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UPS: rethinking backup power for AI

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Podcast

Aug 04, 2026

Podcast

In this episode of Podcast4Engineers, host Peter Balint speaks with Nancy Wang, Director of Application Marketing for UPS at Infineon, about why backup power is becoming a critical enabler of AI infrastructure. They explore how AI data centers are driving new approaches to uninterruptible power supplies (UPS), distributed battery backup, high-voltage DC architectures, and layered energy storage to meet increasingly dynamic power demands. The discussion also looks ahead to how future data centers will evolve from passive energy consumers into active participants in the power grid, making intelligent power management just as important as computing performance.

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Speakers

In this episode of the Podcast4Engineers, host Peter Balint speaks with Nancy Wang, Director of Application Marketing for UPS at Infineon.

Peter Balint

Host:

Peter Balint has shaped visual and audio narratives at Infineon since 2021. He’s a video producer with 20 years of experience and has produced podcasts for the past 10 years. Over his career, Peter has interviewed speakers from all over Europe, bringing high-quality media production and engaging conversations to the forefront of his work.

Nancy Wang

Guest:

Nancy Wang is a technology strategist with more than 20 years of experience bringing emerging technologies to market across software, entrepreneurship, and semiconductors. She currently serves as Director of Application Marketing for Uninterruptible Power Supplies (UPS) at Infineon Technologies, where she focuses on backup power systems for AI data centers and mission-critical infrastructure.  After beginning her career as a Software Engineer at IBM, she spent more than 10 years building and growing technology companies before joining Infineon in 2022. Since then, she has held product and application marketing leadership roles, helping shape semiconductor solutions for AI and industrial applications.

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Guest: You can design or develop the fastest processor in the world, but if you don't provide it with clean, efficient, and reliable power, then the performance doesn't matter.

 

Host: Hello and welcome to this episode of Podcast4Engineers. It's the podcast you just have to listen to if you're interested in what's happening in the semiconductor market. I'm your host, Peter Balint, and today we're talking with Nancy Wang. She's the Director of Application Marketing here at Infineon, specifically for UPS. So welcome and thanks for joining today.

 

Guest: Thanks, Peter. Thanks for having me. I'm happy to be here.

 

Host: So the first question I have is everyone's talking about AI and GPUs and the energy required. to power these AI data centers. But we're looking at something that's maybe not talked about so often, and this is backup energy. From your perspective, in your role here at Infineon, why is this an important topic to look at and why should this get the attention of our audience right now?

 

Guest: Right. One of the least visible, but perhaps the most important part of AI infrastructure is the UPS, so the uninterruptible power supply. I like to think of it as the seatbelt of the data centers, because no one really buys a car for the seatbelt, but when you get into trouble, that is now the most important system in the vehicle. If you lose power for just a few milliseconds, tens of thousands of GPUs may have to reset. That means that days of computing might have to be lost. This is no longer just an engineering issue, it is a business issue. And the importance of it will continue to increase because in the future, AI systems may be used to run hospitals or support the power grids and other very critical services. And in these situations, 5 nines of availability are not just desirable, it is a necessity. So 5 nines mean 99.999% of uptime. So, this means that when a UPS is doing its job perfectly, no one notices it. But in many ways, it is just as important as compute.

 

Host: Yeah, that's a great analogy about the seatbelt. I think that's a really good point to drive this point home. Thanks for that. We talked a little bit before the show, and the one thing that you mentioned, which I think was kind of interesting, was that AI is moving so rapidly. And one of the side effects of this is the terminology seems to be in a sort of an unsure state. Nobody's sure they're referring to the same things when they refer to an item or a process. So, can you talk about that a little bit?

 

Guest: Yes, of course. Some years ago, it was quite easy to describe backup. It was just one large UPS sitting in a power room, and that supported the entire data center. But what we're seeing is that moving forward, it's becoming a little bit more distributed and layered. You might have capacitors, rack batteries, you might have distributed UPS systems, campus scale energy storage, or even assets that support the grid, and they're all working together. So, terms like BBU, UPS, ESS, they are starting to be used interchangeably depending on who you're speaking to and where you are in the world. What I find is more useful is actually to focus on the function and not so much on the label. If someone says UPS or calls it whatever they want, you ask 3 questions. How fast does it respond? How long does it provide backup for? And where does it sit in the power chain? And the answers to these questions are a lot more useful and much more relevant than the name on the box. So, what we're also seeing though is that there are regional differences. So those in the US and in Europe, they tend to follow more of the Open Compute Project. But in Greater China, they are targeting the similar goals, but the implementation path may be slightly different. The terminology will likely continue evolving for some time.

 

Host: Okay. And I guess as rapidly as things change, the terminology sort of follows along.

 

Guest: Yes.

 

Host: So with AI, with the advent of AI, we're really just at the beginning of all this, but we see increasing workloads in terms of what is expected out of a data center, for example, or out of a rack from a data center. This is all on the rise. Is it possible to look at today's demand in a data center for backup energy and say that the solutions of today can work in the future, that they're scalable, or do we need a whole new kind of architecture here?

 

Guest: The biggest change in AI data centers is that not only is it using more power, but it's using the power differently. So traditional data centers are like cars driving on a highway at a very steady speed, but AI data centers are like cars racing through city traffic and constantly accelerating and braking. So backup power is no longer just responsible for managing the outages, it also has to manage the volatility of these rapid swings in power demand. So, what we're seeing is that there are layers that are needed to fulfill this need. The challenge, though, however, is that these disturbances tend to happen at different timescales. So high-frequency disturbances, so in the microseconds or milliseconds, need to be handled close to the GPU, whereas lower-frequency disturbances, so in the seconds, or minutes can be handled by larger energy storage further away. What we're seeing is a layered approach to backup power. For a more specific example, you might have a rack-level BBU that supports seconds of backup to the GPU. You might have a distributed UPS that supports minutes of backup to a pod of IT racks. You might have a central UPS that bridges a little longer until the generators kick in, and you might have a utility-scale BESS that can support the facility and the grid for hours. The future is not one single UPS, it is multiple layers of backup, and each layer is designed specifically for a particular timescale.

 

Host: Okay, so this represents just another layer of complexity when we're talking about AI in a data center.

 

Guest: Yeah.

 

Host: Amazing. What we're hearing now is that it's inevitable that we're going to be going towards high voltage inside the facility and to the rack, DC as opposed to AC. What does this change in terms of this backup energy supply?

 

Guest: So yeah, so data centers in the past distributed AC, but batteries are inherently DC devices. And the GPU also runs on DC. All this kind of AC to DC to AZ conversion is losing energy, it's adding costs, complexity, as well as potential failure points. And this is why a lot of hyperscalers are investigating high voltage DC distribution. So plus minus 400, 800, or 1,500 V DC. So higher voltage means lower current, means less conduction losses, it means less need for copper. This of course also changes the role of UPS. As we switch to DC distribution, we'll start to see more DC-coupled UPS architectures and less of these traditional double conversion UPS.

 

Host: Okay. So we could say with every stage of power conversion, there are losses. And if we don't have to convert from AC to DC, then this eliminates at least one of the areas for loss.

 

Guest: Yes. And that's not the only benefit. So, efficiency is not only the benefit. This also enables more modular designs. It also allows operators to have flexibility to decide where to attach the backup. It could be on the DC bus, it could be next to a solid-state transformer, or could it be even further upstream at the medium voltage interface and be part of a larger energy storage that can interact with renewables or even provide services to the grid. There is, however, one very important caveat. DC distribution does not mean that the entire data center is on DC. There are still critical systems, especially cooling, that require AC power. This is why AC UPS together with DC UPS will coexist for at least some time.

 

Host: Okay, so are these AC systems that are required, are those also supported by UPS?

 

Guest: Yes. They are AC double conversion UPS and also DC coupled UPS.

 

Host: Ok. So, I have to wonder, the one thing that we have established here is that things are changing at such a rapid pace. I wonder how it is for you, for example, or Infineon to keep up with the changes. I mean, how do you know what's happening in the industry next? You have to be guided in some way, right?

 

Guest: Yeah, so one of the advantages that we have here in Infineon is that we don't just see one piece of the puzzle, we actually see the entire power chain. From the grid infrastructure through all of the systems, all the way to the processor. And this is because we are working deeply with the industry, working with hyperscalers, system designers, and this gives us— unique visibility on how the whole energy ecosystem and see where it's heading and much sooner than before it becomes mainstream. This then allows us to take these insights and translate them into products. So actually, I have here today an example. In my hands here is part of our Easy Power Module family. This is the EasyPACK™ HD3. It has an integrated 3-level AMPC topology using our latest silicon carbide and IGBT technologies. It also has a baseplate which is optimized for cooling liquids. This is good for compact, high-efficiency UPS building blocks up to 200 kW.

 

Host: It's pretty clear and it's pretty public that in the future for power generation that maybe these AI data centers will be responsible for generating and managing their own power. What do you have to say about that?

 

Guest: To some extent, that would be true. We are already starting to see this large-scale energy storage in the next-generation data center plants. So pairing renewables such as solar, with energy storage is a good way to improve sustainability and economics. And some regions will also start to have regulations that encourage this approach. However, I don't believe that data centers will be fully self-sufficient. And this is because the scale of the power that is needed is just too large. An AI campus, for example, needs several megawatts, perhaps gigawatts of power, and this is comparable to a utility-scale power plant. The AI power demand is just simply too large. But what I do believe will happen is that data centers will become active participants of the grid instead of just consuming energy. It can store power, it can shift the load, it can also integrate with renewables and perhaps provide grid services back to the grid. It will not replace the grid, but it will work alongside the grid.

 

Host: And when you say store energy, you must be talking about batteries, right?

 

Guest: Yes. Well, massive blocks of batteries. True.

 

Host: Okay. And if we were to sit down in, let's say, for 5 years and maybe talk about this same topic, what do you think would shock you the most about what has happened or what might be reality?

 

Guest: I wouldn't be shocked, but I guess what we will all kind of see happening is that the power infrastructure becomes more strategic. So, in the past, progress in the data centers meant faster processors. Okay, but what AI is teaching us is that computing is only half of the equation. You can design or develop the fastest processor in the world, but if you don't provide it clean, efficient, and reliable power, then the performance doesn't matter. What I also believe will happen in the future is that we will no longer look at UPS as a box, The boundaries of what a BBU or UPS or ESS are will become blurrier. The functions are likely to merge and perhaps even be integrated into other systems in the power chain, and they will all kinds of work together as a coordinated energy platform. But perhaps what's most surprising or important to remember is that power delivery is no longer just supporting AI, it is determining what is possible. The AI revolution will not be limited by how fast we can compute, it will be limited by how intelligently we can generate, move, convert, and store energy.

 

Host: So maybe we should get together in 5 years and see where things are at, at that point.

 

Guest: That's right.

 

Host: Yeah. Thank you, Nancy, for taking the time to share a little bit about what you do on a daily basis with our audience.

 

Guest: My pleasure.

 

Host: And what you're doing at Infineon and what Infineon is doing. We appreciate all the information. And to our audience, I say thank you and stay tuned for more episodes of We Power AI. And thanks for watching the Podcast4Engineers.