Whither tech stocks? Should we ask ChatGPT? – Viewpoint
The sustainable investor for a changing world
Shares in technology companies have often, rightly or wrongly, been tied to megatrends – rising as the wave grabs attention and deflating when reality sets in – but the furore over artificial intelligence and generative AI apps such as ChatGPT looks to be different, more pervasive. What can investors do? Pamela Hegarty provides some guidance.
After a steep run-up, memories of the turn-of-the-century ‘dotcom bust’ are revived easily, but in fairness, many tech companies are now on a sounder footing, including when it comes to profitability and capital expenditure cycles. For context, the NASDAQ index is up some 28% this year, but still down more than 10% from the start of 2022. That is hardly a bubble.
It is essential to distinguish between companies that benefit from technological megatrends in the long run and those that have merely been swept to the surface by the latest tech wave. The key is to recognise the fundamental drivers that are unaffected by hypes. One such factor is the dynamic development of the semiconductor industry and, yes, AI (Artificial Intelligence) is a factor here too.
In the near term, semiconductor demand remains in a cyclical downturn. Sales of personal computers and smartphones have been lagging. Hopes for a mid-year inflection point have been pushed back. This could weigh on share prices across the industry, especially after the strong recovery already seen in so far this year.
Longer term, the main driver of increased demand for microchips is the digital transformation. Semiconductors are a fundamental technology for cloud applications, automation and the Internet of Things. They are used in products ranging from large computers and smartphones to diversified industrial, car and high-performance products.
Overall, we believe generative AI will accelerate the adoption of artificial intelligence.
The rise of machine learning (where an AI model learns from the data) versus the legacy rules-based version of AI (where specific rules are coded) began to really speed up around 2010 with the confluence of low-cost computing and storage (via the cloud), massive data sets (via the internet and social media) and advancements in algorithms in the AI field.
AI is likely to be embedded widely across industries to boost productivity through automation and robotisation and to speed innovation. Market leaders in software services, IT applications and beyond have begun leveraging the technology.
AI has recently hit an inflection point in capability and use cases. Applications include virtual assistants, recommendation systems on e-commerce platforms, fraud detection at financial institutions, autonomous vehicles, customer interaction and self-service, image and facial recognition, medical diagnosis and healthcare systems, and many more areas.
The latest McKinsey Global Survey described the growth of generative AI tools as explosive: one third of survey respondents said their organisations were using gen AI regularly in at least one business function and 40% of respondents said their organisations would increase investment in AI overall because of the advances in gen AI.
Recent developments in generative AI will create considerable demand for semiconductors as computing resources rise exponentially with successive iterations of generative AI systems.
The global AI software market had a market size of USD 9.5 billion in 2018 and is forecast to grow to USD 118.6 billion in 2025. The chatbot market is forecast to reach around USD 1.25 billion in 2025, up from USD 190.8 million in 2016, according to Statista.
The greater prominence of AI, on the back of the increasingly practical uses and apps for generative AI, has arguably created investment opportunities, although we must not lose sight of the significant changes it could bring about in our daily lives. There are implications not just for semiconductor demand and efficiency, but also for jobs and the environment.
As an example, the help that ChatGPT can give students might undermine the business model of education technology companies, but business information providers could benefit as AI allows them to combine private and public data in a way that boosts the productivity of their customers, justifying higher fees for their services.
On the environmental side, there could be pros and cons as well: the expansion of computing power will be energy and emissions intense, but optimisation and efficiency gains in business and daily life could help save energy and support (the development of) green solutions.
The possible wide applications of AI demand that an assessment of AI-related risk be further embedded into our fundamental investment and sustainability analysis. For industries such as energy, healthcare, professional services, and media and advertising, it is worth thinking about how to incorporate AI into our assessment frameworks, both in terms of risks and benefits.
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