Close All Tabs - Beyond the AI Hype Machine

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Starting point is 00:00:25 Ugh, you also having trouble with scammers trying to poke holes in your dam? We need a phone plan that stops these pensions. at the perimeter. That's why I switched to Google File Wireless, a wireless plan built with industry-leading security. Google AI helps block pesky scammers so my info stays secure, and best of all unlimited plans start at just $35 a month. Whatever you do, your sake with Google. Explore Google File Wireless plans today. Plus taxes and government fees, block spam known to Google may not detect all spam calls. From KQED. A few weeks ago, OpenAI launched an app, SORA. It's a vertical video social platform, similar to TikTok, except all the videos are generated by the company's AI image generator, SORA 2.

Starting point is 00:01:10 Within days, the app was a copyright infringement nightmare. There were videos of SpongeBob cooking meth, unsanctioned Rick and Morty ads for crypto startups, and many, many videos of OpenAI CEO, Sam Altman, doing depraved things to copyrighted characters. Like, the one where he brutally barbecues and carves up Pikachu. Pikachu on the grill here. It's already got a beautiful char and it smells like somebody plugged in a chicken. Let's give it a flip. I'm going to carve it into some thick steaks.

Starting point is 00:01:41 Look at that. Cross on the outside, pink and juicy in the middle. Cheers. All of these 10-second videos require an immense amount of computing power, which is extremely costly to maintain. In a blog post, Sam Altman admitted that the company still needs to figure out how to make money off of SORA. He wrote, People are generating much more than we expected per user, and a lot of videos are being generated for very small audiences.

Starting point is 00:02:06 Facing heat from copyright holders like Disney and Nintendo, Altman also announced extra guardrails for the app to curb infringement. Now, users are complaining that everything they try to generate using SORA2 gets flagged as a violation of the copyrighted content policy. They're already getting bored of the app. This whole cycle has been described as the AI hype machine. Big investments are made based on big promises of innovation, disruption, revolution. This hype fuels more investment, which in turn fuels the hype. The cycle continues when a new product launches. For instance, last month, Meta launched its own AI social video app called Fives, but it was quickly forgotten when SORA launched.

Starting point is 00:02:53 AI hype is effectively premised on fear of missing out. It is the fear that if you don't get onto this new technology, you are going to be left behind. That's Alex Hanna, a sociologist and the director of research at the Distributed AI Research Institute. She says this fear of missing out is about a lot more than just funny videos. If you're a corporate manager, you're going to have your competitors, just leave you in the dust.

Starting point is 00:03:22 If you're a teacher, you're doing a disservice to your students by not preparing them for the job market of the future. If you're a student, you're going to miss out on all the skills and all your classmates are going to be outperforming you. And as a worker, you're going to be doing things the old way, the analog way, and everyone's going to be outpacing you. Alex and her co-author, Emily M. Bender, recently published a book, The AI Con, How to Fight Big Tech's hype and Create the Future We Want. Emily runs the computational linguistics program at the University of Washington. This is a field of study that combines human language with machine learning. I often get asked the question, aren't you worried that students are going to get left behind, etc. And my answer to that is often, where is everybody going?

Starting point is 00:04:11 Like this metaphor of left behind suggests that people are running off into some brilliant future. I just don't see it, you know, setting aside the fact that the technology doesn't do what it's being sold to do, that is overhyped and overpomised. The idea that we'd be better off with, instead of interacting with people at all stages, interacting the screens, that's just not the future that I want. In this episode, we're talking about the AI hype machine. When it started, how it's fed, and why a growing corner of critics say they see right through it. This is Close All Taps.

Starting point is 00:04:45 I'm Morgan Sung, tech journalist, and your chronically online friend here to open as many browser tabs as it takes to help you understand how the digital world affects our real lives. Let's get into it. All right, like we always do, we're starting by opening a new tab. What is P. Doom? In their book, The AICon, Alex and Emily talk about these two groups. There are the AI boosters, the people who are optimistic that AI will pave the way to our utopian future. Then there are the AI Dumeers, the people who catastrophes, and believe that AI progress will usher in an era of societal collapse and human extinction. It's very Matrix.

Starting point is 00:05:42 The Matrix is a system, Neil. That system is our enemy. But before we break this down further, let's start by defining our terms. Here's Emily. So artificial intelligence does not refer to a coherent set of technologies, and it has throughout its history, since it was coined by John McCarthy in 1955, basically been used to sell this idea of some magic do-everything technology in order to get money. Initially, it was research funding and then DOD money, and now a lot of it is venture capital money. Yeah, and the way that this has

Starting point is 00:06:18 proliferated in the modern day is that so many things get called AI, so that could be automated decision-making systems used for determining whether someone gets so social services. And so that gets looped in. And then we also get recommendation systems, things like the TikTok algorithm, the Instagram Reels algorithm, pick your short form video. But then it's really manifest in these large language models and diffusion models that are looped into the category of generative AI. You start this book from this one moment in 2023 when Chuck Schumer at the time, the Senate Majority Leader, held a series of forums around AI. Can you take you? Take us back to that moment and set the scene for us.

Starting point is 00:07:04 So, late 23, Takamur is convening the eighth of a total nine Senate Insight forums around AI. And he asked, folks, this is very weird. He asked, what is folks' probability of doom? And this is abbreviated as P. Doom. And for this is an audio platform that is P, open parentheses, doom, close, parentheses. And you also ask what people's P hope is. And so this is a, this means what is your probability that there's going to be some kind of a doom scenario, uh, and which through, you know, hook or crook, um, some kind of thing called AI is going to outperform or, uh, outsmart humans

Starting point is 00:07:51 and take over and lead to a human extinction. And in the book we start and we say, well, this is the wrong question. But also, if you're looking at harms that are happening in the here and now, there are many that exist, whether that be deep fake porn, being made out of non-consensual adults and children, the use of automated decision-making and weapons targeting, especially in Gaza. And then we also talk about students having their exams effectively being judged by these automated tools. So talking about P-Doom in this register is asking the wrong question and focusing on the wrong things. Oftentimes it looks like the Dumer is the people with a high P-Doom value, the people who take that question seriously in the first place. And the boosters, the people who say this is going to solve all our problems are like the opposite ends of a spectrum. And that is how these people present themselves.

Starting point is 00:08:53 It is how the media often presents what's going on. And it is very misleading. I think that one of the points that we make is that that dumerism is another kind of AI hype because it's saying our system is very powerful. It's so powerful it's going to kill us all as a way of saying it's very powerful. But also we make the point that the Dumers and the boosters are two sides of the same coin. And it I think becomes very clear if you look at it this way, which is to say the Dumers say AI is a thing, it's imminent, it's inevitable, and it's going to kill us all. And the boosters say AI is a thing, it's imminent, it's inevitable, and it's going to solve all of our problems. And it's pretty easy to

Starting point is 00:09:27 see, these are the same position with just a different twist at the end. And the funny thing about this boosterism, dumerism, dichotomy is that these are many of the same people or they run in many of the same circles. So, you know, there was this document that was put out called AI 2027 in which it ends with humanity dying in the kind of choose your own adventure. There's only two endings here. They choose your own adventure. And one of them, you know, everyone dies. But the lead author of this works at Open AI. And there's many such cases of people who are working on quote unquote AI alignment who are in these industries. So it's again not as if they're against the building of AI or we should just say no. It's actually a very narrow segment of people.

Starting point is 00:10:19 You described this industry as the modern AI hype machine. What does it look like? I mean, who are the players here? Yeah, I mean, the players are many of the big tech players that we know, so Microsoft, Google, Amazon, meta, but with some new entrance, Open AI being the most significant one, and along with Open AI, a few offshoots, so Anthropic is kind of the most notable one. And then the company that's creating the shovels for the Gold Brush, so that's your NVIDIA, and then your Taiwanese semi-conductor manufacturing company reviewed as T-A. as MC. I want to say that we see AI hype not just originating from those big players. That is a large source of it. Also, we hear over and over and over again about people working in various businesses

Starting point is 00:11:06 being told by their higher-ups that they have to try this new AI thing. And so there's this sort of secondary promulgation of hype that comes from mental management and up that have been sold on the idea that this is going to really increase productivity. And on the one hand, it's a very useful excuse for doing layoffs that they may have other. I already wanted to do. But then on the other hand, some people seem to have really bought into the idea. So they tell that people working for them, you have to spend time figuring out how to make yourself more productive by using these so-called AI tools, because everyone's telling me that that's the way of the future. I mean, the obvious way people are, these players are feeding into the

Starting point is 00:11:47 AI hype machine is by extolling the virtues of AI or, you know, kind of spreading this very dumerous sci-fi rhetoric. But what other strategies are being used to feed this machine? So one important strategy is what I sometimes call citations to the future. So people will say, yeah, yeah, it's got problems now, but it's going to do all of these things. And I think it really is the only technology that we are expected to evaluate based on promises of what it will be doing, right? That car that I just bought only gets, you know, 35 miles to the gallon.

Starting point is 00:12:22 But that's okay because the later one's going to get 50. We don't talk about it that way, except with the so-called AI technology. So Citations to the Future is one big strategy, and another one is anthropomorphizing language. Talking about things that have happened as if the computer systems themselves did it of their own volition and autonomously, instead of people having used the system to do it or done something in order to build the system. So it'll be something like AI needs lots and lots of data. Well, no. People who want to build the system that they're calling AI are amassing lots and lots of data in order to build them.

Starting point is 00:12:57 Or AI is thirsty. It needs lots of water. Or AI was able to identify something in a blurry image. It's like in no sense, right? People used XYZ tool in order to do a thing. Or in order to build these tools, they are using lots of water and so on. So this anthropomorphizing language sort of shifts the people out of the frame and hides a bunch of accountability. And at the same time, makes the systems. sound cooler than they are.

Starting point is 00:13:24 Alex and Demily also pointed out that players in the AI industry push this adoption of AI into our everyday lives by really trying to humanize the product. We're going to dive into that in a new tab. First, a quick break. Support for KQED podcasts comes from Star One Credit Union. Give your savings account the love it deserves. When you keep your money with Star One, you keep more of your money. Star One Credit Union, in your best.

Starting point is 00:13:58 interest. So good, so good, so good. Everything you want for summer is at Nordstrom Rack stores now and up to 60% off. Stock up and save on the brands you love like Vince, Sam Edelman, frame, and free people. Join the Norty Club to unlock exclusive discounts, shop new arrivals first, and more. Plus, buy online and pick up at your favorite rack store for free. Great brands, great prices. That's why you rack. Time for a new tab. Are we really Just Neat Machines. Let's talk about the technology itself, like the way people talk about large language models as AI, chat GPT, Claude, GROC, many people understand that these models are basically predicting the words that most often go together. But can you break it down further?

Starting point is 00:14:51 Like, what's really going on under the hood there? So the first very important lesson is that when we say word, we're actually talking about two things. We're talking about the way the word is spelled and pronounced and what it is used to mean. And one thing that makes that hard to keep in mind is that as proficient speakers of the languages we speak, pretty much anytime we encounter the spelling or sound of a word, we are also encountering what the person using it is using it to talk about. And so we always experience the form and meaning together. But a language model, so the core component of something like Gemini or GROC or Claude or ChatGPT is literally a system for modeling which bits of words go with which other bits of words in whatever the input collection of text was

Starting point is 00:15:37 to create that model. And so what we have are models that are very good at putting literally like spellings of parts of words next to each other in a way that looks like something somebody might say. Emily and Alex have come up with a few phrases that illustrate what large language models really are, which also describe the limitations of this tech. We've got synthetic tech, text extruding machine. The choice of the word extrude is very intentional because it's a little gross. Racist pile of linear algebra. Spicy autocomplete. And one phrase that really took off. Stochastic, parrot. Emily coined the phrase in a research paper she co-authored in 2020. Parrots can mimic human speech, but whether they can really comprehend it, that's dubious.

Starting point is 00:16:29 Stochastic comes from probability theory. It means randomly determined. So a statistical. A stochastic. Stochastic parrot essentially mimics language, in a random order, and does so convincingly. But it doesn't understand it. Starting with OpenAI's GPT2 and GPT3, they were using it to create synthetic text. And so one of the things we worried about in that paper is what happens if someone comes across synthetic text and doesn't know that it was synthetic? What we didn't realize at the time is that people would be happy to look at synthetic text while knowing that it's synthetic, That is very surprising to me. And so the phrase stochastic parrots was this attempt to make vivid what's going on,

Starting point is 00:17:14 to help people understand why the output of a language model run to repeatedly answer the question, what's a likely next word, is not the same thing as text produced by a person or group of people with something to communicate. And what's happened, it's been fascinating as a linguist to watch that phrase go out into the world. So for the first little while, it was people referring to the paper. And then it sort of became people talking about that claim that large language models are not understanding. They're just repeatedly predicting a likely next word. And then it got picked up or interpreted as an insult, which is surprising to me.

Starting point is 00:17:56 Because in order for it to be an insult, the thing that it's being applied to would have to be the kind of thing that could be insulted. Then, in 2022, Sam Altman tweeted, I am a stochastic parrot, and so are you. So I think what happens when Sam Altman picks it up and tweets that is that it is, on the one hand, sort of an attempt to reclaim what is understood as an insult or slur by people in that mindset. But also, and very importantly, it is about minimizing what it is to be human so that he can claim that the system that he's built is as good as a person. Emily and Alex say this concept of comparing humans to essentially flesh machines is a classic move in the AI hype machine playbook. It's reducing humanity and what it means to be human to programming, like Eliza in the 60s. Eliza was an early natural language processing program designed to mimic a therapist.

Starting point is 00:18:56 Think of it as a great, great, great, great, grand chat bot of chat GPT. A lot of people, from academics to government leaders to tech industry giants, bought into the Eliza hype. And that freaked out Eliza's own creator, Joseph Weisenbaum. In a book he published in the 70s, Weizenbaum warned that machines would never be able to make the same decisions that humans make because they don't have human empathy. His criticism of AI caused a stir in the research community.

Starting point is 00:19:26 And decades later, AI boosters are still making that same claim. that humans and machines aren't that different. But what does this devaluing of humanity really mean for us? Yeah, I mean, it means a lot of things. It really seems to emphasize that there is aspects of human behavior that can just be reduced to our observable outputs, right? Humans are just things that output language or output actions. When that's not true, humans have a much more vivid internal life. we think about others, we think about kind of co-presence. But it's more about saying how we're

Starting point is 00:20:07 comparing ourselves to machines that are programmed by people, and those people and those institutions have particular types of incentives to make machines that behave as such. So that's the kind of implications that it has, and it also has the implications of other kinds of moves into humanism, dehumanization and what that does and how we treat people in with regards to dignity and propriety and rights. Can you also give any concrete examples of where we see this kind of devaluing of humans? So I think if we say that humans can be reduced to their outputs, that that leads to lots of problems. And one is we end up saying, you know, the form of, or the words that teachers and students say in the classroom is the learning situation. And so we can replace the teacher with a system for outputting words.

Starting point is 00:20:59 And then those students will get as much, and maybe it'll be personalized, it'll be better. And that is dehumanizing to teachers clearly and also to students because it removes everything that is about the student and teachers' internal life and about their relationship and about their community from the situation. But I think it's also really important in terms of the workforce more generally that basically if we say, well, humans like large language models are systems for, outputting words, then it's a very small step to basically saying the whole value of this person is how many words they can output and doing a very, very dehumanizing work environment to people. We also see this in other domains like the Amazon WorkFor and the ways that these many robots flip from place to place. and the so-called, quote-unquote pickers, people on Amazon work warehouses have to pick things and then deliver them. So there's a lot of implications for that. And I think also in seeing the humanity and other folks and how we treat other folks, you know,

Starting point is 00:22:04 if they're merely kind of meat machines, then what does it say about how we view them with respect to kind of personal rights and human rights and what kind of rights they should be afforded? This idea of human beings just being walking meat machines is chilling. It definitely creeped me out. What are the other real-world consequences of this thinking? Let's open a new tab. Who's really harmed by AI hype? Alex and Emily have said that their goal with writing the AI con is to reduce the harm caused by AI hype.

Starting point is 00:22:43 Automation, for example, doesn't just replace jobs. healthcare providers are increasingly relying on AI products for medical triage to decide which patients to see first. Free legal representation, a guaranteed right in criminal cases, can be replaced by a lawyer using a chatbot. All of this potentially lowers the quality of these services and introduces bias into these systems. Artists and other creatives, meanwhile, are struggling to make ends meet as AI generators, sometimes trained on their own work, are used as a cheaper, faster alternative. And then there's how large language models are disrupting our whole information ecosystem. There's a metaphor we use in the book, the idea that information is being output from these models

Starting point is 00:23:32 and results in information ecosystem spills, like toxic spills, that really can't be cleaned up. There's not really a reliable way to detect synthetic text. And so you're having to deal with and navigate and try to understand whether something on the internet is actually reflective of truth claims that are being made and perhaps researched more deeply by human individuals. You've written that the strongest critiques against AI boosterism come from black, brown, poor, queer, and disabled scholars and activists. Can you talk about some examples of these critiques and why these groups specifically are so uniquely positioned to make them? So we wrote about that in the register of thinking about the ways in which systems, and here I want to say data-driven systems, not just large language models, but the even-driven systems don't work for black-brown communities, queer and trans people, and then people like refugees and people on the move. the kind of pioneering work of doctors to meet to Peru and Joy Bull and Weenie in their paper gender shades talks about facial analysis systems, specifically the way that facial analysis

Starting point is 00:24:50 systems do very poorly on darker-skinned women and that there's a huge delta between darker-skinned women and lighter-skinned men. Sasha Kucenza Chuck talks about how tools like TSA scanners do very poorly on trans. people typically flagging genitals as anomalies or chest areas as anomalies. And then the kind of disparities of how systems talk about women. So there's been a few papers talking about the ways in which different tools. In this case, a word in bedding space makes associations between people in occupations. So man is to doctor, women is to. Women is to and typically the completion is nurse.

Starting point is 00:25:40 So it makes kind of presuppositions of this. All of this stuff effectively happens in large language models and happens in image generation models as well. There's some great research by the Bloomberg data team that shows that if you input something like a nurse typically or a housekeeper outputs kind of a phenotypically looking darker skins, woman, if you type in CEO, white man. And so those kinds of elements are the bias element of it. Ruha Benjamin sums it up really nicely in this beautiful essay called the New Artificial

Starting point is 00:26:18 Intelligencia that appeared in the LA Review of Books in 2024. And she's talking about this ideas of like transhumanism and merging with the machines. And she says, this zealous desire to transcend humanity ignores the fact that we have not all had the chance to be fully human. And my interpretation of what she's saying is that that the people that society does not accord full humanity to have a very different experience of technology, both in the ways, as I was saying, is being used on them, in the ways that doesn't work well for them, and just in the ways that it intrudes on their life. And so people who have the privilege of not experience any of that tend to be less sensitized to what's going

Starting point is 00:26:59 on and to have a less informed perspective. And this less informed perspective encourages AI boosters, who continue to fuel the hype machine. This means investing in and launching new products at a breakneck pace, often overlooking the real-world impact. The MIT technology review recently reported that generating one five-second AI video uses about 3.4 million joules, the equivalent of running a microwave for over an hour. At scale, this amount of energy consumption is devastating for the environment. And running all of this comes at a a steep price for AI companies, too. Like we talked about earlier, Open AI SORA app is proving to be wildly expensive, with more users generating videos than actually watching them. And after the

Starting point is 00:27:48 copyright fiasco and subsequent new guardrails, it seems like some initial adopters are already moving on. Can the hype machine sustain this kind of frenzied investment with such limited return? Okay, we're opening one last tab. Is the hype machine breaking? Do you think the AI hype bubble is going to burst? I mean, like, are there economic critiques? You've heard the social ones, but is there anything pointing to the AI hype bubble, possibly at least deflating? Yeah, well, the problem is that there's so much capital expenditure going into building things like data centers. And they're going into these massive data center built out where, you know, the kind of projections in how much OpenAI, Microsoft, Google, Amazon, and Meta are spending on this all is.

Starting point is 00:28:40 astronomical, I mean, hundreds of billions of dollars, just some of the largest technological infrastructure projects that we've ever seen. At the same time, OpenAI, again, the company that has the most queries to chat DBT, people using most of its products, is making revenue on the order of maybe $10 billion a year. So it's orders of magnitude less. And the kind of metaphor that's being used as well. We have to build the railroads first, and then once the railroads get going, we can put, we can put rail cars on them. But that metaphor doesn't work at all. People are already using the product. And, you know, companies are already saying, we're not getting a lot of value out of this. You know, there was something that was coming out of MIT, which said

Starting point is 00:29:30 95% of companies just haven't really gained value from quote-unquote AI. So what's happening? This is very bubble-shaped, you know, and I don't know how this story ends, but it's very alarming that these four to seven companies are propping up the U.S. and world economy right now. So what happens when the bubble deflates or bursts? It's not going to be good. Like you said, you finished this book in September 2024. The AI industry has only grown since then. What have you learned about the state of the AI hype machine from the reception to your book? I would say what I've learned the most about is about the resilience of people and the importance of connection and community. So the antidote to the hype is a variety of things. One is ridicule as

Starting point is 00:30:25 practice, as we say in the book, and also solidarity and labor movements, but also just sort of connection. And one form of that connection is that there's a lot of people who are, who feel isolated, in a workplace or a social circle where everyone around them seems just completely gaga for this technology, and they're the odd one out. And so one of the joys of both our podcasts in this book has been to find those people and be found by those people who say, oh, so glad I'm not the only one. And then they can form community with other people who have the same reaction. And I think that that is super important. One of the things we grapple a lot just like within close all tabs is where to draw the line with AI use, you know? And again, that's complicated. What is AI? For example, we don't use chat

Starting point is 00:31:13 GPT, but we use an AI transcription tool for our interviews. Are there conditions under which using large language models AI tools are reasonable or justified, appropriate? And then what's your message to the average listener who maybe uses chat GPT in their daily life? But they're not necessarily AI boosters and not necessarily AI doomers. Yeah. Um, So to the first question, I would say, I never call it an AI transcription tool. I would say automatic transcription, right? And that is a use case where you want to look at the labor conditions of the people who produced it. Where do the training data come from?

Starting point is 00:31:50 And it's also a use case where you are well positioned to check the output and see if it's working well for you. Right? You've got something that has been recorded. You've got an automatically produced transcript. You're presumably going through and correcting it. And if it is wrong all the time or if you have one that is particularly bad, for non-Anglo names, for example, you might start looking for something that's better.

Starting point is 00:32:13 So that is a case of automation that I think can be okay. You still want to look into who produced it. Are there privacy implications? Can I use this tool without uploading my data to somebody else? And so on, but there's reasonable uses and reasonable ways to produce automatic transcription. If we're talking about chatbots of the form of chat GPT, I don't see reasonable use cases there.

Starting point is 00:32:37 And partially, we know that the labor and environmental costs are extraordinarily high. That this is not produced ethically. But even setting that aside, every time you turn to chat GPT for information, you're cutting yourself off from important sense making. One of the examples I like to use, if you think about an old-fashioned search engine that gave you back, you know, the 10 blue links, and you've got a medical query, what might come back in those links is a link to, a link to, you know, something like the Mayo Clinic and then your regional university medical center. So in the Bay Area, you know, UCSF. And you might get a link to Dr. Oz's page. And you might get a link to a discussion forum where people with the same medical questions are talking to each other. And you can then look at those and understand the information that's there based on what you know about the Mayo

Starting point is 00:33:33 Clinic and UCSF and Dr. Oz and discussion forums. But that also helps you continue to update what you know about those kinds of sites. Whereas if you asked a chat bot and you got back something that was just sort of some paper mache made up out of some combination of what's in those sites, you not only don't know how to contextualize what you've seen, but you're also cut off from the ability to continue to understand the information environment. And then very importantly, if you think about that discussion forum, any given sentence from that discussion forum interpret it as information, you're going to want to take with a big

Starting point is 00:34:07 grain of salt. But the chance to connect with people who are going through the same medical journey is priceless. And there's a, the scholar Chris Gileard describes these technologies as technologies of isolation. And I think it's really important to think about anytime you might turn to a chatbot, what would you have done three years ago? What would you have done when chat GPT was not in your world? And what are you missing out on by not doing that now? The connections that you would make with people, the ongoing maintenance of relationships, the building of community, the deeper sense of what's going on in the world around you. All of these are precious, and I think not to be thrown away for the semblance of convenience. And then I think the final thing that I would say is

Starting point is 00:34:52 look out for, identify, and reject the inevitability narrative. So the tech companies would like us to believe that AI is the future. It's definitely coming. Even if you don't like it, you have to resign yourself to it. And you'll get people saying, well, it's here to stay. We have to learn to live with it. And I refuse that. I say that is also a bid to steal our agency because the future is not written. Those are all my questions. Thank you so much for joining us. Yeah, thank you. This is a pleasure. Let's close all these tabs. Close All Tabs is a production of KQED Studios and is reported and hosted by me, Morgan Sung. Close All Tabs producer is Maya Kueva. Chris Agusa is our senior editor. Additional editing by

Starting point is 00:35:45 Chris Hambrick and Jen Cheyenne, who's KQED's director of podcasts. Production support from Gabriela Glick. Original music, including our theme song and credits, by Chris Agusa. Additional music by APM. Audio engineering by Brendan Willard and Brian Douglas. Audience engagement support from Maha Sinod. Katie Springer is our podcast operations manager, and Ethan Tovin Lindsay is our editor-in-chief. Some members of the KQED podcast team are represented by the Screen Actors Guild,

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Close All Tabs - Beyond the AI Hype Machine

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