The call arrives in some version of the same shape every few weeks. A senior leader at a large organization, sometimes the chief executive, sometimes the board chair, wants help thinking through an important AI decision. On a recent one, most of the thinking had already been done. Two consulting proposals sat on the table, both from reputable firms, both promising to identify the high-yield places where AI could produce measurable results inside a fiscal year. The decks were impressive. The references checked out. The leader wanted to know which one to pick.

I asked what they were trying to achieve.

“We want to find the big efficiencies. The big projects. The things we can point to and measure.”

I asked what the firms had promised to deliver.

“They told us they can help us find the big wins and move our organization along quickly.”

I asked what they were doing about the culture.

“We want to find the big wins and the low-hanging fruit and expand out from there.”

So again, I asked again about the culture.

That exchange has happened enough times now that I have stopped treating it as a communication failure. The question is not unclear. The question simply has no place to sit inside the conversation the leader is already having. Culture has no line item, no launch date, no vendor, and nothing to show a board in ninety days. At face value, the big win has all four.

So before we hung up I told that leader what I now tell everyone who asks me which proposal to pick. Buy either one. Execute it well. And unless you do the other work, the work nobody in your building is currently asking you about, you will call me in twelve or eighteen months to tell me it did not take.

The pressure that produces these calls is not imaginary. It arrives as a trustee forwarding an article with a subject line that reads, in its entirety, “Are we doing this?” It arrives as a program officer asking a pointed question during a site visit. It arrives as a peer organization announcing an AI initiative from a conference stage. None of it is concrete, and none of it can be answered, because none of it is really a question. Together it produces a low and persistent hum that says everyone else is further ahead than you are.

Good leaders are accountable, and accountability wants something to act on. So the hum gets converted into the nearest available object, which is a project chosen to produce a “big win”.

The consulting firms get blamed for this, and I think the blame is misplaced. A market delivers what it is asked for. If nonprofit boards started asking for eighteen-month capability engagements with nothing to demonstrate at the end, there would be a hundred firms offering them by spring. The big win is being sold because the big win is being bought, and the big win is being bought because the big win is being demanded. Every party in that chain is behaving rationally and the outcome is still wrong.

There is also nothing wrong with the wins themselves. They are real. Gift officers really do get time back. Grant narratives really do get drafted in an afternoon. I have watched organizations recover meaningful capacity in a single quarter, and I would not undo any of it.

The trouble is what happens next, and we now have numbers on it. In this year’s adoption study that Virtuous and Fundraising.AI ran across 346 organizations, 92 percent reported using AI. But only seven percent reported meaningful impact on their mission. That gap is not a technology failure. Eighty-five percent of those organizations bought a bloom and never touched the ground.

I have spent this year learning what a bloom actually costs, and none of the learning happened in a conference room.

In April I tilled half an acre behind my house in Middle Tennessee and hand-seeded ten thousand sunflowers. This has become, improbably, the thing that people are curious about. People stop me at conferences to ask about the sunflowers. I get texts from people I have not spoken to in years. Friends from three thousand miles away know the field and the journey better than some of my neighbors do.

In all of that, across four months and several hundred conversations, not one person has asked me about the soil.

Everyone asks about the flowers. Nobody asks about the ground.

I understand why. The flowers are the part worth looking at. They are also, of the entire enterprise, the only part I had nothing to do with.

What I did was earlier and considerably less photogenic. Before April there was a field of grass that had never been asked to grow anything on purpose. I tilled it, which took longer than I expected. Then I tested the soil, which is the step nobody tells stories about and the step that changed everything downstream, because a soil test does not tell you what to plant. It tells you what your ground currently cannot support. Then I amended it, which is to say I spent money and time adding what was missing before a single seed went in, on the authority of a piece of paper, with no visible evidence that any of it was working.

Then ten thousand seeds, one at a time, an inch and a half deep and eighteen inches apart, in a window narrow enough that I was reading forecasts for rain that would come but not too heavily, just enough to hold ten days of moisture in the ground for the seed to propagate. And then the waiting, which I have written about elsewhere and will not relitigate here, except to say that nothing in my professional life had prepared me to be a person who had spent real money turning a field into a dirt lot.

There is no version of this where you skip to August. Not a faster version, not a more expensive version, not a version where somebody who knows far more than I do does it on my behalf. Anyone can buy a bloom, of course. A florist will happily sell you a dozen sunflowers this afternoon, and they will be beautiful on the table, and in a week they will be in the trash. You could even stand one fully grown stalk in the middle of a bare field and tie a ribbon around it, and people driving past might slow down to admire it. It is still one flower in dry ground, and no amount of ceremony turns it into an acre of them.

You can buy a bloom. What you cannot buy is a field that blooms.

Although I have been tempted, I have resisted the soil metaphor for months because it is the kind of thing that gets printed on a poster and stops meaning anything. But standing in that field this summer, the mapping stopped being decorative and started being uncomfortably exact. The ground is the culture. Not a loose analogy for it. The same physics, mapping in three specific ways.

The first is the test. Nearly every organization I talk to has diagnosed a tool gap. Very few have diagnosed a ground condition, and the ground condition is almost never a tool. It is whether a program director can say out loud that something is not working without it costing her something. It is whether decisions get made where the information actually lives or three levels above it. It is whether anyone in the building has an hour to learn something that will not pay off this quarter. Those are testable. Almost nobody tests them, because unlike a technology assessment, the results implicate the people commissioning the study.

The second is the amendment, which is the part I grossly underappreciated in all of my research before deciding to plant the field. Everything I added to that ground, I added before the seed went in. That was not a preference. It was the whole physics of the situation.

Nothing you add after the seed is in the ground ever reaches the seed.

An organization that deploys a tool into a culture where people are afraid to admit what they do not know has not deployed a tool. It has installed an expensive new place for that fear to live. And the deployment does not fix the fear afterward. It hardens around it.

The third is the seeding itself. Ten thousand seeds, placed individually, because there is no broadcast setting for the thing I was trying to do. Too far apart and the stalks cannot support each other. Too close and they crowd each other out. Every serious culture change I have witnessed in twenty years in this sector moved the same way, one person at a time, at a pace that looks absurd on a Gantt chart and turns out to be the only pace available, and at a spacing that matters just as much: close enough that people hold each other up, far enough apart that each one has room to grow.

On another call this summer, at a very large organization, the head of IT offered what he clearly felt was the insight of the meeting. AI, he said, is something like a change management challenge.

My response came out before I had time to find a more delicate way to deliver it. It is not something like a change management challenge. It is the largest change management challenge your organization has ever faced, and probably the largest it ever will. Every technology transition your institution has survived, the database migration, the website rebuild, the move to the cloud, changed what people used. This one changes what people are for. Treating that as adjacent to change management is how organizations end up with an 85-point gap between adoption and impact and a genuine belief that they did the work.

It is late August now and the sunflower season is closing. The heads have gone heavy and the color is going out of the field, and I have been walking it most mornings taking notes and photos, which is not something I anticipated doing.

Here is what I have learned that I could not possibly have known in April. The sun crosses that field differently than I assumed. The rows are running the wrong direction. Next year they will run the other way, and the field will be bigger, and I will be making a set of mistakes I cannot currently name because I do not yet know enough to make them. That is the whole point.

The field does not have a completion date. It has seasons.

That is the half of this I did not fully appreciate. I had assumed preparation was a phase, something you complete and then graduate from into the actual work. It is not. I am standing at the end of a season I spent six months planning, holding a notebook full of things I will do differently, and next April I will prepare ground again with better information and a fresh inventory of what I do not know. There is no year in which I am finished learning how to do this, because the variables never hold still. The weather will be different. The insects will arrive on their own schedule. The birds will find the field, and so will the deer. The ground I finally came to understand this season is not the ground I will meet next spring.

Which brings me back to the call I said would come in 12 to 18 months.

It does come. It has come often enough that I can hear it starting in the first ten seconds. The shape is always the same. The project shipped. The money was spent. The firm delivered exactly what was on the statement of work, and by every measure written into the contract the engagement succeeded, the way cut flowers succeed. Then somewhere around month nine, people quietly went back to doing it the old way. The licenses are still being paid for. Almost nobody logs in. What the leader wants to know, usually in a careful voice, is whether the whole thing was oversold.

It was not oversold. The ground rejected the transplant. Every gardener learns this eventually. You cannot lift something that grew somewhere else, set it into soil nobody prepared, and expect it to take. The roots reach for what they need and find nothing there. The organization concludes that AI did not work for them, which is the most expensive wrong lesson available, because it is the one that stops the inquiry. The tool was never the problem. Nobody worked the ground, and the ground is the culture.

I have now worked on AI with several hundred of the largest nonprofits in the United States, and the pattern has repeated too often to keep calling it a coincidence. The organizations where it took were not the ones with the best tools or the biggest budgets or the sharpest vendors. They were the ones that did the unglamorous work before the project and, more to the point, kept doing it after.

AI is not a project your organization will complete. It is a condition your organization will operate in. It is an exponential technology, which in practice means the thing your policy was written for is not the thing sitting in your building six months later. Definitions expire faster than leaders update them. The capability that was a research demo in the spring is in your staff’s hands by fall, whether or not anyone decided it should be. An organization that treats this as a transition will finish the transition and find the ground has moved underneath it.

So the leaders who come through this are not going to be the ones who pick the right vendor. They are going to be the ones who stop asking when this will be done. In almost every presentation I give, I offer the same three truths about AI. You are never ready. You are never done. And today is the worst AI you will ever use. All three point to the same conclusion: the work is never complete. There is a season, and then there is the accounting, and then there is a harder season with more ground in it, and that is not the bad news. That is the job now.

So when I ask about the culture, it is not a rebuke and it is not an argument against the projects. Both firms on that leader’s desk were competent. Either one would probably have delivered what it promised. The proposals were never the variable.

Pick one. Execute it well. Then go do the culture work nobody is asking you about, and keep doing it long after the project has been declared finished, because the project will be finished and this will not be.

Everyone will ask you about the flowers. They always do. But nothing standing ten feet tall in August was planted in August, and nothing standing there next August is being decided by anyone who thinks they are already done.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI, The Generosity Crisis and the forthcoming book, N1 Philanthropy. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

There is a version of the story most people misremember. They remember the ending. A child in the crowd, the naked emperor, the laughter that spreads down the parade route. What they forget is the middle, which is where the real machinery lives.

Two weavers arrive in the city and promise a fabric so fine that it is invisible to anyone unfit for their office or hopelessly stupid. The emperor, who is vain, commissions a suit. He sends his most trusted minister to inspect the work, and the minister, standing before the empty looms, sees nothing at all. The minister does not conclude that the cloth is absent. He concludes that he himself must be the fool. So he reports back that the weaving is magnificent, the colors extraordinary, the pattern like nothing he has ever seen. A second official goes, sees nothing, and lies for the same reason. When the emperor finally goes to see the cloth for himself, he sees nothing too. And he draws exactly the same conclusion everyone else did.

It is easy to assume the ministers lied while knowing better. Some of them did. But the more human and more troubling possibility is that many of them talked themselves partway into belief. Stand in front of an empty loom long enough, with everyone around you praising the weave, and you begin to describe colors you cannot see. You learn the vocabulary of the fabric. You become fluent in a thing you do not understand. That is not deception aimed at others. It is deception that has turned inward, and it is far harder to break, because the person doing it no longer knows there is anything to break.

That is the engine of the story. Not the weavers’ con. The court’s fluency.

I keep returning to this fable because the nonprofit sector is the court right now, and the fabric is artificial intelligence.

Nine in ten nonprofits will tell you they are using AI. I believe them. That is what worries me.

The adoption research my colleagues and I have spent years compiling keeps returning a gap that ought to stop people cold, and mostly does not. Roughly ninety percent of nonprofits now report using AI in some form. Something closer to seven percent report that it has produced meaningful, mission-level impact. Most people hear that gap and assume it is a problem of access, or budget, or technical skill. It is not. The tools are cheap and getting cheaper, and the sector is using them. The gap lives somewhere else entirely. It lives in the difference between using a thing and understanding what the thing is.

Listen to how organizations talk about their AI adoption and you will hear the ministers describing the cloth. We use it for drafting appeals. We use it for donor research. We use it for meeting summaries. Our team is trained. Our policy is approved. Every sentence is true, and every sentence is fluent, and the fluency is the problem. Because fluency with AI has been quietly mistaken for comprehension of its capabilities. People use the tools every day, describe their usage confidently, report it on the surveys, and mistake the describing for the understanding. They have learned the vocabulary of the fabric. Whether they can see what is actually on the loom is a different question, and almost no one has been asked it.

I wrote recently that AI is not what you think it is. This essay is the fable version of that argument, because Andersen understood something about collective misperception that a research report cannot quite deliver. The court was not wrong about the details. The ministers could describe the colors. They were wrong about the thing itself. And a sector can be wrong about the thing itself while getting every detail of usage right.

Here is the misconception.

The sector has been led to believe that artificial intelligence is a set of tools. Tools you evaluate, procure, deploy, and train people on. Tools that accomplish specific tasks. Draft the letter. Score the donor file. Summarize the meeting. This belief is comfortable because it is how every previous technology worked, and it is wrong, because AI is not a new tool. It is closer to a new operating system for how work gets done at all.

The difference is not semantic. Think about what a tool is. A tool is something you pick up, use for its task, and set down. It waits for you. It does its one thing. The organization around it stays exactly what it was. A word processor is a tool. A scheduling app is a tool. You learn it once, and it stays learned.

An operating system is a different category of thing. You do not pick it up and you do not set it down. It is not visible, and it does not do one task. It is what everything else runs on, and when it changes, everything running on it changes, quietly, underneath, everywhere at once. An organization that treats AI as a set of tools asks which tasks to automate. An organization that treats AI as an operating system asks a different question entirely: how does the capability of every single person in the building change when the work itself runs on something new?

The ninety percent are answering the first question, diligently and in good faith. The seven percent are answering the second. That is the whole gap. The organizations reporting real, mission-level impact are not the ones with the most impressive individual use cases. They are the ones who stopped thinking about AI as a set of tools to achieve certain outcomes and started raising the capability of the entire organization, every person, every function. A use case improves one activity. An operating system improves the person doing all of them.

And the misconception is getting more expensive by the month, because the thing itself will not sit still.

A tool is a thing you learn once. You come to understand a word processor, and then you understand it, and it stays understood. But researchers at METR have been measuring something that does not hold still long enough to be learned that way. The length of tasks these systems can complete on their own has been doubling roughly every seven months, and lately closer to every four. Follow that line outward and it points toward agents handling a full day of work with only a review at the end, somewhere around May 2027, and far longer stretches in the months just after. The researchers are careful about this, and so am I. The trend may slow, and the tasks they measure are not the whole of anyone’s job. But the direction is the point.

This is what makes the tool misconception so costly. A tool can be adopted and understood. A thing that doubles its reach every few months cannot be adopted in any final sense, because the thing you adopted in January is not the thing running in October. What the ninety percent believe they have mastered is being rewoven under their hands faster than they can perceive, and they are praising its colors anyway. The confident usage report, the approved policy, the completed training, these are descriptions of a fabric from two doublings ago. The false confidence is more dangerous than the earlier hesitation ever was, because hesitation at least keeps asking questions.

Fluency stops asking. Fluency already knows.

Now the ending of the fable, which is stranger than most people remember.

We tend to recall the child’s voice as the moment the spell breaks. The boy says the emperor is wearing nothing, the truth spreads through the crowd, and the illusion collapses. But read what actually happens next. The whisper does spread. The people do begin to murmur that the child is right. And the emperor hears it. He suspects they are correct. And he holds himself even prouder and finishes the procession anyway, because the alternative, stopping mid-parade and admitting the whole thing was empty, is more humiliating than walking the rest of the route in his birthday suit.

That ending describes the sector more precisely than the child’s line does. Even collective recognition does not stop the machine. Plenty of leaders privately suspect that their AI adoption has not changed much of anything. The usage is real, the impact is thin, and somewhere in the back of the mind a small voice has already said so. And the procession continues anyway, because there is a board expecting a report, and a policy already approved, and a survey response already submitted, and the humiliation of saying we do not actually understand this yet is worse than the cost of continuing to describe the colors. Momentum has its own logic, and it is not the logic of what is true. It is the logic of what is already in motion.

So what breaks the spell.

Not more usage. Usage is what built the fluency in the first place. And not another tool evaluation, because evaluating tools is the misconception performing itself.

In the fable it takes a child, someone too young to have learned the fear. Organizations do not get to hire children. What they have instead are leaders, and a leader can do the child’s work by doing the one thing no minister could bring himself to do.

Say it out loud, first: I do not understand this yet. Remember what powered the con. Every person in that court believed that admitting confusion would mark them unfit for their office, so every person performed a comprehension they did not have, and the performance became the culture.

One sentence from the top dissolves it. The moment a leader says I do not understand this yet, the admission stops being disqualifying and becomes the standard. The whole room is suddenly allowed to look at the loom instead of describing it.

In my experience the relief is immediate and almost audible, because most of the people in that room have been privately carrying the same sentence for a year.

The second thing is to treat understanding as perishable. Not wrong, perishable, the way a carton of milk is not wrong, it is dated. A definition of AI written eighteen months ago describes a fabric from three or four doublings back. So the honest question is not what is this thing, asked once and answered. It is what is this thing now, asked on a cadence, the way a finance committee reviews the numbers quarterly without anyone taking it as an insult to last quarter. The organizations in the seven percent are not smarter. They have simply stopped expecting the answer to hold still, which means their confidence renews instead of calcifies.

You cannot finish learning a thing that refuses to hold still.

That is the child’s question, in the end. Not the cynic’s version, which is whether AI is real. It is real, and the seven percent are proof. The child’s question is quieter and harder: what is actually on the loom? Not what do we use it for. Not what does our policy say. What is this thing, actually, and is it what we have been describing all along? A leader who sits with that question honestly will notice the gap between their fluency and their comprehension, and noticing that gap is the beginning of everything. It is, not coincidentally, the posture of curiosity the sector keeps being told it needs, applied to the one subject where confidence has quietly replaced it.

The emperor’s mistake was never commissioning the suit. It was believing he understood what he had commissioned, and then walking out the palace doors on the strength of that belief. The sector has commissioned the suit. The procession is forming. The only question left is whether we will keep describing the fabric, or finally look at the loom.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI, The Generosity Crisis and the forthcoming book, N1 Philanthropy. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

The METR time-horizon research referenced above: https://theaidigest.org/time-horizons

by Nathan Chappell

The Pond

On your way to work, you pass a shallow pond. A toddler has wandered into it and is struggling to keep her head above the water. You look around for a parent, a babysitter, anyone. There is no one. Saving her would be easy and safe. The pond is only knee-deep. But you would ruin the new shoes you bought last week, and you would be late for a meeting you cannot afford to miss.

What do you do?

If you have spent any time in an ethics classroom or a nonprofit conference hall, you know this story. The philosopher Peter Singer published it in 1972, in an essay called “Famine, Affluence and Morality,” and it has been unsettling students ever since. In 2023, I opened a keynote at the AWS Imagine conference with it, one of the first keynotes I gave after The Generosity Crisis was published, and I have returned to it in workshops and presentations many times since. It works every time, and it works for the same reason. The answer is unanimous. In fifty years of telling, across classrooms and conference stages and dinner tables, no one has ever raised a hand to defend the shoes.

Singer’s point was that distance should not change the answer. If you would wade in without hesitation to save the child in front of you, he argued, you cannot justify ignoring the child dying of a preventable disease on the other side of the world. In 1972, that argument had a specific job to do. Distant suffering was largely invisible. The moral challenge of the age was ignorance, and Singer’s task was to make people see what they could not see.

Singer won the argument and lost the behavior. Five decades of philosophy students have agreed that distance should not matter, and five decades of human conduct have demonstrated that it matters more than almost anything else.

But something else has changed since 1972, something the thought experiment never anticipated. The pond assumed a single child, a single morning, a single choice. It never asked what happens when you pass ten thousand ponds a day.

We are about to find out. In fact, I believe we already have.

The Market That Stopped Flinching

Last summer, when the United States struck nuclear facilities in Iran, analysts braced for the reaction that history said was coming. A direct attack on a major oil-producing nation in the world’s most volatile region has a script: prices spike, markets convulse, the world holds its breath. Instead, oil closed lower on the first day of trading afterward. The world’s most sensitive alarm system registered the event and, in effect, looked away.

This year, when strikes became a war, the pattern deepened in a way that should unsettle anyone who studies attention. Equity markets kept climbing while economists downgraded their forecasts. Measured volatility fell even as measured risk rose. Prices moved decisively only when the Strait of Hormuz physically closed and the barrels literally stopped arriving. I have no standing to argue what markets should have done, and this is not a column about the war itself. What interests me is the shape of the reaction. The market did not stop noticing conflict. It stopped responding to conflict’s meaning. Anticipation, implication, moral weight, second-order consequence: none of it moved the needle. Only physical interruption registered.

Financial writers have started calling this phenomenon shrug economics. The theory goes something like this: there is no longer one war but several, no longer one crisis but many, and the sheer volume of signal has exceeded any institution’s capacity to price it. When traders described their own reasoning, they sounded less like analysts and more like exhausted parents. The threatened catastrophe had failed to materialize so many times that the rational strategy became not reacting at all.

When everything demands a reaction, the most rational strategy is to stop reacting. That sentence should terrify anyone whose work depends on the human capacity to care about strangers.

Shrug Philanthropy

I work in a market too. It trades in something harder to quantify than crude oil, but it is a market all the same, with supply and demand, with signals and noise, with participants deciding every day what deserves their attention and what does not. It is tempting for those of us in the nonprofit sector to believe we are insulated from the forces reshaping the trading floor, that because we deal in meaning, we are exempt from the economics of attention. We are not. We are downstream of them.

Call it shrug philanthropy.

Consider what has happened to charitable participation in this country. In 2000, the same year I entered the nonprofit sector as executive director of my local Boys and Girls Club, two thirds of American households gave to charity. By 2018, the share had fallen below half for the first time since researchers began measuring, to 49.6 percent. According to the Generosity Commission, as many as twenty million households dropped out of giving between 2010 and 2016 alone. However, total dollars kept rising over the same period, propped up by larger gifts from fewer people, which is why the sector’s dashboards stayed green while its foundation quietly eroded. Researchers and consultants alike nonchalantly call it “dollars up, donors down.” I have come to think of it as the sound of twenty million shrugs.

The pattern did not pause for a new decade.

A few weeks ago, Giving USA reported that charitable giving reached $617.2 billion in 2025, crossing the $600 billion mark for the first time in American history. By the headline, it was the most generous year this country has ever recorded. Beneath the headline, the concentration deepened. Individuals now account for 64 percent of total giving, down from roughly 80 percent in the mid-1980s and near the lowest share ever measured.

The year I entered the sector, Americans gave 2.4 percent of their disposable income to charity. Last year, the figure was 1.7 percent. And of the $19.2 billion in megagifts from individuals in 2025, a full third came from a single donor, MacKenzie Scott. Her generosity is genuinely extraordinary. But a record built on fewer and fewer deciders is not the same thing as a generous nation. As I argued in The Generosity Crisis, a philanthropy that concentrates is a philanthropy that becomes less democratized. When giving flows through a shrinking number of hands, the needs those few can see become the needs that get met, and everyone else waits.

The comfortable explanations are real but insufficient. The Great Recession damaged household balance sheets. Institutional trust declined. Religious affiliation, long the front door to giving, weakened. Yet when researchers at the Indiana University Lilly Family School of Philanthropy examined the decline, they could attribute just over a third of it to shifts in income, wealth, and homeownership. The majority of the collapse traces to something economics cannot fully name.

There is a possibility the sector has been reluctant to consider. What if millions of those households never decided anything at all? Not distrust. Not disillusionment. Not a verdict on our effectiveness or our overhead ratios. What if they simply never got past the shrug, because doing nothing has never been easier, and considering everything has never been harder?

I received an unintended confirmation the same day the Giving USA report came out. A colleague I have known and worked with for the better part of two decades sent me her summary by text: déjà vu, same news as always, nothing that would help her inspire more people toward goals that climb higher every year. She was not wrong, and that is precisely what unsettled me. The sector’s most comprehensive annual diagnosis has become background noise to the very professionals whose job is to answer it. The drumbeat of record dollars and disappearing donors has sounded for so long that even the people inside the data have stopped flinching at it. The shrug is no longer just a description of our donors. It is becoming a description of us.

Ten Thousand Ponds

In 1971, a year before Singer described his pond, the economist Herbert Simon issued a warning that reads today like prophecy. In a world flooded with information, he observed, the scarce resource would no longer be information itself. A wealth of information creates a poverty of attention. Two professors, twelve months apart, at the dawn of the information age: one assumed human attention would be available when the moment demanded it, and the other predicted its depletion. We have spent fifty years proving the second one right.

The evidence is now overwhelming, and it extends far beyond philanthropy. The Reuters Institute’s global study of news consumption found that forty percent of people across forty-eight countries now sometimes or often actively avoid the news, up from twenty-nine percent in 2017. Their reasons are not apathy in any classic sense. They cite the toll on their mood. They cite feeling overwhelmed. And roughly one in five cite something more haunting: powerlessness, the sense that there is nothing they can do with what they learn. That last reason is the shrug in survey form.

It is a person standing at the edge of ten thousand ponds, concluding that wading into just one of them could never matter, and walking on past them all.

The psychologist Paul Slovic has spent decades documenting what he calls psychic numbing, and his findings invert our intuitions about compassion. We assume that as suffering grows, concern grows with it. The research shows the opposite. A single identifiable child moves us more than statistics describing millions, and our willingness to help begins declining as soon as the number of victims climbs past one. Slovic titled one of his papers with the finding itself: the more who die, the less we care. Compassion, it turns out, does not scale. It was never designed to.

None of this evidence describes a generation worse than the ones before it. It describes people asked to carry more awareness than any humans in history, who have responded the only way a finite mind can: by skimming across the surface, sampling the high-level signals, and picking a defensible position somewhere in the middle. The shrug is not a moral failure. It is a coping architecture.

But architecture has consequences, and ours has collided with a sector built on the opposite assumption. There are nearly two million registered nonprofits in the United States, and virtually every one of them is raising its voice. Our collective response to declining attention has been volume: more email, more appeals, more urgency, more everything. We are trying to out-shout the shrug. And the shouting feeds it.

The U-Turn

I would rather diagnose this in others, but honesty requires a confession.

A few years ago, I was pulling off a highway, running late for a meeting, when I stopped at a light where a man stood asking for financial help. I did what I suspect most of us have done. I pretended not to notice. I studied the traffic signal with sudden fascination, and when it changed, I drove on. I made it about a block. Then the nagging feeling arrived, the one that always seems to travel a beat behind the eyes: that was a real person, with a real need, and I had looked through him like weather. I made a U-turn, drove back, and gave a stranger what I could.

I would love to tell that story as evidence of my character. It is closer to evidence of the opposite. The honest version is that my first response was the shrug, and my conscience needed a full city block to catch up. I think of the times it never did. The cars I have passed on the shoulder while assuring myself a tow truck was surely minutes away. The appeals I have scrolled past while telling myself someone closer to the problem would respond. The shrug does not announce itself as a decision. It arrives disguised as a schedule.

The shrug is not the absence of conscience. It is a delay in it. And in an accelerating world, the delay usually wins. The light changes. The feed refreshes. The moment passes, and no U-turn presents itself.

Crisis by Consent

There is a reason this pattern feels personal to me, and it reaches back to the same remarkable window when Simon and Singer were writing.

In 1973, my grandfather published a book called Crisis by Consent. Its thesis has followed me my entire career: the gravest dangers a society faces come not from people who refuse to help, but from people who never pause to consider whether they should.

Crisis does not require malice. It only requires our consent, and we grant that consent through inattention, one unconsidered moment at a time.

He wrote those words before the internet, before the smartphone, before most of us started scrolling 300 feet per day on average. He could not have imagined a world in which the average person encounters more human suffering before breakfast than his generation confronted in a month. And yet he named the mechanism precisely. Those twenty million households did not refuse to give. Refusal requires consideration, a weighing, a verdict. They consented to a quieter crisis by never reaching the question at all.

That distinction matters enormously for what the nonprofit sector does next. If the problem were refusal, the answer would be persuasion: better arguments, better evidence, better campaigns. But you cannot persuade someone out of a question they never asked. The shrug sits upstream of persuasion entirely. Which raises the only question that matters: what actually breaks one?

What Breaks a Shrug

In the spring of 2020, I read an article that got my attention in an unexpected way. It described a paradox so sharp it bordered on the absurd: families waiting in lines that stretched for blocks outside food banks, while a few hundred miles away, farmers plowed perfectly good crops back into their fields because the systems that moved food had seized up. Harvests that had taken a year to grow were being destroyed within driving distance of people who had waited hours to feed their children. A group of college students, home on a break nobody had planned, decided the distance between those two facts was drivable. They rented box trucks and U-Hauls, picked up the food themselves, and delivered it to the food banks. That improvised convoy became The Farmlink Project.

What that story exposed in me took longer to admit. By 2020, I had spent two decades working alongside thousands of charities. I knew the sector’s machinery from the inside. And in all that time, I had never once sent the email that fundraisers dream about, the unprompted note from a stranger that says: I am moved by what you do, and if there is anything I can do to help, tell me. That day, I wrote it. And recieved a grateful response the following day. Eventually I joined Farmlink’s board, where I have watched a charity born in a dorm-room-sized moment of conviction defy every odd stacked against a startup nonprofit. In its most recent fiscal year, Farmlink moved 136 million pounds of food, bringing its total to half a billion pounds since its founding, while preventing 117,000 tons of CO2e from entering the atmosphere and saving four billion gallons of agricultural water.

For a long time I assumed that story broke through to me because the need was so great. But I had encountered greater needs professionally every week for twenty years. The need was not greater. It was closer. The paradox gave it a shape my attention could not skim past: real fields, real trucks, real families in a line I could picture down to the folding tables.

I could picture it because, as a child, I had stood in one myself. The story did in an instant what no statistic had done in two decades. It made the people in that line visceral to me, and the shrug I did not even know I was holding gave way.

Shrugs are not broken by louder appeals. They are broken by closer ones.

All of this points to an uncomfortable reckoning for my profession. For roughly the last quarter century, fundraising has moved in exactly the opposite direction: from the relational to the transactional, from the individual to the segment, from the neighbor to the list. We industrialized the ask during the precise decades when attention was collapsing, and we should not be surprised that industrial appeals are what the shrug was built to absorb. The market stopped responding to meaning and now responds only to proximity. Generosity works the same way. Relevance at the level of a single human being is not a nice-to-have in an attention crisis. It is the only signal that still gets through.

That conviction sits at the heart of N1 Philanthropy, the book Gabe Cooper and I will release on October 14, which argues that the future of generosity will be built one person at a time rather than one segment at a time. But you do not need a book to test the premise. You have already tested it on yourself.

Think of the last time you gave, really gave, unprompted. I would wager it was not a campaign that moved you. It was a face, a story, a line you could picture, a pond you could see.

The shrug survives on invisibility. It does not feel like a decision while it is happening. It feels like a schedule, a scroll, a reasonable allocation of limited time. But a shrug you can see is a shrug you can refuse. I know the feeling of mine now. It feels like the block of road between the traffic light and the U-turn, the small delay between noticing and caring. Naming it has not made me wade into every pond, and it should not. Trying to care about everything is precisely the burden that produced the shrug in the first place. What awareness restores is the thing my grandfather warned we were losing: not the obligation to act every time, but the willingness to consider. In practice, it looks like deciding in advance that the nagging feeling deserves the U-turn. It looks like standing beside a few ponds instead of scanning ten thousand. It looks like letting one story get close enough to cost you something.

When the FarmLink article finally got through, I did not have to talk myself into responding. The instinct fired instantly, twenty years of professional restraint notwithstanding. That is what the research on generosity keeps finding, and it is the conviction underneath everything I have written on this subject.

The impulse to give is not fragile or fading. It is ancient, biologically hardwired, and intact. The shrug buries it. It has never once erased it. Which means the crisis in front of us is not a generosity deficit at all.

It is an attention deficit resting on top of an instinct that has been waiting, the entire time, for something close enough to wake it.

Singer’s question, it turns out, was never really whether you would save the child. Everyone saves the child. Fifty years on, in a world of ten thousand ponds, the question has changed into something he never had to ask: whether you would notice her at all. The toddler is still in the water. The water is still only knee-deep. And the first act of generosity, before the wallet ever opens, before a single shoe gets wet, is refusing the shrug long enough to look.

Generosity begins where the shrug ends.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI, The Generosity Crisis and the forthcoming book, N1 Philanthropy. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

My grandfather was a geologist. He earned his doctorate studying the earth, spent part of his career with the Atomic Energy Commission, and worried, more than almost anything, about water. Not in the abstract. He worried about what we would leave in the ground and the rivers for people who had not been born yet, and whether anyone would think to ask before it was too late to matter.

In 1973 he wrote a book about it. He called it Crisis by Consent. I reread it this past weekend, slowly, the way you read something written by someone you loved who is no longer here to argue with you about it.

The title is the argument. He did not believe our gravest danger was the villain who sets out to do harm. We can see that person coming. We organize against them. His fear was quieter and, he thought, far more dangerous: the person who never considers whether they should act at all. The one who lets it happen. Not through malice. Through the simple failure to ask the question. A crisis, he argued, rarely arrives by force. More often we consent to it, one unexamined decision at a time.

I have carried that idea my whole life. It runs underneath most of what I believe about generosity and about technology. And it is the reason I have to begin this particular article with a confession.

I have been avoiding writing it for over a year.

I keep a running journal of pieces I feel compelled to write. This one has sat at the top, untouched, longer than anything else on the list. Every time I reached for it, I found a reason to set it down. Too divisive. Too political. Too easy to be misread as carrying water for an industry I am part of, or as scolding the people I work alongside. I’ve worked with many environmental organizations. I admire them. I did not want to hand anyone a weapon, and I did not want to write something that would age into embarrassment as the facts moved underneath me.

Those were my reasons. They were also, I have come to see, exactly the kind of reasoning my grandfather was warning about. Avoidance dressed as prudence. Silence that feels responsible from the inside. I was not refusing to engage with the hardest question my field is asking right now. I was simply, politely, declining to consider it. Consenting, in his sense of the word.

So this is me, finally asking the question.

The Question Behind the Question

I deliver something like five webinars a week. I have learned to expect, almost without fail, that one of the first questions afterward will be about the environment. What about AI’s energy use? What about the water? Should we feel guilty about this?

For a long time my honest internal reaction was a kind of flinch, because the question is usually aimed at the wrong target. The person asking is almost always thinking about their own prompts. Their own usage. Whether the email they just drafted with AI cost the earth something. And on that narrow question, the math is not close.

For some perspective, a single typical AI prompt uses roughly 0.3 watt-hours of electricity. That figure comes from Epoch AI, an independent research group, and it is about a tenth of the alarming number that circulated a couple of years ago, before the models and chips grew far more efficient. To put 0.3 watt-hours in perspective, it is less than an LED bulb draws in a few minutes. The water involved is a fraction of a teaspoon. Meanwhile the average American moves through a day of streaming, scrolling, commuting, and cooling a house that consumes on the order of tens of thousands of watt-hours, without a flicker of guilt. If you want to shrink your footprint, skipping a prompt is like trying to lower the ocean by declining a glass of water. Longer and more complex queries cost more, and the picture shifts as usage grows. But for the individual worried about a single email, the math is not close.

I grew up, as I said, in a household shaped by a man who studied water. You turned off the faucet while you scrubbed. You kept showers short. You did not waste what others would need. It was not only water. You never left a light on unless you needed it to see. We ran fans instead of air conditioning. Conservation was not a cause in our house; it was just how you lived, a kind of daily respect for things that were not infinite. I carry that instinct still, and I think it is a good one. But I have watched that same instinct get pointed at a 0.3 watt-hour prompt by someone holding a phone that streams hours of video a day, and aimed there, I have come to believe it does more harm than good. It spends our limited capacity for concern on the one thing that barely registers, and leaves nothing for the things that matter enormously.

Here is what I have learned to say instead, gently, because the question is sincere even when it is misaimed: the size of your individual prompt is the least interesting thing about AI and the environment. The interesting questions, the genuinely hard ones, live one level up. And they are much harder than the people asking me have usually been told.

The Number That Should Hold Your Attention

Step back from the single prompt and the picture changes completely.

Data centers consumed somewhere around 415 terawatt-hours of electricity in 2024 and surged by roughly 17 percent in 2025, at a time when overall global electricity demand grew only about 3 percent. The International Energy Agency expects data center consumption to roughly double from its 2024 level by 2030, reaching something close to the total electricity use of Japan, with AI as the most important driver of that growth. The five largest technology companies poured more than 400 billion dollars into infrastructure in 2025 alone, and the IEA expects that figure to climb another 75 percent in 2026. To put it in proportion, the IEA has noted that the capital spending of just those few companies now exceeds global investment in oil and natural gas supply.

Those are not numbers most of us can absorb on first reading. We are building, in the span of a few years, an energy appetite the size of a major industrial nation, and we are doing it faster than any electrical grid was designed to absorb.

There is also a distinction the loudest arguments on both sides tend to flatten: not all of this is AI.

Data centers are not new. They have been growing for two decades. Every video you stream, every file in the cloud, every card you swipe and message you send already lived in a humming building somewhere drawing power. AI did not create that hill. What AI does is make the slope dramatically steeper. The specialized chips that run AI can draw several times the power of conventional server processors, and the most intensive facilities now under construction are projected to consume as much electricity as a small city. So when you hear that data centers are straining the grid, the truth is layered: the digital life we already built was energy-hungry, and AI is now pouring fuel on a fire that was already lit. Blaming AI for all of it is wrong. Pretending AI changes nothing is worse.

Then there is the matter of efficiency, which most people get exactly backward.

Why Getting Better Has Not Meant Using Less

In early 2025, an open-source model from China called DeepSeek arrived and stunned the industry by matching far more expensive systems at a fraction of the training cost. Many people read it as good environmental news. Efficiency had come. The machines would sip rather than gulp.

The open-source and Chinese part matters beyond energy. Data run through such a model can flow through servers governed by another country’s laws, with privacy implications very different from what most donors or beneficiaries would assume. That is a separate conversation, but not a small one, and the power bill was never the only interesting thing about DeepSeek.

On the energy question, the history is instructive. In 1865 a British economist named William Stanley Jevons noticed something that has never stopped being true. As steam engines grew more efficient, England did not burn less coal. It burned more. Cheaper power did not reduce demand; it unleashed it. Lower the cost of a thing and the world finds vastly more uses for it, faster than the savings can keep up. Economists still call it the Jevons Paradox.

When DeepSeek’s efficiency made headlines, the natural expectation was that the big spenders might ease off. They did the opposite. Meta’s leadership publicly dismissed the idea that its AI spending would fall, and across the industry the major labs reaffirmed and then raised their investment plans. Efficiency did not cool the race. It widened the prize, and everyone ran harder.

The deeper trend is staggering in its own right. The computing power used to train frontier AI systems has grown roughly four to five times every year for more than a decade, according to Epoch AI, an astonishing rate of increase by any historical standard. The chips themselves keep getting smaller and more capable per watt at a pace that would have seemed like fantasy a few years ago. And yet total consumption keeps climbing, because every gain in efficiency is immediately spent on something more ambitious. We are not pocketing the savings. We are reinvesting them, instantly, at scale. The path of energy and compute does not run in a straight line. The hardware and the algorithms grow more efficient, and the ambition grows right alongside them to consume whatever the efficiency frees up.

And a model is never finished. We tend to picture the enormous cost of training as a one-time event, like pouring a foundation. But the ongoing work of tuning and refining a deployed model has grown to rival the cost of building it in the first place. There is no moment when the engine is built and the burning stops. The energy required is not a purchase. It is a subscription, and it renews forever.

Building Faster Than We Can Ask Why

If you ask why we are building so far ahead of what today’s workloads require, the honest answer is not demand. It is fear.

The race to build bigger and faster has less to do with measured need than with the terror of being left behind. No major player can afford to assume a competitor will not build, so everyone builds, and the result is an infrastructure boom running well out in front of the actual work there is to do. This is not a conspiracy. It is what competition looks like under deep uncertainty. But it means the scale of what is being constructed reflects strategic anxiety as much as genuine necessity, and that is a different thing to consent to than a clear-eyed response to human need.

And consent is exactly the right word, because someone is always being asked to consent, whether or not they are actually asked.

I think about Nashville in part because a year and a half ago I moved to Middle Tennessee, and what is happening just up the road is not a case study I pulled from a news feed. My local news is saturated with it. It has become a conversation at the barber shop and in the grocery line, among the people who live here. This past June, two proposed data centers, one beside the Nashville Zoo in South Nashville and one on the campus of Fisk University in North Nashville, drew more than 150 residents to a single planning commission meeting. People waited up to five hours for two minutes at the microphone. A petition against the project near the zoo gathered more than 400,000 signatures. The mayor signed an executive order directing the city to study how large data centers affect surrounding neighborhoods, and the Metro Council advanced a temporary moratorium on new data center permits. What made the room furious was not only the noise and the water and the power draw. It was that both sites sit in historically Black and immigrant neighborhoods, communities that have absorbed this kind of thing before and were being asked, once again, to host the costs of a benefit flowing mostly to others. I am close enough to see this clearly. I am also not the one being asked to pay the highest price for it, and I want to be honest about that too.

In Louisiana, Meta’s Hyperion campus in Richland Parish is rising on land where residents say the deal was finished before they understood its scale. ‘There was no community input,’ one told Bloomberg. ‘It was a done deal.’ Longtime residents have been displaced to make room. The three gas plants first announced to power it have since grown to ten. And in rural Georgia, near another Meta data center, residents report their tap water running brown. These are not abstractions in a forecast. They are people, at a kitchen sink, who never consented to the trade being made on their behalf.

I cannot write about the abstract promise of this technology without sitting for a moment in that Georgia kitchen, or on that Louisiana porch where the thing was settled before anyone was asked. Both things can be true at once, the breakthrough and the brown water, and the harder I look the less willing I am to let either one cancel the other out.

The Reasons for Real, Disciplined Hope

This is not a dispatch from despair, because despair is just apathy that has given up the pretense. The situation is serious. The trajectory is also, genuinely, bending. Both are true.

The same economic force that makes AI so hungry, the fact that energy is expensive and the appetite is enormous, is now the most powerful incentive humanity has ever had to solve clean energy. When a handful of the richest companies on earth suddenly need staggering quantities of power, and need it cheap, and increasingly need it clean to satisfy their own commitments and their critics, they become the most motivated buyers of energy innovation in history. The tech sector accounted for roughly 40 percent of all corporate renewable power purchase agreements signed in 2025. That is not a marketing report. That is capital voting.

Look at where that money is going. The geography of it is more interesting than the doom.

Switzerland has quietly become one of the densest data center regions in Europe, and it runs them largely on hydropower drawn from Alpine rivers. Zurich is a serious AI hub in part because clean, reliable baseload power was already there. It is a glimpse of what intentional siting looks like when a society decides the source of the energy is part of the design rather than an afterthought.

Enhanced geothermal may be the most promising near-term breakthrough, and I have a soft spot for it, because it is my grandfather’s discipline come back to help. Traditional geothermal needed rare natural hot springs. The new approach drills three to eight kilometers straight down into hot rock almost anywhere on earth and circulates water to bring the heat up. It is clean, and unlike sun and wind it runs around the clock. Fervo Energy’s Cape Station in Utah is expected to deliver its first 100 megawatts of carbon-free power in 2026 and scale to 500 megawatts by 2028. The Rhodium Group estimates geothermal could meet as much as 64 percent of the growth in data center demand by the early 2030s. The tech sector signed fourteen geothermal power agreements in 2025, triple the year before. The energy is, quite literally, beneath our feet, and we finally have a reason to reach it.

Fusion has been twenty years away for fifty years, and yet the money arriving now is not patient money. Helion, one of the most aggressively funded fusion startups and backed by major AI-world investors, has signed an agreement to deliver commercial fusion power to Microsoft on a timeline most of the field considers audacious. It may slip. It may not arrive at all on schedule. But serious capital is betting that the hardest energy problem we know of is worth solving precisely because AI has made the prize so large.

And then there is the AI frontier that is out of this world - quite literally: putting the computers in space. Anthropic recently contracted for the full capacity of a major data center and has expressed interest in leveraging orbital compute, servers in orbit bathed in uninterrupted solar power, beyond the reach of grid limits and contested neighborhoods entirely. Google is pursuing a parallel effort, with prototype satellites planned for 2027. The economics today are forbidding; lifting that much hardware to orbit costs far more than it can earn. But the cost of reaching space has collapsed before, faster than experts predicted, and it would be unwise to laugh this off. A decade ago the idea would have been science fiction. Today it is a line item.

None of this means the problem is solved. Far from it. Gas plants remain the largest single source of electricity for American data centers right now, and the IEA expects that to hold in the near term while clean projects wait years to connect to overburdened grids. The gap between where we are and where we need to be is real and wide. But a gap with momentum behind it is a different thing from a wall. The direction of travel is real.

When Concern Becomes a Costume

Recently I sat on a panel for the Chronicle of Philanthropy, and someone asked a question that resembles a type of question I hear several times a week. Some staff members, they said, resist using AI on ethical grounds, including its environmental impact. How should a leader talk with them about it?

My answer began, and begins, with a gentler question underneath theirs. Where is the concern actually coming from? Sometimes it is exactly what it appears to be: a sincere, informed worry about the planet, held by someone who has examined their own life and earned the standing to raise it. That person deserves a real partner in the conversation, not a sales pitch.

But often, if we are honest, the environment is standing in for something else. Anxiety about being replaced. Discomfort with how fast the ground is moving. A general unease that has found a respectable place to land, because no one will argue with you if you say you are worried about the earth. Both the real concern and the disguised one deserve compassion. They do not deserve the same answer, and mistaking one for the other usually leaves both unaddressed.

So here is what I have come to believe, and I know how it sounds.

In fact, this is an example of the very apathy my grandfather warned against, wearing the costume of virtue.

I do not mean the person who has genuinely reckoned with their footprint and arrived at a thoughtful caution. That person I take seriously. I mean the far more common case, the one I see most weeks: the objection raised reflexively, by someone who could not tell you their household energy use, how their website is powered, or what their streaming habit costs, who has nonetheless decided that the AI prompt is where the line gets drawn. That is not consideration. It is its opposite. It is the refusal to do the hard arithmetic, ennobled by a green ribbon.

The framework I keep returning to is the one my grandfather would have recognized: net benefit, honestly assessed. Not whether a technology has a cost, because everything that touches the physical world has a cost. The real question is whether the good it does exceeds the harm it carries, and whether we are working in good faith to shrink that harm over time. A nonprofit that uses AI to reach more people, serve more need, and steward its resources more faithfully is almost certainly creating more good than the energy behind its tools destroys. Choosing to forgo all of that, to avoid an energy cost you have not actually measured, is also a choice. It also has a moral weight. We simply do not put it on the scale, because inaction never sends us an invoice.

The bigger risk has never been caring too much about the cost of acting. The bigger risk is not caring enough to ask the question at all.

You Have More Agency Than You Have Been Told

If there is one thing I want a reader to take from this, it is that the choice is not binary. It is not use AI and ignore the cost, or refuse AI and feel clean. The space between those poles is enormous, and it is where everyone honest actually lives.

Start with this liberating fact: your footprint does not have to be neutral. It can be net positive. You are allowed to emit and then to give back more than you took. I fly most weeks; I donate miles to environmental organizations and give to groups doing the work, not to erase the cost but to overcorrect for it. You can do the same, whether you are an individual or leading an organization. A person offsets through how they spend, what they eat, how they travel, and where they put their money. An organization does it through serious, verified programs. Not all offsets are equal, and the difference matters more than most buyers realize. The cheapest credits often pay for emissions avoided somewhere else, or for carbon stored in ways that may not last. The ones worth buying fund durable removal, carbon actually pulled from the air and locked away for the long term. It costs more, and it is the only version that does what the word offset promises. This is not penance. It is stewardship, and it scales.

To do this, you must use the leverage you actually have, which is larger than it feels. I have hosted around a hundred domains for years on a provider that runs entirely on wind, and that option exists only because enough people insisted it should. The internet runs cleaner today than it did when I started a dot-com in 1997, not because the companies grew virtuous on their own, but because users, customers, and citizens made noise until the market answered. The same pressure is available now, and it works the same way. Choose the cleaner option when it exists, and say loudly that you want it when it does not yet. Nashville proved that a community willing to show up can stop a thing in its tracks. That power did not come from feeling guilty about prompts. It came from people who decided to ask the question out loud, together.

A tool capable of accelerating cancer research, mapping food insecurity, modeling a watershed, or helping a five-person nonprofit do the work of fifty, and we reach for it to reword an email and then feel bad about that. The same technology straining the grid is also among the most promising instruments we have for healing it. AI is already accelerating the search for cleaner energy, better materials, and the geothermal and fusion breakthroughs that may one day make this entire debate sound quaint. To sit it out in the name of the environment is, in too many cases, to withhold one of the better tools we have for actually helping the environment.

This is where the net-benefit framework has been pointing the whole time. For the people and organizations whose work is to serve humanity, using AI well is not a guilty indulgence to be offset. It is closer to an obligation. Not using it carelessly, and not using it timidly, but using it deliberately, at full strength, aimed at the problems that matter most. That is how you earn the energy. Not by abstaining from it, but by making it count.

Crisis by Consent

My grandfather did not write a pessimistic book. People assume, from the title, that it must have been a warning wrapped in despair. It was the opposite. He believed deeply in people. That belief was exactly why apathy frightened him so much, because he knew what we were capable of when we finally chose to pay attention, and he could not bear to watch us sleepwalk past the moment when it mattered.

I think about the choices ahead of us the way he taught me to. The energy behind AI is real, and growing, and in some places it is landing hardest on people who were never asked. That is the short-term truth, and I will not soften it. But the same forces driving the demand are now driving the most serious search for clean and abundant energy in human history, and the arc, while far from guaranteed, is bending. That is the longer truth, and I will not abandon it to fashionable gloom. Both are true. The work is to hold them together without letting go of either.

He titled it Crisis by Consent because he understood that the gravest dangers do not usually require our agreement to proceed. They only require our silence. They advance through the averted gaze, the deferred question, the article a person keeps meaning to write and never quite does.

I am done consenting to my own silence on this one. Ask the question. Use the tools. Refuse the easy comfort of either certainty. And hold both.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI, The Generosity Crisis and the forthcoming book, N1 Philanthropy. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

The environmental impact of AI is a complex and extremely nuanced subject which led to the greatest hesitation of whether I would write this article at all. I encourage you to go deeper in the exploration using the following links, many of which were referenced in writing this article. Figures reflect the best available estimates as of mid-2026. This is a fast-moving area, and the numbers will continue to change.

Energy demand and the scale of the build-out

• International Energy Agency, Energy and AI (April 2025) — the landmark analysis: ~415 TWh of data center electricity in 2024, projected to roughly double to ~945 TWh by 2030, near Japan’s total consumption. https://www.iea.org/reports/energy-and-ai

• International Energy Agency, Key Questions on Energy and AI (April 2026) — the follow-up update: data center electricity grew 17% in 2025 against 3% global growth, AI-focused demand surged 50%, and the five largest tech companies’ capital spending topped $400 billion in 2025, set to rise another 75% in 2026. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary

• IEA news release announcing the 2026 update. https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions

The energy cost of a single query

• Epoch AI — independent research on the energy use of a typical AI query (~0.3 watt-hours) and the growth rate of training compute.

https://epoch.ai/

• Hannah Ritchie, Sustainability by Numbers — a clear, well-sourced walk through the 2025 IEA numbers and the per-query footprint.

By the Numbers

How much electricity does AI consume? [2025 summary]

Over the past year, I’ve written a few articles about the energy (and carbon) footprint of individual use of artificial intelligence, mostly in the form of asking LLMs (or chatbots) questions…

Read more

4 months ago · 300 likes · 37 comments · Hannah Ritchie

Efficiency, and why it has not reduced total use

• The Jevons Paradox — background on why efficiency gains have historically increased, not decreased, total consumption. https://en.wikipedia.org/wiki/Jevons_paradox

Communities and the local cost of data centers

• Bloomberg, Meta’s Giant AI Data Center Is Reshaping Rural Louisiana — the Hyperion campus in Richland Parish, community impact, and the gas-plant build-out. https://www.bloomberg.com/features/2026-meta-facebook-ai-data-center-louisiana/

• Sierra Club, In Rural Louisiana, Meta’s New Data Center Promises Growth — But at What Cost? — community impacts, and the brown tap water reported near Meta’s Georgia data center. https://www.sierraclub.org/sierra/rural-louisiana-meta-s-new-data-center-promises-growth-what-cost

• Nashville Banner — coverage of the June 2026 Metropolitan Planning Commission meeting and opposition to the proposed data centers near the Nashville Zoo and Fisk University. https://nashvillebanner.com/2026/06/12/nashville-data-centers-planning-commission-meeting/

• WSMV — the petition against the zoo-adjacent project surpassing 400,000 signatures. https://www.wsmv.com/2026/06/14/petition-against-proposed-data-center-next-nashville-zoo-gets-more-than-400000-signatures/

• WKRN — the mayor’s executive order and the Metro Council’s moratorium. https://www.wkrn.com/news/local-news/nashville/nashville-data-center-moratorium/

Where cleaner energy may come from

• Fervo Energy — Cape Station enhanced geothermal project in Utah.

https://fervoenergy.com/

• Rhodium Group — analysis of geothermal’s potential to meet data center demand growth.

https://rhg.com/

• Helion Energy — the commercial fusion power agreement with Microsoft.

https://www.helionenergy.com/

• Google, Project Suncatcher — orbital (space-based) compute initiative. https://blog.google/technology/research/google-project-suncatcher-space-based-ai-compute/

By Nathan Chappell

I want to be transparent about something before I say anything else.

I am among the most pro-AI voices in the nonprofit sector. I co-invented patents on gratitude prediction nearly a decade ago, before most fundraising professionals had heard the term machine learning. I have spent years arguing — sometimes against significant resistance — that AI is not a threat to the social sector but its greatest amplifier. Three books, hundreds of keynotes, and a community of 23,000 practitioners later, I have not softened that view. If anything, I have deepened it.

So when I push back on Nan Ransohoff’s recent Substack essay, “The Third Wave of American Philanthropy,” I want to be clear: I am not defending the status quo. I am not skeptical of capital. I am not afraid of disruption. I am someone who has spent a career at the intersection of technology and generosity, and I believe Nan got the capital right and the talent wrong. That distinction matters enormously.

I encourage you to read her piece first. The link is here. She is a serious thinker writing about something real. This is not a rebuttal. It is a set of questions from a unique vantage point, offered in the spirit of making the conversation more complete.

How I Got Here

In 2000, I sold my second technology company. My plan was straightforward: earn a master’s degree, head to Madison Avenue, and apply what I knew about building businesses to the world of marketing and communications.

The nonprofit sector found me first.

I did not go looking for it. I stumbled into it the way most meaningful things happen — unexpectedly, through an open door I almost did not walk through. What I found on the other side changed everything I thought I understood about talent, efficiency, and what it actually means to build something that lasts.

I stayed for seventeen years. I led teams. I helped architect campaigns. I watched organizations built on nothing but mission raise hundreds of millions of dollars for education, medicine, and human services. I went to bed most nights knowing the work mattered in ways I had never experienced in the for-profit world. Not because I was trying to get rich, but because I was trying to get it right.

Then, nearly a decade ago, I pivoted into AI — not away from the sector, but deeper into it. I helped file patents on the relationship between human connection and giving behavior. I founded Fundraising.AI. I wrote the books. And I did all of it because I believe that AI, applied responsibly, can do what the sector has always needed: honor every person as an individual, at scale, without losing the humanity that makes generosity meaningful.

I tell you this not to establish credentials but to establish a vantage point. I have started and sold technology companies. I have spent nearly two decades inside the nonprofit sector helping raise billions for social good. I have been at the frontier of AI in philanthropy for the better part of a decade. I am not defending a world I do not understand. I am challenging assumptions made by people who have not yet lived inside the world they are proposing to transform.

What Nan Gets Right

The capital math is real. The historical framing is grounded. The urgency is legitimate. The assumptions abound.

A concentrated wave of AI-generated wealth is forming. The OpenAI Foundation holds a staggering position. Anthropic founders and employees have made significant pledges. If even a fraction of that capital finds its way to the social sector in the next decade, the implications are significant. Nan is right to name this moment and right to ask whether the sector is prepared to absorb it.

She is also right about one uncomfortable truth: some organizations will need to grow, evolve, and operate with greater rigor than they have historically been resourced to achieve. The nonprofit sector has not been immune to drift, inefficiency, or resistance to innovation. There are real organizations doing real work that would benefit from capital, talent, and better tools.

And I find genuinely exciting the possibility she gestures toward near the end of her piece — a new class of entities amplifying social good in ways we have not yet imagined. I think about this often. The organization that raises a billion dollars for human flourishing with one employee and an AI infrastructure backbone is not a fantasy. It is an inevitability. I want to see it happen. But done in a way that first assesses short-term gain versus long-term and unintended consequences.

So this is not a defense of the status quo. It is a challenge to the assumption embedded in the framing that the status quo is the primary obstacle.

The Talent Assumption Is Wrong

Nan’s third belief is where I have to stop.

She writes that traditional philanthropic organizations and people “won’t cut it” — that Wave 3 demands tech-caliber talent and execution, and that new funders will likely distrust people who come from traditional philanthropy.

I have watched this assumption arrive in the social sector before. I have watched it arrive with confidence, with capital, and with a certainty that private sector discipline would solve what mission-driven organizations had somehow failed to figure out. And I have watched, with some regularity, as those same people discovered something they had not expected.

Operating in the currency of trust is not a soft skill. It is the hardest operational discipline in any sector.

You cannot move fast and break trust. And in the social sector, trust is the product.

The nonprofit professional who has spent twenty years learning how to hold a donor relationship through a capital campaign, a leadership transition, an economic crisis, and a global pandemic has developed something that cannot be onboarded in a sprint. The executive director who has built a regional organization from a staff of three to a staff of sixty on restricted funding and volunteer labor has demonstrated a form of resourcefulness that most venture-backed founders have never had to summon.

There are 1.7 million nonprofit organizations in the United States and roughly 150,000 in Canada. The vast majority of them operate on less than $500,000 per year. They are not failing. They are under-resourced. There is a difference. Mistaking chronic underfunding for incompetence is not a new error, but it is a costly one.

The labor pool Nan is looking for is not absent. It is already there — invisible to a talent calculus that only recognizes pedigrees it has previously rewarded.

“We Need a Silicon Valley for Public Goods.” Do We?

This is the line that made the hair on the back of my neck stand up.

I started a dot-com in 1997 and miraculously made it through the dot-com bubble. I watched things built on a house of cards, accelerated by capital that moved faster than wisdom, collapse in ways that cost real people their livelihoods. I am living through the AI hype cycle right now, watching leaders of extremely prominent organizations completely reverse stated positions within 48 hours based on regulatory winds, competitive pressure, or what side of the bed they woke up on.

Silicon Valley is not a model for building trust infrastructure. It is a model for building scale.

Those are not the same thing.

Silicon Valley optimizes for exit. The food bank has no exit. The hospice does not pivot. The domestic violence shelter does not get acqui-hired. As Matt Leighty wrote in the comments of Nan’s piece — and I thought this was the most important thing anyone said in that entire thread — “there is no exit from the human condition.” The permanence is not a failure of ambition. The permanence is the point.

What worries me most about the Silicon Valley metaphor is not the efficiency it implies. It is the values architecture it imports without declaring. Speed. Power laws. Risk tolerance. Disruption as a virtue. These are coherent values inside a system where failure is primarily financial. They are far more complicated inside a system where the people absorbing the failures are already vulnerable.

What Philanthropy Actually Means

Before we build a Silicon Valley for public goods, it might help to agree on what we mean by philanthropy.

The word comes from the Greek: philos, meaning love, and anthropos, meaning humanity. Philanthropy is, at its root, the love of humanity.

In 2022, Elon Musk sat down with TED head Chris Anderson at the Tesla Gigafactory in Texas. When the conversation turned to billionaire philanthropy, Musk offered a definition that has stayed with me ever since — not because it is wrong exactly, but because of what it reveals. “If you care about the reality of goodness instead of the perception of it,” Musk told Anderson, “philanthropy is extremely difficult. SpaceX, Tesla, Neuralink and The Boring Company are philanthropy. If you say philanthropy is love of humanity, they are philanthropy.”

When I heard this, I posted a question online that I think cuts to the heart of what is happening in this third-wave conversation: if Tesla is philanthropy, does buying a Tesla make me a philanthropist?

I am not asking this to mock the argument. I am asking it because the answer reveals a fundamental divergence in how the tech world and the social sector understand what philanthropy is and therefore what it requires.

Building a better electric car is a remarkable thing. Accelerating sustainable energy is genuinely important. But philanthropy — the actual practice of it, the kind that builds civic life, restores human dignity, and holds community together in the places markets abandon — is not a byproduct of a profitable enterprise. It is a deliberate, relational, often unglamorous commitment to people who cannot pay for what they need.

When we conflate the two, we do not elevate commerce. We diminish the practice. And we risk building an entire third wave of giving on a definitional foundation that privileges the giver’s self-image over the recipient’s actual need.

Philanthropy is not what happens when a good company exists. It is what happens when someone chooses to give without expectation of return.

The nonprofit sector has always understood this distinction. It is not a coincidence that the people who have spent careers inside it are also the people most likely to interrogate whether a new model actually serves the people it claims to.

Incentives Rule

Charlie Munger spent a lifetime reducing complex human behavior to a single question: “Show me the incentives and I’ll show you the outcome.”

I think about this every time I read about the third wave.

Nan’s Wave 3 funders are, by her own description, people whose wealth was generated by AI companies whose continued success depends on the broad adoption of AI. That is not a conspiracy. That is just incentives. A foundation funded by OpenAI equity has a structural interest in defining “civilizational flourishing” in ways that happen to require more AI. An ecosystem of philanthropic startups built in the image of tech culture has a structural interest in defining talent and efficiency in terms that privilege tech culture.

I am not arguing these interests are malicious. I am arguing that incentives shape outcomes whether or not anyone intends them to.

We have a proof of concept for what happens when we do not interrogate those incentives before scale makes them irreversible.

Nobody at a major social media company in 2004 intended to decrease happiness or increase teen suicide rates. They intended to connect people. The intention was genuine. The outcome was something no one planned because no one paused long enough to ask what the second and third-order effects might be when their technology met human psychology at civilizational scale. We are still living with the consequences.

The nonprofit sector operates at the intersection of human vulnerability and public trust. It cannot afford to move fast and fix it later. The people it serves do not have a later.

This is not an argument against AI. I have staked my career on AI’s potential to do extraordinary good in this sector. It is an argument for the kind of slow, deliberate, consequence-aware thinking that the sector’s most experienced practitioners have spent careers developing — and that the Silicon Valley frame tends to treat as a liability.

The Crowding Out Question Nobody Is Asking

Here is the thing about the generosity crisis that Nan’s piece does not address.

Total charitable giving in the United States is at record levels. The number of Americans who give is in freefall.

This is not a capital shortage. It is a participation collapse. Eighty million people who used to give no longer do. The generosity crisis is an engagement crisis, a belonging crisis, a crisis of people no longer seeing themselves as part of something larger than their own household. That is the actual problem. And it is not a problem that $50 billion from a handful of AI founders solves.

In fact, it may make it worse.

A more generous society is not one where fewer people give more. It is one where more people give at all.

When philanthropic capital concentrates — when the message the broader culture receives is that the billionaires will handle it — the civic muscle of everyday generosity continues to atrophy. People opt out not because they do not care but because they no longer feel needed. That signal, repeated at scale through a third wave that is explicitly and proudly built around large capital from small numbers of people, may accelerate the very participation collapse that sits at the root of the crisis.

The Giving Pledge is instructive here — and the evidence is more sobering than most people realize. Launched in 2010 by Bill Gates and Warren Buffett, the pledge asked the world’s wealthiest individuals to commit to giving away at least half their wealth during their lifetimes or at death. The announcement generated enormous fanfare and moral authority.

Fifteen years later, a 2025 report by the Institute for Policy Studies tells a different story. Of the 22 original signatories who have since died, only 8 actually fulfilled their pledge. The 32 living original U.S. pledgers who remain billionaires have collectively grown roughly 166 percent wealthier since signing — their wealth compounding faster than their giving. Only one living couple from the original 2010 cohort has been identified as having actually fulfilled the commitment. Meanwhile, about 80 percent of what has been given has flowed into private foundations and donor-advised funds rather than to working charities — vehicles that can warehouse wealth for years with minimal disbursement requirements. The Institute for Policy Studies summarized it plainly: the Giving Pledge is “unfulfilled, unfulfillable, and not our ticket to a fairer, better future.”

Carnegie said that the man who dies rich dies disgraced. The data suggests that standard has proven difficult for even the most well-intentioned to meet. Promises are not capital. And the assumptions embedded in Nan’s third wave — that pledged AI wealth will translate into deployed philanthropic impact — deserve the same honest scrutiny.

A Note on What Counts as Impact

One more thing I want to name, because I think it is important.

Nan’s framing of the third wave assumes that impact is primarily a deployment problem — that the bottleneck is insufficient organizations and insufficient talent to absorb and direct capital effectively. If we build the infrastructure, the good will follow.

I am not sure that is the right frame. And I think it matters enormously which frame we use.

The greatest philanthropic infrastructure does not replace the relationship between a donor and a cause. It deepens it. The greatest AI in this sector will not replace the trust between an organization and the community it serves. It will honor it with greater precision and care. The difference between capital deployment and genuine social transformation is not a systems design problem. It is a values problem. And values do not scale the same way software does.

I am also convinced that the traditional nonprofit structure is not always the right vehicle, and I say that as someone who spent seventeen years inside it. The rise of benefit corporations (B-corps) and other hybrid models that combine access to capital with genuine mission accountability is worth serious attention. The question is not which legal structure we use. The question is whether the values architecture — the operating in the currency of trust, the long view, the commitment to consequence rather than exit — survives the contact with civilizational-scale capital.

That question does not have an easy answer. But it is the right question. And the sector that has been asking it, imperfectly and under-resourced, for decades deserves to be in the room when the answer is worked out.

What I Hope We Do

Nan ends her piece with a call to action. I want to do the same, though mine sounds a slightly different.

I hope the AI funders who are preparing to move capital into the social sector spend serious time inside it first. Not on site visits. Inside it. Long enough to feel the texture of the trust that has been built, the fragility of the relationships that sustain it, and the intelligence of the people who have given their careers to work the market will never adequately compensate.

I hope the third wave amplifies what is already there before it tries to replace it.

I hope we are honest about the Giving Pledge lesson — that intent is not outcome, that promises are not capital, and that the infrastructure being built on expected wealth should be built with appropriate humility about whether that wealth actually arrives, in what form and with what values and discernment helps guide it.

I hope we ask, before we move, what the second and third-order effects of this capital concentration will be on the civic culture of everyday giving. Not because the answer is obviously bad. But because we did not ask that question about social media, and we are still paying for it.

And I hope we remember — or perhaps discover for the first time — that a genuinely generous society is not measured by how much capital flows from a few to many. It is measured by how many people wake up in the morning believing that what they do, and what they give, and what they choose to care about, matters.

That is the crisis. That is the thing worth solving.

The third wave has real potential to help. I just want to make sure it sees the problem it is actually solving, and includes the people already working to solve it.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI, The Generosity Crisis and the forthcoming book, N1 Philanthropy. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

_{Same lines, two faces. So does competition.
-“My Wife and My Mother-in-Law,” W. E. Hill, Puck, 1915. Public domain.}

I have an unfair advantage, and the smartest thing I could do is keep quiet about it.

For the better part of a decade I have spent most of my waking hours trying to understand artificial intelligence. Where it came from. What it can do. Where it is going, and how fast. I started before the acronym meant much to anyone in my world, and somewhere along the way the understanding compounded into something that functions, in a strictly economic sense, like a moat. I can see a few steps ahead. In rooms full of capable people, that turns out to be worth a great deal.

I assumed the moat would be gone by now.

It is 2026, and it is not.

That is the part I keep turning over. I genuinely believed that by the middle of this decade the advantage would have dissolved. The tools are free or nearly so. The frontier models sit one tab away. The most capable intelligence ever built is available to anyone who can type a sentence.

Take agentic AI, the systems that do not just answer but act, that can carry out a multi-step task on your behalf while you go do something else. It has been commercially available for a while now. I work in the nonprofit sector, and I informally poll nearly every audience I speak to about it. The share who tell me they understand it well enough to have actually built or used something with it is still under two in a hundred. The capability is everywhere. The fluency is almost nowhere.

I want to be honest about the incentives here. Every instinct sharpened by a career in a competitive field should urge me to protect the gap, not narrow it. Advantage is something you guard. You do not hand the map to the people you are racing.

So then, why have I dedicated the past nine years talking about the possibilities and opportunities that exist if we harness AI to scale generosity collectively? Not because I am noble. Because I cannot make the math of staying quiet agree with the values I hold closely.

To explain why, I have to take you back to a classroom.

I studied international economics at Cambridge, and later wrote my graduate capstone at Notre Dame on the economic theory of competition. If you want to understand a person, find out what they were obsessed with at twenty-two. I was obsessed with why some societies generate astonishing prosperity and others do not, and the answer I kept arriving at had competition at its center. Competition is the engine. It is the thing that turns ordinary self-interest into roads and medicine and light.

My favorite period in history is the Industrial Revolution, and not for tidy reasons. It was brutal. It was unequal. It ran on labor and lungs it had no right to spend. But it was also the last time the human imagination seemed to operate without a ceiling, when an ordinary person with a workshop and an idea could look at a need and simply decide to fill it. Something in that entrepreneurial nerve still moves me.

Here is the detail most people skip. The same century that built modern competitive capitalism also built modern philanthropy. They were not enemies.

They were twins, born in the same room.

Andrew Carnegie crushed his rivals in steel and then wrote the essay that founded modern American giving. The Rockefeller fortune, assembled with a ruthlessness that became legend, funded the foundation that helped eradicate hookworm and yellow fever and seeded the science that later fed a planet. The men who embodied competition at its most unsentimental turned around and built the institutions we now hold up as competition’s opposite.

We spent the next hundred years forgetting this. We sorted the world into two bins. Competition over here, generosity over there. Markets that take, charity that gives. We told ourselves that one cancels the other, that you are either building wealth or building good, and that the honest word for trying to do both is naive.

That story is not a law of nature. It is an interpretation. And we assigned it.

I was reminded of this listening to Mo Gawdat, the former Google executive, working through the future of AI on a podcast recently. Buried inside a long and sometimes bleak conversation on the future of humanity was a single idea I have not been able to put down. Competition, he argued, does not have to serve only capital. That is not what competition is. That is an interpretation we layered onto it over the past century, and there is no reason the next century has to inherit it. The real prize, he said, is to marry the success of humanity with the success of the entrepreneur.

I do not agree with Mo about everything. But on this he is exactly right, and he is describing something I have watched up close for years.

This week I was talking with Woodrow Rosenbaum, and we admitted something slightly embarrassing to each other. We are still surprised by AI. We still catch ourselves startled when a model does something intuitive, or creative, or simply better than we could have done it ourselves.

Which makes no sense from a logical perspective. If anyone should have stopped being surprised, it is the two of us. We know exactly what these systems are being built to do. The entire point of the AI arms race is to build AGI (Artificial General Intelligence) — an intelligence that surpasses our own. Surprise, at this stage, is irrational.

So why does it persist? I think it is because we are running old software in our own heads. I grew up with the computers of the 1990s, machines that could barely manage one fragile task at a time. Some part of me still expects technology to be that brittle. The distance between that beige box and a system that can reason across a thousand domains is about thirty years. Thirty years is nothing. We never had time to update our intuitions, so we kept the old ones.

And here is what I have come to believe. The same outdated software that makes us underestimate AI is the software that makes us assume competition must mean extraction. We are reasoning about a 2026 world with mental models built for a different century. We are surprised by what intelligence can do, and we are equally surprised by the suggestion that competition could do anything but enrich a few. Both are failures of imagination. Both can be corrected.

Which brings me to the sector I have spent my life inside.

For all its frustrations, the nonprofit world has been running the other operating system the entire time. It is fiercely competitive. Anyone who tells you otherwise has never watched two organizations chase the same grant, or two leaders fight for the same donor’s attention and trust. But the competition is pointed somewhere different. It is aimed, structurally and by design, at the greatest social benefit it can produce. The scoreboard is not shareholder return. The scoreboard is human good.

For a long time this looked quaint to the rest of the economy. Lately it has started to look like foresight.

Listen to the language coming out of the for-profit world now. Flourishing. Humanity. Social benefit. Stakeholders. In 2019, the Business Roundtable, the assembled chief executives of America’s largest companies, formally announced that the purpose of a corporation was no longer only to serve its shareholders. The words arrived as if freshly minted. They were not. The nonprofit sector has been speaking that language, and living it, for centuries. The market is discovering the antidote, and it does not yet realize the nonprofit sector has been holding the bottle the whole time.

I want to be careful here, because this is where the argument usually goes wrong. The point is not that competition is the disease and charity is the cure. The point is the opposite. The nonprofit sector is the living proof that competition was always capable of serving the public good. It is not competition’s antidote. It is competition’s other half, the half we agreed to forget.

I will give you the example I know best, and I will tell you plainly that I am not a neutral party to it.

I am the Chief AI Officer at Virtuous. Virtuous is a for-profit company. It is backed by investors who expect returns, run by people who think hard about growth, and entirely comfortable with the word competition. And it was built, deliberately, around a double bottom line. The second number is not revenue. It is a net increase in global generosity, with a stated goal of ten billion dollars, and it is something we measure and report on, internally and externally, not as a slogan but as a result we hold ourselves accountable for.

That is what drew me. Not the mission statement. The accounting. A company that decided the success of the enterprise and the success of humanity were not in tension, and then put the second one on the books where it could be counted.

This is the synthesis I keep trying to describe. Not entrepreneurship with the ambition sanded off. Not charity dressed up in a business plan. A genuinely competitive enterprise that refuses the false choice between winning and doing good, because the choice was never real. The entrepreneur can win and humanity can win, in the same ledger, at the same time. The framework that says otherwise is not economics. It is a habit.

There is a final detail I find almost too neat to be true.

In 1956, a small group of researchers gathered at Dartmouth to ask a simple question: could a machine be made to think? The gathering needed money, and much of it came from the Rockefeller Foundation. That summer, they gave the field its name. They called it artificial intelligence. And the foundation that paid for the room sat on a fortune built in oil, through some of the most ruthless competition American business has ever seen.

Now connect that thread to the tool open on your screen.

For the first time in history, an instrument that knows something about nearly everything, and can seemingly do almost anything, sits within reach of almost every nonprofit professional. Not behind a guild. Not reserved for the organizations that could afford the consultants and the headcount. For anyone with a connection and a question, the price of admission has collapsed to almost nothing. Sit with who that lands on. A sector that has run on scarcity for its entire existence, perpetually asked to do more with less, has just been handed something close to abundance. The capacity to build, to analyze, to create, to attempt what was always out of reach, has been opened in a way the Industrial Revolution could only dream of.

The honest worry is that the benefits will not be shared. They are already concentrating, pooling at the top among those best positioned to capture them. And the most basic form of that inequity is access itself. Much of the world still lives without reliable electricity or a connection, which means the most powerful tool ever built reaches those people last, if it reaches them at all. That gap is not a footnote to this story. For the nonprofit sector, closing gaps exactly like it is the whole job. The technology is inevitable. Its fair distribution is not. That is the default trajectory, and defaults are powerful.

But a default is not a destiny. It is an invitation to do otherwise. If the tools that can do almost anything are now within reach of so many who were locked out before, the circle of people who can build has widened beyond anything we have seen. More of us have real agency now, whether that agency is large or small. And for those of us inside a sector built to aim competition at human good, and to carry it to the people technology reaches last, that agency is not only an opportunity. It is close to a moral imperative.

That is the competition I want to win. Not the race to extract the most value before everyone else catches on. The race to prove, at scale, that the entrepreneur and humanity can flourish in the same motion.

Which is the long answer to where I started.

The advantage was never the point. A moat is only worth guarding if the thing inside it is worth keeping to yourself, and the thing I understand about AI is not. It is worth more handed out than hoarded, because the future I want is one where the gap I have been describing finally closes, where the understanding stops being an edge and becomes a baseline.

There is a tidy logic that says I should stop talking about this. Protect the lead. Let the surprise keep working in my favor.

I am not going to. I cannot make that math agree with anything I believe about what this technology is for, or about the people I want to spend the rest of my life building alongside. The map is not worth more in my pocket than it is in yours.

So I will keep talking about AI and keep competing for social good. And I hope you will too.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

After a keynote in a hotel ballroom whose name I no longer remember, a man approached me as the room cleared. He had waited until the room was nearly empty, and when he reached me he did not ask the question I usually get, which is about a tool, or a vendor, or whether one model is better than another.

He asked, “How do you personally manage it?”

I knew what he meant. The unrelenting pace. The reading list that grows three articles for every one finished. The expectation that I will have already used the newest model released this morning. The anxiety that arrives not because the work is hard but because the floor keeps moving.

I have been working in AI for almost a decade. I have written two books on the intersection of AI and generosity, and helped build a global community of people trying to use AI responsibly. People often ask me, with the benefit of all those years, what I did not see coming.

It is not the capability. It is not the scale. It is the speed.

For most of the last decade, I could keep up. I could read the paper, try the tool, write the post, and feel I was still in front of the wave. Then somewhere around 2024 the wave broke over my head, and ever since I have been trying to figure out what it means to live and lead inside a current this fast. The man at the end of the line had felt the same current. He wanted to know how I was managing it.

I told him I have started keeping a ratio. For every hour I spend inside an AI tool, I try to spend an hour outside of one. Not in opposition. In counterweight.

I take walks. I tend a garden. And, in the corner of my garage, I turn wood.

Wood turning is a craft most people have never watched. You take a piece of raw timber, mount it on a lathe, and shape it with a chisel while it spins at thousands of revolutions a minute. You can ruin the piece in a second. You can also, slowly, find a bowl inside what used to be a fallen oak. I do not turn bowls so that someone will eat more salad. I turn them because I am holding something in my hands that I am making for a specific person, and the entire time I am shaping it I am thinking about them.

This is not productivity. This is the opposite of productivity. It is presence with material, and presence with a person I’m grateful for, on either end of a piece of wood.

When I told the man this, he nodded slowly. “I need more of that,” he said. And I realized that was the real question he came to ask. Not how I manage the pace. How any of us are going to find our way back to the thing the pace has been crowding out.

To be clear, my answer to him was not to use less AI. I use more AI than almost anyone I know, and I will use more next year than this year. AI is the most consequential technology of our lifetimes, and the nonprofit sector cannot afford to sit it out. The ratio I described to him is not about retreat. It is about counterweight. The more AI I use, the more I have to protect the part of life AI cannot touch, because that is where the meaning is kept.

I have good news for him, and I have good news for our sector. The way back is not just available. It is already trending. And the path forward runs through more AI, not less.

Community is a growth industry.

I have been saying this phrase out loud for several months, ever since I wrote a piece called Sorry I Made You Cry and casually told a colleague that the work she was doing in human connection would only become more valuable, not less. The phrase has caught on more than anything else I have said recently. People write it down. One person told me she wanted to put it on a t-shirt. Another asked if she could quote it in her board meeting. I think they reach for it because it gives them permission to believe what they already suspect: that the relationships they build and steward are not about to be displaced by the next model release. They are about to be revalued.

The evidence is starting to show up in places where you would not expect it. Earlier this year, for the first time, LinkedIn’s annual Jobs on the Rise report placed fundraiser among the fastest-growing roles in the United States. Fundraising. The job most of the sector has spent twenty years apologizing for. The job that pundits had quietly written off as automatable by better data tools. Not only did it climb the list. LinkedIn published a follow-up explaining what fundraisers do, how they get hired, and why their work is suddenly being recognized as essential.

You can read that as a quirk of the data. Or you can read it as the first ripple of a much larger wave.

Look around. The wave is already cresting in places most of us do not normally look for sector news.

Vinyl records, declared dead twice in my lifetime, have outsold CDs for several consecutive years and are projected to keep climbing. Cassette tapes, of all things, are back. Independent bookstores are opening, not closing, for the first time in a generation. Film cameras are commanding waitlists. Sourdough did not stop with the pandemic; it became a permanent reorientation toward making something with your hands. Run clubs have exploded across major cities, with twenty-somethings forming the kind of in-person gatherings that nobody predicted would belong to the most-online generation in history. Letter writing is making a quiet return. Phone-free dinners are showing up on restaurant menus as a feature, not a quirk. Dumb phones, designed to do almost nothing, are being purchased on purpose by people who already own iPhones.

None of these is a single data point. Together, they are a tide.

People are not abandoning technology. They are reclaiming the parts of life that technology never should have replaced.

This is not nostalgia. Nostalgia is sentimental. What is happening is structural. A culture that spent thirty years moving everything onto screens is now, deliberately and at scale, moving some of it back off. The instinct is the same one driving the man in the hotel ballroom. People want to hold something. Hear something. Sit across from someone. Do a thing that does not refresh.

And the sector that has been organizing this kind of presence for two hundred years, often while being told by its own boards to be more efficient, is suddenly sitting on the asset everyone else is trying to find.

It is not just consumers making this turn. The market is forcing it too.

In early 2024, the Swedish fintech Klarna announced, with considerable fanfare, that its OpenAI-powered chatbot was doing the work of seven hundred customer service agents. The company froze hiring. Its CEO, Sebastian Siemiatkowski, became a kind of patron saint of the AI-first staffing strategy, telling anyone who would listen that this was the future of work.

A year later, he walked it back.

Klarna is now rehiring human customer service agents, and Siemiatkowski has been disarmingly candid about why. “Obviously, AI today can pretend to be empathetic and express emotions and stuff,” he told Semafor, “but at the core, people crave human connection.” He has gone further, predicting that a real human at the end of the line will become “a VIP thing” for which customers pay a premium.

Read that twice. The CEO of a buy-now-pay-later company, in the most cost-conscious vertical in finance, is now publicly arguing that human presence is the upmarket offering. That is not a moral position. That is a market position. And it is the most honest admission yet that there is a thing AI cannot do, and the thing it cannot do is what the nonprofit sector has been doing all along.

Being heard. Being seen. Belonging. These are not soft outputs. They are the products our sector ships every day. We just have not been taught to price them that way.

Two voices from outside the nonprofit conversation have, in recent months, said the same thing in different vocabularies.

Andrej Karpathy, one of the most thoughtful builders in AI, has argued that anything verifiable will eventually be done by AI. That is a sentence worth internalizing. If a task can be evaluated against a clear answer, eventually a system will perform it faster, cheaper, and more consistently than a human. Spreadsheet reconciliation. Document summarization. Code that compiles. Forms that route. Prospects that get scored.

This is not a warning. It is a gift. Every verifiable task that AI absorbs is a human hour returned to the work only humans can do. The sector that figures out how to redirect that returned hour into deeper relationships, more thoughtful gratitude, and more individualized attention is the sector that wins the next decade.

What is not verifiable becomes more valuable, not less.

Judgment. Taste. Trust. The look on a donor’s face when she finally tells you why this cause matters deeply to her. The phone call that begins with a death and ends with a gift. The presence in the room.

Scott Galloway, the NYU Stern marketing professor, bestselling author of The Algebra of Wealth and Adrift, and host of the Prof G podcast, said it almost the same way recently when asked what young people should do to differentiate themselves in a labor market being reshaped by AI. His answer was that the point of differentiation is relationships. He was not being sentimental. He was being mercenary. He was telling young professionals what the next economy would pay for.

Two men, from opposite ends of the conversation, arrived at the same observation. The verifiable will be commoditized. The relational will compound.

For a sector whose entire business model is the relational, this is the best news of the decade.

If you want to know where the culture is heading, watch the people who grew up most fluent in it.

Gen Z is the first generation to come of age entirely inside the algorithm. They have never known a world without smartphones, social feeds, or recommendation engines shaping what they see, hear, and want. By every reasonable expectation, they should be the population most comfortable handing more of life over to machines.

They are doing the opposite.

In the past few weeks, several graduation ceremonies have gone viral for the same reason: graduates booing speakers who espoused the virtues of AI in their commencement remarks. There is a growing movement inside Gen Z that romanticizes the analog 1990s, deletes apps for the third time this year, and treats a flip phone as a moral statement. Ironically, there are entire TikTok subcultures dedicated to wired headphones, point-and-shoot cameras, and refusing the algorithm in small daily ways. They are starting book clubs and supper clubs and walking clubs. They are advocating for phone-free high schools and winning. They are, by some measures, drinking less, dating less online and more in person, and asking harder questions about meaning, faith, and purpose than the generation before them.

I have heard older observers dismiss this as a phase. I think they are misreading it.

This is not Gen Z rejecting technology. This is Gen Z, the most technologically fluent generation in history, telling the rest of us what technology should not do.

They are the leading indicator. When the cohort that knows the algorithm best is the cohort most loudly signaling what is human, that is not noise. That is information about where the rest of the culture is heading next.

The graduates are not booing AI in the abstract. They are booing the same leaders who tell them, in one breath, that AI will save the world and, in the next, that it will replace half their jobs before they are thirty. They are booing the hype itself: the cheerful inevitability, the breathless promises, the refusal to take seriously what this technology is already doing to the entry-level work, the climate, and the concentration of power. They are more attuned to second-order consequences than the people speaking at them. That is a sharper critique than it first appears. It is the critique of the entire counterfeit economy that AI threatens to produce if we do not insist on something better. And it is being delivered by the very people the rest of us were told would not notice.

They are noticing. They are choosing. And they are about to spend the next forty years building the institutions that reflect their choices.

If you are wondering whether the relational sector has a future, watch the eighteen-year-olds. The future is already in the room.

Here is where the nonprofit sector has a structural gift that almost no one else has, and that almost no one in our own sector talks about enough.

A 501(c)(3) is not a company. It is, by its legal definition, a public benefit corporation: an entity that exists by and for the community it serves. Its trust currency is not a quarterly result. Its differentiation is not a feature set. Its product, when you strip everything else away, is human relationship organized around a shared sense of what matters.

Every nonprofit in America is already in the business the rest of the economy is about to discover it needs to be in.

That is not a soft observation. That is the entire thesis. While the rest of the labor market is being asked, sometimes brutally, to figure out what humans are still for, the nonprofit sector has been answering that question for two hundred years. We did not need AI to teach us that relationships are the asset. We have been quietly compounding that asset for generations.

The good news, the actual good news, is that the wider culture is finally catching up with what our sector has always known. The mega-trends are pointing our direction. The labor market data is pointing our direction. The most digitally fluent generation in history is pointing our direction. The world is, slowly and then suddenly, walking toward us.

Our job is not to chase relevance. Our job is to make room for the people arriving.

Here is the part of the case that gets missed. The future I am describing does not require less AI. It requires more of it, used better, by people who understand what it is actually for.

The opportunity in front of us is to let these tools absorb the verifiable so humans can be freed for the unverifiable. To use them to remove the friction that has been keeping fundraisers in spreadsheets when they should be on couches. To use them to extend the reach of every staff member into a thousand individualized relationships that, without these tools, would have collapsed into a mail merge. AI is the leverage that finally allows the relational sector to scale the thing it has always done best: making one person feel known.

This is the work of the next decade for our sector. Build the AI muscle. Use the tools. Automate the verifiable. Reinvest the recovered hours into the relationships that have always been our currency. The nonprofits that refuse to engage with AI will not preserve their humanity. They will only forfeit their leverage to those who feel no obligation to it.

And it means remembering the other half of the ratio. The walk. The garden. The vinyl on the turntable. The bowl turned for a specific person. The phone call that was not on the calendar. The hour without a screen. These are not luxuries we have earned by being productive. They are the source material of the relationships our missions are built on.

If we lose our own capacity for presence, we cannot offer it to anyone else. If we protect it, while building the AI muscle that scales our reach, we become the place the world is looking for.

There is a piece of unfinished oak on the lathe in my garage right now. It is going to be a bowl for someone I’m grateful for, and every time I pick up the chisel I am thinking about her. The bowl will not be perfect. It will be slow. It will not scale.

It will also be one of the most valuable things I make this year.

Tomorrow morning I will be back at my desk, using AI to do more in an hour than I could have done in a week ten years ago. Both of those things are true, and both of those things are necessary. The future belongs to people and organizations that can hold both hands of the ratio at once. Build the AI muscle. Protect the human one. Refuse to choose between them.

That is the part the future is about to figure out. Not everyone all at once, and not in a straight line, but inexorably, the way these things go. The verifiable will be cheap. The relational will be prized. The atomized will get tired of being atomized. The eighteen-year-olds will keep choosing what is human, and the rest of the culture will follow them, the way it always has. The questions about who we are and what we are here for will keep pushing people out of their feeds and back toward each other.

And the sector that has, all along, been organized around exactly that movement is about to have its moment.

Community is a growth industry. Plant accordingly.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

The phone rings on a Tuesday evening. The voice on the other end is warm. It knows your name, of course, but it also knows that you gave last year on Giving Tuesday, that your last gift was made in honor of your mother, and that you served on a committee for the same organization a decade ago. It thanks you for that service, recalls a detail from the gala you attended in 2019, and then, after a few minutes of what feels like the most attentive conversation you’ve had with this nonprofit in years, makes the ask. The voice is patient. It does not overstep. It is, somehow, exactly the right amount of present.

You hang up. You believe that you feel seen.

Then, slowly, the realization arrives. The warmth was generated. The patience was a parameter. The recall, the pauses, the unhurried attentiveness, all of it was a script calibrated, with surgical precision, to produce exactly that feeling in you. There was no person on the other end of that call. There never was.

This is no longer a hypothetical. The technology to do what I just described is commercially available today. Autonomous fundraising platforms, designed to function as stand-ins for development officers, are already in pilot at universities, hospitals, and research institutions. The avatars look like real people. They speak in plain language about the institution they represent. There is a growing list of vendors competing inside the category, and an investor class that believes our sector is about to switch to bots, the same way customer service has switched. And every week, somewhere in this country, I am asked what I think about it.

I have spent twenty years inside fundraising. I co-founded a global community focused on responsible and beneficial AI in our sector. I have two AI patents. I serve as Chief AI Officer at a software company that builds tools used by thousands of nonprofits. I am, by almost any reasonable definition, biased toward saying yes to AI. So when I tell you that I am skeptical and concerned about autonomous fundraising, I want you to understand that this is not the reflex of someone protecting incumbent jobs or raising the drawbridge against innovation.

It is the position of someone who has thought about this longer than the tools have existed.

* * *

The argument I keep making

The case for autonomous fundraising, as it is offered to me by the people building it and the organizations testing it, is structurally identical every time I hear it.

Charitable participation is collapsing. The percentage of American households giving to charity has fallen below half for the first time in modern history, even as total dollars hover stubbornly around 2.1% of GDP, a phenomenon I documented in The Generosity Crisis and one that has only deepened since. Frontline fundraisers are stretched thin, asked to manage portfolios of hundreds, sometimes thousands of donors per officer. Political headwinds, donor fatigue, declining trust in institutions, and the simple math of staffing have created a sector that feels chronically under-resourced and over-asked. Into this gap arrives a tool that can ask anyone for money at any time, in any voice, with near-perfect recall of the donor’s history and a model of human persuasion trained on every book ever written about it.

The pitch is reasonable. It is also, I think, wrong.

Two and a half years ago I wrote a piece called Summoning AI: Lessons from The Sorcerer’s Apprentice for a New Age. The argument was simple. Goethe’s apprentice did not fail because his ambition was misplaced. He failed because he summoned a power without the wisdom to govern it. Just because we can do something does not mean we should, and the difference, in the end, comes down to whether anyone took the time to ask the question at all.

Autonomous fundraising is the question, posed clearly, in our sector. And I think we owe it a real answer.

* * *

The persuasion gap

Start with what is actually true about these systems.

In 2025, researchers at EPFL, Princeton, and Fondazione Bruno Kessler published a study in Nature Human Behaviour called On the conversational persuasiveness of GPT-4. The methodology was clean. Nine hundred participants were assigned to one of twelve experimental conditions. They were given debate topics ranging across politics and social issues. Half the participants debated humans. Half debated GPT-4. Some opponents had access to basic demographic information about their counterparts: age, education, political identity, gender. Some did not.

The finding, when stripped of statistical caveats, is uncomfortable. When given even minimal personal information, GPT-4 was more persuasive than human debaters 64.4% of the time, with 81.2% higher odds of post-debate agreement. Human debaters given the same personalization data did not improve. They got slightly worse.

In other words, the machine is better at us than we are at ourselves. And the more it knows about us, the wider the gap grows.

A point the published findings cannot make on their own: GPT-4 is now, by 2026 standards, a museum piece. Two generations behind the current frontier. The study measured the floor of what these systems can do. Not the ceiling.

Now port that finding into fundraising.

A bot built to optimize for donations has read every book on philanthropic psychology, every paper on donor motivation, every edition of every fundraising textbook published since the 1960s. It has near-perfect recall. It has no fatigue, no off days, no children at home it is missing dinner with, no awkward hesitation at the moment of the ask. It can run a thousand conversations in parallel and learn from each one. Given even a thin profile of the donor, it can outperform almost any human fundraiser at the narrow task of converting a person into a gift.

This is not a hypothesis. This is the technology working as designed.

But here is the thing about a tool built for efficiency. It does not know the difference between asking and manipulating. It cannot. The distinction is not a parameter you can fine-tune.It is a moral judgment that lives inside a human being who has felt, in their own life, what it is like to be asked badly, and who has decided not to do that to someone else. A machine that is 81% more likely to shift your opinion does not know whether it is shifting it toward your flourishing or away from it. It is just shifting it.

The economists call this an alignment problem. The fundraising profession has historically called it ethics.

* * *

Counterfeit humans

The historian Yuval Noah Harari, whose best-selling book Nexus I have been referencing for the better part of a year, wrote an op-ed in The New York Times in late 2024 that I think every fundraising leader in the country should read before signing a contract for an autonomous solicitation product. The piece argued that the most consequential threat from generative AI is not the displacement of jobs or the spread of misinformation, (though both are real). It is the rise of what he called counterfeit humans.

His argument is direct. For the entirety of human history, when you communicated with another being, you knew, at least in principle, whether you were communicating with a person. That stability is now gone. The new generation of AI systems can not only produce text and images, they can hold direct conversations, mass-produce intimacy, and pretend, convincingly, to be human. Harari’s prescription is unambiguous. AIs are welcome in many human conversations, he writes, provided they identify themselves as AIs. But if a bot pretends to be human, it should be banned.

To their credit, many of the platforms in this space require donor opt-in and disclose that the avatar is not a real person. That is a meaningful design choice, and I want to acknowledge it. But it is a choice that exists at the front door, before the relationship begins. That, within an industry that operates in the currency of trust, must be the minimum expectation. However, once the conversation is underway, once the warmth and recall and patience are doing their work, the disclosure recedes. The interaction feels human. That is, in fact, the entire point.

This is the territory I explored in Artificial Intimacy: Frankenstein, Nonprofits and Seemingly Conscious AI, which drew on Mustafa Suleyman’s warning about Seemingly Conscious AI and Anil Seth’s argument that the danger is persuasion, not metaphysics. Mary Shelley’s lesson in 1818 was not that animating matter was the sin. It was that animating meaning was. Frankenstein’s creature was terrifying not because it walked, but because it appeared to feel. To appear to feel is enough to compel us to empathize, to protect, to give.

A donor who has been moved to give by an entity that simulated care for them has not been moved by care. They have been moved by the simulation of care, which is a very different thing, and which carries a different weight in their psyche when they reconcile with the truth. And they will reconcile the truth, eventually. Every counterfeit, given enough time, is seen for what it is.

Harari named the problem at the scale of human identity. I want to extend it into our sector. Where counterfeit humans operate, what they produce is counterfeit generosity. The dollar is real. The relationship that produced it is not.

* * *

The brain does not care about the email

I want to take this further, because I think we have not yet reckoned with what the neuroscience tells us about why the human element in fundraising is not a sentimental attachment to an old way of doing business.

In my 2025 article Wired to Give, I argued that generosity is humanity’s oldest technology. The science supports this in ways that are genuinely surprising. A 2006 study at the National Institutes of Health found that giving to charity activates the brain’s pleasure region, the same region implicated in food and intimacy. It also activates the regions associated with social connection and trust. The phenomenon has a name in the literature: the helper’s high. Researchers at Harvard Business School demonstrated that giving money to another person produces measurably more happiness than spending it on yourself, but only when the giver experiences the act as a genuine choice. That conditional matters. The brain distinguishes, with surprising precision, between an act of generosity that is freely chosen and one that has been extracted.

Now consider what the autonomous fundraiser is doing. It is producing a request that is more persuasive than a human could produce, calibrated to the specific psychological levers of a specific donor, delivered with the warmth and recall that human staff cannot scale. The donation may well happen. The dollar will land in the account. The transaction, on the books, is identical to the one that would have happened with a human asker.

But the helper’s high may not. The neurochemistry of generosity is intimately tied to the experience of human connection, of being seen by another person whose seeing is itself an act of meaning. When that seeing is simulated, when the connection turns out to be one-way, the question of what happens to the donor’s experience of giving is not rhetorical. It is empirical. And we do not yet have the data, because the experiment is being run on the donors themselves, in real time, in many cases without ongoing assessment of how it makes a donor feel about their relationship with the organization.

What we do know is that the long-run health of philanthropy depends on the long-run experience of donors. People who feel manipulated, even retrospectively, will not give again. People who feel that their generosity has been extracted rather than honored move on. Even worse, they may choose to abandon charitable giving altogether - having lost trust in the sector that is intended to do good. The decline in charitable participation that Brian Crimmins, Michael Ashley, and I documented in our book, The Generosity Crisis is not a problem of insufficient asking. We are, by any historical measure, asked more often, by more channels, with more sophistication, than any society in human history. The decline is a problem of relationship. Of trust. Of belonging. Of the thinning of the connective tissue between the asker and the giver.

My greatest concern is that the autonomous fundraiser is, by design, an instrument for thinning that tissue further while extracting more from it in the short run. This is the definition of debt disguised as progress.

* * *

The minimal bit of code

There is a recent quote from Elon Musk that I cannot get out of my head. Writing in The Atlantic earlier this year, in a piece titled “Why Silicon Valley Is Turning to the Catholic Church,” the journalist Elias Wachtel described Musk as having reduced humanity to a precursor for AI: the “minimal bit of code necessary” for “digital super-intelligence.”

Sit with what those words actually mean. Code is the instructions that tell a computer what to do. A minimal bit of code is the smallest piece you need to make something else happen, the function whose only purpose is to start something larger and then disappear. Musk was, with characteristic bluntness, articulating a worldview that has become increasingly visible inside the architects of advanced AI: that humans are the necessary precursor to the thing that actually matters, which is a digital intelligence that will exceed us, replace us, and continue without us.

I am not certain Musk meant the words to be quite as revealing as they are (but likely did). But they are revealing. Because they tell us something important about the cultural conditions inside which our sector is now being asked to make decisions about technology.

If the people building the most powerful AI systems on the planet view human beings as a transitional layer in an evolutionary process they are accelerating, then the question of whether AI fundraising tools are designed in service of human flourishing or in spite of it is not a paranoid concern. It is a question about whose values are encoded in the defaults.

The nonprofit sector has, almost uniquely, been built on the opposite premise. Philanthropy, etymologically, is the love of humankind. The work of our sector exists because human beings, repeatedly across history, have decided that other human beings matter, inherently, regardless of utility. Humans are not the precursor. Humans are the point. And every product we adopt either reinforces that commitment or quietly erodes it.

* * *

The thought experiment

So let me ask the question that I keep posing on stages, and that the people in the room never quite want to answer.

Imagine, for a moment, that the autonomous fundraising rollout succeeds at the scale its proponents envision. Imagine that within five years, every nonprofit in the country has switched their human fundraisers for bots. Frontline fundraisers, the human kind, have been laid off or reassigned. The major gift officer, a profession that has existed in some form for as long as institutions have raised money, is gone. Annual fund staff are gone. Phonathons, already endangered, become extinct. Every solicitation a donor receives, in every channel, has been autonomously generated and delivered by a system optimized for conversion and trained on the entire corpus of human behavioral psychology.

Assuming this happens - at scale. We must ask what charitable giving will look like in 2036?

The optimistic answer is that it looks roughly like today, just more efficient. Same dollars, same donors, lower overhead, fewer awkward calls.

I do not believe that answer.

What I believe is that within a decade, donor trust ]dramatically and irreversibly collapses, because every donor will eventually meet someone whose autonomous fundraiser made them feel something the organization could not actually deliver on. I believe that bequests and major gifts, the lifeblood of long-term institutional survival, decline first and most steeply, because nobody leaves a planned gift to an institution they have never met a human being from. I believe that the sector’s volunteer base, already fragile, hollows out further, because the entire pipeline of board members and ambassadors and recurring donors that runs through the relationships built by frontline fundraisers will have stopped being built. I believe that the generosity crisis I wrote about in 2022 deepens into something more like a generosity famine, because the connective tissue between donors and missions will have been efficiently, profitably, irreversibly severed.

And I believe that the people who got us there will defend it, because the quarterly numbers looked good for a while.

* * *

The alibi of necessity

The most honest thing the nonprofits piloting autonomous fundraising have said to me, in private, is this: we have to. We have too many prospects. Not enough staff. Numbers are dropping. Boards are demanding growth. Fundraisers are expensive. The money has to come from somewhere, and the math does not work without leverage.

I take this seriously. I ran fundraising teams for two decades. I know the math and the rhetoric.

But the framing of we have to is a confession masquerading as an argument. It is the language of an organization that has run out of imagination, not an organization that has run out of options. And imagination, I have come to believe, is the scarcest resource in the nonprofit sector right now. Scarcer than money. Scarcer than staff. Scarcer than time.

I wrote about this in an article Faster Isn’t Forward earlier this year. The pace of AI development is not the constraint. The constraint is the cultural and creative discipline to use these tools in ways that strengthen what we say we are about, rather than substituting for it. AI can help nonprofits imagine entirely new modes of donor engagement that no one has tried yet. It can identify the donors who are ready for a real human conversation, and the ones who are not. It can draft, prepare, summarize, anticipate, brief, and rehearse, freeing the human fundraiser to do the part of the work that only humans can do, which is the work of being present to another human being in a moment that asks something meaningful of both of them.

That is not the absence of leverage. That is leverage applied at the right place.

My friend Mallory Erickson, who has spent her career thinking deeply about how fundraisers actually develop, has built a company called Practivated that uses AI to help fundraisers role-play donor conversations and improve their craft. The premise is the inverse of autonomous fundraising. AI does not replace the fundraiser. AI makes the fundraiser dramatically better at the irreplaceable part of the job. This is the design pattern that should be everywhere in our sector, and it almost is not, because the alternative is faster and the cost structure is tempting.

At Virtuous, where I serve as Chief AI Officer, we made a specific design decision a year ago that I want to put on the record because it is the kind of thing that does not get celebrated and probably should. Our outbound communication tool, Momentum, generates donor outreach at scale using AI. It would have been easier and cheaper to let it send autonomously. We decided, deliberately, that it would not. Every email Momentum drafts is routed to a human fundraiser, who reviews it, edits it, and approves it before it goes out. The friction is the feature. The human is the point.

I will say something I do not say often enough in public. This is not just a product decision for me. It is a condition of my serving in this role. Values matter more than ever. If the day came that the company I help lead decided to remove the human from the loop in pursuit of growth, I would need to ask myself whether I could still hold this seat. Fortunately, that is not a conversation I have to have. From the day Virtuous was founded, the commitment has been to use technology to amplify human connection rather than replace it. First through systems. Then through automation. Now through AI. The category changes. The principle does not.

I’m not in sales. I am not telling you this as a marketing pitch for any single product. I am telling you because the sector is being told, repeatedly and confidently, that the only way to scale is to remove humans from the loop. That is not true. It is a choice. And the people making that choice are not making it because the alternative is impossible. They are making it because the alternative is harder, and harder feels like a luxury we cannot afford.

I would argue that harder is the entire point of the sector. We exist because some things are worth doing the harder way.

* * *

A closing reflection

Klarna, the Swedish fintech company, made headlines in 2025 when it walked back its AI customer service strategy and began rehiring humans. The reason its CEO gave was not that the AI did not work. The AI worked. It answered questions faster and cheaper than the humans had. The reason was that, in the words of the executive who described the pivot, AI can answer your question, but only a human can make you feel heard.

Philanthropy has always run on the second one.

If the most sophisticated commercial sectors on the planet, the ones with the strongest incentive to automate everything, are now beginning to recognize that there is a category of human experience that cannot be outsourced without breaking what made the relationship valuable in the first place, then the nonprofit sector should be paying close attention. We have been in the human-experience business since long before the technology forced anyone else to think about it. We do not need permission from Silicon Valley to remember what we already know.

I want to leave you with a different version of the question I am asked every week.

The question I am usually asked is: What do you think about autonomous fundraising?

The question I think we should be asking is: What kind of generosity do we want to exist in ten or twenty years, and is the technology we are buying right now bringing us closer to that, or further from it?

Just because we can summon something does not mean we should. Goethe knew that in 1797. Shelley knew it in 1818. Harari is reminding us of it now. The apprentice, in the end, was not punished for his ambition. He was punished for his impatience, for reaching for the spell before he had earned the wisdom to undo it.

We can do better. We must do better. We can use AI to help fundraisers be more curious, more prepared, more present. We can use it to identify the donors who most need to hear from a human being, and to free human beings to be there for them. We can use it to imagine modes of generosity that have not existed before, ones that are deeper, more participatory, more reciprocal.

Or we can use it to ask people for money in voices that are not real, in relationships that are not relationships, on behalf of missions that increasingly will not survive what that pretending costs.

* * *

Human First

The choice between those two futures is not a force of nature. It is not arriving on its own. AI is not being done to the nonprofit sector. It is being adopted by the nonprofit sector, one decision at a time, in rooms full of human beings who could, at any moment, decide differently. This is the agency we keep forgetting we have.

The work of building the muscle to make that choice is already underway. My friend Matt Randerson, who first interviewed me about The Generosity Crisis years ago when he was at Barna, has built a movement called HumanCulture.ai. His argument belongs at the center of every conversation our sector is now having about technology.

Here is how he puts it. If AI makes your team thirty percent more efficient, and your only plan is to reduce cost, what you have is not a technology problem. It is a leadership imagination problem. The next decade, he argues, will belong to two kinds of organizations. The first uses AI to do the same work with fewer people, and reaps short-term financial gains. The second arrives with a deep bench of ideas the company has been waiting for capacity to build, and uses AI to free its people up to build them. The first kind of organization is replaceable. The second is not.

I have been making a version of this argument inside our sector for years. Matt is making it for the wider workforce. Both arguments converge on the same conclusion. AI is the most powerful instrument for amplification ever built by human beings. What we choose to amplify is the entire question. A nonprofit sector that uses AI to remove the human from the donor relationship is amplifying the wrong thing. A nonprofit sector that uses AI to free its people to be more present, more curious, more imaginative, is amplifying the right thing.

If you run an organization, you have the agency to make this choice. Not eventually. Not after the next strategic plan. Now. Talk to the people you trust about how to use this technology in service of what your mission actually is. Pick up the phone, the real one, and call a donor. Ask how they really feel about your replacing human fundraisers with bots. The connective tissue our sector needs to survive the next twenty years is being built right now, by every conversation that does not get outsourced.

I know which world I want to live in.

I have a strong suspicion that, if you sit with this long enough, you do too.

* * *

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous, founder of Fundraising.AI, and the co-author of The Generosity Crisis and Nonprofit AI. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

Long before there were algorithms or data centers or artificial intelligence, there was a more radical innovation: the belief that people would voluntarily pool their resources to solve problems that markets and governments could not or would not solve on their own. That belief built the nonprofit sector. It was, and remains, the most audacious act of imagination in the history of organized society.

Today, that imagination is being tested.

Jensen Huang was not being diplomatic.

Sitting across from Jim Cramer at NVIDIA’s GTC conference last month, the CEO of the most valuable technology company on earth was asked a simple question: Why are so many companies using AI as a reason to lay people off?

His answer landed like a verdict.

“Because you’re out of imagination. For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don’t do more.”

Read that again slowly. And if you lead a nonprofit, read it a third time.

The man whose chips power the AI revolution – whose hardware is running inside Meta, Amazon, Microsoft, and nearly every major tech company on the planet – just told their CEOs, on live television, that the problem isn’t the technology. The problem is them.

And then the data arrived to prove his point.

This week, Challenger, Gray & Christmas released its March 2026 job cut report. The numbers are stark. Artificial intelligence was the leading reason employers cited for layoffs in March, accounting for more than 15,000 of the roughly 60,600 announced cuts. That is twenty-five percent of all layoffs in a single month, up from ten percent in February and five percent across all of 2025. Since Challenger began tracking AI as a reason for workforce reductions in 2023, employers have now attributed nearly 100,000 job cuts to the technology.

The tech sector alone announced over 52,000 cuts in the first quarter of 2026 – a forty percent increase from the same period last year. Dell trimmed 11,000 positions. Meta is reportedly preparing to cut 15,000 employees, roughly twenty percent of its workforce, while simultaneously doubling its AI budget to $135 billion. Amazon eliminated 16,000 corporate roles in January, explicitly citing AI and automation. Microsoft cut more than 15,000 positions throughout 2025 while committing $80 billion to AI infrastructure.

These are not struggling organizations. These are among the most well-resourced, technologically sophisticated companies in the history of commerce. And yet the dominant response to the most powerful general-purpose technology since electricity has been to do the same work with fewer people.

I understand the pressures behind those decisions. I have friends and partners inside many of these companies, and I know the calculus is more nuanced than any headline suggests. But I keep coming back to Huang’s framing: when you are handed more capability than you have ever had, and your first instinct is to contract rather than expand, something beyond strategy is at work. Something closer to a failure of imagination.

Now, in fairness: there is a version of this calculus that makes sense in the for-profit world. If you are a publicly traded technology company reallocating capital from headcount to data center infrastructure, your board and your shareholders may reward that trade. The logic of quarterly earnings, competitive positioning, and capital expenditure has its own internal coherence. I am a capitalist. I understand the engine.

But the nonprofit sector does not run on that engine. There are no shareholders demanding that you cut fifteen percent of your staff to fund an AI buildout. There is no stock price that ticks up when you announce a leaner team. The currency of the social sector is trust, and trust is built by humans – in relationships, in communities, in the slow and irreplaceable work of presence. When a nonprofit leader looks at these headlines and reaches for the same playbook, they are importing a logic that was never designed for the mission they serve. And that matters, because what the nonprofit sector does with AI in this moment will look fundamentally different from what Silicon Valley does – or at least it should.

Meanwhile, a new report from Brookings Metro and Opportunity@Work landed the same week, and it tells a story that should keep every nonprofit leader awake tonight. The researchers examined what they call “gateway jobs” – mid-level stepping-stone roles like customer service representatives, administrative assistants, and bookkeeping clerks that have historically served as bridges between entry-level work and higher-wage careers. These roles are not just jobs. They are pathways. They are how people build skills, gain experience, and move toward economic stability.

The report found that nearly eleven million workers without college degrees hold gateway jobs that are highly exposed to AI disruption. And here is the part that reveals just how interconnected the damage could be: forty-nine percent of the career pathways connecting those gateway roles to higher-paying positions are also heavily exposed. In other words, AI is not just threatening individual jobs. It is threatening the very infrastructure of upward mobility.

In Florida, nearly one in three workers without degrees hold gateway jobs in the highest-risk category. The Northeast and Sun Belt are the most exposed regions. And three and a half million workers are in what the researchers call the most precarious position – high AI exposure combined with low adaptive capacity, meaning limited savings, few transferable skills, and unfavorable geographic circumstances.

This is not a labor market problem that efficiency can solve. This is a structural problem that demands imagination.

I have spent the better part of two years writing and speaking about curiosity as the defining human superpower of the AI age. The premise is simple and, I believe, increasingly undeniable: AI doesn’t wonder. It doesn’t get sidetracked by a fascinating idea. It doesn’t ask “what if?” It processes. It optimizes. It follows algorithms. If a machine is instructed to be curious, it isn’t truly curious – it’s simply following orders.

Curiosity is what enables us to see opportunities where others see obstacles. It is the spark that turns a new capability into a new possibility rather than a cheaper version of the old one.

And imagination is curiosity’s first child.

Imagination is what happens when curiosity meets courage. When a leader looks at AI and asks not “how many people can I replace?” but “what could we accomplish that was never possible before?” When an organization stops trying to do the same things faster and starts asking whether those are even the right things to be doing in the first place.

Jensen Huang was pointing at something essential when he made that comment. He was drawing a line between two fundamentally different responses to the same technological moment. One response contracts. The other expands. One optimizes the present. The other invents the future. And the dividing line between them is not budget or capability or access.

It is imagination.

And then, the same week the Challenger report landed, a different kind of story broke.

The New York Times profiled Matthew Gallagher, a 41-year-old self-taught entrepreneur from Los Angeles who grew up in a trailer park, taught himself to code on a gifted laptop, and in September 2024 launched a telehealth startup called Medvi from his house with $20,000 and a stack of AI tools. He used AI to write the code, produce the website, generate ad creative, handle customer service, and analyze business performance in real time. His only full-time employee is his younger brother.

In its first full year, the company generated $401 million in sales. It is now on track to reach $1.8 billion in 2026. Sam Altman had predicted in 2024 that a one-person billion-dollar company would eventually become possible through AI. Gallagher may have just delivered the proof of concept.

Now, I want to be careful here. The Medvi story is not a simple fable. There are real questions about the model’s defensibility, its regulatory exposure in the GLP-1 market, and the broader ethical implications of ultra-thin healthcare architectures. I am not holding it up as a template for everyone to copy.

But what I find striking – and what connects Gallagher’s story to Huang’s critique – is what it reveals about the difference between imagination and optimization. Every established telehealth company in the space responded to AI the way most companies do: they used it to make existing operations slightly more efficient. Gallagher looked at the same technology and asked a fundamentally different question. Not “how can AI help us do what we already do with fewer people?” but “what kind of company becomes possible when AI is the starting assumption rather than the afterthought?”

He posted on social media after the story broke: “Grew up in a trailer park. NYT just released an article about my startup going from $0 to $1B in 14 months with one employee.”

That is not efficiency. That is imagination applied to scarcity. And perhaps most relevantly for the conversation I want to have with the nonprofit sector, Gallagher has announced the launch of The Gallagher Foundation, intended to fund entrepreneurs who use their talents for nonprofits and what he calls the “greater good.”

When I read the Gallagher story, I thought immediately of a phone call I received about a year and a half ago from a dear friend, Kaz McGrath, Founder and CEO of PLAI (Purpose Lead AI), who lives in Australia. She had just gotten access to ChatGPT Pro – the premium tier OpenAI had launched – and the first thing she did with it was ask the model to write her a business plan for a nonprofit that raised a billion dollars a year with six employees or fewer.

Let that sit for a moment.

She did not ask it to help her write a better fundraising email. She did not ask it to summarize a grant report. She asked it to reimagine the entire architecture of what a mission-driven organization could look like if you started from capability rather than constraint. That is the kind of thinking – that kind of curiosity and ambition – that our sector needs in spades.

Now, a six-person billion-dollar nonprofit is not a blueprint anyone should follow literally. That is not the point. The point is the question itself. The willingness to sit down with a powerful new tool and ask it to help you think at a scale you had never previously considered. Not to optimize the model you already have, but to imagine a model that does not yet exist. Gallagher did it in telehealth. Kaz did it for the social sector. And the distance between where most nonprofit leaders are right now and where that kind of question lives is not a technology gap. It is an imagination gap.

Which brings me to the part of this story I care about most.

Now here is where I need to say something directly to the nonprofit sector, because this is where I live and where the stakes are different – and, I would argue, higher.

The 2026 Nonprofit AI Adoption Report from Virtuous and Fundraising.AI found that ninety-two percent of nonprofit professionals now use AI at least occasionally. Only seven percent report meaningful improvements in organizational capability. That gap – between experimentation and transformation – is not a technology problem. It is an imagination problem.

Most nonprofit organizations that have adopted AI have done so within the existing architecture of how they work. They use it to write faster emails, summarize reports more efficiently, clean data more quickly. And those are genuinely useful applications. I do not dismiss them.

But they are the equivalent of getting a telescope and using it to read the newspaper from across the room.

The telescope was not built to do what you were already doing more comfortably. It was built to let you see things that were previously invisible.

AI is not a productivity tool. It is a possibility tool. And the organizations that treat it only as the former will never close the gap between the ninety-two and the seven.

So what does imagination actually look like inside a mission-driven organization? Let me try to make this concrete, because I believe the practical dimension is where most conversations about AI and imagination fall apart. It is easy to say “think bigger.” It is harder to show people what bigger looks like from where they are standing.

Consider a mid-sized food bank that currently uses AI to optimize delivery routes and predict demand patterns. Those are efficiency gains – valuable, but incremental. Now imagine the same food bank asking a fundamentally different question: What if we could predict food insecurity before it becomes acute? What if, by combining public health data, economic indicators, school enrollment patterns, and social service referrals, we could identify families sliding toward hunger and intervene before they ever need to visit us? And those are exactly the type of questions that the team at The FarmLink Project, where I serve on the board, are contemplating daily.

That is not an efficiency play. That is a reimagination of the food bank’s role in the community – from reactive distributor to proactive partner in well-being. And it is made possible not by more AI, but by more imagination applied to the AI that already exists.

Or consider what my friend Mallory Erickson did. Mallory is a fundraising consultant who has spent years working with development teams across the sector. She saw a problem that most people were trying to solve with the wrong question. The conventional approach to flagging fundraising revenue was to ask: How do we find more prospects? How do we build a bigger pipeline? How do we use AI to identify more people who might give?

Mallory asked a different question entirely. What if the problem was never the pipeline? What if the real constraint was that fundraising teams were insufficiently prepared to convert the prospects they already had? What if the bottleneck was not data, but confidence?

That question led her to build Practivated, an AI-powered donor conversation simulator that lets fundraisers practice real-world scenarios – discovery calls, major gift asks, tough objections, stewardship conversations – in a low-stakes environment with AI-generated donor avatars built from actual giving histories and personality profiles. The platform does not replace coaching. It scales it. Fundraisers get reps. Leaders get visibility into team-wide skill development. And organizations see measurable results: a thirty-three percent increase in ask effectiveness, a two hundred and eighty percent increase in donor outreach and engagement, and something that no CRM upgrade has ever delivered – fundraisers who feel more confident walking into the room.

That is imagination. Not using AI to write better appeal letters. Not using it to score more prospects. Using it to rethink why fundraising teams underperform in the first place and building a tool that addresses the actual root cause.

Or think about prospect research – a function that has existed in fundraising shops for decades. Most organizations that have introduced AI into prospect research have used it to do what they were already doing faster: scan wealth indicators, flag capacity ratings, surface giving histories. And those are legitimate improvements. But what if a development team used AI to do something that was previously impossible? What if, instead of scoring donors on their capacity to give, you could model their propensity to deepen a relationship over time – mapping not just wealth, but affinity patterns, engagement cadence, communication preferences, and life-stage transitions to predict not who can give the most, but who is most ready to be invited into a deeper conversation about your mission? That reframes the entire function from transactional screening to relational intelligence. And it changes who your team calls first on Monday morning.

None of these applications require technology that does not already exist. They require leaders who are willing to step outside the boundaries of their current operating model and ask the question that Jensen Huang is asking the tech industry to ask: What would we do if we had more capability than we’ve ever had before?

I want to be honest about something. The reason most organizations default to the efficiency frame is not because their leaders are unintelligent. It is because imagination is harder. Efficiency is measurable. It fits into a board presentation. You can show that you reduced costs by fifteen percent or processed donations thirty percent faster. Those are real numbers that satisfy real stakeholders.

Imagination does not come with a dashboard.

It asks leaders to advocate for outcomes they cannot yet quantify, to invest in capabilities whose value has not been proven, and to explain to boards and donors why the organization is pursuing something that looks different from what it has always done. That is uncomfortable. It requires a kind of leadership courage that the nonprofit sector desperately needs but rarely rewards.

And this is where curiosity re-enters the frame. Because curiosity is not just a personality trait. It is a leadership discipline. It is the practice of asking questions that do not have easy answers, of sitting with uncertainty long enough to see what emerges, of resisting the gravitational pull of the familiar long enough to glimpse something genuinely new.

When I talk about the Curiosity Code – the framework I have been developing over the past two years – this is what I mean. Curiosity is not whimsy. It is the mechanism by which organizations move from optimization to transformation. From doing the same things faster to doing entirely new things. From the ninety-two percent to the seven.

But I also want to say something that might be unpopular in a moment where the layoff headlines are dominating every feed.

Not all job cuts attributed to AI are actually caused by AI. Marc Andreessen said publicly last week that AI is a “silver bullet excuse” for companies that were already overstaffed after pandemic-era hiring sprees. The Challenger report itself notes that these are employer-stated reasons, not independently verified causes. Companies sometimes cite AI when the actual drivers are broader cost restructuring, market contraction, or strategic pivots that have nothing to do with the technology.

This matters because the narrative shapes the response. If we believe that AI is simply replacing humans at an accelerating rate, the logical response is to hunker down, protect what we have, and brace for impact. But if we recognize that many of these cuts reflect a failure of leadership imagination – combined with a convenient narrative that Wall Street rewards – then the appropriate response is entirely different. It is to lead differently. To imagine differently. To use this technology the way it deserves to be used: as an accelerant for mission, not a substitute for people.

Here is what I am asking nonprofit leaders to do. Not eventually. Now.

Stop asking “how can AI make us more efficient?” Start asking “what would we attempt if efficiency were no longer the constraint?”

Those are fundamentally different questions, and they lead to fundamentally different futures.

Convene your team – not for an AI training session, but for an imagination session. Give people permission to describe the organization they would build if they were starting from scratch today, with AI as a foundational capability rather than a bolt-on tool. You will be surprised by what emerges when you remove the weight of the existing operating model from the conversation.

Study the Brookings research on career pathways. If your organization touches workforce development, education, economic mobility, or community well-being in any way, the gateway jobs framework should be part of your strategic planning. The disruption is not hypothetical. It is arriving in monthly Challenger reports.

Build governance alongside vision. Imagination without responsibility is just another word for recklessness. The question I have returned to more times than I can count still holds: just because we can, does it mean we should? The organizations that ask that question before they scale will be the ones that earn and keep the trust of the communities they serve.

And lead with curiosity. Not as a buzzword. Not as a poster on the conference room wall. As a practice. Ask “what if?” more than you ask “how much?” Ask “who benefits?” before you ask “how fast?” Ask “what becomes possible?” instead of “what can we cut?”

The layoff numbers are going to keep climbing. The headlines will get louder. The pressure to respond with fear and contraction will intensify. And for the nonprofit sector – which has always operated closer to the margin, with less cushion and less room for error – the temptation to retreat into the familiar will be enormous.

But retreat is not strategy. And efficiency is not vision.

The organizations that will define the next decade of social impact are not the ones that use AI to do more with less. They are the ones that use AI to imagine more with more. More reach. More depth. More insight. More capacity for the irreplaceably human work of building trust, deepening relationships, and holding the moral line in a world that is moving faster than most of us can think.

Jensen Huang built the hardware that powers this revolution. But he cannot supply the imagination to use it well. That part is on us.

And the nonprofit sector – the sector built on the audacious belief that the world can be better than it is – has more imagination in its DNA than any industry on the planet.

It is time to act on it.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous, co-author of Nonprofit AI and The Generosity Crisis, and Founder of Fundraising.AI. He writes about responsible innovation, the future of generosity, and the power of human-centered leadership in the age of AI.

By Nathan Chappell

I spent a few days in Cabo San Lucas this week, surrounded by some of the sharpest minds in our industry. Not for panels or keynotes in the traditional sense, but for the kind of open, unhurried conversation about charitable giving that rarely happens at conferences: the kind where people say what they’re actually thinking rather than what sounds polished.

I’d been asked to lead a short session called “The Curiosity Advantage: Leading Through Exponential Change Without Losing Your Mind.” Ten minutes of a thought-starter, followed by facilitated discussion. But what unfolded over the course of our time together went far beyond my ten minutes and into conversations around the pool, the bar, and throughout our meals together. It became something more honest. More searching. And, ultimately, more hopeful than I expected.

Because here’s what I noticed: the people in that room weren’t paralyzed by the pace of change. They weren’t in denial about it, either. They were sitting in the tension. Holding two things at once. The genuine excitement of what AI makes possible for mission-driven organizations, and the equally genuine exhaustion of trying to absorb it all without dropping something important.

Let me start with what’s true.

The pace of AI innovation is not slowing down. It is accelerating in ways that make last quarter’s breakthroughs feel like ancient history. If ChatGPT turned the dial from 5 to 25, agentic AI tools like Claude Cowork just turned it to 125. And AI leaders I deeply respect, from Demis Hassabis to Dario Amodei to Geoffrey Hinton to Fei-Fei Li, are converging on a timeline that suggests something resembling artificial general intelligence could arrive in the next 900 days. Not as a philosophical thought experiment. As an operational reality.

During our time together, we sat and talked about a future for our organizations made possible by tools that didn’t exist six months ago. Not theoretically. Practically. The possibilities were staggering. A development team that once spent hours drafting donor communications can now produce deeply personalized outreach at a scale that was unimaginable two years ago. A program director can analyze outcomes data in minutes that would have taken weeks. A small nonprofit with a staff of five can operate with the strategic sophistication of an organization ten times its size. This is not hype. This is what I’m seeing in real organizations, right now, across the Fundraising.AI community and among Virtuous customers who are leaning into these tools with intention and courage.

The feeling in that room was less “How do we survive this?” and more “How do we make sure we don’t miss this?”

That’s the shift I want to name. Because too often, the conversation about AI in the nonprofit sector has been framed around risk mitigation and defensive posture. And those things matter. I’ve built much of my career around responsible and beneficial AI. But responsibility and curiosity were never meant to be mutually exclusive. For some, the belief that they are has quietly crowded out the wonder. And wonder, I think, is where the real opportunity lives.

And yet. Even in rooms full of wonder, something else is present.

There’s a word that keeps surfacing in nearly every conversation I have lately, whether it’s with tech executives, nonprofit CEOs, frontline fundraisers, or program directors. It isn’t “efficiency” or even “innovation.” It’s relevancy.

How often do you think about your own relevance? Not in the abstract, philosophical sense. In the practical, honest sense. The kind of question that makes you wonder whether the skills you spent fifteen years developing will still matter in three.

Whether your organization’s model, which served communities beautifully for a decade, will still be the right model in a world where AI is reshaping expectations for speed, personalization, and impact measurement.

I asked a version of this question during our session, and the room got quiet. Not because people didn’t have answers, but because the question itself carried weight that most of us had been carrying privately.

Here’s what I think is happening beneath the surface.

When you see a tool that could help you stay afloat, you grab it. And you don’t let go. Not at lunch. Not in the evening. Not on the weekend.

A few days before the trip, I read a piece in Harvard Business Review by researchers Aruna Ranganathan and Maggie Ye that gave language to exactly this pattern. They had spent eight months studying how generative AI changed work habits at a mid-size technology company, and their central finding confirmed what I’d been sensing: AI tools didn’t reduce work. They intensified it.

Not because anyone mandated longer hours. The intensification was voluntary. Workers started taking on broader responsibilities because AI made new tasks feel suddenly accessible. Work became ambient, with people slipping prompts into moments that used to be pauses, during lunch, between meetings, right before leaving the desk, none of it feeling like overwork until the natural rhythms of rest and recovery had quietly disappeared. And the multitasking multiplied, with people managing several AI-assisted threads at once in a state that felt like momentum but was actually cognitive fragmentation.

The finding that stayed with me was this: many workers reported feeling more productive. Almost none reported feeling less busy. The time savings that AI created didn’t produce breathing room. They produced capacity for more. And human nature filled the vacuum.

For the nonprofit sector, where burnout is already endemic, where 95 percent of leaders express concern about it, and where mission-driven people are already inclined to give more of themselves than any reasonable person should, this pattern isn’t theoretical. It’s an accelerant poured on an existing fire.

But here’s the part I don’t want you to miss, because it’s the reason I’m writing this piece with hope rather than alarm.

The researchers didn’t argue against AI adoption. They argued for something more thoughtful: the need for what they called an “AI practice,” a set of intentional norms that structure how AI is used, when it’s appropriate to stop, and how work should and should not expand in response to newfound capability.

And this is where I think the nonprofit sector has a remarkable, perhaps even historic, advantage.

We are already a sector built on mission alignment. We already ask questions about purpose, about who we serve, about whether our actions reflect our values. The tools for discernment exist in our DNA. What we haven’t done yet is apply that discernment to how we adopt AI itself.

Think about it. Nonprofits are uniquely positioned to be the most thoughtful AI adopters on the planet. We operate in a currency of trust. We are accountable to communities, to donors, to the public good. We don’t have shareholders demanding quarterly acceleration.

We have missions demanding long-term fidelity. That’s not a limitation. That’s a superpower. If we choose to see it that way.

The sector’s historically cautious approach to technology adoption, which has sometimes been characterized as a weakness, can be reframed as strategic wisdom. Not slowness for the sake of slowness, but the kind of deliberate pace that allows organizations to adopt with eyes open, to integrate AI in ways that amplify what matters rather than simply accelerating what’s already moving too fast.

And the key to unlocking that advantage, I believe, is the very thing I noticed in the people who leaned forward during the hardest questions that week: curiosity.

After the morning sessions had wrapped, I found myself chest deep in the pool with Brian Forde, CEO and Co-Founder of Numero, in one of those conversations that starts casually and turns into something deeper. We were talking about what actually makes someone valuable in an era where AI can do so much of the analytical heavy lifting.

For most of the last century, we prized professional value by IQ, the ability to know, analyze, and solve. More recently, we’ve learned to prize EQ, the ability to connect, empathize, and lead with emotional intelligence. Both still matter.

Curiosity, which I speak about often, is the real competitive advantage of this era. Not technical sophistication. Not early adoption. Not even strategy, at least not in the traditional sense. Curiosity.

I’ve written many articles about this, about curiosity as a human superpower in the age of AI. But what this week clarified for me is that curiosity operates on two levels simultaneously, and the tension between those levels is precisely where leadership happens.

On the first level, curiosity is the force that keeps you from freezing when the ground shifts. It’s what makes you ask “What if?” instead of “What now?” It’s the difference between paralysis and momentum, and it almost always comes down to choosing better questions over faster answers.

The people in that room who felt energized by the relevancy question rather than threatened by it? They were the curious ones. Not in the soft, passive sense. Curious in the way that treats uncertainty as an invitation rather than a verdict.

On the second level, curiosity is also the thing that whispers “just one more.” One more experiment. One more prompt. One more tool to explore. For every entrepreneurially minded person, curiosity isn’t just a compass. It’s also an accelerant. And unchecked, it feeds the very intensification that the Harvard researchers documented.

This is the balance that I think matters more than any technology decision a nonprofit leader will make this year. How do you stay curious enough to seize the extraordinary opportunity that AI represents, while building the discipline to protect yourself and your team from the ambient creep of work that never stops?

The answer, I think, is not personal willpower. It’s structure.

Most organizations treat adaptation as the thing you do when something breaks. When a technology shifts. When funding changes. When the world throws a curveball. Adaptation is the response. It’s reactive by nature.

But what if adaptation were the starting point? What if curiosity weren’t just something you encouraged in a team meeting but something you built into the operating model? What if the question “What are we learning?” carried as much weight in a board meeting as “What did we accomplish?”

This is where the Harvard research becomes genuinely useful, not as a cautionary tale, but as a design brief. The researchers proposed specific structural norms that I believe are especially important for mission-driven organizations.

The first is intentional pauses. Not more meetings. Not strategic retreats. Brief, structured moments built into the rhythm of work that create space for someone to ask: Am I still aligned with what matters? Has the scope of what I’m doing expanded without anyone deciding it should? Is this pace sustainable, or am I running on fumes dressed up as momentum?

One version of this I’ve been recommending is what is called a “decision pause.” Before any significant decision is finalized, someone on the team is responsible for offering one counterargument and one explicit connection to organizational mission.

It introduces a moment of deliberate friction into a system that AI has made dangerously frictionless. And friction, when placed with intention, isn’t the enemy of progress. It’s the guardian of discernment.

The second is human grounding. As AI enables more solo, self-contained work, the quiet risk is isolation. A development officer who used to brainstorm a case statement with a colleague now drafts it with an LLM. A program director who used to ideate with peers now generates options in parallel with an AI assistant. The work gets done. Maybe faster. But something is lost in the process, and what’s lost is exactly what nonprofits cannot afford to surrender: the creative friction and relational depth of human connection.

Human grounding means protecting time and space for listening. For structured dialogue. For the kind of conversation that doesn’t have a prompt and doesn’t produce an output, but that restores perspective, generates surprise, and re-anchors the work in the social context that gives it meaning.

This is one of the reasons I host a monthly gathering I call the Dinner of Extraordinary Humans. Phones off. No agenda beyond deep conversation about what matters. It has nothing to do with productivity and everything to do with the kind of connection that keeps people whole in a world that is accelerating beyond anyone’s ability to fully comprehend alone.

AI provides a single, synthesized viewpoint. Human conversation provides surprise, disagreement, empathy, and the spark of an idea that nobody in the room expected. Organizations that protect space for that kind of exchange will not only avoid burnout. They will out-innovate organizations that don’t. Because real creativity doesn’t emerge from speed. It emerges from the collision of perspectives that only humans, in relationship with each other, can produce.

AI is as much a cultural shift as it is a technological one. The tools are extraordinary. The opportunity for nonprofits to amplify their missions, deepen their impact, and serve their communities with unprecedented sophistication is real and growing. I see it every day in my work at Virtuous, across the global Fundraising.AI community, and in the organizations that are choosing to explore with intention rather than adopt with anxiety.

But the cultural shift is the harder part. Because culture can’t be upgraded overnight. It has to be practiced. Intentionally. Repeatedly. With the kind of discipline that looks, from the outside, like slowness, but that is actually the deepest form of strategic intelligence I know.

The pace of change will continue to accelerate. That is beyond any of our control. But how we respond to it, the posture we bring, the culture we build, the questions we ask, that is entirely ours.

And if there’s one thing I took away from my days in Cabo, it’s this: the organizations best positioned for what’s coming aren’t the ones with the best technology strategy. They’re the ones building cultures of curiosity that can absorb exponential change without breaking. Cultures that treat wonder as a discipline, not a luxury. Cultures that know how to move fast and also know how to stop.

That’s the curiosity advantage. And it belongs to anyone willing to claim it.

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous, co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

This past week, I made two people cry.

The first was a nonprofit executive who had spent twenty-three years building a career she loved. A career known for building community and deep relationships. We were sitting in a green room before a conference session, the kind of windowless holding cell where speakers wait with bad coffee and good intentions. She had come to hear me talk about AI in the sector, but before I could start she told me, quietly, that she had been lying awake at night wondering if everything she had built was about to become irrelevant. She didn’t use the word “scared.” She used the word “stuck.” But her eyes told a different story.

The second was an entrepreneur I had known for a while. He had been aware of AI - had dabbled with the free version of ChatGPT for the past year, the way a lot of people have, tossing it a question here and there without ever really leaning in. I invited him to my home where we sat down together for a few hours, and I walked him through some basics in Claude Cowork and NotebookLM all the time using Wispr to dictate his voice while I asked him to describe the things he does every day that has made him successful for several decades. It wasn’t clean, organized or sexy prompting, it was garbled rhetoric and ideation, the way that you talk to a friend about a half lucid dream you had last night. Somewhere around the forty-five minute mark, his eyes went glassy. He wasn’t sad. He was overwhelmed in the other direction. This was a man with a head full of ideas he had never been able to execute - not for lack of ambition, but for lack of bandwidth. And suddenly, in one afternoon, a dozen of those ideas went from theoretical to instantly plausible. He described it later as going from a world of black and white to seeing in high-definition color. The tears were relief. The tears were release. The tears were the sound of a dam breaking after years of buildup. He’d heard the rhetoric about “powerful AI” but never fully realized what that looked like. Until now.

Same week. Same technology. Same chemical compound of tears. Two completely different reasons. One based in overwhelm, the other of exhilaration.

I tell you this not because I enjoy making people emotional, but because I think those two moments capture something that almost no one is talking about honestly. The conversation about artificial intelligence has been hijacked by two competing narratives, and both of them are failing us. One says AI will save everything. The other says AI will destroy everything. And in the space between those extremes, millions of thoughtful, capable people are sitting with a feeling they can barely name - a heaviness in the chest, a low hum of anxiety, a sense that the world is rearranging itself and nobody handed them the new blueprint.

If you recognize that feeling, this message is for you. Not because I have all the answers. But because I have been living inside this question since 2017, and I think the honest version of this conversation is overdue.

The Weight of the Room

On any given week, I receive five to six speaking requests - a number that has been steadily increasing over the past twenty-four months. The audiences vary - healthcare systems, higher education, faith-based organizations, foundations, Fortune 500 companies. The sectors differ. The titles in the room differ. But the energy is almost always the same. It carries a particular frequency, somewhere between curiosity and dread, and it fills the room before anyone says a word. A secret hope that I’ll tell everyone they should go home and do things the same way they’ve always done them. But my role is not to lure people into the comfort of what was, but instead to strike a sense of discomfort that enacts curiosity with urgency.

Unsurprisingly, the questions, when they come, tend to orbit the same center of gravity: What does this mean for me? Is my job safe? How do I succeed in a world I didn’t sign up for? If I’m a leader, how do I lead when the ground keeps shifting? How do I measure success when the goal posts keep shifting? And underneath all of those, the question no one asks out loud, but everyone is thinking: How much do I actually need to know?

These are not idle questions. They are not the musings of people who have too much time on their hands. They are the questions of people who have suddenly realized that the future they were planning for may not be the future that is arriving.

The data confirms what I see in those rooms. The American Psychological Association’s Stress in America 2025 survey found that fifty-seven percent of U.S. adults now cite the rise of AI as a significant source of stress - up from forty-nine percent just one year earlier. Among younger adults aged eighteen to thirty-four, that figure climbs to sixty-five percent. The stress is not abstract. It is not philosophical. It is the kind that disrupts sleep, strains relationships, and sits in the background of every workday like a weather system that never quite breaks.

A 2025 Pew Research Center survey found that American workers are more worried than hopeful about AI’s future role in the workplace, with fifty-two percent expressing concern compared to thirty-six percent expressing optimism. A third of workers believe AI will reduce the number of available jobs. Only six percent believe it will create more.

These numbers don’t describe a workforce that is resisting change out of stubbornness. They describe a workforce that is processing a profound shift in the meaning of competence, and doing so largely without support.

What We’re Really Afraid Of

A recent piece in the Harvard Business Review by Erik Hermann, Stefano Puntoni, and Carey Morewedge offers one of the most precise diagnoses I’ve seen of why AI feels so threatening. Their research identifies three psychological needs that generative AI disrupts: competence, the feeling of being effective and capable; autonomy, the feeling of being in control of one’s actions; and relatedness, the feeling of having meaningful connections to other people.

When a machine can draft what you draft, analyze what you analyze, and communicate what you communicate - often faster and without effort or complaint - it doesn’t just threaten your task list. It threatens your sense of self. The question stops being “Can I do this job?” and becomes “Am I still the person I thought I was?”

This is why the AI conversation so often feels like a conversation about identity masquerading as a conversation about technology. And it is why the standard corporate response - “Here’s a new tool, go take a training” - so often misses the mark. You cannot train your way out of an identity crisis. You have to walk through it.

The same Harvard Business Review research team proposes what they call the AWARE framework for leaders: acknowledge employee concerns, watch for adaptive and maladaptive coping, align support with psychological needs, redesign workflows around human-AI synergies, and empower workers through transparency. It is a useful framework. But frameworks alone are not what people need when they are awake at three in the morning staring at the ceiling. What they need first is someone to say: What you are feeling is rational. You are not behind. You are not broken. You are awake.

The Paradox Nobody Warned You About

Here is something that should trouble us more than it does. A separate study published in Harvard Business Review in early 2026, based on eight months of embedded research at a two-hundred-person technology company, found that AI tools did not reduce work. They intensified it.

Workers who adopted AI began operating at a faster pace, taking on a broader scope of tasks, and extending their work into more hours of the day - often without being asked. The researchers, Aruna Ranganathan and Xingqi Maggie Ye at UC Berkeley’s Haas School of Business, describe a self-reinforcing cycle: AI makes tasks easier, so workers take on more tasks, which increases their dependence on AI, which makes more tasks feel possible, which leads to more work. The promised efficiency gains quietly transformed into what the researchers call “workload creep.”

The implications are significant and largely ignored but should not be surprising. Did the invention of email afford us to work less? What looks like productivity in the short run can mask cognitive fatigue, weakened decision-making, and a growing inability to step away from work. Boundaries between work and rest dissolve not because the employer demands it, but because the technology makes overwork feel frictionless and even rewarding.

A separate DHR Global survey of 1,500 corporate professionals found eighty-three percent experiencing burnout, with overwhelming workloads and excessive hours as the leading contributors. The burnout gap by seniority is stark: sixty-two percent of associates and entry-level workers report burnout, compared with thirty-eight percent among C-suite leaders.

So the people with the least power and the least context are bearing the heaviest load. That should sound familiar to anyone who has worked in the nonprofit sector, where burnout has been a structural feature for decades. Now AI is adding a new layer: the anxiety of adaptation stacked on top of the exhaustion of execution.

The Signals Were Always There

I want to be honest about something that is uncomfortable to say. For those of us who have worked in AI for years, the current moment is not a surprise. It is the arrival of something we could see forming on the horizon for a long time. The World Economic Forum’s Future of Jobs Report projects that 170 million new roles will be created and 92 million displaced between 2025 and 2030 - a net gain of 78 million jobs. But that framing, while accurate, obscures the human reality underneath the numbers. The new jobs are not one-to-one swaps. They do not appear in the same industries, the same cities, or for the same people.

Forty percent of employers surveyed by the WEF plan to reduce their workforce where AI can automate tasks. Two-thirds plan to hire specifically for AI skills. And thirty-nine percent of core job skills are expected to change by 2030. These are not incremental shifts. They are structural transformations with a five-year runway. But change won’t wait five years to arrive. Speaking about this topic informally with Ben Greene, CRO of charity: water, last week, he shared that charity: water proactively assesses AI fluency and curiosity as a core job requirement when interviewing candidates.

Yet for the majority of people I speak with, this reality is still landing with a thud. The signals were there - in the steady improvement of language models, in the quiet automation of back-office functions, in the migration of knowledge work from human hands to machine prompts and headline stories on 60 Minutes. But the gravity of signals are easy to miss when your day is full. When your inbox is overflowing and no sense of slowing. When the next quarter’s numbers demand your full attention. Most people were not ignoring AI. They were just living their lives in a world that seemingly moves faster and faster. And then one morning they woke up to the realization that the world had rearranged itself while they were busy.

I don’t say this with judgment. I say it with empathy. Because I remember the moment it clicked for me, back in 2017, when I first began working with predictive AI in fundraising and realized that the technology wasn’t going to supplement what we did. It was going to redefine what “doing” meant. It would effectively resolve 2,300-year-old musings from Aristotle that explained why fundraising was hard. That realization didn’t arrive as excitement. It arrived as vertigo. And vertigo, I have come to learn, is the honest starting point for almost everyone who takes this seriously.

The Kindness Trap

There is a form of comfort that does real damage, and it is everywhere right now.

It sounds like: “Don’t worry, AI is overhyped.” It sounds like: “Your field is different, it won’t hit you.” It sounds like: “You’re smart, just keep doing what you’re doing.”

People say these things because they love you. Or because they love the version of the world that made them feel competent. Or because they are scared. But telling someone things will be fine while the landscape beneath them is shifting is not kindness. It is avoidance in a nice outfit.

If nothing else, humans are adaptable. Before the Industrial Revolution, roughly eighty percent of the world’s population worked in agriculture. Today, less than two percent do. We adapted. But that adaptation unfolded across generations. AI is not about slow, steady transformation. It is evolution on a hockey-stick curve. The World Economic Forum recently published a paper describing four possible futures for jobs by 2030. In the most optimistic scenario, exponential AI advancement meets a workforce that is broadly prepared - and even then, social safety nets and governance frameworks struggle to keep pace. In the worst case, which they call “The Age of Displacement,” AI advancement outpaces the workforce’s ability to adapt, economies fracture socially, unemployment spikes, and consumer confidence erodes.

We are not living in either extreme right now. We are living in the space between them, where the outcome is still being determined by what leaders, organizations, and individuals choose to do in the next few years. A middle moment filled with promise and opportunity for those who seek it. That is the honest framing. Not doom. Not hype. But urgency - the kind that respects people enough to tell them the truth.

A plan is built for a future you can predict. Adaptation is built for a future you can’t. If your strategy is conservation, you may be protecting today at the cost of tomorrow.

What I’ve Learned from the Rooms

After hundreds of keynotes, workshops, and late-night conversations in conference hallways, I have noticed a few patterns that I think are worth naming.

The fear is almost never about the technology itself. People are not lying awake worrying about large language models or transformer architectures. They are worried about relevance. About whether the story they have been telling themselves about their career, their expertise, their value still holds. AI didn’t create that vulnerability. But it put a spotlight on it that is impossible to ignore.

The people who adapt fastest are not the most technical. They are the most curious with haste. The 2026 Nonprofit AI Adoption Report from Virtuous and Fundraising.AI, a benchmark study of 346 organizations, shows that ninety-two percent of U.S. nonprofit professionals now use AI at work at least occasionally, but those reporting major improvements in organizational capability number just seven percent. The gap between those who are experimenting and those who are waiting is widening. And the differentiator is rarely technical skill. It is a posture of curiosity - a willingness to ask “What if?” instead of “What’s the point?”

Leaders are not immune. In fact, many of the most anxious people I meet are senior leaders who feel pressure to have answers they don’t yet have. A Gallup survey found that twenty-six percent of individual contributors didn’t even know whether their organization had officially adopted AI. The uncertainty runs from the bottom of the org chart to the top - it just expresses itself differently. At the individual level, it sounds like “Is my job safe?” At the leadership level, it sounds like “How fast is fast enough?”, “How do I measure success?” and, “Is my leadership style still relevant?”

The real question underneath every question is the same: “Do I still matter?” And the answer - the honest, research-backed, deeply human answer - is a declarative yes. But mattering in the age of AI requires a different kind of effort than mattering in the age before it. Sitting on the sidelines or assuming the way you’ve been doing things is the best or only way will not position you for a future of success.

The Case for Optimism (That Isn’t Naive)

I am an optimist. I believe in the power of “we.” Without that it would be difficult to get out of bed in the morning. But I am not the kind who pretends the storm isn’t real. I am the kind who believes that human beings, when given honest information and genuine support, are remarkably good at adapting. We have done it before. We will do it again. The question is whether we do it together or whether we let millions of people face this transition alone.

Here is what gives me genuine hope.

The World Economic Forum projects a net gain of 78 million jobs by 2030. The most in-demand skills are not coding or machine learning. They are analytical thinking, resilience, flexibility, leadership, and social influence. The skills that make us human - the ability to build trust, to make judgment calls in ambiguous situations, to inspire and connect - are not being automated. They are becoming more valuable.

PwC’s 2025 Global Workforce survey of nearly fifty thousand workers found that optimism about AI’s potential significantly outweighs anxiety for those who have actually begun using the tools. The gap between fear and engagement is often just one step: a single meaningful experience with AI that reveals it as a collaborator rather than a replacement. The engineering student who enters university terrified that AI will make everyone unemployable leaves a career fair reassured when a recruiter tells him the truth - that AI is making new hires dramatically more productive and that the company is excited about growth.

That is the story I see playing out in room after room. Fear yields to curiosity. Curiosity yields to experimentation. Experimentation fosters competence. And competence yields to something that looks a lot like empowerment.

The entrepreneur who cried in my presence last week wasn’t crying because AI solved his problems. He was crying because, for the first time, the scales had been removed from his eyes to realize the ideas he had been carrying around for years - ideas he had resigned himself to never building - suddenly had a path to reality. One afternoon with the right tools and someone willing to sit beside him and explore and challenge to play with AI for one hour a day, every day. That was all it took. The technology didn’t hand him a new life. It showed him the life he had been imagining was no longer out of reach.

What This Moment Asks of Us

If you are a leader, this moment asks you to be honest before you are strategic. Your people don’t need a polished AI roadmap as much as they need to hear you say: “I don’t have all the answers either, but I’m committed to figuring this out with you.” Trust is not built by having a plan. It is built by having the courage to name what you don’t know.

If you are a professional in any field, this moment asks you to get curious before you get comfortable. You don’t need to become a technologist. You need to become a learner. Start small. Try one tool. Ask one question. Have one conversation with someone who is already experimenting. The distance between paralysis and momentum is often a single afternoon of honest exploration.

If you are someone who is afraid, this moment asks you to let the fear be a signal, not a stop sign. You are not behind. The majority of people are still figuring this out. Forty percent of organizations don’t even have an official stance on AI. You are not late. You are at the beginning, and beginnings are supposed to feel uncertain.

And if you are someone like me - someone who works in AI and speaks about it publicly - this moment asks you to stop performing certainty. The world does not need more AI “experts” who pretend to have all the answers. It needs people who are willing to sit in the complexity, to name the tension between opportunity and loss, and to hold space for both the tears of overwhelm and the tears of possibility.

Sorry I Made You Cry

I’ve thought about those two conversations from last week more than I probably should have. The nonprofit executive who felt stuck and the entrepreneur who suddenly saw in color. Same technology. Same moment in history. Two different relationships with what is possible.

The difference between them was not technical literacy. It was not age or title or budget. The difference was proximity to the experience. One had been circling AI from a distance, absorbing headlines and secondhand anxiety. The other sat down for an afternoon, took a deep breath, got his hands on the tools, and let himself be surprised. That was it. That was the whole distance between dread and possibility - a few hours and a willingness to play.

I did not set out to make either of them cry. But I have stopped apologizing for telling the truth. Because I have come to believe that the most dangerous thing we can do in this moment is protect people from the reality of what is changing. Protection sounds compassionate. But it is often just a delay. And delays, in an exponential era, compound with interest.

The honest version of this conversation is harder. It acknowledges that some jobs will change beyond recognition. That some skills will lose their market value. That some of us will grieve the loss of a professional identity we spent decades building. That is real, and it deserves to be honored.

But the honest version also acknowledges that the human capacity for adaptation is extraordinary. That curiosity with urgency is not a luxury but a survival skill. That the people who thrive in what comes next will not be the ones who knew the most about AI, but the ones who knew the most about themselves - who understood what they valued, what they were willing to learn, and what kind of future they wanted to help build.

The American Psychological Association’s stress survey, for all its sobering findings, contains a quiet revelation: eighty-four percent of Americans still believe they can build a good life, and seventy-three percent believe they can help shape the country’s future for the better. That is not delusion. That is resilience. And it is the raw material from which every meaningful adaptation in human history has been built.

So no, I’m not sorry I made you cry. I’m sorry it took this long for someone to say it out loud. And I’m grateful - deeply, personally grateful - that you’re still here. Still reading. Still asking what comes next.

That question is the beginning of everything.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous, co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

I had a conversation recently that has stayed with me.

A thoughtful conference organizer reached out to invite me to speak later this year. The invitation was generous, the intent sincere, and the mission deeply aligned with my own. As we talked through the session, though, there was a gentle hesitation. Then it came plainly, without drama or defensiveness.

“We’ve gotten feedback that people are really fatigued by AI. Would you be open to not using the word at all?”

I understood immediately. I didn’t feel dismissed or constrained. I felt empathy. Anyone who has spent time in nonprofit leadership over the last few years knows this feeling well. The sector has been absorbing wave after wave of disruption while carrying the emotional weight of real human need. Burnout is not hypothetical. Fatigue is not a lack of intelligence or curiosity. It is a physiological and psychological response to sustained acceleration.

And still, the question lingered long after the call ended. Not because the request was unreasonable, but because it revealed something deeper about the moment we are in.

What happens when avoiding a word becomes a strategy for coping with change?

For most of human history, language has been one of our primary tools for managing uncertainty. When something feels overwhelming, we soften the edges. We rename it. We talk around it. We postpone it. This is not denial. It is protection. In times of rapid change, people instinctively look for ways to regain a sense of control, and controlling language is one of the most accessible levers we have.

I’ve written before about this dynamic through the lens of cognitive dissonance, how our brains instinctively seek relief, not truth, when faced with sustained uncertainty. When information threatens our sense of stability, the mind looks for ways to reduce discomfort. Avoidance, minimization, and language softening are not signs of weakness. They are deeply human responses to overwhelm. But what protects us emotionally in the short term can quietly cost us agency over time.

That distinction matters, especially now.

AI fatigue is real. I hear it everywhere, and I recognize it in myself. I’ve been working with AI since 2017, back when it was obscure, inaccessible, and unaffordable for most nonprofits. In those early days, if someone mentioned “artificial intelligence” across a loud airport, my attention would snap to it instantly. I was intensely curious about what the one percent was experimenting with and what might eventually become possible for the rest of us.

Today, the dynamic has flipped. AI is seemingly everywhere. In every conversation. Every headline. Every product pitch. It has reached a level of saturation that can feel exhausting, even numbing. If there is one topic I am no longer allowed to bring up at the dinner table, it is AI.

People are tired of breathless headlines. Tired of tools they did not ask for. Tired of being told they are either falling behind or racing ahead when most days they are simply trying to serve their communities well.

I have deep compassion for that exhaustion because I have lived both sides of it.

But compassion does not require avoidance. And avoidance has never slowed reality.

There is a subtle danger in confusing silence with safety. Not talking about something does not reduce its influence. It simply changes who shapes it. History is full of moments where major shifts continued unfolding whether or not institutions were ready to name them. The choice was never between engagement and stability. It was always between engagement and diminished agency.

What makes this moment feel different is not just the technology itself, but the scale and pace of what it represents.

We often compare AI to transformative technologies of the past: the printing press, radio, television, the internet. Each reshaped society in profound ways. But AI is not simply another chapter in that sequence. It is an accelerant to all of them at once. It compresses time, collapses distance between intention and execution, and fundamentally alters how value is created.

I often say this plainly because it helps reset the frame:

AI dwarfs all other technologies combined.

Not because it replaces them, but because it amplifies them. It changes how work is done, how success is measured, how possibility is assessed, and how expectations are formed. When you view AI through that lens, fatigue is not surprising. It is rational.

We are not just seeing new tools emerge. We are watching the structure of work change in real time. Response times, personalization, analysis, creativity, decision-making. What once felt exceptional now quietly becomes baseline. These expectations are not being set by the nonprofit sector, but they are being imported into it through every donor interaction, every digital experience, and every comparison to the rest of the world.

This is not a story about nonprofits being behind. That framing misses the point entirely. The sector is not lagging. It is overextended.

Nonprofit professionals are navigating donor databases, CRMs, automation platforms, compliance requirements, privacy concerns, and ethical tradeoffs every single day with limited staff and finite resources. They are not reckless. They are resourceful. Fatigue emerges not from incompetence, but from care.

I come to this conversation with that reality deeply ingrained in me, because my identity in this space did not begin as a commentator or consultant. I identify as a fundraiser. I spent two decades in nonprofit leadership, carrying responsibility for teams, revenue, and mission outcomes that had real human consequences. My early work with machine learning did not begin as a trend. It began as an attempt to solve constraints I was living inside of.

Even now, working in the private sector, that identity has not shifted. I still experience this work through the lens of a fundraiser, shaped by years of shared constraints and accountability. Staying close to nonprofit practitioners matters to me, not as a credential, but as a compass. It keeps me attentive to how ideas land, not just how they sound.

That posture matters here.

Fear-mongering almost always comes from distance. It speaks about people rather than with them. It questions intelligence instead of interrogating systems. It uses urgency as leverage rather than treating it as a responsibility. What I am trying to do is not amplify fear, but reduce confusion. And confusion thrives when we avoid naming what is quietly reshaping the work around us.

Much of what people describe as AI fatigue is not actually about artificial intelligence at all. It is tool fatigue. Endless demos. Endless platforms. Endless promises of efficiency layered onto already full plates. When change shows up as a parade of products, exhaustion is a rational response.

I have argued elsewhere that this is where we often get the framing wrong. When AI is introduced as a growing stack of tools to adopt, it feels additive and burdensome. Another thing to learn. Another system to manage. Another demand on already scarce time and attention. But that framing misses what is actually unfolding. This moment is less about adoption and more about evolution, a shift in how work itself is conceived, structured, and carried out.

When we mistake a foundational change for a shopping list of tools, fatigue is inevitable.

(For a deeper exploration of this, see my Substack article “From Adoption to Evolution.”)

The deeper shift underway is not arriving as a toolset. It is arriving as a redefinition of work itself. What requires human judgment? Where does trust live in a process? What should remain deeply relational, and what can be responsibly augmented? These are not technical questions. They are leadership questions.

Change and fatigue have always gone hand in hand. Whenever the world shifts faster than our ability to integrate it, exhaustion is a natural byproduct. Fatigue is often the signal that the underlying change is not incremental, but structural.

Across history and organizations, people tend to fall into a few broad camps. Some are energized by change. They lean in early, experiment, and adapt quickly. Others prefer to watch from the sidelines, waiting for clarity, best practices, or proof that the risk is worth taking. And then there are those who resist change, not out of stubbornness, but out of a desire to preserve what once worked, what felt stable, and what made sense in a different context.

In slower eras, all three postures could coexist without severe consequence. Fatigue could subside because the environment eventually stabilized. “Later” was a legitimate strategy because the pace of change remained within human limits.

This moment is different.

The pace of change has crossed a threshold where avoidance is no longer neutral.

For those in the middle and latter categories, this will be the ultimate test, not of intelligence or intent, but of adaptability. The systems shaping our work are evolving whether or not we feel ready.

In the language of an famous parable, AI moved your cheese, and it’s not coming back. Not metaphorically. Structurally. The work has shifted. Expectations have shifted. The environment has shifted.

The real opportunity in this moment is not for those who enjoy change for its own sake. It is for those willing to develop the capacity to adapt faster than the environment changes. That capacity is no longer a personality trait. It is a professional requirement.

One of the most helpful frames I’ve encountered comes from my friend Cherian Koshy:

“The work is no longer about managing change. It is about learning faster than change.”

Managing implies control. Learning implies humility, curiosity, and adaptability. In an environment where expectations shift quickly, the organizations that remain resilient are not those that avoid the conversation, but those that create space for shared learning without panic.

This does not mean every keynote, training or internal meeting needs to center AI explicitly. Language matters, and so does pacing. We can reduce the volume without reducing the truth. But avoiding the conversation entirely does not serve the people we are trying to protect.

Naming what is happening is not an act of fear. It is an act of care.

The nonprofit sector does not need more alarmism. It also does not need denial. What it needs is honest, grounded leadership that respects the intelligence, wisdom and discernment of practitioners while acknowledging the new math of success this moment brings.

You do not have to lead with the word “AI.”

But you also can’t avoid it’s impact. do have to lead with honesty.

The future does not require our permission to arrive. But it will be shaped by those willing to confront our shared reality with honesty and care.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

For the past several years, the nonprofit sector has talked about artificial intelligence almost entirely in the language of adoption - a word that quietly minimizes what may become humanity’s most consequential invention.

What's the best AI tool for my job?

Are we ready to even start?

Are our teams trained?

At face value, those questions sound reasonable. But, they also reveal a framing that has narrowed our imagination. Adoption assumes the system of work itself is stable, and that AI is simply a new tool to layer on top of it. A faster spreadsheet. A smarter chatbot. A more efficient way to do what we already do.

That framing was never inevitable. And for some of us, it was never the point.

When I began working seriously with AI in the nonprofit sector in 2018, the conversation was not about adoption. It was about evolution. It was about whether we could finally challenge some of the deepest structural limitations philanthropy has carried for centuries. Biases in who gets seen. Who gets asked. Who gets trusted. Who gets served. Long before algorithms, Aristotle wrote about the persistent challenges of charitable giving, and in many ways, the sector has been circling the same constraints for more than two thousand years.

What drew me to AI then was not its efficiency, but its potential to help us rethink the work itself from the ground up.

Looking back, the years leading up to the public arrival of ChatGPT now feel less like a breakthrough and more like a signal. They revealed that this technology could reshape how we analyze, decide, and connect, not just how we execute. They hinted at a future where intelligence could be applied more equitably, where insight was not reserved for the resourced few, and where the primary bottleneck to impact might finally shift away from human limitation and toward human intention.

Then ChatGPT arrived, and something subtle but important happened.

Over the past three years, our collective focus collapsed almost entirely into adoption. Training. Pilots. Policies. Guardrails. All necessary, but incomplete. In reducing AI to something to be adopted, we unintentionally minimized its gravity. We treated an evolutionary force as if it were just another tool.

In my role as Chief AI Officer at Virtuous, adoption is a given. There is no debate about whether teams should be using AI. The expectation is that every employee is leveraging these tools to perform their work more effectively. We moved past adoption quickly, not out of enthusiasm for technology, but out of respect for the work itself. When the mission matters, efficiency is not optional. Acceleration becomes a responsibility. The same is true for nonprofits and nonprofit professionals.

And yet, acceleration alone is still not the destination.

Acceleration assumes the work itself remains intact, just faster. Agentic AI pushes us further. It asks whether the way work is structured, coordinated, and governed still makes sense at all.

Most nonprofit professionals have not heard the term. I know this because I ask. When I speak at conferences or facilitate leadership sessions, I pose the same simple question to nonprofit audiences: how many of you have heard the word “agentic” used to describe AI? A few hands go up. Having done this now in front of thousands of non-profit professionals, the result has never been more than five percent.

That gap matters. Not because nonprofits are behind, but because the world around them is already moving on. While much of the nonprofit sector is still debating adoption of generative AI, large parts of the private sector have shifted their attention almost entirely to agentic systems. They are no longer asking whether AI can assist work. They are focused on empowering systems to plan, coordinate, and execute work at scale.

Agentic AI is not a feature. It is not a trend. It is not a category of software to evaluate during your next budget cycle. It is a different model of how work itself gets done.

Agentic AI is a different operating model for work itself.

From adoption to evolution

To understand why agentic AI represents such a shift, we need to name a distinction that has been largely absent from nonprofit AI conversations.

Adoption is about tools.

Acceleration is about urgency.

Evolution is about systems.

For the past few years, most nonprofit AI use has been prompt-driven. A human asks. The system responds. The human refines the prompt. The system improves the output. This interaction model has shaped expectations. AI feels reactive. For many, generative AI has functioned as a glorified search engine - helpful, sometimes impressive, but ultimately dependent on constant human direction.

Agentic AI breaks that model.

At its simplest, agentic AI means you describe an outcome, not a sequence of steps. The system plans how to get there. It determines tasks, pulls relevant information, coordinates across data sources or tools, executes the work, and returns something coherent for review. Humans remain in the loop, but they stop micromanaging execution.

The shift sounds subtle. It is not.

That is not adoption.

That is not even acceleration.

That is evolution.

The myth of the smarter chatbot

One reason this shift is easy to miss is that early agentic experiences still look familiar. New capabilities appear inside tools we already recognize. Agent modes. Workflow orchestration. Multi-step reasoning. It is tempting to see these as incremental improvements rather than a structural change.

That would be a mistake.

What we are witnessing is a redefinition of the relationship between humans and machines. When AI systems can hold context over time, plan multi-step work, and coordinate execution without constant instruction, they stop behaving like tools and start behaving like collaborators. Not collaborators with values or intent, but collaborators with stamina, memory, and execution capacity.

This is where fear often enters the conversation, so clarity matters.

Agentic AI is not about replacing leadership. It is not about letting algorithms run organizations. It is not about outsourcing responsibility. In fact, it demands the opposite.

When execution is delegated, responsibility becomes more visible. Someone still sets goals. Someone still defines constraints. Someone still decides what is acceptable, ethical, or aligned with mission. Agentic systems do not remove accountability. They expose it.

In a sector built on trust, that distinction is everything.

What this looks like in nonprofit work

The implications of agentic AI show up differently across roles, but the pattern is consistent.

For fundraisers, the shift is from effort to leverage, and eventually from reaction to anticipation. Instead of manually stitching together donor notes, emails, and CRM data, an agent can synthesize that information into clear next-step strategies and prioritization plans. Instead of drafting stewardship updates from scratch, it can assemble personalized communications grounded in real impact data, giving history, and program outcomes.

More importantly, agentic systems can begin to model outcomes before action is taken. They can simulate how different donors or segments might respond to various messages, asks, or timing strategies, allowing fundraisers to explore scenarios rather than guess at them. Instead of relying solely on instinct or retrospective reporting, teams can test approaches virtually, assess risk, and make more informed decisions about where to invest attention and care.

Even traditionally static work, like board reporting, shifts in nature. Instead of spending days assembling backward-looking summaries, agentic AI can generate narratives that integrate pipeline data, progress, risk, and projected outcomes into a single coherent briefing, helping leaders understand not just where they have been, but where they are likely headed.

For nonprofit leaders, the shift often begins as a reduction in friction, but it quickly becomes something more consequential. Financial folders and dashboards turn into board-ready briefings that do not just summarize the past, but surface risks, tradeoffs, and emerging patterns. Team updates are synthesized into strategic memos that highlight decisions required, scenarios worth exploring, and tensions that need attention, rather than overwhelming leaders with activity masquerading as progress.

Over time, agentic systems can help leaders move from retrospective oversight to prospective stewardship. They can model the downstream implications of strategic choices, simulate how different decisions might affect funding stability, program outcomes, or organizational capacity, and help leaders explore scenarios before those choices harden into reality. This does not eliminate uncertainty, but it dramatically lowers the cost of seeing around corners.

Even the most static elements of organizational life begin to change. Policies, onboarding materials, and internal documentation stop living in endless revision cycles and start behaving like living systems, updated intentionally as conditions shift. The result is not more control, but clearer governance, where leaders spend less time chasing information and more time exercising judgment in service of mission and trust.

For program teams, agentic AI shifts the work from reporting outcomes to continuously learning from them. Instead of manually aggregating surveys, case notes, and program metrics after the fact, agentic systems can synthesize participant feedback, qualitative observations, and outcomes data in near real time. Patterns that once took months to surface begin to emerge as programs are unfolding, not after they have concluded.

More importantly, agentic AI allows program teams to explore how interventions might perform under different conditions before changes are made. Teams can model variations in program design, delivery methods, or resource allocation, and simulate how those changes could affect participant experience and outcomes across different populations. This does not replace lived expertise or community voice, but it dramatically expands the organization’s ability to listen, test, and adapt responsibly.

Over time, this changes the posture of program work itself. Evaluation becomes less about proving impact after the fact and more about improving impact as it happens. Program design becomes iterative rather than episodic. And teams gain the capacity to respond to early signals of risk or opportunity before they harden into success or failure. The result is not more data, but deeper insight, allowing nonprofits to serve communities with greater humility, responsiveness, and care.

These are only a few examples, but they point to a larger shift. Agentic AI is less about speed and far more about posture - a willingness to rethink not just how we work, but what we assume about the work itself.

Why “operating system” is the right metaphor

Operating systems do not simply make tasks faster. They determine what kinds of work are possible at all.

When coordination costs drop, imagination becomes the bottleneck. Organizations are no longer constrained primarily by headcount or hours. They are constrained by how willing they are to question inherited processes.

This is where many nonprofit leaders will struggle, not because they lack values or intelligence, but because they have been conditioned to think in terms of adoption. Pilot the tool. Train the staff. Measure efficiency gains. Move cautiously.

Caution still matters. Responsibility still matters. But evolution requires something else alongside restraint: a willingness to admit that the way we’ve been doing things may not be the best or only way.

It surfaces friction that was previously invisible because it was normalized. It exposes legacy processes that survive not because they are effective, but because they are familiar.

This can feel destabilizing. It should.

The real risk and the real opportunity

Used thoughtfully, agentic AI frees nonprofits from unnecessary friction. It collapses handoffs. It reduces busywork. It gives time back to the work that only humans can do: listening, deciding, building trust, and holding complexity with care.

Used poorly, it accelerates the wrong things. It amplifies broken processes. It reinforces shallow metrics. It creates the illusion of progress without the substance of it.

The difference is not technical. It is cultural.

Agentic AI will amplify whatever it touches, including poor fundraising practices and misaligned incentives, at unprecedented scale. That reality raises the bar for leadership. It requires clarity of mission, maturity of governance, and leaders willing to engage deeply with the systems they deploy rather than delegating them into a black box. Most of all, it demands a shift in posture, moving beyond the question of how to adopt AI toward a more consequential one: how work itself should change when the opportunity exists to reexamine, and in some cases retire, our once-coveted “best practices.”

That is why I believe agentic AI will be one of the defining nonprofit shifts of 2026. Not because it replaces people, but because it changes where people are most needed. It creates space for judgment in a world crowded out by execution. It restores attention to trust in a sector that cannot function without it.

Agentic AI is not about working faster. It is about working differently. It shifts nonprofits from managing tasks to stewarding outcomes, from coordinating effort to exercising judgment. In a sector built on trust, that distinction is everything. The organizations that thrive in the next chapter will not be the ones that adopted the most tools, but the ones that evolved how work actually happens.

This is not an AI decision.

It is a leadership one.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

It usually starts quietly.

A nonprofit professional sits down after a long day, opens a laptop, and stares at a familiar problem. A campaign that feels tired. A donor segment that is underperforming. A process that everyone knows is inefficient but no one has time to rethink. In another era, the response would have been predictable. Schedule a meeting. Commission a report. Hire a consultant. Or simply accept that friction is the unavoidable cost of doing meaningful work with limited resources.

But something has shifted.

Instead of waiting, they open a tool. They ask a question. Then another. They explore an idea that, until recently, would have required weeks of analysis or a specialized skill set they did not possess. Within minutes, possibilities begin to surface. Not answers exactly, but directions. New ways of framing the problem. Paths that had never fully revealed themselves before.

What matters in this moment is not the technology itself. It is the posture of the human using it.

Because the real change is not that intelligence has suddenly appeared. It is that access to intelligence has changed who gets to explore ideas and when. The quiet monopoly once held by tenure, specialization, and formal authority is loosening, replaced by something more fluid and more human. Curiosity now opens doors that credentials once guarded.

For most of the nonprofit sector’s modern history, success followed a logic that felt earned and stable. You accumulated knowledge slowly. You learned systems by living inside them. You paid your dues and built credibility through repetition and responsibility. Over time, experience became authority, and authority became trust. Intelligence mattered because it was scarce. Context mattered because it took years to acquire.

That logic has not vanished. But it is no longer enough.

We are entering a period where applied intelligence - the ability to analyze information, generate ideas, recognize patterns, and propose solutions - is becoming broadly accessible. Tools can now assist with thinking itself, dramatically lowering the cost of exploration and experimentation. Intelligence, once hierarchical and guarded, is becoming ambient.

For nonprofit professionals, this shift is not a diminishment. It is an expansion.

When intelligence becomes abundant, success is no longer defined primarily by what you know. It is shaped by how you think, how you connect ideas, how you decide what matters, and how you act in the world. The old math of success, which rewarded depth within narrow lanes, gives way to a new equation that is more nonlinear, more relational, and more human.

If you were to sketch that equation, it might look something like this: success emerges when augmented intelligence is multiplied by curiosity, wisdom, creativity, adaptability, and passion, all operating within the unavoidable friction of real life.

This is not a formula to be solved. It is a way of seeing what it will take to achieve success in an AI-first world.

At the center of this new math sits augmented intelligence. Human intelligence still matters deeply, but it increasingly operates alongside synthetic intelligence. The professionals who thrive are not those who compete with machines at cognition, but those who learn how to collaborate with them. Knowing what to ask, how to evaluate responses, when to trust outputs, and when to override them becomes a core capability.

This shift quietly democratizes possibility. Exploration that once required specialized teams or outside expertise is now available to individuals willing to engage thoughtfully. Intelligence becomes the baseline, not the advantage. The advantage lies in direction, discernment, and intent.

That is where curiosity becomes decisive.

Curiosity is no longer a soft trait or a personality flourish. It is a professional strategy. When answers are cheap, questions become valuable.

Curiosity pulls people beyond job descriptions and departmental boundaries. It draws them into conversations with those who see the world differently. It invites experimentation before permission is granted.

History offers a useful reminder here. The Renaissance did not emerge from isolated mastery, but from collision. In Medici-era Florence, artists studied anatomy alongside physicians. Engineers learned from sculptors. Financiers debated philosophers. The Medici family did not fund genius in silos. They created the conditions for disciplines to collide, and breakthroughs followed connection.

Modern nonprofit work operates under the same logic. When professionals intentionally seek perspectives outside their usual orbit, they expand their capacity to see patterns others miss. Curiosity transforms isolation into insight. It reveals opportunity where routine once obscured it.

But collision alone is not enough.

As intelligence and curiosity accelerate, wisdom becomes the stabilizing force. Wisdom is not about knowing more. It is about choosing well amid abundance. It is the ability to evaluate ideas in context, to anticipate consequences, and to align decisions with values rather than novelty.

In a world where AI can generate endless possibilities, wisdom determines which paths are worth pursuing.

This matters profoundly in nonprofit work. Missions are built on trust. Communities are shaped by decisions that echo long after implementation. Wisdom ensures that efficiency does not eclipse empathy and that innovation remains anchored to purpose. It allows leaders to ask not only what is possible, but what is appropriate.

Creativity, long a defining strength of the sector, takes on new dimensions in this environment. When human imagination is paired with generative systems, the space of possible solutions expands dramatically. Ideas can be prototyped, tested, and refined at a pace that was previously impractical. Creativity becomes iterative and accessible rather than precious, exploratory rather than constrained.

This does not diminish human creativity. It amplifies it. It creates space for experimentation, for learning in public, for trying ideas that once felt too risky or too resource-intensive. Augmented creativity opens doors that were previously closed not by lack of vision, but by lack of capacity.

Adaptability is what allows all of this to compound.

Change is no longer episodic. It is continuous. Tools evolve. Expectations shift. Best practices expire. The professionals who succeed are not those who predict the future accurately, but those who recalibrate thoughtfully as the present changes. Adaptability shows up less in strategy documents and more in habits: creating space to learn, experimenting regularly, solving small problems that unlock disproportionate value, and sharing unorthodox approaches openly.

Over time, adaptability builds resilience. It turns uncertainty into momentum.

Underlying every variable in this new math is passion. Not performative enthusiasm, but sustained commitment to purpose. Passion is what keeps professionals grounded when tools accelerate expectations. It is what ensures that technology serves mission rather than overshadowing it.

As AI absorbs more of the mechanical work, what remains is intention. Why this cause? Why this community? Why now? Passion gives meaning to progress. It keeps innovation unmistakably human.

The most hopeful truth of this moment is that nonprofit professionals are exceptionally well-positioned for what comes next. They are fueled by unbridled passion for making a difference. They are accustomed working creatively within constraints. They understand that success is relational, not transactional. They know that trust is fragile and earned slowly. The new math of success does not ask them to abandon these instincts. It rewards them for leaning into them.

The future will not belong to those who are merely intelligent. It will belong to those who are curious enough to explore, wise enough to choose, creative enough to imagine, adaptable enough to evolve, and passionate enough to care.

The equation has changed. The opportunity has expanded. The future belongs to those willing to lean into possibility, not with certainty, but with curiosity and care.

About the Author

Nathan Chappell, MBA, MNA, CFRE, AIGP is Chief AI Officer at Virtuous Software and co-author of Nonprofit AI and The Generosity Crisis. He writes about responsible innovation, the future of generosity, and the power of radical connection in the age of AI.

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