- Nvidia’s USD500bn initiative to unlock substantial institutional capital available for AI buildout
- AI compute may emerge as a financeable asset class, despite execution and refinancing risks
- Third-party funding could ease hyperscaler balance sheet strain and extend AI spending
- Stay invested in AI, favouring prudent hyperscalers and selective semiconductor plays
AI capex continues to dominate headlines. In our previous CIO Perspectives report (Global Technology: Decoding AI Capex Trends, published in Jun 2026), we covered how AI capex has been elevated from a cyclical trend to a structural one, and why there remains a substantial runway for AI infrastructure buildout. This report expands on that theme by examining a recent landmark financing deal for AI compute and its potential knock-on effects on financial markets. Amid the numerous uncertainties surrounding the AI revolution, one thing remains irrefutable: infrastructure continues to be a key focus for both industry players and investors alike.
Nvidia spearheads landmark financing deal for AI compute. On 10 August, Nvidia announced that it had signed memoranda of understanding with six major financial institutions, namely Apollo, Blackstone, BlackRock, Brookfield Asset Management, Goldman Sachs, and KKR, to mobilise over USD500bn of third-party capital for AI infrastructure development. This includes the construction of data centres, power infrastructure, and the acquisition of Nvidia chips. The move underscores CEO Jensen Huang's view that GPUs and AI compute should be regarded similarly to infrastructure assets such as toll roads, power grids, and commercial real estate, which can be financed through institutional capital and credit markets.
The start of a new “financeable asset class”? Jensen Huang described Nvidia's compute infrastructure as a productive, long-lived, revenue-generating, and transferrable/fungible asset, arguing that lenders should be able to securitise AI hardware and data centres in much the same way as other infrastructure projects. Leaders of financial institutions, which have been eager to deploy capital into AI infrastructure, have echoed Huang's view. Goldman Sachs CEO David Solomon said there is an opportunity to create a new credit market backed by Nvidia compute. Blackstone president Jon Gray said AI compute could become a "financeable asset class" akin to residential mortgages. BlackRock CEO Larry Fink compared the initiative to the creation of mortgage-backed securities, calling it "the next future of financial engineering".
What does this mean for markets?Not a foregone conclusion... While the excitement on Wall Street is palpable, it is not a foregone conclusion that AI compute will successfully evolve into a mainstream “financeable asset class”. Unlike traditional infrastructure assets such as power plants or toll roads, GPUs face rapid technological obsolescence, with economic useful lives potentially limited to around four to seven years. This creates a maturity mismatch that makes long-duration infrastructure financing difficult and raises refinancing risk as successive generations of hardware require replacement. Residual values are also uncertain; lenders cannot assume that today’s GPUs will retain meaningful collateral value once substantially more powerful chips become available. Financing structures will therefore need to rely increasingly on cash flows rather than hardware values alone. They will likely be supported by long-term customer contracts, minimum-purchase commitments, and faster debt amortisation. Third-party financing can broaden AI’s capital pool, but these structural risks must first become sufficiently measurable and transferable.
Potential decoupling of AI’s growth trajectory from hyperscaler balance sheets. If these risks can be successfully quantified and third-party financing for AI compute does become mainstream, it would help to alleviate investor concerns around the bloating of hyperscaler balance sheets. Under traditional financing models, a company (e.g., a hyperscaler, AI lab, or cloud provider) that wants to build a data centre must fund the project through free cash flow (FCF), debt, or equity. The company would have to use its balance sheet both for funding and holding the finished asset upon completion. With third-party financing, however, there is significantly less upfront balance sheet involvement. Instead, an investor consortium and its financing vehicle would raise the capital and subsequently own the asset, while the company simply commits to using the asset. In other words, the company no longer needs to raise all the capital by itself; the capital-intensive asset can be increasingly financed by pension funds, insurers, private credit funds, infrastructure funds, and other institutional capital. This is conceptually similar to what happened in aircraft leasing, renewable energy, telecom towers, and real estate, where the user of an asset does not necessarily need to own or finance it. In short, such a model will help to lengthen and stretch the AI investment cycle.
Who benefits from third-party financing and a lengthened AI investment cycle?
- Hyperscalers – A key beneficiary of this additional source of capital is undoubtedly the hyperscalers, as it will allow them to pursue AI infrastructure expansion with less incremental debt, reduced pressure on credit ratings, and less need to sacrifice buybacks or dividends, or issue equity. This could act as a positive share price catalyst for some of the hyperscalers, as it shifts the focus from balance sheet management back to earnings growth. An important caveat is that while such financing arrangements reduce upfront debt, they do not eliminate financial risk completely; commitments to use/pay for the infrastructure still represent long-term financial obligations that need to be fulfilled.
- Semiconductor and hardware players – The semiconductor value chain is another clear beneficiary. A larger and more diversified funding pool should accelerate and broaden the AI infrastructure buildout, directly supporting suppliers across the ecosystem, from upstream equipment makers and IC design companies to downstream chip fabricators. With semiconductors and related components accounting for roughly two-thirds of the total cost of a modern-day AI data centre, stronger and more sustained demand should translate into higher—and potentially front-loaded—orders for advanced chips, High Bandwidth Memory (HBM), custom silicon, optical interconnects, and power infrastructure. This could in turn reinforce earnings momentum for key suppliers.
Can’t stop this tr(AI)n. In short, Nvidia’s financing deal could mark another important milestone in the evolution of the AI megatrend. If AI compute successfully emerges as a new “financeable asset class” and asset-backed funding gains traction, it could unlock a much larger pool of third-party capital for AI infrastructure, extending and deepening the investment cycle. That said, investors should remain mindful of key risks, including potential maturity mismatches between financing tenors and the useful lives of AI compute assets, as well as the fact that hyperscaler balance sheet risks are not fully removed, since leased data centres still create long-term financial obligations. On that note, we remain cautiously optimistic about AI adoption as a long-term secular trend and continue to advocate having meaningful exposure to AI-related names on the growth-end of the barbell portfolio construct.
A good time to revisit hyperscalers. Hyperscalers have faced pressure for much of the year amid concerns over rising debt levels and uncertainty around potential software disruption from frontier AI models. However, Nvidia’s financing initiative could provide a positive catalyst for investors. The proposed USD500bn of third-party capital is already substantial in its in own right. If asset-backed financing gains traction, it could further ease the funding burden on hyperscalers. At the same time, recent earnings results from SaaS companies have generally shown resilience, helping to alleviate software-related concerns. Against this backdrop, we believe this may be an opportune time to revisit hyperscalers. That said, the group is far from homogeneous; we continue to prefer companies that have managed their debt levels more prudently.
Semiconductors remain a favoured sector. The semiconductor complex remains one of our preferred sectors given its direct exposure to AI capex. However, following the sector’s strong YTD run-up, investors need to adopt a more nuanced approach:
- Memory: The recent correction, combined with rapid earnings growth, makes the memory segment attractive from a valuation perspective in the immediate time frame. However, cyclical risks are rising as growth visibility becomes less clear for 2027/2028 and incremental supply comes online overtime.
- Fabless/IC design: Fabless players remain structural winners of the AI trend, but sky-high expectations expose the segment to volatility and execution risks; be selective and stick to names with pioneering architecture and strong ecosystem moats.
- Semiconductor foundries: We continue to favour leading-edge foundries that are expanding capacity while diversifying the geographical footprint of their plants.
- Semiconductor equipment makers: Lithography equipment makers as well as testing and assembly devices remain attractive second-derivative semiconductor trades. However, valuations are becoming increasingly rich for the former. Equipment stocks have increasingly been recognised as the “picks and shovels” of the AI buildout and have re-rated accordingly. Buying on dips is a sensible strategy.
Figure 1: The proposed USD500bn of third-party funding will enlarge the AI capex pool substantially
Source: Bloomberg, DBS
Note: Oracle's CY2026 capex figure is calculated by taking the average of its historical FY2026 and the midpoint of its FY2027 capex guidance
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