Next Big Thing in AI

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  Speaker 1:

  Hello and welcome to today’s podcast webinar. I’m Mike Collins, the founder and CEO of Alumni Ventures. Today we’re going to talk about where we think AI is going, where it’s investible, where we’re seeing really strong leads, and placing their investment bets. And this is real-time in early 2025. Obviously, a landscape that’s moving very quickly. So we want to get this to our community and let you know what we’re seeing, what we think.

  So before we get going, kind of normal disclosures that this is a point of view on the investment landscape. It’s for informational purposes only. It is not an offer to buy or sell securities, which are only made pursuant to formal legal documents.

  So just a couple of minutes. First of all, a quick overview. We think there’s a lot of activity going on right now in healthcare and health tech. We’re seeing new kinds of strategic business models emerging because of AI. We’re seeing things going on with integration within various other systems, high tech, high touch. We have points of view on where one wants to avoid investing and where one does want to invest, and we’ll point to how you stay up to date on this stuff.

  So very quickly from my background, I’ve been in venture capital, entrepreneurship, technology for 30-some years. Started Alumni Ventures a decade ago from a personal issue of bringing access to a large number of great deals to retail investors. So we have a really good trophy case of success. I think we’re very well thought of as a strong lead investor.

  Next slide. We’ve been around for a decade. We’re coming up on 1.5 billion, 11,000 customers. We’re exclusively a co-investor with 10 investment teams around the country. We are well thought of. I think we really try to be a good co-investor for our portfolio companies. Very transparent. We try not to oversell, but we’re going to change the trajectory of a great company or turn around one that isn’t so great. But we try to be helpful and valuable, and I think our reputation is strong in that regard.

  And at the end of the day, we believe there’s a real strong correlation between great companies and great lead investors. And this isn’t a complete list. We work with probably a hundred, 200 names pretty regularly, but these are names that I think are pretty recognizable and give you a sense of the number of deals we’ve done with them.

  Okay. I also want to just address the kind of thing that a lot of our community has been responding to, which is announcements out of China from DeepSeek and Alibaba really with innovative approaches to language models and kind of the world going into having a stroke about it. Here’s our view. Competition is good. It was inevitable that China was going to be a player and this is a worldwide phenomenon that has huge stakes, and we try to invest in companies that benefit from this competition and are using AI to solve real-world problems.

  So the vast majority of deals that we invest in want to invest in, deploy capital in, ride the wave of this. Our company ourselves, Alumni Ventures—it’s like we use a spectrum of things to do our jobs better, from OpenAI to Gemini to vertical experts. These are tools, and very, very powerful tools, I think.

  So that’s point one. Point two is, oh, does this impact these huge capital investments in computer centers? My life experience is having gotten a Macintosh with 128K that if you make more computers, people will use it. If you make more energy, people will use it. And I don’t think we’ve seen any indication that there’s anything but going to be a huge investment over the coming years in enormous expensive data centers that consume a lot of chips, a lot of connective tissue, a lot of energy, and those are important investible areas for us. So take a breath.

  So I just want to give you three or four little insights, not long lectures, on areas where we see a lot of action coming out of incubators, top VCs putting money to work. And I’ll just touch on those briefly to give people a sense of being prepared.

  Okay, healthcare is obviously a huge part of the economy, prime for disruption. I think most people view U.S. healthcare, which has huge strengths and amazing people who have dedicated their lives to this, as pretty crappy. And so there is kind of a sickness industrial complex in the United States that I am very excited can be chipped away at.

  So we’re seeing AI attacking this industry from multiple vectors, from drug discovery to patient experience. Obviously, that’s not going to happen uniformly. You’re going to see AI-assisted counseling before you’re going to see comprehensive robotic surgery for broken arms.

  So these things happen over time. They get chipped away at. These are three of our portfolio companies we have. I’d encourage people to check out our Health Tech 20 list on our website if you want to dig into really cool companies that have really strong lead investors that are taking on the healthcare industrial complex. But AI is a really powerful tool to help do that.

  Just speaking personally, all the data we’re getting now from our rings and our watches and our at-home tests, diagnostic things, we can all run through AI now to get the equivalent of a very good doctor—probably the world’s best doctor very shortly. So we’re going to see healthcare transformation, and VCs are putting money to work today along the line of the entire health tech stack.

  Point two, which is—we’re seeing VCs create AI businesses going after a job. So I’m going to create an AI system to do fund accounting. I am going to be a junior social media manager. So the ability to quickly understand that, okay, here’s a job, meaning there’s a problem, there’s an opportunity, there’s something that needs to get done. I need to be a tax assistant. It can be incredibly niche, it can be incredibly vertical, but I think it’s also very digestible for the market to understand—I’m taking on this job of a compliance lawyer.

  And a lot of times we’re looking for opportunities where you have kind of a nice high tech, high touch, data combination, and then you can ride the wave of better AI. So you just want to be developing businesses and investment portfolios that, again, ride the wave of innovation, because these things are going to get better.

  Assume that, and if you are trying to build something about a static thing, you’re going to lose and get wiped out here. So very strategic criteria should be considered. So you’re also going to look at a particular customer. I am going to use AI to solve a customer’s problem. So pick a customer, pick a problem set, and focus all of your energy on serving that person and making their life better in a way that they can handle and understand.

  So just again, a real-life example that’s very personal is our customer is a successful retail investor who wants a strategic, broad, high-quality venture capital portfolio. Our job is to use AI to help give them that better than anybody on the planet.

  So we like AI systems that focus on a job, focus on a customer problem set. So again, some examples from our portfolio that are kind of on theme there, and I’m going to double-click on this idea of technology looking for problems, horizontal technology where you really have a clash of the titans.

  You don’t want to play in that space. You do not want to be going head-to-head with the resources and the brain power and the capabilities and the aggression of Meta, Google, Amazon, Apple, China. You want to solve a problem and just dedicate your life to addressing it.

  So this comes back to the framework of jobs to be done by Clayton Christensen. What job is your customer being hired to fulfill? Is it a product? Is it a service? Is it a blend? What is the dimension of data? High-touch human wrapper around deep technology. But at the end of the day—solve a problem, right? This is fundamental to venture capital. What’s the pain point?

  How acute is it? How much are people willing to pay to get it solved? Disruption theory. So never lose sight of the fundamentals.

  I also think we’re seeing really exciting stuff in what I call business model innovation and disruption. So we’re seeing a kind of a geometric decline in the amount of money and the amount of time and the size of teams it takes to do things. So again, classic disruption, which makes a market more accessible.

  So if you say there’s an ability to have a good, productive, one-hour mental health session—old models were: find a therapist, schedule an appointment, get in your car, go there, pay a bunch of money. Maybe you live in a place where you don’t have a great choice of providers. That gets disrupted and is being disrupted by companies who do this online. And the experience of not physically being in the room I’m sure is not quite as good, but it’s almost as good.

  And there are huge other benefits of being able to find somebody that’s a good fit for you. A lot more scheduling flexibility, a lot more convenience, and thus cheaper. And so that disruption is taking place. And I think the next level of disruption is the AI version of that, which again will be even more anytime, anyplace, better, cheaper—order of magnitude.

  So I think we’re looking to see those kinds—and oh by the way, that company can maybe be run by a team that is an order of magnitude smaller than the current innovation trend.

  Now, will some of the technology companies that are kind of on their journey pivot and evolve to an AI-forward one, or is this going to be one of those things that just skips from the old model to the new AI-forward model? I think that’s really interesting, and I think it’ll depend.

  I know within our portfolio, we have a lot of the middle category. They’re doing great today, but they’re really wanting to use this to do things even an order of magnitude better.

  So the vectors are: Can I do it better? Can I do it cheaper? Can I do it less expensively? Can I do it with fewer people? All of that stuff—market by market, case by case. But there’s really interesting stuff going on there.

   

  In the big picture, whenever big technologies happen, there is a reshuffling of the deck. And with that reshuffling of the deck, there are enormous opportunities. I think this is early, but I think it is moving very, very fast. And so I think the value that is going to be created over the next five years in a lot of sectors of the economy—healthcare, education—AI being driven as a fundamental technology, robotics being a fundamental enabling technology, and you’re going to see a mix of stuff take place.

  You’re going to see the big guys win in certain vectors where money and being big wins. And you’re going to see people coming out of the woodwork to disrupt the whole industry. And you’re going to see other companies that are flexible, pivot, and become AI-forward to do what they’re already doing much, much better.

  But the big takeaway is: big technology shifts create new winners and losers, enormous value creation, and you want to be on the right side of that trade.

  So a lot of people over the past decade have ridden big tech as there’s been a lot of reasons why the big are getting bigger in technology with huge worldwide markets that they’ve been able to crack. And I think the next decade is going to be the emergence of a lot more. So I think it may be a thoughtful time to kind of take stock and reallocate the balanced portfolio. Look at those kinds of things that may make sense.

  Okay. Again, we’re venture capitalists. Hopefully, you found this of interest. As a co-investor, we see patterns I think versus being kind of geographically or vertically oriented. We’re broad, and we co-invest. So I think we have good insights on where things are and heading.

  We are always looking to bring more people into the venture capital investing class. So if you’re happy with your venture portfolio—if not—we’d like to be your venture partner. So obviously, we have funds that can fit a whole variety of interests, from picking individual deals to being vertically oriented to just, “Hey, I’m new to this and I want to learn and have my very first venture portfolio.”

  We encourage people to get on the phone, talk to us, visit our website. In particular, here we have an AI fund, a deep tech fund, a strategic tech fund, and then for newbies, we have our Foundation Fund, which is a well-diversified fund. So I encourage people to check us out.

  And with that, I got five minutes to take some questions from the audience.

  Q&A Session

  Question: Can you share your thoughts about DeepSeek and Alibaba?
  Answer: Yes. So I think I addressed that.

  Question: How does AV approach follow-on funding decisions for companies and what indicators determine that?
  Answer: So it’s a good point. One of the most important things VCs do is they monitor their portfolio. They look for traction, they look for KPIs, they look for a company’s ability to execute. One of the keys is to allocate more capital to your really great companies. I mean, the rule of VC is to get in early and chase your winners, with the understanding it’s a power law business. So obviously, it’s great to make 100x on a dollar, but it can be as important to make 5x on a hundred dollars. So you want to deploy more capital into the companies that are separating.

  So a big part of our job as VCs and our investment teams is to monitor our portfolio. By not being on the board, it’s actually a huge advantage. We think we can stay more objective. There’s no signaling about us leading a round. It’s very easy to fall in love with your own company, so we think that that’s a good decision.

  So we run it like we do when we make an initial investment. Is this a good use of reserves? Is this a good opportunity for fresh capital? What I would say is: the same kind of VC decision-making should take place on the initial investment, on deploying more capital, and on the exit side of the equation. So for us, those things stay the same.

  Question: What is the typical timeline for AI-focused investments?
  Answer: Venture capital is a 10-year fund. I think if you get in early, then a 5- to 10-year horizon is still what it takes. Building sustainable businesses is hard, and it takes a long time. And there’s real value to the compounding of time. So the typical J-curve in venture capital—and AI—we don’t think of any differently than this, which is: it’s power law.

  You make a hundred investments. You’ll know within a couple of years probably a third of them are just wrong place, wrong time, wrong team, a million ways to fail. A third are likely to have some kind of middling exit in kind of the 3- to 6-year time horizon. And then your home run investments—you want to run really a long time. And typically, you’re going to see that in the 5- to 10-year time horizon.

  One of the things going on today—and this is just again not AI-specific but just general in venture capital—is a more liquid market for later-stage deals. So the ability to take chips off the table every year keeps getting a little bit better, which I think is super healthy for the market. It used to be going public or selling the company was the only way to provide liquidity. And I think that ended up in kind of non–value-maximizing situations. Companies would go public too soon or feel there was too much pressure to sell.

  And by providing some liquidity—providing an ability to take chips off the table and diversify for early investors and founders—I think leads one to pick the right time to go public or the right time to sell the company. So I think that’s a trend that we’re going to see more of over the next five years than we have in the last five years.

  Question: How do you distinguish between good opportunities and hype?
  Answer: It’s harder than it sounds, right? It comes with pattern recognition. It comes with experience. It comes from being independent. It comes from working with people who have been around the block.

  And so there’s no simple answer to that. You go back to fundamentals. What is the problem? What is the vision of the founder? How compelling are they? What is their grit? How have they displayed that? How does this team work together? Time is a good judge, right? So how are they able to execute?

  Keeping track of this—as a co-investor, you can see, like, okay, I got an update a year ago, here’s what they said, six months later, here’s what they actually did. So again, entrepreneurs are great salespeople. You have to really watch their ability to move the goalpost or tell a good story. Those are essential characteristics, but you have to parse that stuff.

  And again, you’re going to get it wrong. And that is, again, just part of this asset class. Again, for anybody that doesn’t believe that, I encourage them to go check out the Bessemer Anti-Portfolio. Again, one of the great firms in the industry publishes all the mistakes they’ve made.

  And so it is for those that have been around and not just hopped in, gotten lucky at the right time. One builds humility. But just appreciate that if you grind it out, if you’re patient, if you invest in amazing people trying to tackle really hard things, that this is an asset class that we think more people should have an allocation for.

  So thanks for your time today. Hopefully, you got a couple of nuggets, and we’ll see you down the road. Thanks.