The global AI infrastructure market is projected to grow from USD 28.7 billion in 2022 to USD 96.6 billion by 2027, at a CAGR of 27.5% during the forecast period from 2022 to 2027.

The growth of this market is driven by factors such as increased data traffic and need for high computing power, increasing adoption of cloud-based machine learning platforms, increasingly large and complex dataset, growing number of cross-industry partnerships and collaborations, increasing adoption of AI due to the COVID-19 pandemic, and, rising focus on parallel computing in AI data centers.

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NVIDIA Corporation (US), Intel Corporation (US), Oracle Corporation (US), Samsung Electronics Co., Ltd. (South Korea), Micron Technology, Inc (US), Advanced Micro Devices, Inc. (US), International Business Machines (IBM) Corporation (US), Google LLC (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US) , SK Hynix, Inc. (South Korea), MIPS (US), Toshiba Corporation (Japan), Imagination Technologies (UK), Cambricon Technologies Corp. Ltd. (China), Graphcore (UK), Gyrfalcon Technology Inc (US) , Cadence Design Systems, Inc. (US), Tenstorrent Inc. (US), Cisco Systems, Inc. (US), Arm Limited (US), Dell Technologies (US), Hewlett Packard Enterprise (US), Synopsys, Inc. (US), and SenseTime Group Inc. (China).

Opportunity: Rising need for co-processors due to slowdown of Moore’s Law

Moore's law states that the number of transistors per square inch on integrated circuits will double about every 18 months until at least 2020. In April 2015, Intel Corporation stated that it could sustain Moore's law for another few years by developing 7 nm and 5 nm fabrication technologies. However, moving forward, it would be challenging to further reduce the size of processors; doing so would also reduce the space between electrons and holes, which will create problems such as current leakage and overheating in ICs. These problems would lead to slower performance, higher power consumption by ICs, and a further reduction in durability. Thus, the need to find an alternate way to increase the computational power of chips has fuelled the development of accelerators or co-processor chips, which are critical elements of AI infrastructure.

Challenge: Concerns regarding data privacy in AI platforms

AI has several applications in the healthcare industry. However, the adoption of AI in the industry is restricted to an extent owing to data privacy concerns. Patients' health data is protected under federal laws in many countries, and any breach or failure to maintain its integrity can result in legal and financial penalties. As AI used for patient care requires access to multiple health datasets, it is essential for AI-based tools to adhere to all data security protocols mandated by governments and regulatory authorities. This is a challenging task as most AI platforms are consolidated and require extensive computing power, owing to which, patient data or parts of it can be required to reside in a vendor's data center. This is a major challenge in the market.