📌 TOPINDIATOURS Hot ai: King Gizzard Responds to Being Impersonated by AI on Spoti

Acclaimed Australian prog rock band King Gizzard & the Lizard Wizard made headlines earlier this year when it quit Spotify, protesting the platform’s CEO, Daniel Ek, who heavily invested in an AI weapons company.

The band was one of several music acts to pull their music from Spotify over ethical concerns. Many of them have taken issue with artists earning very little money per stream on the platform, or the company donating a sizable sum to president Donald Trump’s inauguration ceremony.

Next, something extremely dark happened: an impostor created a band on Spotify with the extremely similar band name of “King Lizard Wizard” and used AI to generate songs with the same titles as actual King Gizzard songs that ripped off their entire lyrics and sound, accumulating tens of thousands of streams while remaining on the streaming service for weeks without detection.

Outspoken King Gizzard & The Lizard Wizard frontman Stu Mackenzie has now excoriated the platform after finding out about the ruse.

“[I’m] trying to see the irony in this situation,” he said in a statement quoted by The Music. “But seriously wtf we are truly doomed.”

Spotify has since pulled down the offending material, with a spokesperson telling Futurism in a statement that it “strictly prohibits any form of artist impersonation.”

“The content in question was removed for violating our platform policies, and no royalties were paid out for any streams generated,” the spokesperson added.

But the company’s reactive cat-and-mouse game isn’t exactly assuring artists, given Mackenzie’s reaction.

The incident highlights how Spotify is seriously struggling to keep AI slop at bay on its platform. While the company announced new policies to protect artists against “spam, impersonation, and deception” in September, we continue to see offending AI impersonations landing in users’ Release Radar and Discover Weekly playlists, which the company prominently recommends to them.

Worse yet, as  Platfomer reported last month, a separate King Gizzard impersonator had previously attempted to cash in on the band’s royalties using AI — meaning that if there was one band that Spotify should have been manually screening for impostors, it should have been King Gizzard.

In short, Spotify has a major PR headache to clean up as it reels from an onslaught of AI slop.

And a growing number of artists, including King Gizzard, have finally had enough and are looking for greener pastures. Who could blame them?

More on the incident: King Gizzard Pulled Their Music From Spotify in Protest, and Now Spotify Is Hosting AI Knockoffs of Their Songs

The post King Gizzard Responds to Being Impersonated by AI on Spotify: “We Are Truly Doomed” appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Update ai: OpenAI report reveals a 6x productivity gap between AI

The tools are available to everyone. The subscription is company-wide. The training sessions have been held. And yet, in offices from Wall Street to Silicon Valley, a stark divide is opening between workers who have woven artificial intelligence into the fabric of their daily work and colleagues who have barely touched it.

The gap is not small. According to a new report from OpenAI analyzing usage patterns across its more than one million business customers, workers at the 95th percentile of AI adoption are sending six times as many messages to ChatGPT as the median employee at the same companies. For specific tasks, the divide is even more dramatic: frontier workers send 17 times as many coding-related messages as their typical peers, and among data analysts, the heaviest users engage the data analysis tool 16 times more frequently than the median.

This is not a story about access. It is a story about a new form of workplace stratification emerging in real time — one that may be reshaping who gets ahead, who falls behind, and what it means to be a skilled worker in the age of artificial intelligence.

Everyone has the same tools, but not everyone is using them

Perhaps the most striking finding in the OpenAI report is how little access explains. ChatGPT Enterprise is now deployed across more than 7 million workplace seats globally, a nine-fold increase from a year ago. The tools are the same for everyone. The capabilities are identical. And yet usage varies by orders of magnitude.

Among monthly active users — people who have logged in at least once in the past 30 days — 19 percent have never tried the data analysis feature. Fourteen percent have never used reasoning capabilities. Twelve percent have never used search. These are not obscure features buried in submenus; they are core functionality that OpenAI highlights as transformative for knowledge work.

The pattern inverts among daily users. Only 3 percent of people who use ChatGPT every day have never tried data analysis; just 1 percent have skipped reasoning or search. The implication is clear: the divide is not between those who have access and those who don't, but between those who have made AI a daily habit and those for whom it remains an occasional novelty.

Employees who experiment more are saving dramatically more time

The OpenAI report suggests that AI productivity gains are not evenly distributed across all users but concentrated among those who use the technology most intensively. Workers who engage across approximately seven distinct task types — data analysis, coding, image generation, translation, writing, and others — report saving five times as much time as those who use only four. Employees who save more than 10 hours per week consume eight times more AI credits than those who report no time savings at all.

This creates a compounding dynamic. Workers who experiment broadly discover more uses. More uses lead to greater productivity gains. Greater productivity gains presumably lead to better performance reviews, more interesting assignments, and faster advancement—which in turn provides more opportunity and incentive to deepen AI usage further.

Seventy-five percent of surveyed workers report being able to complete tasks they previously could not perform, including programming support, spreadsheet automation, and technical troubleshooting. For workers who have embraced these capabilities, the boundaries of their roles are expanding. For those who have not, the boundaries may be contracting by comparison.

The corporate AI paradox: $40 billion spent, 95 percent seeing no return

The individual usage gap documented by OpenAI mirrors a broader pattern identified by a separate study from MIT's Project NANDA. Despite $30 billion to $40 billion invested in generative AI initiatives, only 5 percent of organizations are seeing transformative returns. The researchers call this the "GenAI Divide" — a gap separating the few organizations that succeed in transforming processes with adaptive AI systems from the majority that remain stuck in pilots.

The MIT report found limited disruption across industries: only two of nine major sectors—technology and media—show material business transformation from generative AI use. Large firms lead in pilot volume but lag in successful deployment.

The pattern is consistent across both studies. Organizations and individuals are buying the technology. They are launching pilots. They are attending training sessions. But somewhere between adoption and transformation, most are getting stuck.

While official AI projects stall, a shadow economy is thriving

The MIT study reveals a striking disconnect: while only 40 percent of companies have purchased official LLM subscriptions, employees in over 90 percent of companies regularly use personal AI tools for work. Nearly every respondent reported using LLMs in some form as part of their regular workflow.

"This 'shadow AI' often delivers better ROI than formal initiatives and reveals what actually works for bridging the divide," MIT's Project NANDA found.

The shadow economy offers a clue to what's happening at the individual level within organizations. Employees who take initiative — who sign up for personal subscriptions, who experiment on their own time, who figure out how to integrate AI into their workflows without waiting for IT approval — are pulling ahead of colleagues who wait for official guidance that may never come.

These shadow systems, largely unsanctioned, often deliver better performance and faster adoption than corporate tools. Worker sentiment reveals a preference for flexible, responsive tools — precisely the kind of experimentation that separates OpenAI's frontier workers from the median.

The biggest gaps show up in technical work that used to require specialists

The largest relative gaps between frontier and median workers appear in coding, writing, and analysis — precisely the task categories where AI capabilities have advanced most rapidly. Frontier workers are not just doing…

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🔗 Sumber: venturebeat.com