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You might have seen MainFT’s story on investor angst over Big Tech’s AI spending spilling into the bond market. It features some interesting JPMorgan research that’s worth digging into and quoting at length.
Here is the killer lead quote from the JPMorgan report, written by a horde of analysts from the bank’s investment grade credit, high-yield bonds, industrials, semis, utilities, and asset-backed securities team (their emphasis):
The global data center and AI build-out will be an extraordinary and sustained capital markets event. Building out global data center and AI infrastructure and related power supplies could cost over $5 trillion in our view, with at least one consultant calling for $7 trillion of global AI-related capex.
Funding that extraordinary growth will likely require participation from every public capital market as well as private credit, alternative capital providers and even government involvement. The question is not “which market will finance the AI-boom”. Rather, the question is “how will financings be structured to access every capital market.”
The sheer scale of those investments has critical implications across the credit market landscape. Big picture, for various reasons public bond and syndicated loan market growth has slowed post-COVID recovery (shrunk in the case of High Yield Bonds). AI/Data Centers are likely to drive a re-acceleration of market growth.
Extraordinary and sustained indeed. JPMorgan’s base case is that another 122 gigawatts of data centres will be built globally in 2026-30. Using the 1.2 rule-of-thumb multiplier to get the energy usage, this would equate to 146.4 gigawatts of electricity. That’s . . . a lot.
In fact, JPMorgan’s analysts say their data centre forecast would have been even higher if it hadn’t been for the increasingly acute constraint of energy supply.
The scale of demand for compute remains astronomical, with actual growth somewhat constrained by physical limitations. Our base case estimates call for 122 GW of global data center infrastructure capacity installations from 2026-2030, at a rapidly accelerating rate. We have written in the past about data center growth being constrained by real estate, energy and power, water, commodity prices and capital. Thankfully, that remains true, else the potential funding needs would be even higher. Our colleagues in Asia produce an intriguing bottoms up unconstrained forecast based on semiconductor orders that implies growth of 144 GW globally through YE2028 — i.e.basically the same amount of growth in the next three years as we forecast over the next five years once factoring critical constraints.
Power is the most important of those constraints. Current lead times for new natural gas turbines have ballooned to 3-4 years, and nuclear plants have historically taken 10+ years to build. Adding 150 GW of power in a timely manner is a remarkable challenge, particularly in light of grid upgrade requirements. Recent comments from certain power producers suggest some flexibility is possible from ramping up peakers more aggressively, but that has implications for retail prices. Balancing ultimate retail electricity prices (still stable as a percentage of income, at least in the US) is a politically important and sensitive aspect of managing the data center boom.
So how is all this going to be paid for? A mix of earnings and investment grade bonds, but eventually everything including the kitchen sink will have to be chucked at it.
Funding curve manageable now, larger call on alternatives down the road. Annual data center funding needs in 2026 are on the order of $700 billion, which could be entirely financed by hyperscaler cash flow and High Grade bond markets. However, 2030 funding needs are in excess of $1.4 trillion, which will likely require funding contributions from all capital providing markets.
Hyperscaler cash flow will have to do an enormous amount of the heavy lifting. Hyperscalers are generating over $700 billion of operating cash flow per annum, and reinvesting ~$500 billion of that back into capex . Note that ~$500 billion of reinvestment back into the collective businesses does not include ~$250 billion of research and development spending per year. We are assuming approximately $300 billion per year of hyperscaler capex reflects cash flow funded AI and data center capex (while AI is crowding out a lot of spending, it’s not crowding out all spending).
As an aside, we do wonder if the equity market has appropriately priced in so much of the hyperscaler earnings getting ploughed into capex rather than buybacks.
Anyway, JPMorgan then looks at how this will reshape the US corporate bond market:
High Grade capital markets will look very different. We think High Grade markets could absorb $300 billion of AI/data center related paper over the next year, and are assuming $1.5 trillion of funding from High Grade bond markets over the next five years. AI/Data center capex-related sectors already represent 14.5% of the JULI Index, which is larger than the US Banks component of the index. Mathematically, if anything like our forecast plays out, AI/Data Center related sectors could represent north of 20% of the market by 2030. Historically, lumpy issuance from select sectors is not “new” to High Grade. Recent examples like Healthcare (‘21-’24) and Telecom (‘16-’19) have been manageable and in both cases led to 15-20bp of underperformance from the impacted sector.
The securitization market is a natural home for data center funding, but… We expect the securitization markets to absorb $30-$40 billion per annum of data center risk, ramping up modestly over time. Critically, the securitization market is providing construction financing via fully amortizing structures, as opposed to permanent stable financings. That somewhat limits the footprint….perhaps for now.
There is capacity for meaningful data center funding from the Leveraged Finance markets, but the history of “the new largest sector” has been poor. We think the new issue market across Leveraged Finance could comfortably finance $150 billion over the next five years, with the recent examples of Financials supportive of that number.
However, the history of High Yield Telecom in the 1990s and Energy from 2010-2015 as both grew to become the largest sector in the marketplace at the time does at least deserve some consideration.
Umm, yeah.
JPMorgan’s analysts say that private credit and public money will also have big roles to play, while banks will primarily play the role of bridging capital, arranging and underwriting financing packages but quickly dumping the risk on to investors:
Potentially large call on alternative capital and governments to bring this to fruition. Private credit has become a key part of the leveraged finance ecosystem, and the sheer amount of dry powder (~$466 billion on an unleveraged basis today prior to any incremental fundraising) implies a meaningful role in filling the potential ~$1.4 trillion funding gap in our analysis. There has been substantial focus on recent private deal structures (with the Beignet private to public structure particularly notable). The flexibility within private credit and alternative structures to better match cash flows and solve for ratings outcomes does argue for a role for private credit beyond just providing capital.
Some might ask — where are the banks? We expect the banking system to continue to be an important source of temporary/bridge capital, but financing long-term assets permanently with short-duration bank loans would be a material asset/liability mismatch.
Government involvement has run the gamut from aggressively and publicly supportive in the US to a safety-focused regime in the EU. More aggressive financial support by governments is possible, particularly if/when national defense concerns around AI grow.
So is this all just another dotcom/cable/railroad/canal bubble, as even some AI insiders say? Unsurprisingly, JPMorgan’s analysts heavily hedge their bets.
The path from here to there will not just be “up and to the right”. Our biggest fear would be a repeat of the telecom and fiber build-out experiences, where the revenue curve failed to materialize at a pace that justified continued investment. For now, commentary from large corporates suggests benefits are starting to be realized at scale. More interestingly, OpenAI just publicly commented that they have achieved a $20 billion annualized revenue run-rate already. However, breakthroughs or accelerated efficiency gains — as people initially thought occurred with Deepseek — could drive an overcapacity/dark fiber situation.
Big picture, to drive a 10% return on our modeled AI investments through 2030 would require ~$650 billion of annual revenue into perpetuity, which is an astonishingly large number. But for context, that equates to 58bp of global GDP, or $34.72/month from every current iPhone user, or $180/month from every Netflix subscriber. How that is apportioned between corporations, governments and consumers is, of course, a long-term debate. Regardless, even if everything works, there will be (continued) spectacular winners, and probably some equally spectacular losers as well given the amount of capital involved and winner takes all nature of portions of the AI ecosystem.
These are all extracts from JPMorgan’s synopsis. The full 52-page report is definitely worth reading if you have access. As the bank’s analysts note: “It is hard to imagine the world deploying $5 trillion of capital without at least some hiccups.”
