Goldman Sachs: AI’s Hidden Trillion-Dollar Spending Wave

Goldman Sachs has issued a sweeping forecast predicting that non-hardware investments in artificial intelligence — spanning data infrastructure, software development, organizational restructuring, and labor retraining — could exceed $1 trillion globally over the AI adoption cycle. Analyst Joseph Briggs outlined in a client note that U.S. labor costs tied to the AI transition already run at $150 billion per year, with workforce reorganization alone potentially costing $800 to $900 billion. The bank argues that these massive intangible investments are consistent with decades-long trends in advanced economies and will ultimately produce a productivity surge, even though early gains may be obscured by the scale of internal corporate transformation underway.

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Key Takeaways

• $1 trillion+ in non-hardware AI spending is projected globally across the AI adoption cycle, covering data, software, and organizational capital.
• U.S. AI-related labor costs are already running at approximately $150 billion per year, with an additional $40 billion annually in executive-level organizational capital investment.
• Workforce reorganization could cost between $800 billion and $900 billion over the full adoption cycle.
• Intangible capital investment in G10 economies has risen to roughly the same size as traditional capital expenditure.
• A productivity J-curve means current U.S. productivity growth is likely understated as firms pour resources into internal AI adoption infrastructure.
• A widening competitive gap is emerging, with firms that effectively deploy AI agents likely to become the next generation of “superstar” companies.

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The artificial intelligence spending narrative has been dominated by headline-grabbing hardware figures — the hundreds of billions flowing into GPUs, data centers, and chip fabrication. But a new analysis from Goldman Sachs argues that the less visible side of the AI buildout may ultimately prove even larger. According to the bank, non-hardware investments in AI — encompassing data infrastructure, software, workforce retraining, and organizational redesign — could surpass $1 trillion globally in the coming years.

The estimate, published in the bank’s Global Economics Analyst report, marks a significant expansion of how Wall Street is framing the economic impact of AI. Where most investment bank research has focused on semiconductor demand and cloud infrastructure spending, Goldman Sachs is now drawing attention to the sprawling intangible costs that companies must absorb before AI translates into measurable productivity gains.

The Scale of Hidden AI Spending

Goldman Sachs analyst Joseph Briggs laid out the numbers in stark terms. Labor costs directly tied to the AI transition in the United States are already running at approximately $150 billion per year, encompassing everything from hiring AI specialists and data engineers to retraining existing staff for new workflows. On top of that, executive time allocations — the hours that senior leaders spend planning and overseeing AI integration — imply a further $40 billion annually in what the bank categorizes as organizational capital investment.

These figures become even more striking when projected forward. “Extrapolating labor restructuring costs incurred so far suggests that workforce reorganization could cost $800-900bn over the AI adoption cycle,” Briggs wrote in the note. That range alone approaches the total annual capital expenditure budgets of entire industry sectors, underscoring just how transformative — and expensive — the internal overhaul demanded by AI actually is.

The estimate aligns with what enterprises are reporting on the ground. A 2026 survey by Writer and Workplace Intelligence found that 97 percent of executives say their company deployed AI agents in the past year, yet 79 percent of organizations face significant challenges in translating adoption into business value. More than half of C-suite leaders admitted that adopting AI is creating deep structural and cultural upheaval within their organizations, even as 59 percent of companies invest over $1 million annually in AI technology alone.

Beyond Hardware: Why Intangible Investment Matters

Goldman Sachs frames these non-hardware outlays as part of a much longer historical pattern. The bank notes that intangible capital investment — which includes spending on software, organizational processes, workforce training, and proprietary data systems — has risen to roughly the same scale as traditional capital expenditure across G10 economies, according to EU KLEMS productivity data. Much of that growth over the past two decades has been driven by increased spending on organizational capital and software management.

This trend is not new, but AI is accelerating it dramatically. Research from the European Commission using EU KLEMS data has shown that in countries like the United Kingdom and Germany, investment in intangible assets surpassed traditional tangible capital as far back as the late 1990s. What AI is doing now is compressing a similar transition into a much shorter timeframe, forcing companies to simultaneously overhaul data architectures, retrain workforces, and redesign business processes.

The hardware spending, meanwhile, continues to surge at a staggering pace. Combined capital expenditure from the five largest U.S. hyperscalers — Microsoft, Alphabet, Amazon, Meta, and Oracle — is projected to reach between $660 billion and $690 billion in 2026, nearly double the 2025 level. Goldman Sachs’ own research estimates that annual AI capital expenditure could hit $765 billion in 2026, growing to $1.6 trillion by 2031, with a cumulative $7.6 trillion deployed between 2026 and 2031 across compute, data centers, and power infrastructure. But hardware alone does not deliver productivity — it is the organizational and software layers built on top that determine whether the investment pays off.

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The Productivity J-Curve: Why Gains Are Delayed

One of the most consequential arguments in Goldman Sachs’ analysis is that current U.S. productivity growth is “likely understated.” The bank attributes this to a well-documented economic phenomenon known as the productivity J-curve, a concept developed by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson at the National Bureau of Economic Research.

The J-curve theory explains why general purpose technologies like AI often fail to produce visible productivity gains in their early years. When companies adopt a transformative technology, they must first make large complementary investments — rewriting software, reorganizing teams, retraining workers, building new data pipelines — that consume resources without immediately generating measurable output. These investments are largely intangible and poorly captured in national accounting statistics. As a result, official productivity metrics undercount the true economic activity taking place.

The pattern has historical precedent. Both electrification and the personal computer triggered productivity booms roughly 20 years after their initial breakthroughs, at the point when approximately half of U.S. businesses had adopted the technology. Goldman Sachs economists, including Briggs, have previously estimated that widespread AI adoption could eventually drive a 7 percent increase in annual global GDP — roughly $7 trillion — over a ten-year period.

Recent U.S. data lends some support to the idea that productivity is already responding, even if unevenly. According to the Bureau of Labor Statistics, U.S. productivity growth surged to 4.9 percent in the third quarter of 2025, driven by a sharp increase in output relative to hours worked. A Federal Reserve Bank of Kansas City analysis found that the upward trend in U.S. labor productivity from 2022 through 2025 coincides with the commercial emergence of generative AI tools, though researchers caution that establishing direct causation remains difficult.

Yet the broader academic consensus is that we are still in the early, investment-heavy phase of the curve. A San Francisco Federal Reserve economic letter published in 2026 noted that most macroeconomic studies find limited evidence of a significant aggregate AI productivity effect so far, even as firm-level case studies consistently show cost savings. The Goldman Sachs note suggests that this disconnect is precisely what the J-curve predicts: the benefits are real but hidden behind the wall of intangible investment currently underway.

The Rise of AI Superstar Firms

Perhaps the most forward-looking element of Goldman Sachs’ analysis is its warning about a widening competitive gap between companies. The bank suggests that firms that more effectively deploy AI agents and invest in data infrastructure are likely to pull away from industry peers and emerge as the next generation of “superstar” firms — companies that capture disproportionate market share and generate outsized returns.

This is already playing out in the technology sector. Goldman Sachs Research has noted that equity gains in recent years have been heavily concentrated in AI infrastructure companies, including semiconductor makers, hyperscalers, and power providers. The average stock in Goldman Sachs’ basket of AI infrastructure companies returned 44 percent, far outpacing earnings growth estimates for the group. As the AI trade matures, the bank expects attention to shift toward companies capable of generating AI-enabled revenue growth rather than those simply building infrastructure.

The concentration effect extends beyond technology. Data from Charles Schwab and Bloomberg show that the ten largest companies in the S&P 500 now control roughly 40 percent of the entire index by market capitalization, exceeding the peak concentration seen during the dot-com bubble. In the most recent 28-session rally through early May 2026, analysts found that just ten stocks drove 69 percent of the index gains, with Alphabet, Nvidia, Amazon, Broadcom, and Microsoft leading.

Goldman Sachs’ note explicitly warns that this dynamic could intensify, with “companies focused on data structure and AI deployment” becoming key to unlocking the economic value AI promises. The implication is that the non-hardware spending Goldman identifies — the organizational capital, the data architecture investments, the workforce retraining — is not just a cost to be endured but a competitive moat being built in real time. Companies that invest early and effectively in these intangible assets may cement structural advantages that are difficult for laggards to replicate.

Enterprise AI Agents: The Next Spending Frontier

A significant portion of the non-hardware investment Goldman Sachs describes is flowing toward the deployment of AI agents — autonomous systems capable of executing complex, multi-step business workflows. Gartner projects that 40 percent of enterprise applications will embed task-specific AI agents by the end of 2026, and total worldwide AI spending is forecast to reach $2.52 trillion in 2026, a 44 percent increase year-over-year.

The enterprise adoption picture, however, remains complex. While nearly all executives report deploying AI agents, the gap between individual productivity gains and organization-wide returns on investment remains wide. AI super-users reportedly deliver five times normal productivity gains, yet only 29 percent of organizations see significant ROI from generative AI. The structural investments Goldman Sachs identifies — in data systems, organizational processes, and governance frameworks — are precisely what bridges that gap.

Companies like ServiceNow are building entire product strategies around this opportunity. At its Knowledge 2026 conference, the enterprise software giant unveiled a suite of autonomous AI “specialists” designed to complete entire business processes — from IT operations to HR to legal — without human intervention. Early results from customers include AI agents resolving IT service desk cases 99 percent faster than human agents and deflecting up to 98 percent of routine employee requests.

What This Means for the Global Economy

Goldman Sachs’ trillion-dollar estimate for non-hardware AI investment carries significant implications for economic forecasting and corporate strategy. If the bank is correct that current productivity gains are understated, then official economic growth figures may be systematically underrepresenting the true pace of AI-driven transformation. As the J-curve’s upward bend materializes — potentially within the next several years — the measured productivity surge could be dramatic.

The broader spending picture reinforces this. Gartner forecasts worldwide IT spending will reach $6.15 trillion in 2026, an increase of 10.8 percent from 2025, with AI infrastructure growth remaining rapid across both hardware and software categories. IDC reports that full-year 2025 AI infrastructure spending totaled $318 billion, more than doubling from $153 billion in 2024, and projects global AI infrastructure will exceed $1 trillion by 2029.

But the Goldman Sachs note makes a crucial distinction: hardware spending, while enormous, is only the foundation. The real economic transformation happens in the layers above — in the organizational restructuring, the data pipeline construction, the software customization, and the workforce retraining that enable companies to actually use the infrastructure being built. That layer of spending, the bank argues, could itself surpass $1 trillion, and it is where the winners and losers of the AI era will ultimately be decided.

For corporate leaders, the message is clear: the AI transition is not primarily a technology procurement exercise. It is a wholesale organizational transformation, and the companies that treat it as such — investing aggressively in intangible capital alongside hardware — are the ones most likely to capture the productivity gains that Goldman Sachs and others believe are coming.

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