The Sheconomy Webinar Series Part II: Women in AI Perspectives and Predictions for 2024

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  Speaker 1:
  Hi, good morning and afternoon everyone. Hope you’re doing well. Welcome to the She Economy Webinar Series Part Two, where we’ve invited distinguished women professionals in the exciting sector of AI to share their experience and predictions for 2024.

  Before we get started, this presentation is for informational purposes only and is not an offer to buy or sell securities, which are only made pursuant to the formal offering documents for the fund. Please review important disclosures in the materials provided for the webinar, which you can access at triplewavfunds.com/disclosures.

  Please note you will be on mute for the entire presentation. This webinar is recorded and will be shared after the event. We encourage you to submit questions throughout the webinar, and we’ll try to answer your questions during the Q&A session.

  In terms of today’s agenda, we’ll do a quick overview of Alumni Ventures and then dive straight into our panel discussion, followed by audience Q&A. Thank you to those of you who submitted questions prior to the event.

  So, in terms of Alumni Ventures, we are the largest VC firm democratizing venture investments for accredited investors. According to PitchBook’s 2022 and 2023 rankings, we are the number one most active firm in the US and third most active globally. Since 2014, we’ve raised over $1.3 billion in capital and have supported over 1,300 portfolio companies across all sectors and stages. This has been achieved by our team of 130 talented individuals rallying behind one team, one dream.

  I am thrilled to be on both our AI Fund as well as the Women’s Fund. Both funds make 15 to 20 investments over a 12 to 18-month period that are diversified by stage, sector, geography, and lead investors. One of our secret sauces is that we co-invest with established venture capitalists with sector expertise. We leverage their diligence and term sheets while conducting our own disciplined process. We seek pro-rata rights and reserve 20 to 25% for follow-ons.

  There’s a $25,000 minimum investment, and our investors also have the opportunity to participate in syndications. For instance, when we invested in Cohere, which is a Canadian LLM company and now a unicorn, we ran a syndication that allowed our community and investors to invest more capital into that unique opportunity. The only difference between the AI Fund and the Women’s Fund is the thematic focus. For the AI Fund, we focus on everything AI, while the Women’s Fund backs great women founders and leaders across all sectors.

  Here’s what we’ve invested in for our AI portfolio so far. I want to highlight our investment in Cohere as well as Xanadu Quantum Computing. The CEO of Xanadu just announced in Bloomberg last week that he’s considering an IPO in three to five years, and we’re very excited about that prospect for the company.

  The AI Fund team is composed of seasoned VCs and operators in the tech and SaaS sectors. We recently led investments into Lambda, an enterprise GPU player and key Nvidia strategic partner, as well as SafeBase, which is an AI-enabled security posture platform.

  We are building from strength: we’ve invested over $200 million in over 350 startups that are founded, co-founded, or led by women. We’ve been investing at a higher rate than the current VC market for women (26% versus 24%).

  Here are some of the startups we want to spotlight. For instance, Kindbody, a fertility care company, is now valued at over $1 billion. Ellevest is a robo-advisory investment platform with venture capital provided in part by Melinda Gates’ Pivotal Ventures, Valerie Jarrett, and Eric Schmidt.

  Our Women’s Fund team is composed entirely of women investors—from serial entrepreneurs to finance professionals from the Yale Endowment and corporate finance product managers—from enterprise SaaS to Web3 and AI. We’re excited to back women leaders because we need more of them, and statistically speaking, they are more capital-efficient and generate better returns.

  I think this is a good segue into the main course of today’s fireside chat, which is a conversation with amazing women professionals in the AI space. Looking at these stats, I sincerely hope they will change and that we can be a force for that change.

  According to a 2022 World Economic Forum report, women make up only 26% of data and AI positions in the workforce. According to the Stanford Institute for Human-Centric AI 2021 AI Index, women make up just 16% of tenure-track faculty focused on AI globally. Recently, I came across a New York Times article highlighting who’s who in the world of AI, and I was appalled to see no women mentioned.

  I could think of two women immediately: one is Dr. Fei-Fei Li, the inaugural Sequoia Professor at Stanford, and the other is Sara Hooker, Head of Cohere for AI at Cohere.

  Anyway, enough of my monologue. Let’s invite our panelists to join our virtual space. Please come on camera. I’d love for you to introduce yourself as well as your firm. I’ll invite our amazing women panelists to appear on screen. Perfect—thank you.

  So wonderful to have you all. Maybe we’ll start from left to right… actually, let’s start with Vivian. Thank you very much. She just joined Next47—super excited for her amazing journey. Please take it away, Vivian.

   

  Speaker 2:
  Yeah, great to meet everyone today on this panel. My background is really that of an operator turned investor. I started out my career early at Uber as the second or third finance hire and was one of the first 200 employees. Eventually, I moved into product and then joined the VC world about seven years ago.

  I’m currently at a firm called Next47, having just joined as a Partner—this is actually my first week. I’ll be very much focused on investing in application-layer AI as my primary focus.

  In the past, I recently moved from CRV, where my focus was very similar: AI application-layer investing.

  A little bit about Next47: it’s a global venture firm built for enterprise founders. We’re based in Silicon Valley but have offices in the US, Europe, and Israel. We lead investments in early- and growth-stage companies. It’s a $2 billion venture fund focused on SaaS, AI, and enterprise. We typically lead Series A to Series C rounds.

  We have a passion for products that change the world. We build a lot of conviction in the categories we invest in and are very committed to the founders who choose to partner with us.

  What makes us different and helps us really add value to our portfolio is our go-to-market team that we often pair with our portfolio companies. They’ve closed more than $60 million in bookings and over 300 deals for our portfolio companies.

  We also have a network of about 250 Fortune 500 companies that we constantly leverage to make introductions between our portfolio and these potential customers.

  Some sample companies from our portfolio include Ada, Observe.AI, and Vast Data. Everyone on our team is focused on AI, and we’re excited for its potential to be the next major platform shift.

  I’ll pause here and let Sophia take it away.

   

  Speaker 1:
  Thank you very much, Vivian. So much to unpack there, and we’ll definitely get into all the details. Next, Sanjana.

   

  Speaker 3:
  Hi everyone. Nice to be part of a panel like this, and thank you Sophia for organizing it.

  My name is Sanjana, and I’m an investor at Radical Ventures. I started my career in investment banking and have been in venture for the last seven years. I joined Radical at inception, and it’s been a fun ride to see how the conviction we had on AI as a category has come to fruition.

  We’re now at a point in time where there’s a lot of activity and interest in space.

  A little bit about Radical: we’re a purely AI-focused fund that emerged from the deep learning ecosystem in Toronto, the birthplace of modern AI. We have folks like Geoffrey Hinton involved with our fund, as well as a core group of AI luminaries who are top in the field—investors in the fund, partners in the fund, and portfolio company advisors.

  Professor Fei-Fei Li, who Sophia mentioned earlier, is a Scientific Partner at Radical.

  We’ve been investing across the AI stack for several years—at the foundation model layer, infrastructure and tooling layer, as well as the applied layer. We like to invest in differentiated technology and AI companies innovating at the frontier.

  For example, Cohere was a company we incubated back in 2019—long before the hype we see today.

  We’ve invested in close to 50 companies across the US, Canada, the UK, and Israel. We are now investing out of our third fund, which is a $550 million fund, and we have nearly $1 billion of assets under management over the last four years.

  For Radical, AI is part of our DNA—it’s very core to our thesis. That’s all we invest in. We’ve never invested in anything else and we’ll continue this focus.

  This comes from the fund’s founders, who were AI operators and had built and sold three AI companies before founding Radical, as well as the core architects of today’s ecosystem.

  We’re excited to continue backing these companies and to share more about our investments in AI founders, including women AI founders.

  Speaker 1:
  Great, amazing. Thank you very much for that very thorough intro. Great. Taylor?

  Speaker 4:
  Yes, thank you, Sophia, once again for organizing this initiative. I’m so excited to be sharing this space with these highly accomplished other women. Really looking forward to our discussion today.

  My name is Taylor Chartier. I’m the CEO of Modus Prime. I’m a proud portfolio company of Alumni Ventures, and I’m also a venture analyst for Valley Capital Partners, backing AI and data-driven startups across the Midwest and Silicon Valley.

  My background, formally and by training, is actually in data science and engineering within the pharmaceutical domain. I’ve supported FDA biologics license applications by advancing quality-by-design initiatives across various unit operations in pharma, working from Boston—the biotech hub on the East Coast—to the San Francisco area.

  My education, as I mentioned, is engineering. I’m a chemical engineer by trade with a master’s from the University of Rochester. I started the AI company Modus Prime to provide pharma with the exact compliant AI tools required to rigorously apply AI to improve drug quality and safely deliver drugs to patients.

  Our technology is AI-based—a computer vision system—designed to solve cost issues, legal challenges, and waste liabilities resulting from drug quality failures. We’re very proud to be working with multiple organizations. We are a Johnson & Johnson JLABS company working out of the Texas Medical Center, and our GXP software enables scientists to apply hands-on techniques and domain expertise to improve drug quality.

  I’m excited to talk about my journey and share my perspective during this panel as a female entrepreneur operating in a highly regulated landscape that’s excited about applying AI but must account for extensive regulations.

  Speaker 1:
  Yeah, absolutely. Thank you very much. It seems like all three panelists have had relationships with AI going way back. Taylor, you’ve been in space for a while and chose AI as a career. Sanjana, you’ve been with Radical Ventures from the start, focusing exclusively on AI. Vivian, you’ve been looking at application tools and AI for as long as you can remember.

  So maybe just a general question to kick us off: how did you get to where you are? What got you into AI? Why are you focused in this particular sector? Perhaps you could share a bit of your personal or professional journey.

  Speaker 4:
  Did you ask one of the panelists to kick off answering that question, Sophia?

  Speaker 1:
  Yeah, it’s a general question. Anyone can feel free to jump in. We’d love for everyone to share their journey of how they got here and why AI.

  Speaker 4:
  Sure, I’ll kick it off—or Vivian, would you like to go?

  Speaker 2:
  No, no. Taylor, please feel free.

  Speaker 4:
  Sure, I’ll start. Speaking from the health space, we probably have similar journeys into AI. Currently, only 5% of the roughly 7,000 rare diseases have cures. That initially motivated me in my pharma career—to try to speed up the discovery and manufacturing of new drugs.

  As it stands today, approximately $50 billion is lost each year due to drug quality failures. The statistical techniques currently implemented in pharma are clearly not enough. That’s what drove me into the AI space. There’s an incredible need for scalable solutions that are faster, more accurate, and more specific—exactly what AI promises.

  That’s why I’ve implemented many AI technologies in this space. I see the huge unmet need and the incredible benefits that AI can bring to healthcare by improving efficiency and outcomes.

  Speaker 2:
  And I can go next. For me, it all comes from being passionate about technology that changes the world. I’ll never forget the first time I took an Uber—it was still a black car service—and it truly felt like magic: tap a button, and a car shows up in minutes compared to flagging down a taxi.

  I think there will be many more moments like that. The first time someone uses ChatGPT, Character.AI, or MidJourney—it’s a magical experience. And that’s not even mentioning what AI can do for enterprises.

  I don’t think it’s optional to be an AI investor in technology anymore. Everyone will have to become an AI investor, just as everyone became a SaaS investor. AI is going to permeate everything.

  In general, my passion and interest come from being fascinated by technology that moves the world forward. AI is going to give people superpowers we can barely imagine now. I’m super excited for what’s to come.

  Speaker 3:
  I can go next. For me, it started in 2016 when I was at my previous fund. We had a fund-of-funds program investing in deep tech and AI-focused university funds—innovations coming out of academic research. That’s when I got exposed to AI technology and saw some frontier applications being built very early on.

  At the same time, I worked daily with some portfolio companies integrating basic supply chain machine learning tools into their tech stack. I saw a significant positive financial and operational impact. That got me interested—not just seeing practical applications but also the fundamental research happening in AI.

  That’s when I learned about Toronto as a fundamental AI research hub, not well-known outside a small group. That drove me to Radical Ventures. I wanted to keep investing because, as Vivian said, AI is a massive platform shift.

  We think AI will impact every single sector. Some sectors will feel it more—like healthcare and biotech, as Taylor mentioned. That’s even more exciting because AI can fundamentally change outcomes, improve access, and reduce costs for some of the toughest unsolved problems.

  Speaker 1:
  Yeah, there are so many directions we can go from what’s already been shared. I’ll quickly chime in about Toronto and eastern Canada as an AI hub. There’s McGill, University of Waterloo, and Creative Destruction Lab with its AI and blockchain streams, producing amazing startups.

  I almost want to ask about regional differences between AI startups in Canada versus the US, but I’ll hold that thought and pull back to an overarching theme shared by the panelists: curiosity.

  Curiosity is essential for successful exploration and continued AI advancement. I was watching NVIDIA’s GTC 2024 keynote and was blown away toward the end when the two Star Wars BD droids appeared on stage. It was the cutest thing—and it made me think about combining AI with robotics and physics to improve service industries, from heavy machinery to healthcare and elder care.

  As Vivian mentioned, AI will touch everything. I’m very excited about its potential. Back in 2023, AI exploded into the mainstream. What do you think will happen this year? What are your predictions for 2024?

  Speaker 3:
  Yeah, maybe I can get started. I think everyone on this panel and in the audience is probably aware that AI innovations have been ongoing for years. There was a lot of research and work happening in AI, and it has been deployed in our day-to-day lives for a while—on our phones with Face ID, in computer vision, and even in algorithms used by companies like Uber, which have been applying machine learning techniques for prediction for some time.

  What really changed was that we had a consumer moment for AI. A big platform shift occurred with the development of large language models—transformers—created by folks like Aiden, founder of Cohere. These models were produced in ChatGPT in 2022, and that’s when people had their “aha” moment.

  AI started generating information and content that people could touch, feel, and understand. That’s when it hit the mainstream.

  I think we’ll continue to see a lot more. If transformers were the big technology innovation of the last five years since their launch, we’ll see further innovations. At Radical, we’re always thinking about what’s next after transformers.

  My prediction for 2024 is that we’ll see additional research innovations at the model and foundational layers, with new companies emerging. Also, AI will permeate applications across all sectors.

  We’re already seeing this in healthcare, education, supply chain, climate, and beyond. But one area I believe will experience massive growth is AI for sciences—biology, chemistry, physics. There’s a new level of innovation happening with different model architectures and scaffolding approaches that are transforming drug development and new material discovery. These advances could have a huge global impact and will soon affect all of us.

  Speaker 4:
  Absolutely, Sanjana. I have to second everything you’re saying. Now that AI is more tangible—something people can see, hold, and use daily—it has truly entered the mainstream.

  I’ve also seen significant developments in AI recently, and one thing I’d add for 2024 is the growing sense of alarm: What do we do with this AI now? Questions around safety and potential issues are driving new governance efforts.

  We’re seeing this globally—Europe has its AI Act, the EMA has issued healthcare-specific guidance, and there are big regulatory shifts. Interestingly, these governing bodies are turning to industry for guidance on how to regulate AI.

  Because the landscape is changing so quickly, they’re just trying to keep up. I’ve even contributed to some of these guidance efforts that regulators are using, helping them understand how to manage this rapid evolution.

  Ensuring “AI explainability” is crucial—communicating how AI makes decisions in plain language. The technology is developing fast, but ensuring AI is safe, explainable, and used for good is an evolving conversation.

  In 2024, I think we’ll see more discussions and collaboration between industry and regulators.

  Speaker 2:
  I agree with everything Sanjana and Taylor said. I’m also excited to see this year’s model evolutions—whether it’s what comes after transformers, innovations like Mamba or SSMs.

  My prediction for 2024 is that 2023 was the year of large language models processing structured data. That’s why we saw an explosion of companies building AI medical scribes, legal tech tools, and other products that handle structured word data, which is relatively easy to process.

  This year, I think we’ll move into the unstructured data era. AI will begin permeating industries beyond legal and medicine.

  We’ll see exciting progress in supply chain and manufacturing, where there are complex, messy data silos that LLMs can now handle. Sciences are another promising area with lots of unstructured data.

  And as models become increasingly multimodal—everyone’s seen the Gemini demo—AI will be able to see, hear, and interpret text and images simultaneously. That makes 2024 a very exciting year ahead.

  Speaker 1:
  Yeah, absolutely. I’m also looking forward to this multimodal phase, where different data sources and types—text, images, voice—come together to build more robust datasets. This will allow machines to generate richer, more engaging content.

  In layman’s terms for our community, imagine asking an AI bot: “Show me how to make seafood cioppino.” The AI could respond not only with text instructions but also with a generated tutorial video, complete with Italian dinner music.

  Thinking about these possibilities is exciting because they can make our lives and work more interesting, efficient, and enjoyable.

  So, for each of you: Are these the areas you’re personally focusing on this year? Vivian and Sanjana, you’ve mentioned healthcare, transformers, and foundational models. Are these your main focus areas, or do you have separate curiosity-driven topics you’re exploring?

  Speaker 2:
  I can start. It’s similar to my predictions for the year. I’m very interested in application-layer SaaS in industries that are currently underexplored but could benefit massively from AI handling unstructured data.

  I’m particularly excited about supply chain, manufacturing, hardware design, and even video game design. There’s so much potential beyond simply reading structured text, and I’m eager to dive deeper into these vertical workflows.

  Speaker 3:
  And Sophia, to your question, my focus is similar to what I mentioned earlier: the frontier of fundamental research. I’m interested in what comes after transformers, the next evolution of models, and especially how AI intersects with the sciences.

  I’m excited about founders and researchers who are tackling hard, non-obvious problems with AI—not just easy wins. Teams innovating at the frontier in sciences, using multimodal models, working with unstructured data, and having deep domain expertise to sell into challenging industries—that’s what really excites me.

  These are difficult spaces to operate in—they’re capital-intensive and take time—but the payoff is massive if successful.

  Of course, there’s also a lot happening in more obvious areas. We’ve invested in enterprise search, legal tech, video generation, and video search. Many of our portfolio companies in these horizontal markets are growing rapidly in revenue and usage.

  The consumerization of AI is another fascinating trend, and we’ll continue to see even more of that this year.

  Speaker 1:
  Yeah, you mentioned researchers, and I suppose that’s one of the technical moats you’re looking for when evaluating founders. Is that right? Could you also share whether there are any regional differences between AI startups in Canada versus the US, given that Toronto and the east coast of Canada are such strong research and tech hubs? I’m curious—are there notable differences?

  Speaker 3:
  Yeah, that’s an interesting question. I think there are obviously geographical and cultural differences between Canadians and Americans, but outside of that, I wouldn’t say there are many major differences.

  A lot of innovation from Toronto or Montreal is fundamental AI research. Institutes like UFT, Mila, Vector, McGill, the University of Montreal, and Amy in Edmonton produce a lot of cutting-edge AI work. Of course, similar research happens in the Bay Area with Stanford and Berkeley, and in places like Pittsburgh and Princeton. Essentially, wherever there are strong universities and top talent, you’ll find fundamental AI research.

  Toronto and Canada have made notable contributions—cohere, transformers, and Aiden’s specific work emerged from this ecosystem.

  But otherwise, when we evaluate companies, we’re looking for the same traits globally: an ambitious vision, technically differentiated approaches, strong technical and talent moats, and the ability to execute rapidly. These founder traits apply everywhere—Canada, US, UK, Israel.

  Of course, there are cultural nuances. For example, Canadian companies might project $50 million in revenue in five years, while American companies project $300 million. But we know how to evaluate those differences.

  Speaker 1:
  Yeah, absolutely. Thank you. Vivian, we’d love to hear how you evaluate AI startups’ technical moats.

  Speaker 2:
  That’s such a great question and something I’ve thought about a lot, especially in application-layer SaaS where you risk being just a wrapper on top of GPT or other models founders build upon.

  At the end of the day, there are two main approaches to building a moat—and ideally, you have both.

  First, think about it as simply building great software. If you’re in vertical SaaS, you build deep workflows, a system of record—software that locks in users. That’s the best kind of moat. People overcomplicate it by focusing only on “AI moats.” Just ask: where’s your moat as a software company? Building enterprise software isn’t fundamentally different here.

  Second, you have to consider your tech moat. One of my favorite examples is comparing Jasper and Writer.com. Both started strong with impressive revenue traction early on. But their trajectories have diverged.

  Writer.com invested heavily in deep enterprise workflows and built its own model, fine-tuned on business jargon data. Their outputs are simply better and more accurate. They also allow enterprises to customize models to their brand and style.

  Jasper, on the other hand, remains largely a wrapper on GPT. As a result, the companies have taken very different paths.

  When I talk to AI app-layer founders, I emphasize that yes, you need strong workflows and software, but you also need the AI research talent—like Writer has—to build a better long-term LLM or help enterprises fine-tune models using proprietary data.

  That said, one researcher I spoke with recently suggested that as LLMs get more powerful and we approach AGI, many of these fine-tuned models could become obsolete. That’s the exciting part about AI—it’s evolving constantly. Every month, something changes, and companies realize parts of what they’re building might be commoditized.

  We don’t even know if transformers will still be dominant in a few years. So you have to build products that truly serve customers, differentiate your workflows, and be willing to iterate quickly as technology shifts.

  Speaker 1:
  Yeah, for sure. Thank you for sharing your perspective on tech moats. Taylor, you’ve checked off two tough boxes for women—science and AI. How do you position yourself as a successful founder and tech expert with domain expertise? How do you define your moat, and what advice would you give aspiring founders looking to create startups in the science-and-AI space?

  Speaker 4:
  I’m happy to share some insights and perspectives. This ties into what Vivian said about constant innovation. Every month, new models and modalities emerge.

  AI today is truly becoming more of a science than it was traditionally seen as. The flexibility required to keep up with this landscape is incredible.

  In my experience, at the intersection of science and AI, the moat is constantly evolving. When building products in scientific domains using AI, you must frequently iterate and integrate newer AI modalities to stay competitive.

  Within a year, a model you’ve built could become obsolete, and customers might say, “We’ve seen better performance elsewhere.” So adaptability is key.

  Your moat ultimately comes down to meeting customer expectations and KPIs. If you can do that, you can leverage whatever AI engine or model is most promising at the time.

  I’ve seen AI innovation outpace scientific innovation itself in just a few years as an AI founder. We’ve had to stay extremely flexible and adaptable.

  For women aspiring to enter this space, every industry is undergoing digital transformation, creating opportunities for AI applications.

  My advice:

  • Join advocacy and industry groups. Engage in conversations and understand how AI is impacting these industries.

  • Use your voice. Share your expertise and insights to help shape these discussions and uplift the community.

  • Stay informed. What’s considered “current” last week may already be outdated this week.

  It’s an evolving dialogue, and being actively engaged is essential to building a strong moat and thriving as a founder.

  Speaker 1:
  Yeah, Taylor, I misspoke—you’ve actually checked off three tough boxes for women: science, being a founder, and working in AI. Kudos to you!

  In the last few minutes, I want to switch gears and talk about personal journeys and advice for other women professionals.

  Taylor, this is a tough question that many women can relate to: how do you deal with people who doubt you as a CEO—especially as a younger woman professional, engineer, scientist, AI expert, and founder?

  Speaker 4:
  This is a very tough question that I know many women can relate to. It’s not an easy topic, so I’ll give a raw response.

  I’ve faced significant challenges—not just technically leading a team in this space but also with fundraising, which we know is incredibly difficult for women (only 1.9% of VC funding goes to women-led startups).

  I’ve had experiences where I was offered a full-priced seed round with a VC firm. Things were going well, but suddenly my leadership and integrity were questioned.

  I felt immense pressure from surrounding investors to take the deal: “You have to do this.”

  But the deal was only for $1 million. At that time, in 2022, the average seed round was $2.5 million.

  Speaker 4:
  So it was very low. And with the VCs we were working with, there just wasn’t really a level of trust there. They clearly did not believe in me as a founder—as a female founder. So I ended up saying no to that deal.

  It wasn’t until a year later that everyone said, “Great job—that was the best decision.” That deal was not good for us because, as we all know, between seed and Series A is the “valley of death” for startups, and $1 million would not have gotten us through it.

  So instead, we just kept raising SAFEs. Now, my investors see that many startups unfortunately didn’t make it through 2023, while we did—and it turned out to be a great leadership call.

  Sometimes you just have to build a track record, stick with your gut, and say, “Look, I know what I’m doing.” Over time, you’ll build believers around you by making those difficult decisions despite the doubt.

   

  Speaker 1:
  Yeah, oh, I can totally relate. I’ve seen many women-led deals where women ask for less capital than others and at lower valuations. It’s tough out there—not only for women founders but also for women investors.

  Maybe I’ll turn the table to Vivian and Sanjana. Really quickly, what advice do you have for women out there?

   

  Speaker 3:
  Yeah, I can start. Honestly, every day I wake up and just want to be like my mom. She was a working professional and my biggest inspiration.

  Come what may, I want to get up each day and make her proud. So my advice is to have a role model you can look up to and a North Star of values—leadership, integrity, hard work, intelligence—that you are fully convinced of.

  Remind yourself of those values every single moment they’re questioned.

  It’s not easy—I struggle with it myself. It’s easy to say, difficult to implement. But having strong values and a role model that inspires you daily, especially when things get tough, really helps.

   

  Speaker 2:
  Yeah, I think in venture, it’s getting better but is still an incredibly male-dominated industry. Unfortunately, Taylor, I think you have it even tougher—there are so many more male entrepreneurs, and the numbers aren’t where they should be.

  In fact, with AI, it sometimes feels worse. I walk into AI events or happy hours and think, “Wow, this is actually worse than B2B SaaS events used to be.”

  It’s not the easiest environment. The thing that’s helped me most is that women in the industry are very willing to help each other. I’ve been very lucky at different firms to have incredible female mentors, far along in their careers, giving me advice.

  Great organizations like All Raise are helpful, but having one-on-one individual mentors—both internally at your firm and externally—has made a huge difference for me.

  These mentors have been my biggest advocates, my strongest references. They’ve gone through the challenges themselves and recognize how difficult this journey is.

  The best thing I can do is to pay it forward—supporting people who are newer in the industry.

  And, of course, events like the breakfasts Sophia organizes and more female-led gatherings help build a stronger community for us all.

   

  Speaker 1:
  Absolutely. I totally echo everyone’s points. It’s super important to keep building a supportive community and network—lifting each other up—because we have so much to contribute.

  We’re here because we love what we do. We’re curious and passionate about AI, and passionate about backing amazing founders and supporting other investors.

  Thank you so much to our panelists for joining today. We deeply appreciate you taking the time to share your thoughts and perspectives.

  I hope we can keep in touch and stay connected within your networks.

  Feel free to leave the camera now.

  For the audience: I believe you’re here because you’re also curious about AI and the women leaders making their mark in this field.

  At Alumni Ventures, we want to back founders at this unique intersection, and we invite you to join our fund. Please book a call with us to learn more and access detailed information.

  My colleague will provide two links in the chat.

  If you’re a founder looking to partner with a large network to grow your company, please visit us. I’m on both the AI Fund and the Women’s Fund, and I’d love to talk to founders working on frontier tech that’s changing our work and lives for the better.

  Lastly, as the webinar ends, you’ll see three quick survey questions on your screen. We’d appreciate you completing them so we can improve future events.

  We’ll follow up with the event recording. Thank you again for taking the time to join this live session.

  We look forward to seeing you at our next event.

  Take care, everyone—and thanks again!