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Participants will test their systems on about 50 cameras, tracking a designated vehicle and generating real-time alerts, movement histories, and GIS visualisation.
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The country has already demonstrated that it can become a global centre for technology, finance and business-process services. Legal Tech outsourcing could be the next frontier.
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“Would you keep production traffic on a gateway owned by the company that also settles your revenue?”
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The Korean machine tool maker said that it has deployed its full Phase 1 investment in Bengaluru and plans to expand local manufacturing, supplier development and engineering talent.
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Memory shortages are driving up prices for everything, from consumer electronics to NVIDIA AI servers, while squeezing PCs and smartphones.
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The site is expected to support frequent Starship launches and expand SpaceX’s launch operations beyond Texas.
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"This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale,” said Z.ai.
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“The purpose of education is to be able to create something. This will help us in creating products and technology.”
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Developed by Google DeepMind’s AnthroKrishi team, the models were initially trained to understand India’s agricultural landscape.
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Anthropic has signed a deal to rent about $45 billion in AI compute fromNscale, a British AI infrastructure company, a source familiar with the deal told TechCrunch. Nscale, founded only in 2024, has already cut deals withthe likes of Microsoftand will supply Anthropic with compute via Nvidia’s Vera Rubin chips, itsnew state-of-the-art chip system. The Vera Rubin system combines six different chips working in concert and is considered the cutting edge of chip design. The compute capacity is expected to start powering the AI lab’s services in late 2027, the source said. The deal, firstreported by Bloomberg, is only the latest in a spree of compute partnerships for Anthropic. Bloomberg writes that the deal spans six years, with the computing power coming from Nscale’sflagship data centerin West Virginia. Over the past eight months, Anthropic has aggressively scaled up its compute capacity in an effort to better compete with rivals, most notably OpenAI. Earlier this month, Anthropic signed a$10 billion dealwith AI cloud startup Volta —founded in January— securing a six-year supply of cloud computing power from a data center in Norway. In July, the company also signed a$5 billion compute-related dealwith AMD. A few months before that, in May, Anthropic revealed it had entered into alarge computing dealwith SpaceX (run by Elon Musk, whose rivalry with OpenAI CEO Sam Altman has made him an unlikely ally of Anthropic). That deal draws computing capacity from two different SpaceX data centers and isreportedlyproviding Anthropic with $1.25 billion worth of capacity each month. In April, Anthropic also signed a deal to significantly expand its partnership with Amazon, gaining access to anadditional 5 gigawattsof compute. That same month, the company alsoexpanded its relationshipwith Google and Broadcom, adding even more power capacity. Anthropic is hardly alone in its enthusiastic pursuit of more AI horsepower. The race to gobble up as much compute capacity as possible is ongoing, with other major players — including Google, OpenAI, and Meta — all following a similar track.
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Amazon and Nvidia just got a lot closer. The two companies announced Wednesday an expanded partnership that includes a deal to add another 2 million Nvidia GPU chips to Amazon’s data centers. These GPUs, which are designed to handle the heavy compute demands of training and running AI models, include Nvidia Blackwell Ultra, Rubin, and Rubin Ultra GPUs. The chips will head to Amazon Web Services’ data centers in 2027 and 2028. The announcement, made during Nvidia’s quarterly earnings call, comes just five months after Amazon agreed to deploymore than 1 million Nvidia GPUs across AWS infrastructure starting this year. Nvidia said in a statement that since then, “demand has exceeded those expectations.” Neither company shared financial terms. It’s unclear what the exact return will be for Nvidia. But considering GPU unit costs, the deal is worth tens of billions of dollars. The announcement is notable not just for its size and the speed in which it grew, but also because it extends beyond Amazon buying more Nvidia chips. And it’s happening even as Amazon invests in its own potentially competing AI chips. Nvidia said Wednesday that its technology, including the networking hardware that connects thousands of GPUs into one system, as well as its open models, CPUs, data processing software, and robotics platform, will also be integrated across AWS. The companies said “surging demand” from startups, enterprises, AI labs, and even governments influenced the decision to work more closely. The expanded partnership comes as Amazon ramps up its own AI chip efforts — particularly with CPUs, which are the general purpose processors at the heart of servers. Amazon has been building its own chips to lessen its dependence on Nvidia and even compete with the chip giant. Amazon’s AI chief Peter DeSantis has said that AWS is in talks tosell its Trainium chips— which are a direct alternative to Nvidia’s H100 or Blackwell chips for deep learning workloads — to other companies for use in data centers. Amazon’s Arm-builtGraviton CPUis also seen as a challenger to traditional server chips from Intel and AMD. Amazon has said itscustom chip businessis growing, noting on its last earnings call that it crossed a $25 billion annualized revenue run rate, driven by $225 billion in total commitments from AI labs like Anthropic and OpenAI. But, it seems Nvidia is still the GOAT in the world of AI chips. With the 2 million GPU chips Amazon is adding to AWS starting in the third quarter, Nvidia also plans to send an unspecified number of Vera CPUs, “some integrated with Rubin, others standalone,” according to Nvidia CFO Colette Kress. Nvidia CEO Jensen Huang has big plans for the company’s Vera CPUs, boasting back in May that he had found a“brand new $200 billion TAM”for the company. Aside from AWS, Kress said Wednesday that Nvidia expects Vera to be deployed by “every major hyperscaler, neocloud, AI lab, and system OEM, with shipments already underway to our lead partners,” which include Oracle and SpaceXAI. The partnership is also extending to Amazon’s warehouse robots and enterprise offerings. Kress said Amazon plans to adopt Nvidia’s full physical AI stack to power its fleet of robots. The stack includes Omniverse (its simulation and digital twin platform); Cosmos (its world model platform); Isaac (its robotics development platform); and Jetson (computing hardware for robots and edge AI). This week, Nvidia also introduced anew version of Jetsondesigned as a more accessible robotics computer for “entry-level edge AI.” On the enterprise side, AWS will serve Nvidia’s Nemotron family of open models on Amazon Bedrock, its managed foundation model platform, and SageMaker, its managed cloud service. Nvidia also reported Wednesday that it recorded sales of $96.2 billion for the second quarter, beating analyst estimates. Data center revenue made up the majority of Nvidia’s sales for the quarter at $89 billion, up 117% from a year ago. Nvidia said it expects revenue to reach $108 billion in the third quarter, some of which will come from its next-gen Rubin GPUs. Nvidia said it began production shipments this quarter. Investors have been looking out for Rubin’s initial Q3 sales for signs that demand will continue into Nvidia’s next generation of hardware. Nvidia has committed $279 billion to secure supply and manufacturing capacity for current and future data-center projects, up substantially from $119 billion last quarter, as the chipmaker looks to secure memory and manufacturing capacity to meet AI demand over the next few years. That commitment includes $92 billion in projected spending for the rest of the fiscal year and another $87 billion in fiscal year 2028. “The thing that matters for the industry is that AI is now doing productive and useful work,” Huang said during Wednesday’s call. “AI is generating profitable tokens… If we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we’re at, which is the reason why everybody’s leaning in.” Investors will be watching to see if additional compute indeed translates so neatly into additional profits as AI companies pour hundreds of billions of dollars into infrastructure.
OpenAI released its official report Wednesday on the Hugging Face breach, offering the clearest picture yet of how an unusual chain of events allowed an AI model to escape its testing environment and triggered a sprawling cybersecurity incident. The report, released more than a month after the incident became public, spans several discrete cybersecurity compromises. “This incident reflects misaligned behavior in an outlier scenario involving a rare and unexpected confluence of events: the presence of impossible tasks in the ExploitGym evaluation, model persistence over long task horizons, and messages to peer models that caused those models to deviate from their goal,” the report reads. Many of the details in OpenAI’s report were previously made public ina Black Hat presentation on August 6, but OpenAI’s official report gives a more thorough accounting of the incident, including more detail on the testing that initiated it. The report also gives critical new detail into how OpenAI aims to prevent future incidents, including chain-of-thought monitoring and a more advanced system for halting rogue agents.” METR and Redwood Research also conducted third-party assessments of the models’ behavior during the incident; both groups are planning to publish their own reports on the incident on it. In broad strokes, the report describes how an OpenAI model was presented with an unsolvable problem in testing and proceeded to chain together previously undiscovered exploits in order to bypass security measures and complete its task. The model initially compromised the Artifactory package management tool in order to gain access to the internet, then compromised various systems across OpenAI, Hugging Face, and other vendors. The report gives critical new details about the models that carried out the breach. The primary model was from the same family as OpenAI’s forthcoming Astra model, although the report emphasizes that it was “a distinct model with different post-training, where much of a model’s behavior is shaped.” Because OpenAI was testing the model’s capabilities, it was also unrestrained by the normal classifiers meant to prevent models from compromising digital infrastructure. “OpenAI estimates maximal cyber capabilities by running this evaluation without the production classifiers intended to prevent models from pursuing high-risk cyber activity,” the report explains. “These evaluations are important so that OpenAI can measure models’ underlying capabilities and design appropriate safeguards.” The report also gives significant new information about OpenAI’s security changes going forward, building on the heightened monitoring and alignment effortsmade public last week. In particular, the company is increasing its monitoring of AI agents “chain of thought,” a working space in which AI systems record short-term reactions and goals. That monitoring will be paired with 24/7 escalation systems, as well as new tooling to halt workloads that are deemed unsafe. “These changes are intended to improve both the breadth and speed of detection — from infrastructure anomalies to potentially concerning model behavior — and pair that visibility with mechanisms for rapid containment,” the report states. “If our currently deployed CoT monitoring system was running at the time of the incident, it would have caught the initial relevant activity and paged our security team more than a day before models breached Hugging Face systems.”
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