Introduction and overview of the McKinsey report

The McKinsey Technology Trends Outlook 2024 sheds light on which technological developments are currently particularly significant and how they are influencing various industries. McKinsey produces this report annually to provide companies with an overview of the most important technology trends and their long-term impact. This year, the focus is on topics that are fundamentally changing our daily lives and work, such as:

• Artificial intelligence (especially generative AI)
• Electrification and
• Renewable energies

For us as a provider of storage and server solutions, the report provides valuable insights that help us to better understand future trends in the hardware market - especially in the area of storage and servers. Although we cannot make exact predictions about prices or availability, the report gives us a good big picture from which we can deduce where the market is heading.

In this article, we summarize the most important findings of the McKinsey report in a compact and practical way. If you would like to read the full report, you will find the link below**.

Research methodology - How McKinsey gains its insights

McKinsey analyzes patents, research, search queries, news, and investments. In addition, executives around the world are surveyed to understand the extent to which these technologies are already in use. This creates a comprehensive picture of the most important technology trends.

**Link to the McKinsey Technology Trends Outlook 2024: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech

Overview of the report and relevant technology trends

The McKinsey Technology Trends Outlook 2024 highlights 15 key technology trends, including:

• Artificial intelligence (AI)
• Advanced connectivity
• Embedded solutions

In particular, the rapid development of generative AI and the growing need for edge and embedded solutions are driving demand for high-performance hardware. Despite difficult market conditions, the demand for computing power and storage solutions remains unbroken, as real-world use cases - particularly in the field of AI and machine learning - are constantly increasing.

Generative AI: what it is and why it's important

Generative AI (gen AI) is a technology that is making great progress and significantly expanding the limits of what machines can do. Gen AI models are trained with huge, diverse data sets. They take in unstructured data (such as text, images or videos) and generate completely new content based on it - also unstructured. This includes text, images, music and even 3D models.

What is an example of gen AI
One example is OpenAI's GPT-4, which produces human-like texts, or DALL-E 3, which can generate photorealistic images based on text descriptions. These technologies make it possible to create completely new content based on existing data.

How gen AI has developed since 2022
This technology has received an enormous boost since 2022. The increase in public awareness and the growth in investment is particularly remarkable. McKinsey reports that Google searches for Generative AI have increased by 700%. The number of job vacancies in this area has also exploded.

In which areas gen AI is used
Companies around the world are integrating gen AI into their software tools to automate or improve various tasks. One application example is AI-based chatbots in customer service, but gen AI is also used for advertising campaigns, drug discovery and much more.

The impact of technological advances on companies' infrastructure

Increasingly complex content and larger amounts of data. Rapid progress in the field of generative AI means that language models (LLMs) can now process far more than just text - images, videos and audio files are also included. These developments are putting enormous pressure on infrastructure, which is why companies around the world are increasingly investing in high-performance data centers and storage solutions to provide the necessary computing power.

How investments in the gen AI sector are developing

The market for generative AI is booming - and this is clearly reflected in investments. In 2023, over 40 billion US dollars were invested in AI technologies. This investment reflects the expectation that Generative AI can speed up many processes and make them more efficient, which is particularly beneficial for data-driven companies. The demand for high-performance hardware that supports these applications is increasing accordingly.

Which industries use AI and how it is used there

• Financial services: ING is improving customer service with the help of AI, which has significantly reduced waiting times for customers.
• Biotechnology: The company Recursion uses AI to accelerate the development of new drugs and discover potential active ingredients more quickly.
• Marketing: Itaú Unibanco, one of the largest private banks in Latin America, used AI in an advertising campaign for women's soccer to generate creative content based on historical events.

What role gen AI will play in the future and what challenges it will pose

Generative AI will continue to play a central role in the future. Companies that integrate this technology at an early stage can secure decisive competitive advantages. At the same time, they will have to adapt their capabilities in order to keep pace with rapid technological developments. An exciting question remains as to whether open or proprietary solutions will prevail on the market.

What implications this has for hardware requirements

SSD

Bigger and faster - this is how the SSD trend can be summarized by the demand for AI. PCIe 5.0 and soon PCIe 6.0 provide the necessary speeds to process the growing volumes of data efficiently. SAS and SATA are becoming less important as they do not meet the performance requirements. NVMe SSDs are becoming the standard as they are faster and more reliable. At the same time, the demand for larger capacities to handle AI workloads is increasing.

RAM

Higher capacity and speed are crucial for processing the large amounts of data generated by Generative AI in real time. DDR5 offers higher bandwidths and lower latencies, which increases the demand for high RAM capacities, especially in data centers and for cloud providers. Low latency in combination with high bandwidth is also becoming increasingly important for AI workloads.

Server

More performance and flexibility. This is how the trend in the server sector can be summarized due to the increasing demand for Generative AI. AI models require massive computing power, especially from GPUs, in order to process the huge amounts of data efficiently. The focus is therefore on GPU-optimized servers with high scalability in order to process AI workloads flexibly and efficiently.

To what extent Supermicro's H13 series is suitable for gen AI
Supermicro's H13 series, especially the GPU servers and BigTwin® systems, are currently of particular interest for generative AI applications. They are based on the latest AMD EPYC™ processors and offer high core counts and energy efficiency - ideal for parallel computing tasks and GPU-supported workloads. However, a look into the future shows that the H14 series with the AMD EPYC 9005 ("Turin") processors will be launched at the beginning of 2025, which should bring another significant performance boost.

To what extent Supermicro's X14 series is suitable for gen AI
At the same time, the X14 series based on the latest Intel Xeon Scalable processors offers a powerful alternative. In the future, Intel will change its naming strategy and the upcoming platform, based on the Sierra Forest and Granite Rapids architectures, will be called Intel Xeon 6 Processors. With support for PCIe 5.0 and DDR5-6400 as well as optimized architectures for high-performance computing and AI workloads, the X14 series is ideal for companies that prefer Intel-based solutions.

The Supermicro universe offers a wealth of options, and our specialized Custom Server Solutions business unit not only advises customers on the selection of suitable systems, but also provides individual configurations and assemblies to meet the specific requirements of Generative AI.