Africa's AI Investment Gap Widens as Global Spending Surges Past $1 Trillion

Goldman Sachs economists Joseph Briggs and Sarah Dong have put a number on the global AI investment pool that should unsettle anyone tracking Africa’s position in the race to build digital infrastructure: $1.019 trillion for 2026, roughly $200 billion above the figure most analysts have been using.

That gap matters for ordinary citizens across Africa because the continent’s share of global AI capital is calculated against that denominator. A finding from the Lawyers Hub and Agence Française de Développement report, presented at the Africa Forward Summit in Nairobi, holds that Africa captures approximately 2.5% of global AI investment. That percentage was derived using the smaller $794 billion figure. If Goldman’s $1.019 trillion estimate is accurate, Africa’s real share drops meaningfully below 2.5%, widening the distance between what the continent needs to build competitive public infrastructure and what it currently receives.

Additional reference context is available at https://iafrica.com/goldman-sachs-puts-2026-global-ai-investment-at-1-trillion-which-makes-africas-share-smaller-than-commonly-cited/.

The consequences are not abstract. Every projection about AI’s potential contribution to African economies assumes a level of infrastructure the continent does not yet possess. Africa accounts for roughly 1% of the world’s data centres. McKinsey projects that continental capacity needs to rise from 0.4 gigawatts to as much as 2.2 gigawatts by 2030, a build-out requiring $10 billion to $20 billion in new investment. Against a $1.019 trillion global pool, that requirement represents between 1% and 2% of a single year’s global AI capital expenditure. The constraint has never been that the money does not exist. It is that almost none of it currently flows to Africa.

The Goldman analysis, published in a Global Economics Analyst report, identifies four structural problems with the standard metric, which relies on capital expenditure from a handful of large hyperscalers as a proxy for total AI investment. First, it ignores investment from US private companies outside that category, which Goldman’s credit team data indicates account for 60% of AI-related supply in 2026. Second, it omits non-US companies entirely, particularly major investors in China and other Asian economies. Third, hyperscaler capital expenditure already exceeded $150 billion before the current AI boom, meaning a portion of current spending is unrelated to artificial intelligence. Fourth, US hyperscalers operate globally, and a significant share of their capital expenditure is deployed outside the United States yet counted as domestic US investment.

After adjusting for all four factors, Goldman estimates US domestic AI investment in 2026 at approximately $581 billion, with the global total reaching $1.019 trillion.

Three independent methodologies produced consistent results. The primary enhanced-capex approach yields $1.019 trillion. A second method, tracking revisions in gross margin forecasts for AI-related listed companies against a 2022 baseline, produces approximately $1.06 trillion. A third, built from official national accounts and global trade data, generates approximately $1.002 trillion. That third approach offers particular granularity: US national accounts show annualized AI-related hardware investment reaching approximately $463 billion as of May 2026, with roughly $100 billion in AI-related research and development and intellectual property investment on top, bringing the US annualized total close to $600 billion.

By contrast, on geographic distribution, Goldman allocates hyperscaler capital expenditure based on announced project locations, estimating roughly 70% flows to US domestic projects, 15% to Asia, and 9% to Europe. The remaining 6% covers everywhere else, a category that includes Africa, and that figure alone underlines how concentrated global AI infrastructure investment remains.

Goldman projects US AI capital expenditure rising from 1.8% of GDP in 2026 to 2.5% in 2027 and 2.8% in 2028. Globally the figures are 0.9%, 1.3%, and 1.4%. Those levels sit within the 2% to 5% peak investment shocks observed during historical general-purpose technology build-out cycles, suggesting that even with substantial upward revisions to 2027 forecasts, AI investment would remain within historical range rather than constituting an unprecedented bubble.

The report flags two cautions relevant to policymakers. Cost inflation is eroding real gains: 8% of the nominal increase in US AI-related hardware spending so far in 2026 is attributable to inflation rather than real investment expansion. Additionally, AI investment’s contribution to GDP is structurally constrained by measurement conventions. National accounts do not count semiconductor purchases as investment goods, and the high import content of AI hardware is netted out of GDP calculations.

For African governments and the citizens who depend on them to close the infrastructure gap, the revised Goldman figure reframes the policy question. The shortfall is not a matter of global scarcity. It is a matter of direction. Whether the policy tools exist to redirect even a fraction of that $1.019 trillion toward the continent’s data centre deficit is the question that will shape who benefits from AI’s next decade.