This piece is based on my research, The Global AI Race amid Asset Bubble Dynamics, which I will present on Friday, 15 May 2026, at King’s College London. Room KIN G21, King’s Building. 10pm-6pm.
Deescalation in the Iran war has unleashed a market rebound led by AI and semiconductor stocks. Its strength in the face of major challenges actually raises questions about the sustainability of the broader boom. Physical disruptions persist, from energy shortages to critical materials, and the de-escalation remains fragile, with Donald Trump unwilling to resume hostilities yet unable to consolidate a lasting and realistic agreement. Long periods of strong performance, abundant liquidity and dominance by a few large firms have trained markets to treat disruptions as temporary and manageable.
Despite AI’s many promises, circular funding and loss-making business models underpin the financial dynamic. The rise of passive investing supports this mechanism. Large tech firms receive a major share of these investments and in turn keep fueling circular financing within the sector, helping maintain their own earnings. This echoes various theoretical frameworks on asset bubbles.
The global bet on the scalability of LLMs to reach human-level intelligence fits perfectly within this financial logic. Meanwhile, essential developments in physical AI, particularly for robotics, suggest a different technological path and financial structure.
The Financial Mechanic and Its Technological Impact
The rally in AI-related stocks reflects a market downplaying industrial effects from disruptions in the Gulf. Markets quickly returned to the dominant narrative of rising AI investment, continued infrastructure expansion and strong demand for advanced semiconductors. Nvidia, its suppliers, memory manufacturers and cloud infrastructure firms led the rebound. So far, there have been no major visible disruptions to semiconductor production, reinforcing investor positioning in the sector despite geopolitical uncertainty. Investors largely treated the situation as a temporary geopolitical disturbance rather than systemic stress.
Semiconductor stocks are led by AI infrastructure growth expectations. Strong earnings guidance reinforces the belief that compute demand remains effectively unconstrained. As long as digital giants maintain investment plans and financing conditions remain supportive, markets tend to interpret geopolitical shocks as disturbance rather than turning points. The current rally reflects confidence in AI-driven earnings growth, continued hyperscaler investment and passive flows reinforcing index concentration in a small group of large technology firms. The so-called Magnificent Seven now represent 30–45% of S&P 500 capitalization and Nvidia’s market cap has exceeded $5 trillion.
Part of this strength reflects the increasingly interconnected structure of AI-related investment. Amazon, Microsoft, Alphabet and Meta are projected to spend well over $600 billion on AI infrastructure in 2026, with some estimates approaching or exceeding $700 billion. Microsoft has invested approximately $13 billion in OpenAI, whose workloads reinforce Azure demand and revenue. Alphabet has invested heavily in Anthropic, with reported financing commitments potentially reaching $40 billion. Anthropic has reportedly agreed to spend roughly $200 billion on Google Cloud infrastructure over five years. Amazon has also expanded its investment in Anthropic beyond the original $8 billion commitment, alongside reported long-term AWS procurement agreements exceeding $100 billion over a decade.
The prospect of usage-based pricing models (per token) introduce uncertainty over whether the observed expansion in usage will translate into durable willingness to pay once investors “subsidies”, credits, or strategic cross-subsidisation are reduced. This creates a gap between measured demand growth and underlying monetisation capacity.
Obsolescence add to the risk. Semiconductor progress is rapid enough that data centers built on current GPUs may become less efficient within 1.5–3 years, while remaining useful for inference and secondary applications. Depreciation schedules of 3–5 years stretch asset lifetimes beyond their peak economic usefulness, potentially masking underutilized infrastructure. Off-balance-sheet leasing structures can further delay visibility of long-term obligations.
Furthermore capital concentration shapes technological choice. Large language models fit the current financial structure well. They scale on GPU infrastructure and align with subscription-based revenue models. Physical AI, particularly robotics, operates differently. It depends on physical systems, long development cycles and data that cannot be easily replicated in cloud environments.
Theoretical frameworks explain these dynamics. The notion of reflexivity highlights how rising valuations attract further capital. Minsky’s instability hypothesis describes how stability encourages risk-taking. Shiller’s irrational exuberance emphasizes the role of narratives in driving capital flows. The unabated rise of passive investing reinforces these effects through index-linked momentum.
The US–China chip competition adds complexity. US export controls on H100 and H200 GPUs force China to rely on domestic alternatives, though performance gaps vary by workload as local accelerators improve in certain use cases. State-backed funding reduces political constraints but does not remove inefficiencies. Chinese firms such as Alibaba and Tencent have significantly increased AI and cloud-related spending, while hardware constraints force higher spending for equivalent compute output. The result is a more fragmented semiconductor ecosystem, with duplicated investment.
Physical Supply Chains and Semiconductor Production
Financial markets and physical supply systems are not fully aligned. The Strait of Hormuz remains a critical energy and logistics corridor. It also affects flows of industrial inputs that are difficult to substitute quickly. Semiconductor production depends on LNG, helium, specialty gases, copper, cooling systems and stable electricity supply. Several of these inputs remain exposed to maritime risk in the Gulf region, though current data suggest limited constraint at this point.
Helium illustrates this dependence clearly. Qatar is one of the world’s largest helium exporters and any disruption to transport routes can quickly affect semiconductor manufacturers in East Asia. Industry groups in Taiwan have raised concerns about LNG and helium security. The issue is the gradual reduction of redundancy in already tight supply systems.
Market pricing assumes that conditions will normalize before supply constraints affect production or expansion. This assumption may prove correct. The semiconductor industry has repeatedly adapted to logistics shocks through inventory management, supplier diversification and government coordination. Markets are not ignoring geopolitical risk entirely. Oil prices, shipping costs and insurance premiums have all reacted. But equity investors—especially in AI-linked sectors—are still assigning relatively low probability to prolonged industrial disruption compared with current earnings momentum and investment trends.
Physical Constraints, Valuation and Fragility
The AI investment cycle has created a tight link between financial expectations and physical infrastructure. Unlike earlier software cycles, frontier AI requires continuous expansion in electricity use, data center capacity, semiconductor fabrication and cooling systems. This exposes the sector to physical constraints as well as financial conditions. Transformer shortages, copper constraints and grid delays are already emerging in several regions. Hyperscale expansion increasingly competes with broader industrial and public electricity demand.
These constraints are only partially reflected in market pricing, which remains focused on growth narratives and future monetization potential. Index composition also plays a structural role. Semiconductor and hyperscaler firms now account for a large share of global equity indices. As their valuations rise, passive investment flows reinforce their dominance, strengthening momentum regardless of changes in underlying risk conditions. Capital concentration therefore becomes self-reinforcing during periods of uncertainty.
Financial resilience and industrial resilience are no longer moving in lockstep. This does not imply current valuations are irrational or that a correction is imminent. The AI buildout is producing real revenue growth, infrastructure expansion and rising demand for compute. The issue is that markets increasingly extrapolate this trajectory as sustainable or unlimited, while assuming geopolitical and industrial disruptions remain temporary.
Historically, strong technological cycles often produce similar assumptions. During periods of rapid capital concentration, markets tend to prioritize scale and dominance Fragility tends to appear only when constraints persist longer than expected or when financing tightens alongside operational stress. The current environment contains elements of both outcomes. Semiconductor demand remains strong, but its supporting infrastructure is increasingly exposed to geopolitical concentration. Taiwan remains central to advanced manufacturing. Gulf shipping routes remain important for energy and industrial inputs. Electricity systems are under pressure from AI-driven demand growth. Much of this expansion still depends on sustained capital expenditure and favorable financial conditions.
The recent market rally may reflect less a resolution of geopolitical risk than the growing dominance of the AI investment framework in global markets. Investors still appear to assume that the importance of AI infrastructure will continue to justify large-scale capital deployment despite visible physical constraints and the lack of a viable business model.