Jill Chase is a Partner at CapitalG where she leads the fund's investments in AI/ML. Her most recent investment was in the AI code generation company Magic.dev. She has spent the past few years working with senior Googlers and industry experts to round out her AI/ML thesis and has had the fortune of working with several tremendous founders and technologists in the space. Prior to CapitalG, she was the CEO of a PE-backed technology company and founded a YC-backed startup. Jill graduated magna cum laude from Williams College with a dual degree in Economics and Psychology and was captain of the women’s basketball team. She came out to the West Coast to earn an MBA from the Stanford Graduate School of Business, but she was born and raised in Boston where she had the opportunity to cheer on the most impressive era of professional sports a city has ever experienced (Go Patriots). She lives in the Bay Area with her husband where they spend weekends doing as many outdoor activities as possible, such as pickleball, tennis, hiking, and running.
EVCA: Describe a defining moment in your career and how it shaped where you are today.
Jill: I have a much less traditional background than most growth investors. Rather than get experience in finance, I decided to spend my time after business school in operating roles, with the hope that it would prepare me for a future in investing. When I first started at CapitalG about three years ago, someone gave me the wise advice to pick a category and really spend time upfront studying it and learning it, as it would differentiate me from more generalist investors. As I reflected on what a good area could be, I immediately gravitated towards AI/ML for three reasons: (1) It is a technical category (raising the barrier to entry for investors to learn), (2) I was well positioned to deep dive and learn it (being surrounded by some of the world's leading experts at Google), and (3) I was incredibly interested in its potential (ML was a core differentiator of the product we built in YC). I started down the path and spent my first year in the job learning everything I could about AI/ML technology. The most defining moment, however, was when I found myself on a call with Google's SVP of Research and AI Jeff Dean, sharing my research and picking his brain about the true potential of this technology and his view on how the world would pan out. I remember vividly leaving that call thinking: "This is even more transformational than I expected. This technology will fundamentally change the world." This was at the end of 2021 before the recent explosion of AI hype, but from that moment on I dedicated myself entirely to studying and investing in this technology, and I couldn't be happier that I did as I genuinely believe AI will be at the center of every great company for the next decade.
EVCA: What is an emerging technology trend that will have a significant impact on the world in the next decade?
Jill: Artificial intelligence will fundamentally change the world as we know it. The pace of technical innovation has been truly incredible, and as a result there have been a significant number of products that have been released that show only the beginning of the value that this technology can create. Looking forward, this transformational technology has the capacity to inspire people and the potential to change all aspects of human interaction--from consumer products (like Character.AI), to enterprise products (like Adept), to code generation (like Magic.dev). This technology will shift the type of work people do from output generation to thinking and ideating--it won't be about how to make a movie, but rather what the movie should be about. The most exciting thing about this technology is that we are in the earliest stages of what it can accomplish. We are in the first at-bat in the first inning of a baseball game for this category. Technical innovation and research are fueling this first part of the hype cycle; more innovations in productization and business model development will come soon--and I can't wait.
Asif Moosani joined Gradient Ventures in 2019 and serves as a Principal on the investing team, focusing on early-stage enterprise SaaS companies globally. He has led rounds in and covers companies including Mural, Gigs, Contractbook, Mage AI, Syrup.tech, Sona, Weflow, Payload CMS, PopSQL, Zowie AI, Labelbox, and others.
Prior to Gradient, Asif worked in Technology Mergers & Acquisitions at Lazard Frères & Co where he advised leading technology majors, participating in 14 transactions totaling $100+ billion of aggregate deal value. Before Lazard, Asif co-founded Evolutionary Technologies, where he provided predictive analytics software serving clients in education and real estate. Earlier, he was a software developer at IBM focused on cloud services products including Tivoli and WebSphere.
Asif graduated with a B.A. in Business Economics from the University of Texas and M.S. in Finance from the McCombs School of Business at the University of Texas.
EVCA: Describe a defining moment in your career and how it shaped where you are today.
Asif: A defining core memory is knowing my father’s story of how he immigrated to the US, and how hard he worked to provide us the opportunities we’ve benefited from today. While his story is quite nuanced and best left for another time, it’s always been a driving force, instilling perseverance and the desire to overcome any obstacles necessary in pursuit of operating as the best in our given fields. It has also instilled in me a strong degree of empathy in accounting for the situation and context through which an individual is shaped. As it relates to my role as an early-stage venture investor, it’s helped me to evaluate founders on the basis of what they’ve overcome and how they’ve preserved, allowing me to support the strongest individuals in pursuit of their world-changing aspirations.
EVCA: What is an emerging technology trend that will have a significant impact on the world in the next decade?
Asif: Over the past decade, there has been an explosion of data and machine learning infrastructure tools. These technologies have served as the foundational layer, and have proliferated through the ML-stack in modern solutions like data labeling (Labelbox) and pipeline orchestration (Mage AI). The emergence of such technologies, enables a wider range of enterprises to adopt ML into both their core workflows and their customer-facing offerings. Additionally, it provides the basis for many startups to leverage this infrastructure when building more automated solutions to legacy problems (e.g., Syrup.tech, Weflow, and Zowie).
Similar to the industrial revolution, these technological developments will unlock an immense amount of productivity. In the same manner, the adoption of ML can serve as an automated assistant for humans across all sectors. Examples would include financial analysts spending less time building spreadsheets and data cleansing, and working on higher level strategic work; or teachers not having to write assignments (and partially offload grading too), and rather focus on working in a personalized manner with the students where they can have the most impact.
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