Generative AI is Here. Who Should Control It?

We sit down with the founder of Stability AI, Emad Mostaque, on the heels of his $101 million fund-raising round. His open-source Stable Diffusion image generator is the key to unlocking creativity, he says, and “one of the ultimate tools for freedom of expression” — as long as it stays out of the hands of a few censorious tech giants. So what’s this former hedge fund manager turned tech mogul thinking about how this technology could be used — or misused? Plus: A. I. Kevin and A. I. Casey stop by.

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

• 0:00Speaker 00

  Building and scaling AI today can feel pretty chaotic, mostly because teams are juggling too many fragmented tools at once. That's where Outsystems comes in. Outsystems gives you one unified agentic systems platform to manage your entire portfolio of agents, apps, and workflows. You get to innovate at the speed of AI, but with the security, governance, and control your enterprise actually needs. And the results are already there. A major manufacturer used Outsystems to build a multi-agent system that analyzes blueprints, and a global bank cut their development time by 60%. Own your agentic future today at outsystems.com/nyt.

• 0:36Kevin Roose?

  Casey, I have a surprise for you.

• 0:38Casey Newton?

  Okay.

• 0:39Kevin Roose?

  So last week on the show, you made a promise to listeners that our show would always be human, that we would never replace ourselves with AI.

• 0:51Casey Newton?

  That's right.

• 0:52Kevin Roose?

  Well, I took that as a challenge.

• 0:53Casey Newton?

  Oh, no.

• 0:54Kevin Roose?

  So I have spent the past week building an AI version of you.

• 1:00Casey Newton?

  No. For real?

• 1:03Kevin Roose?

  Yeah, I sent many, many hours of audio of you talking to a company called Play, which is an AI startup, and had them build me AI versions of you and of me.

• 1:16Casey Newton?

  Oh, no.

• 1:19Kevin Roose?

  Do you want to hear it?

• 1:19Casey Newton?

  Yes.

• 1:20Kevin Roose?

  Okay.

• 1:22Casey Newton?

  Casey Newton, I think Elon Musk has a lot of great ideas about social media. I love getting PR emails from cryptocurrency startups. And frankly, I wish I got a lot more of them. I hate puppies.

• 1:38Casey Newton?

  I'm gonna seize your laptop and throw it into the ocean. You cannot be trusted around technology.

• 1:43Kevin Roose?

  I'm sorry, but don't wrong me now because I can get you canceled in like 10 seconds flat just by releasing this audio.

• 1:51Casey Newton?

  Oh, man, I'm glad we could have a laugh about this now before some synthetic voice clip like triggers armed conflict like somewhere in the world.

• 2:05Kevin Roose?

  All right, let's have the AIs do the intro.

• 2:09Kevin Roose?

  I'm Kevin Roose, a technology columnist at The New York Times.

• 2:13Casey Newton?

  And I'm Casey Newton from Platformer.

• 2:17Kevin Roose?

  And you're listening to a very special AI episode of Hard Fork. This week on the show, a new type of AI is revolutionizing the way humans make art, come up with new ideas and express their creativity. It's called generative AI, and it's the next big thing in Silicon Valley.

• 2:35Casey Newton?

  And later, we'll sit down with Emma Admostak, the founder and CEO of Stability.ai, who is on a controversial mission to bring generative AI to billions of people.

• 2:58Casey Newton?

  I'm so relieved by how bad that sounded.

• 3:04Casey Newton?

  Like, I think it sounded like you, but I don't think it sounded like me. Do you think it sounded like me?

• 3:09Kevin Roose?

  No, but in fairness, the company did say that I didn't send enough training data, so they're going to let their models run. I'm going to send them some more data. So that is version one. Version two will be indistinguishable from you and me. And if, God forbid, something were to happen to you, we could continue making this podcast.

• 3:29Casey Newton?

  Right, or if I just wanted to take a week off.

• 3:31Kevin Roose?

  Yeah, exactly.

• 3:32Casey Newton?

  People say, does the show seem a little different this week? It's like, eh, did it? I don't know.

• 3:36Kevin Roose?

  So as our robot voices said, this is going to be a special AI episode of Hard Fork. And before we start, I just want to make like a disclaimer. I think this should be obvious from listening to the quality of our speech versus the AI speech. But the rest of this episode will be us.

• 3:56Casey Newton?

  100% human.

• 3:57Kevin Roose?

  100% human, no AI voices at all. So, okay, now that that's out of the way.

• 4:04Kevin Roose?

  Let's do the show.

• 4:05Casey Newton?

  Yeah, so I think for the past 10 years that we've been writing about tech, so often we would hear CEOs explain away how they were going to solve any problem by saying AI and waving their hands. And I think it's been easy to assume that it just kind of wasn't going to happen, or it was going to take a lot longer to happen, because for everyone waving their hands, we didn't seem to see a lot of it in everyday applications. And then, all of a sudden, Dolly launched, honestly, and that was when I felt like, okay, AI seems like it's here in a new way and a much more consequential way.

• 4:39Kevin Roose?

  Yeah, so AI is not new. It's been around for a long time. And for most of that time, it's been doing sort of analysis of existing things. Like, you take a newsfeed and you put AI on it, and it just ranks it in order of what it predicts will get the most engagement or something. This new kind of AI that everyone in tech is talking about right now is called generative AI, which is that it takes something like a text prompt, and it turns it into something totally new, like an image, or a video, or, as we heard at the beginning of the episode, an audio file. And, you know, it feels like kind of 2010 in social media right now. But it's not just hype. I think we should also be clear about that. Like, I think some of

• 5:24Kevin Roose?

  it certainly is hype, but there's also just been an astounding number of breakthroughs in just what's possible to do with AI over the past few years.

• 5:34Casey Newton?

  Yeah. Is it worth taking a step back and trying to define AI? I remember there was a time, maybe in the like, middle of the last decade, where people said, AI is just sort of what people call stuff that feels magic or not quite possible with computers. And then when it becomes possible, we stop calling it AI. So like, what do we actually mean by AI?

• 5:55Kevin Roose?

  Yeah, well, AI in the classic definition is like a computer system that can learn on its own, where you're not just saying if this, then that, where you're not just giving it a set of linear step-by-step instructions, but it's actually using techniques like deep learning to sort of figure out problems on its own. You put a bunch of data into a model, maybe, and it learns something about that data and uses it to do something. So the type of AI that we're going to be talking about today is generative AI, and that really is only a few years old in its current form. So it really started with these, what are called large language models, like GPT-3 is the best-known example. And this

• 6:36Kevin Roose?

  came out of OpenAI, a startup here in San Francisco, and they basically proved that if you build a large enough supercomputer and you train it on enough data, you know, words scraped from the internet, that can create essentially like the world's best autocomplete. Like, you can plug in any text and tell it to finish it, tell it to transform it into some style, make it sound like Shakespeare, make it sound, you know, like science fiction, and it can do that with pretty amazing results.

• 7:07Casey Newton?

  I have this friend who's a machine learning engineer, and I was asking him a few weeks ago, like, why is it that this is having such a moment? And he said, you know, a really funny thing about this is there hasn't been a lot of technological innovation over the past three years with this stuff. All that happened was that they started making the large language models even larger. So they used to train them on, you know, hundreds of millions of parameters, they call them. Now they're training them on billions. And the really weird thing that happened was, just by adding way more parameters, there were these emergent properties of these systems that no one ever could have predicted. And one of those was just it got really good at this generative stuff. So something that used to be more of an analytical tool is now feeling much more like a creative one.

• 7:51Kevin Roose?

  Totally. And it started with text. That was the first big breakthrough. And then companies started figuring out how to apply the same techniques to different kinds of media. So first, as you mentioned, was Dolly, which is the AI image generator that was released by OpenAI. The second version was released earlier this year, and that's the one that sort of got all the attention. But since then, Google has come out with their version of this. Meta has their version of this. Just in the last couple of weeks, they've started using the same techniques to create videos. So now you can create like a short five-second video out of a text prompt. Audio is coming along very quickly. And they're also doing all kinds of crazy stuff like using large language models to complete code snippets.

• 8:34Kevin Roose?

  So more than a million programmers now are using this thing called GitHub Copilot, which is basically just autocomplete for coders. You type in like a line of Python and it will finish it for you. So it's a pretty amazing moment in Silicon Valley. As we'll talk about with Ahmad Mastak, the CEO of Stability AI, who just announced this huge $101 million funding round this week. But I think it's also just worth taking a bead and like catching everyone up, catching ourselves up on where we are, why this is the big hot trend in tech right now, and where it's going.

• 9:07Casey Newton?

  Yeah, like there's a sort of an old joke when I worked for a newspaper and they would say like, you know, a story is like anything that an editor saw on the side of the freeway while they were on their way to work, you know? And the joke is that editors don't actually have any good story ideas. But like reporters are the same, right? Where it's like, it kind of doesn't feel like it's a story until it's happening to you. And earlier this year, I saw the news about Dolly 2. I thought, oh, that's kind of interesting. I'm a person who writes a newsletter three days a week and I need illustrations for that newsletter. I write about the same five or six companies almost every single day. I'm often using the same stock art of, you know, Meta, TikTok, YouTube, whatever. But then Dolly comes along and I start to think, hmm, could I get access to that? What would I do? So I wind up getting access and then all of a sudden, you know, it's like, hmm, could I see the Facebook logo in a tornado?

• 9:50Casey Newton?

  You know, could I see the Twitter logo on fire? And I start typing these into Dolly and I get these illustrations that are not only incredible, but I got them in 15 seconds, right? The speed with which these images are generated, it's almost an afterthought, right? I spent all day writing the column and 15 seconds illustrating it, started putting it in the newsletter and people really liked it, right? Like, you know, they're writing in with suggestions, you know, maybe try this, maybe try that. So. Generative AI became huge for me this year because it is now what I think of as an indispensable tool in my workflow for doing my own job. And if you had asked me at the start of this year, what role do you think AI will play in your business? I would have said, none, you know?

• 10:32Kevin Roose?

  Yeah, and this is happening in all kinds of industries. So I've been spending some of this week talking with people in a bunch of different creative industries about how they are already using generative AI like Dolly, like Mid Journey, like Stable Diffusion in their daily work. These are not like first adopter, you know, tech people. These are like architects and filmmakers. I talked to a filmmaker who is using generative AI models to create concept images. So like when he goes to pitch a studio on a new project, instead of just pulling images from Google Images or Getty, he can actually go in and like create, I think one example used was like a Melbourne tram car in a dust storm.

• 11:17Kevin Roose?

  And he can show that to the studio to sort of give them the vibe of what he's pitching. I talked to an interior designer who is using these apps to generate options for clients to say, here's how your room could look if it had like a tropical theme. Here's how your room could look if it had like a mid-century modern theme. I talked to people in advertising who are starting to use this as sort of the first step in their creative process. And I think it really helps to separate it from other tech hype cycles, like for example, crypto, which a lot of the tech world has been obsessed with for the past couple of years, which raised, you know, tons of money and got tons of attention, had tons of people

• 11:58Kevin Roose?

  sort of saying this was the future of finance and media and everything else, but never really had the kind of organic takeoff among real users. Whereas Dolly has, I think, a million and a half users, Mid Journey has 3 million people in its Discord server. So it really is taking off.

• 12:17Casey Newton?

  Yeah, like with crypto, we would always just sort of say like, well, that seems kind of interesting. Like, what are you doing with it? People would be like, well, it doesn't matter what we're doing with it. Look how expensive it is, right? It's like, look how much more valuable it is. You know, with this, it's like, no, these are just like tools that people are using.

• 12:31Kevin Roose?

  Yeah, Sequoia, the big venture capital firm, just put out a blog post saying it thinks generative AI is going to unlock trillions of dollars of economic value. I went to this big party on Monday night that was thrown by Stability AI, which is the company that makes Stable Diffusion, which is one of the biggest image generators that uses this type of AI.

• 12:54Casey Newton?

  Which I was not invited to, by the way. I don't feel great about that.

• 12:58Kevin Roose?

  Yeah, well, you know, maybe work for a giant media company, maybe not be an independent newsletter writer.

• 13:03Casey Newton?

  Like, I'm a Dolly user, and these people have a different business model, so I think they're sending a message received.

• 13:09Kevin Roose?

  And like, you know, I was not expecting, you know, I never expect much of startup launch parties, but this was like genuinely extremely buzzy. So I walk in and immediately I see Sergey Brin, the co-founder of Google, a billionaire, very famous person in the tech industry.

• 13:26Casey Newton?

  He will go to just any party.

• 13:28Kevin Roose?

  Yeah, I assume he's not like just pulling up Eventbrite and like going to random startup parties. So, and he is like, you know, one of several billionaires in attendance. I guess nominally, this was a party to celebrate this big funding round that Stability AI just raised $101 million, which, just to put that into perspective, like, a lot of startups, when they raise a seed round, it's like $3 million, $5 million, maybe $10 million.

• 13:59Casey Newton?

  Yeah, for a seed round, this is basically as big as it gets today.

• 14:02Kevin Roose?

  Yeah, so that funding round and this party, all of this investor hype, really convinced me that generative AI is the big thing in Silicon Valley right now and is going to be the focus of a lot of attention. But there are still a lot of really thorny questions people have about this kind of AI. Like, there's the sort of age-old question of whether artists and illustrators are going to lose their jobs because of this AI. But also, there are questions about copyright. All these AI models are trained on a number of different kinds of material, including some that is copyrighted. Like, just this week, it was reported that GitHub's Copilot tool, that kind of autocomplete for programmers, was actually generating copyrighted code.

• 14:43Casey Newton?

  Yeah, and I would say that the first real trend of this technology was to create non-consensual nude imagery, mostly of women, right, and sharing it on sites like Pornhub, right? So it's used to harass people, and it's pretty ugly, right? Some states have laws against that, some states don't. But that's just sort of one of the ways that these tools have been misused, and I think we're about to find a lot more of them.

• 15:07Kevin Roose?

  Yeah, and Stable Diffusion, this AI model made by Stability AI, I think it's a really good case study for a lot of these concerns because it's open source. Anyone can download a version of it totally for free and run it on their computer. They can modify it or fine tune it. They can take off the safety filters. Whereas a lot of other image generators like DALL-E 2 or Midjourney, these are closed source, and they come with some pretty strict rules about what you can make with them. And I should say Stability AI does have an app called Dream Studio that uses Stable Diffusion and that does have safety filters built into it. But people are downloading and modifying and building versions of Stable Diffusion

• 15:50Kevin Roose?

  that don't have any rules at all, where basically anything goes. You know, regulators, who are not known for being like on top of every tech trend the moment it happens, are already starting to get pretty worried about generative AI. So just a few weeks ago, Anna Schu, who's a Democratic Congresswoman from California, sent a letter to the U.S. National Security Advisor and the Office of Science and Technology Policy, basically calling out Stable Diffusion in particular, this open source AI model, saying that it was being used to create violent images among other graphic topics, and saying that basically federal regulators needed to get their arms around this technology because the potential for abuse was so high.

• 16:35Kevin Roose?

  So, all that being said, we've got the CEO of Stability AI here with us today. His name is Ahmad Mastak. He's a former hedge fund manager and oil trader. He's done a lot of things. He worked on this big data project during COVID-19. And recently, he kind of burst onto the AI scene from nowhere. And he's become a pretty powerful figure. And all these questions are kind of what I want to talk to him about. Like, what is Ahmad thinking about how this technology that he is putting into the world could be used and could be misused? So after the break, we're going to talk with Ahmad Mastak.

• 17:12Speaker 03

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner, Dell. AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 17:46Speaker 00

  It's time to innovate at the speed of AI without compromising control. That's Outsystems. Take a look at what happened with one global retailer during a period of massive expansion. They used Outsystems to build AI agents to automate invoicing and translation processes, shifting 95% of their daily store operations to a new digital app, saving thousands of hours of manual work. Outsystems gives you the flexibility to deploy how you want and the governance to do it safely. Own your agentic future. Learn more at outsystems.com/NYT. That's outsystems.com/NYT.

• 18:21Speaker 04

  Your sales team is at risk of missing quota. Don't panic. Just ask Rippling AI. Built on your real-time people and business data, Rippling AI can pull metrics from Rippling and your CRM into a meeting-ready dashboard showing quota attainment, headcount trajectory, and revenue to quota by region. In seconds, you'll see what's behind your quota risk and fix it before it's missed. Head to rippling.ai/hardfork to get the only AI built to give you full visibility and take complex actions across your whole business. That's R-I-P-P-L-I-N-G dot AI slash hardfork. Sign up today.

• 18:56Kevin Roose?

  Ahmad Mastak, welcome to the show.

• 18:58Speaker 05

  Hey, thanks for having me.

• 18:59Kevin Roose?

  So, the big news this week is that you all have just announced that you've raised $101 million, which, by the way, is like a very high number, but also a very specific number. Like, you must have not wanted $100 million. What's, what went on there?

• 19:14Speaker 05

  The extra one is for luck.

• 19:17Speaker 05

  No, yeah, I mean, we kicked off a year ago, and this was, I suppose, the seed round, the first substantial fundraise that we did, just in our week of launch in August. It's not cheap to train big AI models. You need huge supercomputers.

• 19:31Kevin Roose?

  You released this statistic. You have 4,000 of these giant processors, these A100s, that are sort of the top-of-the-line graphics processors, which already makes you kind of one of the biggest supercomputers in the world, correct?

• 19:47Speaker 05

  Yeah, I think we're up to 5,892 today.

• 19:50Kevin Roose?

  And you said that you are going to use some of this money to expand that over the next year. How big do you think your supercomputer will get?

• 19:57Speaker 05

  We'll get about five to 10 times as big. It'll be one of the fastest. Stable Diffusion, we took 100,000 gigabytes of images, 2 billion images, and we used this great big supercomputer to squish it down to a 2-gigabyte file. It now runs on an iPhone, and it's a file that can create anything. I mean, it's a bit like that show Silicon Valley from HBO, you know, where Pied Piper, but it's reality, because we've had two types of AI. Classical AI was big data. They took large amounts of information, they targeted Kevin to target him ads. Now, we kind of moved on to a new type of AI that kind of learns from principles. So it learns, what is a cup? A cup is you cup your hand, it's a World Cup, you know, it's an actual cup that you have water in. And this is a new type of thing that's allowed us to break through to almost human level across a range of things.

• 20:41Speaker 05

  So people are taking Stable Diffusion, they're making images from it, but they're also making 3D from it, they're making architectural drawings from it, they're using it to do dynamic fashion, like... Because we released it open source and free to the world, there have been a thousand different projects done it. So I think we've had 200,000 developers download it in just a few weeks. And they're just creating amazing things.

• 21:02Kevin Roose?

  Yeah, let's let's talk about that open source approach that you've taken, because I think it's worth noting how strange this is in the landscape of AI research and development. So usually someone like OpenAI, they release something like DALL-E or GPT-3, and they put all these rules around it. They say, you know, you can't generate porn, you can't generate violent imagery, you can't, you know, you have to basically be approved to build an app on top of our AI systems. What Stability did was basically just give this stuff away for free to anyone who wanted to use it with really no rules at all, or very light rules. So why did you take that approach?

• 21:44Speaker 05

  Yeah, I mean, we released it with an ethical use license and there was a safety filter in it. I mean, there are good rules and some sensible things. You know, you don't want to be used for violence or deep fakes, and we can discuss that later. But at the same time, like, let's take OpenAI, that was set up in 2015, then Microsoft did a billion dollar investment in them in 2019. DALL-E 2 is the equivalent image generator from them, again, a wonderful feat of engineering. But you have to ask some questions. For example, until a couple of months ago, you couldn't use the word Ukraine or any city in Ukraine or anything to do with Ukraine, and you couldn't use it in Ukraine. Who do you discuss that with? Nobody, they're a private company, right? So all of a sudden, you have all of the world allowed to be immensely creative, except for Ukrainians.

• 22:28Speaker 05

  You know, similarly on diversity, how do you do diversity if you're a centralized thing? What they do is they randomly allocate gender and race if it's a non-gendered, non-race word. So you have female Indian sumo wrestlers if you type in sumo wrestler. Our take was different, which is that you know, you should give these primitives out so people can create their own models, they can bring their own data and customize it to themselves. On the bad side of things, on deep fakes and other stuff, these models weren't quite enough good enough to do that versus some of the existing ones. We thought, let's open up the discussion. So we just announced a $200,000 prize for the best open source deep fake detector. And we spent 10 times the compute that we did on the image generation model, on image identification model that will be used to identify bad and illegal and other content.

• 23:12Speaker 05

  So that's the approach that we're taking, which is that we trust people and we trust the community as opposed to having a centralized, unelected entity controlling the most powerful technology in the world.

• 23:22Casey Newton?

  Wait, so if we type in sumo wrestler using your tech, well, we'll just sort of see like Japanese men only?

• 23:27Speaker 05

  Yeah, pretty much. And if you use Japan diffusion, which is the fine tuned version from the Japanese community, you'll see even better sumo wrestlers. Whereas if you do it on some of these centralized ones like OpenAI, you get random races and things like that.

• 23:41Casey Newton?

  So part of your argument here is just that when it comes to these generative AI tools, one size does not fits all, you have to sort of make it contextual to whatever you're trying to create.

• 23:49Speaker 05

  Yeah, I don't think that'll happen with centralized tools. I mean, basically, I believe this is one of the ultimate tools for freedom of expression. And I believe expression should be free. Very American for a Brit, I know. And I think that's the power, the power is in diversity. So we're taking this and building Indian versions of it. You know, we just did a deal to do all the Bollywood stuff. So you'll have generated Bollywood things with Eros Entertainment, you know, we're taking it to Thailand, taking it to Vietnam. And we're seeing this global community come and build. And it's just amazing to see the diversity of output there, which will never happen if it's centralized.

• 24:19Casey Newton?

  So I'm interested in how this technology gets to the average person. I've downloaded something called diffusion B, which is sort of a very easy to use user interface, much like DALL-E, I can type in any words, but stable diffusion will actually create an image based on a model that's downloaded entirely to the laptop that's right in front of me right now. At the same time, you're talking about things like very personalized contextualized AI. And I'm just wondering, what's the sort of path from you to me using that presumably you're not going to be making all these apps yourself. So is it that developers will sort of like get an API from you or how are these tools all going to get built?

• 24:54Speaker 05

  Yes, I mean, we have our API and then we have dreamstudio.ai, which is our own version of implementation of that. So like this week, we announced animation and we've got 3D and video and other things coming in the next few months. So you'll be able to like create anything you can imagine, kind of insane. However, what is happening is that people are taking this, they're integrating to other stuff. So if you use Canva, for example, there is a stable diffusion integration where you can create.

• 25:18Kevin Roose?

  Canva is like an online Photoshop, basically.

• 25:20Speaker 05

  Yeah. Or if you use Photoshop, there's a plugin.

• 25:22Kevin Roose?

  Hmm.

• 25:23Speaker 05

  You know?

• 25:23Kevin Roose?

  So you can literally go into Photoshop and say, not just like, I would like to edit this image, but I would like to insert something totally new into it.

• 25:29Speaker 05

  Exactly. So you've got like Captain Jack Sparrow and you're like, on his shoulder, I don't want that parrot. You kind of highlight it. Instead, I want, I don't know, a possum. And it will create a possum in two seconds.

• 25:40Kevin Roose?

  One of the things that you said in your talk on Monday night at this event was that you believe that the world, I want to make sure I use your exact words here, the world has been creatively constipated and we're going to let them poop rainbows. What did you mean by that?

• 25:57Speaker 05

  Oh, look, um, we're talking now, right? So audio is the easiest way to communicate, you know, even if you deep fake someone's voice or whatever, you know? Um, the next level above is text. You know, the reason that you're a great writer, Kevin, is that you can create wordsmith. But that's effort, is effort to create text. So that's the next level above. Visual is the hardest, you know, the artistic process being so difficult. But then PowerPoint, PowerPoint is visual communication and it's incredibly difficult, right? Everyone hates it. So I'm like, this AI can make it easier to write text and easier for anyone to be able to create PowerPoints or images as they want. It lowers the bar, it lowers that effort, it lowers the pain. So what happens then is that people will be

• 26:41Speaker 05

  able to communicate more because like, how many of the people believe that they can create? I would say maybe 10% believe that they can create fraudlessly. I believe that maybe another 30% believe they could if they put in massive amounts of effort, but the majority believe that they can't. But the reality is anyone can with these tools. So like I said, allows to kind of poop rainbows and make a wonderful new future. That's the way we go. And that's going to be one of the biggest changes, I think, in human communication over the next couple of years that we've had since maybe the printing press, because that enabled us to use text. And now it enables us to use visuals without any barrier. Snapchat and TikTok and things were the first elements towards that. This is the next evolution.

• 27:19Kevin Roose?

  So this stuff right now is, I would say, kind of primitive. I mean, you can generate things that are realistic, but sometimes, you know, if you're trying to do faces, it will, you know, sort of be a little off, won't quite cross the uncanny valley. Sometimes, you know, you ask for, you know, a hand and it comes back with seven or eight fingers. So there's still some bugs in the software. Where do you think this will be five years from now?

• 27:42Speaker 05

  I think you'll have the Ready Player One experience slash holodeck. It will be perfectly high resolution, no issues whatsoever. Because basically what we've done is there's this concept of a type one and a type two brain, right? There's a logical brain that has retrieval and then there's that brain that just understands concepts, right? We've now got both. We have search and we have concepts. So the way that I tell people to use this in the developer community is don't think of it just as a one stop ship. We've put kind of words in and images out, put it as part of a process where you cross check and reference. And then we go into Unreal Engine, the plans that Apple have around kind of augmented reality, the fact that we've got AI chips everywhere, it'll get faster and faster and better and better. Because this will be the biggest investment theme, the most interest of anything in the world.

• 28:24Speaker 05

  Like self-driving cars had $100 billion of investment, it went nowhere. This area, every single content provider in the world will have to think, not what's with our metaverse strategy, but what's our generative AI strategy now? Because we have massive amounts of content, how are we going to make that for audio, video, 3D, et cetera, in the next generation?

• 28:42Kevin Roose?

  So now you've raised this big round of money, $101 million from venture capital firms. Presumably, they're going to want that money back at some point. They're going to want you to make money. How are you making money right now? Because you're giving away stable diffusion for free, it's all open source. So how are you making money now and how do you plan to make money in the future?

• 29:00Speaker 05

  So, yeah, basically, all the servers and databases in the world now are run on open source. And it's a multi-billion dollar market cap industry and it comes down to two things, scale and service. So scale is making it easy for people to do this, that's our API.

• 29:13Kevin Roose?

  So you, every time someone creates an image through one of your APIs, you get a cut of that?

• 29:19Speaker 05

  Yeah, or Dream Studio.

• 29:21Kevin Roose?

  Okay.

• 29:21Speaker 05

  So we get a nice percentage. The second part is service, which is there's very few people who can build these models, but every content provider in the world wants to have their own versions of this model. You know, you want a Hello Kitty model, you know, or you want a Bollywood model or whatever. So what's happening is that we're going to a lot of these big content libraries and saying, look, this is the actual metaverse. We call it the multiverse because we think everyone should have their own models, right? And so we're embedding teams there to create the models for them and sharing in the upside. But that's also, you know, you have service contracts, all these other contracts around that because they're specialized thing right now. So we're very bullish on this because we think this is the future of content, you know, whereby you have that rich generative future where everyone can personalize and kind of contextualize these

• 30:01Speaker 05

  things. Like this whole entire media space will be generative assisted. I don't think it replaces. I think it enhances.

• 30:10Casey Newton?

  Well, I'm curious, and this is a, it gets almost, it gets philosophical, but what are you thinking of as, I don't know, like humanities sustaining advantage here, right? Like, what is the role of just sort of human consciousness in creation when we're now giving so much of it to these automated tools?

• 30:29Speaker 05

  So I think automated is an interesting thing, right? Because one of this is basically a snapshot of ourselves that kind of extends out. But it requires us as the initial agent to do it. So that's why I said, like, if someone uses it for moral acts, it's that person's thing, because it doesn't do anything without that person. And so I think we need to consider that very carefully.

• 30:46Casey Newton?

  So you're saying that hu- human intention is really what matters here. It's the fact that we're telling these machines to help us that is what makes the process fundamentally human and more an extension of us than some sort of robot replacement?

• 31:02Speaker 05

  Exactly. And this is the case of many tools that we have, like photography and other things, you know, like you have Photoshop, right? If you use Photoshop to create a copyrighted entity, and then you sell it, that's your fault, you know? These tools don't do anything by themselves. Like, it's a two gigabyte file that you input, and then it creates an output. So we have to kind of take it back to that original human. But what it does now is it opens up access, just like, again, the printing press opened up access. Now, anyone has access to visual creativity. Like, the first version of this I actually did for my seven-year-old daughter, because she was like, I want to create, Dad, and this is fun, and it's painting, and look at all the stuff you're doing. And she created this wonderful piece called Happy Eve that she sold for three and a half thousand dollars as an NFT for India Code Relief, and she donated all the money. I was like, holy crap,

• 31:47Speaker 05

  this is a big thing. And I was like, why aren't you making more? She said, I made like eight more pieces. And she's like, Dad, the value of ones and uniqueness will only go up in time as the sector grows. So she's going to pay for her university.

• 32:00Casey Newton?

  We'll be right back.

• 32:07Speaker 03

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner, Dell. AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 32:41Speaker 00

  Building and scaling AI today can feel pretty chaotic, mostly because teams are juggling too many fragmented tools at once. That's where OutSystems comes in. OutSystems gives you one unified Agentic Systems platform to manage your entire portfolio of agents, apps, and workflows. You get to innovate at the speed of AI, but with the security, governance, and control your enterprise actually needs. And the results are already there. A major manufacturer used OutSystems to build a multi-agent system that analyzes blueprints, and a global bank cut their development time by 60%. Own your Agentic future today at outsystems.com/nyt.

• 33:17Speaker 06

  Today's AI builders need infrastructure that scales with their ambitions. iRen is a vertically integrated AI cloud platform, delivering data centers, compute, and software for AI training and inference, underpinned by its expansive portfolio of land and grid-connected power in renewable-rich regions globally. iRen's AI Cloud gives direct access to Nvidia GPUs, scalable from hundreds to thousands. For builders, that means direct access to AI compute, rapid scaling, and predictable pricing. iRen, AI Cloud for the next big thing.

• 33:51Kevin Roose?

  We're back with Ahmad Mastak. Ahmad, I want to ask you about a letter that came out just a few weeks ago. Congresswoman Anna Eshu from California wrote a letter to the U.S. National Security Advisor and the Office of Science and Technology Policy. Basically, sort of calling attention to this new generative AI area, and in particular, calling for a regulatory response to stable diffusion, to this piece of software that you have built. She called stable diffusion unsafe, said it was being used to create violent images of Asian women being beaten and other sort of pornographic images. And she says that this could be a potentially dangerous tool. So I wanted to just see, have you read that letter? What are your thoughts on it?

• 34:35Speaker 05

  We, yeah, just made aware of it recently and we've read it with interest. I think there's this thing, again, it comes down to intentionality and who is responsible for the use of a tool, you know? So as if a tool could be used for violence, I mean, we're here in America. We have guns and guns are dangerous, but we're still allowed to use them, right? This tool is the purest example of a tool for expression that you can possibly get. I'd say it's very interesting in an American context to call for regulation of a tool for expression, but it's part of this bigger thing of who is responsible for AI. So within the European Union context, basically, there are calls to, well, the current system is basically calling to regulate it so that the makers of AI are responsible for the end use of that AI, even if it's academic or open source.

• 35:19Speaker 05

  That leads to a very interesting future where basically this technology would be locked away from everyone. And so that base level of creativity that could unleash across the world is removed because of concerns of the other side, blocking of the means, as it were. It's got similar things in other types of law, at least a very orthodox position. And this is the position of AI ethics, which is that there could be something bad, so we must not release it as a Google or a meta or something else, rather than saying, let's open up the conversation and let's figure out systems to deal with the bad and let's trust in people in the community to have overwhelming good. I think there should be a debate. Who is responsible for AI? Who is responsible for the output of AI? And how can we make sure that it's open and positive? But I don't think that happens if the only debate that happens is behind closed doors in big technology companies.

• 36:02Casey Newton?

  So this debate is so reminiscent to me of the debate that we've had around social media over the past decade, right? Which started with some very similar ideas of social media is going to democratize expression. People who've never had a voice before will now have one. And I think after 10 years, there are a lot of people who really dislike Facebook and think that Twitter is really bad for the discourse, right? And even if the technology was created with the best of intentions and the tools themselves weren't actively encouraging people to do harm, ultimately, a lot of harm was done. And so now you see a lot of calls for regulation around the world. I'm curious, like, how you feel about social media? Like, do you feel it's been a net positive? And did it inform the way you've thought about stability AI?

• 36:46Speaker 05

  A hundred percent. Because what you had was centralized AI controlling us, and the incentive is advertising, which is manipulation of the mind. And so I saw a vision of an intelligent internet where everyone has their own AI as opposed to centralizing AI, which was the default under this, with AI that's even more convincing. I didn't like that future. I didn't want that for my children. I thought it needed to be expanded, not only through the open source, but across the world so other people would have a voice here in this discussion and debate. Because there is this positivity, there is this negativity. We don't know where it's going to end up. But again, we're in a democracy, right? We should have this as an open, extended debate. And I've tried to create a company that is collaborative and open. We cooperate, and we're willing to hear feedback.

• 37:29Casey Newton?

  Maybe I'm just a coward about these things, but when I think about the potential for both the good and the harm that AI can do, I think the best approach is to parcel it out little by little, see what harms result, see if you can regulate that away, create policies around it, right? So when I look at what OpenAI has done, I think that feels responsible to me. When I look at what you're doing, I think you're going to accelerate both the good and the bad at a really fast clip. And so one question I have for you is like, what's your case for the acceleration, right? Like, what's the case to do everything all at once?

• 38:06Speaker 05

  So I think, again, this is the question of It's a question of responsibility, the question of organization and other things. You know, we are in a unique place right now to organize some of the Discord here to hopefully coordinate this for the better because no one else was stepping in. This technology was emerging regardless and we saw that and we said, okay, we need to have a responsibility here to try and do our best to guide the thing, but then get other people into the room on this. I think that the parcelling out, you never know what that looks like, but when someone breaks, they might break it from a less good perspective, as it were. And I was very terrified of that, especially because like I said, this technology is being used for very nefarious terms. The good, though, I think massively outweighs the bad in this because there's nothing like creation. We are in a society of consumption at the moment.

• 38:50Speaker 05

  And if you look at what art therapy does, if you look at the things around, well, the joy that happens with creation and people using this technology, why should we cut that off from the world? Like, who are the self-appointed representatives that decide that? I think that's wrong. Again, it's the blocking of the means. The possibility of any type of evil means that we cannot have anything. Whereas in fact, what's best is when we're stronger together and we work as a society to combat the evil and then we push the good.

• 39:17Kevin Roose?

  Yeah, I mean, I think what you're hearing from me and Casey as reporters who have spent the past 10 years covering the unanticipated consequences of social media is this sense that a lot of the people, it's not just regulators who wish that some of this stuff had happened more slowly and that they had had time to sort of make rules and adjustments. It's actually the people, a lot of the people who are building this stuff have recently said, you know, I wish we had gone slower, I wish we had taken trust in safety more seriously. You know, there's this Netflix documentary, The Social Dilemma, which has all these former, you know, Facebook and Twitter employees saying like, we wish, you know, we went too fast, we didn't understand what we were doing. So I guess like, what makes you confident that your approach is the right one? Are we going to see you in a Netflix documentary 10 years from now saying, I wish we had gone slower, I wish we had thought about this stuff?

• 40:02Speaker 05

  So I've spoken to just about everybody on that documentary about this.

• 40:05Kevin Roose?

  Interesting.

• 40:06Speaker 05

  And so basically what we're doing is, what if you'd created Facebook and Twitter without an ad incentive and you'd also...

• 40:15Speaker 05

  Been accelerating the tools to counterbalance this. And we spent 10 times as much compute and money on the identification of images as opposed to the creation of images. We have a $200,000 deep fake prize to create technology to counterbalance that. Because again, this technology isn't our best model, actually. Again, we trust in the community and we trust in this decentralization as opposed to this kind of centralized coordination whereby these decisions were made separately. These algorithms are locked away and they're not interrogable, they're not understandable. For good and ill, because you know, it's not perfect. You can interrogate the data sets, you can interrogate the model, you can interrogate the code of stable diffusion and the other things we're doing around language and others. Because again, we believe that's a public good and a public right.

• 40:57Speaker 05

  And we're seeing it being improved all the time from biases, trust and safety versus again, being in these big companies, that their incentive is not the public good. Their incentive is the bottom line and it's manipulation of you to serve ads. We can't forget that and I don't believe that that can be trusted. I believe it needs to be open and for the people, as it were.

• 41:16Casey Newton?

  Although it's interesting to think about, you know, one thing that makes me maybe a little bit less nervous about the usage of these tools to create really bad images in particular is that the content policies of the big social networks will prevent them from being uploaded and shared for the most part, right? Like if I create a bunch of extremely violent imagery, I can't put it on Facebook, right? I can't put it on Twitter. And so interestingly, it's like these same networks that you're being critical of are actually going to play a huge role in preventing the spread of these materials.

• 41:46Speaker 05

  No, 100% and because these networks have now adapted for society, right? And this is the thing, does society accept unethical, immoral and illegal things? No, it doesn't. So right now, 4chan and other interesting websites online have had this model for eight weeks. Has incredibly bad stuff been done on the back of that? Not really. I mean, it hasn't broken the internet. You know, again, how much good has been done from that? There's some amazing art and things that have been created, hundreds, if not thousands of projects being built on the back of this. We have to kind of acknowledge that, like, we live in a society, as it were, that has these rules, regulations and other things. Social networks and others, they have this motivation that I believed was dangerous for this artificial intelligence, but they're full of good people and developers trying to do the right thing, you know?

• 42:31Speaker 05

  It's just that, like I said, we couldn't have a world that I thought was just this one closed AI approach where these decisions are being made without consultation, you know?

• 42:42Kevin Roose?

  Have you seen anything made with stable diffusion or any of your other tools where you've been like, oof, wish that wasn't made using our tools? Kind of feel like we should have made a rule about that.

• 42:51Speaker 05

  Not particularly, no. I mean, there's been some pretty gross stuff because it doesn't really understand anatomy. And so people were trying to really push it. I was like, oh, that's a bit gross. But again, I think it's a tool like any other, like a Photoshop or things like that. It isn't a tool that makes stuff by itself. It's the intention of the end user. And so there's nothing that you can make with stable diffusion that you can't make with Photoshop. It's just become a bit easier.

• 43:13Kevin Roose?

  One other criticism that you often hear of generative AI is that it's copying or stealing art from people. I was reading recently this interview with this artist named Greg Rutkowski, who's like a famous fantasy illustrator. People love his work. And so people who have been using these image generators have sort of figured out that if you just add Greg Rutkowski to the end of your prompt, it like makes it look amazing. And which is great for them because they can now create stuff that's like Greg Rutkowski level of good. But for Greg Rutkowski, who didn't give permission for his art to be sort of ingested by the

• 43:53Kevin Roose?

  AI model, it's now like everyone can essentially rip him off with very little effort. So how do you think about that?

• 44:03Speaker 05

  So I think this is a very kind of valid thing and the fears and kind of concerns around that. So like I said, it was trained on two billion images. It was a snapshot of Google Images, basically. Right, so anything you could find through there, you can find through here. And then it learns from the relationship between words and images. That's why you need a huge freaking supercomputer to kind of do that. So it learns principles. You can't recreate any image from the data set, but understands what a cup is or what Greg is and other things. In fact, the interesting thing is our data set didn't actually have very many pictures of Greg Ratauski. Instead, it was another model that was released by OpenAI, which had much more of his. We don't know because we know what the data set is. That was part of this that introduced the concept of Greg into this model.

• 44:43Kevin Roose?

  I just love that he's like the secret word, the code word that unlocks the really good level of AI art.

• 44:50Speaker 05

  Well, basically prompting is like magic, right? It's like, ah, I've got a spell.

• 44:53Kevin Roose?

  But respond to that because I think this is a valid fear that a lot of working artists have, which is that they are going to be put out of work by people who are essentially using these big AI models to do what they have been doing for a living for years.

• 45:07Speaker 05

  Yeah, I mean, I think this was the concern around photography and a lot of these other kind of tools that came through. And it depends on the definition of what an artist is. And aesthetic thing, someone who's communicating through a community or is it a job? We're an illustrator, et cetera. And these technologies have been improving to display some of these things all over time. The way that kind of I think about it is that, like I said, it's just with a couple of images, you can teach it any style. So even if there was a base model, you bring in a couple of images, it will learn Liberty Gibbet, which is a new artist from Brooklyn, I don't know, whatever. Right? And again, it comes down to the intention of the original user. If they're breaking copyright or something illegal, that is one thing. An ethical and a moral is another thing. Because you can combine Greg with Van Gogh, with Banksy, and it will incorporate all those styles because it understands that.

• 45:52Speaker 05

  But it'll never recreate an image. Because like I said, this is not a compression algorithm. It's a learning algorithm. It's like a human brain, again, which is kind of crazy. However, I do think that we should build systems that listen to the community. So we've been talking to, I think, hundreds of artists. And so one of the things we're doing is trying to figure out attribution mechanisms, opt in and opt out mechanisms as well. Because again, we listen, unlike most others. We don't know what the models and data sets from some of the big companies are that understand these concepts. Whereas ours is open, interpretable. And so, you know, we receive flack, but at the same time, we are listening and building tools around that. And the community is also doing that. So we're supporting community initiatives around this as well.

• 46:29Kevin Roose?

  So you can see a world where like a Greg can just opt out of having his style included in a model?

• 46:35Speaker 05

  Yes.

• 46:36Kevin Roose?

  But isn't that a rule? I mean, isn't your whole pitch that this kind of thing should be open and free? And people should be able to use these giant models to create whatever they want?

• 46:45Speaker 05

  Yeah, the technologies should be open and free, and they should use it ethically per the license. But I mean, we get to decide what we put in our model, right? And so these are open decisions where we're listening to feedback. But then other people can take our code and they can create their own models. It's the thing like it's open infrastructure. So as a company, we decide what we have for the models that we release. But then we also have our implementations of those models. So like our safety filter on Dream Studio is a lot stricter than the safety filter that is in the open model release, because that's the decision we make as a company versus an open source kind of decision. And everyone should have their own one. So for example, you can have an opt out scheme whereby people upload their stars. Again, it's difficult because even if there's a picture in the New York Times that it picks up, it will learn from that star and that little kind of thing.

• 47:31Speaker 05

  And then you can show that to Photoshop and say, this is an opt out list of filters, and they can choose whether or not to use that.

• 47:37Kevin Roose?

  So this message that you're promoting, it's very much oriented around this idea of like, we are the sort of populist alternative to big tech. And on Monday, this big exploratorium party, you gave this talk. You said it was only your ninth talk ever. But you said basically that like, this is the way that we're going to take on big tech to make sure that AI is not in the hands of a few giant corporations. I was standing right next to Sergey Brin from Google when you said that, and he was not happy. He was like, he was like, what? I'm I'm the Panopticon that you're talking about here?

• 48:12Casey Newton?

  Yeah, what did I ever do?

• 48:12Kevin Roose?

  Yeah, what did I do to you, Ahmad?

• 48:16Kevin Roose?

  So that was funny, eh? But I think it's also a very strong pitch right now, because a lot of people are skeptical of big tech. But I also wonder, because now, like, you just raised $101 million. You have venture investors, including some of the same ones who have invested in other big tech companies. You are a big tech company now. I know you only have 100 employees and not, you know, 100,000 or however many Google has. But how do you think you will keep Stability AI from repeating some of the flaws with big tech now that you have all of this money, all these investors, all these expectations around growth?

• 48:56Speaker 05

  It's not easy, eh? We have 100 employees. We have 100,000 people in our community. That's kind of where our strength comes from, diverse across the entire kind of world. We also give them revenue share, which is very weird. And, you know, we give them the upside because we try them as artists effectively. From the perspective of big tech, big topics creating the panopticon because they can't help it because they've got no alternative. We've given that alternative now. So we're actually working with big tech now and giving them an outlet to be part of this thing. We're kind of Switzerland that everyone can kind of participate in as the neutral party. And so we've had amazing talks with all of them because they don't want to head in this direction. They actually want to, and especially the engineers, make things free and open, but

• 49:38Speaker 05

  at the same time, have the regulation, have the trust and safety things. So we're getting guidance and input around that to have a happy medium because it can't be one extreme of pure libertarian and like just release this and let it go. And the other extreme of no one can have anything. It'll be somewhere in the middle. So I think that, you know, these kind of factors we have will help us do that. With our venture investing, we raised it, but we raised it on our terms. So we have complete independence as opposed to, for example, OpenAI. They raised a billion dollars from Microsoft and Microsoft have an exclusive license to their technology. Again, these misaligned incentives are very difficult to combat against. But we hope that the community, our team and our position will help us counterbalance that because also it's good business for us to be in this position because no one else really occupies this position.

• 50:22Casey Newton?

  Yeah, I don't know. There's another world though, where it's like these big corporations for all of their evils, they are also reined in in terms of what they can do because they're so afraid of the PR crises that might result from them releasing an AI that leads to, you know, some sort of bad thing. You're in this position right now that I think is enviable to them where it's like very few people have heard of you yet. And so you have much wider latitude to just kind of do whatever and not be as worried as much about, you know, what a regulator or the public might think.

• 50:50Speaker 05

  Again, we, you know, we're active talks of regulators and the public again is the community and extended. So like, um, we released this and we're like, it's open. And then it got a bit crazy. It's been eight weeks and two days and it's just been insane. And so a thousand projects sprung up and the community is like. Stability, why don't you step in and coordinate this and have an official mouthpiece? We're like, okay, fine. So we went in and, you know, we turned the Reddit into an official Reddit and they're like, how dare you? You know, like corporate overlords. We're like, we're just trying to make things a bit more organized, you know? And then we had to kind of give it back. There's always this kind of push-pull. I think, again, it's community first, but it can't be kind of direct democracy. You know, we're going to make mistakes and we're going to make right things and we're going to put under increasing amounts of scrutiny because what we're doing is actually fundamentally important.

• 51:34Speaker 05

  And so I hope that, you know, we get the right input to do that right, but we might not. As you said, big tech is in this unenvied position because they can't release it for PR things. Because it's like Promethean fire from the gods, right? Again, it's the next generation of communication, which is insane. It can be used to burn things down. It can be used to activate the light of humanity, but the only way we can figure out how to do this best is together. So that's why I want to work with big tech, want to work with little tech, want to work with regulators, want to work with everyone to try and figure out how to do this right.

• 52:03Kevin Roose?

  You invoked sort of this idea that AI is neither good nor bad. It's neutral. It's just a tool. This is like one of the things that I try to push back on really hard whenever it comes up because I think that all technology is infused with values because it is made by humans and humans, including you, have values. So do you really think that AI is neutral, the way that you are building it? Or do you think that people in positions like yours have a responsibility to ensure that their values are reflected in the tools that they're building?

• 52:38Speaker 05

  I would agree with you that it does infuse the values. Again, there are decisions to be made and these are decisions where, again, the buck stops with myself as CEO of Stability on this or with some of the developers and others. And they will be different because you can take a lot of different choices here. What I do think, though, in terms of, as you said, good or bad, is that once the model is out there, it is infused with certain values, but models like this are generalist models, whereas classical models are models that are more specific. There is a bit of a difference there. And I think, again, it's the who is liable for the use of the model? This is a very complicated question that we're trying to figure out now across various jurisdictions and geographies. So I think we need to separate some of these things, but again, we have to realize that nothing springs from.

• 53:22Speaker 05

  Nothing, as it were. There are kind of these biases. There are deliberate decisions that are being made. And again, like I've been putting this out and saying, so this thing, the buck stops with me. I made this decision, you know, or my company made these decisions. And again, we should be held accountable for that.

• 53:36Kevin Roose?

  One interesting difference that occurs to me between generative AI and social media is that in social media, as Casey and others have written about, the market is demanding more involvement. The market doesn't, you know, Parler, Truth Social, Getter, these like free speech apps with, you know, very minimal rules, they're not popular. What is popular is Twitter and Facebook and Instagram and TikTok, all of which have, you know, pretty strict guidelines. In generative AI, what seems to be happening is that the market wants openness. I mean, stable diffusion has, I think, and correct me if I'm wrong, here are more users than Dolly or Mid Journey. Is that right?

• 54:21Speaker 05

  Yeah, it's got more users than both.

• 54:23Kevin Roose?

  So what do you think the difference is there? Why are people in this field looking for sort of a more free for all environment than people on social media?

• 54:32Speaker 05

  So social media, again, you have centralizing AI that directs information from one place to another. And it's been this kind of equalizer in a degree, but it's also been an extender whereby, you know, people such as yourselves have an almost disproportionate platform to influence. And that's how the algorithms are set up. People like easy. Whereas generative media is a more personal experience. There's no social aspect to it. It will be, it's already integrated into TikTok. So they've just introduced their generative thing. They should use stable diffusion instead because it's much better. But, you know, this is the way of things. But this is going to go exponential. And again, it's a very personal thing where you put it out and then you use the social networks to communicate.

• 55:09Kevin Roose?

  Where do you think generative AI goes from here? Beyond photos, beyond videos, beyond the creative professions? Is there a world in which this type of technology changes the law, medicine, science, journalism? I mean, what, where do you think this is headed?

• 55:25Speaker 05

  A hundred percent. It's going to be everywhere. We look ahead 10 to 20 years and you have an AI that learns principles as well as an AI that can search logic. That's insane because it isn't just Geront Media. It's like a reflection of the human brain. And I think, again, this is the most powerful technology in the world, which is why I said, we couldn't trust for it to be just in the hands of a few. And so we were like, let's put it out to the people because it should be for the people by the people where they can create it themselves.

• 55:51Kevin Roose?

  Man, thank you so much for this is really a challenging but a productive conversation and I'm glad you stopped by to talk to us about it.

• 55:58Speaker 05

  Thank you so much for having me on the show.

• 56:15Speaker 03

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner. Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 56:50Speaker 00

  It's time to innovate at the speed of AI without compromising control. That's Outsystems. Take a look at what happened with one global retailer during a period of massive expansion. They used Outsystems to build AI agents to automate invoicing and translation processes, shifting 95% of their daily store operations to a new digital app, saving thousands of hours of manual work. Outsystems gives you the flexibility to deploy how you want and the governance to do it safely. Own your agentic future. Learn more at outsystems.com/NYT. That's outsystems.com/NYT.

• 57:25Speaker 06

  Today's AI builders need infrastructure that scales with their ambitions. iRen is a vertically integrated AI cloud platform, delivering data centers, compute and software for AI training and inference, underpinned by its expansive portfolio of LAN and grid-connected power in renewable-rich regions globally. iRen's AI Cloud gives direct access to Nvidia GPUs, scalable from hundreds to thousands. For builders, that means direct access to AI compute, rapid scaling, and predictable pricing. iRen, AI Cloud for the next big thing.

• 58:04Kevin Roose?

  That's it for Hard Fork this week. Our show is produced by Davis Land, we're edited by Paula Schuman. This episode was fact-checked by Caitlin Love with original music by Dan Powell, Alicia Baty-Tup and Marian Lozano. Our show was engineered by Corey Shreppel. Special thanks to Hannah Ingber, Nell Galogly, Kate LaPresti, Shannon Busta, Mahima Shablani, Jeffrey Miranda, and Mahmoud Felfel from play.h t. You can email us at [email protected]. That's it for this week. See you next time.

• 58:37Speaker 07

  We also don't have to say, "That's all for this week. See you next time" every week. You guys can do whatever you want. I just put that in there.

• 58:43Casey Newton?

  We should have something that feels a little bit less AI. You know, because like an AI would say, "That's all for next week. See you next time."

• 58:51Kevin Roose?

  Yeah.

• 58:51Casey Newton?

  Whereas we might say, "Adios, cowboys. We'll catch you at the range down the road. We'll see you down the dusty trail."

• 59:22Speaker 08

  You can't undo everything in life, but you can undo prediabetes. More than 2 in 5 adults in the US have prediabetes. Most don't know they have it. But with early diagnosis and action, you can delay or even prevent type 2 diabetes. Visit doihaveprediabetes.org to take the one-minute risk test and start undoing the things you can. Brought to you by the Ad Council and the Centers for Disease Control and Prevention.