To understand why Nvidia’s valuation has ascended to an extraordinary $5 trillion, one must closely examine the underlying data and accompanying charts that illustrate how this titan of the technology industry has become the foremost beneficiary of the explosive wave of capital directed toward artificial intelligence infrastructure. These visual and quantitative indicators reveal the extent to which Nvidia is capturing an exceptional portion of the world’s escalating investment in AI capabilities — an investment spree reshaping entire sectors of global industry.
Artificial intelligence has now progressed well beyond its experimental origins into a dynamic industrial phase characterized by massive-scale deployments of computational power. In this new era, the measure of a data center’s magnitude is no longer confined to metrics such as square footage, the number of racks, or the raw count of servers it houses. Instead, the benchmark has evolved toward an energy-based standard — specifically, the number of gigawatts of computing capacity it delivers. Financial analysts and investors on Wall Street, recognizing the magnitude of this transformation, have begun quantifying and forecasting the cost of each of these gigawatts, calculating which corporations are most likely to emerge as winners from this trillion-dollar expansion in technological infrastructure.
According to insights from TD Cowen analysts, one gigawatt of computing capacity roughly equates to the energy output of an entire nuclear power reactor. This comparison provides a vivid contextual anchor for grasping the monumental scale of current AI data center projects — facilities such as xAI’s colossal Colossus 2 complex in Memphis, Meta’s Prometheus installation in Ohio, Hyperion in Louisiana, OpenAI’s ambitious Stargate development, and Amazon’s Mount Rainier project in Indiana. These installations symbolize a new industrial archetype: immense technological structures that draw staggering amounts of power while fusing capital, silicon, and advanced engineering to manufacture machine intelligence. The result is an extraordinarily resource-intensive process that redefines both the architecture of computing and the economics of energy.
Bernstein Research’s recent analysis assigns a concrete cost to this transformation: constructing one gigawatt of AI data center capacity now requires approximately $35 billion in investment. Although that figure may appear almost implausibly large, it marks the financial foundation of the emerging AI economy. Each gigawatt, therefore, represents far more than a simple measure of electrical throughput; it acts as a proxy for an evolving ecosystem that integrates semiconductor manufacturing, high-speed networking, power infrastructure, building construction, and large-scale energy generation into a unified industrial symphony.
Within this enormous capital outlay, certain components account for the bulk of spending. Chief among them are graphics processing units, or GPUs, which function as the computational heart of AI systems. Bernstein estimates that GPUs alone comprise roughly 39% of all expenditures within such a facility. Nvidia, through its market-dominating chips like the GB200 and the forthcoming Rubin series, commands this segment almost entirely. With gross profit margins hovering around 70%, Bernstein’s calculations suggest Nvidia captures close to 30% of total AI data center expenditure as profit, a level of profitability so vast that it helps explain the company’s unprecedented valuation. TD Cowen further clarifies this picture: each gigawatt of AI computing capacity corresponds to the production of over one million GPU dies — the intricate silicon brains driving these systems. TSMC, Nvidia’s manufacturing partner, therefore garners an estimated $1.3 billion per gigawatt from fabricating many of these components. Even as competitors such as AMD and Intel race to narrow the gap, and cloud hyperscalers including Google, Amazon, and Microsoft develop custom AI accelerators or ASICs to trim hardware costs, GPUs remain — as Bernstein aptly puts it — the gravitational and economic center of the AI universe.
The next critical layer of expenditure involves networking — the dense connective tissue enabling scores of GPUs to act cooperatively as one colossal computational organism. Bernstein attributes about 13% of data center construction costs to this area, encompassing high-performance switches, optical interconnects, and advanced cabling. Firms such as Arista Networks, Broadcom, and Marvell are positioned at the forefront of this domain, designing the chips and systems that enable lightning-fast communication. Owing to its high profit margins, Arista tends to translate a relatively modest revenue share into disproportionately large profits. Meanwhile, companies like Amphenol and Luxshare supply the immense quantities of cabling and connectors required, while optical component producers such as InnoLight, Eoptolink, and Coherent further profit from the sophisticated photonics that make long-distance, high-bandwidth transmission feasible.
Beyond compute and networking, another major cost segment lies in power and cooling infrastructure — the physical foundation that sustains these data centers’ relentless processing activity. Power distribution systems, including generators, transformers, and uninterruptible power supplies, consume nearly 10% of the budget. Key corporate beneficiaries include Eaton, Schneider Electric, ABB, and Vertiv. Vertiv, in particular, is strategically positioned within the thermal management sector, which Bernstein estimates consumes around 4% of total spending. The delicate balance between air-based and liquid-based cooling solutions underscores an increasingly complex challenge: how to prevent densely packed AI chips from overheating while maintaining energy efficiency.
Real estate, electricity, and labor represent additional operational layers. The cost of procuring land and erecting specialized facilities amounts to roughly 10% of upfront capital expenditure. Once operational, however, these digital monoliths run astonishingly lean. Annual electricity costs stand near $1.3 billion per gigawatt of computing capacity, yet day-to-day staffing remains minimal. Bernstein’s research notes that facilities of this magnitude often operate with as few as eight to ten individuals, each earning between $30,000 and $80,000 annually — an almost inconceivable ratio of human oversight to machine-driven output. Despite such operational efficiency, a new constraint is emerging: the availability of sufficient power on an industrial scale. Siemens Energy, GE Vernova, and Mitsubishi Heavy Industries have all reported rapidly increasing demand for turbines and grid infrastructure, as hyperscalers compete to secure the dependable energy sources necessary for perpetual AI computation.
In summary, the $35 billion per gigawatt figure captures not just an investment in technology, but a transformation in how the global economy conceptualizes intelligence, energy, and industrial capacity. Nvidia, occupying the strategic nexus of this ecosystem, continues to channel these monumental flows of capital and innovation. Its ascendancy to a $5 trillion valuation is thus neither an accident nor a speculative anomaly — rather, it reflects the company’s central role in powering the computational revolution that is redefining the modern technological era.
Sourse: https://www.businessinsider.com/why-nvidia-worth-5-trillion-inside-35-billion-ai-datacenter-2025-10
