Global Generosity Skyrocketed Last Year but Less so in the U.S.
3 min
A report based on a GivingTuesday research collaboration delivered hopeful news on global generosity, finding that 83.6% of people worldwide donated to others in some way last year.
But, in a surprising “double-whammy,” the recently released research also found that both the number of givers, and the dollars they donated, fell last year in the U.S. for the first time since 2010. Also, stock market declines in 2022 appeared to cause large donors everywhere to give less.
The research, titled “Rethinking Resilience: Insights from the Giving Ecosystem,” was compiled by GivingTuesday Data Commons, a project involving more than 300 organisations and more than 50 global data labs. The Data Commons looks at “giving behaviours, contexts and patterns, movement growth, and altruistic motivations” with a goal of determining and sharing best practices for driving philanthropy.
GivingTuesday began in 2012 as an effort to encourage charitable contributions on the Tuesday after Thanksgiving in the U.S., and has expanded into a global movement.
“Rethinking Resilience” gleaned data from Asia, Africa, Europe, South America, and North America, finding that 56% of people across the globe gave in at least three ways last year, including donating their time, donating things they owned, or providing money; also, 57% gave to all of the three types of recipients that the report tracked: formal charities, informal groups, and individuals.
The report “makes it clear that in many communities, giving to others is not an optional ‘extra’ but rather a first principle of community membership,” Woodrow Rosenbaum, chief data officer at GivingTuesday said in an introduction to the research.
The Data Commons goal, Rosenbaum said, is to “bring the same sorts of data-driven tools to the social sector that the business sector has had for decades.” These tools should help to counter what has become a narrow view of philanthropy.
“Our research reveals that broadening outreach and engagement to include previously under-represented demographics can significantly improve organizational resilience, especially in times of economic volatility and uncertainty,” Rosenbaum said.
The report found a significant rise in volunteering everywhere, which often happens when the economy is shaky. It also found that young people everywhere were “giving more often and in more ways” than older ones.
Overall, this global data gathering exercise revealed that giving can look far different country to country. The “most inescapable insight,” the report said, is that less wealthy countries were consistently more generous than wealthy ones. Kenya, for instance, demonstrated “a near universal commitment to generosity across all metrics,” with India as a close second.
In the U.S., the number of givers fell by 10%, driven by an 18% drop in new donors and a huge drop in donor retention: 26.4% from first-time donors and 3.5% from repeat givers.
Contributions by “major” philanthropists, who give between US$5,000 and US$50,000, and “supersize” ones who give more than US$50,000 fell the least, but because of the large size of their donations, the drop off was more keenly felt. Total dollar contributions fell by 1.7% last year.
The biggest decline among these philanthropists was in the fourth quarter of last year as a 20% drop in stocks took a toll. That fall off could be “the canary in the coal mine,” the report said. “Should large donors suddenly retreat further, the impact on an unprepared social sector could be devastating.”
The message to nonprofits is to actively build a wider, more diversified base of support beyond big philanthropists to “strengthen resilience and reduce the adverse effects of steadily growing competition for a shrinking pool of increasingly cautious large donors who may be retreating in the face of economic uncertainty and volatility,” the report said.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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