The Nightclub and the Library: Why the Social Sector Can't Afford to Sit Out AI

I spend most of my days building AI tools. Three startups. Coding until one or two in the morning. Shipping an entire marketplace platform over a weekend. The pace feels like a nightclub at peak hour. Relentless. Electric. Exhausting.

Then I step into social impact spaces. And it's like walking out of a rave into a library. Everyone's quiet. Cautious. Moving carefully between the stacks.

Same cities. Same zip codes. A world apart.

That gap isn't just culture. It's power.

The Funder in the Back of the Room

Last month I moderated a panel for Northern California Grantmakers alongside Beth Kanter John Kenyon and Sophie Owens. Room full of funders. Toward the end of the Q&A, a woman in the back raised her hand.

"So you're telling me these AI models are destroying the environment. Built on extraction of copyrighted content. They infringe on privacy. They advance racist bias. What exactly is good about these? Why should I, as a funder, support anything built on these models?"

The room got quiet. She wasn't wrong. She was naming what everyone was thinking.

The Fourth Floor of the Library

Two weeks later I was at the San Francisco Public Library, co-leading a hands-on workshop with about 30 nonprofit practitioners alongside Darian Rodriguez Heyman and Adamaka Ajaelo. The session was a partnership with Jerry Trotter from the San Francisco Office of Economic and Workforce Development and Reymon (Rey) LaChaux at the Mayor's Office of Housing and Community Development.

Fourth floor. Wood-paneled room. Beautiful bookshelves. We had a laptop connected to a large screen, doing live prompts as people asked questions.

Then we hit privacy.

For fifteen minutes, fear filled that room. One woman was worried that information in a 990 tax form about serving immigrant communities could be used against the people they protect. Another woman works with people re-entering society after incarceration. She worried that putting any information about them into AI could violate privacy and increase their vulnerability.

I watched people's faces as we walked through how to turn off the settings that let companies train models on your content. How to use APIs and enterprise accounts. Which companies to trust, and why trust is complicated when we've all seen companies violate privacy in ways that surprised us.

These concerns are legitimate. Nonprofits serve vulnerable populations. Many have fewer than 10 staff. They don't have technical experts to vet every tool. The stakes of getting it wrong are real.

And I need to be honest: turning off training settings doesn't mean no logging or retention. Enterprise accounts don't mean zero risk. Assume anything you paste could be exposed someday, even if unlikely. There's no perfectly clean option here.

And yet.

The Two Leaders in the Front Row

In the same workshop, two Latino leaders sat in the front row. They were trying to figure out how to raise funding for their communities during one of the most difficult fundraising years in memory. DEI headwinds everywhere. Funders pulling back. The political climate hostile.

We ran a live prompt together: "Help me generate a list of funders who, despite the DEI headwinds and this political climate, are still stepping up to serve the Latinx community."

In about 30 seconds, the screen filled with 20+ organizations. Latino Community Foundation. Hispanics in Philanthropy. Hispanic Federation. Ford Foundation. MacKenzie Scott's Yield Giving. The California Endowment. Silicon Valley Community Foundation's LatinXCEL Fund. Each with a short rationale, which we then verified.

The two leaders were floored. What would have taken weeks of research—if they even knew where to start—had just happened in front of them.

They left encouraged. Not because AI solved their problem. But because they saw a tool that could help them navigate impossible headwinds with new ideas.

That's the transformation I keep watching happen. Not "AI will save us." But "oh, I can use this for X, and I know not to use it for Y."

The Fundamental Infrastructure

AI is becoming general-purpose infrastructure. More like electricity than an app.

To generate electricity, you have to consume natural resources. You can burn coal. Burn oil. Dam rivers. Generate solar. There are trade-offs with every source. And yet we all need electricity to be productive, to participate in the economy.

You can advocate for cleaner sources. Push for better regulations. Choose providers more carefully. But refusing to flip the switch isn't an option for most people who want to function in the modern economy. You can opt out individually. But you can't opt out of the systems adopting it around you.

I've learned from two decades in corporate social impact that technology amplifies existing systems. It makes good systems better and broken systems worse. That's why the library is right to be cautious. Broken systems—biased hiring, predatory lending, surveillance of vulnerable communities—get worse when you add AI.

But it's also why we can't sit this out. If we're not shaping the applications, not demonstrating what AI can do for communities, not earning credibility through use, we lose our seat at the table when it's time to advocate for guardrails.

It's hard to design guardrails for a tool you've never used.

The Bridger's Confession

I need to be honest about something.

When I led philanthropy at Google.org, one of my primary functions was softening the regulatory environment. Engaging policymakers. Getting elected officials to attend announcements. Providing quotes for press releases that signaled these companies have society's best interests at heart.

I participated in that machine. I strove to hold the tension between company benefit and community benefit. I didn't always do it perfectly. Some projects I worked on provided a strong halo for the company and reduced regulatory pressure in ways that benefited Google more than the communities we claimed to serve.

That's part of being a bridger. If you're going to bridge, you're going to get your hands dirty. You can't be ideologically pure. You can strive to balance those things well. You won't always succeed.

I say this because I think we need more bridgers. People willing to live in that tension. People who refuse to give into tech utopia but who also aren't afraid to engage. Folks who understand the externalities but won't sit it out while others reshape the economy.

If we allow the private sector to completely monopolize this tool, we're worse off. We need to use it. We need to earn our seat at the table. That means getting our hands dirty.

The Risk Ladder

Here's the practical framework I've been sharing in these workshops. Think of it as a ladder:

Level 1 — Low risk (public-facing content): Draft newsletters. Rewrite emails. Summarize public reports. Generate social media posts. Research funders. None of this involves sensitive data.

Level 2 — Internal, low sensitivity: Meeting recordings and summaries with team consent. Job descriptions. Strategy document updates. Internal communications. Still no client data.

Level 3 — Sensitive, only with guardrails: Enterprise tools only. Data minimization. Redact identifying information before pasting. Documented policies about what goes in and what doesn't.

Never: Client-identifying information or legally sensitive data in consumer chat tools. Period.

The underlying mindset is simple: walk through your day asking, "This task takes me a lot of time. Can AI help?" Then check which level it falls on.

Most of what overwhelms nonprofit staff—grant research, meeting notes, donor communications, strategy docs that are months out of date—lives in Levels 1 and 2. That's where you start.

The Ask

We need people with social conscience and proximity to social problems engaging these tools. Driving solutions. Shaping the public conversation. Finding applications the private sector won't prioritize.

The library is where we ask the questions the nightclub doesn't have time for. We need that caution. But if all we're doing is sitting in the stacks while everyone else is in the dance, that's not where we need to be right now.

Our communities are moving slower than the moment demands. That pace is a rational response to high stakes and low capacity. But it's also leaving us behind while the private sector rewrites the rules of the economy.

Every time I step from the nightclub into the library, the contrast gets more jarring. Not because the concerns in the library are illegitimate. They're not. But because the music in the nightclub isn't stopping.

So here's my question: What's one thing you could try in the next 30 days? One task that takes too long, one workflow that's overwhelming, one place where you'd be willing to flip the switch and see what happens?

I'd love to hear what you're experimenting with, or what's still holding you back.

Justin Steele is co-founder and CEO of Kindora, a Public Benefit Corporation using AI to help social-sector organizations find and win funding. He previously directed nearly $700 million in philanthropy at Google.org over a decade and serves as a trustee at The San Francisco Foundation. This piece was originally published on LinkedIn.