Jaclyn Jones has spent nearly three decades building something in a world that runs on human connection. Now she is watching AI ask whether that world still holds.

Before technology sorted the mail, there were kids like Jaclyn Jones standing in a room with pizza-grease fingers, doing it by hand. Her parents worked in nonprofit fundraising, and growing up she would help sort the direct mail appeals that organizations sent to donors: stacks of envelopes organized by zip code and postal routing category, grouped precisely to qualify for bulk mailing rates and get thousands of donation requests into the right hands across the country. She was, in the most literal sense, helping nonprofits raise the money they needed to do their work. It was unglamorous, repetitive, and, she says, a kind of perfect introduction to a career she would spend nearly three decades building.

Jones has worked in fundraising and development for nonprofits her entire professional life, and she would tell you she cannot imagine doing anything else. She graduated into the tech crisis of the early 2000s and, rather than redirect, she leaned further into the work she already knew. She left Portland and moved to Seattle, then back to Portland, where she and her husband are raising two kids who both inherited her love of numbers.

The through-line of her career, though, is not just the work. It is the way she learned it.

The Rungs Below

In the early 2000s, in her first week at a fundraising agency, Jones met a man named Jon. He was the executive vice president of strategy and analytics, and her ambition was immediate and direct. "I want your job," she told him. He did not seem offended. He invited her into conference rooms, walked her through complex problems as he was solving them, and talked out his thinking while she watched. They worked together for fifteen years, moving through different agencies, and when he eventually retired, the organization offered her his role. She was in her late thirties. She had the job she had been aiming at since week one.

And then, a few years later, the organization moved her into a different role. The goal she had organized her professional life around was behind her, and she found herself in unfamiliar territory: not climbing toward something, but figuring out what comes after the summit. "I don't know why in my head I thought I was going to have the same job for the next forty years," she says. "But I truly did. I had one goal, I hit it, and I thought, that's it. Done."

She is still working through what comes next. But the story of how she got there, the fifteen years of mentorship, of doing the tasks that were handed down, of learning by proximity to someone who had already figured it out, is the story she finds herself thinking about most when she considers what AI is changing.

"There will not be expertise at the top," she says, "if you don't have to climb the rungs below."

What she is pointing at deserves to be unpacked carefully, because it is easy to dismiss as nostalgia and harder to see as a structural warning. She is not saying that doing grunt work for its own sake is valuable. She is saying that the grunt work, the entry-level tasks that feel like inefficiency from the outside, is in fact how expertise gets made. When a junior analyst builds a report from scratch, she does not just learn how to build a report. She learns how data moves through the organization, where errors hide, what the numbers look like when something is wrong. Those lessons accumulate into the judgment that defines a senior professional. If AI absorbs those tasks before the young analyst has had a chance to learn through them, the efficiency gain is real and the knowledge loss is invisible. Until, one day, there is no one left who learned the hard way, and the organization has to figure out what it has lost.

This concern is beginning to show up in workforce data. Researchers at the Stanford Digital Economy Lab documented a roughly 16 percent relative decline in employment for workers between the ages of 22 and 25 in the most AI-exposed occupations, while employment for older workers in those same fields held steady or grew. A Knowledge at Wharton analysis of these trends described the dynamic plainly: "AI disrupts this natural progression by eliminating the foundational experiences... each client interaction, each error corrected by a supervisor, each successful project contributed to their professional development."

The talent pipeline, in other words, does not just feed organizations with labor. It feeds them with future experts, future mentors, and the institutional knowledge that cannot be written in a manual because it was never learned from one, exclusively. When that pipeline is interrupted, the cost does not appear on this quarter's income statement. It appears a decade later, when the people who knew how to do the work, and how to teach it, are gone.

The Struggle Is the Point

A related concern surfaced in a conversation Jones had at a recent conference with colleagues who work in language translation to preserve religious literature. She asked, half-joking, how they expected to raise donor support when AI was about to handle their kind of work overnight. The colleague she was speaking with gently corrected the premise. Their work, she explained, focuses entirely on living spoken language and oral tradition. There is no written text for AI to work from, no digital corpus to train on. Jones recognized the parallel immediately. In her niche, deep donor fundraising, the same is often true. The accumulated relational knowledge of this field is rarely written down. For now, at least, the work is protected by its very specificity.

But the observation that stayed with her was less about job security and more about what happens to the mind when it is no longer required to struggle.

She had read an op-ed about the way AI brainstorming removes the productive friction from creative thinking: when you can ask a model what the right word is and receive an instant answer, you never build the neural pathway that comes from wrestling with the question yourself. The struggle, she thought, is not an obstacle to the work. It is the work. "If you don't have to do the work, if you don't have to make the mistakes, if you don't have to dig in," she says, "how do we learn as humans?"

The research is beginning to answer that question in ways that are not reassuring. A 2025 preprint from MIT's Media Lab found that participants who completed writing tasks using AI showed significantly weaker neural connectivity than those who worked without it, with the researchers describing the cumulative effect as "cognitive debt." A separate study, peer-reviewed and published in the proceedings of the 2025 ACM CHI Conference, surveyed 319 knowledge workers and found that higher reliance on AI tools directly correlated with less critical thinking effort. The mental pathways Jaclyn is describing, the ones built through struggle and repetition and wrestling with the right word, are exactly the ones the research suggests we risk quietly eroding.

Trust Gets Local

Jones tracks donor behavior closely, and one of the patterns she keeps returning to is what research from Barna Group has been showing about where trust is flowing in American life. Trust in local institutions, local libraries, local news, local churches, consistently runs higher than trust in their distant or national counterparts. The finding is not surprising on its face, but Jones thinks its implications for the nonprofit world, and for the broader question of human connection in the age of AI, are significant and underappreciated.

As AI makes it harder to know whether what you are seeing is real, she believes the physical and touchable world will become the primary currency of trust. "As we see AI come in further and further," she says, "it's going to be harder and harder to trust things that we can't directly interact with."

The implications for fundraising are concrete. Local missions, food banks, animal shelters, organizations where a donor can drive over and witness the work in person, may find themselves in an unexpected position of advantage. International relief organizations, however credible and well-accredited, face a different and growing challenge. When images can be generated, when voices can be cloned, when everything visible on a screen could theoretically be fabricated, the question of what a donor can actually verify becomes a live concern. "I can drive to my mission and see them," Jones says. "I have no idea, for some of these organizations, if everything I'm seeing is AI-generated or fake."

There is something larger hiding in this observation. The age of AI may not simply be making people more skeptical. It may be redirecting the instinct for trust toward what the body can verify: the face you can see, the hand you can shake, the place you can visit and confirm with your own senses. In that environment, embodied presence, the simple fact of being physically there with other people in a shared space, may become not just meaningful but essential. Not a nostalgic preference for the old way of doing things, but a rational response to an environment in which presence is the only credential that genuinely cannot be faked.

What She Is Watching

Jones is having conversations with her kids about what it means to develop skills that belong to them, skills not in the sense of being AI-proof, but in the sense of being deeply and irreducibly human. One of those conversations was recent, and she tells it with the laughter of someone who finds it both funny and genuinely unsettling.

Her daughter, who has always been strong in mathematics and has been told throughout her education that quantitative skills are among the most durable things a person can develop, came to her mother with a worry she had been sitting with. "Mom," she said, "I'm not pretty enough to be a model, and AI's coming for math. My only option left is to be poor."

The fear is not entirely without basis in the data. The World Economic Forum has estimated that roughly 30 percent of current tasks in analytical and data-related positions could be significantly transformed by automation within the next several years. Routine computation, standard modeling, repetitive data processing: precisely the skills that introductory math education is designed to develop. These are among the categories most susceptible to what AI can already do quickly and cheaply. But the fuller picture is more complex than the headline suggests. Research tracking AI-exposed job markets consistently shows that advanced mathematical reasoning, combined with the judgment and contextual understanding that can only be built through years of practice, commands the highest wages and the most durable career trajectories in the current economy. The problem is not mathematics. It is the assumption that any single skill, held in isolation, is sufficient protection from the speed of change.

What Jones keeps arriving at, when she thinks about her daughter's generation and what she hopes for them, is the physical, the embodied, the things that require a person to be genuinely present in the world. "What we really defined as most human," she says, "is becoming the embodiment of physical interactions." And what she sees in the young people around her, for all of the anxiety that is entirely warranted, is not fragility. She watches her kids' generation closely and finds something she did not expect: a clarity about what is happening that was earned rather than inherited. They have lived through a pandemic, watched global instability, and now they are looking at a future where the machines seem to be lined up to take the remaining pieces. Most of them, she notes, are not enthusiastic about any of it. "Many of them are in on this," she says. "They're all very anti-AI." And yet they are not retreating. She finds them, on balance, clear-eyed in a way she respects.

"I think this is a truly wild moment for this generation," she says, "and I think they're learning well."

She has spent nearly thirty years trusting that the work matters, that the human connection at the center of it cannot be automated, and that the struggle, the sorting of the donor mail, the fifteen years of watching how a mentor thinks, the wrestling with the right word, is not something to be eliminated but something to be honored. What the age of AI has done, if anything, is make that conviction feel less like a preference and more like a necessary argument. One worth making clearly, and often, to anyone who will listen.

Jaclyn Jones is a fundraising and development professional with nearly three decades of experience in the nonprofit sector. This conversation is part of the Humans & AI: Everyday Stories series by HumanCulture.

Sources

Kos'myna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872. https://www.media.mit.edu/publications/your-brain-on-chatgpt/

Lee, H.-P., et al. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). ACM. https://dl.acm.org/doi/full/10.1145/3706598.3713778

Brynjolfsson, E., Chandar, B., & Chen, R. (2025, updated 2026). Canaries in the coal mine: The early impact of AI on the entry-level labor market. Stanford Digital Economy Lab. Cited in: Stern Strategy Group. (2026). AI and entry-level jobs: The evidence has arrived. https://sternstrategy.com/news/ai-and-the-entry-level-job-the-evidence-has-arrived/

Ang, A. (2025, August 12). Is AI pushing us to break the talent pipeline? Knowledge at Wharton. https://knowledge.wharton.upenn.edu/article/is-ai-pushing-us-to-break-the-talent-pipeline/

World Economic Forum. (2023). Future of jobs report. WEF. https://www.weforum.org/publications/the-future-of-jobs-report-2023/