What would an additional $150 billion in annual giving make possible? Consider this: closing America’s entire food insecurity gap, funding the difference between what food-insecure families have and what they need, costs an estimated $32 billion a year. The jump from 2.5% to 3% of GDP in charitable giving would generate more than four times that amount, every single year. That is the scale of impact hiding inside what looks like a half-point on a chart.
When Scott Brighton articulated the vision of reaching 3% of GDP in charitable giving by 2033, it resonated deeply with me. Not as a slogan, but as a measurable ambition, and a genuine pathway to unlocking new levels of social good that our sector has long aspired to but struggled to reach. Getting there, though, will require us to change more than our tools.
The Warning Signs Are Already Here
When we include both charitable donations and the estimated value of volunteer time, giving in the United States has hovered at roughly 2.5% of GDP for decades. That number has been remarkably resilient. It has also been remarkably stagnant.
Meanwhile, the Fundraising Effectiveness Project and other sector benchmarks continue to show warning signs: donor counts declining, retention under pressure, dollars increasingly concentrated at the top of the pyramid. The system still raises money, but the base is thinning. That should concern all of us.
If we are serious about 3% by 33, we have to confront something uncomfortable. We are trying to grow giving using a fundraising model that was built for a different era.
The Model Was Built for a Different Constraint
The modern direct response system was a breakthrough in its time. It was designed to solve a very real problem, how do you efficiently get in front of people at scale? Lists, phone books, mailboxes, telemarketing, these were innovations that optimized for access. The scarcity was audience. If you could reach enough people consistently, the math worked.
Technology has changed the nature of that constraint. Today, it is easier than ever to get in front of eyeballs. Digital channels, social platforms, streaming services, connected devices, distribution is no longer the problem. Attention is.
We all see it in our personal lives. You search for a pair of shoes once, and suddenly your feeds are filled with relevant sneaker ads. For-profit organizations have become extraordinarily effective at capturing and reallocating attention based on behavior and affinity. They are not guessing. They are learning.
Nonprofits, by contrast, often continue to operate as if the primary challenge is still distribution. We added digital channels to our mail strategies. Then we layered automation on top of that. Now we are layering AI on top of that. But if the underlying objective remains short-term response optimization, we are simply accelerating a model that is already under strain.
That is what I mean when I say we risk optimizing decay.
The Shift We Need
I have spent my career building predictive models. I believe deeply in the power of analytics. Used properly, they help us reduce randomness and make better decisions. But models answer the questions we ask. If the question is, “Who is most likely to respond to this appeal?” we will get a sharper version of yesterday’s answer. If the question is, “How should we allocate resources across acquisition, retention, and upgrade to maximize five-year lifetime value?” we begin to change the system.
The difference is subtle but profound. And there are three specific shifts required to make it real.
- From Campaign Optimization to Capital Allocation
Fundraising cannot be managed as a series of isolated campaigns. It must be managed as a capital allocation system. Acquisition is not a profit center. It is a long-term investment in future cash flow. When first-year attrition is accepted as normal, acquisition becomes a treadmill rather than an investment: you run harder just to stay in place. Retention is not an afterthought. It is the primary lever for compounding value.
Lifetime value should sit at the center of strategic decision-making. Organizations should know their donor gap, understand the fully loaded cost of acquisition, and model how changes in retention alter long-term outcomes. These are not academic exercises. They are the mechanics of sustainability.
- From Capacity to Affinity
Major gifts strategy has often leaned heavily on capacity. Capacity matters, but capacity without affinity is fragile. Long-term growth depends on connecting engagement to lifetime value. When retention strategy is disconnected from major gift development, the middle of the file hollows out and the pipeline weakens.
Major gifts do not emerge from spreadsheets alone. They emerge from sustained engagement over time.
- From Automation to Empowerment
AI has extraordinary potential, but not because it can automate more messages. Its real promise lies in closing the knowledge gap. Scenario planning, retention forecasting, lifetime value modeling, these capabilities no longer require a large finance team or specialized training. The math is accessible. The tools are available. Knowledge is no longer scarce.
What remains scarce is integration. We must embed financial intelligence into daily fundraising practice, not treat it as a separate analytical exercise. There is no reason, in 2026, for any organization to operate without a clear understanding of its donor economics.
A Shared Responsibility
But this cannot be a technology company’s responsibility alone. Consultants and technology providers alike across the sector influence what boards measure, how CFOs evaluate fundraising, and how strategy is framed. If we continue to reinforce campaign-level thinking and short-term net expectations, we will become incrementally better at 2.5%. If we redesign the operating model, treating acquisition as long-term investment, elevating retention as a growth lever, aligning engagement with lifetime value, we create a pathway to 3%.
The leaders who built modern fundraising were bold enough to rethink the system for their era. They did not simply adopt new tools, they redesigned the architecture. Reaching 3% by 33 demands that same level of courage from us.
The tools are advancing rapidly. The knowledge is accessible. The data is abundant. And the stakes are clear: the distance between where we are and where we could be is not just a number. It is food on tables, programs funded, and communities reached. It is the difference between a sector that sustains itself and one that genuinely expands what is possible. The real question is whether we are willing to change the model to get there.