The US-based Siegel Family Endowment, guided by its mission to understand and shape the impact of technology on society, has spent more than a decade investing in public interest technology (PIT) alongside other funders, including the Ford Foundation.¹ More than a field of practice, PIT has grown into a movement grounded in the belief that technology should advance the public good. It brings together software engineers, data scientists, community organizers, ethicists, policymakers, designers, and academics to build, govern, and adapt technologies in ways that are equitable, accountable, and responsive to society’s needs. As the use of generative AI becomes more prevalent throughout society, Siegel sees AI as the latest stage of that movement.  

In this conversation, Katy Knight, Siegel’s president and executive director, and Joshua Elder, Siegel’s senior vice president and head of grantmaking, join Nate Wong, partner at The Bridgespan Group, to reflect on the evolution of the public interest technology field and to share lessons for funders seeking responsible innovation, durable infrastructure, and thoughtful philanthropy. 

Nate Wong: Before we dive in, let’s start with a bit of history. From your perspective, what has the public interest technology landscape looked like over the past decade? What are some of the major milestones or defining moments?

Katy Knight: My connection to public interest technology actually predates my time at Siegel. If we look back beyond the past decade, there were already early rumblings of what eventually became the public interest technology movement. 

For me, one of the seminal moments was when Darren Walker, former president of the Ford Foundation and a leading champion of its public interest technology work, brought everyone together in 2018. Before that, there was a thriving open-source community and the era of Google’s “Don’t Be Evil” motto. Many of us working in technology genuinely believed that technology itself was inherently serving the public interest. There was this belief that technology was both the means and the end. 

Then philanthropy came together and said, “Let’s organize around this. Let’s invest in this field.” At the same time, our understanding of technology started to change. Technology was no longer automatically seen as the good guy. There needed to be a clearer distinction between technology itself and public interest. 

[A number of us were] invited to the White House in 2021 to discuss public interest technology, [which] felt significant because it reflected how firmly established the field had become. More recently, our Tech Together convening was another important milestone.  

The community has grown so much that we wouldn’t be able to fit everyone into one room anymore. It has grown beyond individual organizations into an ecosystem with its own leaders and communities of practice. 

Joshua Elder: Our thinking about public interest technologists themselves has also evolved. Early on, there was a strong emphasis on getting technologists into government and nonprofits so they could bring technical expertise into those environments. 

Now, especially with emerging technologies and the explosion of generative AI, there’s much broader recognition that this isn’t simply a technology issue anymore. Technology is embedded throughout society. Understanding AI isn’t just about understanding the technology itself. It’s about understanding responsible and ethical use. It’s about asking the right questions. It’s about figuring out how these technologies can genuinely serve the public good. 

Wong: Building on what you just described, Josh, how do you think this current AI era is reshaping public interest technology compared to those earlier stages of the movement? 

Elder: This moment is creating an enormous amount of noise. It’s become very difficult for people to distinguish what’s genuinely important from what’s simply hype. 

One of the biggest challenges right now is separating substance from hype. There’s so much attention focused on AI literacy. For us, genuine AI literacy or AI fluency means understanding the technology well enough to ask thoughtful questions. It means understanding how to use AI responsibly and ethically. It means understanding how the technology actually works well enough to evaluate whether it’s appropriate for solving a particular problem. 

Instead, what we often see is people simply throwing AI at problems. They’re trying to force AI products into situations regardless of whether those tools actually serve the public interest. 

Knight: When generative AI entered public consciousness, one question we heard almost immediately was whether we needed an entirely new field called public interest AI. My immediate reaction was absolutely not. 

AI is a technology. We already have a field called public interest technology. Instead, we need to understand which AI questions belong within public interest technology and which are broader questions about society itself. Some issues are fundamentally about technical infrastructure, technical expertise, or how these systems function. Other questions are much larger societal questions. Those aren’t really technology questions at all. They’re public good questions. 

Wong: That’s a helpful distinction. It also reflects how many more kinds of people are involved in the field today. A few years ago, especially around the launch of US Digital Response during COVID, much of the conversation still centered on technologists. Now the conversation has become more mainstream.  

I’d love to shift to how Siegel is thinking about AI strategically. How are you approaching investments in AI today?  

Knight: When public interest technology first emerged, we spent much of our time convincing people that technology mattered for the social problems they cared about. Now we’ve reached almost the opposite situation. Everyone comes to us asking how AI can solve their problem. 

Because everyone wants to “AI something,” we have an opportunity to help them think more deeply. Yes, there may be technical infrastructure that matters. But there are also much bigger conversations they need to engage in around technology, education, and society. 

Elder: Siegel has always focused on understanding and shaping technology’s impact on society. AI simply represents the latest stage of that evolution. For us, AI cuts horizontally across everything we do. 

Within our learning portfolio, especially our K–12 work, there’s enormous interest in AI literacy. But underneath that conversation is work we’ve been doing for years around computer science education. Our goal was never to make every student into a software engineer. It was to develop foundational computational thinking skills. While the technology has evolved, the core skills remain the same. 

On the workforce side, we’re studying AI’s impact on labor markets, workforce disruption, and future jobs. Technology is changing much faster than educational institutions can respond. That forces us to rethink how learners move from K–12 through higher education and into the workforce. 

On the infrastructure side, we’re interested in supporting public interest technologists who are building alternatives to the dominant technology models. Rather than funding isolated projects, we’re asking how philanthropy can support durable infrastructure that allows these communities to design, govern, sustain, and scale alternative approaches over time. 

Wong: Looking back, what are some of the biggest lessons you’ve learned? What are the successes that other funders should build on so they don’t need to start from scratch? And what are some of the failures—or at least challenges—that funders should keep in mind as they begin investing in AI? 

Elder: One of the biggest successes has been building a coalition. We’ve brought together universities, researchers, practitioners, funders, and many other organizations that share a commitment to developing public interest technologists. 

The challenge is making sure we don’t stop there. Coalitions are wonderful. They create momentum. But eventually you have to translate conversations into action. Technology moves incredibly quickly. Traditional institutions move much more slowly. The broader lesson is that coalition building alone isn’t enough. Ideas have to become action. 

Knight: Another example is open source. Philanthropy played an enormously important role in supporting open-source software and the communities that built much of today’s internet infrastructure. Over time, though, both corporate and individual funders gradually stepped away. 

Much of what makes open source valuable isn’t just the software. It’s the people. It’s the individuals who dedicate themselves to maintaining those projects. Those projects aren’t glamorous. They’re deeply infrastructural. 

Now we’ve entered the AI era, and the internet infrastructure that supports everything is under tremendous strain. Looking back, one of our biggest lessons is that we need to remain committed to funding these unglamorous infrastructure projects over the long term. We can’t allow that foundational work to disappear. 

Wong: In my early days as a technologist, the culture of collaboration was very different from today’s. How do you think philanthropy can influence that culture? Does it come down to incentives? 

Knight: Two things come to mind. 

The first is narrative. Philanthropy may not traditionally think of itself as shaping culture, but we absolutely shape narratives. We influence the stories people tell one another about education, poverty, environmental issues, technology, AI, and countless other topics. We have an opportunity to be much more intentional about which narratives we choose to elevate. 

The second thing is speed. Technology feels like it’s accelerating constantly. Philanthropy generally operates much more slowly. We need to ask ourselves how we can speed up where appropriate, slow down where necessary, and become more intentional about when each approach is appropriate. 

Elder: On incentives, everything comes back to money. Originally, collaboration across universities was encouraged because funding was attached to collaborative projects and student challenges. That worked. But it wasn’t sustainable. 

The question now is how to embed collaboration into the culture of those institutions so it continues even when specific funding opportunities disappear. Funding still matters. But the ultimate goal is for collaboration to become the norm rather than simply a response to grant opportunities. 

Wong: Imagine you’re giving advice directly to peer funders. What’s one piece of advice you’d most want them to hear as they think about investing in AI? 

Knight: If I were being particularly blunt, I’d tell funders to figure out what AI actually is before they develop an AI strategy. The term “AI” has become a catch-all phrase that’s increasingly defined by a handful of frontier large language model companies. We shouldn’t simply adopt their definition. We owe it to ourselves, to our grantees, and to the broader public to develop a deeper understanding. 

If we’re going to build strategies around AI—whether that’s responsible development, technical infrastructure, or governance—we need to understand the technology well enough to know where meaningful interventions actually exist. We need deeper knowledge. Technology deserves that same level of understanding. 

Elder: I’d simply encourage all of us—including ourselves—to get more technical. Understanding the problem isn’t enough. Philanthropy should also be building, experimenting, and taking calculated risks. 

Another thing we’ve been thinking about is the global dimension. This isn’t simply a US issue. Even if your funding is restricted to domestic work, there are tremendous opportunities to learn from what’s happening internationally. 

Wong: What should be on philanthropy’s collective learning agenda? A lot of conversations about AI focus on government regulation or industry responsibility. What is the unique contribution philanthropy can make? 

Elder: I keep coming back to the global dimension of this work. One example would be the Tech Together convening. It brings funders together and creates space for open, honest conversations about what’s happening. It allows us to acknowledge both what we know and, perhaps more importantly, what we don’t know. 

Right now, I’m part of four to six different AI funder collaboratives. In many of them, the emphasis is immediately on co-funding and getting money out the door. But there often isn’t enough agreement about what we’re actually trying to accomplish together. 

Humanity AI, which Siegel participates in, is one example of a philanthropic collaborative built around a clear, shared goal: to ensure communities have a real say in how AI is used and governed, and ultimately what its role in society becomes. While participants may differ on specific tactics, having that common North Star enables more effective collaboration. 

Knight: Philanthropy is still approaching AI in a very ad hoc way. We keep saying this is a transformational moment. But then we treat AI as a temporary initiative or a separate portfolio while we slowly build a more comprehensive strategy. 

Instead, we need to ask much bigger questions. What parts of our current way of operating actually need to change? What needs to be broken so that we can replace it with something better? How do we move from today’s emergency response into a sustained way of working?  

Wong: Something you touched on is the importance of knowing when to move quickly and when to slow down. For people working in philanthropy who feel pressure to move quickly to solutions, how do you counterbalance that instinct? 

Knight: We’re actively debating this as a team right now. On a personal level, one of my gut checks happens when I’m interacting with whatever chatbot I’m using. If it starts making me feel like I’m a genius, that’s usually my signal to slow down. 

More broadly, I think the real question is whether something is working because it’s genuinely effective or simply because we’ve always done it that way. AI creates a temptation to throw everything out and rebuild from scratch. I don’t think that’s the answer. 

Instead, I think it’s useful to temporarily set a framework aside and ask: “If we weren’t constrained by our current process, how would we approach this today?” Then we can decide what should actually change and what should remain. 

Elder: We’ve tried to avoid treating this as a simple either-or decision. Our inquiry-driven approach focuses first on asking better questions. Those questions help us understand what we’re actually talking about—in this case, AI and generative AI. As we investigate those questions, we build the technical understanding needed to evaluate the technology and its implications. 

That combination of curiosity and understanding helps us evaluate grant proposals, collaborative opportunities, and potential investments. If you can clearly explain why you’re moving quickly, or why you’re choosing to pause, you’ve probably asked the right questions and developed enough understanding to make a thoughtful decision. 

Wong: What’s currently giving you the most excitement or curiosity? Whether it’s technology itself or examples of community-driven innovation, what feels especially promising right now? 

Knight: One thing that’s genuinely inspiring to me is the energy of younger people. There’s a real “burn-it-all-down” attitude among younger generations. Oddly enough, I find that incredibly energizing. As a pragmatist, I don’t actually want to burn everything down. But I do think that energy creates enormous opportunity. 

I’m also excited about some of the public interest AI projects that are beginning to emerge. One example is Current AI’s Alpha Chat. It’s an early chatbot built entirely on open-source components with safety built into its design. Projects like that are intentionally trying to avoid reproducing many of the societal harms we’ve seen in other AI systems. 

Elder: We spend a lot of time thinking about youth voice. The conviction and energy younger generations are bringing right now is forcing people not only to listen but also to genuinely consider what they want and how we can support them. 

I’m also encouraged by the organizing we’re seeing. Parents, families, and communities are coming together to advocate for change. Underneath all of that is a powerful desire for something better. That energy is incredibly valuable. It now needs to be matched with education and knowledge so people can channel that energy effectively. 

These younger generations are going to inherit and lead the future. Our responsibility is to make sure they have the knowledge, foundation, and agency to shape the society they want to build. 

Wong: We like to get practical. What are your top resources that you recommend for peer funders or others—books, articles, podcasts, learning communities? 

Elder: The Public Interest Technology University Network is a great place to see how colleges and universities are preparing the next generation of public interest technologists, and the PIT Competencies Project offers a helpful framework for thinking about the values, knowledge, skills, and behaviors that underpin the field. 

I’d also point people to All Tech Is Human, which has become an incredible convener with practical resources like Building a Career in Public Interest Tech. And if you’re looking for a more personal window into the field, Tech for Us, the documentary we collaborated on with Roadtrip Nation, introduces you to people taking very different paths into this work.  

Knight: I think Josh covered the public interest tech ecosystem really well, so I’ll add a few of the resources that shape how I think as a funder. 

I read Rest of World because it consistently reminds me that technology doesn’t look the same everywhere. If you haven’t read it already, Karen Hao’s Empire of AI is foundational for understanding the scale of the challenges and power dynamics shaping this moment. 

One book I revisit often is Mitch Resnick’s Lifelong Kindergarten. It’s not about AI or philanthropy directly, but it has influenced how I think about learning, creativity, and building environments where people can adapt alongside technology. 

Finally, I’ll shamelessly plug Better Questions, Better Insights. It’s not a public interest tech resource per se, but I do think it’s relevant to philanthropy. At a moment when we’re surrounded by answers and expected to have them all, we’ve found that asking better questions is often the more valuable place to start. The paper shares the inquiry methodology we’ve developed at Siegel, and I hope it offers a practical tool for other funders navigating similarly complex challenges.