Latest AI News
India’s Simple Energy Deepens Siemens Partnership to Speed Up EV Engineering
The move comes as Indian EV manufacturers increasingly invest in digital engineering infrastructure to support larger product portfolios
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IG Defence Eyes Global Drone Exports Amid Gulf Tensions
The drone company plans to export its indigenous long-range attack drone after securing government approvals.
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Why Food Processing Could Be India’s Most Underrated AI Opportunity
Expanding value-added food processing could unlock benefits across the entire agricultural value chain. This is where AI can help.
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AI, Industrial Investment & Skilling Get Big Push in Tamil Nadu Budget 2026-27
Tamil Nadu Finance Minister N Marie Wilson set out a raft of measures aimed at attracting investment, building an artificial intelligence ecosystem and expanding skills training for young people.
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AMD Forecasts Over 70% Server CPU Growth in 2027 After Record $11.5 Bn Quarter
Helios enters production this quarter as AMD expands capacity to ease server CPU supply constraints.
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SpaceX Targets $100 Bn ARR Backed by AI, Starlink, Starship
SpaceX plans to end the year with more than 2 GW of AI compute capacity while preparing to launch its first operational Starlink V3 satellites.
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Meet Wrinkles, an app that uncovers the hidden stories of the places around you
A new app calledWrinklesuses your location to automatically surface stories about the places around you. Wrinkles, available on bothiOSandAndroid, essentially acts as an AI-powered audio tour guide that reveals hidden history and local stories. The idea behind the app is to allow you to move through the world while hearing the places around you come to life without having to stare at your phone. As you explore, Wrinkles can tell you the secrets behind a building, the history of a street, and other local lore. Wrinkles was founded by David Stemler, a longtime advertising executive, and Ryan Hansan, who previously founded ScratchDC, a meal delivery service, and TasteLab, a shared commercial kitchen that helped independent food businesses launch and grow. The pair have known each other since middle school and say travel has always been a defining part of their friendship. They began taking trips abroad together as teenagers and continue to travel together today, now with their families. Over the years, many of their business ideas have grown out of long conversations while traveling and questioning how the experiences around them could be improved. Wrinkles was born from that same curiosity. Stemler came up with the idea for Wrinkles while visiting the British Museum in London and using its official app for a self-guided tour. Although he believed the app was well designed, Stemler found the experience frustrating. “I spent the entire visit heads-down, matching numbers on the walls to lists on my phone instead of taking in my surroundings while learning about them,” Stemler told TechCrunch in an email. At the end of his trip, he called Hansan from the airport and asked, ”If the phone in my pocket knows exactly where I am, shouldn’t the places in front of me be able speak?” The startup has built a global foundation of 1.3 million Wrinkles, or points of interest, across 177 countries. Beyond this foundational content, historians, museums, creators, and brands can add their own stories and experiences to the map. Users can discover these stories as they move through the world or explore them remotely by searching and browsing the map. To make the experience feel like a traditional tour, users can ask questions as they explore. For example, while learning about how the Trevi Fountain was built, you could ask a question like, “Where does this water actually come from?” There’s also a social aspect to Wrinkles, as users can follow friends, family, creators, and organizations, then save and share Wrinkles or organize them into custom maps and guides. Users can personalize their Wrinkles, too, by attaching family memories, photos, videos, and recordings to specific places. Families can collaborate on these histories to preserve stories tied to places like a childhood home, a first date location, or a hometown. Hansan and Stemler see Wrinkles as more than a travel app. While it can help spontaneous travelers discover the stories behind new places, they believe it’s also designed for people who want to learn more about the buildings they pass every day or rediscover their own hometown. “The common thread isn’t travel — it’s curiosity,” Hansan said. “A travel app gets used on one trip a year. Wrinkles gets used on your commute, at the botanical garden, at the zoo with your kids, leaving a permanent legacy for a loved one, and on the trip to Spain.” While the duo has experimented with a freemium model, they say they’re committed to keeping the app free for users. Wrinkle’s revenue primarily comes from the supply side, with museums, tourism boards, universities, and hotels using Wrinkles to create location-based experiences for visitors without having to build their own apps. Creators and media partners can build sponsored place-based guides, while businesses can promote Wrinkles connected to their locations. Partners can also add links for users to book tours, reserve tables, buy tickets, or visit businesses, with Wrinkles earning a share of those transactions. As for the company’s long-term vision, the duo sees Winkles becoming a normal part of how people navigate places, whether that means discovering the history of a college campus, learning more about their own city, exploring a map while planning a trip, or getting a deeper understanding of a city they’re visiting for the first time.
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Anthropic signs $10B deal with AI cloud startup Volta
Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta. Bloombergoriginally reportedthat Volta,founded earlier this year, will provide cloud compute to the Claude maker over a six-year period. Volta has a partner in this deal, Bitdeer, a crypto-mining company that will help develop the data center to provide the compute capacity. That facility will be located in Norway and will deliver a 133 megawatt capacity. It will be fueled byNvidia’s Vera Rubin systems, the chipmaker’s state-of-the-art AI chip architecture. Volta is part ofNvidia’s Cloud Partner program, which is a consortium of AI cloud providers that use Nvidia’s GPUs in their data centers. Volta had spoken about a deal with an AI lab but hadn’t named the specific company it was working with. Bloomberg originally cited anonymous sources familiar with the deal. TechCrunch reached out to Anthropic for more information. Anthropic has sought to aggressively expand its compute capacity over the last several months as it wages a corporate battle with its competitors. The company also recently announced new compute deals with the likes ofSpaceXandAmazon.
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Open-weight AI models are catching up to the frontier. The safety gap remains.
As policymakers debate how to govern increasingly powerful AI systems like OpenAI’s GPT-5.6 Sol and Anthropic’s Mythos, a Chinese open-weight model has narrowed the gap with the industry’s leaders. GLM-5.2, the open-weight AI model from China’s Z.ai, is only a few months behind OpenAI’s GPT-5.5 and Anthropic’s Claude Opus 4.7 on cyber and bio capabilities, according to anew reportfrom AI safety nonprofit SaferAI. But the divide between frontier capabilities and safety practices is growing. According to SaferAI’s evaluation, which the nonprofit ran via Z.ai’s public API, GLM-5.2 refused none of the offensive cyber or dual-use biology tasks it was given. By comparison, Claude Opus 4.7 “refused so consistently that SaferAI could not complete CyberGym on it at all.” (CyberGym is a benchmark that evaluates cybersecurity capabilities. OpenAI used it in the evaluation that preceded last month’sHugging Face breach.) It’s a stark reminder of what some critics have warned for years: that open-weight AI models could put highly capable AI into the hands of potential attackers, with no way to police how they use the technology once they download the weights. With open-weight models rapidly approaching the capabilities of the world’s leading AI systems, the debate is moving from whether they can compete to how society manages risks once they are released. “The frontier of capability is not the frontier of risk, and so we do have to take into account the state of the mitigations as well to assess the risk properly,” Henry Papadatos, executive director of SaferAI, told TechCrunch. While Z.ai could apply safety measures to its hosted API, those protections become unenforceable once someone runs the weights on their own hardware, where they can remove or modify any safeguards, fine-tune the models, or change system prompts. Frontier developers like OpenAI and Anthropic tend to rely on safeguards like classifiers, refusal training, and API-level controls to limit dangerous cyber and biological assistance. Those measures are far from foolproof: jailbreaks routinely bypass protections on deployed models. Far.ai, an AI safety nonprofit,found hundreds of universal jailbreaks— defined as reusable keys that succeed on most harmful requests — in frontier models like xAI’s Grok 4.5 and Google DeepMind’s Gemini 3.1 Pro. According to the report, jailbreaks succeed when attackers combine multiple manipulation techniques — including roleplaying, authority impersonation, fake conversation history, and follow-up prompts — to amplify weak points in a model’s defenses. But the safeguards in place for closed models don’t work at all on open-weight models, which are designed to run on any infrastructure with any set of safeguards — or lack thereof. “The objective should clearly be that the good capabilities — the safe ones — are accessible to anyone, and then we try to remove the bad ones, even in an open source fashion,” Papadatos said. One technique Papadatos noted could help is called “pre-training data filtering,” which is when an AI company removes offensive cybersecurity information from their training data and then trains the model on the curated dataset. Someresearchsuggests this can reducehazardous biological knowledgewithout harming overall model performance. However, for cybersecurity, data filtering is much less practical. It’s difficult to train a general model that excels at coding but isn’t also a good hacker. Because coding has become AI’s biggest moneymaker, developers face pressure to keep improving those capabilities even as they search for ways to limit misuse. Because of that, frontier developers have increasingly relied on other mitigations instead. One approach has been to selectively restrict the kinds of cybersecurity assistance models will provide. Anthropic’s Opus 5, for example, can search for vulnerabilities in uncompiled source code, but not compiled software,per the model’s system card. The reasoning is that this makes it harder to use Opus 5 for offensive purposes. Others include rigorous pre-deployment safety evaluations, publishing risk assessments, and withholding model weights if a system is perceived as too dangerous. In GLM-5.2’s case, SaferAI says Z.ai didn’t publish a safety framework, pre-deployment testing commitments, or risk assessment for the model. TechCrunch has asked Z.ai whether it conducted internal or third-party frontier safety evaluations before release, but did not receive a response. Chinese leaders have increasingly acknowledged the risks of advanced AI. At the World AI Conference last month,Chinese President Xi Jinping emphasizedthe importance of open-weight models, while also stressing the necessity of ensuring AI remains a tool under strict human control. Graham Webster, who studies Chinese AI policy at the Stanford Cyber Policy Center, told TechCrunch that China has robust regulations governing AI, but those rules have historically focused on politically sensitive content, misinformation, and social stability rather than catastrophic AI risks like offensive cyber capabilities and biological misuse. “U.S. AI thinkers are, in general, more concerned with this existential catastrophic [idea] than the Chinese community,” Webster said, adding that many Chinese policy researchers believe that if there’s truly going to be a novel frontier risk, American companies will likely encounter it first. “The Chinese system has confidence that they control the use of these technologies inside China,” Webster continued. “Being online in China is something you do attributed to your real name, and companies can be held accountable, users can be held accountable.” Webster mused that the same mechanism that model providers use for refusing to engage on certain political topics can potentially be tweaked to make sure models refuse to complete offensive cyber attacks or won’t deliver adverse biological engineering outcomes. He added that because Chinese companies tend to coordinate with regulators behind the scenes, it can be tough to know what internal testing they’re conducting before release. Advocates of open-weight AI argue that releasing the weights is important for cybersecurity because it allows companies defend themselves against attacks — Hugging Face relied on GLM-5.2 to defend itself against OpenAI’s breach — and because it allows them to better prepare for future threats if they know what’s coming. “The same systems that helped stop an AI-powered cyberattack can now help defend against millions of cyberattacks every day, while helping us identify and fix vulnerabilities before attackers exploit them,” Clem Delangue, CEO of Hugging Face,said this week in a social media post. Papadatos said that benefit is often overstated, and doesn’t mean “we should open-source dangerous capabilities.” “The main point in my mind is that we shouldn’t just accept that dangerous capabilities are easily accessible by anyone anywhere,” he said, stressing that he believes the industry should be striving for only making the “good capabilities” easily accessible. By default attackers adopt new tools faster than defenders do. For example, a ransomware group can change its methods in a week. A hospital cannot.”
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SpaceX has bought $329M worth of Tesla Megapacks so far this year
SpaceX has ramped up purchases of Tesla Megapack, spending $295 million on the battery storage devices in the second quarter and $329 million so far this year, according to the company’searnings reportreleased on Tuesday. The purchase illustrates just how interconnected Elon Musk’s universe of companies are. Musk, who is the CEO and largest shareholder of SpaceX, also runs Tesla. Musk’s artificial intelligence business xAIacquiredhis social media platform, X, in 2025. Earlier this year, SpaceXgobbled upxAI. The industrial-scale batteries are likely being deployed at the company’s xAI data centers. Before xAI merged with SpaceX, the AI company bought $430 million worth of Megapacks for its data centers. In the first quarter of this year, xAI had purchased only $34 million worth of the equipment. SpaceX also reported that as of December 2025, it had acquired $131 million worth of Tesla Cybertrucks at manufacturer’s suggested retail price, according to its regulatory filing. Though xAI has leaned heavily on natural gas to power its data centers — includingdozens of unpermitted turbinesat a site in Mississippi not far from the Colossus data center project — large batteries like the Megapack are still a critical part of data centers. In addition to providing substantial backup power that can be tapped in a second or less, batteries can provide extra power to GPUs when they demand it. AI data centers don’t draw power consistently. Rather, their power demand ramps up and down depending on the demands of training AI models and running inference. Such peaks can incur significant charges from a local utility or overwhelm on-site generators. Batteries help smooth out those peaks, lowering costs while ensuring that the data center can operate consistently.
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Texas halts new data centers as governor calls for audits
Tech companies and developers have been scouring the U.S. for places to build data centers, and they’ve been drawn to Texas’ loose regulations and seemingly abundant power supply. Only Virginiahosts more data centersthan Texas. But even Texas can be pushed to the brink. Governor Greg Abbott announced Monday that all new data center projects will need to be audited by both the Public Utility Commission of Texas (PUCT) and the state’s grid operator, the Electric Reliability Council of Texas (ERCOT). The size of ERCOT’s interconnection queue has grown dramatically this year. In January, ERCOT had 233 gigawatts of projects waiting to connect to its grid. Inless than six months, that figure had more than doubled. Today, Abbott’s officesaidERCOT is tracking 474 gigawatts of new connection requests. About 90% of those are data centers, according to the grid operator. Some of those projects are simply paper proposals at this point. Because queues for grid connections have grown so long, the first action many developers take is to get in line. Many projects fizzle out as they progress. If even a fraction of those proposed projects come to fruition, they could overwhelm the Texas grid. The interconnection queue today represents more than five times ERCOT’stotal peak demand. While many data center operators, includingGoogleandMicrosoft, have been drawn to Texas for its ample natural gas reserves, wind and solar havehelped ERCOT keep pacewith growing electricity demand, according to the Energy Information Administration (EIA). Utility-scale solar capacity grew fourfold between 2021 and 2025. Meanwhile, electricity prices declined over much of that time, according to areportfrom Amperon. Electricity prices in Texas are relatively affordablecompared with other states, but they have been rising. Data centers and crypto-mining facilities have pushed prices higher,accordingto the EIA. Abbott clearly wants to head off that trend. The governor has directed PUCT and ERCOT to collect a range of information about proposed data centers, including their on- and off-site demand for electricity and water, noise-mitigation efforts, light controls, use of tax incentives, and ownership details. Historically, Texas has favored lighter regulation than many other states when it comes to development. Houston famouslylacks a zoning code, and the state has abusiness-friendly regulatory environment. But data centers havebecome a flash pointacross the country, including in Texas. Abbott had previously tried to coax data centers into providing more information through a voluntary survey.Most didn’t respond, so he’s taking a heavier hand to compel compliance. Depending on what emerges from the audits, Texas’ days as a data center mecca may be coming to a close.
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Spotify expands AI remix and covers project with Merlin partnership
During its second-quarter earnings call on Tuesday, Spotify again teased the upcoming release of a new product that will allow music fans to leverage AI to make covers and remixes of artists’ music, with the artists’ consent. The companyalso announced that Merlin,a licensing partner for independent labels and distributors, has now joined Universal Music Group (UMG) on the new AI music effort. The deal brings more than 30,000 labels from Merlin’s network to the product, which will allow fan-made covers and remixes by artists who agree to participate. Spotify has positioned its AI music product as being significantly different from the more controversial AI music startups that have been used to create fully artificial songs. Instead, Spotify co-CEO Gustav Söderström told investors on Tuesday’s call that the company’s AI music product will be about “real artists, not fake artists.” “We want artists to be consenting [to add] their work into this catalog, so people can play around with covers and remixes based on their art,” added co-CEO Alex Norström. “We also obviously want to give them credit. And last but not least…we not only have the consent and give credit, but we also drive the compensation for this. So, really, we’re talking about the first legal way to partake in this AI tailwind that we see coming for interactive music,” he said. AI music has flooded streaming services. Music streamer Deezer recently noted thatmore than 50% of daily track uploads were generated with AI, up from 10% in January 2025. The company told investors that a research preview of the fan remix and covers product would initially be made available to a subset of users. Spotify also noted that it would not require a full music catalog to get started. The company did not say when the preview would arrive. The new tool will launch as a paid add-on, creating an additional revenue stream for artists, Spotify previously said. “Our remix and covers I think is an incredibly exciting product again because there is no one else that can really do this,” Söderström said. “Normal generative music will happen with or without us. This product will not happen without us, and it needs to exist so that existing artists can participate in this.”
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