3. Efficiency software
The growth of automation – be that at home or in industry – relies on software. Historically, industrial software, which can be used to operate machines, worked on a buy, install and use forever model. Today, that is increasingly shifting to a software as a service (SaaS) model, through which businesses pay a subscription fee to use the software, with the software itself stored in the cloud, or in a hybrid environment. The industrial software market could more than double to over USD250 billion by 2027, representing a CAGR of 15 per cent.4 This should lead to efficiency gains throughout the process from product design and simulation, to sending the blueprint to the factory or manufacturing partner and optimising the supply chain. Cloud based solutions help reduce the cost and complexities of managing the software itself and also enable data centralisation. Data analytics on top of that is growing strongly as more and more data is generated.5 Business process automation is also growing in popularity, the process through which companies use software solutions to boost the productivity of white collar workers. Furthermore, advances in AI are enabling companies to use large amounts of data to improve operational efficiency.
4. AI and compute
As machines become increasingly sophisticated, they need more processing power to compute and process data. AI is particularly resource intensive, requiring vast amounts of data and processing power to create new content. That means more sophisticated semiconductors. Manufacturers of computing processors appear to be natural beneficiaries of the expansion of AI, but large language models (LLMs) also require other types of chips, such as those that boost memory capacity and bandwidth. Memory, compute and storage semiconductors account for the majority of semiconductor sales. Memory (“DRAM”) and storage (“NAND Flash”) chips are primarily used for storing data and instructions, while processing chips (such as the core “CPU” in a computer or a complementary accelerator chip like a “GPU”) are used for performing calculations and processing data in real time. Also important in the semiconductor food chain are electronic design automation (EDA) companies, like Synopsys, who provide software solutions for the chip designers. The level of innovation and incorporation of AI into the software enable the chip designers to speed up the design phase and improve the power and compute efficiency. Furthermore advances in AI should boost the prospects of semiconductor equipment companies. They provide the chipmaking tools that produce smaller, faster, cheaper, more powerful and energy-efficient microchips. Semiconductor manufacturing plants (known as “fabs”) are some of the most highly automated factories in the world, and in turn require increasing usage of AI processes to improve yields and output.
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