The Rise of Tech Companies and Global Financial Stability Risks

Financial stability is entering a new phase of intelligent interdependence. The AI investment wave is no longer a story that lives only in model outputs or equity multiples — it is now a real-economy construction boom that is rewiring energy systems, capital markets, and corporate balance sheets. The systemic frontier has shifted: the institutions that exercise de facto control over stability are increasingly technology and infrastructure firms whose contracts, capital flows and compute capacities span borders and asset classes. Recent mega-deals, explosive capex and rising debt to fund data-centre buildouts make the systemic picture more urgent and measurable.

The phrase Too Big to Fail dates back to the 1984 rescue of Continental Illinois, when a US Congressman remarked that “we have a new kind of bank.” That phrase became the cornerstone of modern financial-stability thinking: size, concentration, and contagion as the central pillars for identifying and addressing systematic risk. 

Four decades later, the systemic frontier has moved. The institutions that now hold the levers of stability are not banks but technology firms whose scale, reach, and contracts stretch across borders and asset classes.

In 2020, Dr. Carl Öhman and Prof. Nikita Agarwal used the term Systemically Important Technological Institutions to highlight the systemic risks of Big Tech. They argued that network power and data dominance could trigger crises of confidence—just like bank runs.

Building on that, academics Prof. M.P. Ram Mohan and Sai Muralidhar, in 2023, proposed that large tech firms FAAMG, Facebook (Meta), Apple, Amazon, Microsoft and Google, resemble TBTF institutions and need resolution capacity similar to banks.

Once FAAMG, then FAAAMG, the constellation now centres on Microsoft, OpenAI, Oracle, and Nvidia—firms that dominate the global AI stack. Together they control technologies that underpin computation, storage, and model training, and their investment decisions influence energy grids, semiconductor supply chains, and even sovereign fiscal priorities.

Their dominance is reflected in global equity markets. Across the world’s major economies, AI-related companies have accounted for between one-seventh and more than half of all incremental market capitalisation over the past three years. A composite global AI index now stands as the second-largest equity market on the planet, valued at roughly $25 trillion—ahead of China’s $13 trillion, Japan $8 trillion, and India $3 trillion. Market capital has, quite literally, migrated from credit to compute.

Structural Interdependence
The defining risk today is not Too Big to Fail but Too Intertwined to Untangle. These technology giants are linked by contracts, capital flows, and compute dependencies so dense that none can be easily isolated. 

The systemic risk is defined less by size alone and more by depth of interdependence. Supply-chain coupling — GPU roadmaps, semiconductor production, and network hardware are tightly linked to cloud commitments. Contractual coupling — long-term power purchase agreements, capacity leases, and colocations create cross-jurisdictional obligations that bind corporate and sovereign actors. 

Capital coupling — the same hyperscalers and suppliers are both clients and counterparties with strategic equity stakes, convertible instruments, syndicated loans and joint investments blur client/counterparty distinction. Market coupling — public markets are funding infrastructure; bond and equity holders are exposed to the physical rollout of AI capacity. When capital, contracts and computation converge, contagion travels not only through chips and code but through corporate balance sheets and sovereign fiscal exposures

Nvidia’s GPU roadmap influences Oracle’s cloud commitments; Microsoft’s investment determines OpenAI’s solvency; and Broadcom’s network speeds affect all. Together they form a macro-balance-sheet whose stress points move in synchrony. A single failure of confidence could cascade across the AI supply chain just as interbank stress once travelled through money markets.

Risk also arises from interlocking ownership and capital flows. The same companies that depend on one another for compute or data are also investors in one another’s balance sheets. Strategic stakes and joint-funding arrangements blur the line between client and counterparty. 

The AI Infrastructure Loop
At its foundation sit the compute providers such as wafer fabricators such as TSMC and Samsung, chip designers Nvidia and AMD, and network specialists like Broadcom. 

Around them operate the cloud platforms—AWS, Azure, Google Cloud, Oracle Cloud—that lease compute at scale. 

Above this layer sit the model developers—OpenAI, Anthropic, xAI, Gemini, Claude—who transform computation into cognition. 

Encircling them are digital networks—Meta, YouTube, LinkedIn, TikTok, X, Instagram, WhatsApp—that distribute AI-generated content and feed behavioural data back into the models. 

Each layer powers the next in a cascading loop where compute drives intelligence, intelligence drives networks, and networks return data that demand still more compute.

Any imbalance in capacity mismatch in technology can potentially threaten the entire equilibrium system and competitive forces among GPU chips, cloud capacities and electricity power grid system and even water supply to cool 
 
AI investment is now visibly reshaping the real economy. Construction indicators like the Dodge Index signal a coming data-centre boom through 2026. The Dodge Momentum Index is an early economic thermometer — and its spike confirms that the AI wave is spilling into bricks, steel, and energy grids. Mega-deals such as Microsoft–IREN $9.7 billion; AWS–CIFR $5.5 billion, show energy assets rapidly converting into AI-cloud capacity. Big Tech bond issuance of around $75 billion and soaring capex—up to 94% of operating cash flow—mark a deep, balance-sheet-level transformation.

Public Market Exposure
Public capital markets are now part of this loop. Large technology firms are issuing billions of dollars of bonds to finance AI and cloud infrastructure, while new equity offerings will soon extend this exposure to global investors. 

If OpenAI proceeds with its proposed public offering, it will add another layer of systemic coupling—inviting public capital into an already connected web. When capital, contracts, and computation converge, contagion travels not only through chips and codes but through corporate balance sheets. As debt and equity flow into the infrastructure of intelligence, markets become both beneficiaries and potential amplifiers of systemic risk.

What makes this transformation systemic is that the AI loop no longer runs alongside finance—it is embedded within it. Payment systems, trading venues, and market infrastructures now rely on AI for authorisation, execution, and surveillance. Information that travels is also information that transacts. The same architectures that generate predictions now price assets, allocate liquidity, and authenticate value. Credit scoring, fraud detection, and risk management all depend on embedded intelligence.

This fusion has produced a new class of systemic actors—Tech–Finance Systemic Entities—entities that determine value and deliver it, where the intelligence that prices risk, and the infrastructure that moves capital now reside within the same system. Their failure would transmit shocks across the grid of intelligence and the grid of capital. They include large payment processors, AI-driven trading platforms, and cloud or model operators that host mission-critical financial applications. They are not just intermediaries of credit; they are intermediaries of chips, codes, computation and cognition.

Supervision must now extend beyond credit and liquidity to include capital concentration in compute, supply-chain fragility, and cross-contract dependencies among hyperscalers. As AI moves from operating around finance to operating within it, the systemic perimeter has shifted once more—from platforms of users to platforms of machines, and now to platforms of money itself.

Where the 20th century’s crises were driven by leverage and liquidity, the 21st century’s  vulnerabilities will arise from interconnected intelligence—where a fault in Cs (Chips-Codes Computations ) can become a fault in credit and capital, and the shockwaves of information move faster than money itself.