From Physics to Founding

Hardik, you are currently building G-EVAL, a startup focused on robotics and Physical AI. Could you start by introducing yourself and explaining the problem you are trying to solve?

Robotics needs the same kind of breakthrough that language models have already had, but to get there, we need real-world robot data at scale. Unlike language models, robots don’t have an internet-sized dataset to train Physical AI foundation models and world models.

G-EVAL is building that missing data layer: starting with real robot data farms, then helping the robotics ecosystem turn messy robot operations into clean, trainable datasets. Our belief is simple: Machine Learning engineers should train better models, not spend their time cleaning robot data.

Your background combines physics, research, entrepreneurship and emerging technologies. How did your journey develop — and when did you first realise that entrepreneurship could become part of your path?

My journey developed in a non-linear but connected way. I started from physics, where I learned to think in systems, first principles, and uncertainty. Physics trained me to ask deeper questions about how complex systems behave and how theory becomes useful only when it connects with the real world. Over time, I became involved in robotics, XR, research, and emerging technologies in Munich through LMU, TUM, RoboTUM, TUM-XR,TUM Venture lab, LMU IEC, UnternehmerTUM, and the local startup ecosystem. I was also shaped by programs and communities such as InnoLab CDTM, the Falling Walls Foundation and Young Entrepreneurs in Science. These experiences helped me understand how research, technology, storytelling, business, and execution connect.

A major turning point was my work around robotics and Physical AI. I saw that robotics has strong research, but the path from lab prototype to real business value is still difficult. There is not enough real-world robot data, integration is painful, and many teams spend too much time on data and operations instead of improving models and deployment. At the same time, my experience in hospitality and event operations gave me a practical view of real-world workflows. It helped me understand that entrepreneurship is not only about technology; it is about understanding a painful problem, building something useful, and connecting research with real customers.

That is how G-EVAL evolved: from my interest in physics, robotics, Physical AI, and science entrepreneurship into a startup focused on real robot data, robotics integration, and usable infrastructure for the robotics ecosystem.

The Century of Science Entrepreneurship

You have described the 21st century as an era of “science entrepreneurship.” What does that term mean to you personally, and why do you believe it is becoming increasingly important?

If we compare human progress with the knowledge pyramid, civilization has always advanced by increasing its capacity for facts, information, knowledge, and wisdom. Every industrial revolution happened when humanity learned how to process and apply knowledge in a new way.

Today, AI represents the next step. We have built enormous scientific understanding, but now we need faster systems to process, apply, and scale it. NVIDIA’s rise with GPUs is a clear example: we needed new computing infrastructure to unlock the next wave of AI-driven progress.

That is why the rest of the 21st century should focus on science entrepreneurship — turning deep scientific knowledge into consumer products, industrial transformation, and civilization-scale value.

Robotics, Physical AI and the Missing Data Layer

You are particularly interested in robotics and Physical AI. What excites you most about this field right now, and where do you see its biggest potential?

I believe robotics will eventually become a consumer market, just like the iPhone and Android became part of everyday life. Today, robots still feel industrial or experimental, but in the future they will become personal, useful, and integrated into daily routines.

My way of thinking about robotics is shaped by this future consumer persona. Someone like “Tony Stark” from Marvel: a person who uses intelligent machines naturally, creatively, and almost as an extension of themselves. Technology is moving in that direction, and robotics will become one of the most important interfaces between humans and AI.

Apart from that vision, my interest in robotics also comes from my own experiments before college, my hands-on work with emerging technologies, and learning from experts in the robotics ecosystem. Those experiences helped me understand that robotics is not only about machines; it is about building the future relationship between humans, intelligence, and the physical world.

You have said that today’s AI systems still do not fully deliver the real-world interaction people imagine. What do you think is currently missing — and how does your work aim to close that gap?

Today’s AI is powerful, but it still mostly lives in the digital world. It can write, reason, generate images, and process information, but it does not yet fully understand or act inside the physical world the way people imagine. What is missing is real-world interaction: the ability to perceive, manipulate, fail, recover, and learn from physical environments.

For robotics and Physical AI, the biggest gap is data. Language models had the internet. Robots do not have an “internet of physical experience” to train on. They need real task data: motion, force, vision, human intent, failures, corrections, and environmental context. Without that, AI cannot reliably move from intelligence on a screen to intelligence in the real world.

My work with G-EVAL aims to contribute to this missing layer. We focus on real robot data, data farms, dataset validation, and robotics integration. The goal is to help robotics teams access clean, usable, real-world datasets so their Machine Learning engineers can focus on improving models instead of cleaning messy robot data. In simple terms, I believe the next big step for AI is not only better software. It is giving AI a body, real experience, and measurable interaction with the physical world. That is the gap I want to work on: building the data and integration infrastructure that helps robotics move closer to its own “ChatGPT moment.”

Curiosity Meets Execution

You spoke very openly about the mindset shift from academia to entrepreneurship. What were the biggest personal and professional changes you had to make?

The biggest shift was moving from curiosity to execution. In academia, the focus is discovery, research, and asking deep questions. In entrepreneurship, curiosity still matters, but you also need negotiation, psychology, speed, and practical decision-making.

I also started to believe in time parity — respecting everyone’s time and understanding priorities clearly. I am learning to balance politeness and openness with realism, boundaries, and execution. Entrepreneurship taught me that technology alone is not enough; people, timing, and trust matter just as much.

One point you raised was that academics sometimes need to “stop trying to appear like geniuses” when entering entrepreneurship. What do you mean by that?

Entrepreneurship is about repeating a behavior that creates value, and that repeated behavior becomes an economy. In academia, we often celebrate genius, curiosity, and discovery. Germany has a strong civilization of deep engineering, scientific thinking, and early leadership in robotics.

But entrepreneurship requires a different lesson: do not only try to be a genius; learn to repeat the behavior that customers actually want. Companies like Unitree show this clearly. They are not only building impressive robots — they are pushing robotics toward repeatable, affordable, and scalable customer demand. For me, that is the key shift: from admiring technology to understanding which behavior, product, or workflow customers will pay for again and again.

Showing Up: Munich, Networks and Confidence

You mentioned that as a child you were rather shy and often stayed focused on science. How did you gradually step out of your comfort zone and grow into entrepreneurial environments?

Munich played a major role in my journey. Almost every evening, there was a startup meetup, tech event, or ecosystem gathering around 6 PM, and I kept showing up. I met founders, researchers, investors, students, and operators, and those conversations slowly shaped my understanding of entrepreneurship.

At the same time, I was active in student clubs and worked in hospitality, which taught me how to communicate with people, treat guests well, and understand real-world operations. Hospitality is fundamentally about human interaction, service, and trust — and that became very valuable for entrepreneurship.

This journey also opened doors to important experiences. I had the opportunity to present myself twice in front of different Bavarian ministers and share the stage with them through student clubs, and I was also involved twice in the Munich Security Conference and the German Film Ball. These experiences helped me build confidence, professionalism, and a stronger network in Munich’s innovation ecosystem.

You took part in several Young Entrepreneurs in Science workshops, including storytelling and innovation formats. What did you personally take away from them?

YES had an important impact on me because it helped me understand that I was not alone in the transition from academia to entrepreneurship. Meeting other researchers and students who had similar doubts made the journey feel more realistic and possible.

The storytelling exercises were especially useful. They helped me learn how to explain my ideas clearly, not only as research or technology, but as a meaningful problem, journey, and opportunity. Personally, YES influenced my decision-making. It gave me more confidence to explore entrepreneurship seriously and helped me see that moving from academia into startups is not a failure of the academic path — it can be a natural extension of curiosity, impact, and discovery.

A Broader Perspective

What advice would you give to students and researchers who are curious about entrepreneurship but still hesitate to take the first step?

My advice is simple: get started. Nothing is too early, and nothing is too late. Hesitation is normal, but do not allow hesitation to become guilt. Students and researchers already have curiosity, discipline, and the ability to think deeply. Entrepreneurship is just another way to use those qualities for impact. You do not need to know everything before starting. Start with small experiments, talk to people, test your ideas, and learn by doing. We have one life to contribute something meaningful to civilization. So follow the signal that comes from within you, and take the first step.

Looking back at how it all came together — physics, Munich’s ecosystem, hospitality, robotics — what is the bigger thread you take from your journey so far?

One important thing I would like to share is that my journey is not only about technology or entrepreneurship — it is about trying to understand where civilization is moving. I believe the next century will be shaped by science entrepreneurship: people who can take deep research, emerging technologies, and real human problems, and turn them into useful products and systems. For me, robotics and Physical AI are part of that bigger story.

My path has been shaped by physics, Munich’s startup ecosystem, hospitality, student initiatives, robotics communities, and many small experiments. None of it was perfectly planned, but each experience gave me a signal. I learned that progress happens when curiosity meets execution, and when technology is connected to real people and real-world needs.

That is the perspective I want to carry forward: build with curiosity, stay close to reality, and contribute something meaningful to the future of human-machine interaction.

What happens next?

Check out our event calendar for upcoming workshops. Stay tuned for more updates, opportunities, and success stories! Connect with Hardik via LinkedIn.

Interviewer & Editor, Design: Laura Walther