ElevenLabs Music v2 signals the next phase of commercial AI audio. ElevenLabs' Music v2 is another sign that generative audio is moving out of the novelty phase and into the operating layer of modern content production. The new model can shift styles inside a single track, moving between dramatically different genres while maintaining musical continuity. It is also designed to handle more demanding vocal performances, richer arrangements, and more precise editing than early AI music tools. For business leaders, the practical question is no longer whether AI can generate sound, but how quickly these systems can become dependable creative infrastructure. The most important capability is control. Earlier music-generation tools often produced interesting fragments, but they were difficult to revise without starting over. Music v2 introduces a workflow where users can regenerate a selected part of a song through prompts while preserving the surrounding material. That matters for operators because real creative production is iterative, and teams need tools that support revisions, approvals, localization, and campaign-specific adjustments without breaking the whole asset. Section-based creation also changes the value proposition. Instead of generating a brief clip and hoping it fits, users can build music in components such as intros, verses, choruses, transitions, and outros. That structure maps more naturally to how marketing, media, and brand teams already work. It also opens the door for more modular audio systems, where a brand can maintain a consistent sonic identity while adapting mood, tempo, language, or format across channels. The competitive context is equally important. Google, Stability AI, Suno, and other AI labs are racing to release models capable of longer, more complex, and more editable tracks. Google has already been pushing toward cover generation, sectional song editing, and music video creation through its creative tooling. This is becoming a platform race, not a feature race, and the winners will likely be the companies that combine generation quality with rights management, workflow integration, and predictable enterprise controls. ElevenLabs is leaning hard into the commercial-use angle, and that may be the most strategic part of the announcement. The company says Music v2 is built on licensed data and cleared for commercial use, which directly addresses one of the biggest adoption blockers in AI music. Copyright uncertainty has already created legal pressure around other AI music startups, and enterprise buyers are unlikely to standardize on tools that create avoidable rights exposure. In procurement terms, licensing clarity is not a footnote; it is the difference between a fun experiment and a deployable business system. For marketing and branding teams, the near-term use cases are straightforward. AI music can support campaign variants, social edits, product videos, podcast beds, internal training content, event assets, and rapid localization. The ability to add sound effects and shift genres mid-track also gives teams more flexibility when matching creative direction to audience segments or emotional beats. The strongest implementations will not replace creative judgment; they will compress the time between concept, revision, and usable asset. There are still risks to manage. Brands need clear policies on what can be generated, who approves final audio, how rights documentation is stored, and when human composers or agencies should remain in the loop. Teams should also test whether generated music aligns with brand standards, accessibility expectations, cultural context, and platform requirements. The companies that get value fastest will be the ones that treat AI audio as a governed workflow, not an unchecked content shortcut. The broader takeaway is that generative media is becoming more granular, editable, and operationally useful. Music v2 points toward a future where audio assets can be assembled, revised, localized, and deployed with the same agility that teams now expect from text and image tools. For decision-makers, now is the time to assess where AI audio can remove production bottlenecks while preserving quality, compliance, and brand trust. Hitman Technologies helps organizations evaluate these emerging systems, design practical adoption paths, and turn fast-moving AI capabilities into durable business advantage.

Japan's Sakana Fugu just beat Claude Mythos and nobody saw it coming. In the fast-moving world of AI, 79mplus Editor is used to the heavyweight title bouts happening in Silicon Valley. But while everyone was distracted by the latest from San Francisco, a Tokyo-based startup called Sakana AI just pulled off a stunning upset that has the industry reeling. They've launched a system called Fugu, and according to recent benchmarks, its "Ultra" version isn't just competing with the best - it's actively outperforming Anthropic's most powerful (and recently restricted) models, Claude Fable 5 and Mythos. Table of Contents The stealth rise of Sakana AI. Sakana AI isn't your typical underdog. Founded in 2023, it's led by Llion Jones, a co-author of the foundational Google paper "Attention Is All You Need," and David Ha, the former head of research at Stability AI. The name Sakana means "fish" in Japanese, and their logo - a school of small fish forming one large fish - is the perfect metaphor for their breakthrough. How Fugu did the impossible. What makes Fugu so disruptive is that it isn't a single, massive Large Language Model (LLM) in the traditional sense. Instead, it is a multi-agent orchestration system. Think of it like a conductor leading a world-class orchestra. The Fugu system uses a "manager" model trained specifically to break down complex tasks and delegate them to a pool of specialists. It might send a coding task to GPT, a writing task to Claude, and a research task to Gemini, then synthesize the results into one perfect answer. Because it can orchestrate these models dynamically, it achieves "frontier capability" without the same export controls that have recently hampered US-based models. The benchmarks: beating the "unbeatable" The numbers coming out of Sakana's labs are hard to ignore. On LiveCodeBench, which tests regularly refreshed software problem-solving tasks, Fugu Ultra scored 93.2, edging out Claude Fable's 89.8. Even more impressive was its performance on GPQA-D, a rigorous test involving 198 graduate-level science questions in physics, biology, and chemistry. Fugu Ultra scored 95.5, surpassing the Claude Mythos Preview score of 94.6. This is particularly significant because Fable 5 and Mythos were recently rolled back by Anthropic following US government national security concerns. Sakana has essentially stepped into the vacuum left by these restricted models. The reality check: is it too good to be true? While the benchmarks are historic, real-world "battle testing" reveals some significant trade-offs. Independent testers have noted that because Fugu is orchestrating multiple high-end models, it comes with a "tax" on both time and money. * Speed: In head-to-head tests against Claude Opus 4.8, Fugu was roughly 4.5 times slower. * Cost: Because it calls multiple APIs to solve a single problem, it can be 5 times more expensive than using a single frontier model. * Intelligence: Critics argue that Fugu is more of a "smart harness" than a new form of raw intelligence. Since it relies on other models like GPT-5.5 or Opus 4.8 for its "thinking," its power is fundamentally tied to the availability of those underlying systems. Why this matters. Regardless of the current costs, Sakana AI has proven that orchestration might be the new frontier. By focusing on how models work together rather than just building one giant "god-model," they've found a way to squeeze superior performance out of existing tech. Japan just showed the world that you don't need to build the biggest model to win the benchmark war - you just need to be the best at managing them. As the AI landscape becomes more fragmented with various providers and export restrictions, the ability to optimize across "all the worlds" of AI will become a vital skill. Nobody saw Japan coming for the crown, but with Fugu Ultra, Sakana AI has officially put the world on notice.

Getty Images signs deal to show photos in ChatGPT results. Key takeaways. * Getty Images will provide licensed content to ChatGPT and OpenAI search results * The partnership reverses Getty's previous adversarial stance toward AI companies * Getty has not disclosed whether images can be used for AI training Getty Images has signed a multi-year deal with OpenAI to license its photo and video library for display in ChatGPT and OpenAI's search tools. The partnership marks a significant reversal for a company that spent years fighting AI firms in court. "High-quality, licensed visual content makes AI-powered search and discovery more useful and more trustworthy," Getty CEO Craig Peters said in a statement. "This partnership with OpenAI reflects a shared recognition of that, and together we will deliver richer visual experiences to ChatGPT users." The announcement comes less than a year after Getty signed a similar agreement with Perplexity AI. That deal included a requirement for Perplexity to display image credits with links to the source, addressing concerns about how AI tools attribute licensed content. How did Getty go from suing AI companies to partnering with them? Getty's relationship with AI has been complicated. In September 2022, the company banned all AI-generated art from its library. A few months later, Getty sued Stability AI, alleging the image generator had committed copyright violations by training on Getty's photos without permission. The court rejected that argument late last year. The legal defeat appears to have shifted Getty's strategy. Rather than continue fighting in court, the company pivoted to licensing. In 2023, Getty launched its own generative AI tool, trained exclusively on its library and powered by NVIDIA's Edify model. Every image the tool produces comes with a royalty-free license. The Perplexity deal in October 2025 signaled that Getty was open to working with AI search tools. The OpenAI partnership extends that approach to ChatGPT's massive user base. What does this mean for ChatGPT users? ChatGPT users should see Getty's licensed images appearing in search results and conversations. The change addresses a persistent problem with AI tools: when they surface images, the licensing and attribution are often unclear or nonexistent. Getty's library includes over 500 million photos, illustrations, and videos distributed in more than 160 countries. The deal should reduce OpenAI's legal exposure. AI companies have faced waves of copyright lawsuits from content creators, and licensing agreements offer a straightforward defense. Will Getty's images be used to train OpenAI's models? Getty has not disclosed whether the OpenAI deal allows images to be used for AI training. The Perplexity agreement explicitly prohibited training on Getty content. If the OpenAI deal follows the same structure, the images would only be displayed in results, not fed into model development. This distinction matters. Training on copyrighted images was the core allegation in Getty's lawsuit against Stability AI. If Getty is now allowing training, it would represent a complete abandonment of its earlier legal position. If training remains off-limits, the deal is purely about distribution rights. Neither Getty nor OpenAI has clarified this point publicly. The broader pattern: litigation to licensing. Getty's shift mirrors a trend across the content industry. News publishers, record labels, and image libraries initially responded to AI with lawsuits. Many are now signing licensing deals instead. The calculus is simple: courts have been skeptical of copyright claims against AI training, and licensing generates revenue. Perplexity has faced its own lawsuits over alleged illegal use of copyrighted materials. The Getty partnership included requirements around image attribution, which suggests content owners can negotiate specific protections even outside of court. OpenAI has been aggressive about signing content deals. The Getty agreement adds visual content to a portfolio that already includes partnerships with news organizations. For AI companies, these deals provide legal cover and better content. For content owners, they provide revenue from technology that's using their work regardless. Frequently asked questions. Getty images will appear in ChatGPT and OpenAI search results where relevant. The exact implementation depends on how OpenAI integrates the licensed content into its tools. Can OpenAI train its models on Getty's photos? Getty has not disclosed whether the deal allows AI training. The company's similar deal with Perplexity explicitly prohibits training on Getty content. Why did Getty stop fighting AI companies in court? A court rejected Getty's copyright claims against Stability AI late last year. The legal defeat appears to have prompted a shift toward licensing deals instead of litigation. How many images does Getty have? Getty's library includes over 500 million photos, illustrations, and videos distributed across more than 160 countries. Logicity's take. Getty's pivot from plaintiff to partner says more about the courts than about AI ethics. When judges signaled they wouldn't treat AI training as copyright infringement, content owners lost their leverage. Licensing deals are the fallback. The real question is price: are these agreements worth what Getty would have won in a successful lawsuit, or is the company settling for pennies to avoid getting nothing? Until the financial terms leak, Logicity Pvt Ltd won't know whether this is a win or a capitulation. Need help implementing this? If your company is navigating AI content licensing or building products that integrate licensed media, its team can connect you with specialists in AI copyright law and content partnerships. Contact Logicity Pvt Ltd at [email protected]. المقال الجديد يتضمن معلومة مهمة جديدة: ارتفاع سهم Getty Images بنسبة 200% في تداولات ما قبل افتتاح السوق بعد الإعلان عن الصفقة، بعد أن كان قد انخفض بنسبة 55% في وقت سابق من هذا العام. كما يشير إلى أن الشركة لا تزال تنتظر الموافقة على صفقة الاستحواذ على Shutterstock بقيمة 3.7 مليار دولار. Manaal Khan Tech & Innovation Writer