You started your career at BCG and moved quickly into entrepreneurship, operations, and now founder roles in the AI space. Looking back, what were the most formative early experiences that shaped the path you’ve taken?
BCG gave me a strong understanding of how large organizations actually operate. You see how decisions get made, where things slow down, and how often good ideas don’t translate into execution.
The biggest shift came when I moved from advising into building. When you’re operating, you’re accountable for outcomes, not just ideas, and that changes how you think very quickly.
Running early community initiatives and hackathons was also formative. I saw how quickly people could go from zero to building something real when the environment is right. It made me realize that talent isn’t the constraint for most people; it’s access and structure.
It also pushed me toward leverage early on. The highest impact work comes from building things that scale, not just working harder within existing systems.
Most organizations still think AI adoption is about tools, when it’s really about how work gets done.”
You’re now building at the intersection of AI adoption, learning, and enterprise transformation. What do you think most organizations still misunderstand about becoming truly “AI native,” and what does it take to move from experimentation to adoption at scale?
Most organizations still think AI adoption is about tools, when it’s really about how work gets done.
You can roll out tools across a company and still see very little impact if there’s no clarity on where those tools fit into workflows or what “good” usage looks like. It stays optional—and optional tools don’t change behavior.
At a deeper level, becoming AI native is about redesigning work around leverage—not just doing things faster, but also questioning what work should exist in the first place.
At a deeper level, becoming AI native is about redesigning work around leverage—not just doing things faster, but also questioning what work should exist in the first place.”
Your work has a strong community and ecosystem-building element, for example, your venture of creating an AI community at Build Club globally to support founders. What have you learned about building communities that sustain engagement and deliver real value?
People don’t stay for content; they stay for progress. What works is giving people an environment where they’re building alongside others. Inverse Reinforcement Learning (IRL) has been a big unlock for us because it builds trust quickly—and when you add structure like demos or projects, it creates real momentum.
If someone leaves after having built something tangible, they come back. That’s what sustains it.
· I stay in contact with a lot of my friends who were in my associate cohort at BCG, and as we navigate our professional careers and gain more different perspectives, it’s been invaluable to continue to lean on them.”
The pace of change in AI is exceptionally fast, and you’ve been recognized early in your career, including being named in Forbes 30 Under 30. How do you stay grounded in what matters while continuing to move quickly?
I try not to follow everything. Much of what gets attention doesn't actually change how people work. Staying close to customers and our community helps filter what matters. You see very quickly what’s useful and what isn’t.
I also stay in contact with a lot of my friends who were in my associate cohort at BCG, and as we navigate our professional careers and gain more different perspectives, it's been invaluable to continue to lean on them.
For early-career professionals—including BCG alumni—looking to build or operate in the AI space, what advice would you offer on where to focus and how to get started?
What’s changed is that the barrier to building is much lower now. You don’t need to be deeply technical to create something useful.
The biggest gap isn’t access to tools; it’s people actually applying them to real work. If you can do that, you become valuable very quickly.
