Businesses seem to have started to harness the potential of artificial intelligence (AI) to manage day-to-day operations. However, the question asked is how can the technology’s foundation benefit various data structures. From what it’s understood, the solution is present in AI chips, which refer to semiconductors that help develop AIs such as ChatGPT. “I believe AI chips are purpose-built hardware tailored for AI and machine learning (ML) workloads, offering performance, energy efficiency, and reduced latency compared to general-purpose chips. They are meant to be optimised for AI applications that are built using deep learning models,” Mohammed Imran, CTO, E2E Networks, a cloud computing company, told FE TransformX.
Market versus Cost
It is believed that AI chips help fasten the process of learning and evolution. Usage of AI chips can help provide benefits such as high processing speed, increased network bandwidth, and economical latency. Other reasons why corporations employ AI chips are because these help in decreasing energy usage pertaining to digital operations, which can help save costs. As a result, AI chips enable the management of mass data amounts and implementation of different algorithms. Blueprints behind AI chips are designed for handling its features, which include deep learning and ML. Advantages associated with the influence of AI chips over AI landscape are growth in deep learning prospects, speeding up of AI appliances’ creation, and development of real-time AI functions.
To be sure, different kinds of AI chips include Application-Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Central Processing Units (CPUs), and Field-Programmable Gate Arrays (FPGAs). Centre for Security and Emerging Technology (CSET), a policy analytics-based think tank, stated that the progress of current AI applications is based on a scale’s computational capacity. The think tank added that in order to prepare an AI algorithm, it can take computing time worth 30 days and expenses close to $100 million. The various expenses related to full-scale execution of an AI are software costs, training and maintenance costs, labour costs, hardware costs, among others.
“When AI chips get ingrained into use cases ranging from factory automation to traffic control, it should influence the cloud computing market as well as the ML models. That way sensor or data capture nodes on fields that are AI enabled are expected to be seamless and scalable. The AI chipmarket by 2040 has the potential to be at least a half a trillion dollar market spanning all facets of life,” Ajit Thomas, co-founder and CMO, Cavli Wireless, an Internet of Things (IoT) company, specified.
In terms of costs of making an AI chip, Nvidia, one of the leading players in the business, has priced its A100 GPUs between $10,000 to $18,000. The newly created H100 is seemingly available at an over twice as expensive price. Reportedly, OpenAI, an AI company, depended on close to thousand Nvidia A100 Tensor Core Chips for preparing the GPT-3 and GPT-4 AI drives. Estimates have shown that for coaching a large language model, such as GPT-3, expenses can be as high as four million dollars.
Global numbers, in the making
In accordance with Allied Market Research, a market research firm, the international AI chip market had a $14.9 billion valuation in 2022 and is expected to reach $383.7 billion by 2032, at a 28.2% compound annual growth rate (CAGR) between 2023-32. The firm also mentioned that the United States of America (USA) is the maximum occupier of the North American AI chip market, which is expected to grow between 2023-32. In Europe, the United Kingdom (UK) clocked the highest amount of revenue in 2022, However, for the given timeline, Germany has been backed to overtake the UK, at a 39.3% CAGR. In Asia-Pacific, China is expected to dominate the AI chip market, on account of increase in investments and government bodies while the Latin America, Middle East And African (LAMEA) AI chip market should grow at a 42.2% CAGR for 2023-32.
“While underscoring India’s role in the technology landscape, the summit also recognised the potential of AI in advancing UN Sustainable Development Goals (SDGs) and addressing global challenges. With global leaders committing to implement the G20 AI Principles, they should facilitate collaboration between AI innovators and problem solvers,” Sanjay Gupta, chairman, India Energy Storage Alliance (IESA), an energy-oriented industry body, highlighted.
According to BlueWeave Consulting, a market intelligence firm, the Indian AI market is estimated to reach $3,996.51 million by 2029, at a 32.26% CAGR between 2023-29. Industrial evaluation has shown that the Indian AI sector is backed by high data volumes created by businesses, along with impact of data analytics and Internet of Things (IoT). For example, the Indian government unveiled the National Strategy for Artificial Intelligence (NSAI) initiative to help with Indian AI developments, and has plans to introduce AI in sectors such as education, agriculture, healthcare, among others.
In recent developments, Nvidia entered into partnerships with Tata Group and Reliance Industries to grow cloud computing, generative AI applications and language blueprints. From what it’s understood, Reliance will get access to Nvidia’s recent edition of Grace Hopper Superchip, which helps support ChatGPT and related softwares. Previously, SIMa.ai, a US-based AI chip startup, declared plans to introduce its TSMC 16nm technology-backed first-generation AI chip to India.
Furthermore, it is indicated as per market estimates that growth of the AI chip market will ascertain the overall AI future. As predicted by Gartner, a management consultancy company, AI chips will account for $53.4 billion revenue of the global semiconductor industry in 2023. In a blog post, Alan Priestley, VP analyst, Gartner, revealed that generative AI use cases and AI usage in data hubs, along with edge infrastructure and endpoint appliances’ requirement of GPUs and semiconductor devices, should facilitate the future of AI chips. Enterprise purposes are also expected to drive the AI chip market. “I believe AI chips have evolved to become the driving force behind innovation across various sectors. They are faster, energy-efficient, and affordable, which seem to be making AI adoption accessible. This democratisation of AI is considered to be fueling breakthroughs in healthcare, autonomous systems, finance, and beyond,” Ranjan Chopra, MD and CEO, Team Computers, an information technology (IT) consultancy, concluded.
