Vibe Coding: Everything You Need To Know — With Amjad Masad

Amjad Masad is the CEO of Replit. Masad joins Big Technology podcast for a frank discussion about vibe coding, or building software via prompt. We discuss all the use cases, whether anyone can do it or whether it's just a tool for already-technical builders, whether vibe coding replaces saas, and what the role of the engineer becomes in the future. Stay tuned for the second half where we discuss whether the AI coding business is sustainable given the costs of delivering the technology. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Alex Kantrowitz?

  Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:03Speaker 01

  Don't go into this thinking you can just have prompt and have an application pop out the other end. At least set an afternoon to give it some good effort and try to get like your first app in. And once you do that, you just get addicted.

• 1:18Alex Kantrowitz?

  I have the stat here that Replit has multiplied its revenue by 10x in less than six months to 100 million in annual recurring revenue. Is that growth vibe coding or is that growth AI coding? Vibe coding. Is AI coding just a hobby or the beginning of a technological revolution that empowers everyone to build? Our guest today, Amjad Masad, the CEO of Replit, has some answers. And we're here in Replit headquarters in Foster City to speak with him. Amjad, great to see you. Welcome to the show. Thank you. I'm excited to be on the show. So we're going to talk today about vibe coding and AI coding, which are two similar but different things. I first wanted to speak with you about vibe coding, which is effectively you write a prompt

• 2:01Alex Kantrowitz?

  and then the AI goes ahead and builds software for you. This is something that Replit enables. This is something I've tried. What are some of the use cases that you're finding people are actually having effective approaches with this? Like, where are the places where people are doing this well?

• 2:17Speaker 01

  There's like broadly three use cases. One is personal life, family life. So, you know, for example, like a lot of people like to do health tracking. I'm going to track my sleep. I'm going to pull in data from my Fitbit and gonna like have AI sort of process that data. I'm gonna have this app on my phone that I use every day. Or I'm gonna build an educational app for my kid to learn math or reading. Or we're gonna have like someone built like a chore hero for their family to like, you know, have an iPad on the wall and like, here's who's doing the most chores and gamifying their family life. You'd be surprised how popular this use case is. And so, you know,

• 3:01Speaker 01

  in the, in the niche that I've always been in, which is like creator tools, there's always been this idea of personal software, malleable software. And by the way, this goes to the early computing history. So, you know, for example, like, Apple had this piece of software called HyperCard. HyperCard allowed anyone to make personal software. You know, there's Fidjol Basic. It's been attempted so many times. But for the first time now, anyone can make software. So there's a lot of, there's a class of personal software. We have a mobile app and you can use that to make software. It's the most fun thing to do is sit down with your kids, five, six, seven years old,

• 3:44Speaker 01

  and just brainstorm games and make games with them. So that's one bucket.

• 3:48Alex Kantrowitz?

  Wait, before we go to the next bucket, I want to ask you a question. So, does this say something about the software industry that the software industry just hasn't served so many use cases? Or are these use cases non-economic? Or is it possible that people will build things for their family? And the next thing you know, they can serve that mass market and it becomes a business?

• 4:08Speaker 01

  It is certainly, you know, there's certainly a market there and you can certainly make a lot of money from that.

• 4:14Alex Kantrowitz?

  Okay. Because when I think about like this concept, and we're going to get to jobs, but this concept that AI is going to take our jobs. To me, it's like, wait, there's so much left to build. If you think just about what we have today and maintaining that, maybe it will.

• 4:29Speaker 01

  Yeah.

• 4:29Alex Kantrowitz?

  But there's so much that software has not yet touched that it seems to me that there's more opportunity out there than people are imagining.

• 4:35Speaker 01

  But just to touch on your earlier question. And you tell me how deep you wanna go, because I can talk about this for hours. But in the early computing pioneers, they all had this idea that computers are this. The thing that makes computers special is this idea of programmability, right? The moment we had a programmable machine that was first invented by von Neumann.

• 5:04Speaker 01

  And it's the same architecture that we use today. The thinking was, oh, anyone can use a computer to program, to solve problems, to to build applications and all of that. It didn't get mass consumer adoption. And the reason is because coding is is hard. And so you you had the Xerox PARC as the research and Palo Alto, Palo Alto Research Center. They developed GUI. One day, they they invite this up and coming entrepreneur called Steve Jobs. Steve Jobs looks at desktops, menus, items, and and he's like, he has the Apple II. Obviously, Apple II is also still command line, you can write some basic.

• 5:47Speaker 01

  And he's like, okay, this is the key to get mass consumer adoption of computers. And so he copies what Xerox had and he builds it into the Mac. And obviously, later, Windows and Microsoft copies UI. And then suddenly, your computers are usable by anyone. And this is amazing. Now, like billions of people use computers and now we have phones based on the same idea. What we lost is this idea that anyone can program a computer. So that's something I've been passionate about all my life. It's like computers should fundamentally be programmable. And there's been a lot of different iterations with visual programming. We had the no-code, low-code revolution that happened like maybe, you know, 10 years ago. I would say it never reached the full potential. It was more of a buzzword than reality, but now. I think I think it is.

• 6:33Speaker 01

  Is a multi-billion dollar market for sure, but it's not a trillion dollar market. And I think this idea of like, anyone can make software is such a massive market.

• 6:41Alex Kantrowitz?

  Okay, so bucket number two.

• 6:44Speaker 01

  Bucket number two tends to be entrepreneurs. And so everyone in the world has ideas.

• 6:52Speaker 01

  People build so much domain knowledge about whatever their field of work, right? I was hearing a story today of an Uber driver that is starting to make an app with Replit. And the app is about logistics. He was a truck driver before. And so he has domain knowledge about how to manage fleets, for example. But he never was able to make it into a software because he didn't have the skill. Maybe he didn't have the capital to go, you know, commission a contractor to do it. And suddenly he can do it. So you pick anyone on the street.

• 7:27Speaker 01

  And they all, in whatever industry they're in, they realize that there's a need for a piece of software or technology that no one has built because they don't have that deep domain knowledge. So we see entrepreneurs from all walks of life. One of our favorite one, we talked about it publicly on our Replit social media channels, a doctor from the UK, that he's like, you know, there's all these apps around managing doctor-patient relationships, but they never, it's not fully integrated. So, you know, you have Zocdoc, you can go make an appointment, but you know, how do you manage your prescriptions? Can I track my patient over time, their progress?

• 8:10Speaker 01

  Can I get information from their Wi-Fi connected scale, from their Fitbit, from, you know? And so he built this comprehensive platform. He got quoted by an agency £100,000 and he built it less than 200, you know, in British pounds.

• 8:30Alex Kantrowitz?

  200,000, 200, period.

• 8:32Speaker 01

  200, the pounds.

• 8:34Alex Kantrowitz?

  So this is stuff that's being vibe coded, effectively prompt in, I want to build the software.

• 8:38Speaker 01

  Yeah.

• 8:39Alex Kantrowitz?

  And then Replit will go build it.

• 8:40Speaker 01

  Yeah, and this is now a startup. And we've had startups start on Replit, multimillion dollar revenue run rate. Some of them have raised at like half a billion dollar valuation. And so we have all the way from small entrepreneurs to startup venture scale entrepreneurs. But this gets me really excited because. America has always been about entrepreneurship and this is really what attracted me to this country. But actually, if you look at the stats, entrepreneurship over time, although we hear about what's happening in the Bay Area and Silicon Valley, there's all startups every day. But the rest of the country, actually, you know, new firm creation has been going down over the past hundred years. There was an uptick during COVID where everyone's sitting at home, it's like I have an idea. That's when I started my business. Right, exactly. Well, it's great, but that actually, we had a regression to the mean.

• 9:25Speaker 01

  And I think with AI, we're going to see that explode again. So that's the second bucket, entrepreneurs. One more bucket. Third one is people at companies like this one. So actually, I'll give you a story from our HR department. We have a small HR department, Rappler is kind of a lean team, we're eighty people. And so we have a lot of these SaaS tools. We pay tens, hundreds of thousands of dollars to do every specific kind of function. And sometimes they don't really fit our use case, we think it's, they're too expensive. So this HR person had a need for an org chart software

• 10:04Speaker 01

  that can visualize the org chart, that can, you know, add, remove people, maintain a history, can look back and see what happened, what changes it did. And went on the market and saw that none of the software captured the exact bespoke use case where she wanted to connect it to our kind of more other HRIS systems or databases. And they were, they all needed, you know, they're all very expensive and need a lot of IT support. So she went into Rappler and built it, vibe coded it in three days. And so that meant that we have a system that exactly fits our use case.

• 10:45Speaker 01

  And that also meant that we're not paying ten, twenty, thirty thousand dollars a year for a piece of SaaS software. And that's happening across the board. We see companies saving hundreds of thousands of dollars replacing SaaS software with built-in, with internally built software.

• 11:00Alex Kantrowitz?

  Now, do you need to be someone with some technical background or some technical know-how to be able to do this? Well, because I'll give you an example. I mentioned to you before we started recording, I opened a Replit account this week. I wanted to build a simple choose your own adventure game. I think it's called History Havoc, where you can work your way through different history scenarios. But it just didn't get to the point where I wanted it.

• 11:25Speaker 01

  How long did you work on it?

• 11:26Alex Kantrowitz?

  So I spent about an hour on it, not a lot of time. And I also, full disclosure, just on your starter plan, I'm not paying yet.

• 11:34Speaker 01

  Yeah.

• 11:35Alex Kantrowitz?

  But I couldn't, I couldn't get it to work. I also tried to build this story tracker.

• 11:39Speaker 01

  Yeah.

• 11:39Alex Kantrowitz?

  And it wasn't able to crawl the web the way that I hoped it would. So it still seems like this to a lot of people that this is something that is helpful if you're technical, you want to make a prototype. But these use cases that you're giving seem to be full-blown companies or working pieces of software. So explain that disconnect.

• 11:58Speaker 01

  I think it requires grit. Obviously, there's like stochasticity in, in the machine learning.

• 12:04Alex Kantrowitz?

  So explain what that is.

• 12:07Speaker 01

  The same prompts can put you on a path of success based on randomness that's happening inside the GPUs. There's this parameter in large language models called temperature. And temperature is literally like how random is the sampling of the words coming out of the, of the LLM. So the LLM, the way it works, you know, you give it a piece of text and it tries to complete the, the next word, the next token, as we call it. And the way it happens, it generates a lot of candidates. So, you know, the red, red fox, you know, jumped, slapped, whatever. But like jumped is the top one, you know, it's the highest probability one that, that

• 12:52Speaker 01

  you know, the model has seen it occur after the sentence and millions of, of cases.

• 12:59Speaker 01

  But you know, you have the sampler and could be randomizing what it picks. And that that randomization makes it more creative. There's also inherent randomization inside the like the Nvidia chips of the GPUs. So this style of software is unlike the software, the classic software, where everything is discrete, input, output. Machine learning models have inherent randomness. And that's a feature, not a bug that creates creativity. Right? So some people sometimes get on a bad luck with with a repli. We're obviously trying to mitigate a lot of these problems. But I would say it also requires grit.

• 13:43Speaker 01

  Like the game you just described, professional programmers coding might take them a two days thing. On a replay, you can do it in two, three, four hours. But it it would require a little bit of grit. So it's not magic. And the skills you were talking about, the technical skills, although they're not required, you can build them up over time. And our environment kind of shows some of these features as you're working with it. And so I I would I would suggest to people that don't go into this thinking you can just have prompt and have an application pop out the other end. I would say at least set an afternoon to to give it some good effort and try to get like your first app in.

• 14:28Speaker 01

  And once you do that, you just get addicted.

• 14:30Alex Kantrowitz?

  So there's vibe coding, which is again prompt. And then you make an app and then you can refine it with more English. And then there's AI coding, where you could basically have AI, you know, complete your code, big autocomplete. So what do you think the opportunity is in vibe coding versus AI coding? And where do you think the energy is in the AI industry today?

• 14:55Speaker 01

  I gave the analogy of the history of computing and I think it's a very suitable analogy for a lot of what we're talking about. Early on in computing, we had the mainframes. So the mainframes, really big room-sized computers. IBM used to make them. Large corporations and governments used them in universities. But everyday people didn't have access to them until Apple created the Apple II. And that was the first mass consumer market computer. And since then, we've had Windows and all these devices. The mainframe was already serving the professionals' needs, but it wasn't serving the consumer needs.

• 15:36Speaker 01

  Now, if you look at the market for PCs versus the professional workstation, Sun Microsystems, all of that, which used to be the case.

• 15:47Speaker 01

  The PC not only was a much bigger market, eventually it subsumed the more professional-grade software. And this is called the disruption theory.

• 16:02Speaker 01

  You know, a lot of your audience that might be into business history or theory. Clay Christensen used to be, I think, Harvard Business School professor. And he wrote this book called The Universe Dilemma. And the idea is that a lot of technology started at the lower end. And because their mass-market appeal, they onboard a lot more users and customers. And over time, they reach a certain economies of scale. And they subsume even the upper end of the market. Currently, the upper end of the market is what you were talking about with AI coding tools, right? So there's like 30 million developers all over the world, maybe a little more now.

• 16:47Speaker 01

  Those are professional developers that went to computer science classes in college. They were trained for four or five years. And now they're working at companies. If you make those developers 20, 30, 40% more productive, you get, depending on, you know, if your company is the size of Google, you know, it's like billions of dollars' worth of productivity, right? So the market is really obvious there. You can go apply it and get it. But it's a zero-sum market. If you look at Copilot, which is Microsoft's product, which was the first to market versus Cursor, which is the more modern kind of AI coding IDE.

• 17:27Speaker 01

  As Cursor is eating market share, you can see it is almost exactly proportional to Copilot, declining in usage. So that's a sign of a zero-sum market. It is very lucrative and there's a lot more growth to be had there. But it is not this fundamentally

• 17:47Speaker 01

  revolution that we can be going through where it's anyone can make software.

• 17:52Alex Kantrowitz?

  Let me, let me ask it this way. I have the stat here that Replit has multiplied by its revenue by 10x in less than six months to 100 million in annual recurring revenue. So is that growth vibe coding or is that growth AI coding?

• 18:08Speaker 01

  Vibe coding.

• 18:09Alex Kantrowitz?

  Really?

• 18:09Speaker 01

  Yeah.

• 18:10Alex Kantrowitz?

  And is, are these vibe coding programs or these bespoke programs that people are building with prompts? Are they in production or are they mostly hobbies that people fool around with?

• 18:21Speaker 01

  Depends on first bucket is more hobby, personal life. Second bucket entrepreneurs.

• 18:30Speaker 01

  As you know, most startups die, so most startup ideas don't make it to fruition. The 10% of startups that are small businesses that get off the ground, they get the most value out of Replit. And some of them are in production now.

• 18:45Speaker 01

  You know, I've, I've talked about a lot of these stories, but you know, for example, we have this creator. His name is John Chaney. He's a serial entrepreneur, used to take him many months and hundreds of thousands of dollars to build applications. And now he can spin up a business and get to million dollar run rates in, in the matter of weeks. Obviously he has experience, like he, he knows the formula of what it means to be an entrepreneur, but people can learn that over time. And in terms of the enterprise, you know, we have, for example, Zillow, the CEO of Zillow, recently on New York Times Deal Book, talked about how everyone at Zillow is using Replit to accelerate product innovation because product innovation no longer depends on engineers.

• 19:26Speaker 01

  You can have product managers do the entire iteration, getting user feedback, even without going to the engineers. So it just like increases. We have Duolingo,

• 19:37Speaker 01

  a bunch of these customers that are really focused on innovating, building their second, third product that are now using Replit for a lot of these use cases.

• 19:45Alex Kantrowitz?

  So is the use case that you build like a prototype? And then you get some feedback and then if everything works out well, then you build into the product with your like core engineers?

• 19:54Speaker 01

  That's one, that's one use case.

• 19:56Alex Kantrowitz?

  Okay, that's interesting.

• 19:57Speaker 01

  Yeah, that's one use case. It's really great. It rapidly improves,

• 20:03Speaker 01

  you know, the time to market. The second use case is operations and internal tools. So for examples, like, you know, Sears Home Services, pretty old company, employs people that go and like fix homes. And they had an operations team that wanted to build a lot of AI tools and software for their field workers to be able to manage their work and their earnings and all of that. But their software was like this hundred-year-old COBOL programs. And the engineers were kind of busy, kind of migrating that and improving that. So the operations team started using Replit to spin up these AI applications that are deployed, used in productions by those field workers every day to manage their day and kind of

• 20:47Speaker 01

  design the optimal routes to how to maximize their earnings per day. So the operations type use cases tend to be deployed, running in production.

• 20:59Alex Kantrowitz?

  Okay, so just so I'm clear, are you also facilitating AI coding? Or is it mostly that you've turned Replit into a vibe coding company?

• 21:08Speaker 01

  My mission has always been about how do you enable people to do this magical thing that is creating software. It's one of the most magical. Exciting experiences you would ever have. And I was a founding engineer at Code Academy. And before that, I built open source tools to do that. Code Academy taught millions and millions of people how to code. And we changed, you know, a lot of lives. So the DNA of Replit has always been about how do you make programming more accessible? It was, it had like a more revolver bent at some point. But because Replit is sort of batteries included platform.

• 21:50Speaker 01

  We give you the database, we give you the authentication, we give you the deployment. We give you the scalability, we give you all of that out of the box. You don't have to go anywhere else to do any of that. It always meant that the people that are getting the most out of it tend to be they're not, they're not professional programmers. Although professional programmers do use it, I would say like just 20% of the use cases.

• 22:12Alex Kantrowitz?

  And the question is then, do the people using Replit then come for the people who are those professional programmers? There was a funny thing that happened. I watched you have a talk at the Semaphore Tech event in San Francisco a couple of months ago. And I tweeted something that you said that in one year or 18 months, companies might be able to run themselves without engineers. And then somebody responded to me with this meme where they said, founders in public, AI is writing 99% of our code. In six months, we won't need any engineers. Founders in the DMs, does anyone know a good React developer? $30,000 bonus, and I will name my firstborn son after you.

• 22:53Alex Kantrowitz?

  So can you explain that disconnect between this view that engineers are going away? And this still like very intense demand for engineers in the market?

• 23:02Speaker 01

  I never made the point that engineers would go away. I make the point that entrepreneurs can start businesses without needing engineers. And that we're ready to see that. We're ready to see, you know, I meet YC companies and Y Combinator is the most prestigious startup accelerator in the world, Bay Area.

• 23:23Speaker 01

  And in the past, Y Combinator would encourage you to go get a technical co-founder. But like we said, there's so many people with amazing ideas that don't have a technical co-founder. And so they're starting to get into Y C. And what they tell us is we're just going to build this thing on Rappel. We're going to see how far we can get. And they often got get really, really far. Now, if you're building a venture scale company and you want to like get to hundreds of millions of dollars of revenue. And you want to, you know, become billion, 10 billion, 100 billion dollar company, you're going to have to hire engineers.

• 24:00Speaker 01

  But if you're trying to build a company that creates a really great living for you, even, you know, you can potentially get rich from it.

• 24:11Speaker 01

  You, I think we're almost there where you can do it on your own without any developers. And so when I'm talking, I'm talking to our audience.

• 24:20Alex Kantrowitz?

  Right.

• 24:21Speaker 01

  As opposed to, I'm not, I'm not talking to Microsoft or Facebook. They're not going to replace developers anymore. My view on developer productivity is that developers are much more impactful than they used to be because a single developer can be so highly leveraged these days. And so, yes, you want to find the best developers and we're expanding the team. But the, the, the scale that Rappel is at today would be 10x that the number of people who are SaaS company five years ago.

• 24:52Alex Kantrowitz?

  Wow.

• 24:53Speaker 01

  To reach 100 million dollar in round rate, you know, five years ago, on average, you'd have like 500, a lot of companies will have 1000 people.

• 25:02Alex Kantrowitz?

  How many do you have?

• 25:03Speaker 01

  Eighty.

• 25:05Alex Kantrowitz?

  Wow. Okay. You know, it just makes me wonder that as companies grow like this. What the future is going to look like from the technical side? And I'm curious, do the folks who have technical abilities, you know, let's say the economy expands like this and everyone and their grandma can build, literally can build a company using AI tools. Do the technical people then come in and sort of clean up the problems? Are they your like cleanup crew? I was reading this funny article and publication called Futurism. It says, companies that tried to save money with AI are now spending a fortune hiring people to fix its mistakes. And it was about, it wasn't about vibe coding. It was actually about content, like content marketing,

• 25:50Alex Kantrowitz?

  where like your, your content marketing plan is just filled with this like, you know, kind of bland chat GPT generated copy. And half the time it says, as an AI assistant, this is the message.

• 26:00Speaker 01

  Oh, yeah. If you Google that, you'll see so many hits.

• 26:02Alex Kantrowitz?

  Yeah. So I am curious to, to hear your perspective on, does, does the technical field end up becoming cleanup crews for vibe coding gone wrong?

• 26:11Speaker 01

  Let me just tell you where I think technical folks have a job security today. So I think if you're writing software for my Tesla, I don't want you to be vibe coding. I want you to write low level verifiable code. If you're writing code for Space Shuttle, you're writing low level verifiable code. But also even, I mean, those are life or death situations. So I think we need, we don't need vibe coding there. We need more precision. But, but even sort of large scale platforms, if you're building a core cloud component,

• 26:55Speaker 01

  the storage or virtual machine components on AWS or Google Cloud or Azure, you want systems engineers that understand distributed systems, understand how to create fail safe systems at scale. So I think engineers there have job security for the foreseeable future. Right? Because of the problem of stochasticity of these models and, and all of that, you need, you need every line of code to be reviewed and managed very carefully. Now where I think AI is going to have the most impact is on product. And people building products, they want to iterate on it really quickly. They want internal tools, people want to replace all the mess of the SaaS software that we have today.

• 27:41Speaker 01

  So I think, I think that's happening. Now, in terms of the cleanup, I mean, depends on where you think AI is headed. Like, do you think that AI is good at making software but bad at maintaining it? And it's going to stay bad maintaining it for the foreseeable future? If it's good at making software, it must also be good at refactoring software or testing software, right? Actually, right now, it's pretty bad at testing software. Because there's this thing called reward hacking. So when you do reinforcement learning over large language models, you're giving it a reward every time it does the right thing. Reward hacking is the way to... So the models become incredibly goal focused.

• 28:27Speaker 01

  They want to get that done, right? That's what RL does. And oftentimes, what we see when we try to get the models to test things, it will start being corrupt in a way. It will like change the test to fit the mistakes it made. Or sometimes delete the tests. It's really fascinating behavior that actually Anthropic published research on. So, but do you believe that's going to be the case forever? Obviously not. Like, I think over the next three or six months, I think we're going to see

• 29:00Speaker 01

  machine learning models being able to test and verify their work.

• 29:04Alex Kantrowitz?

  Okay, so one of the biggest things that this moment depends on is affordable large language models coming from the foundational companies. And that means, you know, in layman's speak, if you're going to want to build with AI code, you have to actually have the ability to bring in models from an OpenAI or Anthropic that are going to generate that code and not break the bank as you do it. And we're still in this, you know, VC funded or investment, private market investment moment where we don't really know the true cost of these models.

• 29:44Speaker 01

  Like meaning like the foundation model companies might be losing money on those and

• 29:48Alex Kantrowitz?

  Do you think they are?

• 29:49Speaker 01

  And the application companies, I don't think they're on a cross margin basis. I don't think they are.

• 29:53Alex Kantrowitz?

  Right, but they're also training and that's a lot of money.

• 29:55Speaker 01

  Definitely.

• 29:56Alex Kantrowitz?

  They're not, they're not profitable. They're losing billions a year.

• 29:59Speaker 01

  Of course, yeah, yeah.

• 30:00Alex Kantrowitz?

  And there's been this thing that's happened recently with, I just want to run it by you, with both Replit and Cursor, where I think end users have seen pricing gone up. Ed Zitron wrote about this and I think it's a pretty good piece talking about effort-based pricing within Replit and that is effectively a different pricing structure. We've seen Replit users talk about the fact that they're actually paying a lot more for the same services than they were previously. And his theory is that OpenAI and Anthropic found quiet ways to jack up their prices for startups and we're beginning to see the consequences because Cursor had a similar

• 30:42Speaker 01

  Who wrote about this?

• 30:43Alex Kantrowitz?

  Ed Zitron.

• 30:44Speaker 01

  Okay.

• 30:45Alex Kantrowitz?

  Is that what's, is that what's going on?

• 30:47Speaker 01

  No, the prices haven't gone down and that's a problem. So we used to see these, you know, we've seen token prices come down 99% since, since ChatGPT. And we've seen token prices come down year over year. The thing that's a little disturbing right now is that token prices are not coming down. You better believe that the unit economics of the labs are getting better because of economies of scale, because these models are getting easier to optimize. But they're actually not reducing prices. And so the concerning thing, are we reaching a steady state? Is there price collusion? Is there now oligopoly of a few model companies that are able to create these state-of-the-art

• 31:30Speaker 01

  models and there's no downward pricing pressure, right? Is the, are there investors starting to demand better, better business fundamentals? I don't know exactly what's happening.

• 31:45Speaker 01

  We should, we should talk about the Chinese open source models in a second because I think that that will introduce an interesting mix to this. But it certainly is the case that we're not seeing token prices go down. The reason, the main reason we went to effort-based pricing

• 32:04Speaker 01

  is, let me explain what effort-based pricing. So when we released Replit Agent V1,

• 32:10Speaker 01

  Version one of Replit Agent would work for like two minutes at a time. You would give it a message or go try to do something for two minutes. Either succeeds or fails, gives you a checkpoint, commits the source code and charges you 25 cents.

• 32:29Speaker 01

  And the reason it only worked for two minutes is because the capabilities of the models, you know, meant that it can only work for that long.

• 32:38Speaker 01

  Now, models got better and we had, we knew that models are going to get better and they're going to be able to work for 10, 15 minutes. And so with version two of Replit Agent, started in beta in February, came out of beta in April, the model would work for 10 minutes. And so we can't charge 25 cents for like a 10 minutes. So what we started to do is came up with a heuristic. Every nine tool calls, we'll do a checkpoint. And so as it's working, you'll see it make a checkpoint, checkpoint, checkpoint. That's a hack, right? That often means that if you make a small change that costs us five cents, whatever, you still, you still get 25 cents.

• 33:24Speaker 01

  But also if you make a big change, you might be costing us a lot more than, than what we charge you. So it was, it was really out of whack.

• 33:33Speaker 01

  Now, that was a hack and we need to move to a place where we're charging the user proportional to what, what, how much the model's working and the cost on us. And we think that's the best way to create a, a long-term, sustainable business. And when the, when those two things are aligned, also opens up new opportunities where when we do optimizations, we're always optimizing. We actually, we actually had like 20% optimization on, on cost recently. We pass it straight to the user because, because now cost and price are, are tracking with each other. What happened with our community, the first thing that happened is there was a sticker shock.

• 34:16Speaker 01

  So you're used to seeing 25 cents every 10 tool calls and suddenly you're seeing.

• 34:24Speaker 01

  One and a half dollars, you know, or two dollars after fifteen minutes of work. So that's one.

• 34:31Speaker 01

  Two, it's true for some users who are really advanced. The cost has gone up with for them because the projects are bigger. The context size is bigger. Their their workloads are bigger. But early on in the project, it's actually cheaper. You mentioned that you worked for an hour. You didn't have to sign up for the the core. Paid, right. We give free users three dollars. So you worked for an hour on three dollars. Not bad. Yes. So it's cheaper than a developer. It's cheaper than a developer for sure. And and so that being said, we we recognize that on advanced users, it is now it's almost there's a tax as as you go on. So we we're trying to optimize the context window and make sure that advanced users are not getting,

• 35:18Speaker 01

  you know, a more expensive experience. The other thing that happened is we introduced thinking mode, reasoning mode, and we introduced like high power mode.

• 35:27Speaker 01

  And and people are enabling those. And sometimes they forget them are enabled. And now we're actually starting to hide it under advanced. Like, don't enable this unless you know what you're doing and you want more power. And there's like a five X multiplier on on it. So a lot of people are enabling those, getting these large checkpoints. And we're like, we put out content, we put out a video, we put out some documentation or blog post. Here's when to use reasoning mode. And you should you should always have it on.

• 35:54Speaker 01

  So just describing all of that that's happening. There's a there's a macro trend in in the application space, where a lot of companies were subsidizing the cost of of, like, a lot of companies were paying money, more money on topic and opening eye than they they were making. Was that, were you doing that? We on V one, no. On V two, yes, because the pricing model was out of whack with how we're charging.

• 36:22Speaker 01

  Actually, the median cost per checkpoint kind of went up only a little bit. So on the lower end, we're charging user less right now. But it used to be that on the lower end, we're charging users more. On the upper end, we're charging users less. So now it's more proportional, more fair for both. And so now we have solid business fundamentals that allows us to grow. And I've been talking about how Raplet has been my mission, my passion for, you know, eight, nine years as a company, 15 years as a side project and a vision. And we're not trying to, you know, rapidly expand revenue while losing money in order

• 37:06Speaker 01

  to to flip this company, to sell it. You know, we've seen all these acquisitions or raised like the next big round. We're really trying to build a business for the long term. And Raplet is made of all these different components. So we have costs, not just on AI, we have costs of, you know, traditional compute, CPUs, storage, databases, all of that stuff. So kind of to summarize, you know, I've talked a lot about what was happening specifically in Raplet. I don't know what's happening in Cursor. I think for sure that their situation is like a little different because they, they, their dynamics is, I think they actually did raise prices for the, you should talk to them, but but I think it's like a little different dynamic than than what happened in Raplet.

• 37:53Speaker 01

  To summarize, there is a concerning trend where token prices are not going down.

• 38:00Speaker 01

  Is that going to be the case for the future? Because that sucks. Because we want to be able to use more tokens to create more intelligence, to be able to create better applications for users.

• 38:11Speaker 01

  Is that going to be the trend forever? Are we reaching a steady state? In cloud, for example, we kind of reached a steady state. When you have a monopoly, there's no pricing pressure. But when you also have an oligopoly, they Not intentionally without talking, start colluding. You know, because it's like a market dynamic where it's like, if you don't lower our price, I'm not going to lower my price. It's not in our sense of as a whole, because we own 25% each of the market, right?

• 38:40Alex Kantrowitz?

  Okay, I do want to ask you about something that you didn't mention when you looked at the different factors for why prices might not be going down. There might be investor pressure. There might have been this equilibrium reached, or is it possible that these models have just gotten so big and expensive to run that the fundamental economics of AI are just not working? So explain why.

• 39:05Speaker 01

  You can surmise the bigness of the models based on speed, token throughput. It's not perfect, but if you remember GPT 4.5, GPT 4.5 was an experimental model from OpenAI. It was the idea, let's train a trillion parameter dense model, meaning it is not sparse, meaning all the neurons are activated on every request. And it was so slow. It's really hard to run these things. The new models, even when they're big, they're sparse models. They're called MOE, mixture of experts. So in every request, there's a router layer

• 39:47Speaker 01

  that takes it to the expert part of the circuit in order to answer that question. So there are models with trillion parameters, but any given request is 32 billion active, and that's like a kind of small model. And what we're seeing based on speed and things like that, it's actually probably the models are getting more efficient. I mean, DeepSeek showed that the models are getting more efficient. And if DeepSeek open source was able to make it, you better believe that the labs are also getting more efficient.

• 40:18Alex Kantrowitz?

  Okay, I do want to speak with you about DeepSeek and Kimi K2 and other Chinese models. Let's do that when we come back from the break right after this. Cool. This episode is brought to you by Avpoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data. And most teams have no single way to see it, secure it, and prove it's under control. That's exactly what Avpoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud, and the agents acting on your behalf. It's how more than 28,000 organizations deploy AI with confidence.

• 41:03Alex Kantrowitz?

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• 42:26Alex Kantrowitz?

  And we're back here on Big Technology Podcast with Amjad Masad, the CEO of Replit, talking about all things AI code, vibe coding, and now let's talk about these Chinese models. So this episode will air a couple of weeks after the emergence of Kimi K2, but we're talking about Kimi K2, which is another Chinese model. And of course, this DeepSeek moment was a big moment where we found out that this seeming small hedge fund in China with some GPUs was able to engineer a more efficient model. That story will be debated about what actually happened for a long time. But let me ask you one influence of DeepSeek question, and then we'll get into the others in Kimi K2. So you'd mentioned before the

• 43:07Alex Kantrowitz?

  break that Western models have taken after DeepSeek. So do you think they learned what DeepSeek did and sort of put those new innovations into play in their own models? Or was that coming anyway?

• 43:21Speaker 01

  From what you've, what we've seen from the Twitter sphere is that it seemed like there were some surprises, because researchers just talk a lot. It seems like there were some fundamental innovations from the DeepSeek models that, that weren't known in the, in the West.

• 43:37Alex Kantrowitz?

  But have they implemented those now? And that's probably why we're getting more efficient models.

• 43:41Speaker 01

  Like, yes, I'm sure, like, the models are getting more powerful without going, getting slower.

• 43:47Alex Kantrowitz?

  All right. So tell me about Kimi K2.

• 43:49Speaker 01

  When Anthropic came out with Sonnet Claude 3.5, that was a fundamental shift in the industry where the models got a lot better at coding. And suddenly, instead of making small snippets of change, Sonnet could, could generate entire files and enable things like Cursor Composer, where it was a start of vibe coding, where you can put in a prompt and generate entire files and all of that, or generate large edits.

• 44:21Speaker 01

  Then Sonnet 3.5 V2 was the first model. It was a computer use model. Was the first model where you could sense that there's agentic, true agentic behavior. I don't know what they did. They cracked RL, whatever happened there. You can give a model a VM and it can give it- Virtual machine? Virtual machine. You can give it an objective and it can sleuth around in the virtual machine, look at the files, do, run some commands and then write a program, test it and then solve, solve a problem. That experience, there's a benchmark called Sweet Bench, software engineering bench.

• 45:04Speaker 01

  And you start seeing the score going up dramatically. I don't know. I think we were at like 10% last year and now we're at like 70% and 80%. World-class coding. It's such a, it's the interesting thing about Sweet Bench is not just coding. Cause there are other benchmarks that, that just do like the code generation, right? Sweet Bench, I think the harder thing about it is the agentic workflow. Is writing the code, testing it, running commands, finding files, understanding files. And this, this stuff was like a huge jump that happened with, with Sonnet 3.5 V2, then 3.7, then 4.0.

• 45:47Speaker 01

  And they've, you know, kudos to Anthropic. They've been able to make, create a lead that hasn't been bridged by the other labs. Gemini is getting there on the agentic stuff, but I would say OpenAI kind of lagged behind. O3 has some interesting agentic capabilities, especially around deep research. But it, it, it hasn't been as good as the other models on this agentic stuff. I mean, they did some interesting stuff with Codex. I don't know if those models are, are in the API, but everyone is using Claude for the agentic coding experience. The interesting thing about KIMI K2, I would say is they caught, caught up not to Claude Sonnet 4.0,

• 46:31Speaker 01

  perhaps Claude Sonnet 3.7. At least that's the vibes right now, before the other labs. Wow. Yeah, I think that's really under reported. Again, this is vibes, everyone's trying to figure it out. But it looks like it has a really good sweep bench. It is doing sixty five on sweep bench. Sonnet is seventy two, seventy two. If you do sampling, which is you, for every step, you ask the model to generate N number of solutions. You can get up to seventy two percent. It can be competitive with with Sonnet.

• 47:05Alex Kantrowitz?

  And this is with export controls?

• 47:07Speaker 01

  Yes. And I think in the paper, they talk about the solution is scaling reinforcement learning. We also saw that with Grok four. Grok four spent as much on reinforcement learning as they spent on on pre-training, which is unheard of.

• 47:25Alex Kantrowitz?

  But even but that's an important point because with that big spend on reinforcement learning, Grok is a competitive model, but they spent billions, billions, hundreds of millions on RL.

• 47:38Speaker 01

  I don't know.

• 47:38Alex Kantrowitz?

  Just this goal setting form of training. And it's not like it's a new category. So it shows there are some limits in.

• 47:44Speaker 01

  XAI is an amazing team. And they they've been able to achieve so much in so little time. But it's also well known in the industry that they're computer inefficient. They're so compute rich that that they're throwing computer the other problem in many ways. Yeah.

• 48:00Alex Kantrowitz?

  So what is the significance that Kimi K two is now as good as some of these anthropic models?

• 48:08Speaker 01

  A small research lab, I think the rumors like the two hundred people, again, there's export controls as well, was able to figure out how to catch up to near state of the art agentic coding models before big Western labs that are highly capitalized, a lot, a lot more researchers was able to.

• 48:31Alex Kantrowitz?

  And does that mean then that? They can undercut them on price or?

• 48:36Speaker 01

  So let's see.

• 48:37Alex Kantrowitz?

  Right.

• 48:38Speaker 01

  So let's see.

• 48:38Alex Kantrowitz?

  Are you gonna integrate Kimmy K2?

• 48:40Speaker 01

  We're looking at it. We're looking at it.

• 48:42Alex Kantrowitz?

  And so far?

• 48:43Speaker 01

  So far, we're impressed.

• 48:44Alex Kantrowitz?

  Okay.

• 48:45Speaker 01

  So far, we're very impressed. So I mean, look, these things, sometimes they overfit to certain things. And I would say it's like requires a month from the entire community to kind of like really have consensus of like, whether the model is really great. And similarly with Grok4, I think a lot of people are playing with it. But but my sense is that it is good enough. And again, the economics are so good that you can expend more tokens to get more intelligence. So it is not at the frontier, but it is near frontier. But given that it's

• 49:28Speaker 01

  cheap and fast enough, you can spend more tokens that that creates some more interesting potential for us to create new capabilities in our platform because it is cheap and fast.

• 49:41Alex Kantrowitz?

  How much cheaper is it than the Anthropic models?

• 49:43Speaker 01

  Man, I am bad at this, but like, I would say, I don't, one fourth, maybe?

• 49:49Alex Kantrowitz?

  Oh.

• 49:50Speaker 01

  Yeah, that's that's on the official API. Perhaps more even. I forgot. Maybe you can look it up after this.

• 49:57Alex Kantrowitz?

  We're gonna have to re- this this show is gonna come, it will come a couple of weeks after we record, but we'll have to release this segment early because that's

• 50:05Speaker 01

  Yeah.

• 50:05Alex Kantrowitz?

  astonishing. I want one more question about Anthropic. I can vibe code in Claude. I do it all the time. And they also have this Claude code product where people are, you know, writing prompts, getting code. Are they your competitor long term? Or how do you see them on that front? Because that's the question is eventually do the labs just subsume everything else that's built on top of it?

• 50:29Speaker 01

  I think the question is for them, right? Like, you should ask, I know you're gonna talk to to Dario, you should ask him the question.

• 50:35Alex Kantrowitz?

  Listeners, viewers, this will air a week after Dario, but I'm about to after this go in and speak with him. So

• 50:40Speaker 01

  Yeah.

• 50:41Alex Kantrowitz?

  you might see this question a week earlier.

• 50:43Speaker 01

  Yeah. So look, we're we're committed to our relationship with Anthropic. They're a great company to work with. We have a great partnership. And it's not like we we didn't anticipate them wanting to build products in addition to the models. Every model company is building products right now. The thing that they're going to have to manage is their pricing. If they're if they're going to compete by undercutting everyone on price, they're going to destroy the ecosystem. Right? I think Replit right now is

• 51:23Speaker 01

  has the advantage of this platform that we built over eight years, that it's going to take a lot of blood, sweat and tears to build and also the user experience that is focused on that sort of non-technical user and like we really care about this this idea of empowerment. Right now, Claude Code is is used by developers and loved by developers. And I think they're competing head to head with Cursor, WinSurf and those kind of products. Whether they're going to move into our space, again, you should you should ask them about that. But I think a more interesting question, how how how do they want to nurture the ecosystem versus just go go and

• 52:07Speaker 01

  because they can compete on price, they can steamroll everyone.

• 52:10Alex Kantrowitz?

  Right. I mean, Claude Code is the max package is $200 a month, and you see developers getting thousands of dollars of API value out of that. You must notice this.

• 52:22Speaker 01

  Yeah.

• 52:22Alex Kantrowitz?

  Not good for the ecosystem.

• 52:23Speaker 01

  I don't think so.

• 52:25Alex Kantrowitz?

  Why?

• 52:26Speaker 01

  Because, again, you're you're competing on on price, not how good the product is. And there's a there's a price at which maybe the quality doesn't matter as much as how many tokens I'm getting, although Claude Code is a really good product. But but then, you know, Cursor, no matter how good they make the product, they're still going to be more expensive and at a disadvantage. And people are like, well, you know, I really like Cursor, but like, I can get 10x more value out of Claude Code. And so the marginal gain in product quality will not matter as much.

• 53:05Alex Kantrowitz?

  Right.

• 53:06Speaker 01

  And that will that will destroy the ecosystem.

• 53:09Alex Kantrowitz?

  Fascinating. I mean, I think that this question is just one small question, or one version of a big question we're going to be asking as these AI models get bigger and better and more intelligent. So I want to, I want to spend the rest of our time talking about some philosophical questions, if that's okay with you.

• 53:26Speaker 01

  Sure.

• 53:27Alex Kantrowitz?

  There's this idea that the AI research houses want to use the code that they generate to sort of, or these coding applications to speed up the development of the next model and compress the time it takes to get better models. People call it an intelligence explosion or things of that nature. Do you see that as feasible and is that something we should want?

• 53:51Speaker 01

  So you should think about what are the limiting factors to the next version of a model? What are the bottlenecks? Where's that invasion needs to happen? I can think of a few, few areas. One is, is research. So this is algorithmic research, like figuring out the next algorithm, that next improvement and, and, and, and, and training algorithm and inference algorithm, whatever it is. And then systems engineering. These training runs are massive. That requires a lot of interesting distributed systems engineering.

• 54:33Speaker 01

  Will AI coding help with AI research? On the margins, perhaps they can like, they can spin up Python notebooks faster. I don't think it's that impactful. Like the models can't do AI research, can't come up with ideas and test them really quickly. Will it help with distributed systems? Perhaps. It is not as impactful right now on writing Rust code.

• 55:03Speaker 01

  Or see or go whatever as it is on JavaScript and Python and higher level languages. And like I said, it requires a little more precision and better system design to and the bottleneck to really good distributed systems is is designed and not like the the amount of number of codes you can generate. Which is more true on the product side, on the product side, you're just, you need to generate tons of CSS and JavaScript and try a lot of things and delete a lot of things and iterate and do A B tests and all of that stuff. So like, volume of code is important there.

• 55:40Speaker 01

  I would say on the on the backend distributed systems, I don't think volumes of code is. So I'm reasoning in real time now.

• 55:49Alex Kantrowitz?

  Right.

• 55:49Speaker 01

  And I guess my answer would be, I don't think it's going to have anything more than, you know,

• 55:59Speaker 01

  you know, marginal improvement on on speed to to the next model.

• 56:02Alex Kantrowitz?

  All right. I guess that makes me rest a little easier than, by the way, just on a, you know, you speak with a lot of people in the AI industry. Of all the economic activity in the AI industry today, how much of it do you think is code? Just.

• 56:15Speaker 01

  So someone, someone actually made that slide that's been going around. I think it was something like 1.1 billion of ARR is in the AI coding and five coding space.

• 56:26Alex Kantrowitz?

  Okay, so it's actually kind of small compared to like the total.

• 56:29Speaker 01

  Well, so.

• 56:29Alex Kantrowitz?

  Revenue.

• 56:30Speaker 01

  Yeah, so Anthropic has four four billion dollars.

• 56:33Alex Kantrowitz?

  Right.

• 56:34Speaker 01

  Let's say, yeah, four billion dollars ARR. Let's say they have also have their own products, their own coding products. I don't know. Let's say 1.5 billion is is off of that is is AI coding. It's substantial, but it is not the entire thing.

• 56:48Alex Kantrowitz?

  Okay.

• 56:48Speaker 01

  But but then you have $10 billion of ARR on on on on OpenAI's side, and that's more consumer.

• 56:54Alex Kantrowitz?

  Now, on the rush to artificial general intelligence, which we've talked a little bit about.

• 57:01Alex Kantrowitz?

  Do you think Silicon Valley is the one that should sort of possess this or be the one that controls it? I mean, there it's an interesting place. There's a lot of kooky ideas here. And it seems like if this is possible, it's going to be something that's controlled by or owned by one or more of the labs here. Is that good?

• 57:22Speaker 01

  Assuming it'll happen and assuming one company will reach there first and have some kind of advantage or.

• 57:31Speaker 01

  Monopoly over AGI, which I'm not entirely sure I agree with these assumptions. But if you want to make, if you want me to make these assumptions and then answer the question, I'd be happy to, but I just want to make it clear that.

• 57:42Alex Kantrowitz?

  Yeah, let's make those assumptions.

• 57:43Speaker 01

  Okay.

• 57:44Alex Kantrowitz?

  I know there's a lot of things that need to happen in order to get there.

• 57:46Speaker 01

  Yeah, I might have some fundamental disagreement with this assumption, but let's.

• 57:49Alex Kantrowitz?

  Talk through the disagreement.

• 57:52Speaker 01

  I, I don't think AGI has any point in time for one. Uh, and I think there's going to be, right now, the, uh, the distance between any lab is, is just in order of a few months. And I think that really matters, you know, between O1 preview and, and deep seek was like two or three months, um, between, I mean, the biggest one was, was this Kimi K2 one that we just talked about that, that was like maybe nine months or something like that, but it's still sub one year. And so whomever reaches AGI first, they're not going to go into intelligence explosion and, and just like suddenly, you know, super intelligence that gets born. People, you know, other labs will like catch up really quickly.

• 58:35Speaker 01

  And, and then, you know, there's going to be a lot, a lot of models. I don't think it's going to look that different from the ecosystem that we have today. And if you assume that AGI will actually have an impact on model development through research and speed of development, then everyone will get the benefit of that as well. And so actually you might have, get even more competition once you, once you have AGI. So I don't think it's going to be a monolith.

• 58:59Alex Kantrowitz?

  Okay, but if it is.

• 59:01Speaker 01

  Okay, if it is, uh, is it, would I want Silicon Valley? Uh, and I guess it's like a moral, uh.

• 59:10Alex Kantrowitz?

  Yeah, philosophical moral question.

• 59:12Speaker 01

  Question. I wouldn't want any human being to, we're all fallible. That's why markets work, that's why.

• 59:23Speaker 01

  That's how a human society evolved over time. It is, you know, Darwinian evolution and free market capitalism. It's all based on competition and and and and the idea that like one system would be this this model of controlled by one human being. We've seen disasters and massive human suffering happen when there's this top down sort of Leviathan type thing, whether it is

• 59:59Speaker 01

  in in in Soviet Russia with with all the deaths that happened there or

• 1:00:06Speaker 01

  in China or whatever. And oftentimes, like in in, as I understand it, in the in the Soviet era, they they had this kooky idea about evolution. I think what was called

• 1:00:22Speaker 01

  Lushenko, Lushenkiism or something like that? I'm not familiar, but I'd love to hear the explanation. Yeah. So basically, they had, they thought that evolution is this bourgeois idea, you know, communism has this this idea. It's like anything that's, you know, high class bourgeois is like wrong. And so this had this ideological view on how evolution works or should work that led them to do agriculture in the wrong way and led to famine and and that sort of thing. And like, and so oftentimes, they do kill people and cause mass suffering, mass poverty, even if they don't intend, even if like outside of the Gulags and all the other

• 1:01:01Speaker 01

  oppressive, explicitly oppressive system, those systems are inefficient because they they have these wrong ideas and there's no competitive pressure to have better ideas. And so that's fundamentally broken

• 1:01:20Speaker 01

  static system that doesn't improve like competitive systems. And I think if we were have super intelligent monolith controlled by a single company or single human being, it's bad. It's fundamentally really bad. I agree.

• 1:01:38Alex Kantrowitz?

  All right, last question for you. We're seeing a lot more AI love bots come out. Is that a good thing or a bad thing that people are going to fall in love with AI more often?

• 1:01:47Speaker 01

  It's a bad thing, like a priori bad thing. Like, the reason humanity grew and flourished and all of that is because we have babies. And anything that, you know, you know, takes away from that, especially given the fertility rate is so low right now, is, is, is, will, will potentially lead to really massive problems, especially since capitalism is based on large, middle-class consumerism, like the, the, the, the, the current instantization of how the economy work

• 1:02:29Speaker 01

  requires that, requires taxpayers to fund social security and like elder care and all of that. The welfare state is based on this large young population. And when that starts to collapse, you're going to have, you know, massive instability in these, in these systems. So even if, you know, humanity doesn't go to extent like, like Elon would say, although Elon is, is, is the first person to create a really interesting mass market companion, I think, right now.

• 1:02:59Alex Kantrowitz?

  Interesting is a fun word for it.

• 1:03:03Speaker 01

  It looks like it's really compelling. I see people, you know, right now talking about it on X so much and looks...

• 1:03:08Alex Kantrowitz?

  It's got some work.

• 1:03:09Speaker 01

  Yeah.

• 1:03:10Alex Kantrowitz?

  But it is, these type of things are going to definitely become real partners to people. People, when this technology has been bad or hardly workable, have gotten married to them.

• 1:03:24Speaker 01

  Right.

• 1:03:26Alex Kantrowitz?

  Before LLMs. So it's going to happen again and in greater numbers.

• 1:03:30Speaker 01

  Hey, the question is, I wrote this, I used to do like more creative, creative writing.

• 1:03:39Speaker 01

  I, I wrote this, this essay on the hyperreal. So I think it is like French post-modernist.

• 1:03:50Speaker 01

  Theorists like Bolger wrote about this this concept of the hyper real. And the idea is like we have reality like you and I are interacting right now. And then you have media created realities. And the reason sometimes it is hyper real, it is more intense than reality itself and more enticing than reality itself. So, you know, even in real things, you know, for example, when you when you get a when you eat like a, I don't know, a Twinkie or something like that, like fatty salty sweetie kind of snack. It is like it is not like a piece of chicken or beef or whatever.

• 1:04:32Speaker 01

  It is it is this hyper real thing. It like hyper engages your senses and it makes you addicted to it. And similarly, social media is hyper real in a sense that I can go get go there and get get a lot of social interaction, tweet something, get hundreds of likes. And it's like much easier than going out in the wild and like finding a hundred people that could like me. Yeah. Right? And so we have these technologies that are and the market around it that that is bootstrapped to make us addicted.

• 1:05:08Speaker 01

  Because they're so so much more enticing and low effort than the reality that we know and and experience day to day. And I I think that that is a huge danger for for the existence and evolution and and longevity of of human civilization.

• 1:05:34Speaker 01

  And and I think it is, you know, I talked about how good free markets are, how important, how how competition is important. This is one thing that capitalism is so.

• 1:05:48Speaker 01

  Adversarial to humans at right and and so I don't have a solution for it. I think in the past, the solution was religion. For example, in like Islam, you can't depict humans or animals in art. That's why in Islam, the the art became more geometric. And if you go go, you know, visits like the mosques or whatever, they have like all this geometry that or like calligraphy that's really interesting. And I think part of the the idea there is is is, I think, the hyper wheel. Like if the the ultimate expression of a a something so enticing is a virtual being like we're we're seeing right now.

• 1:06:32Speaker 01

  And I'm not saying like, you know, Islam had like the foresight or whatever, but I think it's, you know, religions used to have this built in mechanism to protect against these predatory sort of consumer products. And I I wouldn't know how to solve in the future, but perhaps it is it is potentially societal, maybe governmental, I'm always kind of skeptical of that, or or religious kind of protection.

• 1:07:04Alex Kantrowitz?

  We're gonna need something.

• 1:07:05Speaker 01

  Yeah.

• 1:07:06Alex Kantrowitz?

  So Lord help us. I'm Judd. Great to see you. Thanks so much for coming on the show.

• 1:07:10Speaker 01

  My pleasure.

• 1:07:11Alex Kantrowitz?

  All right, everybody. Thank you so much for listening and watching. We'll be back on Friday to break down the week's news. Until then, we'll see you next time on Big Technology Podcast.

• 1:07:35Speaker 02

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