Hard Fork - The Musketeers Take Washington + Spotify's Ghost Music + Tool Time

Episode Date: February 7, 2025

This week, we’re joined by the Times reporter Jonathan Swan to discuss Elon Musk’s tech takeover of Washington, D.C. Then, Liz Pelly, author of a new book about Spotify, stops by to discuss “ghost musicians” and how Spotify’s algorithms are reshaping music culture. And finally — it’s Tool Time! We’ll tell you all about the new A.I. tools we’re using, plus the one that we wish existed.Guests: Jonathan Swan, a White House reporter for The New York TimesLiz Pelly, author of “Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist”Additional Reading: Inside Musk’s Aggressive Incursion Into the Federal GovernmentThe Ghosts in the MachineChatGPT's deep research might be the first good agent Unlock full access to New York Times podcasts and explore everything from politics to pop culture. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify.

Match Group has a new CEO, Kevin, and it is the former CEO of Zillow, the sort of real estate company, which I imagine you probably spent some time on Zillow recently as you've been looking for houses. Well, this raises the question, Kevin, as you're browsing through your Tinder matches, something I know you do a lot,

do you think that we're soon going to see some sort of a zestiment of that person's worth? I think that's a great idea. You know? I think that they should analyze market conditions and say, you know, the market price for, you know, a tall gay man in San Francisco, down 30% from last year. That's right. Short kings are having a huge moment.

I just think, for me, I think this estimate should say something like, this person probably still has roommates. You know? Like that sort of information that you want, not that it's bad to have roommates, but you know, it can introduce complexity, and maybe, you know, you want to know that

before you swipe. I think you know how on Zilla, you can see the history of every house, of every property? I think you should be able to, like, see the entire relationship history. The entire romantic history. The last, like, three romantic partners. Yes, two relationships ago, this person

got dumped for not being a good communicator. I have to say, we've come up with more good product ideas for Tinder in these past five minutes than Tinder has in the past year. Call us! Call us! product ideas for Tinder in these past five minutes that Tinder has in the past year. Call us! Call us! I'm Kevin Roos, a tech columnist at the New York Times.

I'm Casey Newton from Platformer. And this is Hard Fork. This week, The Times' Jonathan Swan joins us to discuss Elon Musk's tech takeover of Washington, D.C. Then, author Liz Pelly stops by to discuss her new book on Spotify and how its algorithms are reshaping music culture. And finally, it's tool time. We'll tell you about the new AI tools we're using

and the one that we wish existed. Well Casey, the biggest story in tech this week is actually not happening in the Bay Area where we live. It is happening across this great country in Washington DC. It sure is, Kevin. So Elon Musk and his team at DOGE, the Department of Government Efficiency, have been hacking away at the federal government, barging into agencies, demanding data and access to computer systems, basically staging what some people are calling a tech takeover of the federal government. And Musk brought with him to Washington a bunch of people to help him at Doge with this effort, including a number of young men, some of

them in their 20s and even reportedly a teenager or two, who are helping him with this effort, including a number of young men, some of them in their 20s, and even reportedly a teenager or two who are helping him with this effort. Yeah, including Luke Verator, who we mentioned on the show in a previous episode, Kevin, because he was part of an effort to decode ancient scrolls using AI. Yeah, and together they've been pulling late nights, some of them reportedly literally sleeping in their offices,

so that they can work basically around the clock to shrink the federal government. Yeah, and they're doing it in some really aggressive and some would say scary ways. They have already gained access to the Treasury's payment system, they have put on leave nearly the entire workforce of USAID, and they have emailed roughly two million federal workers, Kevin, offering them the option to resign and allegedly to be paid through the end of September. Yes, and the subject line of that email was fork in the road, which is not a hard fork reference.

That we know of. That we know of. But it was the same subject line that was sent to employees at Twitter after Elon Musk took Twitter over giving them the chance to Resign or take severance packages if they didn't want to work there anymore. Yeah, so Casey Why are we talking about this on hard fork this week? We are not a politics show

We are not but Kevin several listeners emailed us saying we want to know more about what is happening. What we're seeing unfold in Washington is unprecedented in the modern history, certainly of the United States, and it involves somebody who has been a main character of this podcast from the beginning in Elon Musk. In 2022, Elon Musk bought and took over Twitter, and what is happening at the federal government, while it is infinitely at the federal government, while it is infinitely more important than Twitter, is unfolding in a very similar fashion.

Yeah, I mean, that to me is what brings this into our lane. I feel like the Twitter takeover was sort of the warm-up act for what is happening with Doge and the federal government. Many of the same tactics and playbooks that were used to take over Twitter, to purge the disloyal, woke employees of that company as Elon Musk saw them, are now being used at a much bigger scale on the federal workforce. So we brought in someone who is an expert in politics and Trump and all things Washington,

Jonathan Swan, my colleague at the New York Times. He was one of the authors of a piece that came out in the Times earlier this week that was basically a broad and sweeping look at all of the ways in which Elon Musk and his allies have been making what they called an aggressive incursion into the federal government. Really great story, people should check it out.

But Jonathan has been covering Trump for years, and he's just really got a feel for the pulse of Washington and how people are reacting to Elon Musk's big invasion. Let's bring him in. Jonathan Swan, welcome to Hard Fork. Thanks for having me. So Jonathan, give us the view from Washington. What is the vibe on the ground as Elon Musk and his band of Silicon Valley programmers

move around trying to call the federal government? Well, it really depends who you talk to. For the career civil servants, it's terror. I mean, these people don't know if they're going to have jobs. They don't, in some cases, don't know if their agency's going to exist in the morning. You know, the website goes dark. At USAID, they get an email after midnight, don't come into work. He's calling their agency evil.

They're sort of following him on X. A lot of it's very opaque. He's got these young guys who work for him at, you know, Tesla and some of the other companies. Some of them are in their 20s. One of them is like 19. And they're roaming around the agencies and they'll do these interviews with folks, but they won't sometimes tell them what their name is because they worry about being docked. So it's like you imagine you're a civil servant.

This guy shows up from Doge and he's wearing a t-shirt and a blazer, and he starts basically interrogating you with the questions all being based on the assumption of you are a lazy, worthless, idiotic federal worker. Justify your existence to me, please. And they say, well, who are you? Well, I'm not telling you my surname or whatever. So this operation is now unfolding Do we know where it's going? Is there a roadmap that anyone can see or do we just have to? Rely on reporting that the folks like yourself are doing to even understand what is happening and what the plan is We know broadly speaking what he wants to do, right? He I mean he has said he wants to cut $2 trillion out of the federal budget.

The federal budget's around $7 trillion. It's almost impossible to imagine how he would actually do that. He's since even Elon Musk, who's famous for setting unrealistic expectations and deadlines and what have you at Tesla and SpaceX, he's downscaled that and said, well, maybe we'll get to a trillion. Even that would be astonishing. And people don't really think that that's plausible. But we know he wants to cut.

We know that there's an ideological agenda. Agencies that are doing things that are seen as not aligned with the Trump movement are going to face more hostility. USAID is sort of the platonic ideal of, in their minds, the evil leftist deep state, because what is the Trump movement? It's quote-unquote America first. Well, what is USAID? It's an agency that spends money overseas in foreign aid, humanitarian assistance. They have found themselves in the crosshairs,

but so have a bunch of other agencies. Their general contempt for the federal workforce was really evident if you just read that email. Right, the fork in the road email. Yeah, I mean, you guys know this because this is your field, like the tech, but it's basically this email went to almost all federal workers, around two million federal workers.

But the email was, I thought it was really revealing. You know, when you talk about what's his plan, what's his thinking, I thought it was such a revealing document because the text of the email basically was, because the text of the email basically was, we'd love you to resign. Whoever you are, you are in a lower productivity job and you should resign and take a higher productivity, private sector job. I don't distinguish between your expertise,

I don't distinguish between your experience, you're all basically worth nothing. Yeah. Jonathan, let's talk a little bit about the cast and characters here. So obviously our audience is very familiar with Elon Musk, but tell us about the people around him,

these young men from Silicon Valley that he's brought in with Doge, who everyone's been talking about this week. You mentioned some of them are in their early twenties, maybe even one who's a teenager still. Who are they? How many of them are in their early 20s, maybe even one who's a teenager still. Who are they? How many of them are there? Do they have any private or public sector experience, or are they just interns from

his companies who he thinks would do a good job helping with this? Well, this is a little opaque to me and I have to give credit to my wonderful colleagues, Ryan Mack, K. Konger, Teddy Shleifer. So from what I can understand, you know, some of these are like very bright and experienced allies of Elon Musk that he's worked with for a long time. You know, Tom Krause, who is I think the one that was given access to the treasury payment system and I think he's the CEO of like a

Software cloud software or something one of them one of them deciphered some ancient scrolls Yeah, that was his claim to fame Luke Farator Yeah, you run the gamut from like that guy the scroll decipherer to you know More seasoned people who've worked with Elon Musk for a long time, but I will say It's not actually that clear to me how many of them are there. I've heard that there were around 40 at inauguration. And they show up and, you know, they're very confident and they ask a lot of questions and want access to the systems.

They want to get their hands on the pipes of government and not sort of take the word of career officials telling them what they're doing. Man, it just makes me think of every time over the last decade that I've heard some, you know, person in Washington saying, "'We've got to get more young people interested in government.'"

And I just pictured like the monkey's paw curls. It's like, you might not have wanted it to go down this way. Careful what you wish for. So Jonathan, one of Musk's first moves was, as you mentioned, to seize the federal government's payment systems. Why did he start there? So Musk has told people in the administration that in his view, the way to control government is to control the computers.

He's got a real history of taking interest in the detail, like the really getting down to the nitty gritty and roaming the floors of the factory and why do we have this part in this machine? Why can't we get rid of it and make it more efficient and cheaper and whatever? So that's the mindset, as far as I can tell from talking to folks in the White House and the government.

And his view is, I don't want my guys at Doge to sit down with deep state, quote unquote, official ex, who's going to tell them everything's great and blah, blah, blah. No, I want my guys to have the source code to go in and see for themselves all this nasty fraud. And he's sort of doing a version of what he did with Twitter. Like he's trying to publicize things that he thinks are ridiculous, that money's being spent on. It's the same playbook that we're seeing publicly. But with Treasury, I mean, it's really important. I mean, this is like, this is a really an important part of America's critical infrastructure. I've talked to former senior Treasury officials. They were really alarmed.

This is the outgoing Biden people in December when they got these requests from these Doge people saying, we need the source code. Because this payment system, it's literally the payment system that distributes more than $5 trillion a year. It's like 88% of federal spending goes through this system. People's social security payments, people depend on this system. And it's historically been managed by a small group of really trusted, really experienced career civil servants. As far as I know from talking to former officials, they've never heard of a situation where a political

appointee has requested access to this, let alone be granted access to it. Now they are insisting to us that it is quote unquote read only access, meaning they can't alter payments, but even that is considered extraordinary. But I mean, here's why I think so many people are concerned about this. The constitution gives the power to spend money to Congress, not to the president. The president cannot unilaterally decide what to spend money on or not spend money on. So give us a sense of the conversation around the law here. And is anyone even trying to make the case that what Musk and his colleagues are doing is legal.

So firstly, you're absolutely right. Congress has the power of the purse. There's no question about that. The White House casts this as a temporary freeze to examine the spending to make sure it doesn't conflict with Trump's policy priorities. But as Charlie Savage, my colleague, has written, it also appears to plant the seeds of a potential Supreme Court fight over how much power a president has to refuse to spend money that Congress has appropriated. So there actually is a legal question here that could be litigated all the way up to the Supreme Court. And Trump's aides, they have long wanted to seize back some of this power to withhold

spending. Right. There's this theory floating around that I want to get your take on that some of this is just a diversion or a tactic that basically, the Doge folks, they know that not all the things they're doing are legal, they know that not all of it will end up passing muster with Trump or with Congress, but they're sort of asking for a foot

and expecting six inches, that they're essentially overreaching on purpose so that even if half of what they're doing gets overturned or overruled or can't actually make it through all of the checks and balances, they will still have gotten a fair bit of what they wanted. Do you buy that, or do you think they legitimately expect all of the things they're doing to stand up?

Oh, no, no, no, no, no. And we've written about this. This is actually a really important part of how they think and their strategy. They learned in their first term, it took them a while, but they learned that the most effective way to get really aggressive policies through is to flood the zone, is to do so many things at once

that are aggressive that your opposition, and when they think about their opposition, their mental map is the media, Democrats, Congress, and these outside nonprofit groups like the ACLU who are likely to sue them. They know that those three institutions have a limited amount of resources, right? There's just only so much mental bandwidth to fight them. And so people need to pick their targets. Meanwhile, you're shooting bullets through one after the other on all these different issues. And that's been absolutely their approach in this onslaught of executive action that we've seen in the last two weeks. Yeah, I mean I'm sure everyone in Washington is very shocked

and surprised. I imagine there's one group of people who are not all that shocked and surprised which is Twitter employees or former Twitter employees. Casey, this is a question more for you but like you covered the must takeover of Twitter and everything that followed that, including layoffs and budget cuts and general madness. As you're watching what's unfolding in Washington, is anything surprising you, or do you kind of feel like we're just seeing a story we've seen play out before just on a much bigger stage?

I mean, the playbook is not surprising. We know that this is how Musk operates. He does have tremendous disdain for anybody who he did not hire himself, right? And so we're seeing so much of the way he treated Twitter employees reflected in the way that he's now treating the workforce of the federal government. What I think is so surprising though, Kevin, is that Twitter was a company that he bought, right? He had the legal right to do most of what he did.

There were some lawsuits related to some agreements that he maybe broke, but for the most part, he bought the company and it was his right to decide who he wanted to work there and what he wanted to do with them. The shocking thing about the case of the federal government was that, as Jonathan just said, this is the richest man in the world.

He was not elected. He's not presented Congress with a plan for what he wants to he's not gotten You know consent from the legislative branch and so that to me is just the real shock Yeah I mean to me these don't feel like perfect comparisons because as you said one of these is a company and one of these is a Government but I am starting to see some parallels in some of the tactics that Elon Musk is using one of them being this idea of some parallels in some of the tactics that Elon Musk is using, one of them being this idea of zero-based budgeting. So Jonathan, tell us about

zero-based budgeting and how it's showing up in Washington. Well, it's the idea that you bring a budget to zero and then justify every dollar that you add in spending. So instead of saying, what should we cut? It's actually, no, let's start from zero and say, what should we add? And it's just a way of forcing people to justify every single dollar that they spend. Elon Musk has told people that the success of this Doge effort, his metric for it will be how many dollars they save per day. And to do that,

they're looking at the treasury payments, the USAID. He's looking at the federal government's real estate portfolio, property portfolio to see what they can offload. So the range is so wide. Yeah. I would say like to me, what happened at Twitter to the extent that that can be used as sort of a preview of what might happen in Washington, is that there was sort of a preview of what might happen in Washington,

is that there was sort of two phases of that takeover of what he considered a hostile institution. One of them was sort of the operational phase where you try to figure out, you know, who's paying what to whom and what are we spending money on that we don't need to and where the inefficiencies and then there's the ideological purge, which happened when he would go around to Twitter employees and ask them to commit to being extremely hardcore and try to figure out who was on his side and who wasn't, and then purge the people who weren't.

Do you see any signs, Jonathan, in Washington that that kind of thing is happening? Are these Doge people going around, you know, asking people to pledge their loyalty to the Trump administration, or is that sort of still to come? Well, I'd have to go back and look at the text of that email that was sent out,

but I think loyalty was one of the criteria on that email. Certainly the Trump team has made loyalty absolutely central to the way that they hire people. There's been some reporting in the Times, Jonathan, that Elon and his crew want to bring AI into government. Do we know anything about how or what they mean by that? I credit my colleagues for this, Kate Conger and Ryan Mack, but this was in our big story

on Musk. Yes. So as we understand it, Musk's allies aim to inject artificial intelligence tools into government systems. And the point, supposedly, is to use them to assess contracts and recommend cuts. So what they were told, Caden and Ryan, was that on Monday, Thomas Shedd, who's a former Tesla engineer, he's been tapped to lead a technology team at the General Services Administration. He told some staff members they hope to put all federal contracts into a centralized system

so they could be analyzed by artificial intelligence. And I know from my own reporting that Elon Musk, privately for months now, has been talking about this idea of using artificial intelligence to identify waste within the federal government. And, you know, it doesn't seem like a crazy idea to me, conceptually. I mean, use whatever tools you can to figure out where the wasteful spending is. Problem is, I don't have visibility into what these tools are. It's all very, very opaque. Right. And I think we should say, like, this is not, this part does not feel unprecedented to me. Like, you know, the various administrations, Democratic and Republican, have tried to bring

in the brightest minds in the tech sector to update and modernize some of the creaky, outdated systems that many government agencies use. We have the US Digital Service, there are groups like 18F, these groups of technologists who are brought in to try to bring things up to date. But that is a process that is established and requires doing things like going through a procurement process if you want to use some new AI tool

because maybe it's not secure, maybe there are privacy concerns. You want to make sure that that is fully vetted before you roll it out into these very important systems. It seems very different to have a group of engineers, programmers, product people coming in and just saying, we're going to use these tools whether you like it or not.

Yeah. I mean, one thing that a source mentioned to me the other day, who's been a very senior person in the government, is the counterintelligence risks here. When you have a bunch of people who are young, who are from Silicon Valley or different private companies, moving very fast, very aggressively and getting really sensitive access to the federal government opens up all sorts of espionage opportunities. I mean, foreign governments are constantly targeting the American government workforce, looking for vulnerabilities. There are all kinds of potential side effects of this that perhaps are not being considered

as they move really quickly and aggressively. Hmm. Well help us think through the next steps here. We know that there are already some lawsuits percolating designed to maybe stop some of this. We've also seen Democrats wake up and start protesting. But can you give us a read, Jonathan? Like, what do you think is likely to happen over the next week or so? Do you imagine that anything is going to put the brakes on doge or are they just going to sort of have their way with the federal government here?

Obviously, there are lawsuits. One of the challenges with lawsuits in general is that the speed at which Musk is moving and Trump is moving far exceeds, I think, the capacity of the legal system to catch up. They're doing so many things at once so quickly that the facts on the ground are changing. In the meantime, a whole bunch of things are happening.

Relief projects in Sudan, all around the world ground are changing. In the meantime, a whole bunch of things are happening. You know, relief projects in Sudan have come, you know, all around the world where US foreign aid is helping people have stopped already. So yes, there's going to be legal challenges. Some of this won't fly ultimately, but some of it will. And we're not seeing much appetite from Congress to assert themselves and assert their authorities. Obviously, the House and Senate are in Republican hands. We're not exactly seeing a very aggressive legislative branch.

And in terms of Musk himself, the limit on him is the extent to which Trump tolerates him. That's the only kind of limiting principle. There's been a lot of people predicting that this relationship would blow up. It's kind of interesting. He's sort of willing to tolerate a lot more from Elon Musk. And it might just be as simple as it's pretty flattering having the richest guy in the world, you know, and pretty convenient having a guy who spent $300 billion helping you, working for you as Trump sees

it. Trump's the president. Elon Musk can never be president. He was born in South Africa. From Trump's point of view, great. And Elon is the one that's been most aggressive at turning his platform into a vehicle to support Donald Trump. Yeah. Jonathan, out here in Silicon Valley, we spent a lot of time over the past year talking about various types of management changes. One of them is founder mode, which is this school of thought that a lot of tech companies have borrowed from Elon Musk, where basically you stop listening to your workers, you take control, you dictate more from the top,

and you try to make things as lean and efficient as possible. I see what Elon Musk is doing in Washington as an extension of founder mode which is a corporate authoritarianism. But I'm wondering if you think there is a parallel to be drawn here between the way that a company is managed in an industry like tech and the government, or do you think that those are just fundamentally two different things?

Of course, of course. And, you know, you're talking about a federal bureaucracy at a really dangerous time in the world, a complex world, a federal government that has to do so many things. Make sure our water is clean, Make sure our food is safe. Take care of our critical infrastructure. Manage national security, including cyber security. Air travel.

The break it to fix it mindset. Like the break it part of it's pretty important because what gets broken, the stakes are just so much higher when you're talking about the entire country and the federal government. Well, I mean, we saw what happened at Twitter, right? Twitter doesn't exist anymore.

That was how the Elon Musk approach worked for Twitter. There's something else now, it's called X, not as good, doesn't make as much money, doesn't have as many people using it. He tries to sue people just to advertise on it to keep it running. So that's how that's going. So I have no confidence that what they're doing

is going to lead to some sort of magically more efficient federal government because nothing they have done so far suggests that they have a plan that is centered around taking care of people and making sure that people still get the services that they depend on, which is one of the key reasons the federal government exists. John, the quick last question, then we know you have to go. So far, Elon Musk and his Doge cadre have gone after Treasury, they have gone after USAID, they're reportedly now setting their sights on the Department of Education. What are the three next agencies that you think are in their crosshairs? Look, this is every agency in the government, although I will say, as far as I can tell,

he hasn't really been that involved at the Defense Department, but I will say, as far as I can tell, he hasn't really been that involved at the Defense Department, but I do expect that that will come because if you're really thinking about how to cut government spending, you can't ignore the Pentagon. It's such a huge, and listen, Trump has really tied their hands to a large extent because he said, you can't touch social security or Medicare, huge entitlement programs. He's promised not to cut money out of them. So if you're Elon Musk and you're looking for savings, eventually he's going to have to turn his eye properly to the Pentagon.

And I'll be very interested to see what they propose there. Again, huge conflict of interest, Elon Musk, SpaceX, huge federal contracts, but I'm going to be keeping a close eye on DoD. Jonathan Swan, thank you so much for joining us. Thanks, Jonathan. Thanks for having me. When we come back, writer Liz Peli tells us why Spotify is increasingly full of ghost musicians.

Spooky. Kevin, if you were a streaming music service playlist, what would you be called? Probably Lo-Fi Beats to podcast to. Hmm. I think of you more as a 2000s hot girl, girly pop Wednesday afternoon. But regardless, Kevin, next on our playlist today, we're going to talk about Spotify. Yes. So Spotify is a company that we really haven't spent much time talking about on the show, but I think they are very important within the world of tech companies.

In part because when we say wherever you get your podcasts, well, Spotify is a place where you can get your podcasts. Many of our listeners are probably using Spotify right now. And Spotify has had a big week. They just reported their first profitable year ever. Daniel Eck, the CEO, was quoted as saying, it only took 18 years for us to get here, but we're here. The company now has 675 million users

and around 263 million premium paying subscribers. Their ad-supported revenue is also up, but that's not what we're really here to talk about today. No, Kevin, because as popular as Spotify is, a new book argues that the company's rise hasn't necessarily benefited artists or listeners. Liz Pelly is a writer based in New York who has a new book out called Mood Machine,

The Rise of Spotify and the Costs of the Perfect Playlist. And I have to tell you, I was captivated by an excerpt of this book that came out in Harper's Magazine recently. And the excerpt focused on what are sometimes called ghost artists. These are musicians who Spotify uses as a way to fill out popular playlists

with lower cost music made exclusively for the company instead of songs from major record labels. And according to Liz, it is proliferating quite quickly. Yeah, so this is the kind of story that doesn't get told about Spotify that often, which is how it is essentially becoming an invisible force in the music world, shaping the tastes of its hundreds of millions

of subscribers in ways that maybe some people, even hardcore Spotify users, don't fully appreciate. Yeah, and we've talked about so many other invisible algorithms on this show that are reshaping culture in one way or another, this is our chance to learn how that is unfolding inside Spotify. So let's bring in Liz Peli.

Liz Peli, welcome to Heart Fork. Thanks for having me. So let's talk about Spotify's evolution as a music service over the years. When I first started using it, I really felt like the person in charge of my music listening. I would search for the artist or album I wanted to listen to. I'd play it and then I'd go look for more. And today, though, it feels like it is Spotify that is more in charge, that it is pushing algorithmic and paid recommendations at me every chance it gets.

So how did that evolution start? When Spotify launched, these things were more like search bars. You would have to know what you were looking for. You would have to know the artist or the album that you wanted to listen to. Because in certain ways, when Spotify launched, it was really sort of competing

for the type of music listener who had become accustomed to the kind of digital library that they had access to in the post-Pirate Bay years, the post Napster years, you know, the type of digital music listener who was used to opening their laptop, opening their music library, and being able to push play on whatever they wanted to hear at any moment. Yeah. So at some point, Spotify begins pushing people away from this search bar experience and more toward playlists.

What's the origin of that? So up until around 2012, when you looked at the branding of Spotify, the way it sort of characterized itself on its own website, it would really focus on words like instant, simple, free, and they would talk about giving you access to a world of music. And it really wasn't until later in 2012, around a year after they launched in the United States, when the way in which they positioned themselves started to change. They had also, around this time, commissioned a research agency to research their user base and try to give them information about what people were actually coming to the platform for.

And in a sense, they had started to realize that their users weren't only looking for access to music, but were also looking for the ability to, you know, get a recommendation or hit play and get, you know, a feed of appropriate music. So, the end of 2012, early 2013 is when you start hearing Daniel Eck and the press talking about how, okay, maybe he'd been too precious about this idea of a non-curated service and they started redesigning the homepage. And by 2013, they really started to lean into this idea of a more curated service.

And that's when I first started hearing about things like the Rap Caviar playlist, which was a very popular playlist that a lot of people were using. And actually, artists were angling to get into it and labels were angling to get their artists into the Spotify playlist because Spotify's increasingly large user base was just sort of discovering new music through the playlist. And so there was an element of that

that I feel like is familiar to, radio had the same thing where artists and labels would fight to get their songs played, but this started to feel like Spotify was actually getting its own market power because it had all these subscribers, and it could start to direct them towards certain music and away from other music. Definitely. So as the years went on,

these playlists became pretty influential in the music business. Like you said, they started to become a really integral part of how record labels thought about promoting music, and musicians, both major label and independent musicians alike, started to be pitched on and sold on the promotional opportunities of this whole system. When I started writing about Spotify, which was in the mid 2010s, one of the things that was really interesting to me at the time was the way in which independent musicians were

being sold on this playlist system as a democratizing force. Spotify always had things like, you know, the playlist ecosystem is going to level the playing field. And they talked a lot about this pyramid of playlist creation where they would start artists on these low-tier feeder playlists, look at streaming data, and then they would move you up in the playlist system if the song reacted or if there was a high completion rate. This was kind of like a myth that was sold to artists, but a lot of independent artists weren't necessarily feeling the magic

of this data-driven driven supposedly marriageocratic system. Right. So and I want to ask about that because I have to say from my perspective, when it comes to the rise of playlists, that's basically okay with me. Like it sounds from the way that you're talking about it. A lot of the reason that playlists came to be on Spotify was just user demand. People wanted kind of a guide to their music. They didn't want to be responsible for thinking up every single thing that they wanted to

listen to at any given moment. But you write in your book that over time, Spotify became increasingly concerned with shaping user behavior on the platform. So aside from the playlist that you described, how does that manifest? How do they try to shape the way that we use the app?

And I think this is similar to across the platform economy. Platforms want to shape user behavior in order to boost engagement, to hook people on their platforms, to extend the amount of minutes and hours that people are spending on their platforms so that people have tighter relationships with their products, see their products as more valuable. In the case of a streaming service, a streaming service endeavors to keep have tighter relationships with their products, see their products as more valuable. In the case of a streaming service, a streaming service endeavors to keep people

on the platform longer so that they view it as a useful part of their lives and retain their subscriptions, right? But it's not just about boosting engagement, right? Because my understanding is that Spotify pays out a huge chunk of its revenue to record labels for their music. I mean, billions of dollars. Spotify pays out a huge chunk of its revenue to record labels for their music.

I mean, billions of dollars. It is paid to artists and record labels. And so if you're Spotify and you're trying to grow your business, you could either grow your subscription base or you could just pay out less money to artists and labels. And one of the ways that you could do that is by steering people away from

sort of headline acts and artists with leverage and negotiating power and the big labels and toward maybe smaller or more lesser known musicians who maybe can't command the same types of market power. So is that something that Spotify was also doing? Yeah, that's a great point too. Part of the reason why a streaming platform wants to control more of the user experience is so that they can have more influence

over the types of music that is being recommended to users. And in the case of Spotify, one of the things that I try to trace throughout my book is this series of cost-saving initiatives that the company developed in order to try to nudge users towards content, and I hate using the word content to describe music, but this is how they refer to it,

in order to nudge users towards content that's cheaper for them to license. So there's two specific instances of this that I talk about in the book, one being around 2017, this phenomenon that people started to notice where their playlists for studying, chilling, sleeping, relaxing, people started noticing that there were tracks on these playlists that didn't necessarily seem to be from artists who were real. People were noticing their playlists increasingly filled with what appeared to be royalty-free

stock music. So one of the investigations in the book is into the rise of what internally at Spotify is called Perfect Fit Content, which is music commissioned for certain playlists and moods with improved margins. What you're describing is music that is made for Spotify. It is not Spotify going out and curating music that exists in the world and putting it into playlists. This is like, we want to make a new Lo-Fi study playlist, and so we are going to go out and have a bunch

of studio musicians make this music and then we're going to pay them much less than we would pay Taylor Swift. Right, so you know there's this handful of production companies that are part of this scheme and those production companies will then go find producers and composers who can make this stuff and one of the most interesting parts of reporting my book was talking to a handful of musicians who had made work for these production companies with these privileged deals. And, you know, they talk about how sometimes they'd be

cranking out 12 or 15 of these tracks in an hour, and the idea is to just sort of create as much content as they can to make it as simple as possible so it goes well in the background. They'd be studying music on certain playlists provided to them by the production companies as examples, which would basically be songs that had done well in the leanback environment on Spotify previously, and then try to kind of replicate similar styles in order to hopefully make content that would stream really well. And I should say, like, I understand the impulse to do this. Like, personally, I am, you know,

what you might call a lean-back listener. I listen to a tremendous amount of Spotify, many hours a day, mostly in the context of trying to go to sleep, and it plays while I'm sleeping or trying to study. I'm a huge consumer of all of the, like, low-fi, you know, music to go to sleep and it plays while I'm sleeping or trying to study. I'm a huge consumer of all of the like lo-fi, you know, music to study to. I assume that most of that is now,

like after reading your book, I assume that most of that is sort of this, this perfect fit content and that artists are not being paid very much for that. But I, that is like useful. I don't really care what the music is that puts me to sleep. I just want some music

that's sort of in the right genre with the right kind of sound. So that's, I should just say like, I understand the market forces at work here because I am part of the the universe of Spotify subscribers who do use this more ambient kind of music. Kevin is one of the reasons why the music industry is falling apart. Well, you know, it's interesting because according to some of the interviews that I did, a justification that would be used is, yeah, that some of the senior executives would say things like, well, most people don't know and also they don't care. And I, you know, I understand that there are certain types of users that won't care, but I think there are certain types of users that would care,

and they can't decide whether or not they care or not if they don't know. So one of the things with these cost saving initiatives to me that stands out as a glaring issue is the fact that none of this material is labeled as sponsored or labeled as, you know, this is being recommended due to a commercial deal. I think from the beginning,

these sorts of playlists have operated under the umbrella of editorial on streaming services and we're not just talking about Spotify. I think that there is reason to believe that other streaming services are likely engaging in similar practices as well. But if something is operating under the umbrella of editorial, I do think that there's some sort of expectation that if something's being recommended due to a commercial partnership, that should be labeled in some way. I completely agree with you. And by the way,

if they did have to label those things through regulation, you'd see a lot less of it because they would be embarrassed. I want to talk in addition, though, about the effect on the culture. And maybe we should use lo-fi beats as a jumping-off point since you brought it up, Kevin. There is a chill Lo-Fi study beats playlist on Spotify that is very popular. It's been saved about 2 million times. And in your book, Liz, you write about how the rise of Lo-Fi Beats really reflects this era. I'm curious if you can tell us what lo-fi beats were

before it became a big Spotify and YouTube phenomenon. Like, what was it as an actual culture before it became like a low-cost alternative to paying record artists? Yeah, I think it's interesting to know that the phenomenon that is now known as lo-Fi hip-hop beats to study and relax to had a sort of prehistory online. In the early 2010s, the Lo-Fi hip-hop community online was based more in forums and as people making these sort of J. Dilla, Mad Lib inspired beats,

sharing them with each other. And it was more based on SoundCloud. People were just, you know, making music inspired by these producers that they really loved and that it involved more kind of sample flipping, people trying to outdo each other with impressive drums, less sort of mellowed out, not exclusively background music made for studying. And according to some of the people I talked to, as this scene kind of moved to YouTube, moved to Spotify, as playlist curators got in the game, there was this sort of flattening effect that happened

where certain types of music from this subculture was being put onto playlists for studying and then the types of music that did well on playlists for studying was financially incentivized so more people started making that type of music. I mean that push and pull that you identify is so interesting, right? Where it's like on one hand, yeah, this doesn't feel great because now people aren't really hearing the authentic lo-fi beats. They're hearing the like the cheap version and they don't even know that they're hearing the cheap version. But on the other hand, the playlist became so popular that they incentivize the creation

of a lot more of this music. And so people, you know, wound up hearing a lot more of this thing that they liked. So how do you think about that kind of push and pull? Is the sort of picture of what Spotify doing to the culture more mixed than just, eh, algorithms are flattening everything? I think for me, it's an important distinction is that I don't necessarily just think the issue

is that people are listening to less authentic music or that people are listening to fake music. For me, my concerns have more to do with the reality that there are so many independent musicians today who are trying to figure out how to make a living in the streaming era or maybe not even make a living, but just how to connect meaningfully with listeners in the streaming era. And they're all impacted by these practices. One of the things that Spotify would say as a defense was that, you know, they turned to the stock music because they had found a need for content. But there's no shortage of music in genres like lo-fi, hip-hop, jazz, classical, or ambient

that fill out these leanback playlists by musicians who could really use the boost. So for me, I'm always thinking more about those musicians who are really impacted by being removed from these playlists, replaced with stock music, or who have never been able to access these sorts of promotional opportunities in the streaming area. There's a great excerpt of your book in Harper's Magazine that I read and loved and shared

on Blue Sky. And I had a surprisingly heated back and forth with a reader who I think was a musician himself. And he said to me, essentially, look, this ghost musician stuff that you're talking about, this stock music, musicians have always taken stock music gigs to pay the rent, right? It's always been a tough job and, you know, on some level, a gig is just a gig. And so let's not shame musicians for taking, you know, these gigs, making stock music. And I know that you're not shaming them. But I wonder what you made of that argument that this isn't as different as maybe we may think?

Well, I would encourage that person to read my book because I'm not shaming the musicians who make this work. And in fact, there's a whole chapter where I talked to a number of musicians who make this work. And what I try to explain is that this practice is as deceptive for listeners as it is for them because when I was interviewing musicians who made music for companies that were part of the PFC practice, they-

That's the perfect fit content for Spotify. Yeah, like these musicians didn't know anything about the broader arrangement that they had signed up to be part of. They would tell me things like they make their tracks, they submit them, get paid, and they don't know what happens after that. But their arrangements are dictated by their contracts

between them and the production company that's hiring them. So that'll look different from contract to contract. Some of them told me that their arrangement was a buyout where they're getting a flat fee for the master. And then maybe there's some other royalty rights that they're entitled to. Each company has its own arrangement. I talked to a couple of composers from this songwriter advocacy group based in the UK

called Ivers Academy. And they talked about how from their perspective, they felt like companies like Epidemic Sound in these arrangements are trying to buy composers out of their luck. How when you make production music, part of what you're doing is making lots and lots of music. You never know which tracks might take off and take on a life of their own and then end up being a really sustainable source of income for you for years to come. And by encouraging these buyouts, encouraging this flat fee model, they were buying out

composers' luck and how this sort of,, and that composers should hold on to their chances of a song going viral or being used in a commercial and then being able to see some success off of it. So I think a lot of listeners to our show will be thinking about AI when it comes to the future of services like Spotify. We've talked on the show about services like Suno and UDO which can basically generate new music along the lines of existing music. To me, it just seems inevitable that at least for

these ambient lean back playlists, Spotify will eventually just start creating music on the fly using AI so that they don't have to pay any royalty to any human artist or any production company. Is that happening already and we don't know about it? Do you think that this is the future of this kind of music? Daniel Eck in the press already in recent years has said things about how he finds

the potentials of generative AI music to be exciting, that it could be great culturally and help boost engagement on Spotify. So to me, that sort of framing or that optimism about it sort of signals to me that it would seem unsurprising if that direction was explored eventually, though I should say that in my reporting on PFC and ghost music, it's not necessarily something that I directly observed, although companies like Epidemic Sound who work with Spotify in this way have directly signaled that they're excited about the potential of their composers working with generative AI tools and open to it.

So it's not definitely hard to imagine. I think that from my perspective, there are certainly a lot of important concerns about generative AI content and its impact on streaming services. There already is so much AI-generated music flooding streaming services every day. But I also think it's as important to remember the different ways in which different systems that might be called AI, systems of machine

learning, automated recommendations, algorithmic recommendations, personalization over the past 15 years have reshaped the way that people understand music, are recommended music, the context within which music is served and presented to listeners, I think is equally worthy of consideration and critique. know, equally, equally worthy of consideration and critique. Yeah. Let me put some of my own cards on the table. Like I have to say that Spotify often feels like a miracle in my life. Like I still remember being a high schooler who had to scrimp and

save to buy a single CD for $18. And I wanted to know so much more about the canon of pop music. And it was just completely inaccessible to me. But now Spotify exists and I could just inhale it But Liz your your writing on this subject really unsettles me because it reveals the extent to which Spotify has built systems to manipulate My listening in ways that are completely invisible to me and I do worry that as the years go on My taste in music is becoming less and less my own so I wanted to ask you that my taste in music is becoming less and less my own. So I wanted to ask you how you might reconcile those two things or how you think I might reconcile them and how you try to personally cultivate your own taste in music in this

age. Absolutely. Yeah. Something that I think can sort of be complicated or seem like a contradiction in some ways is that I actually am in favor of universal access to music. I don't think universal access to music is a bad thing. And I'm someone who came up in the era of Napster and file sharing and being able to access a lot of music

that way was really influential and formative for me, I should say. So I don't necessarily think that it's universal access to music that's the problem. For me, I think it's more, it's the rise of and championing of lean back listening, of a sense of passivity, of this devaluing of music, not just on a financial level, but in some ways, on a more cultural level that I think happens when this relationship with music is sort of watered down in this way. And of course, Spotify didn't, and streaming didn't create these conditions, didn't create

the idea of the lean-back listener, for example. But I think that this way of music has been really exacerbated by streaming, by making lean-back listening sort of, you know, the most frictionless way to engage in music. I think that optimization and frictionlessness has been really disastrous for culture beyond even music. I'm a music critic also, and a cultural critic, and I think that thinking is really important. And I think that encouraging people to think

is really important. And when I talk to former Spotify staffers, when I look at the ways in which optimization and frictionlessness are seen as these goals of streaming curation in the platform era, there is one interview that I did with a former staffer who talked about the goals of the curation ecosystem as trying to reduce cognitive work

that people have to do when they open the app. In my book, I sort of trace Spotify's long-term goal to create a product where the user can open the app and be met with the perfect recommendation at the perfect moment without having to do any deciding or any choosing or any thinking. Well, it seems like the TikTok model just applied to music rather than video. Lots of social media platforms have had the same realization that if we just remove all

of the choice from the user and just give them an endless scroll of algorithmically selected content, we can keep them hooked for longer because statistically, most people don't want to do the work of searching out the things that they want. But that is a very cynical view and I think, unfortunately, it does appear to be profitable. I mean, Spotify just had its first profitable year. So something they're doing is working, but I'm not sure it's working for culture at large. Yeah.

I mean, one of the former Spotify engineers that I spoke with referred to the TikTok feed as the ultimate distillation of lean back listening. You're not putting in any input. You're just giving signals based on how long you linger on something. And I guess, you know, what I was trying to get at earlier is that as a critic, as someone who thinks a lot about the way in which music is contextualized as a way of opening up the possibility of new connections with music, to me, this idea of encouraging people to think less about what they're listening to is troubling.

I think that this process of listening, thinking, deciding, hearing things that you don't like, deciding why you don't like them, being challenged by music that is outside your comfort zone, like this is all important from my perspective. Yeah. I think there should be a ghost musician stage at Coachella this year,

where everyone who's made these playlists that we listen to all year long, they just get on stage and it's just little twinkly piano, like, you know, from, I don't know, 8 to 9 PM. I love that. Let's give these people some attention. And here's my feature request.

I want a toggle switch on Spotify where before I go to sleep and put on my sleep playlist, I can just say, only use human musicians because those people are performing a valuable service for me. And I love the idea of like some obscure, classical pianist just waking up to a giant royalty check from Spotify because millions of people

have been using their music to fall asleep. Spotify of course famous for its giant royalty checks. I mean, you know, I write in my book, there's no shortage of really inspiring ambient music to be discovered these days by actual musicians. So if there was going to be a, you know, ambient stage to a major music festival, I would hope that it was, you know, those artists and some of the musicians making the music for these Ghost Artists Playlist have their own creative practices. So I would also, you know, hope that they'd be able to share that music.

No, I'm very glad that we had this conversation because I think I am realizing that I am the problem. It's me. Which was my hope with this conversation. So I'm excited too. And I actually do think that the sort of loss of agency and loss of, you know, taste, essentially, that you're describing in your book, applies to me. I think that I used to be a person

who sought out specific musicians and artists, and I think I have just gotten lazy about that. And so I do actually feel challenged by what you have told us today, and I am going to start being more intentional about the music so I do actually feel challenged by what you have told us today and I am going to start being more intentional about the music that I choose. Well maybe on our way out Lizzie as you mentioned you are a critic do you have any ambient chill lo-fi artists or artists that you might suggest to Kevin so that when he's in more of a lean back mode when he wants to hear the genuine

article and not the dollar store version, that he might be able to enjoy. Okay. I will say that probably my favorite ambient music of the past few years has been by Emily A. Sprague, who is the singer of this band called Florist, but also makes ambient music that is really beautiful. And I would recommend checking out that.

Perfect. Human beings recommending music to each other, just like in the old days. How do you spell that? Emily A. S-P-R-A-G-U-E. Yeah, and just for a change, Kevin, try listening to Emily while you're awake.

I just want you, it might actually improve your appreciation of music. It would be my guess. That's a good tip. Liz, fascinating conversation. Congratulations on the book. Thank you for joining us on Hard Fork. Thanks so much for having me.

["Tooltips," by David Bowie playing in background.] When we come back, we're gonna tell you what AI tools we're using in a new segment called tool time. Well, Casey, it is time for a new segment that we're calling Tool Time. It's Tool Time. Now, if you are a 90s kid, you might remember home improvement. Only 90s kids will remember home improvement. Tim, the tool man, Taylor had a TV show called tool time, but this is different. That's right.

Because whereas Tim Taylor was often working on his car in his garage, we are working on laptops in our home offices. Yes. This is more of a knowledge worker tool time. But we do get a lot of questions from listeners about the tools that we're using, whether it's AI to help us be more productive at work or maybe just in our personal lives. People want to know what is going on out there and what the latest and greatest tools on the market are.

Yeah, they hear us doing hard work and they think they clearly are not doing this without computer assistance. There is some sort of something that they're using to aid themselves. And today we're actually going to tell you what those things are. And Casey, this is a segment about AI and AI tools, so we should make our AI disclosures. Well, here's one for you, Kevin. Casey's boyfriend works at Anthropic. Kevin works for the New York Times, which is currently suing OpenAI and Microsoft over alleged copyright violations related to the training of large language models. Is that the first time we've ever referred to

ourselves as a third person on the show? No, but I like it. All right. The first tool that we want to talk about on Tool Time today is Deep Research. This is a new feature out from OpenAI. It's available to subscribers to the $200 a month

ChatGPT Pro plan, although they have said that they plan to make it more widely available. And Casey, just explain Deep Research for people who haven't heard about it or tried it. Sure, so Deep Research is a way to get a lengthy, extensive, detailed report on a subject that you are interested in. You access it through the normal chat GPT interface,

but when you type in your query, you click a button that says Deep Research, and then Deep Research will read your query. It'll ask you a few follow-up questions so it can try to really hone in on what you want, and then it will use this as yet unreleased model called O3. And so what sort of reports have you been asking Deep Research to create? So I have been experimenting with this, and I've been really impressed so far.

I mean, I have done it with a couple of different topics. One of them, I was just curious about the history of the term AGI, artificial general intelligence. And so I asked Deep Research to make a report for me about, trace this sort of intellectual history of this term and the idea behind it, a computer capable of doing everything the human brain can. And first it asked me some questions to clarify. It said, are you looking for an academic style research document with citations or a more general historical

overview? What time frame should I focus on? Do you want to include science fiction or just general references to scholars and other people talking about AGI? And I answered those questions, and then it went away for 10 minutes. It consulted 36 sources, and it returned a seven

or eight page report about the intellectual history of the term AGI. And as you read through this report, what did you notice? Like, is this a sort of subject where you actually had a lot of familiarity with and so you were able to kind of follow it or was this something that, where you really didn't know a lot of the information that it was telling you? So I have done this kind of research project

before for my last book. I did a document very similar to this and this was good. It was really good. It went all the way back to 1956 to the Dartmouth workshop where the term artificial intelligence was coined. It went back even further than that into the 17th century when Thomas Hobbes talked about how reasoning was akin to computation. So it just traced the entire intellectual history of this term, and I didn't see anything obviously wrong in it. When I started checking some of the citations, it actually all looked pretty good.

Well, I have been doing my own explorations with deep research, and I have to say this feels like the first good AI agent. There's been a lot of talk over the past six months in particular about how this next era of AI is going to be these advanced tools that can do multi-step projects on your behalf in the background while you're not paying attention. I haven't used any so far that felt like they were meeting that bar until this one. I'm somebody who writes a column three times a week. That column is usually rooted in some set of historical events that I need to refresh my memory about. And because it involves subjects I've written about before,

I'm a little bit more confident as I am using it because while it does make mistakes, and I have to say, I have never done a deep research report where I have not found at least one mistake. Other stuff is actually true. And more importantly, it helps to structure my thinking a bit, right? It can create a timeline of events for me.

It can bracket out different ideas into different buckets and offer citations. And you know, I have to say, if I had an editorial assistant and I said, hey, in the next hour, put together a report for me about, you know, this sort of thing, I would be surprised if they could do something that comprehensive in that short of amount of time.

Yeah, so I think it's also useful for just more personal things. One of the things that I had deep research do, I have this stack of parenting books that I have been meaning to read ever since like before my kid was born. And I just never got around to it. A lot of the advice in parenting books

sort of overlaps with itself or overlaps with other books. So it's not a very efficient way of like understanding sort of what you're supposed to do when a toddler is having a temper tantrum or something. And so I basically just said, go off, read all of the things you can from this set of parenting literature and like give me the cliff notes. And it went out and it did that pretty well.

Wow. And so now for the first time, you'll know what to do when your toddler throws a tantrum, which is what by the way. Well, I haven't made my way through the 10,000 word report yet, but I'll get there and I'll let you know. So we should say deep research from OpenAI. This is not the only deep research tool on the market.

Google also has a product called Gemini Deep Research. How would you say this stacks up to other similar tools that you have tried? So I mean, in short, Google's version is not as good. That is to be expected. Google is using a regular large language model, whereas OpenAI is using what they call a reasoning model, which is just built better to do this sort of thing. I think the fact that OpenAI asks questions before it gets to work is really, really useful because it does help you hone in on what you want.

You can also watch the chain of thought as it goes. We talked about this recently with DeepSeq. It does something similar where you can sort of try to understand what is this model doing, and if it's doing something you don't like, you can sort of ask a follow-up later to maybe guide it better. And then finally, you just get much longer output. So when I ran similar queries in Google's version and OpenAI's version, OpenAI's version was generally at least twice as long. That's a mixed blessing, of course,

because now you have twice as much stuff to read, but in general, I found it much more comprehensive. The final thing that I would say was, there is just more stuff in the OpenAI deep research results that feels like thinking. And I know that will drive some people crazy, and they will scream at us and say you're anthropomorphizing these things.

But I'm telling you, while I do not think that the AI ascension, I do think it can create very good human reasoning that can sort of verge on the insightful. And that's really powerful, and it is something that Google's version cannot yet do. What do you think? Yeah, I think deep research is really useful,

and I think it's potentially a very big deal. I mean, a lot of white collar knowledge work is about research that is sort of the one of the fundamental tasks involved in jobs like consulting or, you know, even finance or journalism, like knowing the sort of capsule history of the thing that you are writing or thinking or preparing a presentation about is often quite useful and and pretty time consuming. So I think the implications of tools like deep research on the sort of white-collar labor market are potentially very steep. But just as a tool I think this is very useful already for people who want to quickly get up to speed on new topics. It's a very good learning tool. I've been using it to teach myself things.

So right now, you only get a 100 queries per month. Even if you pay $200 a month to OpenAI for the Pro subscription, it is very compute intensive, and you are somewhat limited in what you can do. But I think we should keep tabs on this, and I'm personally going to keep my Pro subscription just so that I can have access to this.

I feel the same way. So I subscribed to ChatGPT Pro within the past couple of weeks because I wanted access to this operator agent, which it released a week ago, which we talked about in our most recent episode. And I used it and I wrote about it and I thought, I don't wanna use this anymore, it's not that good.

So I was truly getting ready to cancel and then OpenAI said, well, if you subscribe to Pro, we'll also throw in these 100 deep research queries a month. And I thought, that actually might be worth $200 a month to me. Totally. All right, next tool on our list. This is a tool that I've been using for the past week or two

called Granola AI. Casey, are you a Granola user? I am. And this one tickled me, because I had started using Granola in November, I think. And I just hadn't mentioned it to you yet. And so when you told me you were into it, I was like, that's cool, because I had started using Granola in November, I think, and I just hadn't mentioned it to you yet. And so when you told me you were into it, I was like, that's cool, because I am too.

So the way Granola AI works is it's an app, you download it, you install it on your computer, and then anytime you open a new video meeting, like a Zoom or a Google Meet or- Or a Cisco WebEx. Or a Cisco WebEx, if you're still at one of the three companies in America that still uses that, you can have Granola take

notes on your meeting. And what it does is sort of interesting. It's not recording the meeting. I'm sure you've also seen these meeting note-taking tools where kind of the robot joins the video meeting as a sort a hidden participant, and records and transcribes. Granola works slightly differently.

It doesn't record the meeting. It basically just takes the sound that's coming out of your computer and transcribes it in real-time, and then presents you with a pretty detailed summary of what happened in the meeting. So if you are a person who likes to take notes on meetings, this is a good replacement for that. It's also got some cool features where you can

chat with the meeting transcript afterwards, and you can say, what did Bob say? What are some action items that might come out of this? I used it to say, what was Kevin's worst idea this week in our editorial planning meeting? So I have found this very useful. What about you? Yeah, I have as well.

And look, I'm sure that at this point, people have seen these AI note takers. They might be wondering what's so special about this one. To me, what has made it stand apart is in these summaries that it gives you after the meeting. Like it's really good at identifying here were the most important things that came. Oh, did you talk about some sort of milestone in the meeting? We're going to put that at the meeting. Like it's really good at identifying here were the most important things that came out. Oh, you know, did you talk about some sort of milestone in the meeting? We're going to put that at the top. You know, were there things you wanted to work on? That's going to be at the top. And so it's just really smart and they have different templates depending on what you're doing.

So, you know, granola, I think some of their first big users were venture capitalists. And many of the meetings that VCs are taking are people who are pitching them for their startups. So Granola has a template for that. So the AI essentially knows what information to look for that is gonna be useful to a VC afterwards. Similarly, if you have a one-on-one meeting with the same person every week, Granola has a template for that.

So it's really bringing in a lot of structure to the kinds of regular standing meetings that people have and just making those notes super useful. Yeah, I like the feature where if you have like a bullet point of something that happened in the meeting, you can click on it in the granola transcript and it will like sort of enhance that by giving you like a direct quote from the part of the conversation where you were talking about that thing. It's sort of like hones in on the sentence or the two sentences

that most directly talk about the thing in the bullet point. Yeah. Now, I will say Granola is not perfect for me as a journalist because I want to be able to use this for my interviews as well. But because it is not keeping a recording, I have to use something else in addition, because sometimes I actually do need a direct quote and I need to double check it to make sure I'm quoting

the person absolutely accurately. So I actually just wrote in to the like hello at granola email address and be like, hi, I'm a journalist. I would really like it if I could do that. And they wound up putting me on a meet with the CEO. And I got to make my case in real time. And you know, what I learned was basically they've been nervous to do this sort of thing because they like the fact that they don't keep recordings, it feels much more private and secure.

And I respect that. I think it's good to build technologies that preserve privacy. But I'm like, man, if Granola did this, then I could get rid of my other thing that does the, you know, that does that part for me. Yeah. But we should talk about this because this is an interesting question that's come up a few times in my usage of this, which is that because it is not joining your meetings, it can be running in the background without the other person that you're talking to or

the people that you're talking to knowing. So do you have any privacy concerns about using a note-taking AI like this without informing the other person? I mean look, as a podcaster I assume I'm being recorded at all times and when I'm not I get upset because I think we could use that for the podcast. But yeah, I mean certainly when you are recording somebody in an interview, you always want to tell them that you're doing that upfront. Obviously, there are circumstances where you don't want to be recorded or the other person doesn't want you to record them. You kind of have to work that out. But, you know, I think for the most part, if you're in meetings, it's because you're generating some sort of information that you want to use afterward.

And having a tech tool that helps you do that makes all the sense in the world to me. Yeah, you know, Granola has a page up on their website saying you should definitely get people's consent before you do this. I did not get the consent of our editorial team before I started using this in meetings, although I did notify them afterwards. So I apologize.

I'm sorry. That was bad of me. Well, let me just say, you're in a lot of legal hot water, my friend. So lawyer up. All right, Casey, the third tool that I wanna talk about today is not really a tool. It is a request for a tool.

So for months now, I have been wishing and hoping for a tool that would essentially allow me to automate my email. Email overload is a huge problem for me. I get way more email than I can deal with. I spend hours a day trying to slog through my inbox. It is a huge time expenditure. And so one of the exciting things for me

when large language models came on the scene was like maybe I can have an AI sort of take a first pass at responding to my emails or at least the ones, at least populate a draft for me that I can just sort of go through and click send on or edit to my own liking. But that tool has not arrived yet, at least in a form that I have used. So Casey, when it comes to AI and email, what have you tried? What are you using?

What's your level of automation of your own email inbox? It is much lower than I want it to be, Kevin. I have all of the same frustrations that you do. When I look at email, I see a data extraction problem, right? There are only like nine or 10 different kinds of emails that I get. Some of them are pitches for me to write about. Some of them are people who are inviting me somewhere.

Some are people who want me to like go on the radio. And it seems like it should be almost trivial at this point for some kind of AI to just notice that, bucket it out, draft responses, and let me just click a couple buttons and be done with it. But nothing I have tried gets close. I have tried two different AI enhanced email apps so far. One is in a very early beta Notion,

which makes the popular collaboration software. They have a Notion mail client. I don't wanna give a full review of that one because it truly is in beta. They're making a lot of changes over there, but I would just say that so far it has not been able to do what I wanted it to do. The other one I tried is called Shortwave,

which I like paid a subscription for, which sort of promised to do what I just described in terms of extracting all of that data out of my email. I just found like it couldn't do that. You know, I remember running the query like of the emails in my inbox, which ones have action items that I need to do. And it just, it completely failed to do that. So, you know, I, I canceled the CEO emailed me and was like, you know, we're, we're changing it. We're making it better. And I'm sure they have improved it since the last time I use it, but I I gotta say, I have felt burned by my experiences with AI email, and so I am no longer using them.

What about you? So I have been interested in a few different solutions here, but one of the things that worries me about these third-party apps is I don't wanna send, I have 20 years of Gmail sitting in my account, and I don't wanna send all of those emails to a company that I don't necessarily trust to keep that information private.

All these apps that are sort of popping up, they want to learn how to write like you, which involves ingesting a ton of your previous emails. And that's just a privacy concern for me. I don't want to hand over that many years of my email to OpenAI or Anthropic or another company without knowing if they're training on that or retaining that in some way.

So what I've been trying to do is to build my own homespun email autopilot app. So a couple of days ago, I went into Claude and I just said, here's my problem. I want it to all run locally on my machines that it's not sending my e-mails anywhere else, and can you help me build it? And can it?

Well, TBD. So far, I've only been working on it for a few days, but what I have is a bad prototype now. Claude helped me install local LLMs on my machine. We've been going back and forth about how this app should work, how it's going to learn from an archive of my old emails, how to write like me.

But it is still pretty buggy. It did start responding to spam emails for me. So I just like would. This sounds amazing. I'll take all that Viagra that you got. So I still have to do some more fine tuning. It said, this sounds amazing. I'll take all that Viagra that you got. So I still have to do some more fine tuning, but I think I am rapidly approaching the limits of my own very limited programming skill. And so if there are any hard fork listeners out there

who are programmers, and I know there are, you would earn my undying devotion and gratitude if you helped me build an app that would do essentially the following. Three or four times a day, scan my inbox, pick out anything important and draft a reply to it. Populate a little box and give me one button that I can hit to send it or another button that I can hit to edit it.

Also give me a digest every day of the most important things that happened in my email inbox and any action items. If you're feeling fancy, connect to my calendar. But you don't even have to really do that. I would just settle for the email drafting tool that I just described. Yeah, I think that that's beautiful. And I would like to make a prediction, Kevin. What's that?

We are going to publish this podcast and you are going to get several emails from people who make email apps and they're gonna tell you, we can actually do this already. then you're going to go through the trouble of setting it up and then you're going to find it cannot actually do that. I don't know why this happens, but this happens. Okay, I need to send another message.

What's that? This is to the people who listen to this podcast who work at the Google corporation. The total bardasses. The total bardasses. I need you to do this yesterday. You have my email. You know everything about me. You have my browsing history.

You have my photos. You know everyone that I've ever contacted in my life and everywhere that I've ever been and everything that I've ever searched for. The fact that there is not a tool built into Gmail that allows you to put your inbox on autopilot is a failure of imagination and I want it fixed. This sounds like a great job

for 2.0 flash thinking experimental apps, Kevin. That of course is a new model that Google released this week. Okay. So Casey, let's end this tool time segment with a question from a listener. Love a listener question. So this came in just today. It's from a listener named Ray Keane. And we're keen to answer it. And he asks, if you were to choose just one paid subscription,

I guess he means AI subscription, which would it be? So Casey, what is the answer for you? You know what's crazy about this question, Kevin, is that I feel like my answer to it probably changed within the past couple of days. Because I think the truth is that if I could only pay for one AI subscription, if I'm on some sort

of desert island with only one AI, it would be ChatGPT Pro. And the reason really is deep research. We've talked before about how the AI labs are mostly at parity when it comes to the basic questions that people ask. Some LLMs seem to have a better personality, maybe they're a little bit better at mentoring, tutoring, coaching, whatever. But deep research felt useful to me in a way that made me feel like I am going to use this most days now. And I don't think I would want to be without it. So I don't know, maybe a week or two will go by and the bloom will come off the rose. And I'll say, oh, yeah,

this deep research thing, like it turns out I don't actually want a week or two will go by and the bloom will come off the rose and I'll say oh Yeah, this deep research thing like it turns out I don't actually want to read three ten thousand word reports a day But right now I feel like that you know it obviously at a two hundred dollars a month is extremely expensive for a software Subscription, but if I can only pick one I think be that how about you? Yeah, I think it's it's good for people who have an interest in this stuff to at least try the latest and greatest coming out from OpenAI. But for me, the answer to this question is Claude. I pay for Claude, the pro version.

It's $20 a month. And Claude has some limitations. It can't browse the web. It's not good at everything. It doesn't have some of the same multimodal capabilities that some of the OpenAI models do. But it is just a very good daily driver all around AI model for the things that I use it for. Yeah, makes sense.

Claude is really, really great, but I do wish it could browse the web and I do wish it had some sort of research feature or even just a reasoning model. Yeah. Yeah. Yeah. Hard Fork is produced by Whitney Jones and Rachel Cohn. We're edited this week by Rachel Dry and fact-checked by Ina Alvarado. Today's show was engineered by Chris Wood. Original music by Alicia Beatupe, Marion Lozano, Rowan Nimisto, and Dan Powell. Our executive producer is Jen Poyant.

Our audience editor is Nel Galogey. Video production by Chris Schott. Special thanks to Paula Schuman, Huy Wing Tam, Dahlia Haddad, and Jeffrey Miranda. You can email us at hardforknytimes.com and hopefully my email autopilot bot will respond. Thank you.