As Gemini and the broader AI landscape rapidly evolve, what do you see as the most transformative applications of generative AI over the next two to three years and how do you envision these advancements reshaping the way we work and live?
The biggest change won’t be smarter chatbots, but AI evolving into a true collaborator for problem-solving and creativity. The most transformative applications will be tools where AI is a persistent partner. In business, this means AI “agents” that don’t just find information but automate complex tasks and take action. Imagine a small team performing market analysis with the depth of a huge firm or designing products in a fraction of the time. For learning, tools like Gemini will be the ultimate tutor, offering personalized Socratic dialogue for students or real-time translation for someone exploring a new culture. In our personal lives, AI will become a proactive assistant that manages schedules, handles daily tasks, and provides coaching—all tailored to our individual needs. Across all domains, the technology will shift from a tool you use to get information to a fully integrated system that helps you discover, create, and get things done—at work and in life.
These words feel strikingly relevant today—especially in a world marked by uncertainty, conflict, and a growing sense of powerlessness. This is a story about the courage to hold on to love, dignity, and truth when everything around us falls apart.
Reflecting on your time leading enterprise AI strategy at Scale AI, what key challenges and breakthroughs shaped your approach to helping large enterprises embrace generative AI?
At Scale AI, the key challenge was bridging the gap between the incredible potential of foundation models and the messy reality of enterprise data. Many companies saw the power of general-purpose AI, but couldn’t see how it would solve their specific problems. The breakthrough was showing how to customize these powerful models for a company’s unique context, using their own data and expertise. In an enterprise setting, the stakes are high for things like claims processing, customer support, and legal review. To deliver reliable and accurate AI, you have to leverage internal data and build sophisticated testing and evaluation capabilities to earn trust from both employees and customers.
At BCG, you worked on projects in the AI and machine learning product space, including AI@Scale. How did your BCG experience help shape your foundation and career path in this field?
My interest and expertise in AI were truly homegrown at BCG. As a former congressional policy advisor, I had no technical background. I was incredibly lucky to have leaders like Nick Goad, Taylor Smith, Silvio Palumbo, and many more who gave me the space to pursue my budding interest in AI and data. BCG taught me a strategic toolkit, but more importantly, it provided support to jump into really hard problems and learn from experts along the way. That experience gave me the perspective on enterprise AI that was critical for my transition to the tech industry, helping me build Scale AI’s enterprise business from the ground up. A key lesson I took from BCG was to always ground the solution in the customer’s business problem and organizational context, not the other way around—a principle that enterprise tech firms often miss.
A key lesson I took from BCG was to always ground the solution in the customer’s business problem and organizational context, not the other way around—a principle that enterprise tech firms often miss.
You’ve worked at the intersection of business, technology, and government. How has that cross-sector experience shaped the way you think about innovation and impact?
My time in business, tech, and government has taught me one big thing: all these groups see AI through a different lens because they’re fundamentally trying to solve for different variables.
Technology pushes invention by asking, “What’s possible?” It dreams up powerful new capabilities to help people, like what we're building with Gemini.
Business asks, “How would we?” It grounds innovation in real customer needs and viable models.
Consumers ask, “Why would I?” Technology has to solve a real problem and make their lives simpler.
Government asks, “How do we ensure this is safe?” It sets the crucial guardrails for broad, societal benefit.
Real, lasting innovation happens at the intersection where technical feasibility, business viability, consumer need, and societal responsibility overlap. For something as foundational as AI, you need all these perspectives moving forward together.
What’s a guiding belief or piece of advice that’s helped you navigate career transitions—from public policy to consulting to tech—throughout your journey?
I’ve been incredibly fortunate to be in the right place at the right time. I joined BCG when companies were just starting to grapple with data and AI. I joined Scale AI just before ChatGPT changed the enterprise AI landscape forever. And I started leading strategy for Gemini at Google as model capabilities and consumer awareness exploded. You can’t perfectly engineer those moments, but you can always look for big at bats. My approach has been to find interesting, hard problems and seek out people who believed in me enough to let me take a swing at them. Because I had that experience, I was ready when the next opportunity appeared.
