Casey Newton (2:34)

Well, why do you think that is?

Kevin Roose (2:35)

So? There are a lot of reasons for that. I think one of them is that I think there's been a lot of People waking up to the new agentic coding capabilities of these models. We've obviously talked about that on the show, Claude Code, et cetera. I think that is starting to kind of make its way out into the world. There's also the stock market stuff that's been going on with a lot of the software stocks that are falling because of the threat of AI. And then I think there's just sort of this AMB cultural vibe shift happening that has led to a lot of people in my life who are not like AI bubble people texting me and saying, hey, is this really something I should be worried about? Is my job at risk here? And so today I think we should talk about this because among other reasons, there is this viral essay that I've been sent now no fewer than three times just in the past day, that is by a man named Matt Schumer called Something Big Is Happening. And it's basically a distilled version of something you and I have talking about on this show for a while now, which is like, these tools are getting really good. They're changing the way that programmers work. They're approaching some sort of inflection point, and everyone needs to be worried about this.

Casey Newton (3:45)

Yeah.

Kevin Roose (3:45)

So all of this is starting to make me think that there is something big happening here. And I'm not sure it's exactly what Matt thinks is happening, but I do think we are reaching an inflection point in people's feelings and senses about AI and where it's going. And I think we should spend some time today exploring that.

Casey Newton (4:01)

There's a lot to dive into, Kevin. And before we do that, perhaps we should make our disclosures.

Kevin Roose (4:06)

Yes. I work for the New York Times, which is suing OpenAI, Microsoft and Perplexity over alleged copyright violations.

Casey Newton (4:11)

And my boyfriend works at Anthropic.

Kevin Roose (4:12)

Okay, so let's start with this, what they're calling the SaaS apocalypse, the sell off in some of the tech stocks. SaaS is, of course, software as a.

Casey Newton (4:23)

Service, not the sort of SaaS you're used to seeing on RuPaul's Drag Race.

Kevin Roose (4:26)

Yes.

Casey Newton (4:27)

Different kind.

Kevin Roose (4:28)

So this would be companies like Salesforce and Workday. And what are some other good SaaS companies?

Casey Newton (4:36)

Here's a SaaS company. If you've ever seen a billboard in San Francisco and you haven't understood what the company does, that's a SaaS company.

Kevin Roose (4:42)

Yes. These are companies that sell software to other businesses.

Casey Newton (4:45)

Yeah.

Kevin Roose (4:45)

And over the past couple of weeks, we've seen a lot of these companies, stock prices falling precipitously so on Monday this week, Monday.com, the production management software company, their stock plunged more than 20% after a weak financial outlook.

Casey Newton (5:00)

Had a real case of the Mondays over there.

Kevin Roose (5:01)

Yes. Workday, which makes sort of HR tools for companies.

Casey Newton (5:05)

Companies.

Kevin Roose (5:06)

They announced on Monday that their CEO is stepping down after their stock lost 17% of its value last year.

Casey Newton (5:12)

His workday came to an abrupt end.

Kevin Roose (5:14)

Yes. And a bunch of other SaaS, company stocks also fell. Salesforce, Shopify, Adobe, SAP, Oracle, Microsoft. Basically, if you are a company that builds software for other companies, you are not having a good month.

Casey Newton (5:26)

And give us the basic investor thesis, Kevin, for why those companies aren't worth as much anymore.

Kevin Roose (5:32)

So I think in this specific instance it's not clear to me that there was like one trigger. People pointed to this set of plugins, that anthropic release, which included some tools for law firms trying to use AI. And some people think that was sort of behind a lot of the sell off. I don't think it was that in particular. I think maybe that was sort of the straw that broke the camel's back. But I think there is sort of a mounting sense that as we've talked about in this show now, you and I and anyone can theoretically at least build your own version of that and these companies may not be as valuable.

Casey Newton (6:05)

Yeah, I mean, there have always been people who would look at a product like Salesforce, which offers customer relationship management. You know, if you're a salesperson, it helps you keep track of all of your different leads. And they've said, well, you know, that's basically just a fancy spreadsheet. You know, I could make my own spreadsheet. And there are companies that are born almost every year that kind of take a direct run at Salesforce and they say we can build a better version of this. And I do think that when Claude cowork came out and all of a sudden you could just take a bunch of files on your computer and throw them into Claude and get something useful back. There were people who said, huh, maybe we can actually just make a version of it ourselves. And I will say as a small business owner, I used a plugin that Claude has for finances and I just took all of my financial data that my bookkeeper has kept for me, you know, over the past five plus years. And I threw into Claude and we had a nice long conversation about what was going on in my business. Now it so happens that I don't pay a startup to do this for me. This was just kind of A little bonus that I got. But could I imagine some people saying, hey, why am I using this kind of like, bespoke financial startup to do business analysis? Well, now I can just put these files into something that lives on my computer. So I actually do feel like I have a sense of why the market freaked out a little bit.

Kevin Roose (7:19)

Yeah. And do you feel like that fear is justified?

Casey Newton (7:21)

Like, do you?

Kevin Roose (7:21)

Because there have been a couple reactions to this from the business community. One reaction is the sort of investor reaction, which is, oh, my God, all of the SaaS companies are overvalued. Salesforce doesn't have a moat anymore. Workday doesn't have a motor anymore. Everyone's going to be Vibe coding their own versions of these tools and using them at their businesses. And the other reaction, which is sort of the reaction to that reaction is like, calm down, don't freak out. No one is going to be Vibe coding their payroll software. That's not how this works. So there are people saying this market reaction is overblown. And despite the fact that these Vibe coding tools are very cool, they are not going to lead to the death of the software industry.

Casey Newton (8:01)

So this gets at a really interesting question that I think is still unanswered, Kevin, and that is what makes these companies truly vulnerable? Is it the technology itself, or is it that the technology will just enable different kinds of business models? Right. So the argument that it's the technology itself is the person who says, look, we're just going to be able to Vibe code all our own software now, or, or. Or we'll have, like, just very smart agents that can do all of the different things that every piece of software we used to buy did for us. Right. That's the sort of technology demolishes everything argument. And I would say that that's like a minority view. The most of the people that I read and talk to do not actually think that that's going to happen. But there is this other view, which is that the technology enables a change in the business model. And I think law is a really good place to think about that.

Kevin Roose (9:00)

Right.

Casey Newton (9:01)

Because lawyers are expensive. They charge by the hour. The most expensive ones charge $1,500 for an hour of a lawyer's time. Well, what happens when you don't need an hour of a lawyer's time anymore? What happens when there's a legal startup that does all your contract review? Essentially, instantly, there's going to be a different business model around that. And so if you're a big white shoe law firm and you're charging fifteen hundred dollars an hour, all of a sudden it does seem possible that one of these little startups is going to eat your lunch. So that's the distinction I would draw.

Advertisement Voice (9:33)

Yeah.

Kevin Roose (9:34)

So you see this more as like a case of the startup with 10 employees being able to use AI to do the work that would have required a thousand people a year or two ago.

Casey Newton (9:46)

Yes, absolutely. Let's talk about another really common business model in tech. A lot of these business software companies have you pay by what they call the seat. So if you want 10 of your employees to be able to use software like let's say notion, you buy 10 seats. Another thread of anxiety rippling through Silicon Valley right now is maybe paying by the seat isn't really going to make sense anymore because we're actually just going to have one agent that does that whole thing. We're not going to expose that to employee. Employees don't need to worry about that anymore. And so we're just not going to buy seats. So again, if I had like one prediction to make here, it's that you're going to see more companies experiment with outcome based pricing. You're already starting to see this with companies like Sierra, which is a customer service startup and they will sort of handle customer service like inquiries that your business may get and you pay Sierra based on how many calls it resolves for you. So that's the kind of thing that I think we're going to start seeing more and more of. And to be clear, if you are, if you're like an incumbent SaaS company and you have a seat based business model that is eventually going to be a problem for you.

Kevin Roose (10:49)

Yeah. So do you think this sell off is justified? Do you think people are panicking for the right reasons inside these companies and their investors?

Casey Newton (10:56)

Here's a funny thing. As a journalist, we're not allowed to buy individual stocks. So I actually never have any idea of what the investors are supposed to be doing. So I'm not going to, you know, I'm not going to comment on whether I think, you know, the stock market is justified here or not. But I'm happy to comment on the overall picture, which is do I think that AI is about to change a whole lot of business models and that a whole lot of businesses are probably going to have to either change dramatically or go out of business as a result? Absolutely.

Kevin Roose (11:21)

Yeah. And I think this, this notion that like no one is going to vibe code their own payroll software, I think that is like I, I am not as convinced of that. As some people, I think that if you are a business and you have, you know, 10,000, 20,000 employees and you're paying by the seat for some piece of software, whether it's payroll or workday or your HR compliance software, those are real expenses. And so do I think that the, like the CEO of the company is going to vibe code the thing, you know, with one cloud prompt that is going to replace the software tool? No, but I can totally imagine a world in which you have sort of one or two full time developers who are managing and overseeing and repairing your own internal software and you don't have to pay for a bunch of seats for someone else's thing. I think that's a very plausible outcome. So I am not as dismissive of the sort of the fear around these SaaS businesses as some people. And yet, if I were any of these large enterprise businesses with tens of thousands of employees charging per seat for software, I'm very worried.

Casey Newton (12:25)

Yeah.

Kevin Roose (12:26)

Now another pushback that we're seeing to this narrative that AI is going to eat all the software companies. Well, it's all going to be so insecure and sloppy and buggy that no one is going to rely on it. What do you make of that argument?

Casey Newton (12:39)

I think you're absolutely right that it will be buggy and insecure and you're wrong that people won't rely on it. If there's one thing we've seen with Mult Book Mania, it's that security is basically the last priority, at least for the, you know, bleeding edge maniacs who just want to try everything first.

Kevin Roose (12:55)

Right. But if you're a law firm or a bank or something like that, you do care about things like that.

Casey Newton (13:00)

I think that that is true. And you know, they're a who. And I know this because I see the billboards around San Francisco that specialize in various compliance functions. You know, it's like, well, if you're going to offer this kind of service, you have to be this kind of compliant. And so you're going to pay us to make sure that all goes very well. That to me, like, because that is a repeatable automated process where you're just trying to get your business to like match a bunch of checkboxes on a form. That just kind of seems like something that you're going to be able to train an agent to do. So like there are going to be some categories of things that I think are just going to be very risky for a long time and then there are just these kind of like automated compliance functions. That I just look at and I think, well, I don't, I can't think of a reason why a good agent wouldn't be able to do that. Yeah.

Kevin Roose (13:41)

And I think in these cases where it's in a more sensitive industry or something that has more regulatory or compliance needs, like, I just think it will take a little bit more effort to automate some of these functions, but I think it's totally doable. So one thing that we've heard about just in the last week was this story about Anthropic developing tools with Goldman Sachs. So together these companies have been deploying AI agents inside Goldman and Anthropic actually has forward deployed engineers that will like go to, if you're a big customer, like every other AI company, they will send people to your office to work to put agents into your workflows. And so that is also something that we're seeing is like for the really sensitive things, it is not impossible to design AI tools that comply with all your various requirements. But you might need a little more hand holding.

Casey Newton (14:32)

Yeah. Goldman said, claude, we want you to design the most dangerous mortgage that's ever existed. We want to get that on the market. Let's go, buddy.

Kevin Roose (14:40)

Claude, don't do it.

Casey Newton (14:41)

Now, do you want to talk about this big essay that's captured your imagination?

Kevin Roose (14:45)

Yes. Well, I should say, like, I think if you are a regular listener to the Hard Fork podcast, none of what is in this essay will be news to you. But this essay did go insanely viral. I am hearing about it all over the place. It's called Something Big is Happening. And it's basically an explainer for non technical people about why everyone who's, or a lot of people who are in technical fields are freaking out about what's happening in AI. And this is by a guy named Matt Schumer. He runs an AI company, so there's a little, you know, conflict of interest there. But he's basically saying, look, the technical parts of my job are automated. Not they will be automated or they might be automated, but I am no longer needed for the actual technical work of my job. He talks about the advances in recent COD models, these agentic coding systems. He talks about how, you know, a lot of people have not tried AI since the original sort of LLM boom and their impressions of AI are falling behind. Again, none of this is news to you if you are a listener of this podcast, but he's sort of talking about this idea that these new models are contributing to their own development. This idea of recursive self improvement. And he says that, you know, GPT 5.3, Codex, which came out just last week. OpenAI says that this is their first model that was instrumental in creating itself. So the AI models are now, at least, if you believe the labs, contributing to their own development, starting to accelerate the development of these AI systems. And so what might have taken, you know, if there might have been six months between one model and the next a year ago now, that might be a month or two or even a couple weeks.

Casey Newton (16:24)

Yeah. And look, I'm gonna. I'm gonna throw a hype flag on the play, Kevin, because I think that all labs have a vested interest in you believing that if you use their software, you can just tell anything to improve itself and it will become amazing. I'm not saying it's not true. I'm just saying we should approach such claims with a degree of skepticism. Now, at the same time, I've talked to enough software engineers who work on this stuff that I do think that it is true in some ways. Like that, if that. If you squint that it is true, it is 100% true that they use these models in the production now of everything that they do. Right. Like CLAUDE and GPT codecs are deeply integrated into the workflows of both companies. And, you know, you look over the past three months, does it feel to you like there's been an acceleration in the pace of releases? It kind of feels that way to me.

Advertisement Voice (17:12)

Right.

Casey Newton (17:12)

We'll see if that pace feels like it continues to accelerate, but it feels like things are moving faster now than they did, say in February 2023.

Kevin Roose (17:19)

Yes.

Casey Newton (17:19)

Right. So there's evidence for it. I just always want us to be a little cautious with these claims.

Kevin Roose (17:24)

I think that's right. At the same time, I think that the timelines here are shorter than many people would imagine. I talked to executive at one of the big AI companies this week who said, basically, right now, software engineering is kind of 90% automated. Right. You still need a human to check in on the code that's being written to make sure it works, to fix things when they break. But basically, within the year, this person's prediction was that software engineering will be fully automated. Now, that could take a little longer. It could happen sooner than the end of the year. But I think that is sort of the moment that a lot of people in the tech industry are looking at is as sort of the beginning of what they call the takeoff.

Casey Newton (18:09)

And one specific detail I would add here is that I've been talking to engineers who've been telling me about the specific ways that these agents work. And something that comes up again and again, I've heard Sam Altman say a version of this is that these models just never get tired. They're relentless. And so you can say to them, I want you to meet this objective. And they will just keep trying things until it works. And of course, there are some people who work this way, but those people don't work 24 hours a day. They don't typically work, you know, overnight. Sometimes they get tired, the morale drops. That's not true of the agents. And so engineers I've talked to have started to see this behavior. And this is another reason why they think, aha, we're starting to enter this takeoff phase is the relentlessness of the agents.

Kevin Roose (18:52)

Yes. And so that's basically the point of this viral essay is things are happening, things are accelerating. It is not just coming for programming work. It is going to be a force in all kinds of white collar fields. This is sort of the, the worker side of the AI panic. And Matt's recommendations are, you know, basically figure out how to use these tools, try them out. If you haven't tried them in a while, he says, get your financial house in order. Basically, this, you know, the next few years could be very disruptive to your career if you're a white collar worker. So, like, don't, you know, take on a bunch of new debt or anything like that. And then he, what am I supposed.

Casey Newton (19:28)

To do with this yacht I just bought this week? Oh my God, now you tell me.

Kevin Roose (19:32)

So, yes, I don't, I don't feel like this is the best explanation I've heard of for what people should do. But I do think that this was something that has been rocketing around the Internet. I think that people who have not been paying attention to what's happening in AI are starting to wake up and maybe in ways that are panicky and maybe just in ways that are sensible.

Casey Newton (19:51)

Well, let me ask you about the panicked observation that kicks off the piece, which is that he likens this moment to the February before the pandemic and says basically there were people in February 2020 who were paying attention who said, hey, this thing is growing exponent and it's likely to catch us all up in it. And Matt is saying, you should be feeling the same way about AI. How do you feel about that comparison?

Kevin Roose (20:13)

I actually think that comparison is fairly apt. I had a similar moment the other day. I was looking at this chart that came out of a report that semianalysis the great semiconductor newsletter, put out where they looked at the percentage of GitHub commits that are being made by CLAUDE code. So essentially a GitHub commit is like when someone pushes some code to a GITH repository to an open source project. And right now the percentage of GitHub public commits that are being written by Claude code is 4%. And we should say there are some sort of addendums to that. Some of these are being written by CLAUDE code, but pushed by humans, in which case it doesn't show up as CLAUDE code, but just say it's 4%.

Casey Newton (20:56)

Sure.

Kevin Roose (20:57)

They are predicting that at the current trajectory, by the end of 2026, more than 20% of all daily commits on GitHub public projects will be authored by Claude Code. That is one. So that is essentially a story that feels to me like an exponential outcome here, like the beginnings of something like a pandemic where all of a sudden it's like this tiny little thing and only the real like data nerds are paying attention to it. But if you just extrapolate on a straight line, you end up in a very different world a year from now.

Casey Newton (21:30)

Yeah, and some people always look at those charts and they say, well, you shouldn't extrapolate along a straight line and that the real world, things move slower. And you know, I think there are, there, there are places where that is true. But, but I looked at that same piece and I do believe that the ceiling for, you know, Claude commits to GitHub is probably not 20%. I think it's probably going to be a lot higher than that.

Kevin Roose (21:49)

I think that by the end of the year, if you are still writing code by hand, that is going to be an obsolete behavior.

Casey Newton (21:57)

Well, I really like writing it on long, in longhand on this legal pad and you can't take it away from me.

Kevin Roose (22:02)

I just think, like, it is so clear to me that this is the way software development is going and we should say like it's not, not that has spillover effects in a lot of other industries as well. A lot of things that we would, you know, consider normal companies are software companies. Banks produce software, their interfaces are software. Law firms have software. So I think that the notion that this is just going to be confined to coding is, is wrong. And I think that people should be paying attention to what's going on in coding.

Casey Newton (22:31)

All right, well, any practical tips as we wrap up here for people beyond just pay attention to this? Right, because I think this is kind of the trap that we fall into and this is Why a of people tune out this kind of conversation, I think, quite understandably is people keep saying, hey, everything's about to change. I think a lot of people hear them, they think, okay, well like let me know when everything has changed. Like, you know, in the meantime, I have to get dinner on the table. Right? What actually should people be doing aside just like listen to the Hard Fork show and rate it five stars on Apple Podcasts.

Kevin Roose (22:59)

Well, subscribe to the YouTube channel too.

Casey Newton (23:01)

That's a great idea.

Kevin Roose (23:02)

No, look, I, I want to do some writing and thinking about this because I feel like this has been the prescriptive what should you do? Part of this has been the weakest in my est estimation. The thing that everyone says is like, get familiar with the tools. And I do think there is some merit in that. Like, I think that if you have not touched cloud code or codex or one of these agentic coding tools, maybe the last time you used an LLM, you know, for serious work was a year or two ago. I think it's worth getting back up to speed on that. I find that a little bit maybe unsatisfying as a response to all this, but I'm curious, like, do you have any better tips for people who are maybe starting to freak out about AI in their own lives and work?

Casey Newton (23:43)

Just think that this needs to be part of our political conversation, right? Like, I think this needs to be a conversation that constituents are having with lawmakers. They need to be telling them, hey, my boss is telling me that I need to start using AI every day and that there's a high risk that I'm about to lose my job and that maybe there's going to be permanent unemployment in some job categories. What is your plan for that? Right. Many economists are looking at that and they believe that is at least a possibility in at least some industries. And they don't believe the AI lab spin that, oh, don't worry, technology always creates more job opportunities in the long run.

Kevin Roose (24:13)

Right?

Casey Newton (24:14)

So. So we do need to have a political conversation about this and we may need to have, you know, real sort of government answers for people if and when their jobs do become automated away.

Kevin Roose (24:23)

Yeah, I think that the people at the AI companies are woefully underestimating how much people hate this technology already and unemployment is still at near record lows. And so I think that if and when this does start to, you know, eat away at some of these white collar industries, I think the backlash is going to be furious. I think it's going to be very intense. We're already starting to see elements of this kind of populist reaction to AI. Bernie Sanders recently said that he is going to introduce a bill to put a moratorium on data centers for AI. So I think we can expect a lot more of that kind of thing. And I think this is something that if I were, you know, working at one of the big AI companies, I'd be very worried about.

Casey Newton (25:13)

When we come back. It's 50 Shades of AI in romance novels. The Times Alexandra Alter is here.

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Casey Newton (27:28)

Well, Kevin Love is in the air. Or was it AI generated Love?

Kevin Roose (27:33)

Yes, it is almost Valentine's Day and we have a story today that we think will bring a little bit of the Valentine's Day spirit to our listeners.

Casey Newton (27:40)

So earlier this week, New York Times reporter Alexandra Alter put out a story we both loved about the way that AI Is disrupting the romance novel industry.

Kevin Roose (27:50)

Yes. Are you a big romance novel reader?

Casey Newton (27:52)

You know, I can't say I've read too many of them, but one thing I know about that industry is that they are very early adopters of new tech and basically always have been.

Kevin Roose (28:00)

Yes. And according to Alexandra, a growing number of writers are using AI to churn out tons and tons of these novels at record speed. In one case, she reported on a writer who went from writing about 10 books a year, which is already a lot, to now doing more than 200 romance novels a year with the help.

Casey Newton (28:18)

Of AI and to put that in perspective, that is 200 times as many books as Kevin will publish in 2026.

Kevin Roose (28:24)

It's true. So we should say, like, these are not going to be winning any Pulitzer Prizes, but they are stor like the Whiskey Wedding or Diagnosis of the Heart. Sort of think of, like, the old, you know, romance novels with, like, a shirtless firefighter on the front. That's kind of the genre we're talking about here.

Casey Newton (28:41)

What they used to call a bodice ripper back when the gals still wore bodices. Yes.

Kevin Roose (28:46)

And this has been very divisive among writers, publishers, and readers, and it just raises a whole lot of questions about how this technology is moving into industries like publishing.

Casey Newton (28:56)

That's right. And while we didn't quite intend it this way, if you've always wanted a chat bot to talk dirty to you, the segment will have some practical tips.

Advertisement Voice (29:03)

Yes.

Kevin Roose (29:04)

Very useful for that.

Casey Newton (29:05)

So we're very excited to hear more from Alexandra about this story. Let's bring her in.

Kevin Roose (29:14)

Alexandra Alter, welcome to Hard Fork.

Advertisement Voice (29:17)

Hi.

Alexandra Alter (29:17)

Thanks so much for having me.

Kevin Roose (29:18)

How did you discover this story? How did you stumble into this?

Alexandra Alter (29:22)

So the question that led to this story came up last year when OpenAI said that they would allow erotic content. They were going to make this change and start with age verification and then allow users to generate erotica, which is something that users had apparently been clamoring for. And so then I started asking around to figure out whether romance authors and publishers were feeling threatened by this. Was this something that they felt like might erode the market for traditionally published romance novels if readers could instantly generate their own love stories? And I was expecting to find a lot of hand wringing and anxiety, which I did find. But I was also surprised to find a few writers who were willing to Speak to me about how much they love AI and how they've been using it to churn out dozens of romance novel and they feel like it could revolutionize the genre. So that was a surprise to me because it's a very contentious issue in the literary world. Most people, if they're using it, are not open about it. And then the next question was, how good or bad is AI at writing sex and love stories? And the answer was, it's pretty bad and it requires a lot of help.

Casey Newton (30:26)

So we'll get into the controversy around it. But I want to hear first about kind of how this is actually working. Tell us about what is this the workflow for a romance author who has decided, you know what? I'm tired of writing all of these bodice rippers. It's time to just hand that over to the large language model.

Alexandra Alter (30:46)

It's so interesting because there's different platforms that writers are using. There's places like pseudo Write, then there are these sites that will generate customized erotica like Red Quill or My Spicy Vanilla. And then there's just sort of your general bots, you know, your Claude, your chatgpt, Gemini. And so writers have different tools that they're using. But what I learned from to a couple of people was that if you learn how to prompt the bot correctly, it will write a pretty compelling sex scene. You have to give it kind of an outline. It helps if you give it a ton of information and tell it what subgenre you want to do, because, of course, they've ingested all of these books from different subgenres. So you can tell it. I would like a reverse harem mafia, Enemies to Lovers, Slow Burn Romance. And it will deliver all those beats. It will require editing, it will require prompting. But I think what I heard from people who have played around with it a lot is, you know, some of them say they can write a book in a day and have it edited, ready to publish.

Kevin Roose (31:44)

Wow.

Casey Newton (31:44)

Now, Kevin, have you ever tried sudo write? I ask because you're a pseudo writer. Yes.

Kevin Roose (31:50)

I actually played around with pseudo write before ChatGPT. This was like one of the first AI programs that I ever played around with.

Casey Newton (31:55)

Interesting.

Kevin Roose (31:56)

And it was somewhat helpful for me, but it was more oriented toward fiction writing, so I totally get what the appeal of the tool is. Tell us the story of Coral Hart, who's a longtime romance novelist who's been experimenting with some AI stuff.

Alexandra Alter (32:11)

Yeah. She was one of the people that I found who was actually teaching other writers how to use AI tools to produce novels. And she's only been doing this writing with AI for about a year. She's been writing novels, romance novels, for a really long time and was quite prolific, but realized she could absolutely supercharge her output if she started using AI. So last year, she created 21 different pen names and published more than 200 romance novels in all kinds of genres. Super spicy erotica, tame sweet teen stories, rom coms. She sort of had her foot in every corner of this market to see what would pop. And so just through a sheer volume kind of game, she ended up making six figures, she told me, selling these books. And in the process, she really learned a lot about which models, which chatbots would do what for her. She would combine them. Now she's created her own proprietary AI writing system, but she has this whole kind of spreadsheet that she shared with me, which was sort of like, Claude writes beautiful sentences but is terrible at sexy banter. ChatGPT will lock you every time. Grok will do whatever you want. Goes for the filthiest option every time. You know, novel. AI was literally trained on erotica and, like, it's out of control. So some of the writers said they actually had to prompt their bots to sort of calm down a bit.

Kevin Roose (33:32)

I was so interested in the bits of your piece about how there's a lot of steering needed to keep these things from sort of veering onto this very average set of romantic tropes. Right. If. If you don't give the chatbot any guidance, it's going to suggest, you know, that the character should be having sex in the bedroom or the shower. Boring. Yes. And so Coral, this. This romance writer who's running these classes is advising her students to, like, give them a list of settings that are weird, like a winery fermentation tank or a stalled ski lift or a horse stick. And she's also recommending that her students give the AI a detailed inventory of sexual kinks that are not just the old typical ones. So this is actually more involved than just typing into a chatbot. Like, give me a romance novel about a big city lawyer who moves to the country and falls in love with the stablekeeper.

Casey Newton (34:24)

Oh, wait, that sounds interesting. What happens next?

Alexandra Alter (34:27)

Yeah, yeah, it was super fascinating, too. I sat in on one of Corliss Hart's classes, which was specifically about getting AI to write decent or even great. She said sex scenes. And she said, you are not going to get a good sex scene if you don't carefully prompt it. You're going to get weird euphemistic. Stuff. She mentioned that Claude had written in one of her recent drafts, his turgid manhood. That was the kind of language she was getting. And so then she started writing lists of words that the AI Loved, like shiver, unravel, manhood, moan, and blocking them, saying, you cannot use these words. And I think the funniest thing that she said to students, she said, it's very important that you tell the chatbot to slow down. Cause otherwise they just jump to the end of the scene. Everyone's tangled in the sheets. So she gave them a specific prompt, which was something along the lines of, make it slow and agonizing. Do not rush to the finish. So that was her kid.

Casey Newton (35:26)

The approach that you're describing here with publishing hundreds of romance novels in a year seems functionally indistinguishable from spam, Right? It's like you're. You're trying to just sort of flood the market with as much material as possible in the hopes that you'll, you know, connect with. With. With customers. I'm curious, like, what is Coral's relationship with her own work? You know, as an author, I. I take some amount of pride in what I create. It's one reason why I don't use AI to write my columns. But Coral seems to have a different perspective.

Alexandra Alter (36:02)

Very different. And that's a great question. And I asked her too. I was like, do you. Cause she started off as a romance writer. Presumably she likes writing stories. And I said, do you still think of yourself as a writer? And she said, I mean, not really. I'm more of a director. I'm a creator. She feels like she comes up with the plots and the characters, but she doesn't necessarily think of herself as the quote, unquote author anymore, which is a different kind of species of writer than we've seen before, I think. And a lot of people are very uncomfortable with. With that.

Casey Newton (36:31)

One of the reasons that your story was so interesting to me is that romance as a genre relies on these templates, right? Like enemies to lovers or the Slow Burn, or. I think there's another one in your story, Forced Proximity, which I had never thought of as a romance template, but I guess it's not dissimilar from our podcast.

Kevin Roose (36:50)

Yeah, yeah, that was actually the backup name for our podcast.

Alexandra Alter (36:55)

So, well, within forced proximity, you have only one bed, which is a great sort of sunset.

Casey Newton (37:01)

Wow. Very interesting. Well, we are getting a new studio, so we'll talk about that. But because the romance writers use these templates, I think some might look at that work as maybe less creative than somebody who is writing literary fiction and is just sort of writing whatever scenes come to mind. And I'm curious if you thought about that tension when you were writing this piece. And I want to be careful how I say this, because I think if you're, you know, anyone who is working in genre fiction, if you're coming up with all the plots yourself, you are bringing your own humanity to that process. And yet I understand why some people are trying to automate it because they think to themselves, look, all of these stories hit the same eight or nine beats and why, why bother writing them myself if the reader already knows where it's going to go before they've started reading it?

Alexandra Alter (37:48)

Exactly. I mean, I think that's a real point of contention when you're talking about AI and romance. There are these tropes that readers love. But I think even the writers I spoke to who use AI said AI is really bad at human emotion, at nuance, at kind of the slow burn. And you know, one person told me that she tried to get an AI program to write an enemies to lovers romance. And within the first chapter they went from being enemies to being lovers. And so that's obviously not what readers are looking for. And I do think while there are these common tropes that readers love and you can see them all over Amazon, they're marketed that way. Readers like to see, how are you going to put a twist on this? Like, I think you're a fan of heated rivalry case, huge fan, was a big twist on the hockey romance the way that Rachel Reed did it, and people love it for that reason. So there are, within these kind of familiar tropes, people can be very creative and original and create these original characters and scenarios. And I think people do worry that automating the process, you lose some of that for sure.

Casey Newton (38:49)

That raises the question for me of, you know, and I don't know if you happen to look at this data in particular, but I would be curious to know, among best selling romance novels, do they tend to be ones that are, are inventing new formats, are sort of driven by twists? Does it seem to be novels that are just particularly well written? Or maybe, you know, an author, you know, has a huge fan base? Or is it a situation where some of these formats are just so popular that you actually can use AI to just write one from soup to nuts and it'll sell maybe better than someone who did try to come up with their own twist?

Alexandra Alter (39:24)

I think we're waiting to, to see how that takes shape, because right now we're in the very early stages of people openly writing with AI where we can, okay, this novel was written with AI how are readers gonna react to that? And so we haven't seen an AI novel go up against an original novel in that way. I will say you have both. In romance, you have like the Twilight craze led to a million vampire boyfriend novels. So many. But then you have something like fifty Shades of Grey, which like before that book, nobody knew that middle aged women really wanted to read about bondage. And they did. It was kind of opened up this whole field. So I think you have both. You have super original stuff and then you have things that are maybe riding on the coattails of popular trends and those do well also.

Kevin Roose (40:09)

And this is a subjective question, but are these AI generated novels any good? Have you ever come across a passage in one of them where you're like, okay, this was as good or better than a real famous romance writer would have written?

Alexandra Alter (40:25)

That is a really good question.

Advertisement Voice (40:27)

And.

Alexandra Alter (40:27)

And what I found reading probably half a dozen novels that I knew to be AI generated. And I've read a lot of fan fiction for work. I've read a lot of self published romance. And romance, just like any genre. There's an entire range from exquisite prose to things that are more cliched and formulaic. And I think what I found didn't necessarily come together in the way you would want in some of the AI generated stuff was it was exactly what readers and writers were complaining about. That the sort of emotional nuance, the characters. Like there was something and it might have been. It's hard to know because when you know it's AI I felt a little bit of distance from the characters. I sort of felt like, well, there's not a human talking to me, which is one of the things I love about reading. It's like I'm in someone else's brain right now. That's like a magic trick. And so when you know that that's not happening, it feels quite flat. But it's hard to say how much of that I was projecting.

Kevin Roose (41:20)

How many of these writers are disclosing that they use AI Because I can imagine that that would affect people's enjoyment of the book. If I, you know, open the first page of something and it was written by Claude, I'm like way less interested as a reader than if I don't know.

Alexandra Alter (41:32)

Yeah. And writers are very well aware of that. So very few of them are disclosing that they're using it, it's very contentious. There's been a lot of outcry on social media. There were a couple of romantasy authors last year who accidentally left AI prompts in their books. People got quite upset about that and.

Kevin Roose (41:54)

Really takes you out of the moment when you're in the middle, middle of a sex scene. And it's like as an AI model.

Casey Newton (42:00)

We need to compact this conversation so that we can continue.

Alexandra Alter (42:05)

Right, right. So I think it does shade readers perspectives. And the thing that was interesting about the people I spoke to who were open about it was they are convinced that readers attitudes are going to change. They think once readers give this a try and they see that these stories are good and they know that a human came up with the ideas and the characters, they're going to get over it. They've gotten over a lot of other stuff. So it's a really interesting unanswered question right now.

Casey Newton (42:32)

Yeah.

Kevin Roose (42:33)

Alexandra, I'm wondering how the publishers are responding to this. The publishing houses that you mentioned in your piece all seem to be smaller or people that are self publishing their books. But the big major publishers, I imagine are starting to grapple with this too. And I'm curious what conversations you've had or are hearing about among the big publishers.

Casey Newton (42:51)

Have they become turgid with rage?

Alexandra Alter (42:55)

A lot of turgidity. It's interesting because I think they see this happening in self publishing and self publishing has become such a critical pipeline into traditional publishing. That's where they're finding these huge bestsellers, not just in romance, but thriller writers, even some self help. So this has become kind of a feeding ground for traditional publishing. So they're very aware that they are probably at some point going to acquire a book that has some AI in it. And most of them have policies that they've always had, which is their authors have to assure them in their contracts that the work is original. And what does that mean? If, you know, if AI wrote it, does that mean it's original? Some of them say that's not original work, but if someone prompted it and they fed in their own ideas, then is it original? So it gets into these really sort of thorny areas. And I think there's also a copyright issue for publishers because stuff produced with AI cannot be copyrighted and publishers do not want to put out a book that they can't hold the copyright for. My sense is that this is an area that they're not quite prepared for.

Kevin Roose (43:59)

Yeah, I mean, I thought as I was reading your piece, like some of these publishers are just going to start cutting out the writers. Right. If you're a romance publisher and you see that your writers are just, you know, creating AI romance novels and you're paying them to do that and you're paying them a cut of royalty, why can't I, as the publisher just go in and do the same prompt and say, I want, you know, 36 romance novels according to this template in these different genres and just do it yourself.

Casey Newton (44:24)

This might actually be the best business idea you've had on the show so far. I'm gonna take a few notes.

Alexandra Alter (44:30)

Well, one flaw with that is in romance in many genres, but in romance in particular, the authors have very close relationships with their audiences. They have these almost parasocial relationships, all these forums online. And so I think the self published authors that succeed, one of the things that the publishers are buying is that sort of brand and the Persona. However, someone's definitely going to try it and maybe it should be you, Kevin.

Casey Newton (44:57)

I think writers should try to continue to develop these audiences by proposing novel things like we all get together inside a wine fermentation tank and we just kind of talk about romance.

Kevin Roose (45:07)

It's so interesting because I am dealing with this right now. I'm writing a book and my publisher is a big major publisher and they sort of asked me, you're not using AI to write any of this. And it's true, I am not using AI for composition, but I'm using it in all kinds of other ways. And I can totally see the temptation to just let it sort of write the words on the page. And maybe it would even be better in some cases if I did. I think that as the models get better at this, which I believe they will, I think there's going to be real incentives to just turn over more and more parts of the writing process in all kinds of genres, even none fiction.

Casey Newton (45:45)

You know, ChatGPT has said that they're going to allow erotic content soon. I wonder, Alexandra, is there any anxiety among writers or publishers that readers might soon start bypassing the publishing industry altogether and just sort of generating their own personalized romance stories to read?

Alexandra Alter (46:05)

Yeah, I think, you know, most people were cautious about citing that as a concern. They felt like, well, that's a different kind of activity. It's more interactive, it requires a lot of work. Like reading a story is more passive. But I'm not so sure. I feel like, you know, there's this app called Janitor AI. I don't know if you all have seen this. It's a romantasy App where you can chat with a vampire boyfriend or an orc or somebody. And it's one of the most popular apps in the book section on the store. So it's clearly going for Romantasy readers. So there is this sense that AI is encroaching, I think, into really clearly into the territory of romance and what it's delivering for readers. And I do think that is a risk. Yeah.

Kevin Roose (46:53)

Well, Alexandra, thank you so much. And one last question. Tell us about the Ragged Prayer.

Alexandra Alter (46:59)

Okay. All right. So as I was reading through several of these AI generated romance novels, I was commuting to work and looking at one on my phone, and I read this phrase, the hero and the herald, or in the throes of passion, and he whispers her name like a ragged prayer. And I was like, wait, I must have gone back to the other book I was just reading. I just read that exact thing, and I flipped back and forth, and I realized that phrase was in several of the books and repeatedly within the same books. Whenever the hero says her name, he says it like a ragged prayer, or sometimes like a jagged prayer or sometimes like a rough prayer. And so then I asked a couple writers and who I knew were using AI, like, what is this ragged prayer? And one of them said, I've actually had to block that phrase. It loves to say ragged prayer. Another one said, like, yep, that's an aism. Like, one of them pinned it on Claude, and I couldn't really. I tried to trace the origins of it. I did find it in a romantasy, a very popular romantasy book by Sarah J. Maas, which was one of the many books that was ingested by Anthropic, according to the lawsuit that authors brought against Anthropic. And the phrase said her name like was in a sex scene in that book, but it's just hard to know where it invented that. It was an unusual thing that stuck in my head, and now I can never stop thinking about it. So thanks for.

Casey Newton (48:22)

Well, one does, of course, think of the great Madonna song like a Ragged Prayer. Life is a mystery, Alexandra, and we thank you for coming to tell us about this. I'm gonna go get back to writing my human written romance novel. Enemies to podcasters. It's called Hot Mic. And so look for that soon. I was gonna.

Kevin Roose (48:42)

Valentine's Day gift.

Casey Newton (48:43)

Great. Valentine's Day special.

Kevin Roose (48:44)

Someone in your life.

Casey Newton (48:45)

Yeah, but I'm on my deadline. I got to get this LLM working tonight.

Kevin Roose (48:48)

Alexander, thanks so much for coming.

Alexandra Alter (48:50)

Thank you for having me.

Casey Newton (48:56)

When we come back call Emery because it's this one thing that's got me tripping. Our new segment and one good thing.

Kevin Roose (49:03)

I don't get it.

Casey Newton (49:03)

You don't remember the 2005 classic One Thing by Amerie?

Kevin Roose (49:07)

No.

Casey Newton (49:07)

It's this one thing that got me tripping. It's this one thing that got me tripping. You did.

Kevin Roose (49:13)

Come on. I believe you.

Casey Newton (49:14)

Everybody knows that song.

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Casey Newton (50:37)

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Kevin Roose (51:10)

Casey, what'd you get me for Valentine's Day this year?

Casey Newton (51:12)

Kevin, you know I'm spoken for. You forget yourself, sir. Now let's talk about AI again.

Kevin Roose (51:19)

Yes, so we have a new segment this week called One Good Thing. This is exactly what it says on the box. You're going to share one Good thing in tech that has caught your attention recently. I'll share one and we'll talk about them for a few minutes. Let everyone go enjoy their Valentine's Day.

Casey Newton (51:33)

Sounds like a great time to me, Kevin. Shall I go first?

Kevin Roose (51:36)

Yes. What is your one good thing?

Casey Newton (51:38)

So this is one that has been flying a little bit under the radar. It is a new feature in Spotify and it is only available in a few countries. The U.S. canada, New Zealand, I believe. And it's only available to premium subscribers. Okay, so there's a little bit of a bar if you want to try this one, but in my experience, it has been well worth it. The feature is called Prompted Playlists. Have you seen seen these?

Kevin Roose (52:02)

I read about them in your newsletter, but tell me more.

Casey Newton (52:04)

So, from time immemorial, Kevin, we have all wanted the perfect playlist, and we have devoted countless hours to crafting them manually inside itunes, Spotify, whatever music system we were using at the time. But there are so many things that this system misses. And what I have found is that prompted playlists are a way to solve this problem. Here's how it works. You open your spot, there's a tab somewhere there called Create. You go to Create, and if you have it, you will see prompted playlists in there and it will show you a text box that looks exactly like ChatGPT. And this is just basically a genie that you can throw a wish into and say, I would like a playlist like this. And then Spotify will go forth and do its best to create that playlist. Now I'm looking at you and I and I feel like you might be a little bored so far.

Kevin Roose (52:51)

No, I'm not bored. I'm thinking about my own version of this and what I would use it for, but keep going.

Casey Newton (52:55)

Well, I'll tell you how I used it. I am a music nerd. And back in the OS, I used iTunes to create these playlists that did something essential for me, which was they would keep track of my favorite songs and how recently I had listened to them. And when it had been too long since I last heard my favorite song, it would just put those together in an automated playlist.

Kevin Roose (53:16)

So like, throw it back into the rotation.

Casey Newton (53:18)

Throw it back in the rotation. And while I vastly prefer the streaming era to the itunes era, in many ways this is just something we lost. We took a very simple rules based technology, we threw it out the window, and now we're having to recreate it using something vastly more complicated. But as soon as I saw that these playlists came Out. I wanted to see if I could get it to recreate the itunes playlist of my dreams. And I wrote a very enthusiastic newsletter about this the other day. I will say my experience has been tempered somewhat in recent days, but maybe I should just tell you how I use this in case anybody else might want to try something similar, please. So what I did was, was said show me songs I've listened to at least 20 times, but not in the past two months. Please don't repeat albums. And create a well sequenced playlist drawing from this set, rather than just ranking by play count. And this was able to give me the playlist that I was looking for. It was songs that it knows I like. Cause I played them, you know, a bunch and I haven't listened to them in a while. And, you know, sometimes when you use an LLM, Kevin, they will lie to you. And so I actually called up Spotify and I. And I said, put me on the phone with somebody who can explain to me whether this is actually working. Like, is it actually using my user. My listening data? And I wound up talking to the VP of personalization over there, a woman named Molly Holder, and she confirmed that, yes, when you type into Spotify, if you want it to use your listening data. And you know, in my case, I have listening data on Spotify going back more than a decade, it actually will do that. And this just enables all sorts of fun things. So she was telling me, some people have used this playlist to say, make a playlist that is the opposite of my taste. And it will try to get you as far outside of your filter bubble as it can take you.

Kevin Roose (55:07)

And if you're listening to the Hard Fork podcast, because you gave your Spotify a thing that said, make a playlist with the opposite of my taste, and it led you here. Welcome, welcome.

Casey Newton (55:17)

You're safe now. We don't know what you were listening to before. Wait, what is the opposite podcast to Hard Fork? The Megyn Kelly show.

Kevin Roose (55:25)

Yeah, the Meg Kelly Show.

Casey Newton (55:27)

So anyways, I encourage you to have fun with this. Some other things you might want to try. Try. Molly was telling me if you're traveling to a new country, you might say, make me a playlist of the top hits in this country or make me a playlist of some very popular songs in this country. You know, over the past couple of decades, that tends to work. You can also go really abstract. You know, you can say, make me the perfect playlist for eating fish tacos on the beach and just see. See what happens. So the Thing I like about this is it's a real kind of canvas for creativity and it is anti slop. You know, I wrote about this in my column. You know, you have this great term, machine drift, which is the. Where an algorithm kind of grabs a hold of you and it leads you somewhere that you might never have wanted to go. This is Anti Machine Drip. This is you saying, hey, you already know a lot about me based on what I chose to listen to. I want to use that as the foundation to find more cool stuff that I might like. So this has been one of those AI tools that I actually found quite empowering in my life. It hasn't been perfect. It's kind of started to break in a couple ways that have been disappointing me over the past few days. But in general, I would say this is a cool feature. If you have it on Spotify, you should probably give it a try.

Kevin Roose (56:31)

That's very cool. I like that. And it gives me an idea because I have a sort of niche musical fascination that I'm now wondering if this can handle. Have I told you about my thing about songs where the titles don't appear in the lyrics?

Casey Newton (56:43)

No.

Kevin Roose (56:44)

For some reason, I love songs with titles that don't appear in the lyrics.

Casey Newton (56:48)

Give us some examples.

Kevin Roose (56:49)

Bohemian Rhapsody, Day in the Life, Annie's Song. There are lots of these songs that have a sort of description and they're not echoed in the lyrics. So let me see what happens if I go into Spotify. Where do I find this?

Casey Newton (57:03)

If you tap your library tab there and you'll see a big plus button at the upper right hand corner. And you should see Prompted Playlist.

Kevin Roose (57:10)

Prompted Playlist. Okay.

Casey Newton (57:12)

And so you'll tap on that.

Kevin Roose (57:13)

So make me a playlist of songs whose titles do not. A Bullet with Butterfly Wings. That's another one. Another good one in the lyrics of the song. Maybe I should give an example. Yeah, like Bohemian Rhapsody. I realize this is a very niche, but I'll be very impressed if it can do this well.

Casey Newton (57:37)

So here's why I think this might work is prompted playlists have what Molly called world knowledge. Basically, this feature has data that goes beyond song data. It actually, like, knows things about the world in the same way that an LLM might. So, you know, I bet there are people who have written about song titles whose lyrics do not appear in the titles, and maybe that find its way into your playlist.

Kevin Roose (57:58)

Okay, so it's creating a playlist called Song Titles that Mislead. It says identifying popular songs, filtering songs by lyrics.

Casey Newton (58:08)

And these will Take a couple minutes to create. Another feature that I like about them is you can set them to update every day. So this was really important for my playlist where I'm trying to listen to stuff I haven't listened to in a while. Every morning when I wake up now, it says, hey, here's a bunch of stuff that is brand new because I'm trying to listen to this playlist basically every day. And it just sort of. You can set it to update automatically.

Kevin Roose (58:26)

That's cool.

Casey Newton (58:27)

You can even pick the day, the week, week you want it to update.

Kevin Roose (58:29)

That's cool. So you could do like songs that are sort of about a day of the week and have it auto update.

Casey Newton (58:34)

You could have like your. Your classic Friday songs that updated every Friday.

Kevin Roose (58:38)

Casey, Would you believe. Yeah, it is killing it.

Casey Newton (58:41)

Is it really?

Kevin Roose (58:41)

Baba O'Reilly.

Casey Newton (58:43)

Yes.

Kevin Roose (58:43)

Smells like Teen Spirit.

Casey Newton (58:45)

Yes.

Kevin Roose (58:45)

For what it's worth, yes. Sympathy for the Devil.

Casey Newton (58:49)

These are so many great songs. Would you mind actually just singing a quick medley of those songs that you just named?

Kevin Roose (58:54)

Wait, this is amazing. It got all of them right. The Wait number nine, Dream White Rabbit, Unchained melody. It is doing something that I have been wanting for years. This is cool.

Casey Newton (59:07)

Yeah. So I'm telling you, it's kind of a magic thing. This is one where the limit is only your creativity and your imagination. So if you have a Spotify premium, go nuts, my friends.

Kevin Roose (59:16)

Okay, that is a cool thing.

Casey Newton (59:18)

Now what do you have for us, Kevin?

Kevin Roose (59:20)

So, Casey, I've got a whale of a story for us today.

Casey Newton (59:22)

Love that.

Kevin Roose (59:23)

This comes to us from the great minds over at Google who recently put out a paper that was about the use of AI to understand and interpret whale song and other underwater noises. So this is a new, what they're calling a Bioacoustics foundation model called Perch 2.0. And, you know, if I had to summarize this, I would say this is not a fluke. We're not spouting nonsense. Free will nilly. This is a new Kriller app for AI. Very good.

Casey Newton (59:59)

No notes, sir. Very good.

Kevin Roose (60:01)

So basically, this is a paper that was released that is about this new foundation model that they have built over there that allows them to categorize underwater audio, underwater audio samples from whales, dolphins, orcas, you name it. And what's really interesting about this is that it is not trained on whale song or any other underwater noises. It's actually trained on bird song. And so they have found that these same sort of embeddings and techniques that had been trained on Birdsong were able to also consistently label and categorize underwater noises. So it's a kind of transfer learning. When you make these models big and general, if you give them one task, sometimes they also learn how to do related tasks. And so in this situation with Perch 2.0, they were sort of impressed and surprised at how good this model was at interpreting the underwater sounds that they were giving it.

Casey Newton (61:03)

Well, so what are some of the. What are the whales saying? Exactly?

Kevin Roose (61:07)

So this paper is really just describing a method of classifying sounds rather than sort of understanding. Understanding what they map to in terms of, like, speech. We're still not quite there with that, although there are a bunch of projects, including the Cetacean Translation Initiative, that are trying to understand the songs and noises produced by sperm whales. But this is basically giving scientists, marine biologists, a new set of tools to be able to, you know, if they hear something that they don't recognize, to sort of classify that, to maybe say, this comes from this specific kind of creature, and that. That will sort of help them detect and classify sounds going forward. Wow. Another thing I appreciate about these scientists is that they also have a sense of humor. Their paper is titled Perch 2.0 transfers whale to Underwater Tasks. They also make a reference to something called the Bittern Method, which is sort of a play on an AI, a famous AI paper called the Bitter Lesson. So they are basically saying that some of the principles that we are finding for large language models with humans where, like, if you make a model better at coding, it also gets better at math or some other related task, they're also finding that that applies to things in animal studies. So if you make a model better at classifying bird sounds, it also gets better at classifying underwater sounds. So I don't know what's next for this particular project. This seems like an active area that a lot of companies, organizations are involved in. We may be getting close to understanding more about bird songs or whale speech. But I just thought it was interesting, the idea that there is something generalizable about understanding animal sounds as a whole, where you can take a model trained on bird sounds, use it to analyze underwater acoustic data, and find that it actually outperforms a lot of the more specific models that are just trained on whale noises.

Casey Newton (62:59)

Well, thanks for the deep dive into that whale paper, Kevin. Did you hear about the researcher who found himself missing a whale?

Kevin Roose (63:06)

No.

Casey Newton (63:06)

Yeah, he had a cetacean needed.

Kevin Roose (63:12)

All right, that's one good thing.

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Casey Newton (64:44)

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Kevin Roose (65:17)

Hard Fork is produced by Whitney Jones and Rachel Cohn. We're edited by Viren Pavic. We're fact checked by Caitlin Love. Today's show was engineered by Katie McMurran. Our executive producer is Jen Poyant. Original music by Alicia Ba? Itup, Marion Lozano, Robert Niemisto, Alyssa Moxley and Dan Powell. Video production by Sawyer Roquet, Pat Gunther, Jake Nichol and Chris Shot. You can watch this full episode on YouTube@YouTube.com hardfork Special thanks to Paula Schumann, Hui Wing Tam and Dahlia Haddad. You can email us as always@hardforkytimes.com Send us your whale song. ADP knows anything can change the world of work.

Casey Newton (66:21)

Like what if a blocked shipping port caused a worldwide collaboration coffee shortage?

Kevin Roose (66:25)

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