perfect timing: Just weeks earlier, the U.K.’s AI sector reported record investment numbers, underscoring London’s status as the AI capital of Europe. “AI as the New Power Structure” is, aptly, a central theme of the second edition of the Austin spinoff. In fact, a big reason SXSW made the […]
The transportation sector has benefited from hopes of a Iran peace deal, but also from the build out of data centers needed to power AI.
Here is how Mitsui & Co. (MITSY) and Sumitomo Corp. (SSUMY) have performed compared to their sector so far this year.
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.
Charges against a Google employee who bet on Polymarket have raised concerns that the issue may dent momentum in the fast-growing sector.
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 continent’s public sector. In physics, leverage requires three things: a fulcrum, a lever arm, and the ability to apply force. The Bushveld Complex, the world’s 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 “OPTION” 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 world’s 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 DeepSeek’s large language model with its own cloud and storage infrastructure. The price differential was stark—in 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 country’s data-center market was valued at US $2.16 billion in 2024, the largest in Africa. These are not commercially neutral investments. Huawei’s 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 Africa’s 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 “OPTION,” 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 “Governing Through the Cloud” 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 categories—specific, enforceable, and well within the bargaining power of a market of South Africa’s 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 system—model provider, training data, compute environment, evaluation methods, update cadence, human review points, and failure-reporting procedures—public-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 Africa’s 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 Microsoft’s $300 million investment and Huawei’s 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 Treasury’s Draft General Public Procurement Regulations—the legal instrument that will govern every government AI contract—closes 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 Africa’s leverage into policy. The panel—chaired 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 Research—has 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.
Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all of us knowledge workers. But before you quit your job as a software developer or financial analyst—or tech journalist—and…
This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia. Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems. Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026, the first Australian city to hold the title. Melbourne, Australia’s premier conference destination. Tourism Australia More materially for research and innovation, Melbourne is also the nation’s fastest‑growing capital, attracting increasing concentrations of engineering and technology talent, investment and international engagement. Australia’s artificial intelligence (AI) ecosystem is entering a new phase, defined less by isolated initiatives and more by the convergence of compute infrastructure, research intensity and international collaboration. Melbourne sits at this intersection. Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. Sovereign AI compute, expanding hyperscale data center campuses and a growing pipeline of international research-led conferences are reshaping the city’s research landscape. Together, these elements position Melbourne as a focal point for applied AI research, advanced engineering and data-intensive science. The growing global influence of AI engineering, underscored by NVIDIA CEO Jensen Huang receiving the 2026 IEEE Medal of Honor, reflects the scale of this shift. In Melbourne, these factors form a reinforcing research flywheel linking infrastructure, discovery and collaboration. Rather than focusing on startup density or short-term commercial output, Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. NVIDIA CEO Jensen Huang received the 2026 IEEE Medal of Honor.IEEE Sovereign AI foundations The most recent cornerstone of Melbourne’s AI capability is MAVERIC (Monash AdVanced Environment for Research and Intelligent Computing), Australia’s largest university-based AI supercomputer. Built and deployed by Monash University in partnership with NVIDIA, Dell Technologies, and CDC Data Centres, MAVERIC has been engineered specifically for large scale AI and data intensive science, with medical research representing a key priority. Indeed, in these regards MAVERIC has been designed to function as a Next Generation Trusted Research Environment thus ensuring that it is state-of-the-art and provides a safe and secure framework for the analysis of large sensitive datasets. Designed to support research projects including cancer and neurodegenerative disease detection, clinical trial analysis and drug discovery through to materials science and engineering, MAVERIC enables Australian researchers to train and evaluate large models domestically while keeping highly sensitive datasets secure and under national jurisdiction. This sovereign design is particularly relevant in fields such as medical research where privacy, regulation or intellectual property constraints limit the use of offshore cloud resources. Monash University Vice-Chancellor and President Professor Sharon Pickering with researchers [left to right] Professor Anton Peleg, Professor Victoria Mar, Professor James Whisstock, Vice-President (Strategy and Major Projects) Teresa Finlayson, and Professor Patrick Kwan.Eamon Gallagher (Australian Financial Review) Technically, the system reflects the latest shifts in high performance AI architecture. Built on NVIDIA GB200 NVL72 platforms and integrated using Dell’s rack scale infrastructure, MAVERIC employs closed loop liquid cooling to reduce water consumption compared with conventional air-cooled systems, aligning large scale compute growth with sustainability objectives while supporting high density, high throughput workloads. Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences commented, “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines. It will seed wonderful new cross-disciplinary collaborations, underpin the work of our best and brightest young researchers and will allow our scientists to continue to make major discoveries that positively impact the Australian and global population more broadly.” “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines.” —Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences Monash University frames MAVERIC not as a standalone asset, but as part of the national research infrastructure, intended to strengthen collaboration across academia, healthcare, government and industry. This approach positions Melbourne at the forefront of sovereign AI enabled research in the region. Data center scale as research infrastructure The infrastructure demands of modern AI research extend well beyond individual systems. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Total data center investment, US$ billions.Source: Data Centres Global Report 2025 In February 2026, CDC Data Centres opened its first Melbourne campus in Brooklyn, with two live facilities and a third in planning. Combined with CDC’s Laverton campus, Melbourne is projected to host more than 800 megawatts of sovereign digital capacity, critical for AI workloads requiring sustained access to high-density power, cooling and secure environments. Parallel investment is underway in Fishermans Bend, where NEXTDC is developing a AUD $2 billion AI and digital infrastructure hub adjacent to the Innovation Precinct. Planned facilities include an AI Factory, a Mission Critical Operations Center and a Technology Center of Excellence, enabling sovereign AI, high-performance computing and cross-sector collaboration across health, defence and finance. Melbourne hosts Australia’s largest cluster of AI firms, with 188 companies, and more than 40 data centers currently operate across Victoria. The Victorian Government has complemented this growth with an initial AUD $5.5 million investment in the Sustainable Data Center Action Plan. Together, these developments reinforce Melbourne’s role as a national and increasingly global hub for high-performance AI infrastructure as model complexity and infrastructure dependency continue to accelerate. Applied AI research at scale Monash University is home to MAVERIC, Australia’s largest university-based AI supercomputer, built and deployed by Monash in partnership with NVIDIA, Dell Technologies, and CDC Data Centres.Monash University Melbourne’s research strength is underpinned by a dense university network with deep capability across AI, data science and engineering. Institutions including Monash University, the University of Melbourne, Deakin University, La Trobe University, RMIT University and Swinburne University of Technology collectively support research across machine learning, robotics, human-computer interaction, extended reality and advanced manufacturing. This concentration fosters applied collaboration where AI intersects with medicine, sustainability, cognitive systems and immersive technologies. For visiting researchers, it provides access not only to academic expertise but also to live infrastructure environments where research can be tested and validated, reinforcing Melbourne’s position as one of the Asia-Pacific’s most integrated AI research ecosystems. Conferences as research accelerators Plenary session at Melbourne Convention and Exhibition Center.Melbourne Convention Bureau Melbourne’s selection as host city for a growing number of international technology conferences reflects the convergence of research capability and infrastructure maturity. In September 2026, Data Center World Australia and The AI Summit Australia will be co-located at the Melbourne Convention and Exhibition Center, bringing together global leaders across AI, digital infrastructure and enterprise technology. The pairing highlights a broader reality: advances in AI are inseparable from the infrastructure that enables them. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Research-led conferences are also expanding Melbourne’s global footprint. ICONIP 2026, hosted by Deakin University, will bring up to 700 researchers in neural networks and machine learning, followed in 2027 by IEEE VR, the leading conference on virtual reality and 3D user interfaces, attracting up to 1,000 delegates. In this context, conferences function not simply as events, but as infrastructure for knowledge transfer, supporting standards exchange, collaboration and system-level learning at global scale. A global platform for advancing research Sovereign compute, data center scale and a strong conference pipeline create a reinforcing cycle, enabling researchers to engage directly with infrastructure and industry well beyond the event itself. By closing the gap between theory and deployment, Melbourne supports deeper technical exchange and more enduring global research networks. This role was recognized in 2025 when the IEEE awarded Melbourne Convention Bureau the 2025 Organisational Supporting Friend of IEEE Member and Geographic Activities (MGA) — the first convention bureau in the Asia Pacific region to receive the acknowledgement as a result of the longstanding partnership with the IEEE Victorian Section. Melbourne Convention Bureau (MCB) representative Fatima Aboudrar, Senior Business Development Manager, with Vijay S. Paul, Immediate Past Chair, IEEE Victorian Section, receiving Supporting Friend Member recognition in 2025. As AI research becomes increasingly dependent on infrastructure scale, sovereign capability, and global collaboration, Melbourne is moving beyond hosting conversations to actively enabling the systems that advance AI and data‑driven research at global scale. Conference support in Melbourne Your browser does not support the video tag. Why host a conference in Melbourne, Australia.Melbourne Convention Bureau This ecosystem is underpinned by Melbourne’s highly accessible city center, where world-class venues, research institutions and industry hubs are located in close proximity. Free public transport and a compact city footprint enable seamless movement from conference floor to real-world application. Melbourne Convention Bureau (MCB) is a not-for-profit state government agency with over 60 years’ experience, that provides IEEE and its members with free support to bring international conferences to Melbourne, Australia. MCB’s support spans early-stage exploration and international bidding through to securing government funding, connecting organizers with venues, accommodation and event suppliers, and providing destination support for conference planning and delivery. Organizations considering a conference in Australia are encouraged to connect with MCB’s dedicated team, which supports IEEE conferences in Melbourne. Enquiries can be directed to info@melbournecb.com.au.
More than 30 years ago, in the mountain village of Mbem in northwest Cameroon, the moon and stars in the night sky were the only light young Jude Numfor knew after the sunset. Electricity had not yet reached his rural community. “There was one person in the village with a petrol generator and a small television,” Numfor says. “When he turned it on, all the children would run to his house and peep through the window.” That memory became the spark for Numfor’s mission: to bring electricity to rural communities like his hometown. To accomplish his goal, in 2006 he cofounded Wireless Light and Power, since renamed Renewable Energy Innovators Cameroon, and he serves as its CEO. REI Cameroon designs, installs, and maintains solar minigrids for rural electrification. The minigrids use photovoltaic technology and battery-energy storage systems to generate electricity at 50 hertz. The electricity is distributed through smart meters. In 2017 the company received a grant from IEEE Smart Village to fund the expansion of REI’s minigrid operations and refine its business model. Smart Village supports projects and organizations bringing electricity and educational and employment opportunities to remote communities worldwide. The program is supported by IEEE societies and donations to the IEEE Foundation. The partnership has led to a collaboration developing open source metering, a free, community-driven way of tracking energy usage. Unlike proprietary utility meters, the system allows users, researchers, and utilities to view, customize, and verify how data is collected, ensuring transparency in billing, consumption tracking, and grid management. Smart Village’s support has been pivotal, Numfor says: “It’s not just about money. We share ideas, we get advice, and we have made friends. Entrepreneurship is lonely, but with the [Smart Village] community, it is different.” From teenage tinkerer to entrepreneur Numfor’s first experience of life with electricity was in 2001, after moving in with a missionary family in the small village of Allat. They used solar panels to power their whole home—an unimaginable luxury in Mbem. “I could watch TV, eat ice cream, and turn on lights,” he says. “It made me wish my brothers in Mbem had the same opportunity.” Numfor’s curiosity about electricity was ignited when a motion-sensor solar light in the family’s home stopped working. He tinkered with the device to find out why. “My missionary family told me to play with it like a toy,” he says, laughingly. “I replaced the dead battery with a motorcycle battery and was able to bring the power back for the night.” Jude Numfor [right] testing a rechargeable solar lantern, which aimed to replace hazardous kerosene lamps—known locally as “bush lamps.”REI Cameroon His missionary parents encouraged Numfor to study technology and engineering on his own, as none of the country’s universities offered solar energy educational programs at the time. They built him a library and stocked it with books on engineering, management, and entrepreneurship. In 2006, armed with his new knowledge, Numfor launched Wireless Light and Power with a friend, Ludwig Teichgraber. The nonprofit aimed to replace hazardous kerosene lamps—known locally as “bush lamps”—with rechargeable solar lanterns. These solar lanterns—called “light packs”—were built locally by Numfor and a team of 11 young Cameroonians using PVC pipes, nickel-metal hydride batteries, and LED bulbs. Families rented the lamps for a small fee, swapping discharged lamps for fully charged ones at solar-powered charging kiosks when they ran out of power. The kiosks then recharged the depleted lamps, making them available for the next swap. “The solar lantern was safer and cleaner, plus it gave children a chance to read at night,” Numfor explains. “People loved them.” Between 2006 and 2010, his team replicated the model across several villages. But when the global financial crisis hit in 2008, donor support dwindled, forcing the organization to evolve. “We pivoted from being an NGO to a commercial venture,” he says. “That’s how REI was born.” Building solar minigrids to serve community needs The new company’s goal was to move away from the lanterns and toward full electrification of communities. Villagers’ aspirations changed, Numfor says, as they now wanted to power their TVs, music systems, and mobile phones. In response, in 2010, REI developed one of the first solar minigrids in West Africa. Using locally procured components, the prototype supplied steady power to six households. The minigrid system used 12 123-watt solar photovoltaic panels manufactured by Sharp, 16 12-volt 100 ampere-hour automatic gain control lead acid batteries, and a Xantrex charge controller and inverter. Locally sourced wooden light poles were erected to distribute electricity throughout the village. REI charged each household a fee for the electricity. “It was a product-market-fit moment,” Numfor says. “People immediately asked, ‘When can we get this, too?’” The word-of-mouth, grassroots growth caught the attention of global partners. Numfor connected with Smart Village and in 2017, REI Cameroon received its first seed grant from the program. With that funding, Numfor was able to grow organically and attract additional grants, including one from the U.S. Trade Development Agency (USTDA), in partnership with the U.S. Department of Energy’s National Renewable Energy Laboratory. REI has since expanded to six villages, providing power to more than 1,000 households and businesses. With a dedicated team of 16 people, the company operates in multiple regions of the country, each with unique terrain, languages, and cultural dynamics. “It wasn’t easy,” he acknowledges. “I’m not an academic person—I had to learn everything by doing. [Smart Village] helped me structure the project and grow as an entrepreneur.” Today, Numfor pays it forward by sharing his Smart Village experience and mentoring new entrepreneurs. Launching a coalition for smart metering Minigrids can’t operate efficiently without clarifying operating rules to ensure quality service requirements and consumer protection, while also enabling reliable and effective monitoring of the system, Numfor says. “We need to know how power is being used, detect problems early, and manage the minigrid from a distance,” he explains. Existing commercial smart-meter providers offer limited and proprietary solutions. One major provider left the market, making their technology infrastructure obsolete. “It’s risky for an entire sector to depend on a few companies for such a critical technology,” Numfor says. In 2025, with the help of the Smart Village technical community, Numfor convened a consortium of open-source power advocates, including the Africa Mini-Grid Developers Association, EnAccess, Energy IOT, and NESL. The goal was to develop an open smart metering system that is accessible, transparent, and sustainable for all energy providers. “These organizations are collaborating as Open Advanced Metering Infrastructure [OpenAMI], which is about giving control back to the people who deliver the energy,” he says. Scaling for impact Numfor’s passion has grown from bringing light to local rural communities to bringing light to his entire country. Just 54 percent of Cameroon’s citizens have access to electricity, according to the International Energy Agency. For Numfor, the challenge is not just technological—it’s social and economic as well. “Electricity is the most important enabler of education and economic growth today,” he says. “When you have power, you unlock everything else.” “Electricity changed my life. Now I want to make sure every child can grow up with that same light.” —Jude Numfor Across the villages where REI has installed sustainable electricity solutions, small businesses are flourishing. Barbershops hum with community chatter, food vendors can preserve perishables, and entrepreneurs run companies such as phone-charging stations and small mills. “Some villages even have laundromats now,” Numfor says proudly. “Electricity creates jobs and changes mindsets.” Still, it has been a bumpy journey. It wasn’t until 2025 that REI obtained its official authorization (license) from Cameroon’s government to produce and distribute electricity in off-grid areas using solar minigrids. This was a major milestone because REI is one of the first private enterprises in the country to receive such authorization. “We were stuck between pilot projects and growth,” he explains. “Our projects were successful, and there was community demand for more, but to grow, we needed investors who require legal guarantees before committing funds. Now we can scale up and attract investors.” REI plans to expand its reach dramatically, beginning with 134 new villages identified through a feasibility study supported by the USTDA. Their long-term goal is to electrify 760 villages across Cameroon by 2031. While authorization opens doors, financing remains one of REI’s biggest challenges. “The minigrid space doesn’t attract venture capitalists easily,” Numfor notes. “Our return on investment is under 15 percent, so it’s not a typical tech startup model. The real return here is the impact” on the community. He hopes to attract investors who understand that access to electricity drives education, health care, and entrepreneurship. “There are people out there who want to make meaningful change,” he says. “We just need to connect with them. When you electrify a village, you never know who the next innovator will be. Maybe it’s another kid like me, looking through a window, dreaming.” Finding skilled staff is another challenge, Numfor says. To address this, REI developed an intensive recruitment and training process. “It used to take years to find the right people,” he says. “Now, we can identify who fits our company culture within six months.” Numfor’s wife, Angela Taliklong, who joined the venture in 2010, now oversees administration and human resources. A brighter Cameroon and beyond Numfor offers simple words of advice to other impact-driven entrepreneurs: Keep moving. “One of my mistakes early on was trying to be perfect,” he says. “I was spending time improving prototypes instead of increasing the number of our project installations and scaling how many communities we could electrify. You must keep momentum. Don’t wait until everything is perfect before you move forward.” That mindset, rooted in resilience and experimentation, has defined his journey. Rajan Kapur, president of Smart Village, says Numfor is a “shining example” of the program’s vision: “scalable and enduring impact through local entrepreneurs, local procurement, and community engagement based on the use of IEEE technology in underserved communities.” With the ongoing Smart Village partnership, Numfor is determined to bring light and opportunity to every corner of Cameroon, and beyond. He already has launched REI Nigeria. “Electricity changed my life,” he says. “Now I want to make sure every child can grow up with that same light.”
This article is brought to you by DAIMON Robotics. This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore. The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data. Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision. We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads. Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics The Dataset Initiative This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry? DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies. As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development. This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models. Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community. By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models. The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this? We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset. Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year. “To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products? Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset. Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community. Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics From VLA to VTLA: Why Tactile Sensing Changes the Equation The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback? Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings. It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model. An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture. DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics The Technology: Monochromatic Vision-based Tactile Sensing You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path? Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably. When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand. DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform? The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors. A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential. From Academia to Startup After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics? I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years. However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources. Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe. Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics Business Model and Commercial Strategy What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy? We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments. Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration. Embodied Skills and the Convergence Moment You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved? We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously. “Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems. While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots. The Road to Real-World Deployment Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first? I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels. Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others. Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity. This interview has been edited for length and clarity.