We expect AI to sustain structural double digit earnings growth – well above most other sectors. Recent data shows compute demand rising nearly fourfold in just four months in 2026, signalling a clear acceleration in real-world enterprise adoption. Even with this rapid uptake, penetration remains relatively early: generative AI adoption is estimated at only ~50%, compared with over 90% for previous transformative technologies such as the Internet. This suggests a long growth runway and reinforces our view of AI as a durable, multi-year secular theme. Against such a backdrop, upward revisions in hyperscaler CAPEX to new highs in the recent reporting season would seem justified.
Training and inference tasks have different computational approaches. While training is often done on huge amounts of data at the same time and can take weeks, inference runs on a user’s query as and when it comes in. Unlike training, inference does not require running costly backpropagation. With inference, one of the most important considerations is low latency–users expect their chatbot to answer quickly, and for thinking or reasoning models, inference runs many times over before the user even sees an output.
Hence, the shift from AI training to inference, powered by the rise of agentic AI marks a critical “inference inflection” where AI moves from episodic research and development into continuous, production-scale workloads. This evolution broadens the opportunity set, benefitting the hyperscalers, hardware, networking and memory sectors as the demand for persistent, low-latency compute begins to outpace the initial training surge.
Indeed, markets are starting to reflect the new reality. Even as semiconductors continue to drive technology stocks to an all-time high, winners within the semiconductor space are clearly broadening beyond GPUs to inference-specific application-specific integrated circuits (ASIC), central processing units (CPU), memory, storage and networking. Though industry GPU demand would likely remain strong, each model call now requires more coordination, memory and system-level compute. The CPU-to-GPU ratio could even increase from 1:4-8 to as much as 1:1, creating a seismic shift in the semiconductor landscape.
Exhibit 1: Share price performances within semi/hardware have shifted as AI phase advances
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Note: Each segment is based on a basket of representative stocks Source: Bank of Singapore, Bloomberg
Concerns over internet players’ ability to monetise CAPEX have also eased as major internet players broadly point to accelerating cloud revenue along with backlog growth, suggesting compute demand continues to outweigh existing data centre capacity. The healthy end-user take up rate is further corroborated by the surge in native AI sales. Moreover, the internet players are actively monetising their AI investments through product expansion, moving beyond services to include chip sales. These should ease earlier concerns around their ability to monetise the massive amount of AI CAPEX.
Data centre electricity consumption is projected to grow by around 15% per year from 2024 to 2030 according to IEA, more than four times faster than the growth of total electricity consumption from all other sectors.
The US is expected to experience a step-up in electricity demand growth from 1% p.a. during 2000-2020 to 3% p.a. henceforth until 2050, according to the US Department of Energy. Early signs of the power demand inflection are already evident in US electricity prices which rose 5.5% in 2025, substantially faster than the 0.9% p.a. increase during 2011-2020.
That said, there could be an uneven distribution of rewards between large renewable developers/ utilities and the others. The US industry is ripe for consolidation as projects become larger and more complex (e.g. solar with storage instead of solar-only), and larger participants generally have the bandwidth to adapt to changing policy regulations.
European power demand growth in 2025 was positive following 15 years of decline. Looking ahead, DC buildout in the next decade could boost power demand by as much as 15% by 2035 and act as a tailwind for renewables. Based on data collected by the main power grid operators in Europe, DC connection requests to the European power grid have seen an exponential increase in demand that is estimated to be equivalent to approximately 90% of current EU-28 power demand.
AI's water footprint is growing continually and becoming more water-intensive with semiconductor technology advances as well as higher AI infrastructure buildout globally. AI’s water usage in data centre can be broken down into: (i) scope 1 (directly from operations) – cooling in data centre; (ii) scope 2 (indirectly from purchased energy) – off-site electricity generation; and (iii) scope 3 (indirectly from suppliers) – semiconductor manufacturing. While the AI training efficiency gains could mean the cost of compute falling, it could result in a “Jevons Paradox” where overall demand for AI compute will rise rapidly as growing AI adoption outpaces efficiency gains.
The additional demand for water with the ongoing AI developments will add on to the pressures from climate change and exacerbate water scarcity. The World Resources Institute (WRI) estimates that around 4 billion people, or 50% of the world's population, face severe water stress for at least one month annually currently. The WRI has projected that an additional 1 billion people are expected to live with extremely high water stress by 2050 even under an optimistic scenario (where global temperature rise is limited to 1.3-2.4°C by 2100).
As such, a number of authorities have restricted data centres and semiconductor fabricator water use due to water stress. In 2021, Taiwan imposed water rationing on semiconductor fabricators during a severe drought, and in 2019, Singapore paused the development of new data centres due to water and energy usage concerns. The Netherlands placed a moratorium on hyperscale data centres in 2022. Authorities are also pro-actively promoting the use of water-efficient infrastructure. For example, Minnesota has proposed a bill for data centre developers to ensure adequate water supply and deploy closed-loop cooling systems.
Companies that provide technologies to reduce water consumption offer exposure to regulatory tailwinds and data centres’ operational demand. These include liquid cooling technology players, as well as water recycling and treatment technologies. Liquid cooling solutions are less water intensive when compared to traditional evaporative cooling solutions. On the other hand, investing in advanced water recycling systems is essential for data centres and semiconductor fabricators which consume vast amounts of ultrapure water (UPW).
Critical minerals are defined as minerals that are essential to the economic or national security of a nation and have a supply chain that is vulnerable to disruption. With an increasingly fragmented world and higher risks of trade barriers being put up, critical minerals are becoming top of mind for policymakers and investors alike. This is especially so for critical minerals, as they are the non-negotiable ingredients in the global race for AI leadership, in addition to energy security and defence. The race would arguably only accelerate henceforth with policies being progressively put in place to develop these strategic sectors.
The semiconductors and data centres essential for AI training and applications require a range of critical minerals in their production, including silver, silicon, rare earth elements (REE), gallium, germanium, platinum and, most notably, copper. IEA estimates that copper use in data centres could range from 250kt to 550kt by 2030, amounting to 1-2% of global copper demand.
The ongoing AI arms race and quest for artificial general intelligence (AGI) is leading to surging levels of CAPEX by hyperscalers, massive data centre build outs and a race for AI talent. While the impact of AI will remain a key secular trend over the next few years, we see a few sources of risks that investors will need to take into account as they navigate the landscape ahead.
First, we are seeing AI-linked issuances accelerating in the IG space and also increasingly extending in HY and private credit. Over time, there could be increasing questions around supply absorption given tight spreads. Idiosyncratic names with stretched leverage metrics will become increasingly scrutinised by investors as well.
Second, resource constraints are becoming progressively apparent. Grid access and power generation are fast becoming the limiting factor for data centre buildouts, while concentration of data centres in certain regions like Virginia or Texas in the US introduces vulnerability to the AI value chain should there be any instances of grid failures or water shortages in those areas.
Third, the monetisation model for AI across different layers of the stack beyond the “picks and shovels” are debatable at this juncture. In terms of the LLMs, the deluge of choices today raises questions whether, over the longer run, customers are willing to pay for Western frontier models that cost significantly more than the lower cost Chinese ones. On the software side of things, generative and agentic AI raise questions over the relevance of certain application-based software players, and also calls into question the traditional seat-based model that software players have become accustomed to.
Fourth, the rapid data centre buildouts could also increase the impact of any form of technology obsolescence. For instance, in the face of rapid evolution in GPU technology, older facilities (e.g. 5-10kW rack density) could see lower demand and utilisation over time.
Finally, there is the risk of over-ordering. Given severe bottlenecks across the supply chain, lead-times across GPUs, networking equipment and printed circuit boards (PCB) have all increased significantly. While this is backed by strong demand, there could be some element of “strategic pre-ordering” as customers try to secure as much capacity as possible. This introduces the risk of an inventory correction should hyperscalers, at some point, normalise their ordering patterns as bottlenecks get resolved.
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External presenter views
Bank of Singapore zooms into the 2026 Supertrends: Cycles, Halos and Moonshots – a forward looking framework exploring how capital, risk and opportunity are being reshaped by five structural shifts.
We invite you to read the 2026 Supertrends report and explore how these forces may shape portfolios in the years ahead.
