Preface
Last week, I had the chance to step into an Oxford-style debate at the annual Fundraising.AI Summit. The motion on the table: “Generative AI, on the whole, will be a net positive for the social impact sector.”
Two teams went head-to-head. I was on the con side, the skeptics. My teammates and I argued not against AI entirely, but against the idea that its impact is net positive. The debate was fast, thoughtful, and good-humored. And in the end, the audience agreed: the con side won, shifting people from pro or undecided toward our view.
It felt good to step into the role of skeptic. I’m not anti-AI. I use it every day, I teach it, and I help nonprofits experiment with it. But I’ve learned that the only way forward is through inclusion and education. AI is here whether we want it or not. Do we understand it well enough to use it wisely: to see where it helps, where it harms, and where human judgment is critical and necessary.
What follows is my five-minute opening statement from the debate.
AI Debate
Opening Statement - Con Side
The motion asks whether generative AI will be a net positive for the social impact sector.
Not the tech sector.
Not Wall Street.
The social impact sector is built on relationships, trust, and human dignity.
Our opponents will tell you generative AI is a net positive for the social impact sector. They're wrong. Not misguided or overly optimistic. Wrong.
Jobs, the Environment, Bias, Hallucination, Skill loss …
They promise us AI will revolutionize our sector. Faster grants, smarter donors, optimized outcomes. What they don't mention? The revolution they promise would destroy the foundation of the social impact sector.
Job displacement in a sector already understaffed.
Climate impacts from the energy AI consumes.
The risk of repeating bias and discrimination at scale.
The loss of critical thinking and parroting hallucinated “facts.”
These are only the surface-level dangers.
The deeper risk is subtler and more insidious.
Every benefit they cite assumes we should want to be faster, cheaper, more scalable.
But we exist because some work can't be done faster, cheaper, or at scale without losing its essence.
They'll tell you humans stay 'in control,' that this is just augmentation. But when the algorithm's suggestion is always faster, cheaper, easier, control becomes theater. We're not augmenting human judgment — we're replacing it with algorithmic convenience.
Here's what's already happening: Research from the Max Planck Institute shows that humans are unconsciously adopting AI language patterns. We're starting to sound like ChatGPT without realizing it. Words like 'delve' and 'meticulous' have surged in our conversations since 2022.
Language matters, but this isn't just about vocabulary. When AI changes how we speak, it changes how we think.
Social change happens when people work together and that's slow, messy, human work.
Our processes cannot be optimized away without losing their very essence and purpose: to build relationships and trust; and to ensure human dignity and care of the planet.
Here's how we win this debate: by showing that AI harms and AI-promises require us to stop being the social impact sector at all.
The Principles Under Attack:
The social impact sector runs on three non-negotiable principles: trust, collaboration, and relationship. Everything we do — every grant made, every program designed, every community engaged — depends on these human foundations.
Violation #1: Trust Becomes Transaction
"Trust requires human judgment, institutional memory, and authentic connection. AI disrupts all three.
What looks like efficiency is really automated exclusion at scale. This isn’t trust—it’s transaction."
Violation #2: Relationship Becomes Algorithm
"Relationships take time. They require presence, patience, listening. They cannot be scaled or automated without ceasing to be relationships at all.
Violation #3: Collaboration Becomes Automation
"Collaboration means working together, learning from each other, building shared understanding. AI replaces human-to-human connection with human-to-machine interaction, drifting toward machine-to-machine.
They are importing private sector logic into a sector that exists for something entirely different.
In business, shareholder value justifies everything. In social impact, success is measured in trust, relationships, and collaboration that builds community power.
Our infrastructure isn’t machines. It’s people.
Automating that away erodes the very humanity we exist to protect. That’s why generative AI is not a net positive for the social impact sector.
(the end)
Conclusion
AI equity isn’t a finish line we sprint toward. It’s a terrain we walk together — uneven, unpredictable, and full of choices about who thrives … If we treat it like a race, we’ll do harm; sacrificing the very people and places we claim to serve.
We do not have a strong track record (in capitalist America) of protecting the margins when profit and power are at stake. Not with lumber. Not with coal. Not with water. And now not with AI data centers. Industry extracts more than it gives back; leaving scars on communities and ecosystems.
Barbara Kingsolver’s Demon Copperhead lays this bare. Her story of Appalachia shows how entire regions can be hollowed out by neglect and corporate greed. Walter Tevis’ Mockingbird warns of a different loss — a future where efficiency rules, agency vanishes, and people forget how to read.
Both teach us the same lesson. Progress without protection corrodes from within. The tradeoff is the same: speed and profit for some, silence and suffering for others. Grants managers and nonprofit leaders, you may have experienced organizations that “look efficient” but burn out staff, or policies that “scale” but undermine trust.
So the choice before us is clear. We can chase AI’s promise as if it were a race to win. Or we can insist on a slower, more human path. One built on reflection, regulation, and radical care. What matters is not the polish of AI’s outputs, but whether its use builds a future rooted in justice, trust, and inclusion.
Call to action
If this conversation sparks questions for you, don’t stop here. Read Meena’s AI Equity Report. Book time to chat with colleagues about what it means for your work. Get educated. Keep learning. Because the future of AI in our sector won’t be decided by the tools alone — it will be shaped by how we choose to show up, together.