Investing in the age of AI, part 2: Jeff Bussgang

JEFF BUSSGANG: The thing that I really hope for is that AI allows us to, and frees us up to be more human, to spend our time on the human things that are unique to us, like relationships and love, insight and strategy. And I think the most successful professionals in the age of AI are gonna be the ones that really glean into that.

I don’t think AI’s gonna replace founders anytime soon, but founders who use AI are absolutely going to replace founders who don’t.

RANA EL KALIOUBY: Jeff Bussgang is general partner at Flybridge Capital Partners – an AI-focused fund – and he teaches entrepreneurship at the Harvard Business School. He’s also the author of the new book, The Experimentation Machine – which is a playbook for how founders can harness AI in their startups.

Last week we spoke with legendary investor Vinod Khosla about how AI is disrupting every level of our lives. Now in part two of our investor series, we’re getting granular, and talking with Jeff about how founders can use AI to help elevate their businesses. We’ll talk about what it means to be an AI-native company, how AI can make you a 10x founder, and how to scale without growing.

I’m Rana el Kaliouby and this is Pioneers of AI – a podcast taking you behind-the-scenes of the AI revolution.

[THEME MUSIC]

EL KALIOUBY: Hi Jeff. Welcome to Pioneers of AI, and it’s so good to see you again. Thanks.

BUSSGANG: Thanks for having me.

Copy LinkWhy AI inspired a playbook for modern founders

EL KALIOUBY: So you recently published the book, The Experimentation Machine, which I loved reading and we’re gonna talk about, but what inspired you to write the book?

BUSSGANG: So I have two hats, as you know. One is I teach at Harvard Business School in the entrepreneurship unit, and I focus on launching tech ventures.

So I get all the founder aspiring students. And then secondly, I’m an early stage venture capitalist. My firm Flybridge, all we do is focus on AI investing. And so I was seeing in those two worlds a whole new trend around using and leveraging AI to become 10X founders. And so the book tries to bring that to life.

EL KALIOUBY: Yeah, we’re gonna talk about all of that in a second. But before we get into that, I wanted to hear more about your origin story. You spend a lot of time at actually a number of startups, so you’re kind of an operator turned investor of sorts. Share some of the highlights of your journey and how did you end up with this investor hat.

BUSSGANG: Yeah, so I was an undergraduate in computer science at Harvard. I graduated in 1991. My focus was on AI and natural language processing, which in the late eighties is hard to imagine. And then I went into a business career after also getting my MBA at Harvard as a tech founder. And in the mid nineties was involved in a company that went public, an internet 1.0 company called Open Market.

I was an executive team member and had an amazing ride. And then in the early two thousands, I co-founded a company called You Promise, also venture backed, also tech, also an amazing ride. It sold successfully in the mid two thousands.

And when Greylock shifted West in the early two thousands, two of the young Greylock Boston Partners spun out. And with me, we formed Flybridge. So I had this entrepreneurship background with their investing background in combination.

Copy LinkHow operating experience shapes founder-first investing

EL KALIOUBY: How did your entrepreneurship background influence your lens on investing?

BUSSGANG: I think what I would say is that I have a huge amount of founder empathy. And yes, I have the operating experience of taking a company public and building multiple large companies that were successful.

But more at the core is founder empathy and that emotional rollercoaster and that sense of loneliness that founders undertake when they go on this journey. And just being as much as I can, an advisor, friend, companion, mentor, coach, whatever it takes to help them get through to the next level.

EL KALIOUBY: I think that’s so important, because I’m kind of drawing from my entrepreneurship experience too. It is an emotional rollercoaster. Some days you feel like you’re on top of the world, you got this, and then the next day you get a no from an investor or a customer or you lose some of your talent and you’re like, ugh, it’s existential, right? And so being able to have investors where you can talk about this openly and feel safe to bring this up is really special.

BUSSGANG: One of my founder friends captured it beautifully when he said, I eat nos for breakfast. Just that sense of always having to push through negativity and always focusing on that sense of belief and optimism that the next door, the next door, the next door, whether it’s an investor or a customer or M&A candidate.

Obviously the macro environment has a lot of turmoil and you can’t control that. As a founder, that can be very frustrating. So really just being an empathetic advisor, investor and friend, as I said, is really what I center on.

Copy LinkWhy the biggest AI opportunity is in the application layer

EL KALIOUBY: Yeah. So talk a little bit about Flybridge’s investment thesis, and you’ve been investing in AI for at least a decade, right. So talk about what’s your view on the AI landscape as it relates to investing?

BUSSGANG: Yeah, as you said, Flybridge has been investing in AI for 15 plus years, and our original thesis was more around data and machine learning, and that led to companies like Zest AI and MongoDB, which was an important data infrastructure company. And then later we’ve been more focused on the application layer, and that’s really where we’re centered today.

We believe that the foundation layers are getting commoditized in a very good way. Prices are coming down for AI, compute and inference dramatically, and that enables this massive opportunity at the application layer. I refer to it as the AI dividend, much as we all benefited from the cloud dividend over the last few decades. We’re entering into a moment where application software providers are benefiting from these incredible AI dividends that are coming our way.

EL KALIOUBY: Do you only invest in AI companies?

BUSSGANG: Exclusively.

EL KALIOUBY: Oh, wow. Yeah.

BUSSGANG: So at Flybridge, our thesis has always been around enterprise software and AI was an important component of that over the last few decades. But in recent years, it’s all we do. So we’re exclusively an AI focused investor.

I’ll say there’s one exception though, and that is we are big believers in AI native founders, which I’m sure we’ll get into. And if we see an AI native founder operating in a slightly non-AI native space, we lean in on them as well.

Copy LinkWhat it really means to build an AI native company

EL KALIOUBY: So in the book you talk about AI native companies. Can you define what that means?

BUSSGANG: An AI native company is a company that infuses AI into everything they do, every function and every process. When somebody comes to the CEO and says they wanna hire someone, the first thing the CEO says at an AI native company is, could AI replace that hire? When someone describes a new process or a new workflow that they’re creating, the first thing that is asked in the meeting is, how could we use AI to do that more efficiently? So an AI native company is leveraging AI across engineering, sales, marketing, customer service, executive functions, finance, everything everywhere, all at once.

EL KALIOUBY: You know, I’m starting to see companies that are basically, their product is essentially an AI employee. Like one company I met, they’re building AI managers.

There are AI healthcare administrators. Do you envision a world where an org chart will be a combination of humans and AI?

BUSSGANG: Absolutely. In fact, when I talk about the contents of the book and the moment we’re in, I show the standard traditional org chart of a seed stage company. You or I might invest two or $3 million. The team would hire 10, 12, 15 employees that go build the product, and that’d race to get to the $1 million ARR magic threshold.

Today it’s two or three employees and hundreds, if not thousands of AI agents working in tandem to get to the magic $1 million ARR threshold. So yeah, I think you’re gonna see this dramatic change in org charts.

Sam Altman has this famous sort of throwaway line he uses where he says his founder friends joke about when will we see the solo entrepreneur that has achieved the milestone of building a unicorn, a billion dollar company as a single employee. And I looked in a recent analysis I did of unicorns. We now have a dozen unicorns with less than a hundred employees and the question is, in 2026, will they be able to do that with less than 10 employees, and in 2027 or 2028, five, three, two employees. That’s really the moment we’re in with the age of AI for AI native companies.

Copy LinkHow startups can stay defensible as foundation models evolve

EL KALIOUBY: Alright, so AI is moving so fast and one of the questions I have with my investor hat on is defensibility. I often tell founders, if you wake up every day concerned about the next version of ChatGPT and it’s gonna put your company or your product out of business, then I don’t wanna be an investor. That’s not defensible. However, if the next version of these foundation models strengthens your product and your service, then there’s something there. How do you think about defensibility, especially with the pace of change and acceleration of developments?

BUSSGANG: It’s a great question, and it’s the question in every one of our investment committee meetings. The three things that we focus on outside of team, which I can come back to, but just in the core fundamental business and product, are: one, are there sources of proprietary data? So for example, we have an AI for legal software company called Noa. They ingest thousands and thousands of contracts on private corporate debt transactions, and those debt transactions have proprietary terms in them. And so they now have all this insight into who Citibank lends to and what the terms are and who JPM lends to and what their terms are, all the way down the line, so that when a new lender steps to the table, they can do this incredibly rich terms analysis.

That’s a unique data set that only Noa has. So the data moat is really important. Second is around workflows and that sort of last mile. The platforms that the foundation models are providing are incredible, but they’re not trying to nail the interface for the last mile for the user.

So for example, we have a portfolio company called Allspice, which is for hardware engineers. And it’s that last mile of doing hardware design and collaboration. The GitHub for hardware that allows us to have confidence that the engineers are in that platform at Allspice all day, every day. That’s what they live in, and they’re beginning to really get addicted to that workflow.

And then the third area is around that human AI interface. It’s that insight into how humans will interact with your AI platform. If you look at the history of ChatGPT, ChatGPT was an experiment that was thrown on top of the SDK, the API that OpenAI was launching.

And it turned out to be an incredible interface and the rest is history. So founders that really can understand that interface and the human AI interface to manifest these AI capabilities, but do it simply and in a way that’s usable.

EL KALIOUBY: Yeah. A few thoughts on that. I was speaking at a ServiceNow event recently, and one of the questions that came up is, and I think there are studies, there are now people looking at this. If you build personality into these bots or interfaces, they’re more likely to adapt, especially when it’s part of a team. If these bots have a personality, they kind of fit in better into the team culture and I think we’re gonna start to see more of that.

BUSSGANG: I think you’re totally right. I know this is an area you have a lot of expertise in, but that emotional connection that we’re gonna have with the AI agents that work alongside us in the organizations, I think that’s gonna be really powerful. And so the flip side is the software companies that can bring that emotional intelligence into the AI agents and help them work with the humans in collaboration are gonna be the successful software companies.

EL KALIOUBY: I also have a thesis around the evolution of the human machine interface anyhow. Like if you think of a smartphone, it’s not really AI native today.

And so I think a combination of conversational perceptual interfaces, yeah, we’ll see a lot of innovation there. I haven’t seen the thing that I think is like the iPhone equivalent, but I’m curious, do you invest in these companies? Are you keeping an eye on that?

BUSSGANG: We’re very much keeping an eye on that. Obviously pendants and other interfaces and hardware devices and these consumer hardware interfaces, I think are really rich areas for innovation.

EL KALIOUBY: We’re going to take a short break. When we come back we’ll discuss Jeff’s book, and ways that YOU can harness AI to bolster your business. Stay with us.

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Copy LinkWhy AI is creating the era of the 10x founder

EL KALIOUBY: So let’s next talk about the book and how we bring AI to the startup landscape. First off, you talk about the 10x founder. What is that and how do you look for these 10x founders?

BUSSGANG: So everyone knows about the mythical 10X developer. That’s the developer that’s so effective they’re not just 10% or 20% better than the average developer, they’re 10 times better. And for a while, founders were sort of limited in their ability to use tools to be more productive because the tools were software productivity tools that everybody had at their disposal. But now these AI tools allow us to see founders operating like 10X founders, being 10 times more productive, and organizations becoming 10X organizations, leaders becoming 10X leaders, 10X salespeople, 10X marketers. And it means that founders can now lead organizations far more efficiently with far fewer humans.

EL KALIOUBY: Yeah. I think that is so fascinating for a whole bunch of reasons. First, I think it makes this early check so much more important, right? As an early stage investor, if you’re going to assume that these companies are gonna be very capital efficient, then that early check might have the most leverage. Is that the way you think about it?

BUSSGANG: I think the venture industry is really about to change dramatically for that very reason. So if you look at what’s happening in the marketplace, companies are building without needing capital. They can seed strap their way to a million, 2 million of revenue and then scale with modest capital infusion to over a million of revenue per employee. We have a number of our portfolio companies doing over a million dollars of revenue per employee at early stages, and that’s because the founders and the teams are just so incredibly efficient and leveraging these AI tools.

Copy LinkHow faster experimentation leads to product market fit

EL KALIOUBY: So I’ll ask you one more question and then I wanna bring all of that to life with examples. So let’s talk about product market fit, right? How do you define product market fit, and how does AI help accelerate getting to that product market fit?

BUSSGANG: One of the themes in the book is that we’re in a moment where we’re combining these timely AI tools with timeless methods for business building, and it’s important not to lose track of the timeless methods, not to get too caught up in the technology and the latest tool.

And so the way I describe product market fit and the framework I use, which is the HUNCH framework that I describe in the book, I try to really focus on those timeless methods. It’s five letters. So it begins with H, which is the hair on fire value proposition. It’s not nice to have, it’s beyond must have. It’s your hair is on fire. You need to solve this problem. It has to be a top one or two priority. U is for usage. You wanna see a high degree of usage. As many people know, usage is the most important indicator of product market fit. N is for NPS score.

EL KALIOUBY: For those who don’t know, NPS stands for Net Promoter Score. It’s a metric used to measure customer loyalty. Basically asking customers on a scale from 1 to 10 how likely they are to recommend a product.

BUSSGANG: C is churn. You want the churn to be low. And then the final H is a high LTV to CAC ratio.

Lifetime value to customer acquisition cost. Good unit economics. You can’t forget the timeless importance of profitable, sustainable business building. With those five letters, the five HUNCH letters, then you say, okay, we are gonna have different stages. In the early days, it’s gonna be nascent and we’re gonna see naturally solid performance in some, okay performance in others. And over time you want to build up to more positive product market fit and eventually radical product market fit.

EL KALIOUBY: One of the themes that I really liked in the book too is that it’s all about experimentation. And the faster you can iterate through these hypotheses and testing, the quicker, presumably, you’ll get to product market fit.

BUSSGANG: And that’s the second part of your question, which is how does AI impact the product market fit journey. You can run experiments so much faster and so much more effectively today. Building prototypes happens in an hour and when founders would come to me five, 10 years ago and say, hey, I have this idea and I’ve got these wireframes, my first comment to them was, you need a technical co-founder if you yourself aren’t a builder. Now I say it’s like the revenge of the MBA.

MBAs can build, they can use these tools like Lovable and Replit and Cursor and Vercel and they can build applications. It’s really extraordinary.

EL KALIOUBY: Yeah. You know, I spun out of MIT so I’m part of this venture mentoring network and a lot of the calls are, oh, I’m looking for a technical co-founder to help me with this business idea. Do you think we don’t need that anymore?

BUSSGANG: I wouldn’t say we don’t need it, but I would say business founders or non-technical founders can make so much more progress. And certainly the first thing we say to people is not, do you have somebody with you who’s got a PhD in machine learning, which is what we used to say, but now it’s, do you have capacity to build? And maybe it’s you that’s gonna be the builder as the founder, or maybe you’ve got a team of builders around you. But the capacity to build is so much better now than it ever was.

EL KALIOUBY: Yeah, it’s kind of fascinating. My son is 16 and he’s very AI forward and he’s been able to use Replit to build an app and a website and he uses Manus AI to do a lot of research. And I’m like, wow. I spent like eight years of my career learning how to code and then learning all the machine learning basics and now yeah, the barrier has been lowered and anybody can code and it’s really.

BUSSGANG: It is fascinating. You’re touching on something though that, when I did sort of a mini book tour, I was out with OpenAI. They invited me to come to a book talk at their offices, and I got into a great conversation with their leadership about this question of when will the first principles thinking be learned.

Because you’re right, he may be so facile with the tools. And I’m not saying this is gonna be the case with him, but he may not do the hard first principles thinking that you had to do and that I had to do by learning assembly language as my first programming language and then graduating to C, and then C++.

So this understanding of the depth of the intricate sort of components and building blocks, I think, is gonna be really interesting. Where does the wisdom and the insight come from if the tools are doing these things for you?

Copy LinkWhere AI can transform sales marketing and customer access

EL KALIOUBY: So we talked about the use case around accelerating building the product using these AI copilots. What about using AI for sales and marketing? What are some.

BUSSGANG: Yeah, so I talk in the book about Top Line Pro, which is one of our portfolio companies. That’s a platform that provides an ability for service pros — think landscapers, plumbers, electricians — to build websites and interact with their customers like a CRM, a customer relationship management platform.

They are using AI to not only leverage all these public data sources to identify service pros, but then also to do lookalike matching just like Facebook lookalike does, to match the pros that are gonna be the best fit for them. And then they’re using AI to build the websites, automatically ingesting the Facebook pages and Instagram pages of the pros.

And then they’ve created an AI customer service bot to handle service queries. But one area where they were struggling was around outbound outreach. They were emailing pros and pros aren’t great over email. They’re on a roof, they’re in a house on a job, they’re underneath a sink fixing something that’s clogged.

And so they’re not at their desk doing emails. But they decided to personalize emails using AI, and they created personalized video demos of their website in action. And they would say, here’s this video I’ve created for you. Here’s this website. It’s got your content. This is what it would look like if you had me build it.

Do you want me to build it? And it took their response rates from less than 1% to double digits. It was an incredible impact. And they used these modern video generation tools like HeyGen and 11 Labs, and they bolted them onto all these other workflows and AI sales enablement tools to create this incredibly fully automated sales and marketing pipeline.

EL KALIOUBY: It’s amazing. For our listeners who are not familiar with HeyGen and 11 Labs, I did an episode where I shared my own virtual twin basically using, we used HeyGen and 11 Labs and a bunch of other tools. And I had it speak Spanish, which I don’t speak. I had it speak Mandarin, which I also don’t speak. And unless you know me really well, it looks fine. And it’s the worst the technology will ever be. It’s gonna keep getting better.

BUSSGANG: Getting better. So one of the complaints I used to get as a faculty member at HBS is that my students would say, I don’t have enough office hour capacity. I’m a full-time venture capitalist, I teach part-time at the school. There’s an insatiable desire for office hours amongst our students, and I just don’t have infinite hours in the day. So I created an AI clone using Delphi, and that clone is trained on all three of my books, 80 HBS case studies, and about 20 years of blogging and it’s text, voice or video.

And it’s been launched in public now. I trained it with my students and they loved it. And my students rave about it. They say that it has given them infinite access to my wisdom, to the extent I have any, on startups and startup building. It’s not that I’ve changed my office hour volume, I’m still at the same number of hours in the day. But they have this now all day, 24/7 access to me. So I would come into the classroom sometimes in the morning and my students would say, I had a great conversation about my startup with you last night. Like two in the morning. Yeah.

EL KALIOUBY: Isn’t that fascinating? It’s incredible. How does AI change our hiring practices – and how do we make sure that in this Age of AI – women and other underrepresented founders can shape the AI revolution. We’ll get into that after a short break.

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Copy LinkHow to scale revenue without scaling headcount

EL KALIOUBY: Okay, so in your book you also talk about scaling without growing. Define what you mean by that, and yeah, talk about the different ways some of these AI native companies are hiring or thinking about hiring.

BUSSGANG: Want to give credit to Scott Belsky of Adobe who came up with the phrase, or at least is the first person I saw use the phrase, scaling without growing. We’re entering into the era where startups can scale quite dramatically without adding headcount. And I mentioned some of the efficiencies that we’re seeing. Those efficiencies began with the Magnificent Seven.

If you look at the profile of what the big seven companies, who are the most advanced and sophisticated in using the AI tools that they’ve built for themselves, they’ve been growing 20, 30% year over year without scaling headcount. We may be at a point where we’ve reached peak employment generally in the economy because now so many of our companies and companies all around the world in every sector are able to scale dramatically without growing. What that means is that founders can spend more time on the stuff they love. As you know, when you’re scaling a company, you spend a ton of time on hiring and HR issues and interpersonal issues, and politics and operations, and org building and compensation and reviews.

And wouldn’t it be great if you could do the exact same throughput and output with half the people, with a third of the people, with a tenth of the number of people. Now there’s a whole bunch of implications for the economy and the labor force that we can talk about, but from a founder’s lens, that’s a dream, because you can spend more time on building and on working with customers.

EL KALIOUBY: How do we ensure inclusion, right, especially women, like we need more women in the AI space and also underrepresented minorities and people of color.

BUSSGANG: We desperately need more representation in the AI space. Not only because it’s the right thing to do, but it’s actually good for the companies that are building these products. They are gonna eliminate biases if they have a diverse set of viewpoints. Everybody knows these models have inherent biases based on the training, and the more diverse the viewpoints, the better the models are going to be.

I mentioned in the book this example of Snapchat and the camera not being as easily able to recognize faces of color. An engineer who is Black, who’s a friend and colleague of mine and was a product manager at Snapchat, pointed that out and suddenly he became the head of fixing the problem and making sure the cameras and Snapchat’s application would be able to identify faces of color.

So I think it’s critical that we create pathways for underrepresented individuals to get into the tech sector and to help them become AI native contributors into the ecosystem.

EL KALIOUBY: Now you also talk about the pipeline myth. What do you mean by that?

BUSSGANG: When I say we, I should say, when my partners Chip Hazzard and Anna Palmer co-founded X Factor Ventures, which is a venture fund focused exclusively on backing female founders, we discovered that there were way more female founders than we appreciated or realized as a partnership that was mainly male.

And similarly, when I started Hack Diversity, I realized there are way more Black and Brown engineers and founders than I realized. And so these pools of talent exist, but networks are inherently biased. We have relationships with people who are like us or have been in environments that we’ve been in.

It’s just human nature to have homophily, and so expanding our networks requires intentionality. And that’s why I say there’s a pipeline myth for those of us who are hiring. Finding more women who are qualified, finding more underrepresented individuals who are qualified, it just takes expanding your network into those pools of talent.

Copy LinkWhy inclusion ethics and focus matter in the age of AI

EL KALIOUBY: Right. It’s an access issue, not whether they exist. Yeah, absolutely. So it’s also really important to me that as we are seeing more and more AI native companies, we think about the ethical development and deployment of AI. So how do you think about the ethics angle as these companies are harnessing AI across every aspect of their business?

BUSSGANG: I talk about it in the book, and I do an ethics module in my class at HBS as well. Sometimes you have to slow down a little bit and think about these unintended consequences. I had a number of alumni in my class go work for Juul, the vaping company, and they had good intentions when they went and joined that company.

But when that company began to sell to minors, and when we saw vaping in middle school and high school with cotton candy flavors and other types of flavors, they suddenly realized that the growth of the company obscured some unfortunate unintended consequences and bad behaviors. And I think that future is the present today in the AI ecosystem, that we’re gonna see some unintended consequences, whether it’s around safety or biases. Hopefully we won’t see those unintended consequences be as dire as the parable of the paperclip. There’s this famous parable where you set the AI to maximize the production of paperclips, and the AI realizes at one point in the cycle that the way to really maximize paperclip production is to eliminate all the humans on the planet because humans are taking up resources that would be quite valuable in paperclip production.

And so the AI that you assigned to this very narrow task of maximizing paperclip production ends up eliminating the human race. That’s the AI paperclip.

EL KALIOUBY: Right. Which I always say, we can always pull the plug on AI if it gets to that point, but it does underscore the alignment problem, right? Like how important it is to align the incentives and align basically what AI is trying to achieve with what humans want it to do.

BUSSGANG: For sure.

EL KALIOUBY: Okay. So there is a lot of uncertainty when it comes to the economy and it’s just a very chaotic and volatile market. What’s your advice to founders as they’re starting companies these days?

BUSSGANG: It is a wildly volatile environment right now, and our advice to founders is keep your head down and just focus on what you can control. If you’re a founder, let’s say in an enterprise context, all you need is 10 or 20 great customers that are really happy with you in the early days. So focus all your energy and go get those 10 or 20 and don’t worry about the latest tweet from the president. Just focus on getting product market fit.

EL KALIOUBY: Yeah. In a way, actually, it’s a good time to be starting these companies and it’s a good time to be investing in these amazing, transformative companies, but it is hard to tune out.

BUSSGANG: I think the best founders are incredibly effective at eliminating distractions and just staying hyper-focused. I have a joke with one of my partners that sometimes my best founders are the least responsive to me. Because they’re so focused on whatever it is that they’re doing in the moment that they can’t bother to respond to my email.

EL KALIOUBY: Okay, last question. I love to ask this question to all my guests, because I think about it a lot. In this age of AI where AI can be smart and creative and a thought partner, what does it mean to be human in the age of AI? And I’ll actually share a funny story. It was Mother’s Day and my 16-year-old got me this card and his handwriting sucks.

I’m like, oh my God, I failed as a mom. But anyway, he literally stood next to me reading out the card, and he had this cute line. He was like, and you are a better creative and thought partner than ChatGPT. And I was like, yes, I win. So yeah. What do you think it means to be human in the age of AI?

BUSSGANG: I love that. The thing that I really hope for is that AI allows us to, and frees us up to be more human, to spend our time on the human things that are unique to us, like relationships and love, insight and strategy. Discernment, judgment, taste. I think those are the types of things where we’re gonna see humans really thrive. And I think the most successful professionals in the age of AI are gonna be the ones that really glean into that.

EL KALIOUBY: Amazing. Well, thank you Jeff, so much for coming on the show. This was great.

BUSSGANG: My pleasure. Thanks for having me.

EL KALIOUBY: AI is helping startups become successful … faster. And while the fundamentals of a winning business still look the same – the pathways to get there are a whole lot more capital efficient. Jeff’s point about how AI is enabling founders achieve 10x productivity – whether they’re technical or not – is key. Founders can now utilize AI to solve problems across every aspect of their business – from coding to sales to marketing. All of this means that startups can get to product-market fit quicker. Ultimately this creates more opportunities for people to build their dream businesses with less resources.

AI won’t replace founders – but AI-native founders will replace founders who don’t use AI.