Blitzscaling for AI Startups

Frequently Asked Questions

FAQ

• Where is the transcript for this webinar?

  Speaker 1:
  Welcome everyone to our Blitzscaling webinar. Before we get started, this presentation is for informational purposes only and is not offered 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 for the entire presentation and this webinar is recorded. It will be shared after the event. We encourage you to submit questions throughout the webinar. We’ll try to answer your questions during the Q&A session.

  Next slide, please. Let me make a quick introduction about myself. I am a managing partner of Alumni Ventures AI Fund, based in Silicon Valley. I was an entrepreneur in the first part of my career, then spent almost a decade in corporate ventures—initially with Cisco, leading software infrastructure investments and mergers and acquisitions. Then I ran HP Venture for about four years.

  I helped build several cross-border funds between Asia and the US focusing on AI, SaaS, infrastructure services, and applications. I received my EMBA from both Columbia and UC Berkeley.

  On the agenda today, I’ll give a very quick overview of Alumni Ventures and our AI fund. Then we’ll jump into our discussion with our friend Chris on Blitzscaling and how we can apply Blitzscaling in today’s AI environment. We’ll do Q&A at the end to make sure we have time to answer your questions.

  Next slide. Alumni Ventures is America’s largest venture firm for individual investors. Since starting in 2014, we have raised more than $1.3 billion and have over 1300 portfolio companies. PitchBook ranked us the most active venture fund in the US for the last two years. CB Insights recently included Alumni Ventures as one of the top 20 venture firms in North America among other established VC funds such as a16z, Sequoia, Lightspeed, and NEA. We’re honored to be part of this list.

  Today, we just want to make sure we provide you with the information you need. As a firm, we have more than 130 people in five offices covering venture hubs in the US such as Boston, New York, Chicago, and Silicon Valley.

  Next slide. As I mentioned, I’m a managing partner for our AI fund that covers all AI-related investments ranging from generative AI infrastructure, machine learning, big data, AI tooling, and vertical AI. It is an annual vintage fund that invests in 15 to 20 companies within a 12- to 18-month investment window, diversified by stage, region, and lead investors.

  We co-invest with established venture capitalists with sector expertise, leveraging their due diligence and term sheets or running our own disciplined process. We typically reserve 20 to 25% from each fund for follow-on investments.

  We also provide opportunities for our investors to syndicate into appealing startup opportunities when we have enough allocation. We plan to close this AI fund in less than two weeks, by the end of this month. It is the only AI fund for this year. If you’re interested, please contact us as soon as possible.

  We think venture capital is a complementary asset class to traditional stocks, bonds, and real estate. If you’re interested in innovation, especially around AI, please join us on this journey. It will be fulfilling and will help define the next generation of technologies through AI as well.

  Without further ado, let’s jump into our main discussion with Chris. Next slide. Chris, many people know you from the Blitzscaling book. I read the book when it came out six years ago and was really impressed by it. Before we get started, maybe you can give our audience your background, the history of this book, and your definition of Blitzscaling.

  Speaker 2:
  Absolutely. I’ve actually been involved in the startup world probably nearly as long as you have, Ray—since 1995. So next year will mark my 30th anniversary in the startup world, which is pretty crazy. I was obviously extremely young when I started.

  My background is that I didn’t expect to end up in startups. I didn’t know anything about the startup world, but I was lucky enough to attend Stanford as an undergraduate where I studied product design, engineering, and creative writing. Right when I was graduating back in the mid-1990s, the internet was just starting to take off.

  Even though I didn’t know anything about business or startups, I felt like the internet was going to change everything. It was going to be one of the biggest events in human history, and I wanted to be a part of it.

  I got into startups through DE Shaw, the company Jeff Bezos worked for before founding Amazon. And before you ask—sadly, he left about 18 months before I got there. So I don’t know Jeff personally and can’t offer you any rocket rides.

  I just got hooked. Being in the startup world is amazing, as you know Ray, and as the folks on this call know. To get a chance to be part of shaping the future is exciting, fun, and at times, very lucrative.

  So I’ve been in startups ever since—founder, operator, executive—and later became more of an author and investor. Today I help run Blitzscaling Ventures and we have a Blitzscaling AI fund. I completely agree with your thesis, Ray: AI is going to be enormously valuable.

  Blitzscaling is the book you mentioned, which my co-author Reid Hoffman and I released in 2018. It’s still going strong six years later, and we’re very grateful for that. The whole idea behind Blitzscaling is that when you have a valuable winner-take-most market—and I think there will be many such markets in AI—the right strategy is to prioritize speed over efficiency even when things are uncertain. The goal should be to win the market.

  We call this the Ricky Bobby principle, after the film Talladega Nights starring Will Ferrell, where Ricky Bobby says, “If you ain’t first, you’re last.” Now, that’s not entirely true for many things in life—and Ricky Bobby isn’t the smartest guy—but for a winner-take-most market, it actually is true.

  That’s why Blitzscaling has been so effective over the years—from the dot-com boom with companies like eBay, Craigslist, and Google, to the social media era with LinkedIn and Facebook (Reid was LinkedIn’s co-founder and Facebook’s first investor), to the sharing economy with Uber and Airbnb (where Reid was the lead investor), and now into the AI era with OpenAI (where Reid is a founding board member).

  You can see how we’ve developed the ideas of Blitzscaling and how they’ve applied generation after generation.

  Speaker 1:
  Yeah, that’s great. I was at Nvidia’s GTC conference the last three days and it was packed with excitement. How can the core principles of Blitzscaling be applied in today’s hot AI environment, in your view?

   

  Speaker 2:
  You can already see it at work. When you have a winner-take-most market with these dynamics—often based on network effects—the goal should be to grow quickly.

  It’s amazing when you think about OpenAI: fewer than 1,000 employees competing against the biggest companies in the world—Google, Microsoft, Amazon. Google has been working on AI longer than anyone, yet OpenAI is leading the field.

  Why is that? Part of it is the brilliance of its scientists and their foundational model, but another big reason is that they released ChatGPT to the world.

  If I asked the audience right now how many have used ChatGPT, almost everyone would raise their hand. Every time you use ChatGPT, you’re helping OpenAI. They’ve been gathering information on how humans interact with AI, learning about prompting and reinforcement.

  That scale allowed OpenAI to become the premier AI provider to enterprises via their API and enterprise products. Scale made them dominant—not just the product alone.

   

  Speaker 1:
  Yeah, for sure. I think many core principles still apply. You mentioned network effects—a lot of that traditionally involves people-to-people interactions. How do you see this changing in the new AI environment, where more decisions might be made or optimized by AI algorithms rather than people?

  Speaker 2:
  I think the same dynamics apply. People-to-people interactions will still matter. Ultimately, people are still interacting—it’s just that interactions may be moderated by AI.

  But we’ll also see AI-to-AI interactions demonstrating network effects. AI agents won’t treat everything equally; they’ll find certain sites easier to work with and may even collaborate.

  I recently met the founders of Sanaa AI in Tokyo—they’re talking about evolutionary AI and swarm AI. AI agents will collaborate with each other.

  So we’ll still see network effects: AI agents interacting in marketplaces. The fact that it’s AI communicating doesn’t change the dynamic. If anything, rapid AI decision-making will increase the pace at which markets become winner-take-most because AI agents make decisions faster than humans.

  Speaker 1:
  Well, would that change the decision factors in this case? Traditionally, humans are emotional—we tend to be influenced by many things. AI is more logical to a certain degree. What characteristics would you look for in these kinds of network scaling effect models?

   

  Speaker 2:
  I think it’s going to be the classic factors: who is easiest to work with and who delivers the best economic value. AI will generally be more rational than humans. However, it’s important to point out that generative AI is not devoid of emotion—it’s been trained on the corpus of human knowledge and production, so it can express emotions in various ways as well.

  That said, AI is likely to be a better decision-maker than human beings because the average human is not as rational as the collective knowledge of the internet.

   

  Speaker 1:
  Yeah, absolutely. We’ll see a lot of interesting changes. I noticed that when we talk with startups, we tend to ask founders what they would do but also what they would not do, because venture growth is about focus and optimization. What kind of startup would you not recommend Blitzscaling, even if the market condition is hot today?

   

  Speaker 2:
  The key thing to remember is that Blitzscaling is a competitive strategy. The goal is to be the first to scale, to become the top competitor, win the market, and then print money for decades.

  When you think about Blitzscaling, it’s not just about whether it’s a winner-take-most market; you also have to consider your go-to-market strategy. Can you outgrow the competition? If someone has already won the market, it doesn’t matter how fast you try to scale.

  In AI investing circles right now, there’s debate about whether to invest in foundational models. The question is: has that market already been won? In my opinion, it’s very difficult to build a new foundational model today unless you take a very different approach—perhaps focusing on another language, like Japanese instead of English, or building a fundamentally different type of model.

  But if you try to gear up and take on OpenAI head-to-head, that’s likely a terrible idea. It’s probably a bad investment.

  The important thing is to analyze the competitive landscape. That’s why much of the AI activity is shifting to differentiated infrastructure and vertical-specific applications. Just as vertical SaaS became a massive market, vertical AI will also become a massive opportunity.

   

  Speaker 1:
  Yeah, I absolutely agree. There’s also discussion about smaller language models deployed at the edge, right? Similar to how the internet evolved from centralized to decentralized and back again. That’s an interesting consideration, but it requires a lot of funding.

  Large language models already require huge investments. What are the key considerations for AI startups on fundraising? Any suggestions on how they should approach potential investors?

   

  Speaker 2:
  It’s hard to speak for other investors, so I’ll share how people should approach us—which should help others as well.

  When I think about AI right now, startups must demonstrate utility. Where is the value creation occurring? The obvious way is at the AI engine level—creating something like GPT-4—but that’s not the only way.

  You can also create value by understanding a specific problem area and knowing what to ask. As I like to say, you can’t just tell ChatGPT, “Make my life better in every way,” and expect results. You need domain expertise and a clear understanding of the problem.

  Another aspect is integration of human action—having humans in the loop, using post-processing, or even incorporating non-AI methods to improve the output. That’s another way to create value.

  If you’re pitching to an investor, you must clearly show how you create value. The more concrete the example, the better.

  For example, we’ve looked at a company called Productive AI. They make AI assistants for people in fieldwork who rely on their phones, starting with real estate agents.

  Here’s the value they create: A real estate agent talks to a client while driving around town. Normally, they have to remember follow-up tasks, write notes, and later enter data into a CRM. It’s unreliable and time-consuming.

  Productive AI sits in on calls, facilitates conversations, automatically transcribes them, converts them into action items, drafts emails and texts for follow-ups, and updates the CRM—all without the agent doing any manual data entry.

  That’s a clear example of productivity gains and tangible user value. If you can show that kind of impact, that’s the first step. After that, we’ll want to understand how you’ll build a moat to protect your position—but first and foremost, show value to the user.

   

  Speaker 1:
  Absolutely agree. We’re seeing similar companies. There are many possibilities for investments.

  When you run the Scaling Academy, how do you help founders manage the risks of scaling? Your book discusses burn rates and premature scaling. What criteria determine the best time for a startup to start Blitzscaling?

   

  Speaker 2:
  The key things we teach are about building the right go-to-market strategy:

  • How do you increase virality?

  • How do you leverage existing distribution?

  • Are you building products with features that generate network effects or lock-in?

  • Is your product personalized and able to retain customers over time?

  As for timing: you should delay Blitzscaling as long as possible because it’s inherently risky. The longer you wait, the more you can de-risk the process, making scaling safer.

  But you can’t wait too long—if competitors start scaling rapidly before you do, you could lose the market.

  So one of the most important considerations is the competitive landscape:

  • Are there heavy, aggressive competitors already scaling?

  • Or have you found a market that others have overlooked?

  That context determines when it’s best to start Blitzscaling.

  Speaker 1:
  I mean, what you’re talking about is essentially getting product-market fit and scaling infrastructure and operations required for Blitzscaling, right? Absolutely. In your book, you talk about how they should potentially think about infrastructure—how much infrastructure is enough, how much operations are enough? How does scaling actually, in this model, become sufficient for them to be successful?

   

  Speaker 2:
  I think this is one of the areas where AI can help because AI allows you to leverage resources more effectively than before. But it’s still true that you’ll have to go through various stages of Blitzscaling.

  In Blitzscaling, we describe different stages that help you understand that what works at one stage doesn’t necessarily work at another. That’s true for sales strategy, personnel, and infrastructure.

  You need to build infrastructure that’s sufficient for your current stage, but you don’t need to build the infrastructure you’ll need four or five stages from now. Of course, you don’t want to do anything that foreclose your future growth, but building too much too early doesn’t make sense. You need to focus your effort on reaching the next stage. Once you get there, you can then build for the stage after that, always keeping the overall long-term goal in mind.

   

  Speaker 1:
  Yeah, I absolutely agree with you. I think AI provides a lot of virtual assistance on demand—you don’t need to hire all the employees right away because you can have virtual agents doing tasks as needed. Scaling can now be ramped up or down more easily, which is very helpful in the AI era.

  But even on the people dimension, you still have to hire the right talent. How can an AI startup attract top talent and maintain a strong company culture amid the rapid growth brought on by Blitzscaling?

   

  Speaker 2:
  It’s the same as how startups have always attracted top people. Ultimately, companies are volunteer organizations—you can’t force anyone to work for you, at least not in this country. You have to make people want to work for you.

  Financial incentives are one factor. If the company is very promising and people believe they could become enormously wealthy, that’s a big motivator. But it’s not the only one. Mission and culture matter as well.

  Look at OpenAI during its board turmoil—when Sam Altman was fired, 97% of employees signed a petition saying, “Bring Sam back, or we’re all going to Microsoft.” That’s remarkable. They were willing to sacrifice equity to stand by a principle. That shows it’s not just about money—it’s also about believing in the mission.

  Finally, there’s career growth. In our book The Alliance (which I co-wrote with Reid and Ben Casnocha), we explain that companies should focus on how they’ll help employees build their careers.

  Startups are exciting because they offer uncertainty but also incredible opportunities to accelerate your career. Look at Sheryl Sandberg. With her Harvard MBA and background working for Larry Summers, she could’ve done anything. Instead, she joined Google when it was still a young company and later moved to Facebook when people thought it was messy and risky. Those moves made her a multi-billionaire and a globally recognized leader.

  That’s the appeal of startups—including AI startups: they give people a chance to have a breakthrough in their careers.

   

  Speaker 1:
  Yep, that’s why we’re in this industry together. Absolutely.

  What’s your thought on the long-term effects of Blitzscaling for AI startups, the AI industry, and broader society—especially considering the transformative potential of AI technologies?

   

  Speaker 2:
  I think the broader impact will be an acceleration of change. Companies that Blitzscale effectively will win their markets and dominate those fields for the next decade or more.

  Blitzscaling will shape the AI world for at least the next 12 years. This AI wave will be long-lasting—it’s not a quick one- or two-year trend. We’re still uncovering the implications.

  For example, some speculate that AI could enable three-person billion-dollar companies. That won’t be common, but it’s theoretically possible. If that happens, picking the right people becomes even more important. In a small team working together for 10 years, every hire is critical.

  AI will bring fascinating implications for Blitzscaling—changing business models, cost structures, and even what makes acquisitions financially viable.

   

  Speaker 1:
  Absolutely. Traditional business models that didn’t work before may suddenly work now. A friend of mine in private equity said acquisitions are now financially viable because AI reduces the need for so many human resources. Elon Musk did something similar with Twitter—cutting a huge number of employees, yet the platform still runs well.

   

  Speaker 2:
  Yeah, Twitter was bloated beyond belief. It was one of the most poorly managed companies I’ve ever heard of.

   

  Speaker 1:
  Fundamentally, AI is driving big changes. Let me pause my questions and move to the Q&A session.

   

  Speaker 2:
  Sounds good. I’ll bring those up as well.

   

  Speaker 1:
  I see a bunch of questions. Let me start with some that were submitted beforehand. Maria is asking: Is the information in the Blitzscaling book still relevant today?

   

  Speaker 2:
  Yes, absolutely. But of course, the world has changed a lot since the book was published in 2018.

  We’ve seen major events: a global pandemic, geopolitical shifts, dramatic changes in China’s tech industry, the rise of AI, and the rise (and partial fall, perhaps resurgence) of Web3 and cryptocurrencies.

  The core ideas of Blitzscaling remain the same:

  • Is there a winner-take-most market?

  • Can you win that market?

  But you have to adapt these principles to today’s context.

  AI is probably the biggest factor right now, followed closely by the pandemic. The pandemic massively accelerated digitization and remote work—achieving in a few years what would’ve otherwise taken decades.

  It’s no surprise that AI is now taking off even faster. But interestingly, post-pandemic, people still value face-to-face interaction. That’s why so many still come to Silicon Valley to meet founders and investors in person.

  Regarding AI specifically, its biggest impact is how it reduces barriers to scaling. We’re only beginning to see how AI will make organizations more efficient, but I believe it will drive one of the largest waves of wealth creation in history.

  Speaker 2:
  I mean, for those of you who are investors in public markets, you probably recognize that one of the ways we’ve traditionally valued companies is by looking at earnings or cash flow and applying a multiple. Well, if you look at the entire world, you can think of the market capitalization of the world as being a multiple of GDP, and that’s why GDP growth is so important.

  When the Industrial Revolution happened in the 1750s, global per-capita GDP increased by well over an order of magnitude or more, and that presumably meant that the global market cap would increase by a factor of 10 or more.

  In the case of AI, we could very well see productivity gains of 2x or 3x within a decade. What does it mean if the world were two to three times richer by 2030? The implications are staggering. I’m really excited about the changes that are happening. I think Blitzscaling is still a great guide to how to use those changes, but you have to put it into context.

   

  Speaker 1:
  Yeah, I totally agree. In that case, Raymond asks: Given the rapid advancement of AI and its impact on various industries, what innovative strategy do you believe will maximize returns for AI-driven investment portfolios in the next decade? Let’s talk about the huge amount of opportunities here.

   

  Speaker 2:
  Yeah, and here again, I’m going to talk about my own book. I’m going to say that Blitzscaling is one of the key areas you need to look at because that’s how you’re going to win these winner-take-most markets, and that’s how these $100 billion companies are going to be built. We’re already seeing that happen with OpenAI.

  But let’s talk a little more specifically. Let’s say you want to apply it to investing in traditional industries—doing public markets investing or something similar. What I would say is: you can look at a particular industry and decide for yourself what is the elasticity of the demand curve for that industry.

  The best way to think about AI is that it’s typically going to reduce supplier costs. That means on a supply and demand curve, the supply curve moves downward. The big question becomes: what does that do to the demand curve and the intersection point?

  When you have inelastic demand—meaning the same amount gets demanded regardless of price—that means the industry is likely to shrink. Because if you’re buying the same amount of something and the price goes down, revenues obviously decrease as well. Even if costs decrease, competitive pressure will force prices lower.

  A great example is legal services. Lawyers are going to be decimated over the next decade because law is very amenable to AI—it’s just language. And the demand curve for legal services is pretty inelastic. Nobody says, “Oh, I found a cheaper lawyer; I’m going to sue twice as many people.” Well, maybe a few people, but almost nobody.

  So as a result, you’d look at that industry and say, “Should I be buying into a law firm right now?” Probably not.

  On the other hand, take the entertainment industry, where demand is more elastic. We’ve seen over and over that the more specific and closely aligned content becomes to your personal tastes, the more consumption occurs.

  Today, in the streaming era with endless content, media consumption is far greater than it was when we only had three TV channels. Back then, we watched Gilligan’s Island reruns during the day and went outside to play. Now, it’s a wonder any child goes outside when they have a world of entertainment at their fingertips.

  I think entertainment has an even more elastic demand curve. People will start getting personalized entertainment. Imagine saying, “I have a 22-minute Uber ride; give me a 22-minute documentary on the USS South Dakota Battleship.” Initially, it might be still images and narration, but eventually, full video will be generated. With devices like Vision Pro and advancing generative AI, entertainment will be created on-demand specifically for individual needs.

   

  Speaker 1:
  Yeah, absolutely. We’ve done two legal-plus-AI webinars in the last few weeks. If you’re interested, check those out—we addressed similar concerns Chris has mentioned. Obviously, there’s a tremendous amount of opportunity as well.

   

  Speaker 2:
  Oh yes. You can make money by decimating an industry—you just shouldn’t invest in the law firms.

   

  Speaker 1:
  I’d probably extend a little on what Chris talked about. From an investment perspective, I’d say there are two potential areas.

  First is investing into AI-related startups—technology infrastructure companies. We saw this wave in the PC, internet, and mobile eras. Look at today’s tech leaders—Google, Nvidia, Microsoft, Meta—they’re all infrastructure vendors that benefited from those waves. In the current AI era, similar opportunities exist.

  For example, our AI Fund III invested in a startup that’s the leader in GPT cloud services for AI. They just won the 2024 NVIDIA AI Excellence Partner of the Year award—congratulations to them. That’s a very interesting opportunity.

  The second area is new applications enabled by new technology infrastructure, as Chris discussed. Think of Netflix, Uber, Airbnb—applications made possible by the internet and mobile technology. These startups wouldn’t exist without those technologies.

  They also reduce costs in existing categories. Uber reduced transportation costs compared to taxis; Airbnb reduced accommodation costs compared to hotels. Almost every technological advance brings new markets and adjacent cost savings.

  AI will do the same. Jeff Bezos famously said, “Your margin is my opportunity.” Whoever adopts AI effectively will capture significant cost savings and tremendous growth.

  We invest in multiple companies like this—from financial accounting to patent analytics, to internal legal tools. Chris also mentioned vertical integration and roll-ups driven by AI innovation. There are many possibilities.

   

  Speaker 1:
  Next question: Sanjay asks, are there any optimal paths to scale up self-funded startups?

   

  Speaker 2:
  Sure. A couple of things: First, at some point, you may no longer be able to self-fund, and when that happens, it’s great to look for a venture capital partner like Alumni Ventures or Blitzscaling Ventures to help you grow.

  But while you’re still self-funded, you have a lot of power because bootstrapping gives you essentially infinite runway. That gives you time to figure out product-market fit, the market you’re going after, and the technology and nuances of your product.

  This is often challenging to do when you’re growing rapidly or already have a lot of legacy customers.

  So leverage this flexibility. Use this phase to learn as quickly as possible and gain insights about the market that others don’t have. Those insights will give you a competitive advantage.

  This is your opportunity to identify where conventional wisdom is wrong. That’s how you can be a correct contrarian and grow your market position before others realize what’s happening.

  Speaker 1:
  Yeah, I’ll just extend quickly on this. It really depends on the categories. In this model, a simplified view is that a startup can be classified either as a venture startup or lifestyle startup. A startup does not need to be venture funded unless it has growth potential, a big enough market, unique differentiation, and a strong founding team.

  So if you are a self-funded startup that has potential for Blitzscaling, please reach out to us. Like Chris mentioned, we’d be happy to take a look and help you along your journey.

  If you’re a lifestyle business, I personally think it works well competing with existing businesses with high margins because in this AI era, the cost of computing is approaching zero, and AI copilot assistance is getting better and better. You have a great advantage in making a three-person startup work very well—traditional models might take 20 to 30 people to do the same.

  There’s nothing wrong with a non–venture-funded startup, as long as you can find product-market fit and grow very effectively into it.

  Next question: Gene asked, “Business model innovation seems to deliver the best return in mature industries where some aspect of the value chain can be combined or modified. I’d be interested to hear examples where this is not true.” Chris, do you have any thoughts on that?

   

  Speaker 2:
  That’s interesting because I was just nodding and agreeing—I’m like, right on, that’s exactly right. Business model innovation is the key.

  Of course, for those of you who’ve read Blitzscaling, business model innovation usually occurs because technological innovation allows you to do new things that didn’t exist before. Otherwise, people probably would have undertaken that business model innovation already.

  That’s why we tend not to see it in mature industries where there isn’t some sort of disruptive technology coming in.

  Now, the interesting question is: what are the circumstances under which disruptive technology can come in and yet not change the nature of the industry itself?

  I think it probably happens when the technology itself, while revolutionary, is still relatively adjacent to the previous market leader.

  For example, people once asked: “How’s Facebook going to handle the transition to mobile?” It was a big deal because before the smartphone—before the iPhone—all web traffic was on the desktop. That’s all there was. By definition, 100% of traffic was desktop.

  Now, something like 80% or more of consumption is on mobile devices instead of desktops. That’s a massive shift. If Facebook had been unable to make that shift, it would have been dead in the water.

  But they were able to make the shift despite making some wrong choices early on. They initially said they weren’t going to do a native app and later realized that was wrong.

  How did they manage it? Well, as it turns out, delivering content on mobile wasn’t that different. We watch Netflix on our laptops, but we also watch it on our phones and TVs. We use Facebook on computers and phones.

  So the form factor was most important for industries interacting directly with the physical world because you’d be using it out and about. That’s why traditional taxi services couldn’t adapt to Uber—it was a significant change.

  Looking at how mobile affected different industries differently shows a clue: the extent to which a new technology’s capabilities are used daily in a particular industry influences whether it fundamentally disrupts that industry.

   

  Speaker 1:
  Yeah, a very good point. We probably have time for one more question: “How do AI tools expedite the scaling process for startups? What are the implications of the unprecedented speed of AI model evolution for startups?”

   

  Speaker 2:
  The answer is: it’s going to accelerate everything more than ever before.

  AI tools can help everyone—they can make programmers more productive, marketers more productive, and help you make decisions more confidently and faster than ever. The net effect is acceleration.

  But this isn’t the first time this has happened. If we look back at startup history—Ray, you and I remember the 1990s—if you wanted to have a website or a web application, you had to rent $20,000 a month worth of space in a data center like Exodus or Global Center.

  You had to buy $100,000 worth of computer hardware from Sun Microsystems and another $100,000 worth of Oracle database software. All of this meant you needed millions of dollars just to launch a company.

  That’s completely foreign to people today because of the cloud computing revolution. With AWS, instead of spending $20,000 monthly and $200,000 upfront, you now spend $0 upfront and maybe tens of dollars monthly if you have decent volume.

  What did that do? It made it much easier to start certain types of companies.

  Did that mean we no longer had a startup ecosystem? No—it made it even stronger.

  I think AI will similarly make startups more accessible. People will be able to start more companies, and ultimately, it will make the startup ecosystem even stronger. I’m very excited about the future.

   

  Speaker 1:
  Yeah, absolutely. The speed we’re moving forward is accelerating for sure.

  It’s like a chessboard that’s already 50% full, and now we’re doubling every square—it’s a tremendous growth speed.

  As Chris mentioned, all these virtual tools and capabilities mirror what we’ve seen before.

  I was at Cisco during the internet boom in 2000, watching the networking explosion. Then came the mobile wave. Each of these technological waves accelerated things further.

  Flexibility is critical, and AI enables that.

  As I said, virtual agents and virtual employees can perform many tasks, creating flexibility in how you construct your business.

  More and more, we’ll become architects rather than builders.

  We’ll assemble components, and natural language will become the new programming language.

  I used to work on compilers that translated code into machine language. Now, you just speak—chatbots interpret it and create programs that execute entire models.

  It’s fascinating to see how far we’ve come in the last 20–30 years.

  Speaker 2:
  Absolutely, absolutely.

  Speaker 1:
  This is great because I know you need to run to your next one, so I’m sure the community is excited to learn more. Where can they contact you and get more information? Maybe you can share a little bit more about yourself and the best way to connect.

  Speaker 2:
  Absolutely. You can find me at chrisyeh.com — that’s just C-H-R-I-S-Y-E-H.com. Maybe some folks from the team can put it in the chat or notes.

  You can also reach me directly at [email protected]. I’m always happy to talk about these topics.

  You can find me on LinkedIn as Chris Yeh. If you reach out there, please personalize the invite and mention that you attended this Alumni Ventures webinar.

  And of course, as an Alumni Ventures part-owner, I’m very happy to have people be investors in Alumni Ventures as well.

  Finally, you can learn more about Blitzscaling Ventures at blitzscalingvc.com, and it looks like we’ve put those links in the chat, so you’ve got all the information you need.

  Speaker 1:
  Yep. Look it up and reach out—it’s wonderful. I just want to thank everyone for tuning in, and if you have any trouble reaching Chris (I know how busy he is), just contact us as well. We’re happy to help.

  I also want to use this opportunity to thank our community for your support. Alumni Ventures is all about community—community makes us stronger and more purposeful in our pursuit to democratize the venture asset class for individual investors.

  As I mentioned earlier, our fund will close in the next few days—by the end of the month. Spots are limited. If you’re interested in the AI revolution, please contact us.

  If you’re a startup founder interested in learning more about how to apply Blitzscaling to your business, contact Chris. The book Blitzscaling is one of the bestsellers on Amazon (Audible version as well). Check it out.

  Chris, thanks again for being part of the webinar. We and our community truly appreciate your support.

  Speaker 2:
  Always so great to support Alumni Ventures and Ray, to be a part of what you’re doing.

  Again, I want to emphasize to everyone: I think that investing in AI is one of the most important things you can do, especially if you’re worried about what’s going to happen in the future—whether all the jobs are going away or what the world might look like.

  Guess what? You can hedge that bet—because if you are a successful investor, you can hedge that bet by investing in AI.

  Speaker 1:
  Yeah, I couldn’t have said it better. Thanks a lot, Chris, and I appreciate everyone for tuning in. Thank you.

  Speaker 2:
  Thank you, everyone.