2026-07-04 02:00:02
Many IEEE members who collect historical engineering artifacts often offer them to the IEEE History and Heritage group, which includes the IEEE History Center, to display. To bring these artifacts to the public, the group created the IEEE Global Museum, which curates traveling exhibits for display at conferences and in libraries, universities, and other venues.
The program educates people about how technological progress has unfolded over generations, and how engineers and researchers build on past achievements to benefit humanity.
Curating the exhibits has been rewarding, says Daniel Jon Mitchell, director of the group’s heritage programs.
“People tell me that they are genuinely moved by having history and artifacts explained to them in an accessible, intelligible way,” Mitchell says. “When people are moved and emotionally affected by what you’re doing, they’re going to remember that. And I think that’s part of the power of what we’re doing.”
The most recent traveling exhibit was on display in April in New York City during the IEEE Honors Ceremony, which celebrates engineering pioneers who have developed technologies that changed how people connect with the world. Attendees explored the Microchips That Shook the World exhibit, which drew inspiration from IEEE Spectrum’s Chip Hall of Fame. The exhibit conveys the roles integrated circuits play in fields such as signal processing, audio engineering, and telecommunications. The Commodore 64, one of the artifacts on display, stirred up treasured childhood memories for guests who had used the home computer.
Other exhibits have focused on early radio inventions and power and communications technologies.
The Global Museum works with IEEE societies to mark their anniversaries by interpreting and displaying pertinent items.
The idea of a traveling museum came to fruition in 2024 after Alexander Magoun, IEEE’s outreach historian, connected with Mike Molnar. The IEEE associate member owns one of six superheterodyne radio prototypes developed by Edwin Howard Armstrong, who probably is best known for inventing the FM radio system. Armstrong received the first IEEE Medal of Honor in 1917.
The radio converts incoming frequencies into a fixed, lower intermediate one using a local oscillator and a frequency mixer. The technology paved the way for modern electronic communications devices. The prototype became the focal point of the Global Museum’s flagship Unseen Signals: E. Howard Armstrong’s Radio Revolution exhibit, which celebrates the inventor’s life and his impact on the broadcasting industry and wireless communications.
“The radio prototype is one of the most incredible pieces that we could put on display,” Mitchell says. He and Magoun sourced other artifacts including an Audion used in Armstrong’s experiments on wireless signal amplification; a selection of consumer products that attempted to cash in on radio’s popularity, including a flour sifter and laxatives; and a Motorola Walkie-Talkie from the Korean War. They were from museums or private collectors along the East Coast of the United States.
“Aside from [Guglielmo] Marconi, Armstrong is the most significant contributor to the history of radio,” Mitchell says. “The exhibit is not only a biography but also a story of the cultural and political implications his work had.”
Visitors can play 15 short clips of past radio broadcasts covering politics, religion, sports, or another topic.
The Armstrong exhibit was unveiled in 2024 at the National Museum of Industrial History in Bethlehem, Pa.
The 93-square-meter exhibit is still traveling around the United States. It is on display until 15 August at the Pavek Museum, in St. Louis Park, Minn.
From 21 November until 9 May 2027, it is scheduled to be at the Museum of Innovation and Science in Schenectady, N.Y. Entry to the museum is free for IEEE members with a digital membership card.
The IEEE History and Heritage group collaborates with IEEE societies to create exhibits for special events. In 2024 Mitchell curated an exhibit to celebrate the 75th anniversary of the IEEE Vehicular Technology Society and its 100th Vehicular Technology Conference. The Our Mobile World exhibit was launched at the conference, held in October in Washington, D.C.
“The society’s leadership helped me focus attention on key developments that meant a lot to its members,” Mitchell says.
“The IEEE Global Museum wants to present exhibits that connect with its audiences, whether these are IEEE members or the public,” he says. “Just knowing what was important historically doesn’t mean that this will resonate, so I really appreciated the insight.”
The exhibit’s artifacts included a Motorola DynaTac “brick” cellphone, a CB radio from the 1980s, and one of the earliest handheld GPS receivers. Visitors played an interactive game to test their knowledge spanning a century of wireless technology, motor vehicles, and mobile communication inventions.
Mitchell worked this year with the IEEE Dielectrics and Electrical Insulation Society to launch a virtual exhibit, Powering Up, which is available on the Global Museum website. It provides an overview of high-voltage power engineering, and it highlights the roles that manufacturers General Electric and Westinghouse played in making long-distance, high-voltage transmission of electrical power possible. Videos and photos of impulse generators and tests are featured in the exhibit.
Nvidia CEO and cofounder Jensen Huang, who received the 2026 IEEE Medal of Honor, exploring the Microchips That Shook the World exhibit.IEEE Conferences, Events & Experiences
One photo shows lightning arcing between high-voltage generators. Others show the impulse generators used at the 1939 World’s Fair in New York City, demonstrations of artificial lightning, and U.S. President Ronald Reagan visiting GE’s high-voltage laboratory in Pittsfield, Mass.
The Unseen Signals exhibit was created for large venues, but the Microchips That Shook the World exhibit was designed to be displayed in different spaces, Mitchell says. Artifacts are premounted to ensure easy setup, and they’re encased in glass because many are rare.
Microchips are crucial for signal processing, audio engineering, and telecommunications, making them a point of interest despite their small size, Mitchell says. One rare artifact on display is the Kodak KAF-1300 image sensor. Invented in 1986, it was used in one of the earliest digital cameras made for photojournalists.
The KAF-1300’s image sensor chip “is credited with bringing digital cameras out of the laboratory,” Mitchell says. “Only around 500 were produced.”
Visitors can understand how transistors work, he says, by pressing buttons to turn them on and off.
“There are billions of transistors in modern microchips,” he notes, “and you can combine them in a way that performs logical functions.”
Unseen Signals, one of two identical exhibits, was curated by Mitchell and Stephen Cass, IEEE Spectrum’s special projects editor, with help from several Spectrum colleagues. Together, they served as on-site docents for guests at the IEEE Honors Ceremony.
The display also featured a preview of IEEE’s immersive “Inside the Microchip” video project, which delves beneath the silicon surface of Nvidia’s NV20 chip, using forensic photography and computer-generated renderings. The video, to be released this year, aims to teach middle school students about the microchips that are inside their gaming devices.
The exhibit was on display at the IEEE Electronic Components and Technology Conference, held in May in Orlando, Fla. Later this year, members will be able to visit it at the Computer History Museum in Mountain View, Calif., and the University of Waterloo, in Ontario, Canada.
The IEEE Global Museum is made possible thanks to donations to the IEEE Foundation.
2026-07-03 20:00:01
The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade.
Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters.
This framing captures scale. It misses behavior.
The emerging issue is not simply how much power large-scale compute systems consume, but how increasingly dense and synchronized computational workloads are beginning to alter the operating characteristics of the electrical grid itself through increasingly unpredictable demand that varies rapidly in both time and location, creating new operational challenges for grid operators.
Traditional grid planning assumes relatively predictable demand behavior. Industrial, commercial, and residential loads generally follow established profiles that can be forecast with reasonable accuracy. Even substantial demand growth has historically been manageable through reserve planning, transmission upgrades, and demand management programs.
Large-scale compute infrastructure introduces a different class of electrical load. Training—the computational task of making AI models—tends to be highly synchronized across clusters of GPUs, TPUs, and specialized accelerators operating in parallel, computationally dense, and relatively scheduled. Inference—the process of actually using those models—is generally more distributed and user-driven, making demand less predictable both in time and location. Both differ materially from traditional industrial demand profiles, though for different reasons. Unlike many conventional industrial processes, these workloads can ramp rapidly depending on model training cycles, distributed compute coordination, and workload scheduling strategies.
From the perspective of the grid, this is not simply higher demand. It is more abrupt demand. High-density compute workloads can produce substantial step-changes in electricity consumption over extremely short intervals, including rapid fluctuations occurring within milliseconds. Data center operators are already deploying mitigation technologies, including batteries, power-conditioning systems, and supercapacitors. Collectively, however, data centers’ rapid load changes can place additional stress on backup generation reserves, systems that adjust supply as demand changes, frequency-control mechanisms that maintain grid stability, and local transmission infrastructure.
Compute-related variability differs from the intermittency introduced through renewable energy integration. Wind and solar variability originate primarily on the supply side and is tied to environmental conditions. Compute-related variability emerges on the demand side, driven by workload synchronization, scheduling behavior, and computational intensity. The interaction between increasingly dynamic supply and demand conditions introduces additional uncertainty into forecasting, reserve management, congestion planning, and balancing operations.
Research organizations including the National Renewable Energy Laboratory (NREL) have emphasized the growing complexity associated with integrating highly dynamic resources into modern grid operations.
The issue becomes more significant when compute activity is geographically concentrated. Large-scale data centers tend to cluster in regions with favorable conditions such as fiber connectivity, access to markets, tax incentives, and historically low electricity costs. Northern Virginia, often referred to as “Data Center Alley,” remains the most prominent example. The region hosts the world’s largest concentration of data centers and carries a substantial share of global internet traffic.
Utilities operating in these regions have already identified data center growth as a primary driver of future load expansion. Virginia-based electricity supplier Dominion Energy, for example, has repeatedly highlighted hyperscale demand growth in its integrated resource planning documents.
Virginia has seen one of the largest data center buildouts worldwide. Here, Amazon Web Services and iron mountain data centers dominate the landscape in Manassas, Virginia. Nathan Howard/Bloomberg/Getty Images
A sudden increase in electricity consumption within a constrained geographic area can stress substations, transmission corridors, and local balancing operations even if the broader grid maintains sufficient aggregate capacity. This creates localized reliability challenges that are not always visible through system-wide demand metrics alone.
Thermal management systems further intensify these effects. Cooling infrastructure in high-density compute facilities must respond dynamically to changing workloads. As processing intensity rises, cooling demand rises with it, often nonlinearly. This coupling between compute and thermal systems means that fluctuations in workload can propagate through multiple layers of facility power consumption simultaneously.
High-density compute clusters may also introduce power quality concerns at the local level. Large concentrations of accelerators, switching power supplies, and high-frequency compute equipment can generate harmonics and nonlinear load behavior that place additional stress on distribution infrastructure. While modern facilities incorporate mitigation technologies, the scale and concentration of next-generation compute facilities may require utilities and operators to revisit assumptions surrounding localized power conditioning, harmonics management, and infrastructure resilience. These conditions can also contribute to short-duration electrical transients that place additional stress on localized infrastructure and power-conditioning systems.
Part of the challenge is that many existing regulatory and operational frameworks were designed around relatively stable industrial demand profiles. Large rapidly fluctuating loads have historically been constrained because abrupt cycling can complicate balancing operations, increase stress on transmission equipment, and reduce predictability in system operations. High-density compute clusters do not fit neatly within those assumptions.
This creates pressure for both operational adaptation and regulatory reassessment.
Demand response mechanisms may allow certain compute workloads to be shifted or curtailed during periods of system stress. Data-center operators are exploring flexible scheduling, battery storage, and behind-the-meter generation. Grid operators, meanwhile, are evaluating planning frameworks and interconnection approaches for increasingly large flexible loads.
The Electric Reliability Counsil of Texas (ERCOT), for example, has publicly acknowledged the growing implications of large flexible loads, including data centers, for long-term grid planning and operational stability. Interconnection queues across the United States continue to expand significantly, reflecting mounting pressure on both generation and transmission infrastructure. Grid expansion timelines, however, are measured in years rather than quarters.
This creates a structural mismatch. Compute infrastructure can scale rapidly. Electrical infrastructure generally cannot.
The broader implication is that large-scale compute infrastructure is not simply another industrial load category. It represents a shift in the temporal and spatial characteristics of electricity demand itself.
Framing the issue solely in terms of aggregate energy consumption risks overlooking these second-order operational effects. Capacity expansion alone does not fully address rapid ramping behavior, synchronization, localized congestion, transient instability, reserve compression, or increasingly demanding load-following requirements.
The challenge is not just how much electricity these systems consume. It is how they are beginning to change the operating conditions of the grid itself. The call is not to slow AI development but to recognize that hyperscale computing represents a new category of electrical demand. As AI infrastructure continues to scale, planning frameworks may need to account not only for total energy consumption but also for demand volatility, synchronization effects, and geographic concentration. Grid resilience will increasingly depend on understanding how these facilities consume power, not simply how much power they consume.
2026-07-02 02:15:02
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!
You want to become a senior developer. A CTO, maybe. Start your own company, perhaps. Or maybe you just want to land your first role in tech.
You will not get there from raw engineering skill alone.
There’s a skill that’s quietly essential to technical leadership and yet consistently overlooked: public speaking.
If you’re anything like I used to be, you’re already listing reasons not to. “I got into this to code, not to give presentations.” “I don’t want to lead.” “I’m too junior to speak about anything.” No, no, and no again. There’s a ceiling on the return from technical skill alone.
I was terrified of public speaking for the first three years of my career. I wanted to hide behind code, and for the most part it worked. I did my job and did it well.
Then I joined a startup where hiding wasn’t an option. The whole company was five people. I was one of two developers. I had to form opinions on our technical direction and defend them, and the CTO told me directly that I needed to speak up more.
A few things happened once I did. I took more pride in my work. I said some cringe-worthy stuff, lived through the mini-anxiety attacks, and got better. To my own disbelief, I’m now an engineering manager whose job is largely speaking to groups of developers and leading presentations, online and in person.
Here’s why this is worth your time:
Leadership. Communicating ideas clearly, influencing decisions, and aligning your team are core leadership functions, and they matter more the further you climb.
Visibility. Speaking lets you show your expertise, build a reputation, and connect with people who open doors to better roles.
Durability. As automation absorbs more routine technical work, skills rooted in human interaction and judgment are far harder to replace.
The good news is you can build this deliberately, in low-stakes steps.
Record yourself. Use a screen-recording tool to walk through your work, explain a concept, or narrate your code. You can edit, re-record, and over-think it as much as you want. That’s the point. It gets you comfortable on camera before the stakes are real.
Volunteer for demos. Next time you ship a feature or fix a bug, ask your manager for a short time slot to walk the team through it. No format for that on your team? Suggest a monthly lunch-and-learn and kick it off with a 15-minute lightning talk on something you know.
Start small—really small. If your anxiety is spiking, don’t jump into the deep end. In your next meeting, ask one question. Write it down beforehand if you have to. Then be the first to break the awkward silence when someone else asks one. Developers are a famously quiet bunch, so it doesn’t take much to stand out.
The further you grow, the more you’ll be expected to hold opinions and voice them publicly. So start now. Record yourself, ask questions, get uncomfortable, and notice that it gets easier every time you do it.
—Brian
Salome Mikadze-Struk built her tech company Movadex as an undergraduate student at the height of the COVID-19 pandemic—then kept it running during the outbreak of war in her native Ukraine. Now, she’s channeling what she learned into mentoring tech founders and speaking about the importance of resilience as AI upends the software industry.
LLMs are now part of many engineers’ daily workflow, and the demand for technical expertise in implementing and securing the models is rising. But to build tools that work consistently, developers must have a strong understanding of the core principles that govern how the models work. IEEE is now offering a five-course program to teach how to use LLMs effectively, starting with the fundamental engineering behind the technology.
Two researchers at the City University of Hong Kong developed a method to make a circuit trace by simply bending a piece of paperlike material. With the right ingredients—isopropanol and liquid metal—you can make your own origami circuit board. The researchers also created a toolkit, called LiqMetCraft, with software tools and instructions to make it easy for beginners, whether in papercraft or electronics.
2026-07-02 02:00:02
I started my professional journey as an engineer before moving into product strategy and innovation leadership roles for several global technology organizations. Over the years, I have served as a mentor for a variety of programs including Products That Count’s strategic product management, Women in Product mentorship initiatives, and Alchemist accelerator programs.
In 2024 and 2025 I led Walmart’s Women in Product mentorship program. I was responsible for designing and implementing the programs, including managing participant registration, matching mentors with mentees, and establishing clear standards for how they would interact.
Yet for much of my own early career, I never really had a mentor.
As an individual contributor engineer, I was focused on solving problems, delivering results, and figuring things out independently. I was hesitant to ask for help for fear of being judged for what I didn’t know.
Part of that was also temperament. I am naturally introverted.
That mindset rewarded me well. It made me self-reliant, resilient, and deeply driven. But it also had limits. Looking back, I now realize that believing I had to navigate everything alone was not always a strength. I sometimes wonder how many opportunities I missed simply because I never asked for help.
As I moved into product management and later strategy roles, I began collaborating with larger teams, departments, and organizations. The work itself became more cross-functional and people-centered. Over time, I started recognizing the value of mentorship, sponsorship, and collaborative growth in ways I had not appreciated earlier in my career.
I received valuable advice from different people at important moments throughout my career. Some helped me navigate conflict with more clarity. Others helped me communicate my contributions more effectively. And others gave me perspective on how to approach uncertainty, deal with organizational complexity, and avoid burnout.
But those moments were not the same as mentorship. They were valuable but infrequent interactions, not sustained relationships. No one consistently guided me through difficult decisions, advocated for me with decision-makers and senior leadership, or actively invested in my long-term growth.
My understanding of mentorship changed not as a mentee but as a mentor.
Mentorship is often seen as an act of goodwill: admirable but optional. In reality, effective mentorship can be a competitive advantage for everyone involved.
For mentees, it can accelerate career growth, strengthen decision-making, and create access to opportunities that hard work alone does not always unlock.
Mentorship strengthens an individual’s leadership skills, empathy, and the ability to develop future talent.
For organizations, mentorship builds stronger leadership pipelines, more resilient teams, and healthier cultures of growth and trust.
By getting involved, I began to understand that meaningful mentorship is not simply occasional advice or career guidance. At its best, it is an active investment in another person’s growth. It includes advocacy, sponsorship, honest feedback, visibility, and sometimes helping people access opportunities they may not have reached on their own.
That is why mentorship should not be treated as kindness or incidental support. It is one of the most practical, hands-on, and personal forms of leadership.
Advice can help someone improve, but advocacy and sponsorship can change the direction of a career.
In many organizations, career growth depends not only on talent but also on access to honest feedback, influential networks, and sponsors willing to speak about someone’s potential when opportunities are discussed. Access also includes introductions to people who can recognize the value and impact of a person’s work.
Sometimes the difference between advice and true sponsorship is illustrated more clearly through stories rather than through leadership frameworks. In The Devil Wears Prada and its sequel Nigel’s relationship with Andy evolves far beyond workplace advice. In the 2006 movie, he helps her grow professionally, pushes her to envision a more expansive future, and guides her through an unfamiliar industry.
In the sequel—set two decades later—his investment in her success continues even though their careers diverge. When Andy (played by Anne Hathaway) is laid off during a difficult job market and struggles to find meaningful opportunities, Nigel (Stanley Tucci) quietly recommends her for a role at his firm. She is arguably overqualified for the position, but Nigel recognizes that it is the right opportunity at the right time. His recommendation helps her transition from a career in the news back into working in fashion. She can regain stability and ultimately rebuild career momentum. Over time, the opportunity becomes a turning point, reshaping her professional trajectory.
What makes it meaningful is not just the recommendation itself. It is that Nigel continued paying attention to her career growth over the years, believed in her potential, and supported her when she needed it.
That is what meaningful mentorship and sponsorship often look like in practice: not surface-level guidance but genuine investment in someone’s long-term growth and success.
When mentors provide that kind of support intentionally, mentorship becomes more than guidance. It becomes a competitive advantage—not only for the mentee but also for the mentor and the organization.
Mentorship matters because talent alone does not shape a career. Access is important. In many workplaces, advancement depends not only on capability but on guidance, sponsorship, visibility, and informal knowledge about upcoming job opportunities.
Not everyone has equal access to such advantages. Research from McKinsey and Lean In suggests that women often receive less mentorship, sponsorship, and career support than men do, even in organizations that publicly emphasize inclusion and leadership development.
When mentorship is left entirely to informal networks, opportunity often becomes uneven. And when it’s left to chance, opportunity also is uneven.
That’s why inclusive mentorship matters. It creates a more intentional way to support people who might otherwise be overlooked.
“A mentor is someone who allows you to see the hope inside yourself,” Oprah Winfrey once said.
Great mentorship is not about having all the answers. It’s about showing up with intention. It means listening closely, being candid, and helping someone grow with more confidence and clarity.
The best mentors respect their mentees’ time. They come prepared and listen for what is needed rather than rushing to give advice. They are open about their successes and failures because honesty builds trust faster than polished stories do. Great mentors tailor their guidance to the individual and encourage growth while also creating accountability.
Above all, good mentors create a psychologically safe space. They make it easier for mentees to ask difficult questions, test or pitch ideas, and talk openly about issues without fear of being judged. Growth usually starts at that point.
Organizations have a role to play as well. If mentorship matters, the program should be visible and supported.
That can mean including it in stated expectations of leaders, creating ways to connect mentors and mentees, providing mentorship training, and recognizing outcomes that go beyond performance metrics.
It also can mean broadening the understanding of mentorship. Peer mentorship, cross-functional mentorship, and even cross-industry mentorship can play important roles.
Promoting mentorship should not involve forcing artificial relationships or turning an employee’s growth into a line on someone’s to-do list. Organizations ought to promote the idea that leaders should invest in others, helping to build stronger teams, more capable leaders, and more organizational resiliency.
At a minimum, organizations should ask mentors whether they helped their mentee grow in their career and whether the mentee became more confident, capable, or prepared as a result of the relationship. Did they help junior employees navigate the organization more effectively? What opportunities did they create or find to give the mentees more visibility? Did they help mentees develop communication, leadership, or decision-making skills?
Those questions might be hard to quantify, but they get close to the substance of leadership.
People might remember the strategies a leader shaped, the products the leader created, or the financial targets that were hit. Such accomplishments matter, of course. But another part of leadership lasts longer. It lives in the coworkers whose careers were advanced because someone took the time to invest in them.
2026-07-02 00:01:27
This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.
As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy.
From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.
Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it.
Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed.
Australia’s ambition to lead in artificial intelligence is sharpening focus on the infrastructure required to support it. Data centers are projected to account for up to 11 percent of the nation’s electricity consumption by 2035, placing increasing pressure on generation, transmission, and system reliability.
At the same time, insight from the IEEE Power and Energy Society (PES) highlights that meeting energy demand from AI and digital infrastructure is one of the most significant challenges facing engineers over the next decade.
The implications are clear. In addition to computing challenges, AI poses major energy systems challenges.
“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it” —Professor Thas (Ampalavanapillai) Nirmalathas, University of Melbourne
Victoria has developed one of the most advanced and integrated energy ecosystems in Australia and globally, spanning renewable generation, battery storage, grid modernization, and advanced materials.
What distinguishes Melbourne globally is how these capabilities are connected and applied at system scale.
The city brings together world class engineering research, a rapidly evolving clean energy sector, advanced digital infrastructure, and strong alignment between government, industry, and academia. This convergence is critical in the AI era, where energy, networks and computing systems must be designed together.
Victoria’s coordinated investment across these areas is positioning Melbourne not only as a national leader, but also as a reference point in the global energy system transformation.
The challenge ahead is that generating more power won’t be enough, as engineers need to design systems that respond dynamically to new patterns of demand.
Three priorities are emerging globally:
Addressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel.
Melbourne’s strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application.
Melbourne Connect is a University of Melbourne–led innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions.Atlantic Group
At the centre of this capability is the University of Melbourne, where interdisciplinary research is advancing the systems required to support AI driven energy demand.
Through the Melbourne Energy Institute, for example, researchers are examining how energy technologies interact across entire systems from generation and networks through to end use.
“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,” says Professor Thas (Ampalavanapillai) Nirmalathas, Dean of the Faculty of Engineering and Information Technology at the University of Melbourne.
“This is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.”
Melbourne’s leadership is further strengthened by world-class interdisciplinary facilities such as the Smart Grid Lab in the Department of Electrical and Electronic Engineering, which enables real-time simulation of power systems, allowing engineers to test how solar, batteries, electric vehicles and other distributed resources interact within future grids. This supports the design of more resilient, efficient energy systems before they are deployed at scale.
Melbourne’s Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of power systems. University of Melbourne
These capabilities will become increasingly important as data centers are integrated into the grid.
“AI driven demand is not only increasing computing requirements, but also placing new pressures on underlying energy systems,” says Glen Farivar, Senior Lecturer in Power Electronics at the University of Melbourne. “Designing these systems together is essential to achieving both performance and sustainability outcomes.”
This reflects a critical shift. Future infrastructure must be co designed across energy and digital systems, not developed in isolation.
Victoria’s broader energy ecosystem is translating these insights into practice.
Investment in renewable energy, grid infrastructure and storage is enabling higher levels of clean energy while maintaining reliability. Battery deployment is supporting the flexibility needed to manage both renewable variability and growing AI-driven demand.
At its core, Melbourne offers an integrated environment where research, industry and government collaborate to solve complex system challenges.
Solving the energy demands of the AI era cannot be achieved in isolation.
It requires engineers, researchers, utilities, and policymakers to work together earlier and more often. More than ever, engineering collaboration is a critical enabler of future energy systems.
Environments that bring together global expertise are becoming essential to how solutions are designed and delivered.
“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge” —Professor Pierluigi Mancarella, University of Melbourne
In this context, the University of Melbourne is co-leading, alongside Johns Hopkins University and Imperial College London, one of only seven Global Centres in Climate Change and Clean Energy. Through the Electric Power Innovation for a Carbon Free Society (EPICS) Centre, the University is also the Australian technical lead in advancing future energy systems, with EPICS the only Global Centre focused on future energy infrastructure.
The new Electric Power Innovation for a Carbon-Free Society (EPICS) Centre will address challenges in clean energy production and storage.University of Melbourne
“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge,” says Professor Pierluigi Mancarella, Chair Professor of Electrical Power Systems at the University of Melbourne and Australian director and international co-director of EPICS.
“As electricity grids are increasingly becoming the backbone of future energy systems, optimizing their interactions with other sectors, including AI and digitalization, and fostering interdisciplinary and international collaborations are essential,” he adds.
International conferences are increasingly recognized as critical platforms for advancing engineering solutions at scale. Melbourne’s ability to convene global expertise is central to its leadership.
In 2027, the city will host the IEEE PES Generation Transmission and Distribution (GTD) Asia 2027 Conference and Exposition, bringing together engineers, utilities, researchers and policymakers from across the world to address the challenges shaping the future of power systems.
IEEE PES GTD Asia 2027 Melbourne Committee (left to right): Dr. Mehdi Ghazavi Dozein (Monash University), Dr. Glen Farivar & Professor Pierluigi Mancarella (University of Melbourne) , Dr. Mohammad Mohammadi (Australian Energy Market Operator (AEMO)).MCB
“Melbourne offers a unique environment where world-class research, industry capability and policy leadership come together,” notes the IEEE PES GTD Asia 2027 Local Organising Committee, which includes Professor Pierluigi Mancarella and Dr. Glen Farivar from the University of Melbourne, as well as Dr. Mehdi Ghazavi Dozein of Monash University and Dr. Mohammad Mohammadi of the Australian Energy Market Operator.
“Hosting this event creates an opportunity to advance global collaboration on the systems and technologies required to deliver the energy transition at scale.”
These forums enable knowledge exchange, standards development and interdisciplinary collaboration, accelerating progress on complex engineering challenges.
Attendees view a digital installation at AIME 2025 at Melbourne Connect.MCB
As AI, electrification and digital infrastructure converge, the future of global energy systems will depend on the ability of engineers to collaborate and innovate at scale.
Melbourne provides a proven platform for that collaboration, combining world-class research, a rapidly evolving energy ecosystem, and the infrastructure to connect global expertise.
Melbourne Convention Bureau, IEEE Communications Society, and University of Melbourne Representatives.University of Melbourne
For IEEE members, hosting a conference in Melbourne is more than an event decision.
It is an opportunity to engage with a globally connected engineering community and contribute directly to solving one of the most significant challenges facing the profession today.
Through the support of the Melbourne Convention Bureau, professionals can access tailored, free support to bid for and deliver international conferences, bringing global expertise together in a city actively shaping the future of energy systems.
To explore hosting your next conference in Melbourne, contact the Melbourne Convention Bureau at [email protected].
2026-07-01 20:00:01
“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public.
Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview.
Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever.
Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites.
For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years.
The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.
As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.
Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.”
As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars.
So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers?
Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.”
Michael Pierce, Principal at Technology Strategy Partners
Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system.
Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space.
Matt Hasan, AI strategist and independent consultant
My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation.
What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth.
That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.