Why Every Startup Needs to Be AI-Native

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  Sophia Zhao:
  Good afternoon, everyone. Hope you’re well. Alumni Ventures Women’s Fund is pleased to host this webinar on a very topical topic: why every startup needs to be AI native. We’re definitely excited for our upcoming lively discussion.

  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 www.avfunds.com/disclosures. Please note you’ll be on mute the entire presentation, and this webinar is recorded and will be shared after the event. We encourage you to submit questions throughout the webinar.

  Let’s kick it off by telling you a bit about Alumni Ventures, and we’ll dive into discussions with our esteemed panel of women professionals in AI, and we’ll conclude with Q&A. Next, I’d like to invite our managing partner, Laura of the Women’s Fund, to tell you all about us.

  Laura Rippy:
  So good to meet everyone here. I wish that GoToWebinar meant that we could just see everyone’s faces and welcome you as we learn more about AI and do it from a lens a little bit female-centric with our panelists and with our host Sophia.

  So let me just start things out just to let everyone know who’s here. There’s a lot of folks who are new to Alumni Ventures. We’re going to do a little introduction in a moment and also to understand a little bit about the Women’s Fund that is bringing you this webinar today.

  So let’s start with AV. We’ve been around for about nine years. We are the single most active venture capital firm in North America. We are the third most active globally, and we’re different. Our source of capital is individuals. So all of you out there listening today, you are invited to join us as an investor. We have about 10,000. So far we’ve raised $1.3 billion and poured that into about 1,300 startups, all with incredible founders with ambitious goals and strong lead investors in the rounds in which we participate.

  Talking about today’s fund is the Women’s Fund. So Sophia and I and two other members of our team you’ll meet in a moment — we are building a Women’s Fund here, and we start at Alumni Ventures with a position of strength. We have 357 companies already in the portfolio at Alumni Ventures that are either founded, co-founded, or led by women. So we’re starting from that position of strength.

  The fund itself is a diversified portfolio. So you join us with a minimum commitment of $25K. We build a portfolio that’s diversified on stage, sector, and lead investor. And that portfolio you can think of as exposure to venture, but with an extra special twist in that the leadership of the teams in which we invest are led by women or co-founded by women.

  So the fund team — there’s me on the left. I come to venture from an operator’s perspective. I’ve been a CEO three times, and I’ve been here at Alumni Ventures for about seven years. I run the Harvard Fund, I run the Dartmouth Fund, and the Women’s Fund here, and I’m also on the board. Sophia, you’ll get to know in a minute, and I’ll let her do her own introduction as she kicks things off. But we also have Meera Oak and Brittany Wade building our team, listening to the market, delivering great deals into the portfolio.

  Sophia Zhao:
  Hi everyone, my name is Sophia. I’m a senior principal on our AI team as well as the Women’s team. Prior to becoming an investor, I was in the startup and banking world. I worked with a lot of founders and CXOs in the cloud computing, SaaS, natural resources, and consumer sectors. I’ve immersed myself in the world of Web3 post graduating from Yale School of Management, and I’ve been focused on AI and Web3 investments on our team currently. I’m particularly interested in the application of AI and how it will help optimize how we work and live.

  Laura Rippy:
  And Sophia has really sharpened the sword in that category. I think you’re going to have fun with her as our moderator today.

  So just getting back to the Women’s Fund — why are we focused on it? Better performance, quicker exits, and sector-spanning startups. Female-led, venture-backed startups tend to exit sooner and female-led teams tend to be more capital-efficient.

  Our portfolio will be consistent with what we’ve done already at Alumni Ventures across all of the innovative categories of venture — cybersecurity, machine learning, AI of course — but also in biotech, robotics, cutting across all of the venture categories. So if you join us — and I hope you will — we’ll talk a little bit more about mechanics at the end. It’s an amazing way to add venture to your portfolio and join the 10,000 investors who have already joined us at Alumni Ventures to add venture to their portfolio.

  Some sample deals, just so you know the kinds of deals that we get into. I’ll just highlight a couple. So Kindbody — this is the leader really in fertility treatment. We joined at the Series A led by RRE. It’s now a $1.8 billion company and led by the indomitable Gina Bart. Really fiery, fiery founder. They’ve grown very quickly. Walmart is one of their key accounts. I joined them last year. So this is a really dominant company that we joined again in the Series A.

  Carry1st is another one on the other side. This is an EmTech — excuse me, not EmTech, FinTech — FinTech company in Africa that uses gaming as the entry point for payments in the continent. When we joined, A16Z (formerly known as Andreessen Horowitz) was doing double their pro rata in a round led by a gaming-specific VC called Bitkraft, which was itself doing a supersized check.

  So really amazing opportunities here. We get in because of the school connection — that’s why it’s called Alumni Ventures. We have 20 school funds for basically all the Ivys and equivalents. And as a member of the Women’s Fund, you get deals that cut across the different school ecosystems and you really get some of the best that we think is out there because of course they’re led by female founders.

  Sophia Zhao:
  Great. Well, as the Women’s Fund, we’re here to support women professionals in AI, deep tech, healthcare, gaming — you name it.

  In terms of women in AI — I’m sorry for sharing with you these somber stats. I mean, currently there is very little women representation in AI. For instance, the World Economic Forum report found that women make up only 26% of data and AI positions in the workforce. When it comes to university tenure, women make up about 16% globally. And last November, when we came across the New York Times article highlighting the who’s who — the key contributors to AI — women were excluded.

  And that article has really inspired Alumni Ventures’ Women in AI blog and webinar series because we feel that it is important to highlight the contribution of women and how pivotal our role is that we play in shaping the future of AI.

  So without further ado, let’s welcome our panelists to come on camera. We’re so pleased to have them join us and share their unique perspectives, and perhaps we can ask each and every one of them to introduce themselves.

  Jean, would you do us the honor by going first?

  Jeanne Dunn:
  Sure, sure. So I’m Jean Dunn. I have a French name in between, but most people can’t pronounce it, so I usually call myself Jean Dunn.

  Anyways, I am a 30+ year tech executive. I started in Boston, as a matter of fact — I understand Laura’s from Boston. So I started in Boston with a number of software companies and then came out to the West Coast to do a startup, take a startup public. And then we sold that, and I went off to Cisco after that. I spent 22 years at Cisco, various different roles — running businesses, running global marketing, sales, and running customer services. So I’ve done pretty much every kind of role you could think of in a company. And of course, when you work in tech, it’s all about innovating constantly and finding new ways to do things.

  I’m going to enjoy the discussion today. I am looking forward to meeting the rest of the panelists. And last but not least, I should say my clarinet job now is — I started the company five years ago. What we do is we work with startups and large companies and essentially take ownership in them and help to get them to scale. So one that I’m a founder of, two that I’m an advisor on, and I’m also on the board of three public companies.

  Sophia Zhao:
  Wow, amazing. Thank you very much. Pleased to have you here.

  And next, we want to invite Gabrielle to introduce herself. Gabrielle always brings a very unique international perspective to our panel today. Gabrielle?

  Gabrielle Hurtubise-Radet:
  Yeah, it’s a great pleasure. Thank you so much for welcoming me. I’m currently in Montreal indeed, and I’m in an event, so I hope that you hear me well.

  My name is Gabrielle, and I’m very grateful to be part of this conversation. As a principal manager at ATM in the entrepreneurship team, my goal is to find science–market fit for our scientists. We have a community of 1,200 researchers. They’re writing papers, discovering brilliant things, but they have a hard time transforming their research into a product. So that’s where I help them — taking my experience from Microsoft and working in a startup that was measuring carbon for cities.

  So yes, it’s a pleasure to be here today to welcome not only female scientists but also female entrepreneurs who are willing to develop new ideas. It’s something that I really struggle to develop in our community in the sense that we would like to foster diversity programs, but it’s something that’s still a big challenge. So I’m happy to see that this kind of conversation can take place.

  Sophia Zhao:
  Yeah, absolutely. I’m sure some of our audience members are entrepreneurs, scientists, researchers, and people curious about AI — and I think they’ll benefit from learning and hearing from our perspectives.

  Samantha Huang:
  Hi everybody, I am Samantha. I’m a principal at BMW Ventures. We are a $350 million fund that we invest on behalf of BMW. We focus on Series A and B, primarily automotive, sustainability, manufacturing, supply chain, and enterprise SaaS — which involves, of course, AI and big data — the areas that we play in. Previously, I’m also a lawyer, so AI and engineering and law — they follow Boolean logic. So even though I’m not technical, a lot of the underlying logical systems are the same. And so I’m a very excited deep tech investor.

  I would say on the side, I’m also on the board of the Emerging Venture Capitalists Association, which is a networking org for emerging VCs. And then I also work with a nonprofit called the Asian Law Caucus, which is the oldest civil rights organization for the Asian community in the United States.

  Sophia Zhao:
  Amazing. Thank you so much.

  Alright, well maybe kicking it off for our discussion. As consumers, we often utilize AI without really realizing we’re using AI. So, for example, when we’re unlocking our phone with facial recognition, when we have curated content from Instagram and TikTok, when we’re using navigation tools while driving — and the list can go on and on — AI has become a part of our lives.

  And of course, by natural extension, we are seeing a lot more AI usage, or talk of AI usage, in work. So curious from your perspective: what do you see as the benefits of startups or companies becoming AI-native or adopting AI into their day-to-day operations?

  Jeanne Dunn:
  You want one of us to go?

  Sophia Zhao:
  This question is open to all. Feel free to jump in.

  Jeanne Dunn:
  Yeah, well I’ll start. As I said before, I’m involved with three different tech startups right now, and so I can talk from that perspective as well as from a large company perspective.

  First of all, I think everybody — honestly, with this past year and a half, past two years of OpenAI hitting mass adoption and new tools being available to all of us — I think everybody should be using or probably is using AI to some extent, whether they know it or not.

  But with regard to tech startups — when you think about what you have to do as a startup, you have to do as much as possible with as little as possible in terms of capital. You have to move fast. So it’s all about speed. And you have to be able to, if you will, get more capacity. You can’t hire fast enough. Even if you have the money to hire, you can’t hire fast enough.

  So the wonderful thing about AI tools is you can use them in so many different ways to set up your business. I’m, for example, using them for my support desks. I’m using them for sales tools. We’re using them across just our onboarding to our platform. So we’ve embedded them as a way to onboard to our platform. And I think in the future that’s going to go multimodal. So that’s going to be an interesting twist and turn.

  But even the basic stuff — I know one of our panelists here is a lawyer — I set up most of my early contracts with OpenAI, and then I had my lawyer check them. So we ended up getting a first-draft contract done and then sending it off to our lawyers to just do some tweaks and finalization for us. And that probably saved us about 80% of my legal bills.

  So all of those things are really great uses. And of course, every day you’re writing, so writing tools — Grammarly, other tools to help with that — are terrific. And of course OpenAI — it’s a great research tool, but of course you do have to check it all the time to make sure that your data is accurate. But all of those tools are wonderful, and I can’t imagine startups not using them — because you really have to, right? When you think about all the things you have to do with just a little.

  Samantha Huang:
  I would say those are all great use cases and stuff that I’ve kind of played with before.

  The way that I view AI within my workflow, or just general workflows — it’s all about automation and efficiency. So how do you become more productive? AI is a key enabler of that. It makes you more productive, it allows you to cut down the time that you spend on a certain activity, it also allows you to save money. So you might not need an extra person to be doing the data entry or the emailing and the scheduling — that can all be done by an AI ME agent.

  And then for me personally, the way that I’ve seen really extreme improvement in my productivity with AI is that I have very detailed conversations with ChatGPT so that I can discover and learn about new markets and technologies really fast.

  So I was able to get up to speed on the whole satellite industry — the whole satellite technology/telecom — for an investment called Skylo that we did in December. Literally I was talking to ChatGPT for like three days, asking questions. And that would be using ChatGPT, which is — I think it’s family payment — it’s like $25 a month. Versus, I’d have to call a person, and then I’d take a $1,000 consultation fee. I’d have to call three people to get a little bit of an understanding of a market. But with ChatGPT and these other tools that just put information at your fingertips, your process is so streamlined — and you can just also save a lot of money doing it as well.

  Gabrielle Hurtubise-Radet:
  Yeah, no, I totally echo what has been said. I like the fact that efficiency was mentioned, reduction of cost — but also the discovery and the conversations — is very interesting.

  And on this media, I have a different perspective. Because, as you may know, in Montreal we have an official singular stance on AI within Montreal Declaration and responsible AI, and we really address all aspects of the development.

  So I really believe — just as you said — that all startups should improve and develop AI within their technology. Yet I would encourage them to do it for the right reasons.

  And I’m saying that because all scientists — the only thing that… it’s the beauty of it also — it’s AI. So by design, at the core of everything they’ll do, they’ll add AI. Now the question is: is AI very much rooted? Is it the real answer to the problem you’re trying to solve?

  So this is where I like to encourage people in the audience also to think twice about the use of AI. Yes, it can improve efficiency, it can reduce your cost, but if you implement it not within the operation, but within the product itself, then you need to ask yourself the right questions about whether or not there’s a demand on the market for this.

  And what I encourage them to do is to try to solve the problem by any other way possible. I mean, at the end of the day, if AI is the best solution, then go for it. Then it’s really where you’re going to have a competitive advantage, and you can use the research and expertise to go in that direction.

  But if you find other ways of doing it, then maybe people — because there’s also a strong cultural change linked to AI and to the use of it — then many people would actually go for something that is a little bit less technical.

  So I fully agree with the fact that all startups should — maybe not implement AI — but ask the right questions about whether or not they should implement AI, and see whether it’s in their operations or in their product itself.

  Sophia Zhao:
  Absolutely. I feel like everyone has touched upon some really key points — speed, cost, automation, doing more with less, essentially.

  So I read this survey by HubSpot. It surveyed over 1,300 professionals that are in marketing and sales roles, and they use AI to save at least three hours per content creation. And they save two to three hours by automating or eliminating manual tasks, so that they can allocate their time and prioritize attention to the tasks that only they can do. Maybe it’s that very human relationship-building that can’t really be replaced by an AI.

  So there are definitely benefits of incorporating AI into everyone’s day-to-day work and life. And Jean, you touched upon a really cool point — multimodal. What’s interesting is, early in the year when we were looking at these AI trends to watch in 2024, multimodal was a trend to watch. But fast-forward to April or May — multimodal is already here.

  And for a community member — multimodal basically means it is a machine learning model that’s capable of processing information from different types of data sources, including image, video, and text, for example.

  So you can now ask ChatGPT — where ChatGPT-4o was released I think last week or so — you can ask it: “Hey, show me a recipe of, I don’t know, butternut squash ravioli.” It’ll give you an image of it, it can give you a video of it, it can walk you through a very simple recipe.

  So I feel like the speed at which AI is innovating is astonishing, where a trend that we’re identifying a few months ago is already becoming reality. And I think it’s important for startups to bear in mind that it’s almost like, if you don’t move forward, you’re moving backward.

  And we have an audience question from Christine, specifically wanting to know: What is the best way to learn the basics of integrating AI into core business functions?

  So, if our panelists don’t mind — can you maybe pinpoint a tool or a way that you’d recommend to Christine?

  Jeanne Dunn:
  Well, I think the first thing is: just start using it. First of all, once you start using it and realizing the power, you’ll get a sense of how many things you can do with it.

  There are all kinds of tutorials, all kinds of classes that you can take online for it, just to get some sense of the different ways to use it. The most important thing to understand about, particularly LLMs, is how you ask a question determines what type of answer you’re going to get.

  So I think the trick is: start playing around with OpenAI or whatever tool you like, and get good. Start understanding the best ways to put your questions in a specific context so you’re going to get the right answer.

  And there are frameworks and things that you can use. On my site, in fact, I point to a lot of different frameworks that essentially show you how to ask a question in a certain way to get the right type of answer that you want to get.

  And I think that’s going to be the most important thing to do right now. The other thing is — what role do you have? So this answer’s going to really depend on the role that you have, and the type of work that you have to do every day, and what you want to learn about.

  There’s so much within the general LLMs that you can do, it’s hard to come up with a task, quite frankly, that can’t be done through a general LLM.

  Where LLMs are going — we start off with these large language models, just one big monolithic thing. But what’s happening now is they’re realizing — and this is with ChatGPT and the recent 4o launch — what they’ve essentially done is crafted these, if you will, small multimodels that each can do a specific thing really, really well. And by doing this, they’ve solved a couple of different problems.

  Jeanne Dunn:
  One is this ability to quickly pair on new capabilities and new information without having to rebuild the entire model again. And the second piece of it is it makes it easier to process — you need fewer GPUs to process the model. So all of this is advancing so fast that you can do anything any way you want, and the multimodal model allows you to even do it while you’re in the car. You could talk to it and get an answer. In fact, that’s what we’re doing with some of our applications. We’re moving to that model where we have a lot of users — in fact, 80% of our users are mobile — so using voice to text, back to voice again, those are really powerful tools for people that can’t be attached to a screen all day long.

  So I’d say, figure out exactly what your job and your workflow is. Start working on tools — I can send a link when we finish the webinar to people that shows really all the top 10 different tools that you can start playing with. I think the best way to learn is to try it. And then of course, just take a couple of general AI classes — MIT offers and Stanford offers them. They’re free, they’re on multiple sites. And just read, read, read, read — because everybody’s publishing on it now, so it’s so easy to learn. And of course, just go on and type in some things, type in some questions, and it’ll actually teach you along the way.

  Laura Rippy:
  Hey everyone. Taking a quick break to share more about the Women’s Fund from Alumni Ventures. AV is one of the only VC firms focused on making venture capital accessible to individual accredited investors like you. In fact, AV is one of the most active and best-performing VCs in the U.S., and we co-invest alongside renowned lead investors.

  With the Alumni Ventures Women’s Fund, you’ll have the opportunity to help us invest in fiery female founders. PitchBook reports female-led startups are more capital-efficient and exit faster, yet only receive 15% of all venture capital dollars. We see this as a great opportunity, and we’re starting from a position of strength. Alumni Ventures has already invested in over 350 startups founded, co-founded, or led by women.

  So join us in the Alumni Ventures Women’s Fund to put your investing capital to work backing a diversified portfolio of female-led, high-velocity startups as they change the world. Visit av.vc/funds/womens to learn more.

  Now, back to the show.

  Gabrielle Hurtubise-Radet:
  I couldn’t agree more. Just get started.

  Sophia Zhao:
  Yeah.

  Samantha Huang:
  Yeah. And there’s two camps, right? Oh, go ahead, Gabrielle.

  Gabrielle Hurtubise-Radet:
  I’m sorry — I think there’s a little glitch in the Wi-Fi here — but I was just saying, maybe just to add to what Jean said, and I think it’s pretty comprehensive.

  I’m a strong believer in capitalizing on what you already have. So maybe explore, because I’m pretty sure that AI is already in your life right now. It’s already in our social media, as well as what you mentioned — it’s already in Gmail, for instance. It’s already on so many platforms. So if you try to understand how AI is trying to influence or is already having an impact on your life, then decoding the impact it has — and how you can maybe use it for business obligations — and how companies are currently using it, and what the impact on your life is, will also help you do the other way around.

  And I also believe that, as a non-technical person, the way I went through AI is by turning to my comfort zone. I was studying political sciences, social sciences, and I started going into AI — going in that direction — through those lenses. Trying to see what is social science, what is political science saying about AI. So I was still in my comfort zone, just touching the boundaries of what I know — and it was very helpful.

  Go ahead, I’m sorry — I didn’t mean to interrupt.

  Samantha Huang:
  I could answer, but we can also go to another question in case Sophia wants to move the ball along.

  Sophia Zhao:
  All good. Well, Samantha, since you’re an investor — we have a question from Ellen who’s interested to know: What are some of the AI investment trends you are excited about?

  Samantha Huang:
  Generative AI. That’s the biggest thing out there, right?

  There are two ways to think about it. One is just applications — that’s going to be a huge market. So you have all these new foundational models that are powering all these new applications. It could be reimagining how you do FinTech, reimagining productivity in the workplace. You can potentially replace a business analyst in the workplace through AI-enabled business analyst visualizations.

  And then there’s also the whole range of — apart from application-specific stuff — you have your workflow tools. All the tools that developers need to create an AI model and deploy it. Those are all kind of the hot things right now. That includes not just tools for building out your generative AI model, but also remember: it’s not just the generative AI stuff that is cool. It’s also the old stuff — AI from just like two years ago — which is just predictive stuff. Being able to generalize based on data and make predictions so that you can provide a better application at the end.

  I’m still very much leaning into the old predictive AI stuff because there are tons of applications that can be built on that too. So for example, I’m looking at a company that is using the old-school AI stuff to monitor machines better. There are always applications that can be had with either predictive AI or generative AI.

  So there’s a lot of hype, but there are actually use cases that can solve real business problems. So in some ways, I believe the hype is justified.

  Sophia Zhao:
  Yeah.

  Jeanne Dunn:
  I agree with that, Samantha. One of my companies, DeHart Labs — their whole business is essentially taking new types of data, so earth science data, satellite data, and being able to aggregate that into intelligence through ML and LLM. They use both. They use LLM on the front end of their platform.

  Very challenging — as you know if you’re in the data science space — very challenging sometimes to do the modeling. So LLMs can be used in the front end of it to simplify how customers can use it. Because one of the biggest challenges, honestly, data applications have had for the last 10 years is that people don’t know how to use them. And it’s hard to get the talent in to use it — it takes a fair amount of skill.

  But if you can pair up an LLM with a big data model for a machine language platform that can take all kinds of data analytics, ingest it, and then formulate intelligence out of that through easy-to-use tools and modeling on the front end — boy, you’ve got some magic.

  I think that we’re going to see all kinds of data applications, workflow applications, take off in the next five to ten years. I’m certainly seeing — from all of my enterprise companies — rapid adoption these days of those types of applications.

  Sophia Zhao:
  Yeah, absolutely. I have a follow-up question from Susan.

  Obviously, AI is changing so rapidly — but how do we assess what is forward versus what’s unrealistic? And I actually would love for Gabe to take a stab at this question, because obviously Gabrielle, you work with a lot of scientists, and some of them are turning into entrepreneurs. And of course, sometimes what they research may not necessarily translate into a viable business product — or a product in general.

  So maybe starting with you — how do you make that assessment of what is forward versus unrealistic?

  Gabrielle Hurtubise-Radet:
  It’s so complicated to answer, and such a big question as well.

  I wonder what is meant by “unrealistic”? Is it unrealistic to make it? Or is it unrealistic because I’m not sure if people are going to buy it? Or is it unrealistic… I’m not sure.

  There are two things.

  The first of all is, I believe that at the end of the day, the market is right — the customer, the person who’s going to pay for it.

  And this morning, I was actually talking with one of my founders — we have a portfolio of 40 companies that I’m helping on a daily basis — and one of them was like, “Gabrielle,” and I’ve been following her for two years, “I’ve been building the best product ever. I know that my product is perfect, the technology is working so well, and yet nobody’s paying for it. Nobody knows what I’m building. Nobody knows that what I have may solve this problem. Because I don’t want to have a marketing team, because I’m not sure about the positioning,” etc., etc.

  So if you start with the technology itself — and that’s why I think what was mentioned regarding de-hype on generative AI is very, very relevant. Samantha — yes, absolutely, generative AI is going to change a lot of things. But you need to make sure that it’s applied to a specific problem and it’s going to solve something in society. And not only solve it, but actually that people are willing to pay for it.

  So focusing on the problem itself, focusing on customers, and redoing customer discovery, customer iteration — having this product mindset of doing iterations based on the feedback that you receive, and not just trying to build something in your lab — that’s what I see with my scientists. They just build something when they think it’s great. And sometimes, from a technology adoption point, it’s amazing. And yet the market is not ready for it.

  So that’s my first point: really make sure that you understand the market and that you find your science–market fit.

  The second thing is — because I like the idea of the word “unrealistic,” actually, it’s very interesting to me — I was going to talk this morning also about the kind of imaginary, the kind of story/narrative we build around AI. What we build goes in a specific direction — a specific narrative that we’re all being told, that we all convey. We build a narrative of productivity, we build a narrative of efficiency, etc. And that’s kind of the mainstream direction in which we all go — which is probably normal, and in which we saw finance, where actually AI has been proven to make a major difference.

  Yet I believe that there’s room for other perspectives. I strongly believe that there’s room for other imaginaries, other narratives. And that women actually have a strong responsibility and leverage to build those new narratives.

  So really ask yourself: what is unrealistic? And maybe it’s not that unrealistic. Maybe the market is telling me that it’s okay and I can go in that direction. And maybe I can also tell myself that I have all the tools and resources to build it.

  Sophia Zhao:
  Yeah, great. Thank you for sharing that.

  Maybe just passing the portrait over to Jean — I mean, you’ve been in tech for over 35 years, you’ve worked with a bunch of entrepreneurs, founders, CXOs, etc. When you’re assessing these companies, what do you feel are the key success factors for them to be successful?

  Jeanne Dunn:
  Well, it really depends on if we’re talking about startups. Is the question specific about startups?

  Yeah, so I think about startups — it comes down to two things to start off with, and then it builds over time in terms of its sustainability.

  I think one: it has to be an original — either technology, idea, or business model. And two: it has to be a great team. If you don’t have those two components, it’s very challenging. It’s very challenging to make your way out of the noise.

  Because where we are right now — it’s so much, I don’t want to say easier, but it’s so much… well, I guess it is easier to start a company these days. It doesn’t take as much money — because of AI, because of other tools — it doesn’t take as much capital as it used to, to start a company, particularly in the software business.

   

  Jeanne Dunn:
  And so it really comes down to originality and value — the value of the originality of the idea — and then of course having a team that can sustain that over time. So you start off in this place where you’ve got a great product or a great service or a great approach to a new business model that you’re trying to address.

  And the team has to know how to scale. Number one — because I would say that most startups fail by sitting too long in one place, hoping that something’s going to turn. They don’t know how to pivot when they see early warning signs that what they have is good, but it’s not quite in the pocket yet. So they need to pivot. And that happens to almost every startup that gets going. They find that they have to slightly move to the right or to the left, and they don’t do it early enough — and then they run out of money before they can be successful.

  And then the other piece is just resilience, right? Because doing a startup is not easy. And most people — they go, “Yeah, a company is one to two years.” It takes a good ten years to truly build a company, a high-quality company. And you start to get the essence of it by years three to five, but in order to really see if it can scale to a massive amount — it usually takes a long time. A lot of the successes we’re seeing today, even in the LLMs, have been ten-year projects. They didn’t come about overnight.

  So I think that it’s got to be: team, original idea, original thought. They’ve got to be able to be resilient. They’ve got to be able to sustain. And they’ve got to be able to pivot when they get information and news that says it’s not going exactly the way they had planned.

  And this happens to, I’d say, probably more than 60–70% of startups. So I think those are the key things. And at the end of the day, it’s all about the team. Because if the team at its core isn’t strong enough, isn’t resilient enough, isn’t smart enough to pay attention to all the signals and adjust properly — or if they’re too stubborn to make the right changes — then that’s a problem.

  The other piece is I honestly believe that the best companies — the best companies — kind of start with a bit of not just a technology-centric focus, but a customer focus. They really understand the value, or a value-based focus — like really understand, at the end of the day, what outcome they can deliver, and really get all over that and make sure they’re delivering that. Because if they don’t understand where they’re trying to go, then it’s difficult to figure out what’s going to get them there. So the clarity to outcome is also, to me, a key thing for success that I look for.

  But lots of other things — but I think those are essential.

  Sophia Zhao:
  Yeah, thank you Jean. I feel like that would be a very popular X thread if you put that on X. We will like and re-X.

  Great. Well, in the last few minutes, we have some general questions from Mara, Maura, and Katie — more about advice for women professionals. So, for instance: How can women have a niche role in the future of AI? How can we upskill and empower women with AI knowledge and tools? And if you can share maybe a particular AI tool that you love, that you use, that you like — that would be super helpful. Maybe Samantha, we’ll start with you?

  Samantha Huang:
  A tool that I love… okay, well, I am conflicted because I think ChatGPT is a very cool technology, but I think OpenAI has some issues. And hopefully it doesn’t go into evil territory — but it could go into evil territory.

  But I do heavily rely on ChatGPT, mainly for information discovery and iterative learning.

  Jeanne Dunn:
  And then — how can women have a niche role in the future of AI? I mean, this is a pretty broad question, but if you have any specific ideas or top of mind that you can think of…

  Samantha Huang:
  Yeah, well, so there are different ways. Just — let’s be AI engineers. Let’s be investors. Let’s take up roles in the businesses, because they ultimately will dictate the course of how technology is developed over time.

  I also want women to be heavy on the data creation side, because ultimately these models are going to be based on the data. And I would like women to be watchdogs for biases in the data that the models can train on. So these are all important ways that women, I hope, should continue to develop — and also take strong stances on.

  Because this technology is super empowering, but there are also some dangers to it — with privacy, human agency, and the future of work and the future of people. So all things to take into consideration as we go on into this crazy frontier of AI development.

  Gabrielle Hurtubise-Radet:
  Two great questions.

  For the tool I’m using — I’m also using ChatGPT a lot. As you can see, I’m a non-native English speaker, and it’s helping me develop new ideas and also work with my team. I recall I was newly promoted to manager, and I was having a meeting for which I wasn’t well-prepared, and it really helped me develop and push the boundaries of what I was thinking and challenge also my ideas.

  So, of course, ChatGPT is a great solution. But again, as mentioned, OpenAI has some struggles, and I think it’s just leaving room also for other solutions and other ways of doing it. And what I see in the ecosystem is a lot of startups are developing other kinds of LLMs, and I think that’s where women have a strong role — in terms of regulation, for sure, in terms of development, and in terms of promotion.

  As mentioned, building the right narrative on AI — and not going for the obvious low-hanging fruit of efficiency and productivity, which, yes, are super important — but having just another added layer of philosophy to it, and making sure that everybody’s included in that conversation, I think is very important.

  So my advice would be: feel legitimate in those conversations. Because if you don’t care about AI, AI will care about you — and maybe not in the best way.

  Jeanne Dunn:
  Thanks, Gabrielle.

  Well, my favorite tools — of course we talked about ChatGPT and Nobody AI. I probably use those the most. But I also use Tableau. I use Grammarly, of course — we all use Grammarly every day, whether we realize it or not, because it’s embedded in all our applications.

  I love Freshdesk as well — another great tool — and HubSpot. And then Ada for chatbot. So those are just a bunch of tools that we use within our companies that we really like.

  With regard to advice for women — I think you asked the question: advice for women that want to either get into this area or that want to contribute in a way.

  I honestly think that the future of AI and where the value’s going to be is in the powerful combination of a deep industry vertical or horizontal and the AI itself. So an example: being a medical or a research scientist in the area of medicine, and knowing how to use AI for that purpose. Or being essentially a cybersecurity analyst or a CISO — and using… I mean, you cannot be a CISO these days and not use AI. There’s so much data that comes across all of these security platforms — you’ve got to use AI to parse through the data and make sense of it and figure out what’s a real threat versus not.

  So I think that’s another way: get deep in a space — a horizontal space versus a vertical space — and really get good using both of those things. I think that’s going to be the most powerful combination.

  There’s going to be a lot of general people doing LLMs and doing all the things that Samantha and Gabrielle talked about. And I do believe it’s very important that women get involved in that too. Because I do believe that we are in very real danger — very real danger — of creating systems and technology that start to drive us, versus the other way around.

  And so I think, like anything else, technology can be used for good; it can be used for evil. I’m very concerned about AI’s impact, for example, in the cyberspace. I come from many, many years in this area — and pretty soon, it’s going to be able to crack any code out there with the right level of compute. And what are we going to do if passwords, everything — all the security mechanisms we have to protect our bank accounts and our companies and so forth — if that all goes awry? What do you do?

  So there’s some very real risks out there. So I want to see women involved in both the positive side of this — the promissory part of this, where we can use it to make a better life for all of us, make us healthier and more effective, allow us to be the best in our specific skills as possible — and then also to help prevent things from going wrong.

  And in the middle of that is making sure that LLMs and the data models are set up so they don’t have the kind of bias, the kind of bro culture, if you will, that seems to sometimes surround the technology space. So we do have to be mindful of that. And I want to see — I think if a lot of women enter into this space, we’ll be in good shape.

  Sophia Zhao:
  Absolutely. I couldn’t agree more. I feel like we could carry on this conversation for longer, but we are at time.

  And thank you so much to our wonderful panel for sharing your unique perspectives. I’m taking lots of notes, and I hope our content is informative for our community. And we really encourage everyone to lift each other up, and let’s contribute to this AI revolution in our own way — and hopefully everyone will come out better than before.

  So thank you very much for your time today. Please feel free to exit the camera, and we’d like to invite back our MP, Laura, to share a few closing comments. Thank you again to our panelists.

  Laura Rippy:
  That was great. Super, super fun.

  And I wanted to kind of start with the call to action that you asked everyone at the end there, Sophia — how can they take action here? — and invite folks to invest in female-led, often AI-focused startups as part of our Women’s Fund.

  And just to bring up a bit about the mechanics of how that works: so you’ll see the QR code here, or you can look at av.vc — so Alumni Ventures VC — av.vc/womensfund, and that’s the materials to join us.

  So as we said, it’s open to accredited investors. The definition of accredited is: if you make $200K a year individually, $300K as a married couple, or you have assets over a million dollars, then you fit the definition of accredited — and you can join us, as 10,000 investors already have at Alumni Ventures, to be a part of really what we have as a mission, which is to democratize access to venture capital.

  And to do it in this context, with an investment in the Women’s Fund.

  And I think, Sophia, between the deals that we’ve looked at — what would you say — easily have a component of AI in those opportunities. We’ll see where the final fund wraps up in terms of the mix of the 20 or so portfolio companies that will be in it. But you will absolutely get exposure to this category.

  And we invite you to participate, because maybe not everybody’s ready to go start their own company. Or maybe not everyone is ready to be a part of a startup — but you can certainly invest in one. And that’s where we invite you to participate.

  So thank you to my colleague Sophia for such a great moderation, and to our panelists who really kept it going and opened all of our eyes to all sorts of their expertise in the category of AI — and looking at it through the lens of women.

  So thanks everybody for joining us, and please reach out if you have questions. We’d love to hear them.

  Take care.