2026 Thematic Mid-Year Update

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Key takeaways

AI continues to be the key-theme of 2026, reshaping industries and influencing nearly every corner of the global economy. Our 2026 Thematic Mid-Year Update features key charts exploring how the rapid advancement and adoption of AI is transforming sectors, creating new opportunities and risks, and redefining where and how we invest.

1. AI’s value chain continues to expand as adoption deepens, but winners are beginning to separate amongst supply constraints.
2. Rising demand for AI compute is intensifying the need for critical materials and power, exposing bottlenecks along the way but also potentially opening new frontiers like space.
3. New capabilities emerging in robotics and healthcare may impact our daily lives, while AI & digital assets may reinforce each other through daily use cases.

Welcome to the AI economy

The AI investment opportunity is shifting from a concentrated technology buildout toward a broader, increasingly physical economy, with implications and applications for all sectors and consumers. The next phase of thematic investing may depend on identifying both the beneficiaries of AI adoption and the constraints that determine how quickly the AI economy can scale.

IT: AI's next phase is about putting it to work

For much of the AI buildout, the investment story has centered on the companies building the infrastructure. But the next phase may become about what that infrastructure enables. We are beginning to see signs that years of AI capex are translating into revenue.¹ Cloud revenue growth for the largest providers has been accelerating as AI infrastructure comes online, showing evidence that demand has begun to be monetized.² Not to mention, in one year, the combined cloud backlog, which represents signed customer commitments of Amazon, Alphabet, Microsoft, and Oracle nearly tripled, rising from $800 billion to $2.3 trillion.³

But spending on AI and creating value from AI are not necessarily the same thing. As AI adoption matures, opportunities may increasingly favor companies that can successfully deploy AI across their businesses, not simply those investing in AI technology. Kellanova offers a real-life example. The company behind the popular pringles snack, invested $4 - 5 million in AI to make every chip more consistent (potato chips that is, not to be confused with AI chips). The result? A 10% increase in quality, 13% reduction in waste, and 40% return on investment.⁴

We believe that the next set of AI beneficiaries may include not only the companies building the technology, but also those with the strategy, talent and operating models to put it to work, like Kellanova. As a result, AI’s economic value may be measured not by how much technology companies buy, but by what they are able to do differently because of it.

That shift may already be spreading through the broader enterprise economy faster than in previous technology cycles. In the first three years after the launch of ChatGPT in late 2022, 29% of Fortune 500 companies and 19% of Global 2000 companies adopted AI.⁵ What may be even more telling is where that adoption is beginning to take hold. Coding may be the obvious starting point, but faster-growing enterprise use cases are also emerging in legal and medical administration, industries that by some measures have been historically slower to adopt new technologies, as evidenced in the chart below:

As AI moves beyond the tech sector, the challenge may increasingly shift from whether companies want more compute, to if the physical supply chain can provide it fast enough. Unlike software, semiconductor capacity cannot scale overnight. Chips are estimated to account for roughly 60% of datacenter build costs in 2026,⁶ up from about 40% in 2021, with memory representing much of that increase.

Over a typical four-year hardware cycle, compute capability has improved roughly 250% faster than memory bandwidth,⁷ creating a growing mismatch between what AI systems can process and how quickly data can reach them.

This constraint is difficult to solve quickly because adding semiconductor capacity is a multi-year physical undertaking (see table below).⁸

Materials, energy, & utilities: The AI economy is becoming a physical one

AI bottlenecks aren’t just limited to semiconductors, demand is extending well beyond chips and data centers to the raw materials that underpin them. Copper, lithium, nickel, and more, all sit within broader supply chains supporting electrification, batteries, power infrastructure and advanced technologies. Yet expanding supply is not simply a question of finding more resources.

New mines can face long development timelines, permitting hurdles, declining ore grades and significant capital requirements, creating the potential for supply to lag demand. One example? Copper, where current project pipelines could leave a quarter of 2035 demand unmet.⁹

And copper isn’t the only pain point. The funnel for materials is narrowing at refining; currently the leading refining country accounts for an average of 72% of global refined supply of critical raw materials, including copper, lithium, nickel, cobalt, graphite, and manganese.¹⁰ In our view, not only is more supply needed, but refining must diversify.

DID YOU KNOW? U.S. data center IT power demand is projected to reach 121 GW by 2030.¹¹

Beyond critical materials, the physical demands of AI extend directly to the power system. As data centers scale, electricity demand is beginning to accelerate after decades of relatively muted growth, creating what may be the largest expected step-up in U.S. electricity demand growth in a century.

Industrials: New frontiers are emerging across space & defense

As the physical constraints around AI become more visible, space is emerging as a potential frontier for solving some of those challenges. Falling launch costs and improving space infrastructure are lowering the barriers to experimentation, enabling companies to test ideas that would have seemed far less practical even a decade ago.

DID YOU KNOW? It is 95% less expensive to launch a rocket to space now vs 65 years ago.¹²

Orbital computing is one example, while the technology remains highly experimental and significant engineering challenges persist, placing computing infrastructure in space could potentially offer access to more abundant and consistent solar energy, while reducing dependence on terrestrial land, transmission and grid connections. Solar panels in space can generate roughly four to ten times more power¹³ than comparable systems on Earth, and suitable orbits can provide near-continuous sunlight rather than outputs that vary with weather and seasons.

The broader implication extends beyond data centers. As access to orbit becomes cheaper, and more active satellites are launched, entirely new space-enabled markets may become more economically viable.

The technology may still be early, but the combination of falling costs and growing activity is creating a flywheel, cheaper access enables more experimentation, which can support new infrastructure, applications and ultimately new investable opportunities.

Back on earth, AI and other emerging technologies are broadening the defense ecosystem, creating opportunities across both newer defense-tech innovators and traditional contractors. At the same time, near-term replenishment needs could reinforce a longer defense spending cycle. Depleted inventories, rising defense authorizations and growing contractor backlogs suggest that restocking may be only one part of a broader period of sustained investment in defense capacity and modernization.¹⁴

Consumer & healthcare: AI is moving beyond technology into daily life

Robotics may be one of the clearest examples of AI’s move beyond the digital world and into the physical one. Robotics bring superintelligence into the real world by allowing machines not just to process information, but to perceive, move and act on it. What was once concentrated in controlled industrial settings is increasingly expanding into transportation, manufacturing and over time, everyday consumer use cases. The market remains in its early stages, but the breadth of potential use cases points to a vast long-term addressable opportunity.

DID YOU KNOW? The cost to build a humanoid robot has fallen more than 30x in a decade.¹⁵

Healthcare is another area where AI’s capabilities intersect with a growing structural need. The world is aging, with one in six people globally expected to be aged 60 or older by 2030.¹⁶ Older populations spend roughly twice as much on healthcare as younger groups,¹⁷ and aging demographics means more patients, more medical data and greater demand for care, without a corresponding increase in the resources available to deliver it.

DID YOU KNOW? AI support may increase cancer detection by up to 50%.¹⁸

That imbalance creates a natural role for AI. Rather than replacing clinicians, AI may help healthcare systems make better use of scarce resources by helping doctors process more information, identify issues earlier and extend expertise across a larger patient population. Radiology and diagnostics are one early example of how AI can augment existing workflows.

Financials: AI & Digital Assets may reinforce each other

Tokenization is emerging as a potential next evolution of financial market infrastructure, creating a new digital wrapper for traditional assets and potentially reshaping how investors access, hold and transact in markets. As more real-world assets move on-chain, the opportunity is expanding beyond digital-native assets toward areas such as funds, credit and other traditional securities. Ethereum currently represents the blockchain with the largest share of tokenized real-world assets, underscoring how the development of these digital rails may become an increasingly important part of the evolution of market infrastructure.¹⁹ Learn more about Ethereum.

We also believe AI & digital assets are mutually beneficial technologies that may reinforce adoption of both in every day use cases. Consider an example of using AI agents to book your next trip. AI could help research, optimize, and ultimately book travel logistics autonomously. It could look like this:

At a higher level, this example demonstrates the intersection of AI as machine-native intelligence, with crypto as machine-native money, and blockchains as providing the programmable infrastructure that connects intelligence with economic activity.

What does the expanding AI economy mean for portfolios

AI has entered its next chapter, no longer beheld to just a single sector, but instead reshaping industries and influencing nearly every corner of the global economy. For investors, capturing AI opportunities may increasingly require looking across sectors, understanding where constraints and adoption are emerging, and being selective about where value is ultimately created to fully capitalize upon the AI economy. The next phase of AI investing may reward breadth, selectivity and attention to constraints.

1. Breadth: Investors may need to broaden their lens and ask themselves where is AI spreading? As investing across sectors becomes necessary to capture the full opportunity set
2. Selectivity: Investors should ask themselves who is actually creating value? And seek to separate AI spending from value creation
3. Scarcity: Investors should also explore where is supply hardest to scale? As physical economics may increasingly determine where economics accrue

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