Market confidence in AI infrastructure investment has recently wavered, as a confluence of factors—the rise of open-source models, growing calls for AI safety regulation, and negative free cash flow at hyperscalers—has weighed on sentiment across the tech hardware sector.

In a September 16 report on the Asian technology sector, JPMorgan analysts including Gokul Hariharan addressed each of these three major concerns. Their conclusion: the fundamental demand for computing power is unshaken, AI safety concerns are merely short-term noise, the capital expenditure cycle will persist, and semiconductor equipment is set to become the most critical constraint in the entire supply chain by around 2027.

Buying Compute Is Also a Good Business

The first market concern is that while "selling compute" is profitable, whether "buying compute"—that is, AI model makers and vertical AI application companies—can sustain profitability remains uncertain.

Analysts note that multiple reports currently show inference gross margins running between 60% and 80%.

The report further estimates that a model maker selling AI tokens could generate $20 billion to $40 billion in revenue per gigawatt of compute annually, far higher than the roughly $10 billion per gigawatt projected for 2025.

Citing data from Sapphire Ventures, the report highlights that over 80 vertical AI companies have achieved annual recurring revenue (ARR) exceeding $100 million in a remarkably short period, validating value creation at the downstream consumer of compute.

Open Source Isn't a Threat; It's a Demand Catalyst

The second market concern revolves around open-source models lowering token prices and eroding profitability for model developers. JPMorgan takes the opposite view. Analysts argue that open-source models have historically driven persistent declines in token costs—with long-term per-token cost reductions between 80% and 90%—which accelerates AI adoption across industries rather than dampening overall compute demand.

Analysts cite three key arguments: first, with current compute supply still tight, token consumption for both proprietary frontier models and open-source models will continue to grow strongly; second, the spread of open source will push AI deeper into the broader application layer; and third, more vertical AI companies will leverage low-cost open-source tokens to solve industry-specific problems, creating new revenue streams.

The analysts specifically point out that a temporary rebound in token costs in the first half of 2026 is an "outlier" caused by severe compute shortages combined with the rise of agentic AI—not a reversal of the long-term trend.

AI Safety Regulation: Short-Term Disruption, Not a Change in Training Pace

The third concern comes from the regulatory front. Recent reports citing the CEO of Anthropic publicly calling for a slower pace of AI model improvement have sparked worries about the future of compute demand.

Analysts believe this concern is also exaggerated. They assert that "AI models are evolving at an accelerating pace, and the proximity of RSI (Recursive Self-Improvement) is precisely evidence that technological advancement and AI scaling laws have not slowed down."

They further note that even if the launch of new models could be constrained by alignment issues, the training pace of frontier models itself is unlikely to decelerate. Meanwhile, open-source models will not pause, which will in turn pressure frontier labs to train faster to maintain their lead.

JPMorgan suggests that the AI safety debate stems from regulatory catch-up to rapid technological progress, not a signal of weakening demand. The report anticipates that over the next one to two years, generative AI could unlock entirely new market opportunities, akin to the expansion seen in coding and agentic workflows over the past 18 months.

Capital Expenditure: Low Leverage, Accelerating Cash Flow

The financing capacity for hyperscaler capital expenditure is the fourth market concern. Several cloud providers have entered a phase of negative free cash flow, raising external financing pressures.

JPMorgan's assessment is that there remains room for growth in capital expenditure over the coming years. Three reasons support this view: first, the large hyperscalers' net debt-to-equity ratio is currently only 13%, indicating still-low leverage; second, public cloud business growth is accelerating with higher pricing, serving as a critical source of operating cash flow expansion; and third, equity financing from hyperscalers and downstream compute buyers can provide additional capital support.

Analysts forecast that combined capital expenditure for Amazon, Microsoft, Google, Meta, Oracle, Coreweave, and SpaceX will rise from $443 billion in 2025 to an estimated $933 billion in 2026, and further to $1.577 trillion in 2027, representing year-over-year growth rates of 71%, 110%, and 69%, respectively.

The analysts also caution that rising interest rates and shifts in risk appetite within certain AI infrastructure financing segments remain variables to monitor closely.

Semiconductor Equipment: The Next Bottleneck Is Forming

At the supply chain level, JPMorgan believes that looking towards 2027, semiconductor equipment (SPE) will transition from being a "cleanroom and other areas" bottleneck to the most central constraint across the entire supply chain.

Demand-side drivers include: TSMC significantly increasing equipment investment for its N2, N3, and A14 process nodes; challengers like Intel, Samsung Foundry, and Terafab also raising capital expenditure; mature-node fabs initiating their first new investment cycle in four years; DRAM and NAND Flash makers rapidly expanding equipment procurement in 2027-2028 as cleanroom capacity comes online; and Chinese memory investment accelerating significantly over the next two years.

On the supply side, JPMorgan notes that semiconductor equipment makers are beginning to enjoy a rare pricing power dividend, driven by tight capacity, rising input costs, and rush orders from multiple customers.

Packaging and Substrates: Persistent Bottlenecks and the Rise of Optical Interconnects

At the component level, JPMorgan identifies IC substrates and advanced packaging as continuing critical bottlenecks. Substrate supply is highly concentrated among top-tier players like Unimicron and Ibiden, with capacity expansion cycles taking around 2.5 years. With AI accelerators driving higher substrate usage per package and the introduction of EMIB-T packaging technology by the end of 2027, the supply-demand gap is expected to widen further.

Regarding optical interconnects, JPMorgan expects Google, AWS, and Nvidia to fully adopt NPO (Near-Package Optics) solutions within the next two years to address performance bottlenecks in GPU-to-GPU, GPU-to-CPU, and memory interconnects. CPO (Co-Packaged Optics) remains the long-term direction, but supply chain maturity will take time.

Memory: Healthy Fundamentals, But Sentiment Needs Repair

The memory sub-sector is where JPMorgan holds a relatively cautious stance. The report notes that concerns about HBM spec downgrades, efficiency gains from algorithm improvements (such as Recurrent Transformers) compressing memory usage, and the migration of KV cache to lower-tier storage like DDR or NAND have made the investment thesis for this sector noisy.

However, the fundamentals are not pessimistic: the supply-demand balance is not expected to tighten before 2028, and prices should continue to rise through 2027. JPMorgan believes that if AI sentiment recovers in the fourth quarter of 2026, memory stocks could follow suit. But until then, investor participation may remain subdued relative to other tech sub-sectors.

Which Segments Will See Stronger Price Increases by 2027?

JPMorgan outlines the segments expected to see broader or higher price increases in 2027 compared to 2026: Wafer foundry—TSMC is expected to raise prices across all process nodes in 2027 (with 2026 limits to advanced nodes), with 8-inch mature-node price increases potentially reaching 10-15%, up from 8-10% in 2026; OSAT—price increases expand to include wire bonding and mature flip-chip packaging; Substrates—a pricing upcycle begins, with EMIB-T introduction further tightening supply-demand; High-end CCL (M7 and above)—dual drivers from AI demand and server spec upgrades, with supply growth persistently lagging demand; Memory—the pace of price increases is expected to moderate; PCB materials—pricing momentum is also expected to weaken.

The analysts point out that the EPS revision trend for the Asian tech hardware supply chain remains healthy, and valuations are attractive. Market sentiment is currently weak, but there are no clear signs of an imminent capital expenditure downturn. The key catalyst for the next market upswing will continue to come from further validation of the monetization capabilities of the model layer and the AI application layer.