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AI NewsFrench AI Startup Mistral AI Raises $830 Mn in Debt for its Sovereign AI Infra
5:31 PM IST · March 30, 2026
This investment aims to increase its capacity to 44MW and support the growing demand for AI capabilities from governments and enterprises.
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Music streaming serviceTIDALis the latest to take aim at AI-generated music with the introduction ofa new policythat will prevent fully AI-generated music from making money on its platform. In addition, TIDAL will use automated tools to remove AI-generated music that attempts to impersonate an artist or a group, the company said. “We are committed to protecting and rewarding organic creativity to avoid compromising an artist’s ability to connect with and build their fandom from TIDAL subscribers. Many have told us they do not want to be exposed to — or prompted to listen to — wholly AI-generated music,” wroteTonyGervino, TIDAL EVP and editor-in-chief, in anannouncement. He clarified that TIDAL’s new policy was not meant to “bash technological advancement,” but rather focuses on protecting and rewarding “organic creativity” from artists. With the changes, fully AI-generated music on TIDAL will be identified and tagged as such, allowing listeners to see an “AI” badge next to any tracks deemed to be 100% AI. These tunes will not be able to be monetized or collect royalties, and will not be eligible for direct-to-fan sales, the company noted. TIDAL’s policy joins others in the streaming music space, where services like Spotify, Apple Music, Deezer, andQobuzhave developed their own policies to address the growing number of AI-generated tracks filling their services. Spotify last yearrevamped its policies to label AI musicand better filter spam, while still acknowledging that AI tools would be used in the music-creation process to varying degrees. Apple Music alsotook the tagging approach. Deezer, which said that44% of all new musicuploaded to its platform daily is AI-generated, has taken a tougher position. It actively removes AI tracks from recommendations and excludes them from editorial playlists. It alsooffers its AI-detection technology to rivalsandprovides a consumer-facing toolthat lets you see if AI music has slipped into your playlists on competing services. TIDAL’s policy could be an interesting test to see if demonetization could be the thing to slow the deluge of AI music, which many listeners aren’t interested in. “Regardless of what you are reading elsewhere, AI’s takeover of the music industry (and your recommendations) isn’t inevitable if we take even greater steps now to monitor and control it,” noted Gervino. The company said the new policy is a “living document,” meaning it’s open to changes as the space evolves. It goes into effect on July 15, 2026.
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Cursor isn’t lettingthe $60 billion SpaceX acquisitionslow it down. On Monday, the companyannounceda new mobile appfor iOSdevices designed for users who want to prompt coding agents directly from their phone. The app ties into the Cursor 2.0 changesunveiled in October, which shifted the service towards independent coding agents. With the mobile app, users can spin up new coding agents or interact with agents that were initiated from the desktop client. Cursor’s move to mobile follows similar apps from Anthropic and OpenAI, both of which offer ways to interact with their coding tools on mobile. It’s part of a broader shift in AI-based coding tools, which are increasingly abstracting away from written code and towards oversight of code-writing agents. With no need to access large code bases, many developers are switching away from multi-monitor desktop setups in favor of phones, which allow continuous conversations with remote agents. In a recent talk, Anthropic’s head of Claude Code, Boris Cherny, said he had almost entirely switched to mobile AI coding as a result. “Most of my coding now is on my phone,” Cherny said in the talk. “I would have said ‘you’re crazy’ if you told me that six months ago, but yeah, here we are.”
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Just eight months after launching its commercial service, AI leaderboard providerArena, which originated as a research project at UC Berkeley in 2023, has reached $100 million in annualized run-rate revenue. Arena is best known for its popular crowdsourced AI model performance leaderboard, generated from over 10 million user evaluations. Its consumer website lets a user type a prompt it sends to two models; afterwards, the user chooses which model did a better job. While Arena’s popular AI model leaderboard is free for public use, the company began generating revenue from its platform in September when it introducedAI Evaluations, a service that provides model labs and enterprises with deep-dive performance analytics gathered from its community. Arena’s rapid revenue growth shows that its commercial offerings are as popular with customers as they are with its community of evaluators, who are frequently drawn to the platform for early access to the latest, often unreleased, AI models. “A lot of people don’t even understand that our business is making any money at all; people still see us as like an open-source project,” Anastasios Angelopoulos, Arena’s co-founder and CEO, told TechCrunch. While Arena calls its revenue milestone ARR, a term that traditionally stood forannualized recurring revenue, Angelopoulos clarified that the company charges customers for “consumption,” which means that its revenue is not recurring. While Arena doesn’t have direct competitors – Yupp, another crowdsourced AI model-picking startup,shut downin March— Angelopoulos said the company competes “for the same dollar” with human labeling startups like Mercor, Surge, and Scale AI, all of which assist model makers in refining their AI during post-training. As AI providers strive to maximize model performance, their appetite for post-training refinement services continues to surge. When Arena announced in January that it raised a $150 million Series A at a post-money valuation of $1.7 billion, its annualized revenue was$30 million. Elsewhere, Handshake’s gross annualized revenue from AI training has nearly doubled since January, climbing from $550 million to nearly $1 billion, The Informationreportedin April. Mercor’s annualized revenue also topped $1 billion earlier this year, up from $500 million last September,accordingto The Information. Arena ranks models on a variety of tasks such as text, coding, vision, and image generation, as well as complex, long-running workflows through its recently introduced Agent Mode. Along with Angelopoulos (pictured left), Arena was co-founded by fellow UC Berkeley postdoctoral student Wei-Lin Chiang (pictured center), who serves as the startup’s CTO. The startup was also co-founded by Ion Stoica (pictured right), the renowned UC Berkeley professor and Databricks co-founder who advised the project before it incorporated as a company in April 2025. Arena has raised a total of $250 million from investors including Felicis, Andreessen Horowitz, The House Fund, LDVP, Kleiner Perkins, Lightspeed Venture Partners, Laude Ventures, and UC Investments.
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The world’s two largest memory chip companies plan to invest $518 billion (~800 trillion won) to build four new memory fabs in southwestern South Korea, a region that has historically attracted little semiconductor investment. The announcement is part of the country’s sweeping national investment plan spanning semiconductors, AI data centers, and physical AI, which was unveiled at a presidential briefing on Monday, with the chairmen of Samsung and SK Hynix in attendance. The plan breaks down into three buckets. In the memory chip bucket is $518 billion for four new memory fabs in the southwest, plus $52 billion for an HBM (high bandwidth memory) packaging hub in the central region. Then there’s another $356 billion (550 trillion won) for AI data centers to be built by Korean tech and energy behemoths such as SK, GS and Naver through 2035. All told, South Korean tech companies have committed to spend over $900 billion on AI and the demands for chips it is creating. With this, the nation hopes to catapult itself into becoming more of an AI power player than it already is. Currently, Samsung and SK Hynix (along with U.S. memory chip maker Micron) are all enjoying record demand from what’s been calledRAMageddon, a worldwide shortage of memory chips caused by the AI buildout. “Semiconductors, physical AI, and AI data centers are the triple axis for South Korea’s next industrial era,” President Jae Myung Lee saidin a televised addressMonday, calling 2026 the year South Korea must establish itself as an “irreplaceable” industrial power. Lee said existing chip facilities in Yongin and Pyeongtaek, the heart of South Korea’s semiconductor belt just south of Seoul, have “already reached their limits,” and urged companies to accelerate investment in the southwest, hoping to spreading the AI wealth beyond the nation’s capital. “We must secure overwhelming production capacity in advance,” he said. Yet, Lee pushed back against media reports that the government had pressured companies into the investments,reportedly sayingthe decisions reflected the companies’ own judgment. “The government’s role is to invest its capabilities so that companies can invest without losses and with better prospects,” he was quoted as saying. Samsungseparately published a press release Monday, announcing plans to invest 2,655 trillion won (~$1.7 trillion) over the next decade, with 425 trillion won earmarked for the Honam region, the southwestern corner of the Korean peninsula. The company cited expected incentives around power, water, workforce, and living conditions as key factors in selecting Gwangju, roughly 300 kilometers south of Seoul, for a new semiconductor fab, alongside an AI data center in Haenam, at the southern tip of the peninsula. That is not an outlandish sum compared to U.S. tech giants Alphabet, Amazon, Meta, and Microsoft, who will collectively spend $650 billion on AI infrastructure this year alone,according to Reuters. Meanwhile, SK Group announced a 2,100 trillion won (~$1.4 trillion) medium-to-long term investment roadmap, 1,100 trillion won to expand semiconductor production capacity and 1,000 trillion won for AI data centers nationwide. SK Hynix, the group’s core semiconductor affiliate, is central to the chip expansion push, while SK Telecom will lead the buildout of 15 gigawatts of AI data center capacity across the country. Whether the ambition translates into execution is another question. Deep tech industries like semiconductors and AI don’t move on political or even customer demand timelines. Fabs take years to build and the risk is that, by the time they are ready, the demand that caused them will have ebbed, leaving companies with oversupply and crashing prices. For now, the world’s AI chip supply chain, especially those hungry for all things memory, will be watching to see if South Korea can pull it off.
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