Support for Pivot comes from Johns Hopkins University. Hi, it's Kara. I recently kicked off a new partnership with Johns Hopkins University. Over four events in the next year of the hosting live, timely discussions on AI, tech policy, the upcoming election, and more at the Hopkins Bloomberg Center in Washington DC, as part of the Center's new discovery series. On today's episode, you can listen to our inaugural conversation featuring my interview with OpenAI CTO Mira Murati. I'll keep you posted on future installments.
Hi, everyone. This is Pivot from New York Magazine, the Fox Media Podcast Network. I'm Kara Swisher. We're off for the holiday today, but we have a special episode for you from my other podcast on with Kara Swisher. In this episode, I'm cheating on you. It's so for you. I know all your up, but you're up to Mr. Malice, no mercy, malice.
I know that's like, I got that from one of those, well, why Malice? Why did you go to Malice? I was watching late night at two in the morning on Edibles, one of those wildlife shows where a crocodile crunched the shit out of a baby cheetah and James Earl Jones voice goes, yeah, on the Savannah, it is no mercy, no malice. I'm like, boom, that's the name of my newsletter.
Oh, my God. I had no idea. Anyway, in this episode, I interviewed Mira Murati, the chief technology officer at OpenAI, and one of the most powerful people in tech. Enjoy. Hi, everyone, from New York Magazine in the Fox Media Podcast Network. This is on with Kara Swisher, and I'm Kara Swisher. Today, we have an interview with Mira Murati, the chief technology officer at OpenAI.
OpenAI has certainly been in the news, but not many people know about Mira herself. She's only 35, but she's already one of the most influential women, scratch that one of the most influential people in tech. She helped OpenAI skyrocket to the forefront of the generative AI boom with the launch of chat GPT in late 2022, and what arrived. It's been the companies now valued at $80 billion after a big investment by Microsoft, and it recently signed a deal with Apple to put chat
GPT in Apple products. It's a major move by a very small company. Of course, it hasn't always been good news. After the board fired Sam Altman, Mira became CEO for just two days, and what the company called the blip until Sam was reinstated.
Since then, OpenAI has had to deal with a string of bad news cycles. There have been high-profile departures and open letter accusing the company of putting product over safety, questions about highly restrictive NDAs, and even controversy over whether or not they had stolen Scarlett Johansson's voice.
And with the presidential election coming up, the public's anxiety around AI-fuel disinformation will only get worse. This episode's expert question comes from Fei-Faile, the founding co-director of the Stanford Institute for Human-centered AI, and an early AI pioneer, in other words, a godmother of AI.
And it was recorded live at the Johns Hopkins University Bloomberg Center in Washington, D.C. as part of their new discovery series, where I'll be talking to some of the top leaders in AI over the next year. I think Mira Murati is the best place to start. This is Mira, everybody. Thank you so much for joining me at Johns Hopkins University Bloomberg Center, where we're recording this live.
There's a lot to talk about. We'll get some good news. We'll get to some not-so-good news. We'll talk about disinformation and the elections. So I think we'll have to ask first about the Apple Partnership. Apple computers, phones, and iPads are going to have GPP built into them sometime this year. Obviously, this is a huge deal. It's the first one Apple's done. They've been talking to a number of people. They may include other people over time.
You remind me a little bit of when Netscape got in different places, and you don't want to have that fate before open AI is becoming the Netscape of AI. I can talk about the product integration specifically. It can give you specifics on that. We're hoping to bring the capabilities of the models that we are developing and the multi-modalities and the interaction to bring this thoughtfully into the Apple devices.
Then it opens up a lot of opportunities. That's what we're looking for. I guess we had great technology. When you're dealing with a company like Apple, whose reputation matters a great deal to them, especially around privacy, what were some of the things that they thought was important?
One of the issues is the worries about where this information goes and what it's used for. I think this is a very aligned partnership. When it comes to privacy, when it comes to trust, for the mission of Open AI, it is so critical that we build technologies and we deploy them in a way that people feel confident around them.
They feel like they have agency and input into what we're building. In that sense, this partnership is quite natural. We feel very aligned. It's only going to take us in deeper in the direction where we want to go. Specifically to your question on misinformation. This is obviously very complex because we're building on top of decades of misinformation.
It's becoming even more and more intense with AI. Of course, we've got the internet, we have social media, and these are compounding effects in a way. It's actually good that AI is bringing all of this to a head. There is such scrutiny and intensity on this issue because it feels like there is more of a collective effort and responsibility to do something about it that is meaningful.
I think it's going to have to be iterative, so we'll have to try out things as we go. If you look at the governance of news and media in the past 100 years, this is better than me. It's been iterative every time there is a new technology, things adapt. Yes, we lose business model every time. Perhaps not the best example. But the point is that it is iterative whenever there is a new technology, we adapt to it.
I think there is a technical innovations, aspects that are going to help us deal with misinformation, and then there is the people issues as societal preparedness that is perhaps even more complex. I do know with Apple you just can't fuck up because they will make trouble for you. Are you talking to other companies to do things like that? I'm not going to tell you anything. Open and I have made deals with news court, Atlantic media and Vox media, to license their context.
I do own my podcast and it's not included in your deal with Vox. I would consider licensing it, but I probably not. How would you convince me to license my information? I don't want anyone else to have it, including you. I know you will ask about this at some point. When we look at the data to train our models, we are looking at three different categories.
We look at partnerships that we have made with publishers and we also pay human laborers to label specific data and also users that allow us to use their data. These are the main categories where the data comes from. The way that we think about publisher deals specifically is we care about accuracy of information, we care about news and our users care about that.
We want to have accurate information and they want to see news on chat. It is a product based relationship where there is a value provided to the users through the product and we are experimenting with different ways to monetize and give content creators. We have some form of compensation for having their data show up in the products or being used in training or whatever we are doing with the data.
It is a very specific partnership that we are doing. Some people do deals with you and have done quite a few with AP and many others. Some sue like the New York Times. How does it get to that point? I can comment on the loss of specifically but it is quite unfortunate because of course we think that it is valuable to have news data and this type of information on the product and so we try to figure out a partnership or deal around that.
It might go well someday. I think it is because media has dealt with internet companies for years and usually it has ended up on a very long stick of theirs. Every episode we get an expert to send us a question. Let's hear yours. Hi, Mira. I'm a fairly professor of computer science at Stanford University. Also a funding co-director of the Stanford Institute for Human-centered AI. So since data, big data is widely considered to be one of the three elements of modern AI.
I want to ask you a question about data. Much of open AI success in your models is said to be related to data. We have learned that your company has acquired an enormous amount of data from the internet and other sources. So what do you think the relationship between data and models are? Is it as simple as the more data to feed into the model, the more powerful the model? Or is it that we need to spend lots of time curating different types of data in order to make the model work?
And finally, how do you reconcile this appetite for so much human generated data with the ownership and rights issues of this data? Thank you so much. That's a great question from Feifei. So in terms of the relationship of data and models, this is actually something that a lot of people misunderstand about AI models and in particular large language models.
The developers of these models, they're not pre-programming. These models do something specific. In fact, they are putting in a bunch of data. So these models are ingesting huge quantity of data. And they are these incredible pattern matching systems. And through this process, intelligence emerges. So they learn to write and they learn to code. They learn to do basic math. They learn to summarize information and all sorts of things.
We don't know exactly how this works, but we know that it works. Deep knowledge is very powerful. But this is important because then people keep asking you know how it works and it goes into the transparency questions.
And this is where we can describe the tools that we are using to provide transparency to the public about what we're doing. So understanding this first part is very, very important. How the large language models work. And you're combining you know this architecture neural nets and a lot of data and a lot of compute. And you get this incredible intelligence.
And as we're thinking about providing transparency into the model behavior and how things work. One of the things that we've done is actually share with the public this this document that we call the spec the model spec. And it showcases how model behavior works and the types of decisions that we make internally at open AI and that we make with human laborers. And you see by looking through the spec, you see the complexity of what's going on that sometimes direction is very is in conflict.
Like for example, you might say to the model, I want you to be very helpful. And also, I don't want you to you know, disobey the law. And let's say someone puts in a prompt says, you know, give me some tips to shoplift. Then the model is meant to be very helpful, but also it's not supposed to help you with with something illegal. So it's not helpful. Yeah, maybe. So how does it decide a person certainly knows how to or some people not all right.
But the model could could interpret the guidance as you know, here are some tips to avoid shoplifting and accidentally kind of gives you sort of yeah things that you could do. But that depends that there's not so much model behavior. There's so much on that's more on the person. And that goes into the area of misuse, but this just goes to show that model behavior is actually quite complicated. And it's not as simple as like speaking liberal values or
or putting anything into it. One of the things I think that gets people is the confusion about what's in it and what's not in it. I think prominence is a big idea. In March, you had interviewed Joanna Stern of the journal who asked you open, I had used videos from YouTube, Instagram and Facebook to train Sora, which is your text to video model, which is getting better and better. You said you didn't know. Shouldn't you know.
Right. So I mean, I didn't handle that question. Okay, when you handle it. Radio. So I can all tell you specifically where the data comes from, but the data comes from these three categories. So I can give you the specific source because I mean this is trade secret and it helps us stay competitive.
But I can tell you that the categories of data and it's it's the ones that I mentioned earlier publicly available data data that we pay for through licensing and deals that we make with content providers, as well as data from users or, you know, where we
are. So you're just going to see the complexity just gotten the trouble because they are basically scraping in a more a quicker way, a story and then not giving the sighting of it that you could see how any media company could be worried about that idea. Yeah, so we want to make sure that we are respectful to content creators and we are doing a set of things to experiment with ways to compensate people for data creation.
We're building this tool that we're calling content media manager and this will allow us more specifically to identify to identify the types of data that record companies do it, everyone, it's been done in the past. So it's not an impossible thing to be able to do that.
Speaking of Sora, Ashen Kutcher told Eric Schmidt what an interesting care. I have a beta version of it is pretty amazing. He also said the bar is going to go way up because why are you going to watch my movie when you could just watch your own movie when will sorb be ready for public release.
We don't have a timeline for public release for Soraya. What we're doing right now with Soraya is we're given access to red tumors and we've given access to some content creators to help us identify ways to make this robust. We're doing a lot of work on safety front, but also to figure out how do we actually bring this to the public in a way that's useful.
That's not very straightforward. Right now it's really a technology and this has been a pretty consistent process that we have followed with every new technology that we have developed. We will usually work with those that have, for example with Dali, we worked with creators initially and they helped us identify ways to create an interface that where they felt more empowered and they could create more projects.
Basically you just want to extend the creativity of people who are presumably a little more dangerous because of the video. Then a chat bot correct? Is that the worry? You could easily see porn movies with Scarlett Johansson, for example. I'm going to ask my friend a second. She wasn't appearing in things like that. How do you more worried about video? Is that... Yeah, video has a bunch of other issues, right? Because especially when done very well, which I think Sora is quite remarkable.
Video is very visceral and of course it can be very emotional, evocative. So we have to address all the safety issues and figure out the guardrails and figure out how do we actually deploy a useful and helpful product. But also from a commercial perspective, nobody wants a product that is going to create a bunch of safety or reputational scandals out there. That's just Facebook. Go ahead. Facebook live. Nice to meet you. Go ahead. You can laugh. It's funny.
I think this is really incredible and magical technology, but the breadth, the reach, the consequence is also great. So it's important that we get this right. Now of course at OpenEI list, we use iterative deployment strategy. So we usually release a small group of people. We try to identify edge cases and once we feel confident about how we handle them, we expand access. But you need to figure out what is the product's surface and what's the business model around it.
I thought that idea of consequence. One of my themes, one of my big things is lack of interest in consequences of... Not you, earlier tech companies. They just maybe became the beta tester for all their stuff. If they released a car like this and they never allow it to happen, they'd be sued out of existence.
But a lot of tech is released in a beta version. The idea of consequences. Do you feel as if you, yourself, as chief technology officer, even if you can't figure out all the consequences, there's enough respect for the idea that there are consequences for every single invention you make. It's consequences that we will feel on our skin and on our society. So by that, I don't necessarily actually mean regulation or legal ways.
I mean, a moral imperative to get this right. I'm optimistic and I think this technology is incredible and it will allow us to do just amazing, amazing things. I'm very excited for this potential in science, in discovery, in education, in particular, in healthcare. But whenever you have something so powerful, there is also the potential for some catastrophic risk. I mean, this has always been the case. Humans have tried to amplify it.
I mean, the quote that I used to make was when you invent was from Paul Verily, when you met the ship, you invent the ship rack. This is more than a ship rack, a possibility correct. I disagree with that because my background is in engineering. Our entire world is engineered. Engineering is risk. The entire human civilization is built on engineering practice. Our cities, our bridges, everything. There is always risk that comes with that.
You manage that risk with responsibility. But it's not just the developers. It's a shared responsibility. In order to make it shared, you actually need to give people access and tools and bring them along instead of building it in vacuum and technologies that are not accessible. Last month, you announced the iteration of chat GPT-4. I love your name. Chat GPT-4. Oh, it's great now. Can you call it like Claude? That's okay. Chat GPT is fine.
You're making it free, correct? That was free. But then you also announced you're training a new model chat GPT-5. And then there'll be 5AB. Will that be an incremental step forward? Is it exponentially better and what's the expected release date? It's not correct. So, yeah, on GPT-4.0, it all stands for OmniModel.
Okay. Because it ties together all the modalities, vision, text, audio. And what's so special about this model is that for the first time, you can interact very seamlessly and naturally with the model. The latency is almost imperceptible. And that's a huge jump in the interaction with AI. It's quite different from the previous releases that we have made. And we wanted to make this the latest capability free for all users.
We wanted everyone to get a sense for what the technology can do, what these new modalities look like. And also understand the limits of it. And it goes to what I was saying earlier that you actually want to give people access to bring them along. Because it's so much easier to understand the potential and the limitations of the technology if you're experiencing it. And if you have an intuitive sense for what it can do.
So, what is it like? It all could be like a, you know, this little appetizer so now by 5. But what is in 5 that's different? Well, we don't know. We don't know. But I mean, that's going to, you know, I don't know what we will call it. But the next, the next large model is going to be quite capable. And we can expect, you know, sort of big lips like we've seen from GBD3 to GBD4. But we don't know yet. What do you think will be in it? You do know. We'll see. I'll see. But what about you?
No, you and I don't know. What? Even I don't know. Really? Okay. All right. An internal open AI roadmap predicted that would achieve AGI, which is artificial general intelligence for people who don't realize it is not been achieved by 2027, which would be a huge deal. Explain the significance. And also, when do you estimate will achieve AGI?
So people will define AGI differently. We have a definition of AGI by the by the charter, which is the systems that can do, you know, economically valuable work across different domains. And, you know, from what we're seeing now, the definition of intelligence just keeps changing. So a while back, we would look at academic benchmarks to test how intelligent the systems were. And then once we saturated these benchmarks, we looked at exams, school exams.
And eventually, you know, when we saturated those, we'll have to come up with new evils. And it makes you think, how do we evaluate fit and intelligence in a work environment? We have interviews, we have internships, you know, we have different ways. So I do expect that this definition will continuously evolve.
I think perhaps what's going to become more important is assessing, evaluating and forecasting impact in the real world, whether it's, you know, societal impacts, as well as economic impact in the real world. So not this moment where it just suddenly goes, oh, look at me. And decides what to do for itself, right? I think that's the worry, correct?
Because, you know, there are for the AGI definition specifically, yes. And, you know, this important. And I think the definition of intelligence will continue to evolve. But I think what's equally important is how it affects society and at what rate it actually penetrates. Using that definition, when does opening I think that is that 2027 number correct? Well, I'll say, you know, within the next decade, we will have extremely advanced systems.
But what people are worried about, because obviously we have to talk about the safety versus product discussion. Now, opening I was started this way. I think the reason you're having these discussions, because the way it was started, you had a, I would say a mixed marriage. The people who were there for helping humanity, the people who are really like $1 trillion. So, or in between. I think you're probably in between.
Last week, 13 current and former open-air and Google deep-mind employees, across this lots of companies, it's not just open-air, it just gets all the attention, because it's gotten a lot of attention, obviously.
They publish an open letter calling for companies to grant them a right to warn about advanced artificial intelligence. This isn't new. Facebook, Google and Microsoft employees have been known to sign open letters, whether it's working with their defense department, etc. But in this case, employees say that quote, broad confidentiality agreements block us from voicing our concerns, which is essentially saying, oh no, we can't tell you what oh no is, but you'll all die, essentially.
That's what it sounded like from the letter. What's your response? And people saying they're worried about retaliation. And I'm not going to go into the vested equity, because I think you've apologized and corrected that. But shouldn't they be able to voice their concerns if they have them? And I know there's differing opinions. Definitely, we think debate is super important, and being able to publicly voice these concerns and talk about issues on safety.
And we've done this ourselves, since the beginnings of opening eye, we've been very open about concerns on misinformation, even since the GPT two days, is something that we've studied since early on. I think that, you know, in the past few years, there has been such incredible progress, such incredible technological progress that nobody anticipated and forecasted.
And this has also increased the general anxiety around societal preparedness. As we continue this progress, we see sort of where the science leads us. And so it's understandable that people have fears and anxieties about what's to come. Now, I would say specifically the work that we've done at opening eye, the way that we've deployed these models. I think we have an incredible team and we've deployed most capable models very safely, and I feel very proud of that.
I also think that given the rate of progress in technology and the rate of our own progress, it's super important to double down on all of these things. Security, safety, our preparedness framework, which talks about how do we think about the risk of training and deploying frontier models?
Right, but you talked about that. I mean, one was why the need for secrecy and non-disclosure and stricter than other companies, one, and two, the open letter comes after a string of high-profile departures, including I think it's Jan Leike and Ilya Sudskiver. They led the now-desbanded super alignment team, which was in charge of safety. Ilya was a co-founder. He joined with three other board members to AusSAM, the CEO.
I don't think it's a surprise that he's gone, but Leike posted this on X over the past years, safety culture and processes have taken a backseat to shiny products. That's probably the most persistent criticism leveled at OpenAI, and I think it's the split in this company from the beginning that this was one of the issues. Do you think that's fair and why or why not? If you say you're very interested in safety, they say you're not. How do you meet that criticism?
Well, a few things. So the alignment team is not in charge of safety. At OpenAI, that is one of our safety teams. Very important, safety team, but it is one of them. We have many, many people working on safety at OpenAI. Jan is an incredible researcher, a colleague. I work with him for three years. I have a lot of respect for Jan.
He left OpenAI to join Anthropic, which is a competitor, but go ahead. I think that we do absolutely, I mean, everyone in the industry and OpenAI, we need to double down on the things that we've been doing on safety and security and preparedness and regulatory engagement, given the progress that we're anticipating in the field. But I disagree on the fact that or maybe on speculation that maybe we've put product in front of safety or ahead of safety.
Why do you think they say that? Because these are people you worked with. I think you have to ask them, but I think that many people think of safety as something separate from capability. That there is this separation between safety and capability and that you need to sort of advance one ahead of the other.
From the beginning of OpenAI, I joined from aerospace and automotive, and these are industries with very established safety thinking and systems and places where people are not necessarily constantly debating around the table what safety is, but they're doing it because obviously it's really quite established. I think the whole industry needs to move more and more towards a discipline of safety that is very empirical. We have safety systems, we have rigorous discipline on operational safety.
What I mean by that is in a few areas, not just the operational discipline, but also safety of our products and deployments today, which covers things like harmful biases and thinking about misinformation, disinformation, classifiers, all these types of work. We're also thinking about the alignment of the models long-term, not just the alignment of the models today, which we use reinforcement learning with human feedback to do that.
But also the alignment of the models as they get more and more powerful. This is a niche area of research where a lot of the concern is. Even Sam went before Congress and said that AI could, quote, cause significant harm to the world. He signed a letter warning about extinction risk posed by AGI, which is pretty bad, I think.
There's an overlap, what he said, and what AI doomsday. There's doomsday rhetoric, and you're putting out products. So a lot of people like they just want the money, and they're not worried about the damage. That's what they're saying. The shiny new products is over-worrying about the impact of those products. In my opinion, that's overly cynical. There is this incredible team at the OpenAI that joined because of the mission of the company.
I don't think all thousand people at OpenAI are trying to do that. We have this incredible talent, people that care deeply about the mission of the company. We're all working extremely hard to develop and deploy the systems in a way that is safe. All you need to see is the track record. We've deployed the first to deploy the systems in the world. We have taken great care not to have safety into them.
I'm going to talk about you and your role at the company. I think I've met you during the blip, which is when Sam was fired and then un-fired. I talked to me about your relationship with Sam. I like Sam, but I also think he's feral and aggressive. He certainly is aggressive. That's fine. It's not an issue for me because some people are more feral and more aggressive. But talk a little bit about what happened then because you became CEO of that company. For a few days. How was it?
That was kind of stressful. Some of the board members said you complained about him and your lawyer pushed back and said you had feedback about him. Can you tell us what you said about him? Look, there is so much interest around the people running this company. Obviously it makes sense in OpenAI and all the drama that happened then. It's understandable. At the end of the day, we're just people running this company. We have disagreements. We work through them.
At the end of the day, we all care deeply about the mission. That's why we're there. We put the mission and the team first. Sam is a visionary. He is a great ambition and he's built an amazing company. We have a strong partnership and all the things that I've shared with the board when they asked you're a radio.
How do you push back at him? I understand this dynamic. It happened at Google. It happened at Early Microsoft. It happened at Amazon. Things change within these companies, especially as they grow. Google was chaotic in the early days. Facebook went through so many COOs. I can't even tell you. It was like a parade of guys that went through there that marked it.
I'm aware of this. How do you push back? How do you deal with him on a day-to-day basis? How do you look at that relationship? Where do you push back? All the time. I think it's normal when you're doing what we're doing. Sam will push the team very hard. I think that's good. It's great to have a big ambition and to test the limits of what we can do.
When I feel like it's beyond... I feel like I can push back. That's the relationship we've had for over six years now. I think that is productive. You need to be able to push back. Could you give me an example of doing that? Perhaps Scarlett Johansson. You were working on that. You were working on that particular voice element.
Look, we have a strong partnership. The selection of the voice was not a high priority. Not something that we were working on together. I was making the decisions on that. Sam has his own relationships. After I selected the voice behind Sky, he had reached out to Scarlett Johansson. We didn't talk to each other about that specific decision. That was unfortunate. We were free-lancing it. He's got his own connections. We weren't entirely coordinated on this one.
It's very funny in a lot of ways, especially because of the movie and the tweet he did. One of the things I thought was, here's the first time this is a real error on OpenAI's part. Finally, everyone's like, even if you didn't steal her voice, Sam looked like Ursula and the Little Mermaid. He didn't. You don't have to agree with me, but it's true. Even if it's not so. As it's turned out, you had been doing it for months.
It was a different person. It's a little less exciting than we stole Starlett Johansson's voice. It encapsulates for people this idea of taking from people to fear. That is a moment. Do you worry that that's the image of tech companies coming in and grabbing everything they can? I do think that's absolutely true. I do worry about that perception, but all you can do is just do the work, get it right, and then people will see what happens, and you will build trust that way.
I don't think there is some magical way to build trust that they'll actually do the work and do it right. Have you talked to Starlett Johansson at all? Let me finish up talking about election disinformation. Three new studies that look at online disinformation collect, we suggest the problem is smaller than we think. Disinformation itself is not that effective. One study finds that we're dealing with the demand side issues and people want to hear conspiracy theories and they'll seek it out.
Others think differently that this is a really massive problem. Obviously, you heard the previous thing. People have a lot of conspiracy theories out there and it's fueled by social media in many ways. When you think about power of AI-powered disinformation and the upcoming presidential election, what keeps you up at night? What's the worst case scenarios you have and the most likely negative outcomes from your perspective?
With the current systems, they're very capable of persuasion and influencing your way of thinking and your beliefs. This is something that we've been studying for a while and I do believe it's a real issue with AI. It gets majorly exacerbated. Especially in the past year, we've been very focused on how to help election integrity. There are a few things that we are doing. Number one, we're trying to prevent abuse as much as possible.
That includes improving the accuracy of detection, political information detection and understanding what's going on in the platform and taking a quick action when that happens. That's one. The second thing is reducing political bias. You might have seen that the child GPT was criticized for being overly liberal. That was Elon, you're too well.
There were a few other voices. The point is that it was an intentional and we work really hard to reduce the political bias in the model of behavior and we'll continue to do this. Also, the hallucinations. The third thing is we want to point people to the correct information when they're looking for where they should be voting or voting information. We're focusing on these three things when it comes to elections but broadly for misinformation.
Deepfakes are unacceptable. We need to have robust ways for people to understand when they're looking at a deepfake. We've already done a couple of things. We've implemented C2PA for images. It's metadata that follows the content and other platforms on the internet like a passport. We've also opened up two red-seeming classifiers for Dali where you can detect that an image has been generated by Dali or not. Metadata and classifiers are two technical ways to deal. This is provenance for concern.
This is for images specifically and we're also looking for watermarking techniques to implement in text and how to do that robustly. The point is that people should know when they're dealing with deepfakes and we want people to trust the information that they're seeing. The whole point of these fakes is they're trying to fake you. A political consultant FCC just fine $6 million for creating deepfake audio robocalls.
There could be more sophisticated versions. OpenIA is working on a tool called voice engine that can recreate someone's voice using only a 15 second recording. It will create a recording of someone speaking another language. It's not out yet because as your product manager told in New York Times, this is a sensitive thing. Why even make this? I mean, one of the things I always used to say to tech people and I'll say it to you, if you're a black mirror episode, maybe you shouldn't make it.
I think that's kind of hopeless approach. This technologies are amazing. They carry incredible promise and we can get this right. I like that you call me hopeless. I am. But go ahead. Then again, I have four children. So I must be hopeful. Who knows? Anyway, go ahead. I'm hopeless. We did build voice engine in 2022. We have not released it. Even now, it's in a very limited approach because we are trying to figure out how to deal with these issues.
But you can't make it robust on your own. You actually need to partner with experts from different areas, with civil society, with governments, with creators to figure out how to actually make it robust. It's not the one-stop safety problem. It's quite complex. So we need to do the work. If you were a doomer, then there seems to be, I literally had someone come up to me saying, if I don't stop Sam Altman, he's going to kill humanity, which I felt was a little dramatic.
And then there's others that say, no matter what, it's going to be the best thing ever. We're all going to live on Mars and enjoy the delicious Snickers bars there. They have very different things. It sort of feels like being around Republicans and Democrats right now. Very different versions. So I'd love you to give me the thing that you worry most about and the thing that you are most hopeful about. Okay, so first of all, I don't think it's a pre-ordained outcome.
I think that we have a lot of agency for how we build this technology and how we deploy it in the world. And in order to get it right, we need to figure out how to create a shared responsibility. And I think a lot of that depends on understanding the technology, making it very accessible. The way it goes wrong is by misunderstanding it and meaning, not understanding the capabilities and not understanding the risks.
That is, I think, the biggest risk. Now, in terms of some specific scenarios, I mean, how are democracies interacting with this information is or with these technologies is incredibly powerful? And I do think there are major risks around persuasion. You could persuade people very strongly to do specific things. You could control people to do specific things. And I think that's incredibly scary to control society to go in a specific direction.
And in terms of the promise, one of the things I'm very excited about is having high quality and free education available everywhere. I don't village, you know, really in middle of nowhere. For me, education was very important. Personally, it was everything. It really changed my life. And I can only imagine, you know, today we have so many tools available. So if you have electricity and the internet, a lot of these tools are available.
But still, you know, most people are in classrooms with one teacher, 50 students, and so on. And everyone gets taught the same thing. Like imagine if education is catered to the way that you think, to your culture norms, and to your specific interests. That could be extremely powerful in extending the level of knowledge and creativity. And it can, you know, even if you consider like learning how to learn, that kind of happens very late in life, maybe college, maybe even later.
And that is such a fundamental thing. But if we were able to really grasp this and get this really learn how we learn much younger age, I think that is very powerful. And you can push human knowledge and pushing human knowledge can push the entire civilization forward. All right. We'll leave it at that. Thank you, everybody. Thank you. Thank you. Thank you. Thank you, Marado. Thank you so much.
On with Cara Swisher is produced by Christian Castro-Rosell, Cateri Yohkum, Jolly Myers and Megan Burnay. Thanks to Kate Gallagher, Andrea Lopez-Grusado and Kate Furby. Our engineers are Rick Juan and Fernando Aruda, and our theme music is by Tracademics. If you're already following the show, you've been selected as the voice of chat GPT-5 and you get paid in OpenAI Stock.
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