Three Startups Using AI to Transform Industries

Frequently Asked Questions

FAQ

• Where is the transcript for this webinar?

  Speaker 1:
  So thank you again for joining. I’m Ron Levin, managing partner of the Alumni Venture Seed Fund, and today’s webinar is an Eye for AI. We’re talking with the founders of three startups that are using AI to transform different industries—a very hot topic, you could say, in the venture world today.

  And quick disclosure here: This presentation is for information purposes only and is not intended as an offer to sell securities or the solicitation of an offer to buy securities. If you’d like to read our full disclosures, they are available at avfunds.com/disclosures.

  So, quick overview: I’ll start with a little introduction, give you some background about Alumni Ventures and how we perceive seed investing and what we do here. But we’re going to spend the vast majority of our time with a roundtable discussion with three really dynamic, exciting founders that we’ve invested in over the past year. They are all applying AI in different ways across different industries, which I think will make for a fascinating discussion.

  We’ll end with some Q&A. A number of questions have been submitted ahead of time; we’ll try to get to a few of those. If you’d like to put any questions in the chat, you’re welcome to do so. We won’t take live questions—meaning you don’t have to come on screen to ask anything. Just submit them in the chat, and we’ll try to get to some of those toward the end. We’ll aim for about, call it, 45 minutes in total here today.

  So, I’m Ron Levin. As I said, I lead our seed fund here at Alumni Ventures. I’ve been with our group for a little over five years now, previously with our Yard Ventures team for the Harvard alumni community. I was actually an investor myself personally in Yard Ventures before I came to work here.

  I started angel investing and discovered Alumni Ventures and Yard Ventures as an opportunity to diversify my own personal investments. I really started to like what the firm was offering, got a little bit closer to the team, and that ultimately led to my joining here.

  I moved over to seed investing about a year and a half ago and last year took over responsibility for our seed fund that focuses on pre-seed and seed investing. Before becoming an investor, I was a co-founder and CEO of a company in Barcelona called TravelPerk. It’s an enterprise travel management platform that I started with two co-founders I met while working at Booking.com. The company is now backed by several hundred million in VC, is a unicorn company, and is one of the leaders in the enterprise travel space today.

  Before that, I was at McKinsey and spent most of my career at the intersection of travel and technology in one form or another. I started my career during the early dot-com days in the first wave of the late ’90s and early 2000s with Lycos, an early search engine portal that was one of the leaders before Google came along. Some of you may remember them.

  I’m also very passionate about impact and about using venture and technology to make a difference in the world—to improve society in various ways. I published a book last year called Higher Purpose Venture Capital. So, if impact venture and impact entrepreneurship are of interest to you, please feel free to check that out.

  This webinar is from the Alumni Ventures Seed Fund. All of the companies we’re talking to today are seed-stage enterprises. Seed can run the gamut—early seed, late seed—but it essentially means early-stage ventures that are in the process of developing product-market fit and going to market, but may not yet have fully scaled. That’s where we like to invest because we see a very attractive return profile.

  Early-stage ventures tend to generate greater returns on investment, which has been seen over various time periods. If you look back 10, 20, or 30 years, early-stage venture investment is one of the most exciting asset classes there is. It’s not without risk—returns are commensurate with risk. Early-stage ventures can succeed tremendously, but they can also fail quickly. It’s an asset class that can take years to finally get any liquidity. It can take five, 10 years, or even more for a startup to reach a liquidity event.

  When we invest in seed-stage companies, it’s not necessarily an idea on a napkin but rather something that’s more fleshed out with experienced, dynamic founders. That’s what we look for.

  We believe in diversification, which is why our seed fund builds a well-diversified, broad portfolio. We believe in the power law concept that suggests the returns on a venture portfolio will come from a few companies rather than all of them. That’s just the nature of how this works when investing in early-stage technology startups.

  But we like to invest in companies that have great promise and the opportunity to become very large businesses.

  Our seed fund is currently looking at about 50 to 70 deals over the course of this year. We’re currently investing out of Seed Fund 7 and will shortly start raising Seed Fund 8.

  We diversify across sectors—anything that venture capital touches: highly scalable, technology-driven businesses. We are geography-agnostic as well. We also diversify by co-investing alongside many different lead investors.

  We invest alongside traditional, well-established VC firms. We like to get pro-rata rights when possible, and we reserve around 25% of our capital for follow-ons. We like to place additional bets into companies that are performing well, giving us the opportunity to put additional capital to work. This is one of the ways VC firms can achieve their greatest returns.

  If you’re interested in investing with us, our minimum is only $25,000. We’re trying to democratize venture capital. It used to be that you couldn’t get into a venture fund with less than $1 million, $5 million, or even $10 million. We’re making the asset class much more accessible.

  By investing in one of our funds at the $25K level or greater, you also gain access to syndications. About once a month, we’ll share with our investors a specific deal where there’s an opportunity to put additional capital into what we consider to be some of the most attractive opportunities we’re already investing in.

  That’s a little bit about the seed fund. There’s more information that will come in the deck we’ll share with all of you. Over 10,000 individual investors have already invested with Alumni Ventures. We are the most active venture capital firm in North America and one of the most active in the world. We have over 1,300 portfolio companies to date.

  That’s according to PitchBook, which collects data on the industry. We have over $1.3 billion in assets under management.

  We’ve also recently been ranked by CB Insights, a market research firm that tracks our industry, as one of the top 20 performing VC firms in North America—alongside some of the biggest-name VC firms you’ve probably heard of. This is validation that our model of co-investing into exciting opportunities alongside traditional, well-established leading VC firms works very well. It helps our investors achieve the same attractive returns that top institutional investors like pension funds, endowments, corporate investors, and high-net-worth family offices are able to achieve.

  Our firm is made up of about 40 individual venture investors. We’re spread across about five offices in the US and are supported by close to 90 other investment professionals.

  We also have a Super Angel partner program and are introducing a scout program. We source most of our deals through referrals—folks in our network that we believe are successful and have a track record as investors or entrepreneurs who refer deals to us. We love getting referrals because there are many companies out there, and we don’t always see them all ourselves. A lot of what we see comes through partners who refer to them. Sometimes they’re VC firms, sometimes other founders, and sometimes angel partners—folks who are actively angel investing and putting their own money into early-stage opportunities, which we then follow.

  We are very diversified. We invest globally. We like moonshots, deep tech, enterprise software, digital health, anything in healthcare, cybersecurity, DevOps, robotics, AI, and more. We’re broadly diversified. If that’s of interest to you, please feel free to reach out.

  Now, without further ado, I’m excited to introduce our panel today. You’re welcome to come on video when you’re ready. Rajesh Iyengar, founder and CEO of Link Code Labs; Vincent Chang from Think First; and Wayne Chang from Patented AI. Great to have you join us.

  What these three founders have in common is that they are all using AI to transform particular industries in ways that we found very compelling. We’ve also invested in all three of these companies in the past year, so we truly believe in what they’re doing. We believe in them as founders. I think they’re among the most exciting companies we’ve seen, and we’re really excited to be part of their story—even in these fairly early days.

  What I’d love to do is start by going around and having each founder introduce themselves, as well as let everyone know who their company is and what they’re doing. I’ll go left to right across my screen. Rajesh, would you like to kick it off?

  Speaker 2:
  Sure, yeah. Hi everyone, my name is Rajesh Iyengar. I’m the co-founder and CEO of Link Code. We started in 2017 and we are primarily focused on visual inspections for the manufacturing industries. This is my third venture—I’ve had a couple of exits in the Bay Area—and now I work with my partner currently and we are doing this right now. Happy to answer any questions.

  Speaker 1:
  Great, thank you. Vincent, would you like to go next?

  Speaker 3:
  Sure. Hi everyone. My name’s Vincent, co-founder and CEO of Think First, an AI math tutor for every student. I graduated from Harvard Business School and I’m a three-time mission-driven founder in education. All of my ventures are in the tutoring space because I was a beneficiary of tutoring growing up and now also tutor myself. I saw firsthand how tutoring can change lives. With AI, we saw how tutoring can be democratized and reduce the cost to less than 1% of human tutors. That’s how we started Think First.

  Speaker 1:
  Great. Perfect. Thank you, Wayne.

  Speaker 4:
  Hi everyone. I’m Wayne. This is my second applied AI company. My first one was Digits and this one is Patented. With Patented, we help companies do several things. One is to monetize their IP portfolio, which is often dead assets. The second is, if you’re being asserted with patents—which is inevitable as you get bigger—we help neutralize that. By removing the patents and invalidating them, it removes the threat.

  Speaker 1:
  Excellent. Some very interesting areas to dive into. I want to go a little bit deeper into what each of you are doing individually, and then we’ll come back to some broader topics like implications and where the future is headed.

  Rajesh, you’re in manufacturing. What is AI allowing you to do? I know you work in quality inspection. I think this is already something that isn’t necessarily done manually by most companies—there are already other forms of visual inspection. But what is your technology? What is AI allowing your customers to do now that they couldn’t do before?

  Speaker 2:
  Yeah, sure. Primarily in manufacturing, especially when they had technology before, they were relying heavily on a golden template matching kind of technology. This is mission vision-based, and they were using cameras to do inspections with this golden template matching approach. With that technology, they had various gaps, one of them being high inaccuracies.

  Even though they spent a lot of money on cameras and software, they were still having inaccuracies and were penalized heavily. Specifically in the automotive industry, there are only about 500 OEMs, whereas there are 250,000 suppliers globally. These suppliers are penalized heavily if they are short-supplying or supplying defective materials.

  For example, Jeep charges a supplier $3,000 per minute if they short supply or deliver defective material. Typically, companies are losing about $1 million to $1.5 million every month due to this issue.

  The primary reason goes back to the cameras being ineffective at achieving high accuracy levels. What AI is doing currently—through our technology and tech stack, infused with datasets—is allowing us to provide extremely high accuracy. Out of 1 million inspections done, we see zero to four inaccuracies. This gives manufacturers a very high level of confidence when supplying materials to higher-tier customers. It saves them both money and time.

  Speaker 1:
  Great. How are your customers reacting to this new technology? I imagine many of them have been working in this industry for decades, and with older industrial industries, they might be less adaptable or eager to be first adopters. How are you finding the responses so far?

  Speaker 2:
  Initially, we had a tough time convincing them with AI because they were all scared about how AI would perform and the associated risks. We developed a strategy: we infused data into our solution and designed our platform to have flexibility to add any kind of camera ecosystem.

  With that approach, we could go to customers and say, “Don’t change anything in your existing infrastructure. We’ll work with it as is and prove that we can achieve much higher accuracy than what you currently have.” We were able to prove that, and once customers gained confidence, they expanded deployments to other locations.

  Today, with so many AI companies out there, manufacturers are more open to trying and investing in AI technologies.

  Speaker 1:
  Great to hear. Thank you. Vincent, you’re in the EdTech space, and this has become a lightning rod. What are the implications for AI now that ChatGPT has become widely known and large language models are common?

  One teacher I know commented, “Well, this is the end of homework.” Many people are worried that AI will let students cheat and get away with it. But I assume you see it a little differently. I’d love to hear your broad thoughts on how AI will actually help people learn better.

  Speaker 3:
  Yeah, thank you so much. I think with any new technology when it first comes out, there are going to be bad sides and good sides. We saw the risk of plagiarism and AI doing homework for students. We’ve even seen entrepreneurs building tools to aid student cheating, unfortunately. They sometimes call themselves AI math tutors, but they’re really just homework solvers addressing short-term pain for students rushing homework.

  But we also see many encouraging signs where AI can be used for good in education. AI really offers true personalization in education almost for the first time ever—personalized guidance. When students are struggling, each one struggles with different concepts. AI can meet them where they are today.

  This is really exciting and makes personal tutors so effective. Another exciting aspect AI brings is adaptive assessment and practice that goes along with personalized guidance. If AI diagnoses a student’s learning gaps—A, B, and C—it can give them real-time adaptive assessments or practice to reinforce those concepts.

  Lastly, we’re excited about real-time insights: AI’s ability to summarize interactions with students and provide teachers with real-time insights. We’re excited to bring these benefits to classrooms and education in general.

  Speaker 1:
  And what kind of feedback are you getting from teachers and tutors? How do they see this working? Are there surprises—positive or negative? It’s quite a change for many of them, I assume.

  Speaker 3:
  Yeah, great question. For some background, our company works with schools—we partner with schools—so it’s free for students. We don’t go directly to parents or tutors.

  We’re seen as a teaching assistant by many teachers. They imagine having 30 teaching assistants in their classroom, each helping individual students with guidance and questions. This reduces teacher workload and differentiates the learning experience for each student.

  There is a group of teachers concerned about job security, as we’ve seen in many other industries when AI performs well. But in our opinion, education focuses heavily on human interaction and its social aspects. AI will never replace that component; it can only complement teachers, making classrooms more engaging and personalized.

  Speaker 1:
  Yeah, absolutely. It’s very promising. Wayne, turning to you—you’re focusing on patent searches and disrupting this market. What led you to this problem? You’re not a lawyer by background. Why did you decide to tackle this particular issue?

  Speaker 4:
  Startups have always hammered into me the idea of entrepreneurship: protect your innovation, file patents or trademarks, do all this IP work. But the big question is: after you spend two to four years getting a patent and $30,000 to $60,000 per patent to get it issued, what do you do with it once you have it?

  It turns out this is a very large problem. People without an entire staff—like MIT with a licensing office of 25 or 50 people, or Stanford, or many other universities—are at a disadvantage. Qualcomm, for example, has an enormous team dedicated solely to patent licensing. Why is it that only the largest institutions can truly leverage these types of assets? Qualcomm makes about $6 billion a year, with 20% of its profits coming directly from this.

  If you’re not an attorney or IP professional, you’re locked out. After all my previous startups, I looked back at my portfolio of patents from each venture and thought, “What do I do with this besides having it look great on the wall or having my attorneys stuff their filing cabinets?”

  It turns out this is a really big problem. Some of the smartest people I know—who start companies—keep filing patents and end up wasting so much time, energy, and money to maintain them each year. We wanted to help other entrepreneurs and companies who have already invested so much capital and resources to obtain patents but are locked out from generating returns on them.

  That’s one side of it. The other is that we hate patent trolls. We want to help address situations where entities that don’t have real innovation assert bad patents against others. We figured we could solve both problems simultaneously using the same approach. The more our system is used, the cleaner the overall patent system will be, with only true innovations surviving and those innovators generating real ROI.

  Speaker 1:
  Excellent. That’s a very noble mission. Broadly across legal, a lot of us who’ve had to hire lawyers for one reason or another are shocked when we get the bill and see how much we’re charged hourly. No disrespect to lawyers or the profession, but something feels a bit amiss with how much you pay versus what you get in return.

  I’m wondering—where do you think AI is going? Will it disrupt that dynamic? Will it make lawyers’ lives harder or easier?

  Speaker 4:
  I’m hoping there aren’t too many attorneys on this call watching now, but I think AI is going to bring a full-on assault on that entire business model. Anything billed on an hourly basis is going to be disrupted because AI can do things much cheaper, much faster, and at higher quality.

  We’re seeing this over and over again. I think the typical law firm that’s not adapting to this new reality is like a walking zombie—they don’t know they’re dead yet, but they will be very soon.

  If you’re charging by the hour, you need to quickly figure out how to adapt and survive in a world where someone else can provide the same value much cheaper and much faster, or you won’t survive.

  Speaker 1:
  Yeah, it’ll be interesting to see how they adapt. I’m sure there will be clever ways, and some will figure it out, but others might be left in the dust if they don’t catch on soon enough.

  Let’s go a bit broader and open this to anyone—but Wayne, I’ll start with you. Both of us have roots in the early dot-com wave of entrepreneurial activity. AI isn’t new; it’s been around for decades. But suddenly, it’s everywhere—everyone’s grandmother now knows something about AI. It’s been a real revolution.

  From my perspective, even the deals I’m seeing now versus just two or three years ago are completely different. Companies that weren’t even thinking about AI are now addressing it in one form or another. I’d love your comments on how you see AI’s evolution and how we’ve gotten to where we are today.

  Speaker 4:
  ChatGPT made it very accessible. What used to be science fiction is now science fact. Everyone’s using it differently and learning rapidly. Feedback loops have become much faster.

  The difference in innovation and the types of companies you’re seeing—what’s coming into deal flow inboxes—is the result of this fast iterative cycle, enabled by high-quality feedback loops. That means the bar for startups is rising and will continue to rise as more models and methods emerge to harness these new capabilities.

  What’s really interesting is drawing a line from where we started to where we are now and imagining where we’ll go. I have an unpopular opinion: I think we’ll see the rise of a zero-human company.

  Instead of focusing on copilots that augment human behavior or productivity, imagine a fully AI-first company. Suddenly, you’re no longer bound by human limitations—team members could be the very best in their sector: the best engineer for manufacturing, the best mobile designer, etc.

  It becomes compelling when you can get them all working together as a team—like a SEAL Team Six scenario—working 24/7 for you. I think there will come a time when we see a company hit NASDAQ with no human employees, competing with real companies and outperforming them.

  Speaker 1:
  Amazing. Rajesh, you’ve also been very experienced in this industry. How have you seen evolution from your perspective?

  Speaker 2:
  Yeah, absolutely. In this industry, people used to worry about how automation would develop and whether it would take away their jobs. Those were the initial concerns. But now people realize that even with AI, they’re able to survive—and in many cases, AI is helping them.

  One example is in the Midwest, where there’s a significant shortage of certain resources. It’s been particularly difficult in quality inspection. Companies struggle to find the right people to perform inspections, so automation has become a blessing.

  Everyone’s starting to realize that automation is definitely a benefit. With our approach of requiring less capital and providing a yearly cost model, customers are able to achieve proper ROI. They see results within three or four months, and they’ve really embraced it.

  Speaker 1:
  Absolutely. Vincent, anything else to add on that?

  Speaker 3:
  Yeah. When you think about AI before the generative AI era, teachers and educators didn’t see themselves using it. It felt like you had to be technical to know about AI or use it.

  Now, anyone can log onto GPT—or you don’t even need to log in anymore—and create a lesson plan for themselves, saving two or three hours instantly. Many teachers are seeing this and thinking, “Wow, this is intuitive.”

  Everyone gets it. They immediately see the value and also feel a bit threatened because of how quickly and how high-quality the AI output is. At the end of the day, though, when you talk to teachers, many are already using it to reduce their workload.

  Overall, ChatGPT and generative AI have democratized access to AI and its power for everyone.

  Speaker 1:
  How quickly do you see this advancing? What’s it going to look like five or ten years from now? Will it be radically different or just incremental progress?

  Speaker 3:
  I think education has historically been the lowest adopter of most technologies. But with AI, several background factors are driving adoption.

  For example, there’s a teacher shortage in the U.S. The average teacher works 50 to 60 hours a week and isn’t paid as much as other long-hour professions. These challenges make AI’s ability to reduce workload and supplement teaching resources extremely valuable.

  We’re seeing two forces at play. On one hand, there’s a strong need for AI to address real issues in education. On the other, regulation, policies, and budget constraints create friction for adoption.

  It’ll be interesting to see how policymakers, decision-makers, and budget controllers adapt to embrace AI. But the overall trend is clear—everyone recognizes the value of personalized learning with AI, and education is starting to embrace it, which is very exciting.

  Speaker 1:
  Yeah, absolutely. Education has always been expected to be radically transformed by technology. From the earliest days of the World Wide Web, people thought it would completely change education.

  I think we’ve seen gradual progress, but this feels like one of those monumental moments where we’ll really see what AI can do. Can it completely disrupt higher education? Do people really need to spend $60,000 a year to get a college diploma, just to acquire marketable skills for a good job?

  We’re seeing a lot of evidence that might not be the case in the future. That could be a big step toward solving inequality and leveling the playing field if we can deliver truly high-quality education remotely and inexpensively.

  I’m also fascinated by the intersection of AI, robotics, and human brain interfaces—like Neuralink—and the futuristic concepts around singularity that Ray Kurzweil talks about.

  Rajesh, since you’re closer to the manufacturing world, what are your thoughts on that?

  Speaker 2:
  Yeah, absolutely. People believe that within the next five to ten years, the “lights-out” factory model will definitely become a reality. These are factories that can operate entirely with robots and AI.

  Some factories already operate with minimal human involvement—particularly warehouses. Amazon pioneered this with robotic warehouse automation. Production is now getting a similar push.

  Today, most AI deployment is focused on quality inspection. But it’s increasingly being adopted in production as well. With robots and AI working together, tasks like painting, welding, and grinding are already being automated.

  Speaker 1:
  Yeah, it’s definitely a new world. Wayne, looking ahead—put on your future goggles—what’s coming next?

  Speaker 4:
  I hate being the bearer of bad news, but I think most analyst jobs are probably doomed. AI’s ability to process massive amounts of information and generate incredible insights or actionable items will eliminate the need for many of these roles.

  Whether it’s trading activity, healthcare insights, medical analysis, legal review, or patent evaluation like in our field, tasks that currently require human understanding and lots of manual analysis will disappear.

  But the flip side is positive—we’ll gain these insights and correlations much faster, enabling the next level of productivity.

  I also heard Ray Kurzweil speak at TED a couple of weeks ago. He said the next huge wave he’s excited about is the embodiment of robotics—how AI will allow the objects around us to become more alive and interactive.

  Speaker 4:
  And I’m looking forward to that as well. Seeing Rajesh and Vincent here and the things they’re building is awesome—it enables progress in many different ways, including robotics.

  Speaker 1:
  Absolutely. There are certainly risks to many people out there, but also unbelievable promises. It’ll be very interesting to see how far this gets, whether we can control our worst instincts while also optimizing our brightest innovations and hopefully solving problems for the world.

  We don’t have much time left, but I want to save a couple of minutes for questions that have been submitted. If anyone wants to put something in the chat, you’re welcome to. We’ve had a few questions submitted ahead of time, so we may only have time for one or two.

  Here’s one that’s a bit specific to OpenAI and LLMs—it’s about data accuracy in AI. One person asked: How do you avoid “garbage in, garbage out” and ensure the algorithms we’re using and the processing we’re doing are accurate and performing as intended? I’ll open that to anyone who’d like to jump in.

  Speaker 4:
  I can jump in quickly. This was a huge problem we had to solve early on in our space—high-stakes court litigation. There’s no room for error. These involve massive amounts of information, and it has to be 100% accurate. Anything less is detrimental to the entire case.

  We had to figure out how to achieve zero hallucinations and 100% accuracy with current-generation models. That required breaking down several engineering barriers. In the next few weeks or a month, we’ll be releasing what we’ve built to accomplish that. It will enable software to achieve zero hallucinations and high performance by leveraging a year of work on cleaning our data sets and pipelines.

  Speaker 2:
  In our industry, we have a dataset-cleaning mechanism. Even when we collect just 30 to 40 pictures from each customer on their defect data sets, because we infuse data, we have a data visualizer that allows customers to see what the correct data is.

  This helps us overcome issues with accuracy, which is critical because there’s big money and safety at stake—we don’t want car doors falling from the sky. Ensuring accurate data is the primary thing we do in our AI deployments.

  Speaker 3:
  For us, large language models aren’t math models—they struggle with math. We had to combine the power of large language models with a math engine on our end to make sure we had the right tools for AI to handle different scenarios.

  Speaker 1:
  Makes sense. Let’s go around one last time and give each of you an opportunity to make an ask of our audience. We have entrepreneurs, students, investors, and people interested in technology or AI. Is there something you’re looking for—customers, partners, funding—where someone here could help? Vincent, let’s start with you.

  Speaker 3:
  Thank you, Ron, and thank you everyone for coming. If you’re connected to a school or school district in North America, we’d love a warm introduction to a school principal or district leader. If they’re interested in piloting our AI tutor, that would be awesome. We’d really appreciate that.

  Speaker 1:
  Great, thank you so much. Rajesh?

  Speaker 2:
  We’re looking for partners to help deploy our solutions and bring them to customers. Any partner referrals or customer referrals would be excellent for us, and we’d be very grateful.

  Speaker 1:
  Great, thanks. Wayne?

  Speaker 4:
  I have two questions. First, if you have a patent you want to monetize, or if your portfolio companies have patents they want to monetize, let me know. We can run a free pilot. Xerox said what we provide usually costs about half a million dollars, so think of it as a half-million-dollar gift we’re happy to provide.

  Second, if you’re in an industry where you need 100% accuracy—where precision matters and zero hallucination is critical—reach out. We can get you early access to the solution we’ll be announcing soon.

  Speaker 1:
  Outstanding. To summarize, we have an exciting future ahead of us. It’s clear that AI will transform many industries and many people’s lives—hopefully for the better. There are risks to certain professions, as we’ve discussed, but overall, it’s imperative that we all follow the pace of change and prepare for it.

  These three companies are doing very important work, each moving society forward in its own way. I really appreciate your time, Rajesh, Vincent, Wayne. It’s always a pleasure to connect with you. I’m honored and proud to be an investor.

  We’ll distribute this recording and slides to everyone who signed up, and it’ll be available publicly for anyone else who wants to tune in. Thank you so much to everyone for joining us today—it’s great to have you with us. Please get in touch if we can ever be of help. I appreciate it.

  Speaker 2:
  Yeah, thank you, Ron. Thank you for having us here. Thanks everybody for joining. Bye. Have a good one.

  Speaker 3:
  Bye.