AdmansPraxis Weekly Briefing - 25 September 2026
This week’s reads centres on a common issue for allocators: whether portfolio assumptions still hold as the cost and availability of capital change. AI financing is moving rapidly from an equity story into credit markets, monetary policy has shifted again, while new long-run evidence provides a useful challenge to the way historical returns are used in strategic asset allocation.
CFA Institute Research Foundation — Exponential Wealth: Centuries of Stock and Bond Returns
What changed: Published on 21 September, Roger Ibbotson and Laurence Siegel extend the long-run Stocks, Bonds, Bills and Inflation tradition with new equity and bond indices. The work emphasises total return, income reinvestment, inflation, diversification and costs, while also warning against mechanically extrapolating extraordinary historical compounding into future expected returns.
Worth reading because: This is particularly relevant to strategic asset allocation and capital-market assumptions. Long historical datasets can create a false sense of precision if selectors do not distinguish between realised historical returns and reasonable forward-looking expectations. It provides useful context when challenging multi-asset managers on the assumptions embedded in optimisation models, strategic allocations and long-term return targets.
BlackRock Investment Institute — The AI Buildout Meets the 5% World
What changed: Published on 21 September, BlackRock argues that the scale of AI infrastructure investment is increasingly colliding with a structurally higher cost of capital. Financing requirements are spreading beyond hyperscalers’ balance sheets into investment-grade bonds, private credit and structured financing, increasing competition for capital across markets. BlackRock remains constructive on risk assets but sees greater need for selectivity, particularly within credit.
Worth reading because: This moves the AI discussion beyond simply identifying beneficiaries. For equity, credit and multi-asset managers, selectors should increasingly ask who is financing the investment, at what cost, what return on capital is required and who ultimately bears the risk if expected AI revenues arrive more slowly than anticipated.
BlackRock — AI and Private Credit: Assessing Risk and Finding Opportunity
What changed: Published on 24 September, BlackRock examines the growing role of private credit in financing AI infrastructure. Its central argument is that the opportunity is becoming increasingly uneven: enormous capital requirements can create attractive lending opportunities, but underwriting needs to differentiate carefully between borrowers, collateral, contractual cash flows and exposure to technological obsolescence.
Worth reading because: This is an excellent due-diligence prompt for private-credit managers. “AI infrastructure” can encompass very different risks, from contracted data-centre cash flows to equipment whose economic value may depend on rapidly changing technology. Selectors should establish what actually secures the loan, how residual values are stressed, where refinancing risk sits and how correlated supposedly diversified AI-related exposures really are.
Amundi Investment Institute — The Fed Raises Policy Rates
What changed: Following the September FOMC meeting, the Federal Reserve raised rates by 25 basis points to 3.75–4.00%, its first increase since 2023. Amundi interprets the move primarily as an insurance and credibility measure rather than the beginning of an aggressive tightening cycle. The Fed’s projections indicate one further increase during 2026 before rates remain unchanged in 2027.
Worth reading because: The interesting point for selectors is the changing relationship between inflation, monetary credibility and duration. Short-dated Treasury yields rose after the decision while longer yields fell. That provides a useful test for fixed-income managers: is duration exposure being driven by a directional rate forecast, curve positioning, carry, recession protection or relative value? Similar headline duration numbers can conceal very different sources of risk.
BlackRock Investment Institute — Q4 2026 Global Investment Outlook
What changed: BlackRock’s latest quarterly outlook brings together three increasingly connected forces: the AI capital-investment cycle, higher structural yields and persistent inflation pressure. Rather than relying heavily on conventional asset-class allocations, BlackRock continues to advocate more granular exposure selection and argues that investors need to identify the specific economic risks and themes they want before choosing the appropriate public or private-market vehicle.
Worth reading because: This has a useful implication for multi-asset manager research. Traditional labels such as equities, bonds, infrastructure and private credit can disguise exposures to the same underlying economic driver. A portfolio holding technology equities, data-centre infrastructure and AI-related private credit may look diversified by asset class while remaining heavily dependent on the same investment cycle.
Market evidence — AI Financing Is Beginning to Test Credit-Market Capacity
This is not a single research paper, but I think the development is sufficiently important for allocators to include this week. Recent credit-market evidence shows investors becoming more discriminating as AI-related borrowing accelerates. Reuters reports that AI-linked corporate issuance is requiring wider spreads and larger concessions than some conventional investment-grade borrowers, with concerns increasingly centred on the volume and unpredictability of future financing needs. Reuters
At the same time, increasingly complex financing structures are emerging around data centres and computing equipment, including special-purpose vehicles and residual-value guarantees. The significance is that AI is becoming a capital-structure and credit-underwriting question, not simply an equity theme. That makes cross-asset exposure mapping increasingly important for fund selectors assessing global equity, credit, infrastructure and multi-asset portfolios. Financial Times
AdmansPraxis takeaway
The most useful theme this week is the need to look through asset-class labels to the underlying economic exposure. AI illustrates the point particularly well. Equity managers may own hyperscalers, infrastructure managers may finance data centres, and credit managers may lend against the same investment cycle. Those positions can appear diversified in an asset-allocation report while ultimately depending on similar assumptions about AI demand, capital expenditure and future cash flows.
The CFA Institute research provides a useful counterweight to this very contemporary debate. Strategic portfolio decisions still depend heavily on assumptions derived from history, yet realised long-run returns are not automatically sensible forecasts. Whether assessing capital-market assumptions, duration positioning or AI-related credit, selectors should therefore keep separating historical evidence, forward assumptions and the actual risks being taken to bridge the two.
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