Latest AI News
Spotify Will Label AI-Generated Artist Profiles With New AI Persona Badge
Spotify will start labelling artist profiles that appear to represent AI-generated identities and will keep their music out of most recommendations by default. The company is introducing an AI Persona badge to help users identify profiles that do not represent real people. Artists can disclose their AI identities through Spotify for Artists, but Spotify will also review profiles itself. The labels will begin appearing from mid-September across artist pages, Search and playlist track listings, according to the company.
View
Discovered Materials Raises $9 Mn to Solve Silicon’s Overheating Problem
The round saw participation from Peak XV Partners and angels Paul Graham, Gokul Rajaram, and Thariq Shihipar.
View
Google, Lightspeed to Host AI Sprint for India’s Consumer Tech & Fintech Startups
The one-day programme will focus on making AI commercially viable, covering infrastructure, CAC, margins and deployment.
View
Quantinuum, Oracle Bring Quantum Computing to OCI
Quantinuum’s Helios will be integrated with OCI’s HPC and GPU infrastructure, giving customers a single environment to run hybrid quantum-classical workloads.
View
OpenAI COO Brad Lightcap Leaves After 8 Years to Start New Venture
Lightcap’s exit follows a recent reshuffle of OpenAI’s leadership team, with the executive saying he plans to share details of his next venture soon.
View
SpaceXAI Releases Grok Bot, a ‘24x7’ AI Teammate
Grok Bot is available in early beta to SuperGrok Heavy, Cursor Ultra, and Cursor Premium Teams subscribers on desktop and iOS.
View
Samsung to Train 20,000 Indian Students in AI
The company’s fourth edition of the Samsung Innovation Campus programme is delivered alongside the Electronics Sector Skills Council of India and the Telecom Sector Skill Council.
View
Samsung Cuts Month-Long Chip Verification to Two Days With Claude: Report
Samsung assessed that the Claude-assisted workflow was roughly 15 times faster.
View
Anthropic Adds Hidden Watermarks to Claude AI Text to Help Identify AI-Generated Content
Anthropic on Tuesday announced that it is adding invisible watermarks to text generated by its artificial intelligence (AI) models. According to the company, the content will have a hidden signal that remains even when it is copied and pasted elsewhere. The move builds upon Anthropic's decision to sign the European Union's AI Act Article 50(2) Code of Practice on Transparency of AI-Generated Content. Anthropic says Claude models launched from August 2, 2026, will support the marking system from day one, and the feature is rolling out globally.
View
Accel closes oversubscribed $550M India fund within weeks, 19 months after its last
Accel has closed a new $550 million India fund, less than two years after raising its previous India-focused vehicle, as part of a coordinated $3.5 billion global fundraising effort. The new India fund was oversubscribed and closed within weeks, people familiar with the matter told TechCrunch. Accel still has more than 55% of itsprevious $650 million India fundavailable for investment, the people said, underlining that the latest raise came despite ample capital remaining in its earlier vehicle. The fundraising comes as Accel bets that India’s next startup wave will be driven not only by AI, but also by consumer internet, fintech, and advanced manufacturing. The firm believes that artificial intelligence is becoming a horizontal technology that underpins each of those sectors rather than a standalone investment category. “There is a significant amount of money available in the market for early-stage investing in the categories we have always invested in — AI, consumer, fintech, and now advanced manufacturing, and deep tech,” Shekhar Kirani, a partner at Accel, told TechCrunch. “We will continue to invest, looking for the best of the best local winners, where we can make them into global successes.” Accel is expected to begin deploying capital from the new fund in 2027, Kirani said. Until then, the firm will continue investing from its previous India fund, he added while declining to disclose how much remains. Accel’s renewed commitment comes as global investors debate whether India can produce globally competitive AI startups after the country largely missed the first wave of foundation model companies. Accel sees India’s opportunity specifically in building AI applications, infrastructure, and software aimed at enterprise and consumer use cases. “The early movers have been on the LLM [large language model] side… but there is a significant opportunity in the application layer,” Prayank Swaroop, a partner at Accel, said. Accel expects Indian startups to build AI-powered applications and enterprise software on top of existing models rather than competing with OpenAI or Anthropic. Swaroop told TechCrunch that Indian startups are increasingly combining AI with the country’s existing engineering talent and services expertise to solve enterprise problems, particularly in sectors where human oversight remains critical. Kirani echoed Swaroop and mentionedRapidClaims, an Accel-backed startup that automates medical coding for U.S. healthcare providers, as an example. The startup combines AI with domain expertise to deliver coding accuracy of about 95%, targeting a market that has traditionally relied on outsourced human labor in India and the Philippines. Barath Shankar Subramanian, a partner at Accel, said the firm’s optimism is also being driven by the rapid adoption of AI among Indian consumers and businesses, creating a growing domestic market for AI-native products alongside globally focused software companies. The trend is already visible across leading AI companies. OpenAI and Anthropic have both identified India as theirlargest market outside the U.S., while AI coding platform Cursor recently said India has become one of its fastest-growing developer markets and itslargest market for power users. Accel’s fundraising comes as several global venture firms are renewing their focus on India despite a broader slowdown in venture capital.Peak XV Partners, the former Sequoia Capital India business, recentlyraised $1.3 billionacross new India and Southeast Asia-focused funds, whileGeneral Catalysthas committed todeploying $5 billionin India over the next five years.Lightspeed Venture Partnersis also said to be exploring a new$300-$350 millionIndia-focused fund. Kirani said the renewed interest reflects a shift in the quality and ambition of Indian entrepreneurs. “Compared to several years back,” he said, “the quality of ideas and quality of founders are significantly better than what we have ever seen.” The new India fund was one of four funds Accel raised simultaneously for the first time, alongside dedicated U.S. and Europe funds and a $1.35 billion growth vehicle. The growth fund, Accel said, can back breakout companies emerging from any of its regional funds, including India, allowing the firm to continue investing from inception through IPO and beyond. Kirani told TechCrunch that the coordinated fundraising was driven by investor preference to evaluate Accel’s global platform in a single process rather than through separate regional fundraises. Accel’s investment philosophy, Kirani said, remains rooted in backing founders early rather than chasing later-stage trends. Accel writes the first institutional check in roughly 80% of the companies it backs, a strategy that has helped it invest early in companies includingFlipkart,Swiggy,Freshworks, andZetwerk.
View
An unreleased Anthropic model made progress on one of math’s biggest unsolved problems
For more than 150 years, the Riemann hypothesis has stood as one of the major unsolved problems in mathematics, a long-running mystery about the distribution of prime numbers. There is currentlya $1 million bountyfor a working general proof of the hypothesis, which remains unclaimed. Contemporary AI models still can’t solve it either — but they can make a lot more progress than you might expect, a finding that’s likely to reopen longstanding questions about contemporary AI’s ability to discover new scientific and mathematical ideas. On Monday, Anthropic announced that an as-yet-unreleased model had made significant progress on the Riemann hypothesis, significantly increasing the lower bound of solutions for which the hypothesis holds true. Even more impressive is how the progress was made: An Anthropic staff member without significant mathematical training prompted the model to “take a real stab” at proving the hypothesis, then left the model to coordinate the task across the following day and a half. All told, the model tested 650 different ideas for solving the problem, coordinating across 60 subagents and spending 31 million output tokens in total. “Out of the 60 subagents, two were responsible for developing the key mathematical ideas,” a footnote to the paper explains, “13 contributed ideas to these agents, 30 attempted (but were unable) to develop new ideas, 13 served as validators to check the correctness of the arguments, and the final two helped to write the initial paper.” The finding was confirmed by two of Anthropic’s in-house mathematicians, and formalized using the open source proof assistantLean. This is the latest in a string of mathematical breakthroughs led by large language models, or LLMs.A number of Erdos problemshave been solved by AI models over the course of this year, and the release of more powerful models has led to more impressive results. OpenAIrecently releaseda set of 10 major results proved by its internal “Astra” model, while a separate effort from Anthropicdisproved the longstanding Jacobian conjecture. The growing body of results has caused both excitement and concern in the mathematical field. Ina public declaration signed in June, a group of prominent mathematicians raised concerns that AI could undermine critical values of the field — particularly the standard that true mathematical proofs should be “attributable to specific authors who take credit for their discovery and assume responsibility for their correctness.” But the field is still split on how mathematicians should approach the new research techniques. Ina blog post responding to the declaration, Fields Medal winner Timothy Gowers questioned whether the influence of AI might change mathematics in a more complex and positive way. “If we arrive at a world where mathematical theorems are no longer associated with mathematicians, maybe that won’t be any more problematic than the fact that stars aren’t named after astronomers and most aren’t named at all,” Gowers wrote.
View
General Catalyst leads $1.1B round into 2-month-old River AI
River AI, an AI startup founded by xAI co-founder Igor Babuschkin, has secured $1.1 billion in funding in a seed/Series A round led by General Catalyst and AMP PBC, with participation from Nvidia, AMD Ventures, Y Combinator, and Temasek. (AMP PBC is an AI-focused investment firm founded in 2026 by former Andreessen Horowitz general partner Anjney Midha, backer of companies at a16z like Black Forest Labs, Mistral AI, LMArena and OpenRouter.) River came out of stealth in June with a fascinating mission. Babuschkin, whose resume includes AI roles at DeepMind and OpenAI, intends to reinvent AI from scratch, beginning with how models are trained. This is in order to turn agents into personally trainable assistants, rather than following the trajectory other AI labs are on: human worker replacements. “To get there, we believe the stack has to be rebuilt end to end: training, models, the product layer, and new hardware that lets personal AI live close to you,”he wrotein his launch blog. “Capable agents will be a normal part of everyday life. Less like the assistants you call on today when you need a task done, more like guardian angels: quietly present, on your side, helping with what actually matters to you. They will know you well, and they will be yours, not someone else’s,” he envisions. River already offers an API,billed per 1 million tokens, with rates dependent on the open model used. The API allows developers to use both reinforcement learning (RL) and low-rank adaptation (LoRA) fine-tuning on the models. This first product is intended to be an antidote to prompt engineering. “Prompting steers a model you don’t own and can’t improve. River lets you train open models into ones that are truly yours — and serve them like any other endpoint,” the product literature says. While this round is an eye-popping-size investment for a nascent company, and perhaps another indication of the overheated AI atmosphere, River’s premise comes at a particularly auspicious time. Enterprises are waking up to wanting to control their AI model destiny by using a mix of models, including open weight. River is promising to solve the post-training expertise part of that problem with its neocloud offering. “Any enterprise can complete a complex reinforcement learning run in 15 to 20 minutes with no infrastructure team required, at two to four times the cost savings relative to closed-source alternatives,” it wrote in its funding announcement. The bigger vision of this company is that everyone will have their own agents, trained by themselves, and working on their behalf. We are already seeing this concept in action with the rise of personal, locally running agents in the form of OpenClawand its derivatives. Plus, we are already seeingNvidia partner with PC makers like Dell, Microsoft, and HPfor AI-capable hardware. How River’s tech will differ remains to be seen. But it’s starting out with a war chest full of cash to try.
View
