Latest news with #ReleaseRadar
Geeky Gadgets
21 hours ago
- Entertainment
- Geeky Gadgets
How to Easily Switch from Apple Music to Spotify
If you are an Apple Music user, making the transition to Spotify represents a meaningful shift in how you experience music streaming. This change highlights the distinct differences between the two platforms, particularly in areas such as music discovery, device compatibility, social features, interface design, library management, audio quality, and subscription pricing. By examining these aspects, you can decide whether Spotify better aligns with your music streaming needs and preferences. Watch this video on YouTube. Music Discovery: Spotify's Algorithms Take the Lead Spotify's music discovery capabilities are driven by sophisticated algorithms that provide a highly personalized listening experience. Features like 'Discover Weekly' and 'Release Radar' analyze your listening habits to recommend songs and artists tailored to your tastes. These tools allow Spotify to consistently introduce you to new music that aligns with your preferences. While Apple Music also offers recommendations, its approach relies more heavily on editorial curation, which may feel less dynamic and personalized. If discovering new music is a priority, Spotify's data-driven system offers a more engaging and innovative way to explore fresh sounds. Cross-Platform Compatibility: Access Anywhere, Anytime Spotify excels in cross-platform compatibility, making sure seamless functionality across a wide array of devices. Whether you're using an Android phone, a Windows PC, a smart TV, or even a gaming console, Spotify provides consistent performance. This versatility makes it easy to access your music wherever you are. In contrast, Apple Music is optimized for Apple's ecosystem, which can limit its usability on non-Apple devices. If you frequently switch between platforms or use a variety of devices, Spotify's adaptability ensures a smoother and more convenient experience. Social Sharing: Connecting Through Music Spotify's social features are designed to enhance collaboration and connection through music. You can create collaborative playlists with friends, share songs directly to social media, and follow other users to explore their listening habits. These features make it easy to engage with others and discover music through shared experiences. Apple Music, on the other hand, offers limited social functionality, focusing more on artist-driven content rather than user interaction. If you enjoy sharing music and connecting with others, Spotify's robust social tools provide a richer and more interactive platform for musical engagement. Interface Design: Simplicity Meets Functionality Spotify's interface is known for its streamlined and user-friendly design, which prioritizes functionality and ease of use. Navigating playlists, discovering new music, and managing your library are intuitive processes, thanks to its clean and organized layout. Apple Music, while visually appealing, can feel cluttered due to overlapping features like 'For You' and 'Browse.' If you value a straightforward and accessible interface, Spotify's design ensures a smoother and more enjoyable user experience. Library Management: Flexible and Organized Spotify offers flexible tools for managing your music library, allowing you to create folders, reorder tracks, and share playlists effortlessly. These features make organizing your music simple and efficient. While Apple Music provides similar options, it lacks the same level of customization and ease of use. For users who prioritize efficient library management, Spotify's tools offer a more practical and user-friendly solution. Streaming Quality: Balancing Quality and Features Both platforms deliver high-quality audio, but their approaches differ. Spotify allows users to adjust streaming quality, with a 'Very High' option available for premium subscribers. This flexibility ensures that you can balance audio quality with data usage based on your needs. Apple Music, however, offers lossless audio and spatial audio with Dolby Atmos, providing a richer and more immersive listening experience. If audio quality is your top priority, Apple Music's advanced features may appeal to you. However, Spotify's quality settings are sufficient for most listeners and offer the added benefit of adaptability. Subscription Pricing: Flexibility and Value Spotify and Apple Music both offer a range of subscription plans, including individual, family, and student options. However, Spotify stands out with its free, ad-supported tier, which allows you to explore the platform without committing to a paid plan. This option provides a cost-effective way to experience Spotify's features before deciding on a subscription. Apple Music, by contrast, requires a subscription to access its full library, offering no comparable free tier. If cost is a significant factor in your decision, Spotify's free option and flexible pricing structure provide greater value and accessibility. Why Spotify May Be the Better Choice Switching from Apple Music to Spotify after 10 years highlights the unique strengths of each platform. Spotify excels in areas such as music discovery, cross-platform compatibility, social sharing, and interface design, making it a versatile and user-friendly option. While Apple Music offers advantages like lossless audio and seamless integration within Apple's ecosystem, Spotify's adaptability and focus on user experience make it a compelling choice for many. If you're considering a change, Spotify's features may provide a fresh and engaging way to enjoy your music while offering greater flexibility and convenience. Here are additional guides from our expansive article library that you may find useful. Source & Image Credit: Nikias Molina Filed Under: Apple, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
29-05-2025
- Business
- Yahoo
SPOT vs. PATH: Which Tech Stock Possess Stronger Growth Potential?
Both Spotify Technology S.A. SPOT and UiPath PATH are prominent software-driven tech stocks. SPOT is a leading personalized audio streaming and content platform, while PATH focuses on robotic process automation (RPA). Despite operating in different niches, both companies are linked by AI-backed growth narratives. Our comparative analysis will help investors figure out the tech stock that offers a more optimistic growth prospect. Spotify Technology's AI integration has aided its growth trajectory, evidenced by its improving key performance indicators. By the end of March 2025, the company added 3 million monthly active users (MAUs) and the count increased 10% year over year. Similarly, premium subscriber count increased by 5 million by the end of March and grew 12% year over year. This performance shines a light on AI's ability to create bespoke and engaging user experiences, elevating its business performance. SPOT has tactfully incorporated AI into its recommendation engines. The algorithms are subjected to analyze consumer habits, allowing the company to generate hyper-personalized features such as Discover Weekly, Release Radar and Daily Mixes. These playlists are driving user engagement by increasing time spent on the platform, leading to higher retention and a greater probability of a free user converting into a premium subscriber. Apart from recommendations, AI facilitates an annual marketing campaign — Spotify Wrapped — a user recap feature that provides an individualized summary of user's listening habits from the past year. This feature acts as an organic marketing tool and contributes significantly toward user acquisition and brand visibility due to its virality. Moving on, ad-supported MAUs increased 9% year over year in the first quarter of 2025. AI is a significant driver behind this growth, helping the company optimize target advertising and enhancing ad revenues. Finally, AI DJ and AI Playlist are a few of the company's AI-led innovations that demonstrate commitment to strengthening user interaction. This ultimately improves user retention and paves the path for prolonged growth in the competitive market. PATH, a global player in the RPA domain, leverages AI to fuel its growth, transforming traditional automation into an intelligent one. The strategic collaboration of AI has expanded the scope of processes that can be automated, supporting its growth trajectory positively. In fiscal 2025, the company registered annual recurring revenues (ARR) of $1.7 billion, increasing 14% year over year. Also, $424 in revenues was recorded in the fourth quarter of fiscal 2025, rising 5% from the year-ago quarter. Banking on sophisticated AI-powered solutions, the company has been able to generate strong ARR, indicating a healthy recurring stream of revenues and customer retention. Furthermore, the dollar-based net retention rate hovered at 110%, further bolstering PATH's success in broadening its customer base, which is a direct benefit of its AI-backed offerings. The company has introduced intelligent document processing, communications mining and computer vision to automate unstructured and complex tasks that RPA cannot handle by itself. This agentic automation, which allows AI agents to work alongside robots, generates a higher return on investment for clients by enabling businesses to tackle complicated workflows. For instance, the recent acquisition of Peak aims to strengthen PATH's vertical AI solutions strategy by accelerating AI adoption in retail and manufacturing sectors. Acquisitions as such and AI-led innovations backed by substantial R&D investment are instrumental in retaining market leadership and capitalizing on the rising demand for enterprise-wide AI transformation. The Zacks Consensus Estimate for Spotify Technology's 2025 sales is pegged at $19.9 billion, suggesting 17.4% year-over-year growth. The consensus estimate for earnings is pegged at $9.88, indicating a 66.1% rise from the preceding year's actual. Three estimates for 2025 have moved north in the past 60 days versus four southward revisions. Image Source: Zacks Investment Research The Zacks Consensus Estimate for UiPath's 2025 sales is pegged at $1.5 billion, implying 6.7% year-over-year growth. The consensus estimate for earnings is pegged at 52 cents per share, indicating a 1.9% year-over-year decline. No estimate for 2025 has moved north in the past 60 days versus two southward revisions. Image Source: Zacks Investment Research Spotify Technology is currently trading at a forward 12-month Price/Sales ratio of 6.45X, which is higher than the 12-month median of 4.83X, indicating an overvaluation. UiPath appears slightly overvalued with its 12-month Price/Sales ratio of 4.56X, which is marginally above the 12-month median of 4.53X. While both stocks are trading at a premium compared with their historical valuations, PATH is priced attractively from a valuation standpoint, suggesting greater breadth for expansion. Image Source: Zacks Investment Research While both SPOT and PATH rely heavily on AI to gain a competitive advantage, Spotify Technologies' near-term prospects appear brighter. SPOT's excellent user growth and impressive financial performance paint a detailed picture of a better near-term potential. That being said, we do acknowledge PATH's strength in enterprise automation and its lower valuation compared with Spotify Technology. However, SPOT is a fundamentally stronger stock than UiPath, with a remarkably higher earnings growth outlook, giving investors greater confidence to bet on its growth. SPOT and PATH have a Zacks Rank #3 (Hold) at present. You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report UiPath, Inc. (PATH) : Free Stock Analysis Report Spotify Technology (SPOT) : Free Stock Analysis Report This article originally published on Zacks Investment Research ( Zacks Investment Research Sign in to access your portfolio
Yahoo
02-05-2025
- Entertainment
- Yahoo
Spotify was great at helping you discover new music. Then the staff cuts began.
The music app I've been using for the last 14 years recently decided that I'm obsessed with rainstorms. When I last listened to my Spotify Release Radar playlist — which for years reliably curated a decent selection of newly released music informed by my favorite artists — the lineup quickly took a turn for the worse. After new songs by familiar indie pop names like Japanese Breakfast and The Marias, there was a five-minute recording of rain falling followed by a short, ambient instrumental by a little-known alternative rock artist. Then, for its remaining 25 tracks, my playlist delivered a succession of rainstorms, nature sounds, and brown-noise frequencies — not exactly the stuff of a killer music festival lineup. When Spotify launched Release Radar in 2016, it promised "a weekly selection of the newest releases that matter the most to you." Not only was the feature designed to delight music fans upon first listen, but it would get better over time, the company promised. So why, nearly a decade later, was my latest Release Radar delivering 85% noise? Sure, my girlfriend and I had recently started listening to "sleep sounds" — things like ocean waves and the hum of an airplane's engine — at night, but that didn't fully explain the issue. The algorithm hadn't made such a crucial curation error before. I'm hardly the first Spotify subscriber to notice that the gears of its music recommendation engine have gotten rusty. Over the past year, fans have taken to Reddit, LinkedIn, and other platforms to complain that curated, for you playlists like Release Radar, Discover Weekly, and Daily Mix have gone downhill, resulting in what many describe as an "echo chamber" that feels more repetitive or off the mark than it once did. Random tracks from white-noise playlists or kids' music albums are popping up where they don't belong, ruining the listening experience. Molly Holder, Spotify's senior director of product for personalization, disagreed that the quality of these curated playlists has declined. "People are discovering more new music and spending more time doing so," she said in a statement, adding that the company listens to user feedback, continues to enhance personal recommendations, and that listener engagement metrics are up. But after talking with former Spotify employees, it seems likely that a combination of layoffs and shifting business priorities has hollowed out the platform's music discovery product. Essentially, in a quest for profitability, Spotify broke its algorithm. One of the features that set Spotify apart from its intensifying competition over the years was the platform's man-meets-machine approach to music curation, especially its personalized, algorithmically curated playlists. Discover Weekly promised to introduce listeners to new music that they might like, Daily Mix turned their tastes into themed playlists, and Release Radar highlighted freshly released tracks. For years, these features seemed to elicit near-universal praise online and gave Spotify a competitive advantage just in time to fend off competition from giants like Apple and Amazon. The music discovery features are likely one reason it's been able to lead the global music subscription market. A recent EMARKETER report found that Americans spend over nine times as much time on Spotify as the next closest competitor. Lately, though, those same features have inspired frustration. For many, Discover Weekly has gotten worse at delivering songs that align with their musical tastes, sometimes including songs they've already listened to on Spotify. Other users report that Release Radar misses new releases from artists they like or mistakenly includes tracks that came out weeks earlier. Release Radar has also started including less relevant music from what one Reddit user described as "random artists with under 50k listens" that seemed to come out of nowhere, while others bemoan the inclusion of what sounds like "AI garbage." The most audible groan of disapproval came with the arrival of Spotify Wrapped last year. While the personalized year-end recap playlist had historically sparked a tsunami of positive online buzz each year, the 2024 edition prompted a far more mixed reaction. Listeners said it lacked personality and interesting insights into their music habits. Jeffrey Smith, a self-described "Spotify diehard" who leads marketing for the online music marketplace Discogs, has become so frustrated by the platform's declining music discovery feature that he's considering switching to Apple Music. "Over the last couple of years, Spotify has met my needs less and less," Smith said. "It's not really reflective of my listening behavior as much as it is reflective of what they want me to listen to. It's just a listening machine at this point, not a music platform." Smith's affinity for Spotify started fading when he noticed one song — "Back on 74" by Jungle — kept popping up on his personalized Spotify playlists. While it's possible that he had checked the song out at some point, it wasn't something he enjoyed or actively listened to. Still, the song made its way from one personalized music recommendation feature to another and even started inspiring Spotify's algorithms to play similar songs. "It just continued until I finally said, 'I can't take it,'" Smith said. As a music obsessive working at a major marketplace for vinyl records, Smith has no shortage of sources of quality music recommendations. But the days of Spotify augmenting his organic music discovery appear to be over. If he keeps his Spotify subscription at all, he says it will be for the podcasts and audiobooks the company has added to its premium tier. I've found myself in a similar conundrum. After largely dismissing the 2015 launch of Apple Music as a then-satisfied Spotify subscriber, I recently decided to give Apple's music service another try. Since its debut, Apple Music has prioritized human editors over algorithms for music curation. Where it does use data-driven personalization, the results are pretty solid, and crucially, free of rainstorms. While Spotify's new audiobook library makes it tempting to stay, its playlists no longer feel like the best in the business. Once my free trial of Apple Music is over, I'm planning to make the switch. Music subscription services like Spotify spend an extraordinary amount of money — last year, the company said it spent $10 billion — to license their massive music catalogs from record labels, publishers, and other rightsholders. These high costs make it more challenging to turn an enduring profit, something Spotify only managed to do for the first time in 2024. From Wall Street's perspective, Spotify is killing it as year-over-year metrics like subscribers and revenue continue to grow. But in the quest to please investors, some ex-Spotify employees think it has abandoned too much of the human element that makes music and the culture around it so special. Doug Ford was a Spotify executive who oversaw editorial playlist curation from 2013 to 2018. He arrived via Spotify's acquisition of Tunigo, a Swedish company that specialized in expert-curated music playlists based on different genres and moods. The next year, Spotify acquired Echo Nest, a music data startup founded by MIT grads that used a mix of machine learning and data filtering to build Pandora-style music recommendation algorithms. Both teams built out Spotify's man-meets-machine music discovery system, which helped the company successfully weather new competition from tech giants like Apple and Amazon as it started to explore the possibility of going public. "That was a really beautiful moment in Spotify's history," Ford said. "That mix of the intentional algorithmic and human curation to make a really deep product was great." The results spoke for themselves. Spotify said the 2015 launch of Discover Weekly yielded 1.7 billion streams in its first five months and generated a weekly wave of social media buzz when listeners' Discover Weekly playlists were refreshed. "Our goal with Discover Weekly was to make something that felt like a friend or someone who knew you well was making you a mixtape," Spotify's product lead at the time, Matt Ogle, told me in an interview. To accomplish that feeling, the algorithm drew heavily from how real flesh-and-blood music enthusiasts were curating playlists on the platform. "That's why that thing was so good," Ford said. "It was taking all the inputs equally and letting things happen normally with the audience. Now it feels a little bit different." Ford said things started changing in the lead-up to Spotify's 2018 initial public offering and the ensuing pressure to prove to investors that profitability was possible in the notoriously tight-margined music streaming business. "The culture changed because it had to become a business," Ford said. "You need to be a successful business in order to offer this utility to people. But they've discarded some of the human aspects of it, for sure." One way the company shifted was by finding cheaper content to use. As the journalist Liz Pelly outlines in her book "Mood Machine," Spotify's pre-IPO years saw an increasing reliance on what is known internally as "perfect fit content," a euphemism for cheaper-to-license audio and mood-specific stock music that is optimized for longer listening sessions. As more listeners turned to streaming services like Spotify for "chill vibes" playlists and background music for studying, the company realized it could save money on royalty costs by populating those playlists with the cheap stuff. That would certainly explain the astonishing number of rainstorm audio tracks being released in a given week. Ford watched with dismay as the platform became flooded with generic mood music. Around the same time, he said, respected colleagues started leaving the company. After what he describes as "a particularly bad stretch" of feeling frustrated by what he saw as a dramatic culture shift, Ford accepted an offer from YouTube to lead content for its music subscription service in 2018. In the years since, Spotify's music programming strategy has only skewed more heavily toward automation. Pelly writes that by late 2023, the number of Spotify employees working on editorial music curation (about 200 people globally) was less than one-third the size of the team focused on algorithmic curation and personalization, according to an internal org chart. Notably, that head count was before Spotify laid off 17% of its total workforce in December 2023, a drastic cost-cutting measure that CEO Daniel Ek later admitted "did disrupt our day-to-day operations more than we anticipated." Holder said that there are now over 130 employees working on editorial music curation. One of the more palpable victims of Spotify's 2023 cuts was Glenn McDonald, the company's data alchemist (his real job title), who came from the original Echo Nest team and designed and built much of the original data infrastructure and algorithms powering Spotify's music recommendation engine. He designed the system that identifies a song's genre, which was crucial to effectively sort and recommend music and understand listeners' tastes. "The genre system was human-guided," McDonald said. "After they laid me off, they replaced it with a system that is not human-guided. It's just machine learning. It looks at patterns of words in the titles and descriptions of playlists. That's objectively worse." McDonald said the ripple effects of this change could be felt across Spotify's listening experience. Music was often misclassified, like when Swedish folk-metal bands were suddenly lumped in with all other folk artists. He said it took Spotify engineers a year to fully deactivate and replace the genre system he built, according to data output from the company's public API. One of the many data-filtering tools he built during his tenure at the company was a set of filters designed to fine-tune the music recommendations presented by Discover Weekly and Release Radar — and crucially, prevent things like nature sounds and ambient noise frequencies from showing up where they don't belong. "They would have been flagged as anomalies on those dashboards, and I would have followed up with the teams in charge of those recommendations," McDonald explained. "Without those dashboards, probably nobody is even watching for these issues." Holder said that Spotify's approach to classifying genres and subgenres has "evolved" from the legacy system built by McDonald and other Echo Nest engineers, but declined to get into technical specifics of how it works now or what advantages the new system introduces. "We're always laser-focused on continually enhancing personalized recommendations," she said. Recent updates include improvements to genre accuracy and Discover Weekly recommendations, she added. Ford said the changes at Spotify are part of a broader industry shift away from costlier human curation. "There's been a tradeoff in favor of high metrics, long listening sessions, and music that's just 'good enough,'" he said. "It's happening everywhere." The result is what Ford calls a "rampant wave of algorithmic fatigue" among listeners, who are clamoring for more meaningful cultural experiences as online platforms become increasingly hyper-automated. While the occasional bug and ongoing iteration are to be expected in any product, some longtime subscribers find it odd that Spotify would need to fix or enhance features that worked so well in the first place. My Release Radar delivering mostly white noise and rainstorms, for instance, feels like a fundamental failure of what that feature used to do so well. Over the course of reporting and writing this story over the last few weeks, I've checked back with my Release Radar. Each week, it has a few new songs, but it's still mostly rainstorms. John Paul Titlow is a freelance journalist who writes about technology, digital culture, travel, and mental health. Read the original article on Business Insider
Yahoo
02-05-2025
- Entertainment
- Yahoo
Spotify was great at helping you discover new music. Then the staff cuts began.
The music app I've been using for the last 14 years recently decided that I'm obsessed with rainstorms. When I last listened to my Spotify Release Radar playlist — which for years reliably curated a decent selection of newly released music informed by my favorite artists — the lineup quickly took a turn for the worse. After new songs by familiar indie pop names like Japanese Breakfast and The Marias, there was a five-minute recording of rain falling followed by a short, ambient instrumental by a little-known alternative rock artist. Then, for its remaining 25 tracks, my playlist delivered a succession of rainstorms, nature sounds, and brown-noise frequencies — not exactly the stuff of a killer music festival lineup. When Spotify launched Release Radar in 2016, it promised "a weekly selection of the newest releases that matter the most to you." Not only was the feature designed to delight music fans upon first listen, but it would get better over time, the company promised. So why, nearly a decade later, was my latest Release Radar delivering 85% noise? Sure, my girlfriend and I had recently started listening to "sleep sounds" — things like ocean waves and the hum of an airplane's engine — at night, but that didn't fully explain the issue. The algorithm hadn't made such a crucial curation error before. I'm hardly the first Spotify subscriber to notice that the gears of its music recommendation engine have gotten rusty. Over the past year, fans have taken to Reddit, LinkedIn, and other platforms to complain that curated, for you playlists like Release Radar, Discover Weekly, and Daily Mix have gone downhill, resulting in what many describe as an "echo chamber" that feels more repetitive or off the mark than it once did. Random tracks from white-noise playlists or kids' music albums are popping up where they don't belong, ruining the listening experience. Molly Holder, Spotify's senior director of product for personalization, disagreed that the quality of these curated playlists has declined. "People are discovering more new music and spending more time doing so," she said in a statement, adding that the company listens to user feedback, continues to enhance personal recommendations, and that listener engagement metrics are up. But after talking with former Spotify employees, it seems likely that a combination of layoffs and shifting business priorities has hollowed out the platform's music discovery product. Essentially, in a quest for profitability, Spotify broke its algorithm. One of the features that set Spotify apart from its intensifying competition over the years was the platform's man-meets-machine approach to music curation, especially its personalized, algorithmically curated playlists. Discover Weekly promised to introduce listeners to new music that they might like, Daily Mix turned their tastes into themed playlists, and Release Radar highlighted freshly released tracks. For years, these features seemed to elicit near-universal praise online and gave Spotify a competitive advantage just in time to fend off competition from giants like Apple and Amazon. The music discovery features are likely one reason it's been able to lead the global music subscription market. A recent EMARKETER report found that Americans spend over nine times as much time on Spotify as the next closest competitor. Lately, though, those same features have inspired frustration. For many, Discover Weekly has gotten worse at delivering songs that align with their musical tastes, sometimes including songs they've already listened to on Spotify. Other users report that Release Radar misses new releases from artists they like or mistakenly includes tracks that came out weeks earlier. Release Radar has also started including less relevant music from what one Reddit user described as "random artists with under 50k listens" that seemed to come out of nowhere, while others bemoan the inclusion of what sounds like "AI garbage." It just continued until I finally said, 'I can't take it.' The most audible groan of disapproval came with the arrival of Spotify Wrapped last year. While the personalized year-end recap playlist had historically sparked a tsunami of positive online buzz each year, the 2024 edition prompted a far more mixed reaction. Listeners said it lacked personality and interesting insights into their music habits. Jeffrey Smith, a self-described "Spotify diehard" who leads marketing for the online music marketplace Discogs, has become so frustrated by the platform's declining music discovery feature that he's considering switching to Apple Music. "Over the last couple of years, Spotify has met my needs less and less," Smith said. "It's not really reflective of my listening behavior as much as it is reflective of what they want me to listen to. It's just a listening machine at this point, not a music platform." Smith's affinity for Spotify started fading when he noticed one song — "Back on 74" by Jungle — kept popping up on his personalized Spotify playlists. While it's possible that he had checked the song out at some point, it wasn't something he enjoyed or actively listened to. Still, the song made its way from one personalized music recommendation feature to another and even started inspiring Spotify's algorithms to play similar songs. "It just continued until I finally said, 'I can't take it,'" Smith said. As a music obsessive working at a major marketplace for vinyl records, Smith has no shortage of sources of quality music recommendations. But the days of Spotify augmenting his organic music discovery appear to be over. If he keeps his Spotify subscription at all, he says it will be for the podcasts and audiobooks the company has added to its premium tier. I've found myself in a similar conundrum. After largely dismissing the 2015 launch of Apple Music as a then-satisfied Spotify subscriber, I recently decided to give Apple's music service another try. Since its debut, Apple Music has prioritized human editors over algorithms for music curation. Where it does use data-driven personalization, the results are pretty solid, and crucially, free of rainstorms. While Spotify's new audiobook library makes it tempting to stay, its playlists no longer feel like the best in the business. Once my free trial of Apple Music is over, I'm planning to make the switch. Music subscription services like Spotify spend an extraordinary amount of money — last year, the company said it spent $10 billion — to license their massive music catalogs from record labels, publishers, and other rightsholders. These high costs make it more challenging to turn an enduring profit, something Spotify only managed to do for the first time in 2024. From Wall Street's perspective, Spotify is killing it as year-over-year metrics like subscribers and revenue continue to grow. But in the quest to please investors, some ex-Spotify employees think it has abandoned too much of the human element that makes music and the culture around it so special. Doug Ford was a Spotify executive who oversaw editorial playlist curation from 2013 to 2018. He arrived via Spotify's acquisition of Tunigo, a Swedish company that specialized in expert-curated music playlists based on different genres and moods. The next year, Spotify acquired Echo Nest, a music data startup founded by MIT grads that used a mix of machine learning and data filtering to build Pandora-style music recommendation algorithms. Both teams built out Spotify's man-meets-machine music discovery system, which helped the company successfully weather new competition from tech giants like Apple and Amazon as it started to explore the possibility of going public. You need to be a successful business in order to offer this utility to people. But they've discarded some of the human aspects of it, for sure. "That was a really beautiful moment in Spotify's history," Ford said. "That mix of the intentional algorithmic and human curation to make a really deep product was great." The results spoke for themselves. Spotify said the 2015 launch of Discover Weekly yielded 1.7 billion streams in its first five months and generated a weekly wave of social media buzz when listeners' Discover Weekly playlists were refreshed. "Our goal with Discover Weekly was to make something that felt like a friend or someone who knew you well was making you a mixtape," Spotify's product lead at the time, Matt Ogle, told me in an interview. To accomplish that feeling, the algorithm drew heavily from how real flesh-and-blood music enthusiasts were curating playlists on the platform. "That's why that thing was so good," Ford said. "It was taking all the inputs equally and letting things happen normally with the audience. Now it feels a little bit different." Ford said things started changing in the lead-up to Spotify's 2018 initial public offering and the ensuing pressure to prove to investors that profitability was possible in the notoriously tight-margined music streaming business. "The culture changed because it had to become a business," Ford said. "You need to be a successful business in order to offer this utility to people. But they've discarded some of the human aspects of it, for sure." One way the company shifted was by finding cheaper content to use. As the journalist Liz Pelly outlines in her book "Mood Machine," Spotify's pre-IPO years saw an increasing reliance on what is known internally as "perfect fit content," a euphemism for cheaper-to-license audio and mood-specific stock music that is optimized for longer listening sessions. As more listeners turned to streaming services like Spotify for "chill vibes" playlists and background music for studying, the company realized it could save money on royalty costs by populating those playlists with the cheap stuff. That would certainly explain the astonishing number of rainstorm audio tracks being released in a given week. Ford watched with dismay as the platform became flooded with generic mood music. Around the same time, he said, respected colleagues started leaving the company. After what he describes as "a particularly bad stretch" of feeling frustrated by what he saw as a dramatic culture shift, Ford accepted an offer from YouTube to lead content for its music subscription service in 2018. In the years since, Spotify's music programming strategy has only skewed more heavily toward automation. Pelly writes that by late 2023, the number of Spotify employees working on editorial music curation (about 200 people globally) was less than one-third the size of the team focused on algorithmic curation and personalization, according to an internal org chart. Notably, that head count was before Spotify laid off 17% of its total workforce in December 2023, a drastic cost-cutting measure that CEO Daniel Ek later admitted "did disrupt our day-to-day operations more than we anticipated." Holder said that there are now over 130 employees working on editorial music curation. One of the more palpable victims of Spotify's 2023 cuts was Glenn McDonald, the company's data alchemist (his real job title), who came from the original Echo Nest team and designed and built much of the original data infrastructure and algorithms powering Spotify's music recommendation engine. He designed the system that identifies a song's genre, which was crucial to effectively sort and recommend music and understand listeners' tastes. "The genre system was human-guided," McDonald said. "After they laid me off, they replaced it with a system that is not human-guided. It's just machine learning. It looks at patterns of words in the titles and descriptions of playlists. That's objectively worse." There's been a tradeoff in favor of high metrics, long listening sessions, and music that's just 'good enough.' McDonald said the ripple effects of this change could be felt across Spotify's listening experience. Music was often misclassified, like when Swedish folk-metal bands were suddenly lumped in with all other folk artists. He said it took Spotify engineers a year to fully deactivate and replace the genre system he built, according to data output from the company's public API. One of the many data-filtering tools he built during his tenure at the company was a set of filters designed to fine-tune the music recommendations presented by Discover Weekly and Release Radar — and crucially, prevent things like nature sounds and ambient noise frequencies from showing up where they don't belong. "They would have been flagged as anomalies on those dashboards, and I would have followed up with the teams in charge of those recommendations," McDonald explained. "Without those dashboards, probably nobody is even watching for these issues." Holder said that Spotify's approach to classifying genres and subgenres has "evolved" from the legacy system built by McDonald and other Echo Nest engineers, but declined to get into technical specifics of how it works now or what advantages the new system introduces. "We're always laser-focused on continually enhancing personalized recommendations," she said. Recent updates include improvements to genre accuracy and Discover Weekly recommendations, she added. Ford said the changes at Spotify are part of a broader industry shift away from costlier human curation. "There's been a tradeoff in favor of high metrics, long listening sessions, and music that's just 'good enough,'" he said. "It's happening everywhere." The result is what Ford calls a "rampant wave of algorithmic fatigue" among listeners, who are clamoring for more meaningful cultural experiences as online platforms become increasingly hyper-automated. While the occasional bug and ongoing iteration are to be expected in any product, some longtime subscribers find it odd that Spotify would need to fix or enhance features that worked so well in the first place. My Release Radar delivering mostly white noise and rainstorms, for instance, feels like a fundamental failure of what that feature used to do so well. Over the course of reporting and writing this story over the last few weeks, I've checked back with my Release Radar. Each week, it has a few new songs, but it's still mostly rainstorms. John Paul Titlow is a freelance journalist who writes about technology, digital culture, travel, and mental health. Read the original article on Business Insider
Business Insider
02-05-2025
- Entertainment
- Business Insider
'Algorithmic fatigue'
The music app I've been using for the last 14 years recently decided that I'm obsessed with rainstorms. When I last listened to my Spotify Release Radar playlist — which for years reliably curated a decent selection of newly released music informed by my favorite artists — the lineup quickly took a turn for the worse. After new songs by familiar indie pop names like Japanese Breakfast and The Marias, there was a five-minute recording of rain falling followed by a short, ambient instrumental by a little-known alternative rock artist. Then, for its remaining 25 tracks, my playlist delivered a succession of rainstorms, nature sounds, and brown-noise frequencies — not exactly the stuff of a killer music festival lineup. When Spotify launched Release Radar in 2016, it promised "a weekly selection of the newest releases that matter the most to you." Not only was the feature designed to delight music fans upon first listen, but it would get better over time, the company promised. So why, nearly a decade later, was my latest Release Radar delivering 85% noise? Sure, my girlfriend and I had recently started listening to "sleep sounds" — things like ocean waves and the hum of an airplane's engine — at night, but that didn't fully explain the issue. The algorithm hadn't made such a crucial curation error before. I'm hardly the first Spotify subscriber to notice that the gears of its music recommendation engine have gotten rusty. Over the past year, fans have taken to Reddit, LinkedIn, and other platforms to complain that curated, for you playlists like Release Radar, Discover Weekly, and Daily Mix have gone downhill, resulting in what many describe as an "echo chamber" that feels more repetitive or off the mark than it once did. Random tracks from white-noise playlists or kids' music albums are popping up where they don't belong, ruining the listening experience. Molly Holder, Spotify's senior director of product for personalization, disagreed that the quality of these curated playlists has declined. "People are discovering more new music and spending more time doing so," she said in a statement, adding that the company listens to user feedback, continues to enhance personal recommendations, and that listener engagement metrics are up. But after talking with former Spotify employees, it seems likely that a combination of layoffs and shifting business priorities has hollowed out the platform's music discovery product. Essentially, in a quest for profitability, Spotify broke its algorithm. One of the features that set Spotify apart from its intensifying competition over the years was the platform's man-meets-machine approach to music curation, especially its personalized, algorithmically curated playlists. Discover Weekly promised to introduce listeners to new music that they might like, Daily Mix turned their tastes into themed playlists, and Release Radar highlighted freshly released tracks. For years, these features seemed to elicit near-universal praise online and gave Spotify a competitive advantage just in time to fend off competition from giants like Apple and Amazon. The music discovery features are likely one reason it's been able to lead the global music subscription market. A recent EMARKETER report found that Americans spend over nine times as much time on Spotify as the next closest competitor. Lately, though, those same features have inspired frustration. For many, Discover Weekly has gotten worse at delivering songs that align with their musical tastes, sometimes including songs they've already listened to on Spotify. Other users report that Release Radar misses new releases from artists they like or mistakenly includes tracks that came out weeks earlier. Release Radar has also started including less relevant music from what one Reddit user described as "random artists with under 50k listens" that seemed to come out of nowhere, while others bemoan the inclusion of what sounds like "AI garbage." The most audible groan of disapproval came with the arrival of Spotify Wrapped last year. While the personalized year-end recap playlist had historically sparked a tsunami of positive online buzz each year, the 2024 edition prompted a far more mixed reaction. Listeners said it lacked personality and interesting insights into their music habits. Jeffrey Smith, a self-described "Spotify diehard" who leads marketing for the online music marketplace Discogs, has become so frustrated by the platform's declining music discovery feature that he's considering switching to Apple Music. "Over the last couple of years, Spotify has met my needs less and less," Smith said. "It's not really reflective of my listening behavior as much as it is reflective of what they want me to listen to. It's just a listening machine at this point, not a music platform." Smith's affinity for Spotify started fading when he noticed one song — "Back on 74" by Jungle — kept popping up on his personalized Spotify playlists. While it's possible that he had checked the song out at some point, it wasn't something he enjoyed or actively listened to. Still, the song made its way from one personalized music recommendation feature to another and even started inspiring Spotify's algorithms to play similar songs. "It just continued until I finally said, 'I can't take it,'" Smith said. As a music obsessive working at a major marketplace for vinyl records, Smith has no shortage of sources of quality music recommendations. But the days of Spotify augmenting his organic music discovery appear to be over. If he keeps his Spotify subscription at all, he says it will be for the podcasts and audiobooks the company has added to its premium tier. I've found myself in a similar conundrum. After largely dismissing the 2015 launch of Apple Music as a then-satisfied Spotify subscriber, I recently decided to give Apple's music service another try. Since its debut, Apple Music has prioritized human editors over algorithms for music curation. Where it does use data-driven personalization, the results are pretty solid, and crucially, free of rainstorms. While Spotify's new audiobook library makes it tempting to stay, its playlists no longer feel like the best in the business. Once my free trial of Apple Music is over, I'm planning to make the switch. Music subscription services like Spotify spend an extraordinary amount of money — last year, the company said it spent $10 billion — to license their massive music catalogs from record labels, publishers, and other rightsholders. These high costs make it more challenging to turn an enduring profit, something Spotify only managed to do for the first time in 2024. From Wall Street's perspective, Spotify is killing it as year-over-year metrics like subscribers and revenue continue to grow. But in the quest to please investors, some ex-Spotify employees think it has abandoned too much of the human element that makes music and the culture around it so special. Doug Ford was a Spotify executive who oversaw editorial playlist curation from 2013 to 2018. He arrived via Spotify's acquisition of Tunigo, a Swedish company that specialized in expert-curated music playlists based on different genres and moods. The next year, Spotify acquired Echo Nest, a music data startup founded by MIT grads that used a mix of machine learning and data filtering to build Pandora-style music recommendation algorithms. Both teams built out Spotify's man-meets-machine music discovery system, which helped the company successfully weather new competition from tech giants like Apple and Amazon as it started to explore the possibility of going public. You need to be a successful business in order to offer this utility to people. But they've discarded some of the human aspects of it, for sure. "That was a really beautiful moment in Spotify's history," Ford said. "That mix of the intentional algorithmic and human curation to make a really deep product was great." The results spoke for themselves. Spotify said the 2015 launch of Discover Weekly yielded 1.7 billion streams in its first five months and generated a weekly wave of social media buzz when listeners' Discover Weekly playlists were refreshed. "Our goal with Discover Weekly was to make something that felt like a friend or someone who knew you well was making you a mixtape," Spotify's product lead at the time, Matt Ogle, told me in an interview. To accomplish that feeling, the algorithm drew heavily from how real flesh-and-blood music enthusiasts were curating playlists on the platform. "That's why that thing was so good," Ford said. "It was taking all the inputs equally and letting things happen normally with the audience. Now it feels a little bit different." Ford said things started changing in the lead-up to Spotify's 2018 initial public offering and the ensuing pressure to prove to investors that profitability was possible in the notoriously tight-margined music streaming business. "The culture changed because it had to become a business," Ford said. "You need to be a successful business in order to offer this utility to people. But they've discarded some of the human aspects of it, for sure." One way the company shifted was by finding cheaper content to use. As the journalist Liz Pelly outlines in her book "Mood Machine," Spotify's pre-IPO years saw an increasing reliance on what is known internally as "perfect fit content," a euphemism for cheaper-to-license audio and mood-specific stock music that is optimized for longer listening sessions. As more listeners turned to streaming services like Spotify for "chill vibes" playlists and background music for studying, the company realized it could save money on royalty costs by populating those playlists with the cheap stuff. That would certainly explain the astonishing number of rainstorm audio tracks being released in a given week. Ford watched with dismay as the platform became flooded with generic mood music. Around the same time, he said, respected colleagues started leaving the company. After what he describes as "a particularly bad stretch" of feeling frustrated by what he saw as a dramatic culture shift, Ford accepted an offer from YouTube to lead content for its music subscription service in 2018. In the years since, Spotify's music programming strategy has only skewed more heavily toward automation. Pelly writes that by late 2023, the number of Spotify employees working on editorial music curation (about 200 people globally) was less than one-third the size of the team focused on algorithmic curation and personalization, according to an internal org chart. Notably, that head count was before Spotify laid off 17% of its total workforce in December 2023, a drastic cost-cutting measure that CEO Daniel Ek later admitted "did disrupt our day-to-day operations more than we anticipated." Holder said that there are now over 130 employees working on editorial music curation. One of the more palpable victims of Spotify's 2023 cuts was Glenn McDonald, the company's data alchemist (his real job title), who came from the original Echo Nest team and designed and built much of the original data infrastructure and algorithms powering Spotify's music recommendation engine. He designed the system that identifies a song's genre, which was crucial to effectively sort and recommend music and understand listeners' tastes. "The genre system was human-guided," McDonald said. "After they laid me off, they replaced it with a system that is not human-guided. It's just machine learning. It looks at patterns of words in the titles and descriptions of playlists. That's objectively worse." There's been a tradeoff in favor of high metrics, long listening sessions, and music that's just 'good enough.' McDonald said the ripple effects of this change could be felt across Spotify's listening experience. Music was often misclassified, like when Swedish folk-metal bands were suddenly lumped in with all other folk artists. He said it took Spotify engineers a year to fully deactivate and replace the genre system he built, according to data output from the company's public API. One of the many data-filtering tools he built during his tenure at the company was a set of filters designed to fine-tune the music recommendations presented by Discover Weekly and Release Radar — and crucially, prevent things like nature sounds and ambient noise frequencies from showing up where they don't belong. "They would have been flagged as anomalies on those dashboards, and I would have followed up with the teams in charge of those recommendations," McDonald explained. "Without those dashboards, probably nobody is even watching for these issues." Holder said that Spotify's approach to classifying genres and subgenres has "evolved" from the legacy system built by McDonald and other Echo Nest engineers, but declined to get into technical specifics of how it works now or what advantages the new system introduces. "We're always laser-focused on continually enhancing personalized recommendations," she said. Recent updates include improvements to genre accuracy and Discover Weekly recommendations, she added. Ford said the changes at Spotify are part of a broader industry shift away from costlier human curation. "There's been a tradeoff in favor of high metrics, long listening sessions, and music that's just 'good enough,'" he said. "It's happening everywhere." The result is what Ford calls a "rampant wave of algorithmic fatigue" among listeners, who are clamoring for more meaningful cultural experiences as online platforms become increasingly hyper-automated. While the occasional bug and ongoing iteration are to be expected in any product, some longtime subscribers find it odd that Spotify would need to fix or enhance features that worked so well in the first place. My Release Radar delivering mostly white noise and rainstorms, for instance, feels like a fundamental failure of what that feature used to do so well. Over the course of reporting and writing this story over the last few weeks, I've checked back with my Release Radar. Each week, it has a few new songs, but it's still mostly rainstorms.
