Women + AI Summit 2.0: What Stayed With Me

The hardest part of the Women + AI Summit 2.0 wasn’t deciding what to attend. It was accepting what I’d have to miss. 

The schedule was so packed it was impossible to do everything “right.” There were too many sessions I wanted to attend, too many people I wanted to talk to, and very little room to breathe between them. At one point, I made a conscious choice to step out. 

I ended up sitting with two of my favorite people to play hooky with, Sunny Eaton and Lori Gonzalez, talking instead of listening. The conversation drifted, as it often does at good conferences, from tools to consequences. We circled around how systems like ChatGPT complicate the idea of a “reasonable expectation of privacy.” We treat these tools like private conversations, even though they aren’t. That gap—between how these systems feel and how they actually work—is where much of the risk lives. 

Almost everyone else stayed put. The sessions were too good to miss. And still, that conversation lingered. It was a reminder that even at a tightly programmed conference, some of the most meaningful moments come from choosing where to spend your attention. 

That tension—between structure and spontaneity—defined the weekend for me. In a way, it mirrored the larger conversations we were having about AI itself: how much to automate, when to pause, and how to choose deliberately in the face of overwhelming possibility. 

What Kept Showing Up 

Looking back at the schedule, it would be easy to describe the summit as a progression from talks to workshops to hands-on building. But what stayed with me were the questions that kept resurfacing. 

One of the clearest throughlines was AI literacy—not fluency with tools, but understanding. How these systems behave. Where they fail. And how much agency we hand over when we use them. Several talks traced turning points: fear giving way to curiosity, skepticism shifting into discernment. There was a shared recognition that opting out isn’t neutral. Literacy allows engagement to be intentional rather than reactive. 

As the day shifted from listening to building, the emphasis moved from tools to workflows. The most interesting conversations weren’t about clever outputs. They were about boundaries and judgment. Not just what can be automated, but what should be. 

Ethics showed up not as philosophy, but as practice—especially around data quality and provenance. “Bias in, bias out” wasn’t a slogan. It was a warning. The concern wasn’t only what AI produces, but what we feed it: whose experiences are represented, which sources are trusted, and how quickly flawed assumptions scale once embedded in a system. 

That thread carried directly into access to justice. AI wasn’t framed as a magic fix. If anything, there was a sober recognition that poorly designed systems can widen gaps as easily as close them. Access to justice wasn’t a mission statement. It was a design constraint. 

Underneath all of it was governance—not as a future policy question, but as something already underway. The people choosing vendors, setting internal standards, and defining acceptable use are shaping the future in real time. Governance defaults to whoever is in the room. 

Taken together, the summit wasn’t about celebrating AI. It was about responsibility. About engaging with technology in ways that hold up over time. 

What We’re Taking Home 

I didn’t leave with a list of tools to try. I left with a clearer framework for approaching AI work. 

Literacy comes before leverage. Adoption is an organizational design problem, not just a training issue. Ethics starts with inputs, not outputs. Access to justice must be built into systems from the beginning. And governance is already underway, whether we acknowledge it or not. 

None of that is flashy. But it is foundational. 

If there was a shift, it was this: move deliberately. Build the capacity to pause. Ask better questions before accelerating. The long-term impact of AI won’t be determined by how fast we move, but by how thoughtfully we do. 

Women in the Loop 

Which is why it feels important to name something I’ve intentionally held until now: this was a conference focused on and led by women. 

That mattered—not as branding, but as posture. 

At many AI conferences, there’s a YOLO energy: build fast, deploy faster, sort out consequences later. The emphasis is on scale and upside, with risk treated as friction. 

That wasn’t the posture here. 

Instead of “What can we build?” the questions more often sounded like “What should we build?” and “Who does this affect?” There was comfort with uncertainty. Openness about tradeoffs. A willingness to admit what hadn’t worked. 

Even the design choices reflected that care. Speakers had walk-up songs. Dolly Parton’s 9 to 5 marked transitions. Sessions were labeled mini, midi, and maxi—not by hierarchy, but by scale. None of it felt gimmicky. It felt intentional. Human. 

This wasn’t a lack of ambition. It was a different kind of ambition—one oriented toward durability, impact, and trust. 

Representation didn’t just change who was speaking. It changed what felt worth discussing. 

Shaping What Gets Built 

Cat Moon and her team at Vanderbilt Law created more than a conference. They created a space that modeled a different way of engaging with AI—curious, responsible, and deliberate. 

I left not feeling pressured to adopt more tools, but clearer about the responsibility that comes with adopting any of them. In a field that often rewards speed, this felt like a necessary pause. 

If this is where AI conversations are headed—more reflective, more inclusive, more honest about tradeoffs—it’s a direction worth investing in. 

The future of AI isn’t shaped in the abstract. It’s shaped in moments and weekends like this one. 

This is why it matters who’s in the room when decisions are made.