- AI capex has shifted from cyclical cloud spending to a structural investment trend
- Rising AI spending is tightening operating cash flow coverage, prompting external funding
- Hyperscaler leverage remains modest; debt ratios still below broader technology sector averages
- Investors should be selective, favouring capital-efficient companies with visible monetisation
Decoding AI capex trends. AI capex has been a defining theme in global markets since early 2024, with spending rising steadily and its significance expanding from being an equity earnings driver to a broader GDP growth lever. In this report, we analyse the trends that have shaped AI investments over the past two years and examine their implications for investors.
AI capex has shifted from elevated to structural. In early 2024, hyperscalers (large technology companies involved in the provision of cloud services and computing infrastructure) were already spending heavily on AI, but most investors could still view AI capex as an extension of the existing cloud investment cycle. However, by 2025 and 2026, this interpretation has become less convincing. Capex guidance has been repeatedly revised upward, often within the same fiscal year, suggesting that companies are not just executing against a fixed plan but continuously recalibrating infrastructure needs as AI demand, model complexity, and competitive pressure evolve.
This shift has prompted changes in funding mix. Before 2024, hyperscalers could fully fund cloud capex from operating cash flow while still returning capital to shareholders. High margins, asset-light software/advertising economics, and strong cash balances meant external capital was optional. However, as the nature of capex has shifted from cyclical to structural and amounts have gotten materially higher, coverage from operating cash flow has become tighter. Therefore, post-2024, hyperscalers are now supplementing operating cash flows with debt, and in some cases, equity, leases, and private capital to fund their AI capex.
Bubble fears are ever present. Unsurprisingly, the move towards external funding has exacerbated bubble fears. Broadly speaking, these bubble fears can be distilled into two main concerns:
- Excess leverage and financial risk. Firstly, investors are getting increasingly concerned that hyperscalers are losing financial discipline and heading down the same path of excess leverage and heightened bankruptcy risk of past technology companies during the dotcom crash.
- Returns falling short of sky-high expectations. Secondly, there is a perpetual fear that returns from these AI infrastructure projects may fall short of expectations—the concern is that future payoffs may not be enough to justify the gargantuan spending and near-term pressure on earnings and balance sheets for these companies.
These are the two key issues that we will address in this report.
Leverage remains modest by conventional standards. While external funding has indeed become more common among hyperscalers, their overall debt ratios remain modest by conventional standards. The extent and rationale for taking on more debt also differ significantly by company. Microsoft, for instance, has actually deleveraged over the past three years from a total D/E perspective. Meta and Alphabet, on the other hand, have undergone a major directional shift in leverage, but overall debt levels remain very moderate. Even Amazon, which is the most highly geared among the hyperscalers, has a total D/E of c.51%, comfortably below the NASDAQ Composite company average of 68.5%. On aggregate, it is fair to conclude that hyperscalers are not over-levered though some are becoming much less net-cash-heavy.
Intention matters. When analysing the shift towards external funding, it is also important to distinguish between a shift that is intentional (and part of a wider strategy) and one that is borne of necessity. This distinction matters because the latter almost always comes with reduced financial flexibility. Among the four main hyperscalers that are actively developing AI data centre capacity, we can classify them into three buckets:
- Bucket 1: Strategic capital-structure management – Microsoft. As mentioned earlier, Microsoft is an exception amongst the hyperscalers as it has not turned to external funding. Other than a USD24.4bn debt issuance in 2024, it has not raised additional debt or equity since. Despite having significant AI capex, its cash generation and balance sheet allow it to fund the cycle without a major pivot to external financing.
- Bucket 2: Intentional external funding to preserve flexibility – Alphabet and Meta. Alphabet and Meta have both raised significant amounts of capital (debt and equity) since 2024. However, they are not doing so out of necessity. Rather, the capex opportunity has become large enough that external funding helps preserve liquidity, avoid over-concentration in debt, and maintain optionality around buybacks, dividends, and future infrastructure commitments.
- Bucket 3: External funding is becoming more practically necessary – Amazon. Amazon is closest to a true funding need because capex is absorbing a much larger share of operating cash flow. Unlike Meta and Alphabet, Amazon does not partake in share buybacks or pay dividends. The pressure is more directly from the scale of infrastructure investment itself.
Therefore, on the worry of excess leverage and financial risk, it is fair to say that hyperscalers remain in good stead; external funding, where applicable, is still mostly driven by conscious decisions around capital structure management rather than pure necessity.
So far, so good. On the concern about returns potentially missing the mark, the answer is less straightforward. Our take on this issue is a pragmatic one: While there is always a risk of overbuilding, the risk remains small to negligible for now. AI demand visibility continues to improve faster than supply can be built, and hyperscalers are seeing strong demand signals from enterprise AI workloads, model training, inference, cloud customers, and internal AI products. This robust demand growth has kept profitability metrics such as return on equity, operating margin, and free cash flow per share for Big Tech companies on a steady uptrend so far despite the massive increase in capex and pivot towards external funding.
“Over-spending” is the cost of staying in the AI game. AI, as a mass-market technology, remains in its infancy, having only gained broad traction in late 2022. If we map that against a similar world-changing technology like the internet, which took roughly 25 to 30 years to reach mainstream commercial ubiquity, we can appreciate how AI has a significant amount of growing to do. One could argue that “over-spending” is simply a pre-requisite for these companies to remain relevant and competitive in an AI landscape that is still growing by leaps and bounds. No technology heavyweight wants to be capacity-constrained and risk falling behind if AI demand surprises on the upside. All this to say, bubble concerns are not unfounded, but they remain grounded more in fear than reality for now.
Investment implications
Semiconductors continue to benefit from external funding and larger spending. The shift towards external funding will provide AI companies and hyperscalers with more liquidity to accelerate infrastructure build-outs beyond the USD700-800bn mark that is currently projected for 2026. This acceleration would transmit directly and positively to the semiconductors and upstream supply chain. Stronger and potentially front-loaded demand for advanced chips, High bandwidth memory (HBM), custom silicon, optical interconnects, and power infrastructure should support earnings momentum for suppliers. Order visibility and pricing power in tight segments will be reinforced as more capital flows into productive AI capacity.
More nuanced for hyperscalers. The big cloud platforms remain beneficiaries as well, but investors should adopt a more nuanced approach moving forward. Their scale, data advantages, and distribution reach give them a privileged position to capture enterprise workloads as AI moves from experimentation to production. However, the bar for outperformance is rising. It is no longer enough to only spend heavily—returns must eventually materialise in higher utilisation, incremental revenue, or improved profitability. Signs of overcapacity or disappointing conversion of spending into profitable growth could derail risk sentiment. The increase in external funding for select hyperscalers creates greater potential for balance sheet strain and investor scrutiny if monetisation lags. Not every hyperscaler will clear this hurdle with equal ease.
Constructive but selective. In short, the funding environment is extending, rather than exhausting, the AI investment cycle. The broadest and most durable gains are likely to remain in the semiconductor and infrastructure supply chain where demand tailwinds look durable, while far lower capex requirements make external fundraising needs and shareholder dilution relatively more manageable. For platform companies, the opportunity is real but requires thoughtful handling. Only those that combine visible monetisation with capital efficiency are likely to justify and sustain the valuations that the market is already assigning them. Selectivity, rather than blanket exposure, will determine who wins from the next phase of the AI build-out.
Figure 1: Timeline of hyperscaler capex revisions – Strong upward bias
Source: Bloomberg, DBS, various news outlets and company announcements
Note: Microsoft was not included as the company does not provide full-year capex guidance
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