Infundly Weekly Briefing — Week Ending 10th July 2026
- Sheridan Admans
- Jul 10
- 4 min read
Updated: Jul 24
This week’s briefing focuses on a common due-diligence issue: portfolio labels are becoming less useful than the underlying exposures they conceal. BlackRock frames the outlook around competing regimes, where AI-led productivity gains sit alongside constraints in power, labour, capital and materials. Loomis Sayles and J.P. Morgan both point to AI-related capital expenditure as an important driver of markets, but raise different questions about whether this represents genuine breadth or another form of concentration. Goyal et al. add a manager-selection perspective, showing that backing newer or first-time private-market managers is not automatically rewarded, even where the foundations appear credible.
What changed: BlackRock has moved beyond a conventional base-case outlook toward “multiple incompatible regimes”: AI-led productivity abundance versus continuing scarcity in power, labour, capital and materials. It argues that portfolios should identify their embedded macro assumptions rather than relying on broad asset-class diversification alone.
Worth reading because: The strongest section is its treatment of implementation. It connects an investment theme to the choice between debt and equity, public and private markets, active funds and indices. That is directly useful when assessing whether a manager’s vehicle and portfolio construction genuinely deliver the claimed exposure.
What changed: Using more than 61,000 institutional commitments, the study finds that investors both chase top-quartile performance and allocate surprisingly readily to first-time or young managers. However, the willingness to back newer managers was not associated with superior subsequent returns.
Worth reading because: It challenges two common assumptions: that institutional selection is dominated by track-record conservatism, and that access to emerging managers necessarily creates an excess-return advantage. The findings support separating credible foundations from evidenced repeatability, especially when reviewing newer strategies.
What changed: Loomis remains constructive on the global credit cycle after the recent oil-price reversal, but acknowledges that credit risk premia are somewhat elevated and that sticky inflation remains the main challenge. It expects AI-related capital expenditure to spread beyond technology into industrials, utilities, infrastructure and Asian supply chains.
Worth reading because: It provides a useful counterweight to more defensive macro narratives. For manager meetings, the key question is whether exposure to AI-related growth represents genuine earnings breadth or simply a repackaged concentration in the same economic driver.
What changed: J.P. Morgan argues that equities are being sustained primarily by AI-related earnings and capital expenditure rather than broad economic strength. It sees emerging markets as another expression of the AI theme, while Europe and Japan offer different drivers through defence spending, governance reform and the normalisation of interest rates.
Worth reading because: This is useful for interrogating apparent geographic diversification. A global, US and emerging-markets manager may all be drawing returns from the same AI capital-expenditure cycle, while Japanese or European strategies may provide more genuinely differentiated earnings drivers.
Infundly takeaway
The common thread this week is that categories can give a false sense of diversification. A portfolio may appear diversified by geography, asset class or vehicle type, while still depending on the same underlying drivers: AI investment, infrastructure demand, credit conditions, private-market financing or the availability of capital. For fund selectors and allocators, the key task is to look through the label and test what is really being owned, how the exposure is delivered, and whether the evidence base supports the role the strategy is being asked to play. This is especially important when reviewing newer strategies, where credible foundations should be distinguished from proven repeatability.
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