CapitalMarketLineFromInflationShocktoProductivityShock v3

MULTI-ASSET | AUGUST 2026 Contributors

Capital Market Line: Michael J. Kelly, CFA Global Head of Multi-Asset Hani Redha, CAIA

From Inflation Shock Portfolio Manager Steven Lin, CFA Portfolio Manager

to Productivity Shock Peter Hu, CFA, FRM Portfolio Manager Sunny Ng, CFA Portfolio Manager Mikhail Johaadien Markets have navigated a series of inflationary shocks over the past few years, Research Analyst including Covid, tariffs, and geopolitical tensions, including most recently the closure of the Strait of Hormuz. While disagreements linger between the U.S. Teresa Wang Research Analyst and Iran, neither has shown the appetite for full-fledged war. Markets remain confident in a mutual escalate-to-deescalate episode, or perhaps the two sides can still work out a middle ground. Meanwhile, we expect energy-based inflation to find a new normal as users continue fuel-switching and reducing large dependencies on fuel coming from the Strait.

At that point, we expect investors to turn their attention back toward the AI investment wave, which is on pace to be one of the largest private-sector investment booms in history. Some worry capital will be misallocated, leaving behind a debt overhang and societal disruption. Others foresee a step-up in both real growth and productivity, so that growth does not come with the burden of inflation. We think we are in the early innings of a positive productivity shock that will enhance growth and profitability and bring about disinflation over our Capital Market Line’s 5-year time frame.

Previous technological revolutions have often rewarded a narrow group of beneficiaries, at least initially. This one is no exception, but it has already begun to broaden. Open-weight models are diminishing the risk that two or three

frontier labs will end up controlling the entire market. Usage is spreading to a broader set of more economical models, powering a Jevons’ paradox in which greater efficiency breeds both surging volumes and more widespread adoption. In the process, dollars are shifting away from a few frontier models focused on training future intelligence toward more economical models focused on inference. This, in turn, means the incremental AI dollar is funding a broader range of activity that includes data center construction, electrification, semiconductor fabrication, memory production, power generation, networking infrastructure and, eventually, a redesign of enterprise software and business processes. Frontier labs dependent on venture capital and circular financing are losing compute share to financially sturdier providers financed via equity and investment- grade debt. Thus, the economics of AI are trending toward sustainability.

For investors, the most pertinent issue now is where to find long-term value within the four layers of the AI stack. Semiconductors convert energy into tokens, hyperscalers convert capital expenditure into compute, foundation models convert tokens into intelligence, and applications convert that intelligence into end-user value.

With Chinese open-weight models rapidly catching up, foundation models remain well-poised to compete on their respective frontier capabilities, but their share in overall tasks is likely to diminish quickly from comprising the entire market to carrying out only the most sophisticated tasks. Over time, we also expect them to be less relevant to where tolls are collected.

The hyperscalers remain positioned at the center of enterprise adoption, which is where all the bills are paid today. They maintain trusted enterprise relationships, own secure infrastructure, provide security and compliance frameworks, and increasingly orchestrate the flow of AI workloads among models. They are paid whether workloads are routed to frontier or open-source models, and revenues are accelerating very meaningfully from wider AI usage and adoption. Still, the amount of investment required, and thus the amount of potential return after the AI wave crests, remains an area of vibrant debate.

With supply for many pieces of the AI ecosystem unable to keep up with surging demand, substantial value today is accruing to the bottlenecks, particularly memory. This is the only area…