Blog
Apr 30, 2024
I was always fascinated by the language of universe—the computational patterns that underpin everything from physics to human language. I started my undergrad early in mathematics and after I came to the U.S. as a teenager, my first job was at the Lawrence Berkeley National Lab doing data analytics. Little did I know that my humble coding job was contributing to the revolutionary Human Genome Project—an international effort to map all of the genes in human DNA. It remains the world's largest collaborative biological project. My background in mathematics, computer science, machine learning and visual arts enabled me to look at things very differently and build the first visual analytics tool for genomics—an open-source genomic browser that is still in use by institutions today. It turned out that the same fundamental technology could be applied across fields, so I built one of the first charting engines for the financial markets and created a company around it. All before I turned 24.
I've built my career around the thesis that technology will change the world, and that learnings from one field translate and transfer to others. I believed that AI would become real in my lifetime. Like many early ideas in computer science, it started as a vision and a theory. An idea of the neural net modeled on how our own mind might work. We didn't have the data to train it. We didn't have the computing power we needed. But we modeled theoretically why and how it would work and made more and more progress over the years.
We're living in this moment of creating the wheel—and this technology will change not only our lives, but also who we are as a species. It will change the course of our evolution.
The board hired me shortly after Wikipedia, then the 5th largest website, went into a decline. We reversed the trend, but it quickly became apparent to me that Wikipedia’s model of human creation and curation of knowledge was hitting a scaling barrier. By 2013 our rate of creating information was vastly outpacing our ability to distill it "by hand." It was clear we needed an automated compression engine, supervised by humans—rather than having humans process data, we should have them analyze it (similar to how geneticists analyze and interrogate the genome that has already been sequenced by the machine). We needed a model to process language and deliver a search experience to the users in their native tongue, at the right level of complexity, with accurate references to trusted data sources. In other words, we needed AI. During the following three years we put into place several AI initiatives, although we stopped short of implementing the large transformational project we called the "knowledge engine."
I was lucky to push this farther at Microsoft. I joined to take AI out of MSR (Microsoft’s famous research facility) and turn it into a commercial offering. The set of fundamental AI services and devices quickly turned into a multi-billion-dollar pipeline of business. I was part of one of the largest hyperscale cloud organizations and the advances in transformer architectures signaled a massive opportunity. I believed that Microsoft could lead the industry in a new direction and that it was a once-in-a-lifetime opportunity to support this transformation. This encompassed accelerating Microsoft’s consumer businesses from gaming to search to LinkedIn, the future of data centers from climate impact to fusion energy development, and of course AI. My role as a Deputy CTO was to help a multi-trillion-dollar market cap company shape its 10, 20, 30-year view into the future—and launch what I believe will be the biggest technology wave of our lifetime.
The AI shift is so profound and so multi-pronged, it's critical to have that very broad yet deeply detailed perspective to understand where the opportunities will be one, five or 10 years from now. It's not unlike the shifts to the Internet, mobility, and the cloud where there will be many waves and the opportunities will be up and down the technology stack, from the infrastructure to the application layer—we can already see AI changing the way the data center is built. The difference is that AI is just much, much bigger.
We're at the beginning of the true productization cycle today, but there is also a tremendous amount of fundamental innovation that will continue to unfold. Even as AI adoption and maturation accelerate, there will be many waves of disruption over the course of the next decade and beyond.
Working at Microsoft was an incredible experience, but at the end of the day my work would have been focused on one or several products, with one set of constraints. Yet the world is unconstrained, and the impact will be profound. The only way to tap into that is through a platform that can truly catalyze the innovation that will unfold—a platform to harness the knowledge, advance the technology, empower the entrepreneurs and actually help build the companies that will bring these tools into the world.
We are navigating a massive, historical change. This shift has been decades in the making, and navigating it requires depth of experience and breadth of vision to bring to fruition—that's why I chose to come to NEA. NEA's strength as a firm is really derived from working together across the partnership to tap into a great diversity of markets, industries and sectors. I think of my role as a fusion reactor creating energy across all of these different areas, leveraging expertise across disparate verticals in a way that is very strategic and makes each more effective and more impactful, from driving deal flow to commercializing products to scaling businesses.
NEA has been investing in AI for many years—the first investment was in the late 80s, and the firm has built a substantial portfolio over the last decade. AI has played an increasingly central role not only in the investments we make, but also the implications for existing companies. Diving deep into the analytics of businesses across the portfolio alongside external data, one starts to understand the market forces that are shaping this shift and where the disruption is going to occur.
The team has done a phenomenal job of finding and investing in some really great AI opportunities. Now they’re eager to look at AI more systematically—to think about second and third derivative opportunities that are going to unfold as this wave rises. NEA’s advantage is bringing together disciplines and leveraging knowledge transfer and pattern recognition from one discipline to another. The intersection of healthcare and tech over decades is a great example—each practice continuously draws on the expertise of the other. And because this technology will evolve for decades to come, it will shape not only what we invest in, but how we build companies and how we foster the strength of platform as we pursue this next frontier.
I started out as an entrepreneur, trying to learn as quickly as I possibly could, stumbling in the dark and picking myself up after failures. I am intimately familiar with what it takes to build a company and the things can cause a company to succeed or fail—from strategy to people and relationships, from partnerships to product development, and of course tech. Joining NEA feels a lot like coming full circle.
It is my time to give back. It is immensely valuable to have help and support from those who have struggled through the journey. Sometimes I wish I could download all of my experience so that the next generation of entrepreneurs could upload it and not have to figure everything out first-hand. Perhaps one day one of them will figure out how!
This time belongs to the founders—they will dream up things we can't even imagine. And we will be there to help them make it possible.
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