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23 Minuten
AI may feel like an unprecedented disruption, but the technology industry has navigated major shifts before. In this episode of The Machine Room, Kevin Brown is joined by Ravi Pendekanti, Corporate Vice President of Data Center Solutions Product Management at AMD, to look back at previous technology transitions and explore what they can teach us about the current AI boom. From Y2K and cloud computing to smartphones and open-source architectures, they discuss why major technology shifts rarely unfold exactly as predicted.
Drawing on Ravi’s career across companies including Sun Microsystems, Silicon Graphics, Juniper Networks, Oracle, Dell, Western Digital and AMD, the conversation explores how enterprises respond to disruption, why skills, security and speed to market can determine the pace of adoption, and how AI could represent a democratization of high-performance computing. As compute density rises from traditional rack levels toward hundreds of kilowatts, that evolution is also reshaping the power, cooling and physical infrastructure required to support AI at scale.
Kevin and Ravi also discuss the long-term role of open architectures and collaboration across the technology ecosystem. From compute and networking to power and cooling, delivering AI infrastructure increasingly depends on partners working together around interoperable systems. They close by looking at the collaboration between AMD and Schneider Electric on the Helios reference design, and why speed of deployment, scalability and sustainability will be critical as organizations build the next generation of AI-ready data centers.
Key quote:
“Innovation wins when you have open standards defined.”
Key takeaways:
What previous technology disruptions can teach us about the current AI transformation.
How enterprises navigate disruption, reduce risk and move quickly with business-critical investments.
How AI is helping bring high-performance computing capabilities into the mainstream.
Why rising compute densities are transforming data center power, cooling and infrastructure requirements.
How open architectures can enable greater competition, interoperability and innovation across the AI ecosystem.
Why collaboration across compute, power, cooling and infrastructure will be essential to delivering AI at scale.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people shaping the technologies and infrastructure behind AI.
Drawing on Ravi’s career across companies including Sun Microsystems, Silicon Graphics, Juniper Networks, Oracle, Dell, Western Digital and AMD, the conversation explores how enterprises respond to disruption, why skills, security and speed to market can determine the pace of adoption, and how AI could represent a democratization of high-performance computing. As compute density rises from traditional rack levels toward hundreds of kilowatts, that evolution is also reshaping the power, cooling and physical infrastructure required to support AI at scale.
Kevin and Ravi also discuss the long-term role of open architectures and collaboration across the technology ecosystem. From compute and networking to power and cooling, delivering AI infrastructure increasingly depends on partners working together around interoperable systems. They close by looking at the collaboration between AMD and Schneider Electric on the Helios reference design, and why speed of deployment, scalability and sustainability will be critical as organizations build the next generation of AI-ready data centers.
Key quote:
“Innovation wins when you have open standards defined.”
Key takeaways:
What previous technology disruptions can teach us about the current AI transformation.
How enterprises navigate disruption, reduce risk and move quickly with business-critical investments.
How AI is helping bring high-performance computing capabilities into the mainstream.
Why rising compute densities are transforming data center power, cooling and infrastructure requirements.
How open architectures can enable greater competition, interoperability and innovation across the AI ecosystem.
Why collaboration across compute, power, cooling and infrastructure will be essential to delivering AI at scale.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people shaping the technologies and infrastructure behind AI.
20 Minuten
In Part 2 of Kevin Brown's conversation with Vlad Galabov, the host of The AIDC Debate podcast, the duo explores what it means when a new data center comes to town and how people are becoming acutely aware of new data center buildouts.
Focusing on a new development project in the London area, they discuss the challenges and opportunities for operators facing local opposition - from community concerns and planning debates to transparency, regulations, sustainability and the future of urbanized data centers.
Featured guest: Vlad Galabov, host of The AIDC Debate podcast
Key Quote:
"No one is building a rogue data center on their own...we need to be smarter on all sides in terms of the community, the regulator and the data center industry”
Key takeaways:
What it takes to turn AI ambition into fully operational AI infrastructure.
How investment and development models are evolving as projects scale.
The factors shaping where future data center capacity can be built.
The growing relationship between data centers, energy systems, and local communities.
Why the industry must adapt as AI-driven demand accelerates.
This is Part 2 of Kevin and Vlad's conversation. Click here to listen to Part 1 to hear their discussion on the forces driving AI demand and the scale of the global AI infrastructure challenge.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people shaping the technologies and infrastructure behind AI.
Focusing on a new development project in the London area, they discuss the challenges and opportunities for operators facing local opposition - from community concerns and planning debates to transparency, regulations, sustainability and the future of urbanized data centers.
Featured guest: Vlad Galabov, host of The AIDC Debate podcast
Key Quote:
"No one is building a rogue data center on their own...we need to be smarter on all sides in terms of the community, the regulator and the data center industry”
Key takeaways:
What it takes to turn AI ambition into fully operational AI infrastructure.
How investment and development models are evolving as projects scale.
The factors shaping where future data center capacity can be built.
The growing relationship between data centers, energy systems, and local communities.
Why the industry must adapt as AI-driven demand accelerates.
This is Part 2 of Kevin and Vlad's conversation. Click here to listen to Part 1 to hear their discussion on the forces driving AI demand and the scale of the global AI infrastructure challenge.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people shaping the technologies and infrastructure behind AI.
21 Minuten
AI is driving an extraordinary new wave of demand for high-density compute and AI-Ready data center infrastructure. The first episode of another two-part series, Kevin is joined by self-professed “data center nerd” Vlad Galabov - the host of The AIDC Debate podcast - to unpack the trends driving the AI infrastructure boom and what it could mean for future of the data center industry. Drawing on Vlad’s background in semiconductors and data centers, alongside his role as one of the industry’s leading analysts, the conversation explores his journey into data centers, the extraordinary scale of the global AI opportunity, and how AI infrastructure spending could reach 3.5 trillion dollars by 2030. Featured guest: Vlad Galabov, co-host of The AIDC Debate Key Quote: “The reason why we need this compute capacity is because we’re requiring it. It's somehow in our nature that we want the best." Key takeaways: - What is driving the rapid growth in AI compute demand. - How semiconductors, cloud infrastructure and data centers fit into the wider AI ecosystem. - Why investment in AI infrastructure is accelerating. - The role of resilient supply chains and AI-Ready capacity in future growth. - What the scale of the AI boom could mean for the next generation of AI Factories. Stay tuned for part 2 of the discussion, looking at where the next generation of data centers could be built and how the industry may need to respond to AI-driven growth. Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people shaping the technologies and infrastructure behind AI.
20 Minuten
AI infrastructure is increasing electricity demand at a speed and scale few industries have experienced before. In this episode, Kevin Brown speaks with Mandar Pandit, Chief Strategy and Growth Officer for Data Centers at GE Vernova, about how the energy sector is responding as individual data center campuses begin to require as much power as a small city.
Mandar explains why limited grid capacity and lengthy interconnection timelines are leading more operators to consider behind-the-meter generation. By producing power on site, data centre developers can take greater control of their deployment schedules. However, this also means assuming responsibilities traditionally managed by utilities, from power generation and protection systems to grid stability and overall energy management.
The conversation also explores the technologies that could support this growth more sustainably, including hydrogen-capable gas turbines, carbon capture and small modular nuclear reactors. Rather than viewing AI solely as a threat to the electricity system, Mandar argues that it is exposing existing weaknesses, accelerating innovation and encouraging data centre operators, utilities and technology providers to collaborate in new ways.
Featured guest: Mandar Pandit, Chief Strategy and Growth Officer for Data Centers, GE Vernova
Key quote: “The AI boom is highlighting the weaknesses in the system and showing us where, as an industry, we need to focus quickly.”
Key takeaways:
AI data center campuses are increasingly requesting hundreds of megawatts or more than one gigawatt of power at a single location.
Grid infrastructure and data center development operate on very different timelines, creating an urgent need for alternative power strategies.
Behind-the-meter generation can help operators gain faster access to power by producing electricity on site.
Operating behind the meter is not a simple solution, as data centre operators must also manage protection, control, stability and other utility-like responsibilities.
Gas generation, hydrogen, carbon capture and small modular nuclear reactors could all contribute to a more diverse power mix for AI infrastructure.
Growing data centre demand is stress-testing the energy system, exposing weaknesses and accelerating investment, collaboration and innovation.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people building the energy and infrastructure systems behind AI.
Mandar explains why limited grid capacity and lengthy interconnection timelines are leading more operators to consider behind-the-meter generation. By producing power on site, data centre developers can take greater control of their deployment schedules. However, this also means assuming responsibilities traditionally managed by utilities, from power generation and protection systems to grid stability and overall energy management.
The conversation also explores the technologies that could support this growth more sustainably, including hydrogen-capable gas turbines, carbon capture and small modular nuclear reactors. Rather than viewing AI solely as a threat to the electricity system, Mandar argues that it is exposing existing weaknesses, accelerating innovation and encouraging data centre operators, utilities and technology providers to collaborate in new ways.
Featured guest: Mandar Pandit, Chief Strategy and Growth Officer for Data Centers, GE Vernova
Key quote: “The AI boom is highlighting the weaknesses in the system and showing us where, as an industry, we need to focus quickly.”
Key takeaways:
AI data center campuses are increasingly requesting hundreds of megawatts or more than one gigawatt of power at a single location.
Grid infrastructure and data center development operate on very different timelines, creating an urgent need for alternative power strategies.
Behind-the-meter generation can help operators gain faster access to power by producing electricity on site.
Operating behind the meter is not a simple solution, as data centre operators must also manage protection, control, stability and other utility-like responsibilities.
Gas generation, hydrogen, carbon capture and small modular nuclear reactors could all contribute to a more diverse power mix for AI infrastructure.
Growing data centre demand is stress-testing the energy system, exposing weaknesses and accelerating investment, collaboration and innovation.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people building the energy and infrastructure systems behind AI.
23 Minuten
Enterprise AI has moved rapidly from experimentation to implementation, but turning an idea into a secure, scalable and commercially viable solution remains complex. In this episode, Kevin Brown speaks with Chris Campbell, Senior Director, AI Solutions at World Wide Technology, about how organisations can identify valuable AI use cases, prepare their data and determine where their workloads should run.
Chris shares lessons from WWT’s own AI adoption journey, including its evaluation of approximately 70 initial use cases and the development of tools designed to improve internal efficiency. He explains why successful AI projects require more than access to a model or computing power. Organisations also need strong executive sponsorship, clearly defined business outcomes, reliable data and an infrastructure strategy aligned with the needs of each workload.
The conversation then turns to hybrid AI infrastructure and the growing role of neocloud providers. Chris explores the trade-offs between public cloud, private infrastructure, colocation and rented GPU capacity, as well as how neoclouds can provide a bridge while enterprises develop their longer-term facilities. With significant new data centre capacity under construction, he argues that the industry is still at the beginning of a much longer transformation.
Featured guest: Chris Campbell, Senior Director, AI Solutions, World Wide Technology
Key quote: “We’re in the first mile of a marathon that’s never going to end.”
Key takeaways:
Successful enterprise AI programmes begin with clearly defined business problems and measurable outcomes, rather than technology alone.
A cross-functional AI centre of excellence can help organisations assess use cases, data readiness, risk and potential return.
Clean, accessible and well-governed data remains fundamental to moving AI projects from pilot to production.
AI workload placement should reflect factors such as security, data sovereignty, cost, performance, scalability and time to deployment.
Neocloud providers give organisations access to dedicated GPU capacity without requiring them to build and operate infrastructure immediately.
A hybrid approach can allow enterprises to use rented capacity as a bridge while developing longer-term private, hosted or on-premises AI environments.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people turning AI ambition into operational infrastructure.
Chris shares lessons from WWT’s own AI adoption journey, including its evaluation of approximately 70 initial use cases and the development of tools designed to improve internal efficiency. He explains why successful AI projects require more than access to a model or computing power. Organisations also need strong executive sponsorship, clearly defined business outcomes, reliable data and an infrastructure strategy aligned with the needs of each workload.
The conversation then turns to hybrid AI infrastructure and the growing role of neocloud providers. Chris explores the trade-offs between public cloud, private infrastructure, colocation and rented GPU capacity, as well as how neoclouds can provide a bridge while enterprises develop their longer-term facilities. With significant new data centre capacity under construction, he argues that the industry is still at the beginning of a much longer transformation.
Featured guest: Chris Campbell, Senior Director, AI Solutions, World Wide Technology
Key quote: “We’re in the first mile of a marathon that’s never going to end.”
Key takeaways:
Successful enterprise AI programmes begin with clearly defined business problems and measurable outcomes, rather than technology alone.
A cross-functional AI centre of excellence can help organisations assess use cases, data readiness, risk and potential return.
Clean, accessible and well-governed data remains fundamental to moving AI projects from pilot to production.
AI workload placement should reflect factors such as security, data sovereignty, cost, performance, scalability and time to deployment.
Neocloud providers give organisations access to dedicated GPU capacity without requiring them to build and operate infrastructure immediately.
A hybrid approach can allow enterprises to use rented capacity as a bridge while developing longer-term private, hosted or on-premises AI environments.
Follow or subscribe to The Machine Room on your preferred podcast platform for more conversations with the people turning AI ambition into operational infrastructure.
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Über diesen Podcast
The Machine Room is a brand-new podcast series from Schneider
Electric, exploring the challenges and trends facing the future of
the global digital infrastructure industry.
Led by Schneider Electric’s SVP and CMO for the Data Center
business, Kevin Brown, hear senior leaders from key industry
players for conversations that share insights into the heart of
data centers and AI infrastructure - exploring the future of energy
technology, and the power of digital innovation.
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