For investors, the opportunities from AI are wider than news headlines suggest, the author of this article says.

The following article is from Kwai San Wong, analyst, global equities at Sarasin & Partners. The article examines the investment background of AI and frames the topic in ways that we hope wealth managers find useful. Please remember, the usual editorial disclaimers apply to views of guest writers. To comment and enter the conversation, email tom.burroughes@wealthbriefing.com and amanda.cheesley@clearviewpublishing.com. 

Recently, I travelled to Los Angeles and San Francisco to meet the management teams of some of the world's most important technology companies. What I heard paints a picture that is more confident and more complex than the headlines suggest.

AI is not in a bubble
The most striking thing about spending time with these companies is how concrete and tangible the AI investment cycle appears to have become. The AI story is no longer about possibilities; it is about construction. Data centres are being built at a pace that was unimaginable five years ago. Investments in data centres were estimated at $598 billion in 2025, while investment in AI-specific facilities is projected to grow from $236 billion to $934 billion by 2030 (1). The companies spending enormous sums on computing infrastructure are doing so because the returns, measured in AI-driven revenue and cost savings, are visible and growing. Customer commitments extend years into the future, and the companies supplying the infrastructure have multi-year order books to match. 

The total planned investment in AI infrastructure across the industry has been estimated at several trillion dollars by the end of this decade, with McKinsey estimating that data centres will require $6.7 trillion worldwide by 2030 to keep pace with demand for compute power (2), a figure that would have seemed fanciful not long ago, but which now appears entirely plausible.

The economics behind AI's spending boom 
The companies building and deploying AI models are now making money from analysis, customer interactions, and every automated task their systems handle. That shift from experiment to profit engine is the single most important development of the past year, and it is what gives credibility to the enormous spending plans we are seeing.  Amazon, Microsoft, Alphabet, and Meta are together expected to spend hundreds of billions of dollars on capital expenditure in 2026, with estimates ranging from $130 billion to $220 billion for each company (3).

One of the most useful ways to think about this is through the cost of electricity used to power AI. Every AI request produces what engineers call "tokens," the basic units of information, which cost money to produce. Companies are in a fierce competition to reduce that cost while keeping quality high. The value of faster, more efficient chips is measured directly in the economics of every AI product their customers sell. This is why spending on computing infrastructure behaves less like discretionary technology spending and more like investment in essential industrial machinery.

Beyond the chips
When most people think about AI infrastructure, they think about the chips doing the processing. What is often overlooked is the networking equipment that allows those chips to work together. Imagine tens of thousands of chips in a data centre that need to share information with each other thousands of times per second; the speed and reliability of the connections between them have a direct bearing on how efficiently the whole system operates.

Meetings with Arista Networks, Lumentum and Astera Labs highlighted strong demand for technological improvement at every part of the networking driven by AI inference and agentic AI. Companies are seeing broader customer demand and higher networking requirements at each generation of AI chip.

Physical AI: the next investment frontier
The systems most people are familiar with today, such as the chatbots, the image generators and coding assistants, are digital AI. They exist in software and produce digital outputs.

The next frontier, which several executives described as already arriving, is physical AI: systems that perceive and act in the real world. Autonomous vehicles are the most visible example, with self-driving taxi services now operating commercially in multiple cities. Industrial robots capable of adapting to new tasks with far less programming than before are beginning to appear in factories.

The investment implications of this shift are significant. Physical AI requires not only the training infrastructure that currently drives most spending, but also edge computing, such as processing power installed close to where decisions are being made, whether that is in a vehicle, a factory, or a building. This represents an additional layer of demand on top of an already stretched supply chain.

The primary constraint on how quickly physical AI can expand not the technology itself, but rather regulation and the time required to build consumer and business trust. These are slower-moving forces, which suggests that while physical AI's eventual impact may be profound, the investment cycle will be more gradual than the rapid build-out currently underway in data centres.

What this means for investors
The opportunity is broader than the headlines suggest. The names dominating AI coverage, notably a handful of chip companies and technology giants, represent only part of the picture. The networking companies connecting AI infrastructure, the equipment makers enabling chip manufacturing, and the analogue chip companies managing power delivery across every piece of the system are all benefiting substantially.

Finally, it is worth acknowledging what we do not know the pace at which AI-generated revenue will grow. The ultimate size of the physical AI opportunity, and the timing of any eventual slowdown in capital spending are all uncertain. What is clear is that we are in the middle of a technological transition of considerable importance, one that is creating real and durable value for the companies best positioned within it.

Footnotes
1, The Network Installers, Data Centre Growth Statistics, 2026. https://thenetworkinstallers.com/blog/data-center-growth-statistics/

2, McKinsey & Company, The cost of compute: A $7 trillion dollar race to scale data centres, 2025. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers

3, Company guidance from Amazon, Microsoft, Alphabet, Meta; Cornfordandcross.com, 2026.