Tracking Trillions: the Assumptions Shaping the Scale of the AI Build Out

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Tracking Trillions: The Assumptions Shaping the Scale of the AI Build-OutThe AI CapEx debate is usually framed as a demand-side question—will adoption justify the spend?—but the size of the investment itself is not a single, fixed number. Executive Summary The AI CapEx debate is usually framed as a demand-side question—will adoption justify the spend?—but the size of the investment itself is not a single, fixed number. It is highly sensitive to a small set of assumptions about how the infrastructure itself is built and renewed. 1The economic useful life of AI silicon, where small shifts in replacement cadence move cumulative spend by hundreds of billions 2The cost and complexity of next-generation data centers, which are rising as AI workloads push power density higher and system integration deeper 3The chip and architecture mix, whose impact depends on whether compute demand is elastic (reshaping margins) or inelastic (reshaping totals) 4Elongation from power, labor, and equipment bottlenecks, which in stress scenarios can feed back into demand-side doubt Several widely discussed dynamics matter for returns, volatility, and value distribution across the ecosystem but do not materially change the aggregate scale of capital required. Current estimates of the ultimate scale of the AI build-out—regardless of the demand side—are far more conditional than they appear. For investors and operators, a critical question remains: What fundamental assumptions do we have about the future, and how resilient are our plans to changes in those assumptions? This analysis is a scenario-based framework intended to explore how different infrastructure assumptions may affect aggregate capital requirements, not a forecast of future spending.

A single AI query feels weightless—a question typed, an answer returned, no moving parts in sight. But the progress of AI rests on a deeply physical edifice: millions of processors, hundreds of thousands of kilometers of cabling, industrial cooling systems, and power demands that rival those of midsize countries. Better understanding of the complexity of that physical infrastructure—and the assumptions upon which its build-out rests—should inform how we think about the scale, durability, and risks of today’s AI capital expenditure boom. The scale of these expenditures is enormous. Estimates of $4 trillion to $8 trillion of total capital investment over the next five years have featured prominently in recent market commentary. That capital is used to buy new chips, build new data centers, and construct new power, all in an effort assemble sufficient computing infrastructure to meet the moment. Debates about whether this figure is “too high” are usually framed around a demand-side question: Will AI adoption and monetization justify the spend? But there is an equally important supply-side unknown. The scale of required investment for the AI build-out is itself more uncertain than commonly assumed. Estimates rest on a number of assumptions that, if changed, can significantly increase or decrease the amount of capital required. Not all assumptions matter equally in this equation. A small number of assumptions determines how much capital must ultimately be deployed to build AI infrastructure, while other assumptions—despite commanding significant attention—primarily influence timing, monetization, or the distribution of returns. The most critical assumptions for the level of capital expenditure required for the AI build-out include the following: The economic useful life of AI chips The cost and complexity of building next-generation data centers The way chip architectural choices translate into system-level costs The elongation of the build-out due to physical and institutional bottlenecks Much of the broader debate focuses on dynamics that matter for returns but do not materially alter the amount of capital that must be deployed. This analysis examines these assumptions and suggests a framework for understanding which changes would push the headline capital expenditure figures higher or lower than current estimates.

Baseline aggregate AI CapEx estimates (bn) ~$7.6tr of capital between 2026 and 2031 across compute, data centers, and power We begin with a baseline model that projects the total scale of AI infrastructure investment implied by today’s chip sales estimates. We anchor this baseline to NVIDIA’s forward data center revenue Wall Street estimates as a proxy for prevailing expectations around XPU (GPU and other accelerators) deployment and then infer the associated requirements for data centers, power, and supporting infrastructure. This approach does not attempt to forecast AI adoption or end-market demand; rather, it provides a consistent reference point against which we can test how different supply-side assumptions expand or contract the overall scale of investment. The baseline model implies $765 billion in annual AI CapEx in 2026, growing to $1.6 trillion in annual CapEx in 2031. These figures include a variety of components necessary for the AI build-out. The core unit of AI infrastructure is the accelerator—a processor purpose-built for the parallel computation that AI workloads demand. Today's leading systems, such as NVIDIA's GB300 NVL72, pack 72 of these processors into a single rack, connected by high-speed backplanes and linked across facilities by hundreds of thousands of kilometers of cabling. These systems generate enormous heat, requiring industrial-scale liquid cooling. And all of it sits within data centers equipped with dedicated power delivery, redundancy systems, and grid or behind-the-meter generation. Together these layers account for baseline estimates that anticipate roughly $7.6 trillion of cumulative CapEx between 2026 and 2031. The key question is, how might changes in the useful life of silicon, the cost and complexity of data centers, the mix of chip architecture, or the pace at which physical bottlenecks persist push that figure materially higher or lower? Assumption 1: The Economic Useful Life of AI Chips AI accelerators (GPUs, ASICs, etc.) are the engines of AI infrastructure, and large-scale data centers house hundreds of thousands of these chips. These devices have a useful life—typically estimated at four to six years—bounded by physical degradation on one side and economic obsolescence on the other, as each new generation delivers step-change improvements in performance. Useful life of silicon chips is the single most influential variable in determining the scale of cumulative AI infrastructure investment. Unlike other major components of the stack—data center buildings, which are typically depreciated over roughly 20 years, or power infrastructure, which often spans 25 years or more—AI silicon turns over on much shorter cycles. This fact, paired with its high cost per unit, is what makes the silicon replacement cadence so consequential. Uncertainty around AI silicon’s useful life reflects a core tension: Rapid improvements in performance per dollar between generations of AI silicon push companies to replace hardware quickly, while the growing range of AI tasks means older chips can still deliver value for longer. This tension is sharpened by NVIDIA's unprecedented annual release cadence for GPU architectures, with each generation delivering step-function leaps in capability rather than incremental improvements. Many analysts believe that this mismatch between the annual release schedule and the quantum advances of each new generation makes the prevailing accounting treatment of four-to-six-year depreciation schedules less reflective of the value of the underlying assets. Because silicon accounts for a large share of AI infrastructure CapEx, small changes in assumed useful life have outsize effects on cumulative spend. Extending average economic life from four years to six years materially reduces the number of replacement cycles over a given horizon—while shortening it has the opposite effect. At scale, these differences translate into substantial changes in aggregate capital requirements—and,

The scale of AI infrastructure investment is most determined by assumptions around silicon useful life, data center cost and complexity, and composition and timing of the build-out. Beyond these critical factors, there is enormous movement across the ecosystem—extreme shifts in pricing, supply chain dynamics, and fundamental rewriting of long-held assumptions. But not all of the factors that create headlines move the needle on the overall scale of spend over the medium term. Though they are critical for understanding returns, volatility, and value distribution—especially within individual subsectors—they do not materially alter the overall scale of infrastructure investment. These are some of the factors: Training vs. inference mix Per-chip memory growth and memory pricing volatility Behind-the-meter vs. grid power sourcing The balance between training and inference primarily affects the timing of economic realization rather than the scale of infrastructure investment. A faster transition to inference-heavy workloads accelerates revenue generation by converting a fixed capital base into usage, improving utilization and near-term returns. By contrast, a prolonged training-dominant phase extends the ROI timeline as CapEx and R&D continue to be deployed in advance of broad monetization. This distinction has limited inherent impact on the magnitude of AI infrastructure spend. Instead, changes in training vs. inference mix alter how quickly that infrastructure begins to pay for itself. Memory per accelerator continues to rise, but this trend is largely priced into current infrastructure estimates. Today’s accelerator road maps already assume significantly higher memory density per chip, reflecting longer context windows, more stateful inference, and increasingly agentic workloads. When we consider sensitivities around this trend—whether memory per chip evolves broadly in line with today’s expectations or ends up 25%+ higher or lower—the impact on our aggregate ~$7.6 trillion infrastructure estimate is modest. This is because rising memory intensity is largely baked into prevailing system designs and pricing, therefore shifting the composition of spend within the silicon stack more than it expands the overall envelope of investment. Near-term volatility in memory pricing, even when dramatic, should be understood primarily as a function of supply-demand imbalance at unprecedented and highly lumpy volumes, rather than evidence of a permanent departure from historical cyclicality—informative for margin distribution and vendor positioning but with less impact on the long-run scale of AI infrastructure spend. As with prior chokepoints, we would expect capacity expansion and yield improvements to normalize pricing over time. Memory is unlikely to be the last component to experience this kind of volatility. The “buy out the store before the storm” dynamics that define parts of the AI supply chain suggest that similar episodes of intense, short-term pricing pressure are likely to recur across other critical components such as interconnect, optics, storage, and packaging. Behind-the-meter power does increase absolute spending on power infrastructure relative to a grid-connected world, as it replaces shared generation and transmission assets with bespoke, project-specific solutions. On a project-by-project basis, this represents a real cost difference: Captive generation typically requires higher upfront capital and results in lower average utilization than grid-scale assets. However, power remains a relatively small share of total AI infrastructure investment when compared with silicon, data-center construction, and supporting systems. As a result, even meaningful shifts in power sourcing strategy—such as widespread adoption of behind-the-meter generation—are unlikely to materially change the aggregate ~$7.6 trillion estimates for ecosystem-wide AI spend. This dynamic has important implications for the power sector itself, where suppliers ma

Debates about the scale of AI infrastructure investment are often framed as a referendum on demand: whether AI adoption, monetization, or productivity gains will ultimately justify trillions of dollars of capital investments. But the amount of capital required to support today’s AI ambitions is not a single, fixed number—it is highly sensitive to a small set of structural assumptions about how the infrastructure itself is built and renewed. The implication is not that current estimates are obviously too high or too low, but that they are far more conditional than they appear and so may shift over time. As assumptions around technology progress and system design and market demand behavior shift, estimates of required capital will move with them. For investors and operators, critical questions remain: What fundamental assumptions do we have about the future, and how resilient to changes in those assumptions are our plans? Innovation may be the most important wild card. Current assumptions are largely based on current technologies. But a truly discontinuous innovation—for example, one that materially reduces the compute complexity of both training and inference—could further shift the investment landscape. The DeepSeek moment in January 2025 offered a glimpse of how markets might react to such a development. While events that followed suggest it was not, in the end, the kind of paradigm shift that would fundamentally alter the trajectory of infrastructure investment, the episode was a reminder that this risk exists and that investment frameworks for the AI build-out need to be attuned to such factors. There is a certain circularity hinted at in this analysis. Much of it has focused on how difficult it will be to deploy trillions of dollars of capital against the physical, institutional, and economic constraints we have described. But if the ecosystem does manage to conquer those constraints—if the infrastructure is built, the bottlenecks are cleared, and the cost of compute continues to fall—then the history of technology suggests that the result may not be surplus capacity but rather a new wave of demand and use cases that could not have existed at higher price points. The success of the build-out for today’s AI ambitions may be what ensures that it is not enough for tomorrow’s technological opportunities. Disclaimer This document has been prepared by the Goldman Sachs Global Institute and is not a product of Goldman Sachs Global Investment Research. This analysis draws on publicly available market estimates, company disclosures, and Goldman Sachs Global Institute scenario analysis. Quantitative figures are intended to illustrate sensitivity to key assumptions rather than to represent forecasts or consensus expectations. The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. Company references are illustrative and not an expression of view on the prospects of any issuer. The material provided is intended for informational purposes only, and does not constitute investment, legal, or tax advice, a recommendation from any Goldman Sachs entity to take any particular action or be used as a basis for any other investment decision, or an offer or solicitation to purchase or sell any securities or financial products. Any forward-looking statements, case studies, computations or examples set forth herein are for illustrative purposes only. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for

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