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

“The way to control government is to control the computers.”

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

• 0:00Speaker 00

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

• 0:35Casey Newton?

  Match Group has a new CEO, Kevin.

• 0:38Kevin Roose?

  Yeah.

• 0:38Casey Newton?

  And it is the former CEO of Zillow.

• 0:42Kevin Roose?

  Hmm.

• 0:42Casey Newton?

  The sort of real estate company.

• 0:45Kevin Roose?

  Yes.

• 0:45Casey Newton?

  Which I imagine you probably spent some time on Zillow recently as you've been, you know, looking for houses.

• 0:49Kevin Roose?

  Yes.

• 0:49Casey Newton?

  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 zestimate of that person's worth?

• 1:02Kevin Roose?

  I think that's a great idea.

• 1:03Casey Newton?

  You know?

• 1:04Kevin Roose?

  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.

• 1:15Casey Newton?

  That's right. Short kings are having a huge moment. I just think, for me, I think the zestimate should say something like, this person probably still has roommates. You know? Like, like that sort of information that you want, not that it's bad to have roommates.

• 1:29Kevin Roose?

  Yeah.

• 1:29Casey Newton?

  But, you know, it can, it can introduce complexity and maybe, you know, you want to know that before you swipe.

• 1:34Kevin Roose?

  I think, you know how on Zillow you can see the, the history of every house or of every property? I think you should be able to like, see the entire relationship history.

• 1:43Casey Newton?

  The entire romantic history. The last, like, three romantic partners.

• 1:47Kevin Roose?

  Yes, two relationships ago, this person got dumped for not being a good communicator.

• 1:52Casey Newton?

  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.

• 1:59Kevin Roose?

  Call us!

• 1:59Casey Newton?

  Call us!

• 2:05Kevin Roose?

  I'm Kevin Roos, a tech columnist at The New York Times.

• 2:08Casey Newton?

  I'm Casey Newton from Platformer.

• 2:09Kevin Roose?

  And this is Hard Fork.

• 2:10Casey Newton?

  This week, The Times' Jonathan Swan joins us to discuss Elon Musk's tech takeover of Washington DC. 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.

• 2:42Kevin Roose?

  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.

• 2:50Casey Newton?

  It sure is, Kevin.

• 2:52Kevin Roose?

  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.

• 3:08Casey Newton?

  Yeah.

• 3:09Kevin Roose?

  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.

• 3:23Casey Newton?

  Yeah, including Luke Veritor, who we mentioned on the show in a previous episode, Kevin, because he was part of an effort to decode ancient scrolls using AI.

• 3:32Kevin Roose?

  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.

• 3:43Casey Newton?

  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 2 million federal workers, Kevin, offering them the option to resign and allegedly to be paid through the end of September.

• 4:06Kevin Roose?

  Yes, and the subject line of that email was fork in the road, which is not a hard fork reference.

• 4:12Casey Newton?

  That we know of.

• 4:13Kevin Roose?

  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.

• 4:28Casey Newton?

  Yeah.

• 4:29Kevin Roose?

  So, Casey, why are we talking about this on Hard Fork this week? We are not a politics show.

• 4:33Casey Newton?

  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 more important than Twitter, is unfolding in a very similar fashion.

• 5:04Kevin Roose?

  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

• 5:47Kevin Roose?

  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.

• 6:05Casey Newton?

  Let's bring him in.

• 6:14Kevin Roose?

  Jonathan Swan, welcome to Hard Fork.

• 6:16Speaker 03

  Thanks for having me.

• 6:17Kevin Roose?

  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?

• 6:30Speaker 03

  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. The website goes dark, like 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 Tesla and some of the other companies. Some of them are in their 20s, one of them was 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're worried about being doxxed.

• 7:12Speaker 03

  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, who are you? Well, I'm not telling you my surname or whatever.

• 7:35Casey Newton?

  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 folks like yourself are doing to even understand what is happening and what the plan is?

• 7:51Speaker 03

  We know broadly speaking what he wants to do, right? 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...

• 8:34Speaker 03

  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.

• 9:14Kevin Roose?

  Right, the fork in the road email.

• 9:15Speaker 03

  Yeah, I mean, you guys know this because this is your field, like the tech, but you know, it's basically, this email went to almost all federal workers, you know, around about 2 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, "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."

• 9:56Kevin Roose?

  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, you know, are in their early twenties, 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 sort of interns from his companies who he thinks would do a good job helping with this?

• 10:30Speaker 03

  Well, this is a little opaque to me and I have to give credit to my wonderful colleagues, Ryan Mack, Kate Conger and Teddy Schlefer. 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 Kraus, 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.

• 11:02Kevin Roose?

  One of them deciphered some ancient scrolls.

• 11:05Speaker 03

  Yeah.

• 11:05Kevin Roose?

  That was his claim to fame. Luke Ferrator.

• 11:07Speaker 03

  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.

• 11:42Kevin Roose?

  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.

• 11:58Casey Newton?

  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?

• 12:07Speaker 03

  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 D tech, like the really getting down to the nitty gritty and roaming the force 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 Doge to sit down with deep state quote unquote official X who's going to tell them everything's great and blah, blah, blah.

• 12:47Speaker 03

  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 is 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 part, 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, we need the source code.

• 13:28Speaker 03

  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, you know, people depend on this system. And it's, it's, 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, they are insisting to us that it is quote unquote read only access, meaning they can't alter payments.

• 14:12Speaker 03

  But even that is considered extraordinary.

• 14:15Casey Newton?

  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.

• 14:22Speaker 03

  That's right.

• 14:22Casey Newton?

  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?

• 14:41Speaker 03

  So firstly, you're absolutely right. Congress has the power of the purse. There's no question about that. The White House has cast 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.

• 15:19Speaker 03

  And Trump's aides, they have long wanted to seize back some of this power to withhold spending.

• 15:28Kevin Roose?

  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 those folks, they know that not all the things they're doing are legal, they know that not all of it will end up, you know, passing muster with Trump or with Congress, but they're sort of asking for a foot and expecting, you know, six inches, that there's 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?

• 16:13Speaker 03

  Oh, no, no, no, no. And, you know, we've written about this. This is actually a really important part of how they think in this 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 in Congress, and these outside nonprofit groups like the ACLU who are likely to sue them. They know that those three...

• 16:55Speaker 03

  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.

• 17:20Kevin Roose?

  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 Musk 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?

• 17:51Casey Newton?

  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.

• 18:35Casey Newton?

  He's not presented, you know, 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.

• 18:45Kevin Roose?

  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 zero-based budgeting. So Jonathan, tell us about zero-based budgeting and how it's showing up in Washington.

• 19:06Speaker 03

  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 in the, 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.

• 19:50Kevin Roose?

  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 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 are 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

• 20:35Kevin Roose?

  to the Trump administration, or is that sort of still to come?

• 20:39Speaker 03

  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.

• 20:56Casey Newton?

  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?

• 21:07Speaker 03

  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...

• 21:25Speaker 03

  Supposedly is to use them to assess contracts and recommend cuts. So what they were told, Kayden and Ryan, was that on Monday, Thomas Shed, 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.

• 22:10Speaker 03

  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.

• 22:22Kevin Roose?

  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, you know, 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

• 23:05Kevin Roose?

  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.

• 23:17Speaker 03

  Yeah, I mean, one thing that a source...

• 23:22Speaker 03

  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

• 24:07Speaker 03

  as they move really quickly and aggressively.

• 24:09Casey Newton?

  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?

• 24:36Speaker 03

  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, you know? Relief projects in Sudan have, you know, all around the world where US, you know, 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.

• 25:21Speaker 03

  Obviously, the House and Senate are in Republican hands. We're not exactly seeing like 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. You know, 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.

• 26:03Speaker 03

  He was born in South Africa. You know, 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.

• 26:16Kevin Roose?

  Yeah. Jonathan, out here in Silicon Valley, we've 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 sort of 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 sort of 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 kind of 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?

• 27:01Speaker 03

  Yeah, 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, 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 cybersecurity, 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.

• 27:41Casey Newton?

  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 has 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.

• 28:12Kevin Roose?

  Jonathan, a quick last question, and 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?

• 28:30Speaker 03

  I, look, they go, 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 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.

• 29:09Speaker 03

  Again, huge conflict of interest, Elon Musk, SpaceX, huge federal contracts, but I'm going to be keeping a close eye on DOD.

• 29:20Kevin Roose?

  Jonathan Swan, thank you so much for joining us.

• 29:22Casey Newton?

  Thanks, Jonathan.

• 29:23Speaker 03

  Thanks for having me.

• 29:26Kevin Roose?

  When we come back, writer Liz Pelly tells us why Spotify is increasingly full of ghost musicians. Spooky.

• 29:39Speaker 00

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

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• 31:12Casey Newton?

  Kevin, if you were a streaming music service playlist, what would you be called?

• 31:15Kevin Roose?

  Probably LoFi Beats to Podcast 2.

• 31:17Casey Newton?

  Hmm, I think of you more as a 2000's hot girl girly pop Wednesday afternoon. But regardless, Kevin, next on our playlist today, we're going to talk about Spotify.

• 31:26Kevin Roose?

  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.

• 31:34Casey Newton?

  In part because when we say wherever you get your podcasts, well, Spotify is a place where you can get your podcasts.

• 31:39Kevin Roose?

  Many of our listeners are probably using Spotify right now. And Spotify has had a big week. They just reported their first profit ...year ever. Daniel Ek, 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.

• 32:06Casey Newton?

  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.

• 32:49Kevin Roose?

  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.

• 33:04Casey Newton?

  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 Pelly.

• 33:22Kevin Roose?

  Liz Pelly, welcome to Hard Fork.

• 33:24Speaker 06

  Thanks for having me.

• 33:25Kevin Roose?

  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?

• 33:55Speaker 06

  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.

• 34:31Casey Newton?

  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?

• 34:42Speaker 06

  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'd 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

• 35:27Speaker 06

  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 Ek in 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,

• 35:57Speaker 06

  They really started to lean into this idea of a more curated service.

• 36:01Kevin Roose?

  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, and actually, artists were angling to get into it. And, and labels were angling to get their artists into the Spotify playlist because Spotify's increasingly large user base was just sort of, you know, discovering new music through the playlists. And so there was an element of that that I feel like is familiar to, you know, 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 sort of start to direct them towards certain music and away from other music.

• 36:41Speaker 06

  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 a record label 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 said things like, you know, the playlist ecosystem is going to level the playing field. And they talked a lot about this pyramid of playlist curation where they would start

• 37:26Speaker 06

  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, the magic of this.

• 37:48Speaker 06

  Data-driven, supposedly marriageocratic system.

• 37:51Kevin Roose?

  Right. And I want to ask you about that because I have to say from my perspective, when it comes to the rise of playlists, that's basically OK 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?

• 38:29Speaker 06

  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 people on the platform longer so that they view it as a useful part of their lives and retain their subscriptions, right?

• 39:01Kevin Roose?

  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 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 lesser-known musicians who maybe can't command the same types of market power.

• 39:45Kevin Roose?

  So is that something that Spotify was also doing?

• 39:47Speaker 06

  Yeah, that's a great point, too. You know, part of the reason why a streaming platform wants to control more of the user experience is 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

• 40:32Speaker 06

  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.

• 41:14Kevin Roose?

  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.

• 41:35Speaker 06

  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'd 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

• 42:17Speaker 06

  as examples, which would basically be songs that had done well in the lean back environment on Spotify previously, and then try to kind of replicate similar styles in order to hopefully make content that would stream really well.

• 42:31Kevin Roose?

  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 lofi, 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.

• 43:15Kevin Roose?

  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.

• 43:25Casey Newton?

  Yeah. Kevin is one of the reasons why the music industry is falling apart.

• 43:28Speaker 06

  Well, you know, it's interesting because according to some of the interviews that I did, justification that would be used is, yeah, that some of the senior

• 43:39Speaker 06

  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 clearing 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.

• 44:24Speaker 06

  But if, 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.

• 44:36Casey Newton?

  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 at times. And, and in your, your book, Liz, you write about how the, 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?

• 45:17Casey Newton?

  Like, what was it as an actual culture before it became like a low-cost alternative to paying record artists?

• 45:24Speaker 06

  Yeah, I think it's interesting to, 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, Madlib-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

• 46:05Speaker 06

  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 the 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.

• 46:47Casey Newton?

  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 playlists became so popular that they incentivized 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?

• 47:23Speaker 06

  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 lovi, hip hop, jazz, classical, or ambient

• 48:08Speaker 06

  that fill out these lean back 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 era.

• 48:27Casey Newton?

  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

• 49:09Casey Newton?

  as maybe we may think."

• 49:13Speaker 06

  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 talk 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.

• 49:37Kevin Roose?

  That's the perfect fit content for Spotify.

• 49:39Speaker 06

  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 the 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. You know, each company has its own arrangement. I talked to a couple of composers from this songwriter advocacy group based in the UK called Ivors Academy, and they talked about how, from their perspective,

• 50:21Speaker 06

  they felt like companies like Epidemic Sound and 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, you know, was a change 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.

• 51:00Kevin Roose?

  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. And to me, it just seems inevitable that at least for these ambient sort of 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?

• 51:42Speaker 06

  Daniel Ek 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 like in my reporting on PFC and ghost music, it's not necessarily something that I directly observed, although companies like Epidemic Sounds who work with Spotify in this way have directly signaled that they're, you

• 52:25Speaker 06

  know, excited about the potential of their composers working with generative AI tools and open to it. So it's not definitely not 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

• 53:06Speaker 06

  recommendations, algorithmic recommendations, personalization over the past, you know, 15 years have reshaped the way that people understand music, our recommended music, the context within which music is served and presented to listeners, I think is, you know, equally equally worthy of consideration and critique.

• 53:32Casey Newton?

  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 can just inhale it. But Liz, 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 how you might reconcile those two things, or how you think I might reconcile them,

• 54:15Casey Newton?

  and how you try to personally cultivate your own taste in music in this age.

• 54:20Speaker 06

  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 championing, of lean-back listening, of a sense of passivity, of this devaluing of music,

• 55:05Speaker 06

  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,

• 55:47Speaker 06

  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, you know, like there was 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

• 56:31Speaker 06

  moment without having to do any deciding or any choosing or any thinking.

• 56:36Kevin Roose?

  Well, it seems like the TikTok model just applied to music rather than video. It's, I mean, 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.

• 57:14Speaker 06

  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 know, 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

• 57:58Speaker 06

  why you don't like them, being challenged by music that is outside of your comfort zone, like, this is all important from my perspective.

• 58:06Casey Newton?

  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 p.m.

• 58:20Kevin Roose?

  I love that.

• 58:21Casey Newton?

  Let's give these people some attention.

• 58:22Kevin Roose?

  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.

• 58:45Casey Newton?

  Spotify, of course, famous for its giant royalty checks.

• 58:49Speaker 06

  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 at 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 playlists have their own creative practices. So I would also, you know, hope that

• 59:19Speaker 06

  they'd be able to share that music.

• 59:20Kevin Roose?

  No, I'm very glad that we had this conversation with you, because I think I am realizing that I am the problem. It's me.

• 59:27Casey Newton?

  Which was my hope with this conversation, so I'm excited too.

• 59:29Kevin Roose?

  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 that I choose.

• 59:55Casey Newton?

  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?

• 1:00:13Speaker 06

  Okay. I'll 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 Floris, but also makes ambient music that is really beautiful. And I would recommend checking out that.

• 1:00:34Casey Newton?

  Perfect. Human beings recommending music to each other, just like in the old days.

• 1:00:37Kevin Roose?

  How do you spell that? Emily A.

• 1:00:40Speaker 06

  S P R A G U E.

• 1:00:43Casey Newton?

  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.

• 1:00:54Kevin Roose?

  That's a good tip.

• 1:00:54Casey Newton?

  Liz, fascinating conversation. Congratulations on the book. Thank you for joining us on Hard Fork.

• 1:00:59Speaker 06

  Thanks so much for having me.

• 1:01:10Kevin Roose?

  When we come back, we're going to tell you what AI tools we're using in our new segment called Tool Time.

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• 1:03:11Kevin Roose?

  Well, Casey, it's time for a new segment that we're calling Tool Time.

• 1:03:15Casey Newton?

  It's Tool Time.

• 1:03:28Kevin Roose?

  Now, if you are a 90s kid, you might remember Home Improvement.

• 1:03:33Casey Newton?

  Only 90s kids will remember Home Improvement.

• 1:03:35Kevin Roose?

  Tim the Toolman Taylor had a TV show called Tool Time, but this is different.

• 1:03:38Casey Newton?

  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.

• 1:03:50Kevin Roose?

  Yes, this is more of a knowledge worker Tool Time.

• 1:03:53Casey Newton?

  Yeah.

• 1:03:54Kevin Roose?

  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.

• 1:04:08Casey Newton?

  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.

• 1:04:19Kevin Roose?

  And Casey, this is a segment about AI and AI tools, so we should make our AI disclosures.

• 1:04:24Casey Newton?

  Well, here's one for you, Kevin. Casey's boyfriend works at Anthropic.

• 1:04:28Kevin Roose?

  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.

• 1:04:35Casey Newton?

  Is that the first time we've ever referred to ourselves in the third person on the show?

• 1:04:39Kevin Roose?

  No, but I kind of like it. All right. So 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.

• 1:05:00Casey Newton?

  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 O three. And so what sort of reports have you been asking deep research to create?

• 1:05:36Kevin Roose?

  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, you know, the trace the 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 timeframe should I focus on?

• 1:06:15Kevin Roose?

  Do you want to include like science fiction or just general references to, you know, 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.

• 1:06:37Casey Newton?

  Right. And as you read through this report, uh, what, 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?

• 1:06:51Kevin Roose?

  So I have done this kind of research project before for my last book. I, 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 like the, the entire intellectual history of this term. And I didn't see anything. Obviously wrong in it. And when I started checking some of the citations, it actually all looked pretty good.

• 1:07:25Casey Newton?

  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.

• 1:08:05Casey Newton?

  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 an amount of time.

• 1:08:44Kevin Roose?

  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, you know, parenting literature. And like, give me the the Cliff Notes. And it went out and it did that pretty well.

• 1:09:20Casey Newton?

  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?

• 1:09:26Kevin Roose?

  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?

• 1:09:45Casey Newton?

  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 DeepSeek. 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

• 1:10:30Casey Newton?

  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?

• 1:11:04Kevin Roose?

  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.

• 1:11:48Kevin Roose?

  It's a very good learning tool. I've been using it to teach myself things. So right now you only get 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.

• 1:12:10Casey Newton?

  I feel the same way, you know, 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 want to 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 bucks a month to me.

• 1:12:33Kevin Roose?

  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?

• 1:12:43Casey Newton?

  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 am too.

• 1:12:54Kevin Roose?

  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.

• 1:13:06Casey Newton?

  Or a Cisco WebEx.

• 1:13:09Kevin Roose?

  Or a Cisco WebEx, if you're still at one of the three companies in America that still uses that.

• 1:13:15Kevin Roose?

  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 of, you know, 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 like chat with the meeting transcript afterwards. And you can say, you know, what did Bob say?

• 1:14:00Kevin Roose?

  What are some action items that we might want to, that might come out of this?

• 1:14:03Casey Newton?

  I used it to say, what was Kevin's worst idea this week at our editorial planning meeting?

• 1:14:09Kevin Roose?

  So I have found this very useful. What about you?

• 1:14:12Casey Newton?

  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, you know, 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, 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.

• 1:14:52Casey Newton?

  So it like the AI essentially knows what information to look for that is going to be useful to a VC afterwards. Similarly, you know, 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.

• 1:15:12Kevin Roose?

  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.

• 1:15:33Casey Newton?

  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, right? 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, right? 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.

• 1:16:15Casey Newton?

  But I'm like, man, if granola did this, then I could get rid of my other thing that does the, you know, that is that part for me.

• 1:16:20Kevin Roose?

  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?

• 1:16:42Casey Newton?

  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 setting, 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.

• 1:17:10Kevin Roose?

  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.

• 1:17:24Casey Newton?

  Well, let me just say, you're in a lot of legal hot water, my friend, so lawyer up.

• 1:17:30Kevin Roose?

  All right, Casey, the third tool that I want to talk about today is not really a tool. It is a request for a tool.

• 1:17:36Casey Newton?

  Yeah.

• 1:17:36Kevin Roose?

  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?

• 1:18:21Kevin Roose?

  What are you using? What's your level of automation of your own email inbox?

• 1:18:27Casey Newton?

  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 of 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.

• 1:19:12Casey Newton?

  They have a Notion mail client. I don't want to 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 on 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 canceled. The CEO emailed me and was like, you know, 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 gotta say, I have felt burned by my experiences with AI email and so I am no longer using them. What about you?

• 1:19:56Kevin Roose?

  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 want to send, you know, I have 20 years of Gmail sitting in my account and I don't want to 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.

• 1:20:36Kevin Roose?

  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 emails anywhere else. And can you help me build it?

• 1:20:55Casey Newton?

  And can it?

• 1:20:56Kevin Roose?

  Well, TBD, because so far, I've only been working on it for a few days. But what I have is sort of a bad prototype now. I Claude helped me sort of 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 I?

• 1:21:25Casey Newton?

  It said, this sounds amazing. I'll take all that Viagra that you got.

• 1:21:30Kevin Roose?

  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 drafter apply to it. Populate a little box and I can 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.

• 1:22:15Kevin Roose?

  But you don't even have to really do that. I would just settle for the email drafting tool that I just described.

• 1:22:20Casey Newton?

  Yeah, I think that that's beautiful. And I would like to make a prediction, Kevin.

• 1:22:23Kevin Roose?

  What's that?

• 1:22:24Casey Newton?

  We are going to publish this podcast and you are going to get several emails from people who make email apps. And they're going to tell you, we can actually do this already. And 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.

• 1:22:37Kevin Roose?

  Okay, I need to send another message.

• 1:22:39Casey Newton?

  What's that?

• 1:22:40Kevin Roose?

  This is to the people who listen to this podcast who work at the Google Corporation.

• 1:22:44Casey Newton?

  The total bar of asses.

• 1:22:45Kevin Roose?

  The total bar of asses. 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.

• 1:23:07Casey Newton?

  This sounds like a great job for 2.0 flash thinking experimental apps, Kevin.

• 1:23:14Casey Newton?

  That, of course, is a new model that Google released this week.

• 1:23:17Kevin Roose?

  OK, so Casey, let's end this Tool Time segment with a question from a listener.

• 1:23:22Casey Newton?

  Love a listener question.

• 1:23:23Kevin Roose?

  So this came in just today. It's from a listener named Ray Keen.

• 1:23:26Casey Newton?

  And we're keen to answer it.

• 1:23:28Kevin Roose?

  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?

• 1:23:37Casey Newton?

  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. You know, we've talked before about how the AI labs are mostly at parity when it comes to the the basic questions that people ask. Some LLMs seem to have like 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.

• 1:24:21Casey Newton?

  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. It turns out I don't actually want to read three 10,000 word reports a day. But right now, I feel like that, you know, it obviously at $200 a month is extremely expensive for a software subscription. But if I can only pick one, I think it'd be that. How about you?

• 1:24:44Kevin Roose?

  Yeah, I think 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 bucks 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.

• 1:25:15Casey Newton?

  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.

• 1:25:24Kevin Roose?

  Yeah.

• 1:25:49Speaker 00

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• 1:27:29Kevin Roose?

  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 Baetup, Marion Lozano, Rowan Nimitzdo, and Dan Powell. Our executive producer is Jen Poyant. Our audience editor is Nell Galogly. Video production by Chris Schott. Special thanks to Paula Schuman, Puywing Tam, Dahlia Hadad, and Jeffrey Miranda. You can email us at [email protected]. And hopefully, my email autopilot bot will respond.

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