Many market commentators believe inflation is the most important story for equity markets this year. By contrast, we think the influence and dominance of powerful growth themes is a far more compelling development, despite the inflation data. Witness, for example, the stellar performance of those exposed to electrification and data centre cooling within industrials. Similarly, we’re seeing a premiumisation in the consumer and financial sectors. There’s also been a marked jump in security and defence spending across sectors. And, of course, the most eye-catching has been the huge impact of artificial intelligence (AI).

Thematic investing is growing in importance across global equity markets – a trend we think will gain pace. This is particularly true in the context of a slowdown of global growth, driven by fiscal constraints, demographic shifts, and a new era of globalisation. 

The evolution of AI will be central to this dynamic. No longer the preserve of Nvidia or Microsoft, AI is broadening across sectors, geographies and companies. In our view, AI will be one of the most powerful and important themes over the next decade. 

The great reveal

Last year marked AI’s ‘great reveal’. The emergence of ChatGPT was a wake-up call to markets. The message? We’re likely on the cusp of a multi-year, meaningful change in growth dynamics within specific parts of the economy. 

Generally, new computing cycles emerge every 10 years, heralding a tenfold increase in computer access. These cycles follow a similar pattern. Initially, semiconductor manufacturers benefit, followed by a shift in demand towards infrastructure and, finally, software and services reap the rewards. The 2010 internet boom was a case in point. 
We think AI is following a similar pattern – but with important caveats. The key reason lies in the technology itself. For instance, the GPT2 training model needed 1.5 billion (bn) parameters to operate (the adjustable elements in a model that are learned from training data, such as biases and scaling factors). By contrast, GPT4 requires 100 trillion – equivalent to the number of synapses in the human brain. This rapid growth in complexity requires increasingly sophisticated semiconductor chips, such as Nvidia’s GPUs (graphic processing units).

The fivefold increase in Nvidia’s earnings estimates underscores the lightning growth of this dynamic. It also highlights the underlying silicone intensity of generative AI – the sheer computing horsepower far surpasses that of previous computing cycles by several magnitudes.

This has implications not just for data centres (buildings that house computer systems), but also for factors outside the traditional technology space. This includes the type and volume of power consumption, the configuration of the power grid, power management, and numerous specialised areas within the industrial economy. 

Economic implications         

The other important difference lies in how AI diffuses through the economy. In past cycles, consumers adopted new technologies far more quickly than they have AI. Now, companies are testing and trialling AI applications at a pace that far outstrips consumer engagement. Indeed, despite the initial excitement around ChatGPT, we’ve seen scant evidence of meaningful monetisation of the technology.  
As a result, while we continue to focus on direct AI, we’re increasingly expanding our thematic focus into three core areas, detailed below. 

AI and power infrastructure 

Electrification and reindustrialisation are potentially the most interesting themes, as capital expenditure (capex) increases and permeates the supply chain. Electricity consumption in Western countries is expected to surge by 40%, propelled by energy demands of AI-driven data and green policies. 

In a recent earnings call, Emerson CEO Lal Karsanbhai said a search on ChatGPT consumes six-to-10 times more power than a traditional Google search (Chart one). The estimated capex required to develop AI infrastructure continues to rise. The increase in demand is real and is happening today.

Chart one: ChatGPT queries are 6x-10x as power intensive as traditional Google searches

Source: Google, SemiAnalysis as per Goldman Sachs report “Generational Growth” April 28, 2024.  

 

Of course, we’ve been here before. In 1999, an Energy Information Administration forecast said that 30-50% of the US electric supply would be required to power the internet. Instead, the growth in US electricity demand flattened to zero over the next two decades. Then, significant growth in internet traffic was offset by efficiency gains in performance-to-watt growth as a result of Moore’s Law (the observation that the number of transistors on computer chips doubles approximately every two years). Today’s situation is different. The impact on electricity demand is expected to be significantly greater, compounded by a deceleration in the gains predicted by Moore's Law. 

So, what happens when the unstoppable force of AI meets the immovable object of the electricity grid? In the short term, electrical equipment manufacturers are poised to gain pricing power. Lead times for transformers are expected to double, extending from nine months to two years until delivery. In the long term, however, electricity grids must transform to keep pace with the estimated increase in demand. This includes addressing the unpredictability of supply, especially as renewables increasingly account for a larger share of the energy mix. 

AI is one leg of the stool. The other two are nations seeking energy independence and widespread electrification in the drive to ‘net zero’. This has resulted in a step-change in the electric utilities’ capex plans across Europe and North America.  In the US, data centres alone will need 250 terawatt hours of generation capacity over the next five years – equivalent to Spain’s power consumption. This represents the highest level of growth in the US for over 20 years.

This surge in demand will also be geographically concentrated in areas where the grid infrastructure might not cope. In the US, data centres are predominantly located in North Virginia and Texas, while in Europe, they are heavily concentrated in Frankfurt, Amsterdam, and Dublin. Furthermore, BloombergNEF estimates that grid investments to decarbonise global electricity by 2050 will increase from $300bn in 2022 to $600bn in 2030. Companies best placed to capitalise from this transformation are electrical firms such as Schneider and WEG, and utility-focused equipment manufacturers like Hubbell. Innovation is also needed to enhance grid operations. Here, AI can help design more efficient systems and infrastructure. Potential beneficiaries include Itron, which offers smart meters and analytics software to the grid, and Europe’s Alfen, known for its smart-grid infrastructure and software. 

Opportunities in data centres

Another area poised for structural growth comes from the winners in the data centres sector. These facilities require advanced cooling systems and resilient power supplies to operate efficiently and reliably. 
Previously, air cooling technologies met the needs of common, lower-power-density servers. However, AI servers, while still a small sector, represent some 40% of the current market when adjusted for power density.  As AI server adoption grows, and power density increases, air-cooling technologies will no longer be up to the task. Liquid-cooling technologies are therefore gaining momentum and capturing increasing market share (Chart two). 

Chart two: Increasing thermal design power of server processors

Source: Taiwan Technology, Goldman Sachs, May 2024.

 

We think many of the future leaders in cooling technologies will come from Asia and emerging markets, including AVC and Aurus. In developed markets, Vertiv has the most direct exposure to the data centre theme, with approximately 75% of its revenues tied to liquid cooling.

Meanwhile, the demand for resilient power supplies, coupled with the slow-moving nature of grid upgrades, creates potential opportunities for companies specialising in backup power generation and uninterruptable power-supply equipment. The rebuilding of industrial capacity, particularly in the US, further contributes to this trend. Multi-industry beneficiaries include Schneider, Simens, ABB and Eaton.

Digital twins + Gen AI = a killer combination

A final area that merits attention is the concept of digital twins. This is defined as "virtual representations of assets, people, or processes along with their environments, designed to simulate strategies and optimise behaviours."  Organisations use data twins to enhance data-driven decisions. For example, a heavy industry manufacturing plant might use a digital model to monitor on-the-ground processes and predict machinery wear and tear.  

A successful digital twin requires accurate, abundant, real-time data. Previously, managing this volume of data has been challenging. However, advancements in AI LLMs (large language models) have revolutionised this process. These models can distil crucial information, facilitating efficient data transfer and processing for digital twins. This enhances their utility and effectiveness.

As consulting firm McKinsey highlighted, “The symbiotic relationship between digital twins and Gen AI increases their combined scalability, accessibility, and affordability. This new frontier will allow innovative and dynamic organisations to improve their advantage”. Companies at the forefront here include Dassault, Altair, Siemens, Aveva (a subsidiary of Schneider), and Autodesk.

abrdn’s investment philosophy and research platform are well-placed to take advantage of the diffusion in AI 

The key to successful thematic investing is understanding where value will materialise within the most powerful global growth themes. It’s crucial to distinguish between growth and value creation. We focus on the latter, especially as capex spreads throughout the AI value chain. 

The companies we’ve discussed predominantly operate in consolidated markets, distinguishing themselves as ‘serial value-creators’. Their quality business models enable them to capture an outsized share of the economic gains, particularly as themes expand and evolve. 

In our view, AI will not be the preserve of large caps forever, with smaller players coming to the fore. This is an opportunity for those with capabilities to invest across the market-cap spectrum and various geographies. 

Our research expertise in sustainable, small-cap and emerging markets investing means we’re ideally placed to build thematic portfolios that identify value-creators in the most important global themes. This includes those driving the next stage of the AI revolution.