Shivangi Mishra

I attribute my success to resilience, hard work, and a willingness to keep learning. I’ve never been afraid of difficult or ambiguous problems; in fact, those are often the challenges that have pushed me to grow the most. Building products from 0→1 has taught me to stay persistent when things don’t work, learn quickly from setbacks, and keep moving forward.

I also believe hard work compounds when it’s paired with curiosity and ownership. I take responsibility for outcomes, stay close to the problem and the customer and continuously look for ways to become better at what I do.

The best career advice I’ve received is to evolve with the technology rather than compete with it.

As AI changes what it means to be a Product Manager, my mentor encouraged me to become a PM + Forward Deployed Engineer. Someone who can understand the customer problem, define the product, but also get hands-on with the technology, prototype solutions and work directly with customers to deploy them.

That advice changed how I approach my career. I’ve focused on becoming more technical, working closely with engineers, building AI agents and prototypes myself and getting closer to customers and real-world deployment. I believe the best PMs of the AI era will be builders who can move fluidly between product, technology and customer needs.

My advice would be: don’t wait until you feel fully ready, build anyway.

As women in tech, we may sometimes find ourselves as one of the few women in a room full of technical leaders. I’ve learned not to let that make me question whether I belong there. Ask the question, challenge the assumption, and bring your perspective to the table.

I’d also encourage young women to build technical fluency, not just functional expertise. You don’t need to become an engineer, but understanding how technology works and being willing to prototype and experiment is increasingly important. With today’s AI and design tools, we can move beyond simply explaining an idea to actually building and demonstrating it.

Network intentionally with people who are ahead of you, including product leaders, engineers, founders, and researchers. Learn not just from their career stories, but from how they approach problems, make decisions, and navigate their day-to-day work. That practical knowledge can accelerate your growth enormously.

And above all, build resilience. There will be setbacks and moments of self-doubt. Keep learning, keep building, and keep showing up. You don’t have to know everything to have something valuable to contribute. Don’t make yourself smaller to fit the room. Help shape the room.

I think one of the biggest challenges in AI right now is reliability. We are building systems that can do increasingly sophisticated things, but the real question is how much we can trust the output, the underlying data, and the decisions made from it. In my work building AI agents and a world model for Physical AI, I’ve seen firsthand that better data and reliable evaluation are just as important as better models.

At the same time, I see a huge opportunity in how AI changes the way we work. After building 15+ AI agents, what has stood out to me is how effectively they can take over manual, repetitive work that doesn’t require much judgment. That gives people something much more valuable back: time to think, solve problems creatively and focus on insights and decisions that require human context.

I’ve also seen this shift through my work on APIs, MCP, and developer experiences. Developers can now accomplish in minutes what previously took hours of repetitive work. I think this is only the beginning of a much bigger shift in how humans interact with software.

What excites me most is where this goes next. I’m fascinated by how AI will seamlessly integrate into our everyday lives, both virtually and physically. We’re already seeing glimpses of this with autonomous vehicles and AI-native software, but I think the really transformative question is what happens when intelligent systems can understand our environment, anticipate our needs and work alongside us naturally. We’re entering a few extraordinary years of experimentation and I’m excited to be building through them rather than simply watching them happen.

Integrity and curiosity are probably the two values I hold closest.

Integrity, for me, means doing the right thing even when it is not the easiest or most visible choice. This has become especially important to me as I’ve worked in AI, where decisions about data, privacy and how technology is used can have consequences far beyond the product itself. I believe people should be able to trust the products we build and the people building them.

Privacy is a natural extension of that belief. As AI becomes more embedded in our everyday lives, I think we have a responsibility to be thoughtful about what information we collect, how we use it and what we give users control over. Trust is difficult to build and very easy to lose.

Curiosity is the other value that drives me. I’m most energized by problems where there isn’t an obvious answer. It pushes me to keep learning, ask better questions, challenge my own assumptions and stay open to being wrong.

Ultimately, I want my work to reflect ambition without compromising integrity. I want to build things that are technically ambitious and genuinely useful, while being able to look back and feel proud of how they were built, not just what they achieved.