Diversification and the global AI growth story

The following is an excerpt from our 2026 global outlook

For many investors, capturing AI growth appears to present a troubling compromise: is concentration risk simply the price of entry to the most compelling growth story around?

We believe this dilemma is illusory.

Although the leading names in AI are domiciled in the US, it’s crucial to remember that AI is a global phenomenon, not a regional one. As a result, we expect AI growth to reflect current global GDP trends and divisions, with the US, China and Europe the driving forces, while also capturing growth elsewhere through distributed applications. Sovereign independence requirements will also drive domestic AI investment into data centres, as we are seeing play out now.

Increasing adoption in consumer, enterprise and cross-industry applications can also be expected to drive growth.

When we consider the current stage of AI evolution, we see several near-term developments that we believe should be on investors’ radar screens:

• Data centre capex diversification: diversification beyond NVIDIA to Google[1] TPUs, AMD and custom AI chips, shift from general-purpose to application-specific processing architectures
• Open-source distributed intelligence: state-of-the-art small, local models, often open-source, are creating distributed intelligence layers
• Enterprise deployment acceleration: adoption is expanding from testing to full deployment phases. Complex AI systems are driving demand for robust oversight tools, creating entirely new categories of essential infrastructure for explainability, security and compliance
• The inference economy: AI infrastructure is expanding beyond traditional data centres into interconnected systems (edge devices, specialised processors and distributed networks)
• Next-generation interface revolution: multi-modal AI adoption through earbuds, augmented reality, wrist controllers and ambient computing is creating new platforms that transcend existing market-leading interfaces like smartphones, laptops, and tablets

For investors, the challenge is clear: AI is complex, multifaceted and fast evolving. Picking individual winners is difficult, and concentration risk can be high in market-cap-weighted indices.

We belive a carefully constructed index, equity and or multi-asset strategy can seek to overcome this. For example, to address concentration risk, an equal weight approach can be used to cap exposure to individual names in a transparent fashion while still providing access to underlying growth potential.

A diversified[2] approach that captures full AI value chain – from infrastructure providers to application innovators – is a pragmatic response to the pace of change we are seeing in AI. Rather than picking today’s best-known names and hoping their success continues, investors could instead consider investing in a wide range of companies driving AI adoption across many sectors and geographies, providing much more rounded exposure.

Read our 2026 global outlook

The value of an investment and any income taken from it is not guaranteed and can go down as well as up, and the investor may get back less than the original amount invested.

[1] For illustrative purposes only. Reference to a particular security is on a historic basis and does not mean that the security is currently held or will be held within an L&G portfolio. The above information does not constitute a recommendation to buy or sell any security.
[2] It should be noted that diversification is no guarantee against a loss in a declining market.