Investment Institute

AI impact is emerging in stages and Asia stands to benefit

  • 24 August 2023 (7 min read)

Robust year-to-date performance has pushed the technology sector firmly back into the limelight.

Arguably the main driver behind this has been the intense focus on artificial intelligence (AI), chiefly spurred on by the launch of ChatGPT in November 2022, a chat interface built by OpenAI, which allows the public to interact with its AI model.

The introduction of the conversational engine created a groundswell of interest in generative AI - even though the technology is not new, its potential remains a mystery to many. The roots of AI date back to British mathematician Alan Turing’s 1950 paper, Computing Machinery and Intelligence, while the 1956 Dartmouth Summer Research Project on Artificial Intelligence was a seminal event, where the term ‘artificial intelligence’ was coined.

AI is about a computer’s capability to think and learn – to carry out tasks and mimic cognitive functions usually associated with human beings. Familiar 21st century AI developments are virtual assistants like Amazon’s Alexa or Apple’s Siri, which are subsets of so-called machine learning (ML), where neural networks attempt to simulate human knowledge acquisition. It involves training models to make predictions based on data. But it is generative AI, which can produce a vast array of content including text, images and videos – and led by applications like DALL-E and ChatGPT – that is creating the current buzz.

A few key factors have led to the latest AI breakthroughs: an increasing pool of available data to train the models; access to vastly improved cloud and mobile storage; and advances in raw computing power to scan massive amounts of data. These models are effectively algorithms that take an input and create an output. They are trained using large amounts of data – and the greater the quantity and quality of data, then the more useful the outputs.

Vitally, there needs to be enough computing power to train and run the algorithms. The training process is highly iterative, and requires massively parallel computing, fulfilled by advanced chips produced by companies such as Nvidia.

The pace of technological change in AI is breathtaking; however, its benefits are set to come in stages and we are currently only at the inception of this potential secular shift. In addition, we are still at the early stages of AI investing, with the focus primarily on building AI. Suppliers of materials or equipment for semiconductor manufacturing and semiconductor manufacturers themselves in general are the immediate big winners of generative AI, as more computing, memory and networking solutions are required.

Over time, the leadership is expected to change, and the pool of beneficiaries will deepen, as AI technology flows into operations across industries. Infrastructure and device makers will likely see the next profit wave - those who build the surrounding hardware and software architecture for computer processors, graphics processing units, and other specialised processors. The third wave is expected to come later from companies which create usable applications on top of this infrastructure – and then companies that benefit from associated productivity gains from those applications.

We could see AI packaged as a service, successfully addressing specific needs, and creating significant value. Today's AI training stage precedes potentially significantly larger future revenues from model deployment and AI inference. Enterprises will likely re-architect around AI, and companies that are quick to understand how to harness AI are likely to outgrow those that are not.

Asia stands to benefit from its unique position in each of those stages of development. Taiwan, Korea, and Japan are likely the biggest beneficiaries of generative AI-led first stage of development, with their high-end chip manufacturing capabilities and cloud service.

Data advantage or perhaps differentiated data pools could set Asian companies on a different evolution path. Undoubtedly, there will be concerns about how AI interacts with data privacy from a responsible investment point of view. And until there is a global consensus on how this is regulated, Asian countries where data privacy is less of a focus may see a clear contrast with the West in how AI is used.

On the application side, AI is already being used across sectors, including healthcare. It is helping with administration tasks, clinical decision support and to increase system efficiency and improve patient outcomes. Emerging digital health ecosystems are already impacting more than a billion lives across Asia today, with prominent examples emerging in China, India, Singapore, and Indonesia. Take PingAn Healthcare and Technology as an example: The company has provided over 1.3 billion consultations since its inception in 2014 — this volume of information generated via the ecosystem has the potential to serve as a strong foundation to advance AI for diagnostics, treatment, and prevention.1

An upward trend for AI adoption and investment will likely continue in Asia - report showed that Southeast Asia companies are poised to invest 67% more in AI and ML in 2023 compared to previous year2 . AI has the potential to be transformational for companies: from boosting productivity to accelerating new product development, and in turn, contributing to the region’s GDP.

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