Replit CEO on Vibe Coding and the Future of Software Development w/ Amjad Masad, Dave Blundin & Salim Ismail | EP #196

26:15And I'm shaking their hands, and I felt Michael kind of squeeze my hand a little bit. I don't know what was going on there. The moment I sit down on the chair, his face is red. He's like, why did you Rickroll us? It turns out, as we were sitting outside, they were getting Rickrolled inside before the waiting bus. So they clicked on the video, and they got this Rickroll video place. If you guys haven't seen it, just go Google it. Yeah, there's a meme. You can find it easily. And he was very upset, and he started really kind of giving us a really hard interview. Really good question. And what are you thinking at this point?

26:51I'm thinking we were screwed. Like, we're done. And so I actually exit the room and ordered an Uber. And my co-founder's like, well, we have to wait. He's like, oh, we're not getting in. After we did that, we're not getting in. But then I get a phone call from Adora Chang, and she's like, you got in. And so we go in that same day, and Sam Altman did the kickoff and all of that. How important was the YC experience to you? I mean, would you have accelerated and gotten to this point without it? I think one property that I have is I would never quit. And so I would have kept going, and I think we would have probably made it.

27:30But it probably accelerated our progress by probably years. I mean, it opened a lot of doors. And just being around these amazing people, being around Sam, Paul, and you just raise your ambition a lot. Being around all the ambitious people at Vice. Yeah, well, it's the Silicon Valley thing in general. I think that's what matters. The question about talent is very important. There's talent everywhere. But I think the density of the network at places like Stanford, places like Silicon Valley. Paul Graham had that great quote that being a startup founder is only about one millimeter away from being unemployed.

28:00So what you're telling your parents, you need like-minded people all around you as your support network. Otherwise, it's just really, I mean, doing it in Jordan would have been really emotionally difficult. And you obviously pulled it through. Just to put a pushpin in that story, too, incredible story. But if anyone from Stanford administration is in the room, I know Eric Brynjolfsson and others are, like your application process is in complete fail. But the pull process of discovery is a complete win. So now when you're building your own company, you see all these people and what they're doing.

28:33you don't need them to apply to come and work for you. You have the data. You can analyze the data and pull. The world is clearly going to move from applications to big data-based pull. To discovery. To discovery. Yeah, that's what I'm saying. The world is going to be forced to be more meritocratic. Yes. And I think the problem with institutions like YC, they've gotten better, and Stanford, they're very much about pedigree. Yeah. Where are you from? I'm not faulting them for it. It's a heuristic. If you're Stanford dropout, Stanford did the work for us to choose you, and therefore we're just going to piggyback on that.

29:07But in reality, again, talent comes from everywhere. Philip Rosedale, creator of Second Life, I don't know if you know him. One day, Philip says, do you know why there are more startups in San Francisco than any place else, and why the success story is so high? And he goes, I want to tell you what I found out. So he ran a script on top of LinkedIn where he measured the density of technical founders per square kilometer. And he found that San Francisco had the highest technical density, right? There was Austin, there was Cambridge, there was, you know, Miami starts to pop out. But the notion is if you're taking the risk of starting a company and you fail, instead of going back to your mom or dad's, you know, bakery to work, your friend down the street has another startup you can go and join.

29:59So it is a density issue. But I think increasingly that is moving online. I know you're good friends with Bology and his idea of network state and all of that. And I was in Jordan last week, and I saw a company there that is ostensibly headquartered in San Francisco. They run all their engineering there, but they come here quite often. They meet with VCs. They're incorporated in Delaware, and when they go into meetings, it's almost like they're headquartered in San Francisco. And so it's like San Francisco is going to the cloud in many ways. Yeah. Salim. So this evolution of where things are going with the future of software development is pretty profound.

30:43But at the business level, I use the analogy of Kickstarter. When Kickstarter appeared for the first time in business history, you could get market validation for a product without building the product. That's kind of incredible. What you're doing is essentially providing an environment where people can write software and fully finish that software and then get market validation and figure out what's needed at almost zero cost. That's right. This is taking out the marginal cost of all of this stuff, which is what we talk about a lot in abundance with our exponential organizations, link ventures, et cetera.

31:15As you accelerate that, essentially you get to a point where society is just one huge amount of code being written on a nonstop basis. Yes. How do you think about the future of society in the context of that? Yeah. So that's a great question. Let's zoom out here. Yeah. Yeah. So I think if you think about entrepreneurship, I don't just think about it in terms of like quitting your job and starting a business. It's about being able to solve problems in the world and taking initiative without your boss telling you to do that. So we're seeing it a lot in the enterprise as well. Like, we've seen cases where, you know, a product manager at Zillow being able to deliver millions of dollars in bottom line revenue for the company because although he wasn't an engineer, he was able to increase conversion of, you know, homebuyers at the company.

32:09And then suddenly everyone heard about that story. Now we have 500 licenses at Zillow. And everyone is recommended to VibeCode because they want more entrepreneurial people. That person got promoted. Agency. Agency. It's high agency. And it's the biggest challenge that large corporations have today is they're so structured and they're so fragile in that structure. I mean, Salim, you talk about this all the time, right? It's enabling your employees. You try anything disruptive in a big company and the immune system attacks you. Right. Standard problem. But here's why Vibe Coding is important, is because you can do it anyways and you can show the results, and the results will speak for themselves.

32:49This is a huge breakthrough. Permissionless innovation can't happen inside the organization as well. So we call this PDI, Permissionless Disruptive Innovation, because in the past, whenever you want to do something really disrupted, you had to get the permission from the Medici family or from the church or from the corporation or from the government or from an investor. Somebody had to bless you to do this, right? Today, for the first time in human history, you can do very disruptive things without any permission. My favorite poster child here is Vitalik Buterin, an 18-year-old kid, gets together with a few friends.

33:23They ignore their professors. And now, boom, you have Ethereum, a$600 billion ecosystem that nobody understands. And so that's kind of incredible that you can do that. And what a profound opportunity for everybody in the world to make a massive difference. And what you're doing is now providing the scripting language to take anybody's massive purpose, as we call it, and start making it an actual reality. So here's where it gets even weirder and stranger and more abundant, is that it is not going to be just you doing the disruption. It's going to be your agents. You're going to be able to program agents that are running in the organization, finding the inefficiencies, and actually making progress on your behalf as well.

34:03You can also run a startup that is producing these agents. Let's say you're someone who's worked in HR for 30 years. You know all the HR processes. You have a goldmine in your head. You have an unmonetized goldmine in your head. And what you do in the near future, we're actually launching this tomorrow, Agent 3, heard it here first, but Agent 3 will allow you to create other agents. So to be able to create like an HR agent. How long have you been working on that? It's been about six months since Agent 2. So I think every six months, it commensurate with the progress in LLMs, we're trying to predict what is the next set of capabilities that would unlock the next version.

34:48So we already know what Agent 4 is going to look like. We take it back. We try to build it. And over time, the first version of it would be crappy. But then the foundation models and everyone sort of catches up. So I always tell entrepreneurs, build crappy products. So agents can basically spin off their own agents. What could possibly go wrong? Look, I don't focus on what goes wrong. Other people's problems.

35:19But, you know, it's going to be mind-boggling. It's almost like a singularity moment, what's going to happen once we have agents that can also transact with each other, right? So I can have an agent. Which brings in crypto. Which has a crypto wallet that can go hire other agents. Sure. I'm going to raise my agent to be a trillionaire. Exactly. Yeah, we're probably going to be seeing autonomous agents with their own wallets and net worth and all of that. You know, one thing I heard you speak about is the future of the company. I want to talk about that because we have a lot of entrepreneurs in the audience and the people who will listen to this around the world.

36:00I mean, most corporations today have been built in a very siloed, very, I think you'd use the term of factory production mechanism. And it's, you know, Salim, you railed against this. Yeah, I mean, John Hedel talks about this. All big corporations are designed for two things. They're designed for predictability and for efficiency. But in today's world, it's so volatile. You need to be architected for flexibility, agility, adaptability, speed. Therefore, I actually did a talk with you on stage, Peter, a few years ago in Toronto. And the title of the talk was The Death of the Corporation. We're seeing corporations go to platforms and platforms go to ecosystems.

36:45There's an economist. I think his name is Thomas Coase. And he has this theory. Ronald Coase. Ronald Coase, yeah. The theory of the firm. Yeah, so Ronald Coase wrote a nine-page paper in the 1930s citing that the reason we'll have bigger and bigger companies is that transaction costs inside a big company are cheaper than outside a big company. He won the Nobel Prize in economics for that nine-page paper. The book that Peter and I just wrote, the second edition of Exponential Organizations, we actually declare Coase's law dead because the transaction costs inside a big company are way higher than doing it on the outside.

37:22Exactly, exactly. Therefore, the big companies as a category will start to decline very rapidly. That's right. My takeaway from that paper is that full-time employment is a bug of the system, not a future. In reality, if... And doing what you're told is a bug. Doing what you're told is a bug as well. I mean, honestly, employment is a bug. Tell my kids just go full out. Yeah. So the reason why firms have to hire people, train them, and then put them in this somewhat of a draconian hierarchy is because of the transaction costs of going out of the market tend to be cost prohibitive. If you lower that, for example, Uber lowering the transaction costs from looking for a taxi like that to a couple of clicks a button, if an agent can go hire a human or can go hire another agent, then we're going to see companies shrink.

38:15And we're going to see a lot more entrepreneurship. And the market is going to be more dynamic. I think more people will get rich. And I think there's going to be just a fast life and death cycle of companies. Dave, do you want to jump into your question from earlier about Replit, coding Replit? Dave Kuntzakarathy, You want the easy one or the hard one? Dave Kuntzakarathy, No, give it the hard one. Dave Kuntzakarathy, All right. I'd love to get your insight actually on this very specific topic. You know, we're very quickly going to get through all of the code out the legacy garbage part of society, right?

38:51Right now you can use Replit to build things so quickly, and there's all this garbage software that was, you know, it was cost prohibitive to hire enough engineers to do it right. Now you can just do it. But if we get to the point where you can create 1 ,000 times more code, 10 ,000 times more code, then it's all going to be greenfield. What's the net new thing we can build that adds value that we just couldn't afford to build before? You have a lot more vision into that than anyone else because you can see all these millions and millions of users and what they're starting to build. So what are you seeing them build today that gives you insight into what they'll be building in the greenfield future we're coming into?

39:24I think agents. and the reason we built this, what we're calling the agent stack of Agent 3, is that's sort of the next evolution of software. When you look at most pieces of software, especially inside the enterprise, they're trying to solve a problem, a very specific problem. And usually you create a piece of software and then someone will use that piece of software to solve that problem. That intermediate step is not needed. You create an agent programmed and imbued with the domain knowledge to be able to solve that problem. And then you run that agent in an autonomous fashion inside the organization.

39:59And that's what we're seeing our users struggle with. It's like, hey, I want to build this automation process, but it keeps generating an app. And yes, maybe I need an interface for the chat app to talk to the agent, but I want to be able to run it based on hooks, based on time triggers, based on, so we're building all of that. And that's how our roadmap works. It's like we're seeing what people are trying to do. They're looking at AI, they're looking at AI, They're seeing that LLMs can make good judgment. And I think that's the main thing that happened in the past six months to a year is LLMs with agents, they can do this multi-step reasoning, especially with now GPT-5.

40:37The scale on the thinking of GPT-5, like GPT-5 base is so dumb, but GPT-5 high is so, so intelligent. So we're able to scale that intelligence. and so right now these agents can be nodes in very nuanced decision making. So we had some conversation on this stage earlier today about some fairly sketchy research out of MIT that indicated that, look, in the real world corporate environment, people are getting virtually no benefit out of AI-generated coding. And you're actually living it. Do you have any estimate on how much of Replit is built by Replit or what the force multiplier is inside your own organization?

41:17The impact of Repl.it on our organization is more in how we run the company and even outside of engineering than it is inside of engineering, counterintuitively so. So you're using Repl.it to run Repl.it? Yes, we're using Repl.it to run Repl.it. So our salespeople, for example, will write some software using Repl.it that use an LLM that analyzes all their call transcripts to find the main reason why people might not buy Replit. They will take that. They will go to the product team. They're like, this is the main thing. We're missing this compliance thing. I really want to touch on this topic because you're probably the oldest guy in your Y Combinator cohort, right?

41:57Yeah. When you look across all these people who are Vibe coding or building tools, they have virtually no large-scale management experience. They're like, yeah, this helped me write code more efficiently, but it's actually even better at managing large organizations. That's right. You know, all the planning, all of the, you know, like who did what. And they have no experience with it. I've got like about a$2 to$3 million a week payroll. And it's a goldmine in knowing what's going on across a thousand people that are all scattered around. The embedded data in that payroll is a goldmine. It's a goldmine.

42:29And it's so much easier than the harder challenge of just Sweebench. And you're probably the only guy in that Y Combinator world that's actually running and has run a large organization sees its advantage as a management tool, not just as a leaf node code development tool. Yeah, it's almost like the engineers using AI is stable stakes, and I would certainly challenge a lot of those. I think you call it dubious, which I agree a lot of those studies are coming out. But it's stable stakes, and there's so many products you can pick out of. I think World Replit is really shining today in the enterprise because of the vertical integration that we do and because you don't need prior coding knowledge to do all that.

43:07And by the way, we're launching all these integrations and all the ways in which it can hook into your data infrastructure. Because of all of that, it will help you run the company, your legal team. I mean, the gentleman here, we were talking earlier about his finance team at Dolby using Replit to kind of run the finance team. And I think that's really profound, and not a lot of people are talking about that. It's really profound. You are going to run into some data sovereignty issues and on-premises issues, et cetera, right? Because there's a lot of people that are, we know a medical CEO that got excited by ChatGPT and uploaded all their patient data into it, which happened to you, and now they've got massive legal issues around that, so you're going to face some of that.

43:44So that's an engineering-solvable problem. Or a lawyer-solvable problem. Or a lawyer-solvable problem. What I'm excited about is the fact that, you know, the current stack of business software ERP systems will take certain specific... My co-founder, Harish, talks about this a lot. We'll take certain use cases and automate those. But now you can automate every minutia-level use case across a business, all the hundreds of little things that people are doing. And anybody can automate those. So the legal team will automate contract drafting, and they'll just start replicating those and getting that out there.

44:17Best thing that happened over the last 10, 20, maybe 30 years is the digitization of these organizations and governments and the system of record systems, such as the ERPs, the HR systems, the CRM, Salesforce, whatever. That is the infrastructure for which the next set of automation, and the invention of the future of work. So we're exactly at the right time to be able to build these systems. All right. You want the hard question? Yeah, let's go. I want to talk about the vibe coding wars. All right, that's good. That's exactly the hard question. Or I will. All right, we'll start. There's the easier and the harder of the two.

44:52Okay. The easier one first. All right, lovable versus replet. Okay, good, good answer. Good answer. All right, first of all, if I look three years in the future, is it all boats rising with a tide or is one of you going to kill the other? Are you going to differentiate and become different? Do you think about it every day? Do you not focus on the competition? I think it's already quite differentiated. I think that the reason people kind of conflate all these products is because you can put in a prompt and get something out. Most of our competitors generate a front-end app. For you to connect them to a database, you need to go grab a database somewhere else.

45:29For you to deploy them, you need to go to AWS and deploy them. The replication actually provisions a database for you. It runs migrations of the database for you, provisions a production database for you, does that separation for you, helps you with the deploy process. So the platform, and the reason we've been around for 10 years, we've been building the depth of the platform because we knew that the challenge of programming and making software is not just about the code. A lot of it is about the infrastructure. The chart I'm looking at here is there's full stack and front-end only, right? Right, yeah.

46:04Right, and you're at the full stack side. And then there's non-technical and technical, and you sit sweetly in the middle. That's right. What about Vercel then? Because Vercel's kind of, yeah, I think of them as being deployed historically. And now suddenly they're... Well, on this chart, Vercel's down here in the bottom right. Yeah, Vercel, I think with VZero started as a design tool. And now, I think everyone is seeing that the value is where Replit is, because Replit is winning all the deals. Everyone's trying to come after us, but good luck. That is a perfect segue into the really hard question.

46:38We were over at OpenAI headquarters a few weeks ago with Kevin Wheel. When they launched GPT-5, they brought Michael Truitt on stage, made a big deal out of Cursor. But just a couple of weeks prior, they were trying to buy Windsurf. I was like, okay, wait a minute. I was like, you were friends today in an alternate world where Microsoft didn't torpedo that deal. You guys would be the worst arch enemies in the world today. So you've got these foundation model companies. Are they going to come in your direction, or how is that going to play out? The really interesting thing about our market is it's total war.

47:13There are no friends. Really, there isn't. Arguably, in prior eras of Silicon Valley and tech, they're like natural allies that could form. Right now, today, you're an ally with someone else that are going to come out and attack you tomorrow. So you have to be super paranoid if you're starting a company in this stage. Are you super paranoid? Of course. I think competitions are going to come from everywhere. Google is one of our closest partners. We spend hundreds of millions of dollars with them, and they have three competing products. I still love them for it, and Sundar still uses Replit instead of the other ones.

47:51But, you know, it's... Shout out to Sundar. I think of this as the Dropbox thing where you have OneDrive, you have iCloud, you have Google Drive. But just by focusing on that one product area, wholeheartedly Dropbox does very well. And I think that will be the same no matter what other people do. Their interests are so varied. There's a rising tide of near infinite demand. Yeah, there's that. But also, I remember when I was at Yahoo, you're managing across a large... You were where? I was at Yahoo, by the way, too, for seven months. Yes. I'm just not going to come over there for a few. Anyway, you're managing a big company across 120 different web properties, and you have to allocate resources, et cetera, et cetera.

48:35One dedicated team is always going to beat you. Right. Always. You should crap all over that, by the way. If you said, yeah, we want to be just like Dropbox. Yeah. Dropbox is a great company, but I think that it's a lot more brutal than those errors because... Well, you know, back, let's say, a decade or two ago, you had this politeness between the big companies. Google would say, Microsoft would say to Google, don't come into the office space and don't improve Google Docs too much. We won't come after search too much. Really? It was an implicit. It was this polite thing. Now all the gloves are off now.

49:12The gloves are off because everything's converging on AI. They all settled into their swim lanes and they said, okay, Apple, your phones are fine and Bing will suck forever. We'll just commit to that. Sorry. But anyway, it was all detente, and now all of a sudden it's all at war because everything is converging on, oh, wow, all that matters is AI. So I 100 % agree with Salim on the idea that the customer segment focus is a superpower, and that's very important. That being said, I do think there's a potential that this product will differentiate but eventually converge. So Agent 3, you can see tomorrow, it's the most autonomous agent on the market.

49:53and we ran it like two days ago for four and a half hours because right now it can test its own code and you and I were talking earlier about is AGI here, is AGI not here? The thing that would make me feel like you can define the current era of models as AGI is because if they get real and good environmental feedback, you can run them endlessly and have them actually try to solve a problem. And invariably, what we're seeing in software, if you're able to spend as many tokens as you can and they're running in a good environment, they're solving these problems. I've heard you say that it's this sort of universal expansion of coding capability that you believe is going to lead us to AGI.

50:40Yeah, I think that's... I think AGI is here already, personally. Right. I think we passed the Turing test, didn't notice. I think we passed AGI, haven't noticed. and we'll see if we notice. I call BS on this. We're talking about something we can't define, we can't measure, and we don't have a test for. Yes, that's why I can say... At least the Turing test had a measurable outcome, right? It was very clear. AGI, and now we're talking about ASI, and we still have no idea what any of that means. Anyway, aside from that, I'm fine with the conversation. I have a question, though. Just to answer that, the insight from Turing is that the Turing machine, the computer, is the ultimate problem-solving machine.

51:22Yes, that scales infinitely. That scales infinitely, and that is the milieu in which intelligence will use in order to solve any problem. So whatever AGI system you can imagine, ultimately it's going to be writing code to solve problems. That's my fundamental belief. Interesting. Salim, can I take this in a slightly different direction? Maybe. Just to show of hands, how many of you at this event over the last couple of days have had your minds completely blown? Just to show of hands? A bunch of you. All right, I'm going to try and go for it right now. So we have biology, which is essentially software, right?

52:02You have 50 trillion cells in the human body, each governed by the DNA. Now essentially we can now edit. 3.2 billion lines of code. We can now edit the DNA like you would software. Essentially a human being is now a software engineering problem. When you apply Repl.it to biology code, are you seeing that happen? When do you see that happen? What do you think are the implications of that? The name Repl.it sounds very biological in other words. Yeah, completely. Is that a coincidence? It's right there in the name.

52:31So, I mean, ultimately, the fascinating thing about LLMs, which is similar to the DNA code, is how much knowledge it compresses. I downloaded this app called, I think, Full Moon on my phone that allows you to download open source 1 billion parameter models. Because I was in the plane. I like to read books in the plane. I know there's Starling in the planes right now, but I pretend they don't exist because there's like 12 hours that I can read a book in peace. And so I was like, but I need to look some stuff up. So let me download DeepSeek 1B. And it's amazing how good it is. I mean, obviously, it hallucinates, and you need to check its work and everything like that.

53:09but 1 billion parameters, what is that? Like less than 1 gigabyte. And that embeds a large portion of human knowledge. And if you run it for longer like R1 does, DeepSeq R1, you can solve problems as well. Can't wait to download it into my Neuralink chip. Yeah, exactly. These systems are efficient, and it shows you that something about the nature of the universe that there's so much data noise and redundancy that you can really boil things down to very small,

53:46you can boil things down, the essence of things is actually very simple. Isn't it mind-blowing to you, actually, the data? If you take even a big, big, big neural net, you get about a trillion parameters, it fits easily on your laptop. And this is all human knowledge, everything on Wikipedia, everything from the internet, all learned, and about$100 to$200 million training process, and it all compresses down and it just fits on your laptop. Now, the processing is way too slow, so that's why you need to run the billion-parameter model. But putting that aside, just the amount of storage that we have is just incredible.

54:20And that's why NVIDIA is worth so much. The processing is not so incredible. We're going to look back on this period and just say, God, we've been so inefficient, because I think the ultimate LM will probably be about a billion parameters. Isn't that crazy? And back to DNA and bio, we're seeing OpenAI had big results, obviously AlphaFold before that. Clearly, we have the data to train. And now with the way we train LLMs is unsupervised learning. So you can find this compressible information out of this massive sea of a lot of noise without supervision, without data labeling. And I think LLMs will get really good at that.

55:00And once they're able to generate, be able to code bio, the thing on us would be to create these environments. And that's what I see our company as a tech company. We're about creating environments. We're creating. We want to be the best habitat for LLMs. Right now, the best habitat for LLMs is to be in a virtual machine environment. But in the future... One more question to close out the competitive landscape topic. so if I look at the companies that do coding so you know Replit, Levelable, Cursor, Windsurf Blitzy you guys are probably the only one that built foundation models because you were there early so you actually trained these things I think everyone else just picked up OpenAI or whatever later so then you're like okay well we're not going to spend$100 million building training our own model it's way too expensive so now we're on top of the other guys oh wait now our valuation is in the billions You're raising tons of money.

55:58That's confidential. I just found out. So you're raising tons of money, and you're not actually planning to consume it, right? You haven't spent the money from your last funding. But you have a war chest now that actually is plenty big to actually train foundation models. So is there a version of your future where you get back into the foundation model training business? I think it's cyclical. And the way it works, when we went from GPT-2 to 3, there was a period of time where there wasn't a lot of progress. The tricks were known. So we sort of knew how to do pre-training. The labs was busy cooking up, you know, a mixture of experts and GPT-4, whatever.

56:35There's a period of time where startups can actually use their data and add a layer on top of these foundation models and fine-tune and do all of that. And so Replit code 3B, 3 billion parameter, was state-of-the-art coding model in 22, 23. And that put us ahead of the competition because no one else had access to a model that cheap and that easy. run. But then the foundation models did another big jump, the scaling up of the models. I think we're reaching a point where it makes sense to go back into training because we have a certain data set that we think is very helpful, especially for the kind of use cases that Repli does.

57:15And we're starting to find places in our agent architecture because it's not just one agent. We have so many subagents. We have the testing agent. We were able to build a computer use model, a computer use architecture that is three times faster and 15 times cheaper than the state of the art from the big labs. And I think that we're going to continue finding these things until we get the next frontier model that actually, and that's what happens, is that's the history of machine learning, the better lesson. Then you can pour data and compute and you can consume all these use cases. And so just being open-minded about it, some companies were like, we're going to go, we're going to die, we're going to live or die by training.

57:58But for us, we're ultimately trying to solve a problem and pursue a mission, and we'll just do whatever needs to get there. I really thought in a million years that you would never have the guts to say that. You know, I mean, you have the capital to do it, but I could see there's actually some MIT alum, brilliant Christian Bailey here and others, their eyes lit up as soon as you said that. Because I think that mission statement that you just outlined will attract a ton of talent that doesn't want to go to someplace boring. They want to do something, you know, super. I want to close this out going back to your roots.

58:30So listening to us, you know, our Moonshots podcast, when the three of us discuss taking moonshots, there are entrepreneurs around the world who are a young Amjad someplace in the Middle East, someplace in Southeast Asia, in South and Central America, who are dreaming big. What's your advice to them? What's your advice to inspire them and how should they think about their next few years? Because do you agree that the next few years are really the game? Yes, yes. It's everything. My advice would probably get them into trouble, but I think that's part and parcel. We've had the conversation, skip university, go build a company.

59:19Yeah. I think all those are details. If you get into Stanford, get in and then drop out. What really matters is throw away most of the advice that your parents and your society sort of give you. Because it's such a dynamic world right now. Don't do what you're told. Yeah, and conforming is the worst thing that you could do. Don't do what you're told. And it'll take real self-programming to actually exit that mindset of really doing what you're told. and try to really think from first principles about what you want. Find your inner purpose. I love the quote from Mark Twain. He says, two important days in your life, the day you were born, the day you found out why.

1:00:00Yeah, and I think the meme, the mid-witch curve, I think has a real truth to it in that you can really overplan your life, overthink your life, following your intuition, and being attracted to a problem, I think there's a, you were talking about the human spirit, right? And I agree with you, there's something essential about the human spirit that's probably not going to be captured. We're saying, tweeted last night, the one thing AI will not replicate or displace is the human spirit. And I think because we're humans, we live among other humans, we can see problems that are very important for our communities and the people around us that are not going to be embedded in the machine.

1:00:46And so following your intuition for what problems you want to solve will probably net out a truly unique and differentiated position in the world for you to have. I mean, I'm very blessed to have a long-term mission. Like I've sort of found my purpose and I think the way to do it is to follow your curiosity and intuition. If it takes you to university, then that's fine. If it takes you to a building company, that's fine as well. The implication of this is pretty profound because take education, right? We've been doing education from a supply side perspective for 100 years, where you go get a skill, you become a developer, an accountant, a doctor, whatever, and then you go to the job market to sell those skills.

1:01:25All of that evaporates now, and now we have to move to the demand side and say, what problem do you want to solve? That's right. And now you get the tools and the technologies, the techniques, the repositories to solve that problem. And that, I think, is going to be the most exciting change that we're going to see. Good place to close out and respect the clock here. Ladies and gentlemen, I'm Jad Massad. Thank you. David Blunden, Salinas Mal. Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport, energy, longevity, and more.

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