Op-Ed for Les Echos, Friday, June 5, 2026. The text below is a more detailed version of the article published in the print edition of the newspaper.
The general rebound led by AI and semiconductor stocks, since the onset of de-escalation in the Middle East, is not enough to dispel concerns about the current investment cycle. On the contrary, this exuberance raises questions about its sustainability, against the backdrop of a race among LLM providers to go public.
The already massive and growing weight of the sector in stock indices fuels a self-reinforcing dynamic, driven by passive investments: the more the sector grows, the more it attracts waves of capital, which in turn drive further growth—until a shock finally disrupts this mechanism.
From this perspective, markets have quickly dismissed material risks—such as energy shortages or supply chain pressures for essential semiconductor inputs—as temporary. This reaction is not merely diplomatic optimism. For three years, the financing model has relied on the stratospheric expansion of LLMs, while downplaying questions about their business models, the consequences of pricing adjustments in the era of agentic AI, or the intrinsic limitations of these models in terms of reliability.
The strong performance of cloud and semiconductor companies, combined with abundant liquidity and the dominance of a handful of firms, has reinforced the notion that demand for generative AI infrastructure will remain indefinitely robust.
Circular Capital Flows and Technological Concentration
This dynamic stems in part from the increasingly circular structure of financing. In 2026, projections for investments by Nvidia, Alphabet, Apple, Microsoft, and Amazon in “hyperscale” infrastructure range between $600 and $725 billion. The interconnections within this ecosystem are particularly tight. Nvidia occupies a central position as both the dominant supplier of GPUs and a key investor, reinforcing a loop in which investments, demand for computing capacity, and production capabilities are mutually dependent.
Microsoft has invested $13 billion in OpenAI, whose computing costs rely heavily on Azure—further boosting Microsoft’s cloud revenue and its ability to sustain its investments. Google has poured several billion dollars into Anthropic, which has simultaneously committed to massive cloud infrastructure contracts, split between Google and Amazon. Amazon itself has invested around $8 billion in Anthropic, which then developed subsidized access for developers through the cloud infrastructure of these same companies.
However, usage-based pricing models (per token) introduce uncertainty about whether this growth can translate into sustainable revenue, particularly if mechanisms of “subsidization” or indirect support were to wane. These mechanisms sustain growth expectations but blur the line between independent demand and self-perpetuating capital flows. A significant portion of the sector’s apparent strength rests on a small number of companies that simultaneously finance the infrastructure, provide the computing power, and support the applications consuming that power.
The rise of passive investing further amplifies this phenomenon. As major AI-related companies see their valuations climb, their weight in major indices automatically increases, attracting more financial inflows and intensifying market concentration. The “Magnificent Seven” now account for between 30% and 45% of the S&P 500’s market capitalization, depending on the period, while Nvidia’s market cap has surpassed $5 trillion.
Material Constraints and Financial Fragility
At the same time, the material foundations of this expansion are becoming increasingly critical. Cutting-edge AI depends on massive growth in electricity consumption, semiconductor manufacturing capacity, cooling systems, and data centers. Semiconductor production itself relies on complex industrial supply chains involving LNG, helium, specialty gases, copper, and stable electrical power.
Helium exemplifies this dependency. Qatar is one of the world’s leading exporters, and any disruption to maritime routes in the Gulf could quickly impact semiconductor manufacturers in East Asia. In Taiwan, several industrial groups have already expressed concerns about the security of LNG and helium supplies.
Moreover, the island sits at the heart of the U.S.-China diplomatic chessboard, with Trump’s approach amounting to a refusal to engage on the issue. Meanwhile, China has embraced the U.S. strategy of restricting semiconductor exports and is betting on building its own autonomy—centered around Huawei—which, in the long run, could challenge the dominance of American giants and their financial constructs.
Additionally, the rapid obsolescence of infrastructure adds another layer of fragility. Data centers built around current GPUs could lose a significant portion of their competitiveness for advanced computing workloads in as little as 18 to 36 months, even if they remain usable for inference or secondary applications. Yet, accounting depreciation periods typically span three to five years, potentially obscuring underutilized infrastructure and delaying visibility into long-term financial obligations.
This is not about questioning the AI revolution itself, but how financial markets treat LLMs’ growth as limitless—underestimating physical, financial, industrial, and geopolitical constraints… as well as the opportunities of alternative models. Large language models fit naturally into the current financial architecture because they deploy efficiently through cloud infrastructure and (partial) subscription-based business models. In contrast, physical AI—particularly in robotics—operates under a different logic. It depends more on real-world deployment and longer development cycles, which align less neatly with current financing mechanisms.
This dynamic echoes Minsky’s financial instability hypothesis, which posits that long periods of stability gradually encourage increasing risk-taking. The limits of financial and industrial resilience may soon force a rude awakening, perhaps triggered by profit-taking after a wave of IPOs.