Startup investor Nancy Wang sees the potential for generative artificial intelligence to change the balance of power in fields such as cybersecurity, which is one of her areas of focus, leveraging her background as a former Amazon Web Services general manager and Google lead product manager.
But she has also seen first-hand the ways that AI can unlock opportunities for underrepresented groups in tech, as the founder and board chair of the non-profit Advancing Women in Tech (AWIT).
Collaborating with the U.S. State Department on its Academy for Women Entrepreneurs (AWE) program, Advancing Women in Tech ran a series of classes that culminated in a workshop last fall at an AWE event in Taipei.
Participating in the program were about 75 women entrepreneurs, who did not come from engineering backgrounds but were able to create e-commerce storefronts for their businesses with help from generative AI.
“It lowers the barrier to entry for people, especially underrepresented groups … to be able to create their own livelihoods and be entrepreneurs,” Wang said, describing it as an example of AI’s potential to level the playing field.
That’s one of the takeaways from this episode of the GeekWire Podcast, featuring a conversation with Nancy Wang. A technology product and engineering executive, advisor, and investor, she is a venture partner at Felicis Ventures, where she invests in early-stage startups in cybersecurity, enterprise infrastructure, and business-to-business software as a service. She’s also a contributor to Forbes.
Listen below, or subscribe to GeekWire in Apple Podcasts, Google Podcasts, Spotify or wherever you listen. Continue reading for additional takeaways.
More highlights from Nancy Wang’s comments, edited for clarity and length:
The “virtuous data cycle” in AI: A platform that thrives off user data will only get more valuable the more users and the more data it collects. … If you look at companies that have been born before the generative AI age, they’re now also training their own foundation models, maybe using 10-20 years of, let’s say, network intrusion data, versus a net-new company that will predict network attacks using generative AI. Who’s going to have that deeper moat?
And that represents a lot of the questions or debates that we as investors will have, when we think about the viability of these new companies.
The impact of online learning: The University of Pennsylvania is one of the few universities that has a full Master of Computer and Information Technology degree program on Coursera. The intent behind that is much very much similar to why we, as AWIT, put all of our content on Coursera, which is, we wanted to reach underrepresented students.
If you look at the enrollment rates for the online masters program, it’s nearly 40% to 45% women enrollment, which is unheard of in-person, on campus. So that begs the question: What will we see in terms of the underserved population’s economic abilities if we’re able to provide them these opportunities to flexibly uplevel themselves, to gain more skills, and to then go into more enriching careers?
What drives her work with AWIT: As one of the few women engineering graduates from undergrad, and then going into traditionally very male-dominated industries, such as infrastructure software, data protection, or security … sometimes, I did have to check myself when I was the only woman in a room, and that felt very normal to me. I had to check myself: Wait, this is not normal. This is not what should happen. …
I’m a natural problem-solver. That’s just who I am. I’ve always taken things apart, fixed things, put things back together, probably broken things in the process, as well. I see this as a problem that we need to solve as society. Because if we truly want to advance, and especially using AI products as an example, if we want that to be representative of all of the users, not just people who happen to be building the models, or who happen to be feeding the data, then we do need to bring up some of these underrepresented groups. …
Being the only woman in the room is not a norm that is going to exist much longer.
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