Artificial Intelligence

We power AI - from grid to core

The rapid advances in artificial intelligence (AI) applications have drastically increased the energy demand in data center. This introduces a paramount challenge: improve the scalability of AI technologies while maintaining environmental responsibility. This article provides an overview of solutions to address this challenge.

Internet of Things
Edge AI
Energy
Data centers
Article

Jul 07, 2025

Fueling the Future: Why data is the lifeblood of Artificial Intelligence

AI's role in modern technology is transformative, and it fundamentally relies on the capabilities provided by semiconductors. Semiconductors are at the heart of AI; they help to power, collect, process and manage the vast amounts of data that AI systems require to function. This includes everything from basic computations to complex machine learning tasks that enable AI to 'learn' from data.

Since 2010, the amount of data generated annually has grown year-over-year, starting at 2 zettabytes. By 2025, the volume of data is expected to increase to 175 zettabytes – a more than 145-fold increase in 15 years only.

The importance of data in Artificial Intelligence cannot be overstated. In fact, Artificial Intelligence, particularly machine learning, relies heavily on vast quantities of data to train its models, which can encompass a wide range of formats, including text documents, images, and sensor readings such as temperature and humidity. By analyzing this data, AI systems can identify patterns and relationships, which they then use to make predictions, decisions, and generate outputs. The type of data used to train an AI system is closely tied to the specific task for which the AI is being developed. For instance, a text generation AI system, such as a large language model, requires large amounts of text data to function effectively, whereas predictive analytics around road traffic might rely on sensor data to make accurate predictions. Ultimately, the quality and quantity of the data used by an AI system have a direct impact on its accuracy, reliability, and performance.

Powering the pulse: Data centers are the backbone of AI

Data centres are playing a critical role as the backbone of Artificial Intelligence. They must process these immense streams of data around the clock. We see that AI, and generative AI in particular, will accelerate this data growth and this ever-increasing demand for data requires seamless connectivity, higher bandwidth, wide-area coverage, and lots of computational power.

The rapid advancements in artificial intelligence (AI) applications have drastically increased the power demands within data centers. The reason why training and executing AI models requires energy lies in the computing power required for the complex calculations of the machine learning algorithms. The computing power required to train modern AI models has doubled approximately every 3.4 months since 2012. This exponential growth in computing requirements is directly reflected in higher energy requirements and increases the overall load on the network. The more complex the calculations, the more energy is required. This is not just about the power consumed by the processors themselves, but also the infrastructure required to support them, including cooling systems and power supply networks.

Data centers
Data centers
Data centers

According to the latest Energy and AI report published in April 2025 the Lift-Off trajectory sees global electricity demand from data centres and their necessary infrastructure exceeding the 1700 TWh mark in 2035. Compared to ~460 TWh in 2022, which is around 2% of global electricity demand, this is a 370% increase within 13 years. 

Simply put, there is no AI without power.

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Beyond the buzz: The carbon footprint of AI

So, behind the brilliance of AI lies a computationally and power-intensive process - with a staggering carbon footprint. As we expand AI capabilities, we need to be aware of the massive energy consumption of AI data centers, which often comes from non-renewable energy. Additionally, AI data centers require extensive cooling mechanisms to prevent overheating, which often leads to substantial water usage. The water consumption for cooling these massive data centers can rival that of small cities, putting additional pressure on local water resources.

Data centers
Data centers
Data centers

Given these dynamics, we must focus on enhancing energy efficiency. This means for example developing more efficient AI algorithms or optimizing data center infrastructure by implementing innovative and energy efficient power management solutions that significantly reduce power delivery network losses. Addressing these challenges is essential for environmental sustainability and ensuring the economic viability of scaling up AI technologies.

The need for energy efficiency in powering AI – from grid to core

Data centers
Data centers
Data centers

There are many solutions possible to this new challenge. What is key: Solutions must cover energy conversion   from the grid entering the data center to the core, the AI processor. 

Our innovative portfolio of power semiconductors includes solutions ranging from the grid entering the data center to its core, the AI processor and leverages the benefits of Si, SiC and GaN to achieve the highest efficiency, density, and robustness.  Examples of such applications include top-of-the-rack switches, power supply units, battery back-up units, DC-DC networking and computing (like 48 V Intermediate Bus Converters, power modules and discrete solutions), and protection. Additionally, with our novel power system reliability modeling solution data centers can maximize power supply reliability and uptime, enabling real-time power supply health monitoring based on dynamic system parameter logging.

Data centers
Data centers
Data centers

Worldwide, energy savings of around 48 TWh could be achieved with various types of these advanced power semiconductors. This corresponds to more than 22 million tons of CO₂ emissions, according to Infineon analysis.

Powering a sustainable future: Meeting the challenge of data center power demand

Looking into the future there are many technological challenges ahead we need to address all while continuously enhance energy efficiency and performance. We need to foster bringing clean and reliable energy to the AI data centers. It is about enabling the sustainable growth of AI technologies in a way that is compatible with our environmental responsibilities. After all, there’s no AI without power. This reality drives us to keep advancing our technologies, ensuring that as AI evolves, our solutions for powering it efficiently and effectively evolve as well.