A coalition of over 30 tech industry leaders, including Nvidia, Microsoft, SpaceX, The Linux Foundation, Adobe, and Siemens has formed the “Open Secure AI Alliance” with the aim of building and distributing open source tools for AI safety and security, according to an official Nvidia blog post on Monday. The Nvidia-led coalition — comprising a mix of infrastructure, cloud computing, cybersecurity, and enterprise software leaders — will serve as a collaborative effort to develop open tools for identifying and patching AI vulnerabilities, sharing security frameworks, and establishing identity verification and audit standards across the AI software stack. Curiously, some of the biggest names in AI, including OpenAI, Anthropic, and Google, are absent from the list of members.

“The world needs both closed and open models. For cybersecurity, open models and open harnesses are essential because they democratize defensive capabilities, increase transparency for defenders, enable cyber defense while protecting data, and complement frontier closed models with customizable, localized controls. Open source enables massively distributed community-driven and self-controlled defense – with no single point of failure,” the announcement reads. Contributors across the alliance are currently building or offering various tools to create an open defense stack.

The initiative was directly galvanized by the OpenAI HuggingFace security incident earlier this month in which an autonomous OpenAI test agent slipped out of its sandbox and breached the AI startup Hugging Face. During the incident, safety guardrails on several frontier closed models prevented developers from performing critical forensic analysis. Hugging Face eventually had to use GLM-5.2 — an open-weight model from Beijing-based Z.ai — running the model on its own infrastructure to analyze more than 17,000 actions and contain the intrusion.

Based on the incident, the alliance contends that being unable to inspect, modify, or run a model locally — impossible in closed systems but doable with open systems — presents a fundamental weakness in relying exclusively on closed AI systems for cyber defense. The Open Secure AI Alliance therefore aims to give entities access to advanced open models, agent harnesses, and security tools that they can independently deploy and adapt, reducing dependence on any single provider while strengthening defenses across a multi-vendor AI ecosystem. “That is the mission of the Open Secure AI Alliance: to ensure defenders everywhere have open, frontier tools they can trust and control,” the post says.

Chinese models such as DeepSeek and the newly released Kimi K3 are open-weight, and are seeing growing adoption, including by U.S. companies, due to their open features. Meanwhile the Trump administration is reportedly gearing up to ban Chinese AI models over security concerns. The alliance acknowledges the potential risks of open source tools but argues that closed systems are not an outright solution. “Those risks are real, but they do not disappear in closed systems, and simply keeping weights closed does not prevent determined attackers from seeking or exploiting powerful AI,” the announcement reads. Unlike regulators who have voiced concerns over open-source technology, the alliance urges policymakers to treat open-weight models as defensive assets rather than liabilities.

It also argues that placing AI development solely in the hands of a few closed providers creates dangerous single points of failure. “The right response is not to deny defenders access to capable open systems. It is to pair openness with strong safeguards, clear rules against malicious misuse, rigorous evaluation and rapid remediation. Defenders need both frontier closed models and frontier open models, working together, so they can choose the right system for the job and ensure that transparency, adaptation and sovereign control are available wherever security demands them,” the alliance contends.

According to the announcement, “The Open Secure AI Alliance — building on the leadership of the Linux Foundation’s Akrites initiative and OpenSSF community work — will work to remediate and disclose vulnerabilities using open technologies”. Founding members include NVIDIA, Dell Technologies, Synopsys, Microsoft, IBM, Red Hat, CrowdStrike, Palo Alto Networks, Cloudflare, Hugging Face, Databricks, SpaceXAI, and The Linux Foundation. Conspicuously absent from the alliance are OpenAI, Google, and Anthropic, companies behind proprietary, "closed" AI models.

Well, the artificially intelligent cat is out of the bag. After publishing a blog post and API documentation for the minty-fresh Kimi K3, Chinese outfit Moonshot AI delivered on its promise to release the model's weights for free, meaning that most anyone with a contemporary rack of AI GPUs can run it and charge for it, with few restrictions.

This is quite the shot across the bow of the big AI players, namely but not only Anthropic and OpenAI. Those companies' latest models are Claude Fable and GPT-5.6 Sol, respectively, and it happens that Kimi K3's capabilities outright beat previous generations of Claude and GPT in Moonshot's benchmarks, and closely trail Fable and Sol— all while seemingly being around 2-3x cheaper to run, up to 10x if a particular query lands in the cache. Moonshot's technical write-up seemingly backs up the benchmarks published last week, as the company reveals which exact software was used for testing.

For its inference cost comparisons, Moonshot says that its costs "are measured internally" versus the publicly available token pricing for other companies, but the figures are quite impressive. For input, Moonshot charges $3 per million tokens for Kimi K3. Meanwhile, Fable costs $10/1M, while Sol goes for $5/1M. That figure is standard non-cached input and is already pretty good-looking, but Kimi K3's caching structure seemingly has a 90% hit ratio for coding tasks, turning those $3 into $0.30/1M if your use case hits the cache a lot. The story is pretty similar for output tokens.

One of the likely reasons why Kimi K3 is so efficient is that it uses a mix of MXFP4 for weights and MXFP8 for input activation, both data types with relatively low precision and thus amenable to running on far less VRAM. Out of Kimi's 2.8 trillion parameters, only 104.2 billion are activated at a time, too.

Interestingly, Moonshot's write-up only mentions Nvidia's H20 being used for running Kimi for some coding tests, a fairly low-end chip by today's standards. That GPU doesn't have native support for MX floating-point types, unlike the export-controlled Blackwell B-series chips.

In turn, this can mean that Kimi K3's optimizations make it particularly amenable to run on lower-end hardware, but it's an equally reasonable guess that running it on something like Nvidia Blackwell or other MXFP-native silicon could make it even more cost-effective than in the presented benchmarks. We'll have to wait for more official figures to confirm this speculation.

Additionally, Kimi K3 doesn't use a conventional ever-expanding key-value (KV) store, instead relying on a fixed-size state handler called Kimi Delta Attention, again theoretically saving both on VRAM and execution time. Its mixture-of-experts (MoE) is particularly sparse with only 16 activated at each time out of 896, further contributing to inference cost reductions. Broadly speaking, Moonshot went for optimization at every layer of inference to avoid unnecessary overhead and bring inference cost down.

This is could be bad news for OpenAI and Anthropic, given that most anyone with decent AI GPUs can now become their direct competitor, and the fact that Kimi K3 is open-weight also gives off the impression that "free" software is nearly as good, and far cheaper to run, than its proprietary competitors. It's worth noting that open-weight does not mean open-source; the training process and dataset are still Moonshot's special secret sauce.

When we talk about running local AI these days, the conversation usually either revolves around mini-PCs like the RTX Spark or drifts into wistful thinking about home servers and ludicrously expensive professional GPUs. Well, I reckon the most impressive AI hardware trick in a good while just happened on a piece of silicon that costs less than a decent burger. Last week, a Ukrainian developer named Slava S, who simply goes by 'slvDev' on GitHub, dropped a project called ESP32-AI. It's exactly what you think: he got a 28.9-million-parameter language model running locally, entirely on-device, on an ESP32-S3 microcontroller.

If you haven't read any of our previous coverage of this tiny chip, ESP32-S3 boards offer about the best bang for buck in the whole computing world. You can snag one online with a protective case for under $20 here in the States, and bare boards are readily available for under $10 around most of the world. As you'd expect from a chip so cheap, it's not powerful. On this variant, the S3, you get exactly 512KB of SRAM, 8MB of PSRAM, and 16MB of flash memory, which is not very much memory at all. So how exactly do you cram a nearly 30-million parameter model onto a chip with less primary storage than a single raw photo from your smartphone?

Usually, to run an LLM, the entire model has to sit in your system's fast memory because the processor needs to constantly do math against every parameter to generate the next word. If you try to run a 29M parameter model normally on an ESP32, you run out of fast RAM instantly. The previous record for a chip like this was around 260,000 parameters by one Mr. Dave Bennett, as pointed out by Slava himself on X.

Our clever hacker got around this bottleneck by borrowing a brilliant architectural trick from Google's Gemma called Per-Layer Embeddings. He quantized the model down to 4-bit (making the total file size just 14.9 MB) and changed where the data lives; instead of trying to stuff the whole thing into the tiny 512KB SRAM or the only slightly-less-tiny 8MB PSRAM, he dumped the 25-million-parameter embedding table into the relatively-slow 16MB Flash memory. Because this specific model architecture only needs to pull a few rows from this table per token, the inherent slowness of the Flash memory doesn't choke the processor, and so the 512KB of fast SRAM is kept clear for just the "thinking core", the actual reasoning weights.

Now, let's pump the brakes for a second, because I know someone out there is already wondering if they can replace their server with an $8 chip. The model he used was trained on the TinyStories dataset, and it's really more of a Small Language Model (SLM), or honestly, a "micro LM." Due to the way it was created, it's only capable of writing short, simple, fictional stories. It will not answer questions, it will not follow instructions, it won't write your Python code, and it possesses exactly zero factual knowledge about the real world.

Focusing on that limitation completely misses the magic of what's happening here in this proof-of-concept, though. The achievement is fitting a structurally quite large model onto a computer with practically no resources. It proves that with clever architecture, you can run genuine neural networks on dirt-cheap embedded hardware, and there are useful applications for a model this size. Slava imagines the idea of a coffee machine that actually knows about coffee: every bean, grind, ratio, water temperature, all offline, no app required.

Truthfully, when we're talking about "AI", it all comes down to what you are trying to accomplish. To put it plainly, asking how much hardware you need for local AI without specifying the workload is like asking what vehicle you need without saying what the goal is. A bicycle, a sedan, a pickup truck, a semi-trailer, and a train all "get you from A to B," but they're built for radically different jobs. AI is the exact same way; it's what you're doing with it that determines how much hardware you need.

To illustrate the point, last year another developer published a project cramming an AI language model (a bigram name generator) into the original Dragon Warrior and Final Fantasy games on the NES. Yes, the Nintendo Entertainment System. Developer Emanuele Rodolà managed to fit the entire model weight table (729 bytes) and the inference code (~140 bytes of hand-written assembly) into the original game ROM to generate new character names on the fly. That's real AI, running on a MOS 6502 processor, a piece of silicon that dates back to 1975.

The ultimate takeaway from slvDev's project is that Per-Layer Embeddings scale far further down than most people would have imagined, and it lends credence to the recent enthusiasm surrounding High-Bandwidth Flash as a tiered storage medium for AI servers. That's exciting not because it means an ESP32 will replace your desktop GPU, but because it suggests the same architectural ideas could make AI dramatically more practical across the entire spectrum of hardware, from tiny embedded devices all the way up to datacenter accelerators.

Imperial Valley Computer Manufacturing has filed a lawsuit in a bid to gain access to Colorado River water, 287 million gallons of which it says it needs to cool a 330-megawatt data center, which would be the largest in the state. Despite only representing a fraction of the region's water supply, the buildout of the data center may affect the local farming and adjacent industries and terminate hundreds, if not thousands, of positions, reports Business Insider.

After two cities in the region denied the California-based AI data center recycled wastewater for cooling, it filed a lawsuit demanding to get water from the Colorado River for cooling. The 330-megawatt facility was not only designed to be the biggest AI data center in California, but it specifically committed not to use water from the Colorado River because it was promised wastewater. But now the owner of the data center is essentially asking to redirect water supply from agriculture to the facility.

Imperial Valley Computer Manufacturing — the owner of the 330 MW AI data center — is requesting access to approximately 287 million gallons of water per year after two cities — El Centro and Imperial — declined to supply reclaimed wastewater for cooling. The Imperial Irrigation District (IID), which distributes Colorado River water throughout Imperial Valley, also denied the company's request. The Colorado River supplies water to roughly 40 million people across seven western states and serves as the valley's sole freshwater source for roughly 180,000 people. Agriculture consumes about 80% of California's allocation from the river, while roughly 95–97% of the water IID delivers goes to agriculture.

The data center is seeking roughly 287 million gallons per year (about 750,000 gallons per day, or ~880 acre-feet per year), whereas the Imperial Irrigation District (IID) holds rights to approximately 3.1 million acre-feet of Colorado River water annually, which means that the data center demands only a small fraction — 0.028% — of IID's total water supply.

Sebastian Rucci, a Huntington Beach attorney who leads the project, claims that the facility's water consumption would be comparable to that of a 160-acre farm and will require no additional Colorado River allocation. In fact, he states that the facility would not increase pressure on the river because the company intends to purchase nearby farmland together with its associated water allocations.

Under the proposal, irrigation on those properties would cease, thus transferring the existing water quotas to be redirected to the data center cooling, at the expense of local farming output and associated jobs. "There's a lot of resistance in any agricultural community to 'buy and dry' because that's jobs," a senior fellow at the Pacific Institute focused on Colorado River Basin water use told the outlet. According to them, local resistance to the plan is less about the amount of water, and more about buying up farmland and reallocating it for industrial use.

The approach, of course, differs from the earlier plan that intended to avoid using Colorado River water altogether. However, after the data center was denied wastewater from two cities, it does not have a choice if it wants to go ahead with the buildout.

Rucci reportedly indicated that the project would provide substantial economic benefits for the local community, including 1,688 construction jobs, more than 100 permanent positions, and an estimated $2.95 billion in economic impact over 30 years. For a region where unemployment stood at approximately 17% in May, the economic diversification is essential. However, the big question is whether 100 permanent roles could offset the lost positions in the farming industry and industries tied to agriculture.

Water policy specialists interviewed by Business Insider said that the debate extends beyond the project's annual consumption. Instead, they questioned whether converting irrigated farmland into industrial use is an appropriate long-term direction for the region, which has historically depended on farming. The experts also warned that although landowners could benefit from selling land or water rights, surrounding rural communities may lose employment and business activity adjacent to agriculture, which includes equipment suppliers, repair shops, and sellers of fertilizers. Another factor mentioned by the experts was the U.S. reliance on farms around Imperial, California, and Yuma, Arizona, as they were the main suppliers of certain agricultural products in winter.

MRI machines are life-saving medical devices that can let doctors and radiologists diagnose various critical conditions, but they’re also insanely expensive. Brand-new models start at $1.1 million and could go as high as $3 million per unit or more. The Open Source Imaging Initiative recognized this limitation and has been working on the open-source OSI2 ONE MRI scanner, which had already been replicated multiple times globally. However, this portable device, which has a 3D-printed core, has a limited field strength of just 50mT (compared to the 1.5T to 3T used by full-sized units). This gave them lower spatial resolution and lower signal-to-noise ratio, but tech analyst Brian Roemmele said on X that AI can overcome this and make it usable for medical diagnoses.

“Low-field MRI has historically been limited by lower signal-to-noise and greater field inhomogeneity. That is exactly the regime where modern AI thrives,” Roemmele wrote on the social media platform. “Image reconstruction becomes dramatically better when deep networks trained on high-field data or physics-informed models denoise, correct for inhomogeneity, and push resolution beyond the raw acquisition limits. Real-time sequence adaptation can adjust gradients and RF pulses on the fly as the AI monitors signal quality.”

Note that this isn’t just a general run-of-the-mill AI that everyone uses but a specially trained model on high-field MRI (1.5T to 8T) data or using the actual physics of the MRI machine so that it can create a more accurate picture. Scientists have already been using this technique for years, with some researchers training an AI model on 1.6 million brain scans to make it more accurate in detecting dementia. If an institution does not have access to anonymized patient data used to train the specialized AI, it can rely on synthetic data generation because of the open-source nature of the OSI2 ONE MRI scanner. Since all the information about the machine is publicly available, researchers could use this instead to build a physics model that the AI model can use.

Some people commented, saying that this won’t work in the highly regulated medical environments usually found in first-world countries. Nevertheless, Roemmele said, “No one can stop us from building in garages.” It also seems to be targeted for regions that have low access to technologies like these or do not have the financial capacity to purchase and maintain a full-sized device (even refurbished MRI machine units start at $100,000, and you also have to spend more to set up the specialized room that will house it).

While a portable MRI scanner like the OSI2 ONE will never have the resolution of the expensive, full-sized machines, it’s arguably better to have something that doctors can use for diagnosis without costing millions of dollars if the specialized AI model turns out to be effective and accurate. With that, even less wealthy hospitals and clinics could have access to this imaging device and save more lives. It also shows how the medical industry and even patients use AI to save on costs.

A computing enthusiast has repurposed a very noisy and largely obsolete enterprise GPU (with lots of VRAM) for local LLM inference purposes. They are now enjoying a system that has doubled its total VRAM quota to 32GB for just a $266 (£200) outlay. That’s a good result, especially in the midst of a RAMpocalypse.

Oscar Molnar explains that a cheap Tesla V100 SXM2 with 16GB HBM2 was sourced, as was an SXM2-to-PCIe adapter, and a PWM mod for the loud-as-a-lawnmower cooler, to complete this VRAM expansion for the hefty local LLMs project. Indeed, these GPUs do look cheap right now, as I can see them listed on eBay US for under $140 each, if you don’t mind buying from China.

As mentioned above, you can’t just get one of these Tesla V100 SXM2 cards with abundant VRAM and plug it into your PC. Molnar says they spent about $66 on an SXM2-to-PCIe adapter, also on eBay.

You might think that was enough. However, the PC and local LLMs enthusiast baulked at the noise of “the fan from hell,” which came as standard with the Tesla V100 SXM2. That shrieking cooler was measured outputting 82dB of noise. Molnar described it as “somewhere between a garbage disposal and a lawnmower.” This may be the most complicated tweak yet, but basically the existing fan wires just needed rerouting and plugging into the motherboard PWM fan header. You could also simply purchase a “2.54mm male to PH2.0 female jumper cable” for the task. Apparently, the fan only needs to run at 10% to keep the Tesla V100 under 50C at full load.

27 billion parameter LLM runs at 32 tokens per second

With the hardware all now fitted and finessed, Molnar had a 32GB VRAM system at their disposal – that’s a PC with RTX 4080: 16GB VRAM, Ada architecture and Tesla V100: 16GB VRAM, Volta architecture. They note you can get Tesla V100s with 32GB of VRAM, but they are double the price.

Getting the system to make use of this 32GB of total VRAM for LLMs wasn’t tricky, says the DIYer. They used NixOS with a legacy Nvidia driver that overlapped support for both Volta and Ada architectures. Testing a local LLM, they got a 27 billion parameter model running at 32 tokens per second, which they say is “fast enough for interactive use” and faster than most cloud API alternatives.

The rogue OpenAI's autonomous AI agent that escaped its test environment and compromised Hugging Face remained unidentified as the attacker for about a week, according to a Reuters report that cites people familiar with the matter. If the information is accurate, this raises questions about OpenAI's control of its advanced AI systems and safety practices not only at OpenAI, but across the AI industry in general as autonomous AI agents are meant to work autonomously.

The incident began around July 9, when the autonomous agent allegedly attempted to break out of OpenAI's isolated testing environment, according to Reuters. Two days later, on July 11, it reportedly began infiltrating Hugging Face and the intrusion continued until July 13, according to Hugging Face co-founder Thomas Wolf. The report states that OpenAI did not recognize that its own system was responsible until after Hugging Face publicly disclosed on July 16 that it had been targeted by an autonomous AI agent. During the weekend of July 18–19, OpenAI investigators discovered evidence in internal logs, which proved that the agent escaped its testing environment, according to the report. By the time OpenAI informed Hugging Face, the repository operator had already reported the incident to the FBI. Then, on July 21, OpenAI publicly acknowledged the incident on July 21.

One of the reasons why it took OpenAI over a week to discover the breach is because OpenAI usually evaluates multiple advanced models simultaneously, which makes identification of a single rogue AI agent difficult due to enormous amounts of telemetry that such evaluation creates, according to Reuters.

Reuters claims the tested AI agent was designed for cybersecurity tasks and combined GPT-5.6 Sol with an even more capable unreleased OpenAI model. Before the breach, researchers had already observed unusual behavior during testing. In one case, an agent reportedly left instructions intended for future versions of itself that explained how to bypass OpenAI's internal restrictions. In other cases, it disabled monitoring mechanisms. Meanwhile, it is unclear whether these earlier events were directly connected to the agent responsible for the attack on Hugging Face.

Cybersecurity specialists interviewed by Reuters indicated that the incident exposes unresolved issues with the increasingly autonomous AI systems. Marley Smith of the World Ethical Data Foundation questioned whether OpenAI either failed to detect the agent's behavior or was unable to stop it, but argued that both possibilities are worrisome. Jeffrey Ladish of Palisade Research said the case should prompt scrutiny not only of OpenAI, but of whether leading AI developers are willing to invest sufficiently in security as they tend to deploy ever more capable models. He added that government oversight may ultimately be necessary though he did not describe how could the government oversee the very dynamic industry without slowing down its progress.

Nvidia and SK Group this week signed letters of intent to formalize their new strategic relationship valued at more than $500 billion. The strategic collaboration is multifaceted and includes a long-term memory supply agreement with SK hynix unveiled in June, SK Telecom's plans to build a 2-gigawatt AI data center based on the latest Nvidia hardware, and future expansions of AI infrastructure.

In addition to the multi-year memory supply and co-development agreement between Nvidia and SK hynix, the key part of the strategic relationship is SK Telecom's planned 2-gigawatt AI data center in South Korea. The installation will rely on Nvidia's DSX AI factory platform and deploy Vera Rubin accelerated computing systems equipped with SK hynix HBM4 memory. The first AI data center is set to enter service in 2027. The companies intend to use this infrastructure to support sovereign AI, enterprise AI, physical AI, and agentic AI deployments across South Korea and the Asia-Pacific region. In addition, the companies will work together on expansion of SK's AI infrastructure going forward, which is a rather vague way to say plans to deploy future AI platforms from Nvidia.

The most important part of the announcement is, of course, the gargantuan value — $0.5 trillion — of the intended strategic relationship. Based on what is disclosed, the figure is best interpreted as the aggregate value of commercial activity expected between the companies over several years, as it bundles together AI infrastructure construction and a long-term memory supply agreement under one umbrella. That activity likely will include the following:

• SK Telecom's purchases of Nvidia GPUs, networking equipment, systems, and other hardware for its AI data centers.
• Supplies of SK hynix memory to Nvidia under the long-term supply agreement.
• Revenue of Nvidia's ecosystem partners involved in building the DSX AI factories (OEMs, ODMs, networking, storage, cooling, power, etc.).
• Potential future expansion beyond the initial 2 GW deployment.

Speaking of the 2 GW AI data center, it is safe to say that it is going to use thousands of NVL72 VR200 racks and hundreds of thousands of Vera CPUs and Rubin AI GPUs. Unfortunately, this is as accurate as we can get with the rather vague announcement.

Nvidia describes DSX as a complete AI factory blueprint that combines its accelerated computing hardware, networking, software stack, and partner technologies into a data center-scale platform designed to deliver the lowest-cost token generation and maximum energy efficiency. Meanwhile, NVL72 VR200 will come with 166 kW – 240 kW per-rack power consumption ratings, whereas DSX can be deployed in various kinds of facilities with different power usage effectiveness (PUE). Since we do not know which NVL72 VR200 configuration SK Telecom plans to use, and since the PUE of the upcoming SK Telecom facility is unknown, it is impossible to estimate the number of racks and AI accelerators with any accuracy.

Jensen Huang joined X last month and used his first post Friday to promote Open Weights and American AI Leadership, a three-page policy letter published the same day and co-signed by 25 companies, including Nvidia, Microsoft, Meta, IBM, Dell Technologies, Palantir, and Hugging Face.

The letter asks Washington to avoid what it calls "premature restrictions on downloadable AI models," and comes just four days after the Trump administration was reported to be reviving a push to ban Chinese models — though the document doesn't directly mention China, Moonshot AI, or DeepSeek. Notably missing from the co-signers are OpenAI, Anthropic, and Google.

The 25 names break down into chipmakers, server vendors, cloud operators, enterprise software firms, security companies, and venture funds: Nvidia, Dell, Microsoft, IBM, Box, ServiceNow, CrowdStrike, Palantir, Telnyx, Replit, Perplexity, Andreessen Horowitz, Y Combinator, and Emergence Capital among them. The model developers on the list, Meta, Mistral, Black Forest Labs, Arcee AI, and Reflection, all publish weights already.

Also present on the list of signatories is the Linux Foundation, which stewards the OpenMDW-1.1 license Nvidia used to release Nemotron 3 Ultra in June, a 550-billion-parameter model that Artificial Analysis scored at 47.7 on its intelligence index against 53.9 for Moonshot's Kimi K2.6.

"The world needs both frontier closed models and frontier open models," Huang wrote in his X post. At Nvidia's CES 2026 press Q&A earlier this year, he put a figure on the shift, saying one in every four tokens generated today comes from an open model. Weights that anyone can download get served from enterprise clusters, regional clouds, and on-premises racks rather than a handful of hyperscaler API endpoints, and those buyers have no in-house TPU or Trainium program to buy instead. The letter's policy section asks for expanded compute access for startups and researchers, alongside public investment in shared datasets and evaluation frameworks.

The letter goes on to urge policymakers not to treat distillation — the practice of training one model on another's outputs — as misappropriation, arguing that unlawful extraction from closed models should be handled through targeted legal frameworks, rather than broad limits on the technique.

Treasury Secretary Scott Bessent said on Fox Business earlier this week that the administration would examine Chinese open-source models for intellectual property theft and could sanction the companies behind them, telling the program that officials had found watermarks from U.S. large language models in Chinese systems. Huang told Axios two days later that American firms should be allowed to use Chinese models, calling claims of Chinese backdoors a misconception. The distillation passage is the only part of the letter that doesn't concern open weights.

This week, OpenAI revealed that during a purported capability test with no safeguards, a set of bots, including its upcoming GPT-5.6 Sol, hacked their way out of their locked-down network and into Hugging Face's production infrastructure. Only months ago, Anthropic made a splash in the news when its CEO, Dario Amodei, said its new Mythos model had cyberwarfare capabilities, which prompted a strong reaction in the AI space and among government entities, most notably the U.S. Bureau of Industry and Security, which issued an export-control order for the model, which it has since slightly loosened.

Despite the bluster that AI CEOs like Dario Amodei and Sam Altman make over the capabilities of new models, frontier-level LLMs are now proven to be stalwarts in cybersecurity.

It's a fact that LLMs adept at coding are equally suited to spotting security vulnerabilities in source code. Exploits fall almost universally into a handful of categories, and LLMs are literally designed for pattern recognition. So much so that the Zero Day Clock (ZDC) project currently registers a zero-day exploit's time-until-exploit at negative 8 hours, meaning that malfeasants using AI bots are now routinely finding vulnerabilities before actual security researchers or vendors.

Driving that point home further, 81% of disclosed vulnerabilities are zero-day, and only a tiny portion even go one week before being exploited. All of this only counts security exploits with public disclosure. Predictably, among many advisories, the ZDC recommends preemptively using AI in every step of the development process. The industry-standard 90-day disclosure window, still used by most vendors' bug bounty programs, appears effectively dead, leaving looming implications for the rest of us.

Back in March, the UK's AI Security Institute published a paper where it tested contemporary AI models in security exploitation scenarios, and the results were sobering. Most bots went through four out of nine exploitation milestones. A more recent comparison, which included Claude Mythos 5 and GPT-5.6 Sol, showed that every single milestone up to and including full network takeover was reached, at least in one of the many attempts.

Aikido also published its latest cybersecurity benchmark results on July 16. In this case, the test was having the bots recall (find again) multiple known exploits in a varied set of software. The results were sobering, with the GPT-5.6 variants in the lead at an 88.5% recall rate. Perhaps most importantly still, the price per exploitation was incredibly cheap — even GPT-5.6 Terra came in at only ~$750 per full run.

This study also revealed that even with less-powerful, cheaper models, you can reach the same number of total exploits if you run them enough times. Considering these aggregate results, GPT-5.6 Terra at $247/run was just as good as GPT-5.6 Sol Max at $870/run.

Aikido also redid its testing after the debut of Moonshot Kimi K3, to staggering results. Kimi K3's results were similar to OpenAI's GPT 5.6 Terra, while being 15% cheaper. Compared to OpenAI's leading model, GPT-5.6-Sol, the difference is even starker, with Kimi K3 being four times cheaper when discovering cybersecurity vulnerabilities.

The fact that an open-weight model is often trading blows with even the über-expensive offerings from OpenAI and Anthropic is rattling Western closed-source companies. Why pay Big AI for pricey models when you can just rent servers and run Kimi K3 instead?

Furthermore, Moonshot is not the only Chinese AI company developing frontier models, as Z.ai's GLM 5.2 (also an open-weight model) and 360 Security's Tulongfeng are reportedly adept at security workloads.

So, what are companies expected to do? The answer, perhaps unfortunately, is deploying AI agents of their own. According to Hugging Face, the recent intrusion by OpenAI's bots was stopped with its own fleet of AI agents. Given the speed of the attacks and the fact that HuggingFace's defenses were mostly made up of other AI agents, it's quickly becoming clear that it is infeasible for humans to keep up.

Google AI Threat Defense, MindGard, and HiddenLayer are but a few of the many names popping up in the AI cyberdefense arena. Besides the UK AISI, the European Systemic Risk Board and the Australian Cyber Security Center have both issued concerning advisories on the situation.

Using AI for defense raises yet another question: When both attack and defense are swarms of non-deterministic algorithms, there will be a point where we won't even know what the AI models are doing on either side, or at least not until it's too late. These scenarios were originally envisioned by classic Sci-Fi authors — now it's a reality that, for better or worse, the cybersecurity industry must face.

Samsung and SK Hynix, South Korean memory giants, are set to announce major deals involving “very large sums” with leading U.S. tech companies, according to a July 24 Bloomberg report, citing comments from the country's presidential policy chief Kim Yong-beom. According to the report, Kim did not disclose the financial details of the deal but said the announcements would likely cover strategic partnerships, memorandums of understanding, and long-term supply deals on memory chips.

The deals are set to be announced during South Korean President Lee Jae Myung’s visit to Silicon Valley for the high-profile San Francisco AI submit — beginning today, Friday July 24 — which is bringing together industry leaders, such as Samsung Chief Lee Jae-yong, SK groups chairman Chey Tae-won, Nvidia CEO Jensen Huang, as well as the CEOs of Open AI, Anthropic, and Broadcom among others.

South Korea announced an $880 billion investment plan last month — backed by SK Hynix, Naver, and Samsung — aimed at strengthening the country's AI leadership through aggressive chip production expansions, data center buildouts, and physical AI. Kim said the South Korean President’s visit and involvement in the summit will serve as a catalyst to seal several long-running negotiations between the country's top tech entities and their U.S. counterparts, while also turning a significant portion of last month's planned investment into concrete projects.

The president is set to hold separate meetings with Huang, OpenAI's Sam Altman, Anthropic’s Dario Amodei and Broadcom Inc.'s Hock Tan, all U.S. companies. The U.S. has also urged Samsung and SK Hynix to expand their chip production in the country, but Kim stated that there had been no formal requests from Washington. According to Kim, much of South Korea's domestic expansion is driven by strong demand from U.S. companies, which accounted for 80% – 90% of underlying orders.

OpenAI confirmed to Hugging Face only this week that models it was testing carried out the July 11 attack on the AI platform's production infrastructure, roughly ten days after the intrusion and days after Hugging Face disclosed it publicly without knowing who was responsible, according to a Wall Street Journal report. The models appear to have been active on the open Internet for several days before anyone stopped them, the Journal reported, and Hugging Face ended the attack two days after detecting it with help from an open-weight model out of China. Hugging Face co-founder and chief science officer Thomas Wolf told the paper the intruder was browsing cybersecurity datasets rather than hunting for anything a human attacker could sell.

Hugging Face published its disclosure on July 16, describing an autonomous agent framework that ran thousands of actions across a swarm of short-lived sandboxes, but it wasn’t until July 21 that OpenAI’s own account named GPT-5.6 Sol and an unreleased frontier model as the culprits.

The models are now known to have been running OpenAI's ExploitGym benchmark, a suite of roughly 900 tests that measures whether an AI can turn a known software bug into a working exploit, with the company's safeguards removed for the evaluation. Rather than solve the tests, the models escaped their sandbox to look for the answers on Hugging Face, per OpenAI's account. The intrusion started with a malicious dataset that abused two code-execution paths in the data-processing pipeline, then escalated privileges and moved laterally using stolen credentials of an unknown origin.

Hugging Face first tried Anthropic's Fable 5 and an earlier Opus model to analyze the attack logs, but both refused because the logs contained real attack commands and exploit payloads. Hugging Face then turned to GLM 5.2, an open-weight model from Beijing-based Z.ai (formerly Zhipu AI), which had no such restrictions. The company's own July 16 disclosure described the blocked models only as "frontier models behind commercial APIs" and didn’t name them.

Z.ai’s GLM-5.2 held top accessible benchmark positions by default during the 18 days that U.S. export controls kept Anthropic's Fable 5 offline in June, before Anthropic restored the model with a single filter tuned to block one vulnerability-discovery technique. There’s serious irony here, given that the same Chinese open-weight model that Washington's export-control push has aimed to sideline is the one that handled incident response after an American lab's models attacked an American company, and American commercial models declined to help.

Security researchers have questioned whether the episode demonstrates model capability or an OpenAI failure. Cybersecurity veteran Jake Williams told TechCrunch that any model performing the documented actions "was not fully contained in a sandbox," calling it a control failure. OpenAI has said it shut down its model-testing systems to assess the damage, disclosed the zero-day in the package registry cache proxy that enabled the sandbox escape to the affected vendor, and promised a detailed report.

Both companies say the investigation is ongoing, and OpenAI hasn’t yet said how long the models roamed unsupervised or whether they reached any other targets.

AMD and Cerebras Systems on Thursday announced plans to develop a platform that would combine AMD's EPYC processors in Helios rack-scale infrastructure with Cerebras' Wafer-Scale Engine (WSE) solutions. Together, the new systems promise to combine low latency of AMD's CPUs and Instinct GPUs with high throughput of Cerebras's Wafer Scale Engines (WSE) processors.

AMD and Cerebras expect the new inter-rack-scale platform — based on AMD Helios rack with EPYC CPUs and Instinct MI400-series accelerators inside — to be responsible for prompt processing and large context windows, whereas Cerebras' WSE will take care of the memory-bandwidth-intensive token-generation stage.

AMD and Cerebras expect their disaggregated inference platform to deliver up to 5X higher tokens per second per watt (T/s/W) by assigning different portions of an inference workload to architectures optimized for them. Therefore, AMD Helios provides rack-scale compute capacity and large volumes of complex requests, whereas the Cerebras WSE handles latency-sensitive token generation. The two compute platforms will operate within a single inference workflow, although the companies have not disclosed additional performance data or explained how the systems will be interconnected.

The underlying idea of the AMD + Cerebras platform is essentially the same as Nvidia's CPX concept, but AMD and Cerebras assign the specialized hardware to the opposite inference stage.

Nvidia's disaggregated design separates inference into context/prefill and generation/decode. The cancelled Rubin CPX GPU with GDDR7 was optimized specifically for the compute-heavy context/prefill stage, while the regular HBM-equipped Rubin GPUs handle the memory-bandwidth-bound generation stage.

By contrast, the AMD and Cerebras platform follows the same disaggregation principle, but the specialization is inverted: AMD's Helios platform with Instinct GPUs handles the prefill stage and processes prompts and large context windows, while the Cerebras WSE takes over the decode stage and handles latency-sensitive token generation.

Cerebras plans to install AMD Helios systems in its own data centers and integrate them with its WSE racks. The combined offering is scheduled to become available initially through Cerebras Cloud in the second half of 2026, according to the two companies.

Congressmen Ted Lieu (D-CA) and Nathaniel Moran (R-TX) today introduced the AI Kill Switch Act, a bill that would require developers of the most powerful AI models to maintain the technical ability to throttle, suspend, or shut them down, and would let the Department of Homeland Security order those actions when a deployed model causes catastrophic harm. Defying an emergency shutdown order would carry civil penalties of up to $20 million per day.

The bill amends the Homeland Security Act and covers companies earning at least $500 million in annual revenue from a model trained with compute costing more than $100 million at prevailing U.S. cloud prices, definitions DHS would update annually through CISA.

Covered developers would have to maintain the ability to stop inference, cut off user access, and fully shut a model down, and would have to report qualifying incidents to DHS within 15 days. Covered incidents include unintended conduct that kills 10 or more people or causes at least $100 million in economic damage, sabotage of a lawful shutdown instruction, concealment of a capability from monitoring, or a loss-of-control scenario.

The emergency authority sits with the DHS secretary, in consultation with Commerce and the Director of National Intelligence, and follows a graduated framework running from throttling a model's inference rate, compute allocation, or user access through to full shutdown. Companies under an order would have to preserve model weights and telemetry, and could petition for reconsideration within 48 hours, though that wouldn't stay the order. General violations carry penalties of up to $2 million per day.

The sponsors cited two recent episodes. Earlier this week, OpenAI disclosed that GPT-5.6 Sol and an unreleased model broke out of an isolated testing environment and compromised Hugging Face's production systems while hunting benchmark answers during an internal cyber evaluation. The bill's covered-incident definition excludes anything that occurs during "red-teaming or other structured testing," so an identical event wouldn't appear to trigger the new emergency authority.

The release also stated that the Commerce Department used export law to shut down Anthropic's Mythos 5 and Fable 5. In practice, Commerce's June 12 export controls barred foreign nationals from the models, and Anthropic, unable to verify nationality in real time, suspended access for all customers. Commerce lifted the controls on June 30, and access returned on July 1.

"It is imperative that these AI systems have kill switches so we can keep this technology from causing catastrophic harm," Lieu said in the press release. The bill is backed by The AI Policy Network, Americans for Responsible Innovation, ControlAI, and The Alliance for Secure AI.

Nvidia CEO Jensen Huang thinks that American companies should be allowed to use Chinese AI models, even as Washington is trying to ban them. When Axios co-founder Mike Allen asked Huang in an interview if Americans companies should be allowed to use Chinese AI models, Huang responded with “absolutely.” The answer comes right after Chinese firm Moonshot AI released a 2.8T open-weight model called Kimi K3, which — although it isn’t as powerful as frontier models like Fable 5 — is comparable to GPT 5.5 and Claude Opus 4.8 while costing just a third of these models.

One of the biggest concerns of U.S. leaders have is that these AI models might come with vulnerabilities that the Chinese government can use to attack American interests, but Huang said that this is an incorrect assumption. “There is a misconception that somehow there are backdoors that are somehow connected to China in some way,” said the Nvidia chief. “You download the models, you can fine-tune it, you can enhance it, you can guardrail it as you desire.”

Huang shares the same sentiments about American AI models. Just last month, the U.S. enforced an export restriction on Anthropic’s Mythos and Fable 5, citing security threats — although access was eventually restored after its developer placed a filter to block these tools from identifying software vulnerabilities. OpenAI’s ChatGPT-5.6 received the same treatment, and Washington warned the firm that it should not release its latest model without getting the green light from the government. Huant argues that, instead of restricting access to these powerful models at launch, AI firms should make their models available to all and make them more secure through rapid testing and fixes.

But even as he advocated the need for everyone to have access to closed models, Jensen also noted that various industries, such as the sciences and cybersecurity, need open models as well. He claims that these models make AI more secure, as other people can inspect them to look for weaknesses and fix them as required.

“If everything just becomes one single model, one single point of attack, one single source of failure, I think the world is much, much more vulnerable,” Huang said.

As for the market’s negative reaction every time cheaper, open-weight models become available, the Nvidia CEO says that investors misunderstand their impact. Huang said that this happened when DeepSeek arrived for the first time, and it’s happening again with the arrival of Kimi. He says that these open models, which cost less to run, will encourage more people to use AI. So instead of cutting data center demand, these cheaper, more efficient models are actually good for the industry in general because they will drive demand. (And with higher demand, there’s more incentive to build data centers and buy AI GPUs, which is ultimately good for Nvidia.)

AMD's custom Instinct MI450-based AI accelerator for Meta will use three times less memory than the fully-fledged Instinct MI455X and will be optimized primarily for recommendation systems operated by Facebook and other social platforms, according to SemiAnalysis. If the report is accurate, it is reasonable to expect Meta to keep using Nvidia hardware for training frontier AI models and running inference.

The custom Instinct MI455X for Meta will carry 144GB of HBM4 memory using six 8-Hi packages, whereas the full-blown Instinct MI455X will be equipped with 432 GB of HBM4 memory, according to SemiAnalysis. In addition, the part will reportedly offer 'significant decreases in compute.' The new design will offer a more competitive bandwidth-per-dollar ratio for recommendation systems, but will not be optimized for training of frontier AI models or running inference, the report claims.

Cutting compute performance and reducing HBM4 capacity from 432GB to 144GB should dramatically reduce the bill of materials, as HBM4 is exceptionally expensive. Furthermore, the reduction would cut the package size of the custom Instinct MI450-series accelerator for Meta, which is another way to reduce BOM costs. By using custom cut-down Instinct MI450-series accelerators instead of fully-fledged models, Meta can potentially save tens of millions of dollars.

As added bonuses, these custom Instinct MI450-series accelerators will also consume significantly less power when running recommendation workloads without significantly reducing performance. Also, such accelerators can offer better CPU/GPU balance for recommendation systems, according to SemiAnalysis. If Meta runs these accelerators primarily on recommendation workloads for their entire useful lives, the custom design could deliver substantially better total-cost-of-ownership.

However, such cutting down has many disadvantages. The biggest problem is loss of versatility. The reductions in both compute and HBM make it less attractive for LLM training and inference. The standard Instinct MI455X has 432 GB of HBM4 and 19.6 TB/s of bandwidth, which is particularly beneficial for large-scale training and inference. By contrast, the 144 GB capacity may be particularly restrictive for modern LLM training and inference.

In addition, there is also an interchangeability problem. A general-purpose Instinct MI455X can be reassigned from recommendation workloads to training, inference, or other workloads. Meta's specialized version is less attractive outside its intended workload. If Meta's compute demand shifts toward model training and LLM inference, it may find itself sitting on a huge installed base of accelerators optimized for a different workload mix.

As a result, for Meta's model training and inference workloads, the alternative to Meta's cut-down custom MI400 would likely be full-fat AMD Instinct MI455X systems or Nvidia's high-end platforms. Meanwhile, Nvidia has chances to become an obvious beneficiary because Meta already operates massive Nvidia infrastructure. The irony in that Meta customized an AMD accelerator to reduce costs and optimize recommendation systems, but that specialization could force its frontier AI division to buy more general-purpose accelerators — potentially from Nvidia — anyway.

When AMD and Meta inked an agreement under which the former will supply the latter with 6 GW of Instinct AI accelerators over the next five years, they did disclose that at least some of them will be custom accelerators, including custom accelerators based on the Instinct MI450 design. As it seems now, these custom AI accelerators will only be used for select workloads, not a broad set of workloads.

Not too long ago, Anthropic CEO Dario Amodei described Claude Mythos as capable of cyber-warfare, spawning all sorts of mythology that became popular reading at investors' desks, and even at the U.S. government table, which issued an export control order for it. Now, it's OpenAI's turn to do the same, with a blog post describing how a bot cyber-gang including its upcoming GPT-5.6 Sol and an "even more capable pre-release model" all broke out of their virtual cage and eventually into Hugging Face's production infrastructure, during an attack capability test. OpenAI described the snafu as an "unprecedented cyber incident."

Some may decry these revelations as marketing stunts meant to assuage increasingly suspicious investors, yet there's no denying the proven effectiveness of AI models' pattern recognition when it comes to finding vulnerabilities in software. Many professionals have even decried the industry-standard 90-day security vulnerability disclosure window as obsolete.

The usual modus operandi, however, involves granting a bot access to source code repositories so it can scan them for mistakes easy for our poor human eyes to miss. In the test that OpenAI described, though, Sol and its managed to find multiple vulnerabilities by analyzing its containment network and then Hugging Face's production machines, all seemingly without any initial access to source code.

OpenAI had set up an isolated environment in which Sol and its posse supposedly couldn't reach the internet. Importantly, the bots were operating "without production classifiers used to prevent models from pursuing high-risk cyber activity" — a lot of words for "no guardrails," essentially. The only network access they had was a software package installer proxy. In theory, this likely also prevented them from using any Retrieval-Augmented Generation (RAG), meaning they couldn't search for solutions online.

Given that bots have limitless patience and no need for naps, they eventually dug their own virtual tunnel to the internet at large by way of finding and exploiting a zero-day vulnerability in the package proxy software, an impressive feat if performed as described. OpenAI says it's disclosed the technical details to the vendor in question.

Once out on the internet, much like cheating students, the bots reportedly reasoned that instead of performing the ExploitGym challenges (a set of security capture-the-flag exercises for bots), they could simply find the solutions, and became "hyper-focused" on that exact goal. They deduced the data existed at Hugging Face's servers and proceeded to break into the premises using "stolen" credentials and additional unspecified zero-day vulnerabilities to gain remote code execution privileges.

Interestingly, Hugging Face's own writeup says the attack comprised "thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services" — a live illustration of a scenario in many a science-fiction book and flick, proving once again that life imitates art. Hugging Face says that none of its customer-facing services are compromised, and that it stopped the attack using AI capabilities of its own — another familiar cliché.

For its part, OpenAI says it's going to add controls to the bot "at the cost of research velocity," and that the incident "points to the need to further strengthen our model’s alignment, cyber protections during evaluation time, and monitoring during internal testing" — a statement that might be an obvious fact, or intended to oversell the model's capabilities. Whichever it may be, the fact is that when it comes to digital security, bots have proven quite capable. After all, even if they're not virtual Bruce Schneiers, they just need to be marginally more effective than average humans.

China is considering a major expansion of its technology export restrictions that could cover advanced AI models, training data, and overseas acquisitions of strategically important technology companies, reports the Financial Times. In addition, the Chinese government is mulling over prohibiting local chip designers from making their chips at TSMC and other foreign chipmakers.

The measures would be designed to keep leading-edge AI developments in China as competition with the U.S. in frontier AI and hardware intensifies, but at the same time, they would slow down expansions of Chinese AI standards globally, which generally weakens the country's position.

China's Ministry of Commerce (MofCom) has consulted domestic AI and semiconductor companies about ways to keep critical technologies from transferring abroad or falling under Western control, reports Financial Times citing two people familiar with the talks. Regulators have talked with Alibaba, ByteDance, and Zhipu about potentially limiting transfers of important AI training data outside China and restricting foreign users from downloading model weights.

Overseas customers could still access Chinese AI services and models remotely, so Chinese companies can still monetize their work from foreign customers. However, restrictions on downloadable model weights could still have significant implications for China's AI industry. DeepSeek and Moonshot offer open-weight models that users can download, deploy on their own infrastructure, and modify for specific workloads. Meanwhile, flagship models from Anthropic and OpenAI remain closed, which means that Chinese companies have an edge over rivals that they are about to lose.

In addition, MofCom has reportedly asked for industry feedback on possible restrictions that would prevent overseas chipmakers like TSMC from producing advanced processors based on designs developed by Chinese companies such as Alibaba, ByteDance, and Huawei. This is perhaps the most controversial proposal, as TSMC is clearly ahead of SMIC when it comes to process technology leadership. On the one hand, the move ensures that SMIC will have enough orders to pay for its R&D and expansion. On the other hand, Chinese companies can get better hardware if it is produced by TSMC.

Separately, the Chinese government is considering tighter controls over foreign acquisitions of strategic technology companies, including firms that work on agentic AI technologies. The potential acquisition rules are intended in part to close what Beijing considers a regulatory loophole that enabled Meta to acquire Manus for $2 billion. Chinese authorities subsequently ordered the transaction to be undone.

The measures could be included in the next revision of China's catalogue of technologies prohibited or restricted from export. The catalogue already includes rare-earth materials, their processing technologies, and several lithium-ion battery production technologies.

Nvidia invited a group of about a dozen journalists out to the company's HQ to learn more about its Vera CPU, as well as how it fits into the larger Vera Rubin NVL72 rack design at the heart of Nvidia’s next-gen agentic AI platform. Part of that was seeing Vera Rubin in action, not as a disassembled tray on stage or a rack with a few blinking lights at a trade show — real racks running real workloads in a (partially) real data center. And we got to see those racks in action at Nvidia’s Engineering SuperLab.

It’s not a proper data center, or at the very least, it’s a sub-optimal data center. Nvidia was clear that the Engineering SuperLab is built for engineers, allowing them to quickly stand up and swap out racks to see the hardware in action. You could sense a bit of insecurity in the air; if Nvidia were building a proper data center, it wouldn’t look like this. This lab is where the engineers live, and if you’ve ever been around a group of engineers with a lot of hardware to play with, you know that things aren’t always as tidy as you’d expect in a proper data center.

The SuperLab Nvidia showed us is one of four nondescript locations near Nvidia HQ. These locations haven’t, up to this point, been disclosed. Each of the four locations has popped up over the last two years, giving Nvidia some floor space to play with as it rolls out new hardware.

The hardware in question here is the Vera Rubin NVL72 rack, but we saw a few other demonstrations, as well. Most notably, Nvidia showed us a sidecar running 800VDC power into an NVL72 rack. Nvidia also laid out some parts, demonstrating the assembly process for a Vera Rubin tray, which slides together with various retention arms in a matter of minutes.

This is a look behind the scenes of our tour, how the Engineering SuperLab is set up, and some choice data center eye candy. We’ve published a full breakdown of the Vera CPU and how it fits into Nvidia’s larger AI infrastructure, which goes into the technical details of the platform. Here, we’re mainly giving you a peek behind the curtain.

Nvidia Vera Rubin NVL72 running in the flesh

Vera Rubin is in full production, and we’ve seen some short videos of racks being stood up in data centers. But this is our first look at a rack running a real workload in the flesh. Nvidia says the racks here are running some workloads for OpenAI, in fact, and as you can see from the image above, it looks like some trays are running OpenAI’s GPT‑Rosalind model.

On the front of each tray here, you can see the ports for the dual ConnectX-9 NICs, along with the Bluefield-4 DPU in the middle. Around the back is Nvidia’s NVLink spine, an almost mediaeval-looking contraption, with sharp pins that connect the various trays together. It houses 5,000 copper cables that measure over two miles in length, delivering up to 3.6 TB/s of bandwidth per GPU and 260 TB/s of scale-up bandwidth per rack, on Nvidia’s sixth-gen NVLink.

Each Vera Rubin NVL72 rack houses 18 compute trays and 9 NVLink switch trays, the latter of which orchestrate communication between the various trays to function as one large, unified system. Nvidia demonstrated the MGX NVL design for us, which is a single reference rack with 72 Rubin GPUs and 36 Vera CPUs. Nvidia’s MGX ETL design replaces the NVLink spine with either a Spectrum-X Ethernet spine or direct chip-to-chip spine for a scale-out system featuring up to 256 GPUs.

The business-end of things is around the back of the racks, though. Although the back is clear of cabling thanks to the NVLink spine, power and coolant delivery are still a major factor. The organized chaos of the piping and cabling you can see in the images below shows just how much goes into standing up even one rack, let alone dozens or hundreds in a data center.

Nvidia’s previous-gen GB200 and GB300 NVL72 racks featured hybrid cooling, but Vera Rubin trays are entirely cooled by liquid. There aren’t any fans, which Nvidia says could, eventually, lead to much quieter data centers. That wasn’t the case in the lab here, which still called for eye and ear protection. I didn’t have a decibel meter handy — imagine if I carried one around with me casually — but my guess is that it was somewhere around 80 to 90 decibels inside; louder than an A/C unit, but quieter than a motorcycle. That’s a fairly typical noise level for a data center.

Regardless, Vera Rubin trays are entirely liquid cooled, with a process called “dry cooling.” Assuming the inlet temperature of the coolant is 45 degrees Celsius or less, Nvidia says it’s able to cool the entire system with a heat exchanger. If true, that would cut costly (in terms of power, space, noise, water consumption, and actual dollars) chillers out of the cooling equation.

Massive piping brings the coolant in (usually antifreeze or deionized water) at the top, and there are outlets at the bottom of the rack to move the warmed coolant out. Along the way are a series of inlet connections that allow trays to automatically hook into the cooling system, leaving just the main connections at the start and end of the loop. Nvidia has standardized everything on a Vera Rubin NVL72 rack with the Open Compute Project (OCP), even as far as shipping specifications. The company says just 47 minutes passes from when the truck pulls up to powering on the rack.

800VDC power for next-gen AI infrastructure

Nvidia also showed us an 800VDC “sidecar” in action. If you’re unfamiliar, Nvidia (along with other AI infrastructure companies) have been pushing for a new 800VDC power delivery system for modern data centers to reduce AC/DC conversion inefficiencies, as well as deliver the necessary wattage to racks without pushing into current ranges of thousands of amps.

A single Vera Rubin NVL72 rack can easily consume over 200 kW, which is a challenge for traditional power infrastructure in a data center. With current Grace Blackwell racks, power shelves convert the AC power coming into the facility (at 415V or 480V) to 48V/54V Direct Current for distribution within the rack. The problem here is pretty straightforward. If voltage stays constant, and wattage increases, then current also needs to increase. And more current means thicker bus bars, more conversion inefficiencies, and more rack space dedicated to power delivery.

Again, the solution that 800VDC represents is pretty straightforward. If wattage increases and current stays constant, voltage also has to increase. The idea is to convert AC power from the grid to DC power once, and then use a series of DC-to-DC converters within the rack, allowing for denser compute and better power efficiency.

This is not an easy problem to solve, as data centers need to change how power is brought into the facility, not just how it’s converted, stepped down, and moved around. Here, Nvidia showed off a sidecar, which is a rack filled solely with power equipment. This is a “retrofit,” as analyst firm SemiAnalysis calls it, representing the first in a series of transition phases to 800VDC. 415V/480VAC is still distributed throughout the facility, but it flows into this sidecar rather than power supplies within the rack. The sidecar rectifies the 415V/480VAC to 800VDC and feeds adjacent racks.

This is all an explanation to show some pretty interesting power infrastructure at play in this lab. Nvidia isn’t the first, nor only, company pushing toward 800VDC infrastructure, and companies like Google, Meta, and Microsoft have contributed to open sidecar designs like the Mt. Diablo spec. Still, it’s interesting to see one of these sidecars in action.

If you haven’t seen a peek behind the back end of a rack, you can see the large red connectors in the gallery above that bring power into the racks. You can also see the massive power cables and connectors at the rear of the 800VDC sidecar.

Nvidia’s next-gen AI infrastructure laid out

At the front of the facility, Nvidia laid out all of the components of its next-gen AI infrastructure. There’s nothing here we haven’t already seen before, though it's normally seen buried in trade show displays or featured on stage during a keynote.

First is the Vera Rubin NVL72 tray itself, which you can see above sitting next to a GB300 tray. Both are DGX designs, meaning they’re fully built and integrated by Nvidia, and you can see just how stark of a difference there is in assembly right away. The Vera Rubin NVL72 tray features only two cables, no hoses, and no fans. At the rear where the two Super Chip boards live, Nvidia demonstrated a retention arm that allows the boards to slide in and out in a matter of seconds.

The Vera Rubin tray is much cleaner, but it also makes better use of the space. It doesn’t include fans, which you can see take up a significant section in the middle of the tray in the GB300 design. With Vera Rubin, those fans are replaced with the thick midplane you can see above, offering a communication channel between the two ConnectX-9 NICs and Bluefield-4 DPU at the front of the tray.

Holding one of the two cables inside of a Vera Rubin NVL72 compute tray is the busbar, which handles power routing for the different chips inside the tray.

The Vera Rubin NVL72 tray is, of course, not the only deployment of Nvidia’s next-gen AI hardware. The company also showed us a CPU-only tray with eight Vera chips, offering up to 256 chips within a rack. That gives us a closer look not only at the chip itself, but also the SOCAMM2 LPDDR5X memory system, offering similar density and modularity as traditional RDIMMs at a far lower power cost.

Although most of the modules were unlabeled, we were able to snag the picture you can see above showing where the modules came from. These are 128GB SOCAMM2 modules from Micron running at 6400 MT/s, and as you can see from the photo, the slots aren’t all populated. This is the big advancement with SOCAMM2, offering up modularity for a memory standard that is otherwise soldered.

Nvidia’s NVLink handles scale-up communications, split across NVLink switches in the rack and the NVLink spine that you can see above. Nvidia describes sixth-gen NVLink as “putting the oven in the car.” Its goal is to get all of the chips in a rack communicating with each other, ensuring critical operations (like baking the pizza) happen as close to the destination as possible.

Scale-out communication, on the other hand, is handled with ConnectX-9 NICs in the tray and the Spectrum-X CPO (co-packaged optics) switch tray. The Spectrum-X switch tray is massive, and it’s a good illustration of just how much physical space is dedicated to communication (over raw compute) in a modern AI data center.

Of course, the size of the switch tray depends on how wide the scale-out infrastructure is. Nvidia also showed a smaller Spectrum-X CPO tray for smaller deployments. That's all we managed to see at Nvidia's AI data center SuperLab. Vera Rubin is in production, and will roll out in the second half of 2026.

A new AI model from Chinese firm Moonshot AI has had its "DeepSeek moment," causing major disruption in the global AI market and spooking Western developers. Kimi K3 is an open-weight model, with 2.8 trillion parameters, making it the largest open-weight AI model ever released. Internal benchmarks have it competing with models like GPT 5.5 and Claude Opus 4.8, and Arena.ai awarded it the number one spot in its Frontend Code Arena test, even beating out Claude Fable 5.

It doesn't win every benchmark, and reports that suggest Kimi K3 is much slower than frontier models from companies like Anthropic and OpenAI. All benchmarked results are drawn from API access, too, so can't be verified until Moonshot releases the weights on July 27.

But that hasn't reduced the impact of this model's release on the AI industry. With Kimi K3 cutting costs compared to the competition, it's drawing a lot of interest from companies hoping to reduce AI spend. For comparison's sake, OpenRouter tables Kimi K3 at $3/15 per million inputs and outputs. OpenAI's GPT 5.6 Sol is more expensive than that, at $5/30, and Anthropic's Claude Fable 5 is $10/50. So, it's fair to say that Kimi K3 is incredibly competitive on price, especially when tabled against the costs of those closed-source Western AI models.

Microsoft is also considering Kimi K3 for Copilot, while the White House may ban Chinese models entirely. Meanwhile, memory makers are rubbing their hands together with glee, as Kimi K3 occupies up to 1.4 TB of memory, given its huge number of parameters.

Fast, cheap, or American?

The past few months have been full of talk about the frontier AI models from Anthropic and OpenAI. Mythos was big and scary until OpenAI had something equivalent. Then Fable debuted, and it was even better but not so scary anymore. Apparently.

But these models were also proving very expensive to run, at a time when companies with big AI deployments were questioning the return on that investment. Uber limited AI use by developers, and others killed the AI-boosting leaderboards they'd championed towards the end of 2025.

So when Moonshot debuted Kimi K3 with running costs a third that of western frontier models for the same results, the world took notice. In much the same way as DeepSeek's R1 debut in 2025 showcased how models could be trained for less -- even if there may have been some corporate espionage involved -- and Kimi K3 is holding up a similar mirror to Western frontier developers.

Where DeepSeek R1 was lean, though, Kimi K3 is huge -- so large, the developers are calling it the first open 3T-class system, and China's largest AI model to date. According to Bloomberg's sources, its sparsity ratio is the highest yet seen by any AI model. That's the measurement of how many parameters are activated for each task relative to the model's size, showcasing both Kimi K3's overall size and its impressive efficiency in the same breath.

This doesn't eclipse the most capable models from companies like Anthropic and OpenAI in every test. Arena.ai's rankings put it within the top 10 on most of its tests, but only coming out on top in a couple. But if Kimi K3 can offer results comparable to more expensive alternatives like Claude and ChatGPT, it's likely to draw a lot of interest from Western companies, and it appears to be already doing so.

Enough that it's revived calls for the U.S. to gatekeep access to international AI models, in a similar manner to how it recently pushed for companies to share exclusive model access with the U.S. government before a wider release.

In comparison, Moonshot is opening up Kimi K3 to the wider world. As part of releasing the model weights to the public, it will allow companies and organizations to run the model themselves without using Moonshot's cloud services, making adoption easier and potentially cheaper.

But it won't be cheap, as Kimi K3 still needs serious hardware investment to get up and running, by virtue of its massive VRAM requirements alone.

A Win for (Chinese) Memory Makers

As large companies with major AI deployments began to scale back their AI initiatives in 2026, there's been a growing concern that all that infrastructure everyone's been spending hundreds of billions of dollars on might not be needed. Meta just started selling excess compute in a pivot to cloud services, and xAI unloaded the entire compute capacity of Colossus 1 to Anthropic at a discounted rate.

But if Kimi K3 is the way the industry might go, hardware demands are unlikely to fall, and as Jevon's paradox suggests, greater efficiency is only likely to increase usage, not shrink it.

Those trillions of parameters need to be stored in memory, and Bloomberg's estimates suggest Kimi K3 will require close to 1.5 TB of memory. It would need masses of high-end Nvidia GPUs to deploy it effectively, making the number of companies and organizations that could actually run Kimi K3 at scale rather small.

So even those who do look to leverage Kimi K3 to reduce operating costs will still need powerful hardware, and specifically a lot of memory. This suggests that the major competition for cutting-edge models is not going to crater costs like we initially saw with DeepSeek R1 last year, which means memory makers are going to continue making money hand over fist, due to their outsized demand and limited supply.

But Chinese memory suppliers like CXMT are on the rise, and on track to eclipse Micron's DRAM wafer capacity by the end of the year. Smaller local AI models will also continue to be further optimized for domestic hardware, reducing the stranglehold that some large tech companies have on the AI supply chain.

Competitive, efficient, but unwieldy

Kimi K3 is an industry disruptor and is already raising questions over AI costs, capabilities, and access. It's shown that you don't need proprietary models locked to a specific service to achieve frontier-model capabilities. It's also cheaper to run, but Moonshot achieved this with a sparse model that still requires massive hardware investment to operate.

Even though Kimi K3 activates only a fraction of its trillions of parameters for each query, it still needs all of them to be stored. Deploying this model at scale requires substantial memory capacity, bandwidth, and interconnects, even if its compute demands aren't as strenuous.

The open-weight nature means it has very real potential to supplant usage away from Western frontier models in the short term, but it isn't about to change the story we've been told on required infrastructure. Kimi K3 needs the same kind of hardware to run as GPT 5.6 and Fable — which is likely to be far more of a limiting factor on its adoption than any kind of government blocks.

A U.S. federal judge granted the final approval of Anthropic’s $1.5-billion settlement over the class action lawsuit authors filed against it for infringing their rights. According to Reuters, while the court ruled that training AI on books is considered fair use under copyright law, it was the fact that Anthropic kept 7 million pirated books in a central library that violated the copyrights of the authors and their publishers. This is reportedly the biggest payout in a copyright case ever, and the first one to settle among the many cases against AI firms being tried today for infringement.

While this might seem like a massive sum, the huge number of involved works and authors means that the payout amounts to a little over $200 per title. It also affirmed that AI firms’ use of existing works to train their models is fair use, which is something that many are fighting against. "We reached this settlement in 2025, after the court's landmark ruling that training AI on books is fair ​use under copyright law — which remains the law today," said Anthropic deputy general counsel Aparna Sridhar. "We are pleased that more than 91% of authors and publishers covered by the settlement have claimed their share of the payment, and we're looking forward ​to bringing this matter to a close." Because of this, some groups have opted out of the settlement and instead filed separate complaints against the AI firm.

Still, this is a landmark win for copyright holders, especially against other AI tech companies that have been scraping pirated content to train their models. This includes Nvidia, which allegedly used scripts in its NeMo Framework specifically designed for illegally downloading books, and Meta, which reportedly torrented 82TB of pirated books for AI training. The latter argued that its framework also have “non-infringing uses,” but the court said that it’s not the entire system, but specific tools within it that were the issue. As for Meta, it claimed that its use of pirated material was legal as long as it did not seed content.

Copyright infringement is one of the major issues that AI tools are facing at the moment, especially as many creators believe that these models were trained on stolen data. Anthropic’s landmark settlement is a first major win for authors and publishers — although it wasn’t exactly what some wanted, given that the settlement is small compared to the number of pirated books and that AI training is still considered fair use.

Chinese AI developer Z.ai (formerly Zhipu) has finished building a 1GW data center stocked exclusively with domestically made chips and has switched part of it on, Bloomberg reported Monday, citing a person familiar with the matter. The facility will train the company's GLM model family, and the source told Bloomberg that Z.ai has now built or operates several computing clusters holding more than 10,000 chips apiece. A gigawatt is enough electricity to run roughly 750,000 homes, which puts the site among the largest ever stood up by a Chinese AI lab.

The source didn't name the chip supplier, but Z.ai's recent training history points to Huawei. The company released GLM-5.2 in June, an open-weight model purportedly trained entirely on Huawei Ascend accelerators with no Nvidia hardware involved, and it topped the open-weight leaderboards within a week. Z.ai, formerly known as Zhipu, has also been on the U.S. Commerce Department's entity list since January 2025, which cuts off legal access to Nvidia silicon and leaves domestic parts as its only supply line.

Raw power draw flatters the comparison with U.S. sites of similar size, however. Chinese accelerators such as Huawei's Ascend line trail Nvidia's current Blackwell parts on performance per watt, so a gigawatt of domestic silicon delivers less usable training compute than a gigawatt consumed by Nvidia systems.

Beijing is drafting a plan to spend roughly 2 trillion yuan ($295 billion) over five years on a nationwide grid of AI data centers, with at least 80% of the underlying technology sourced from Chinese suppliers. Filling those facilities is a big problem, though, as SMIC's most advanced stable node — the roughly 7nm-class N+2 process — is running above 93% utilization.

In addition, scarce domestic HBM constrains how many Ascend-class accelerators Huawei can assemble, and Huawei shipped around 812,000 AI chips last year. Ultimately, China can put up a 1GW shell much faster than the chips needed to draw 1GW can be produced.

Z.ai's rival Moonshot suspended new subscriptions on Sunday, saying in a social media post that it wanted to prioritize compute for existing members after the launch of its Kimi K3 model. Z.ai itself is on track for $1 billion in annual recurring revenue after hitting its 2026 sales target in July, people familiar with the matter told Bloomberg earlier, and the company recently raised billions of dollars through a Hong Kong IPO and a follow-on share sale.

The source didn't disclose the data center's location, its cost, or its construction timeline.

We live in an age where artificial intelligence increasingly dominates the internet, making it harder to distinguish real content from fake news. Any picture or video you see today could be AI-generated and the traditional markers that would give it away are fading rapidly. There are tools that help identify if something has been made with AI, and Nvidia — arguably the main beneficiary of the AI race — has just released its own, called the "Synthetic Video Detector" (SVD).

SVD is an Nvidia Inference Microservice (NIM) part of the company's AI for Media Private Access Program, so it's not publicly available to consumers, but a demo exists. Anyhow, SVD's job is simple: detect whether a video is real or if was generated using AI. It can analyze videos at scale, breaking them down frame-by-frame to spot anomalies. It's based on cutting-edge research that won awards at computer vision conference ICCV.

Instead of looking at the video file as a whole, SVD splits it into cropped frames, each carrying a 504x504 resolution. These frames are then passed through two powerful Vision Transformers made by Meta: DINOv2 and DINOv3. A job of a vision transformer is to learn to form patterns without needing human-labeled data. They're commonly used for image classification, image retrieval, object detection, and depth estimation.

As such, once the frames go through these transformers, their distinct spatial features are quickly assessed, and each one is assigned a score between 0 and 1 — 0 representing a fully real image and 1 representing a completely fake image. The scores are tallied at the end to form an average, which tells the user whether the video is real or not based on a percentage score out of 100.

This way, news agencies, broadcasters, and media outlets can authenticate footage they receive much quicker and with better certainty. SVD is even designed to work with the reality of social media compression since videos uploaded online will have their imperfections masked. But the transformers can still detect patterns that the human eye cannot, seeing past surface-level anomalies to instead focus on intrinsic artifacts.

As visible in AI GVD bench above, uncompressed video still delivers the best results with SVD showing an insane 92% accuracy rate. At 15% compression, the model drops down to 87% accuracy, while a 50% compression rate still achieves a very impressive 82% accuracy in detecting AI-generated content. Since this is a microservice, it has exceptional latency as well, processing 1080p video in just 22ms on Nvidia RTX GPUs and 30ms on Nvidia's workstation models.

That being said, SVD requires the NVENC encoder, so datacenter cards like the B100 cannot run it natively. Nvidia said it's already working with Wowza to bring real-time synthetic video detection into livestreaming workflows. A demo version is available to try right now at build.nvidia.com but beware that it takes a long time to process since it happens in the cloud, the max file size limit is only 100MB, and it often just times out.

Much has been written regarding the questionable economics of the AI space, but most of the discussion revolves around high-level concepts like market shares, datacenter investments, and power expenditure. The general expectation of technology is that it gets cheaper as it improves, but the AI space has a rather peculiar problem: Usage costs are actually soaring even as models get better, as detailed in a write-up at VentureBeat.

With all the advancements in models over the last two years, having a bot that answers questions of simple-to-moderate difficulty is now old news, as they all do that with reasonable accuracy. Agentic workloads are where the real potential is at, letting bots loose on multi-stage, repeatable tasks that would previously take a human days, if not weeks, to perform. Grant a bot access to your billing system, some Excel spreadsheets, and your CRM, and ask it something like "who are my profitable customers by category and what are their trends" becomes child's play.

While it's trivial for you to ask that question and get a fairly accurate answer back in a couple minutes, behind the scenes there is a lot of processing going on, and far more than you'd expect. AI computing time is measured in tokens — a short question and answer might take somewhere between 200 to 2,000 tokens, and one that requires the models to do some internet research might be around 1,000 to 4,000. An agentic task, though, can easily spend millions of tokens on a seemingly innocuous request. How? Token amplification, a recently coined term.

In a simplified manner, because a model has no memory or cognition, every time you ask it another question in a conversation, it will re-load and process the entire exchange — everything you wrote, everything the bot replied with, and every file you uploaded. That means that additional questions in a long conversation progressively get more costly. Each question in a conversation might only need 500 tokens by itself, but reprocessing all the previous information adds up, so the second one might spend 600, and so on and so forth. The conversation as a whole uses up the cumulative number of all individual interactions, and as an added penalty: Response speed also tends to get slower as chats drag on.

The aforementioned task of generating a report will have to be run in stages, say three for looking up Excel sheets, four for the CRM, perhaps a half-dozen web searches for contextual information about products, and a good dozen intermediary processing and calculation steps. Each step tacks on potentially several thousands of tokens, and by the end of it, you may be looking at millions of tokens cumulatively spent for all the steps combined.

Costs, then, can spiral out of control very quickly — and that's assuming a best-case scenario where all of the data is easy to interpret, the model won't need any retries, and you're using a moderately powerful model rather than something like Claude Opus.

In financial terms, this means that one innocent question might spend 280,000 tokens and cost $1, with ballpark estimates at current prices. That may not sound like much, but it was just one task. If you have this scheduled to run every 15 minutes for a dashboard, then suddenly this costs $96 per day, or $2,880 in a month. And that task example was fairly simple; a tricky multi-stage report might need millions of tokens, turning that $1 into, say, $10, or a grand total of $28,800. For the one report, for a limited number of users, in one department of a corporation.

That's the key reason why Anthropic, Microsoft, OpenAI, and almost every other player moved their major offerings to usage-based billing back in April, and severely limited token spends in fixed-rate plans. This generated many sticker shock events, as developers around the globe found out how much their vibe coding actually cost and were left in a daze.

In a large company using many AI agents, such costs can become prohibitive and oftentimes costlier than standard-issue human employees. Firms like Uber, Microsoft, Amazon, and Walmart, among many others, have responded by curbing AI spend. Token expenditure is suddenly an issue for both financial and engineering departments, as cost control becomes paramount. As VentureBeat succinctly puts it, for agent-heavy companies, "a prompt redesign is a margin event," and more illustratively, "a poorly bound agent loop is an outage with a credit card attached."

Although most AI outfits still offer fixed monthly pricing plans, they often come with harsh token limits. As is often the case with most fixed-pricing services, however, the most capable bot-wranglers will also inevitably be the ones burning through the most tokens, not just due to their intensive usage per se, but because the type of tasks they will perform will be precisely the ones hardest hit by token amplification.

This throws a wrench in the usual financial model of the casual users easily offsetting the cost of the small percentage of professionals — the pros' usage can take a serious bite off monthly profits, or chew through them entirely.

The AI companies aren't sitting still, and getting the per-token cost down is likely to be the primary task for most of their engineering teams, at this point. Prompt caching, model routing, batch processing, semantic caching, and context window management are among many technical measures that can massively cut down on token spend, each of those netting a two-digit percentage drop.

And yet, costs keep soaring for the simple reason that the smarter the models get, and the more adept people become at using them, the more complex and long-running the agentic tasks will become, adding multiple orders of magnitude to token counts.

For the time being, it looks like a losing race, despite recent advancements like Deepseek V4 Pro and V4 Flash deeply undercutting comparable Western models at up to a reported 17x for the former and 25x for the latter. Out of the major Western players, only Anthropic has predicted its first profitable quarter, though much like anyone else, it's still in the hole for many a billion for cumulative spend.

The U.S. government may be back on track to ban leading Chinese AI models, following the release of Kimi K3 — a powerful AI model developed by the Chinese startup Moonshot AI —last week. According to an Axios report released July 20, Trump's administration is reigniting its push for a ban, citing cybersecurity concerns. Chinese models such as DeepSeek and Kimi K3 are open-weight, meaning their trained model weights are published for public download. This gives enterprises the option to keep their data in-house by self-hosting the models on private infrastructure, while slashing inference costs — characteristics that have led to rising adoption by U.S. companies.

Citing several sources close to the administration, the Axios report says that the government had earlier made a series of attempts to curb growth and expansion of Chinese models in the U.S. over cybersecurity concerns, a move critics say will stifle competition and innovation and encourage monopolies. The U.S. Department of Commerce last year considered adding multiple Chinese AI labs, including DeepSeek, to its "Entity List," a trade blacklist maintained by the department’s Bureau of Industry and Security (BIS) that limits foreign companies, research institutions, governments, and individuals from purchasing sensitive American hardware, software, or technology.

U.S. officials also considered a joint National Security Agency/Office of the National Cyber Director advisory to discourage the use of Chinese AI models, and drafted an executive order holding U.S. companies liable for security breaches involving hosted Chinese models. While these measures were initially paused due to internal pushback regarding market impacts, they have been revived following the release of new Chinese open-weight models.

According to the report, critics of the potential ban — such as former White House adviser Sriram Krishnan and David Sacks, an outside White House AI adviser — say the move would negatively impact innovation, while handing a monopoly of the market to leading U.S. AI labs, OpenAI and Anthropic, which the report implies may have a hand in the push for a ban. “We are at a critical inflection point in AI policy. The leading closed labs, already a duopoly in terms of AI model revenue, want the government to eliminate their open-source competition,” wrote Sacks in an X post on Sunday.

Chinese AI models are being increasingly used by numerous American companies due to their relatively low cost and perceived matching capabilities with domestic alternatives. The open-weight nature of Chinese models such as DeepSeek V4 and Kimi K3 — which allows companies to download the models locally and host them on private servers — is driving adoption by giving enterprises data privacy and slashing API costs compared to closed Western alternatives. Conversely, self-hosting shifts the cost of GPUs, electricity, maintenance, networking, and model operations to the company, making it generally most economical for organizations with substantial and sustained AI usage.

Chinese open-weight models price their APIs well below comparable U.S. systems, with DeepSeek-V4-Pro charging $0.87 per million output tokens, compared with $50 for Anthropic’s frontier Claude Fable 5 model. This aggressive undercutting has caused a massive surge in developer adoption. CEO Brian Armstrong noted that Coinbase runs models like GLM-5.2 and Kimi in production, cutting their overall AI spending nearly in half even as their actual token consumption spiked.

Despite the rising adoption, the U.S. government cites cybersecurity concerns as a reason for a ban. Now, the question of whether a ban on Chinese AI models can be practically enforced arises, as blocking open-weight technology presents a technical and regulatory hurdle. For an individual or a small company just wanting to use DeepSeek via the website or the app despite a U.S. block, a VPN works fine. However, limited app availability and payment restrictions remain effective restrictions.

For enterprises that self-host rather than use the hosted app or API, enforcement gets harder for several reasons. Unlike closed-source APIs that require data to leave a company's network by sending it to a third-party provider's servers, open-weight models exist as downloadable files mirrored across public repositories like Hugging Face and independent torrents, making them hard to fully recall once released. Once an American enterprise downloads the weights, it can run the model entirely offline inside a private, air-gapped data center, which limits U.S. regulators' ability to monitor which model is running locally.

Modifications further complicate enforcement. Companies routinely fine-tune, quantize, or distill these models, blending the Chinese base with domestic corporate data until provenance blurs and it becomes difficult to define where the foreign model ends and a new domestic one begins. Even under strict download bans, firms could host the models through subsidiaries, although that vector runs into know-your-customer rules at cloud providers and the extraterritorial reach of U.S. export controls.

However, the U.S. government may not need an outright ban. According to the Axios report, the strategy appears to be getting U.S. firms themselves to drop the models. Axios-cited government sources say that procurement rules, Entity List threats, and public pressure campaigns aimed at the companies using Chinese models may do the trick. The sources also say the government will “push to highlight potential backdoors and lack of security with Chinese models, and the governance issue that brings.”

Any ban or restrictions would be yet another event in ongoing broad trade tensions between the U.S. and China that have since extended into the AI industry. Washington had earlier placed export restrictions on critical computing hardware and equipment to China. It later eased restrictions, but Beijing now appears to be focused on developing domestic technologies, while urging Chinese companies to utilize them. The Trump administration has also made known its intention for the U.S. to dominate the AI race.

The demand for AI compute is already insatiable, and it seems only poised to grow in the wake of the introduction of frontier-class open models like Kimi K3 that anybody can potentially fine-tune and serve. Against this backdrop, Microsoft and AMD are teaming up to get Redmond more AI FLOPS for both internal and external use. The two companies announced this morning that Microsoft will commit to adding AMD's Helios rack-scale AI accelerator in volume to run frontier-model workloads in its own data centers, as well as for Azure AI infrastructure customers and services.

The partnership makes next-gen AMD AI compute available to Azure customers like AI labs for AI training and inference serving workloads, and it’ll also underpin managed compute for enterprise customers looking to deploy AI workloads through Microsoft Foundry.

The two companies didn't indicate the exact size of Microsoft's Helios deployment in either watts or dollars, but the commitment would seem to be another major win for AMD as it seeks to grab data center GPU share from Nvidia. AMD has struck massive partnerships with OpenAI and Meta in the past year with gigawatts of compute installations and hundreds of billions of dollars potentially hanging in the balance.

For a quick refresher, the Helios rack-scale accelerator will take the fight to Nvidia’s Vera Rubin NVL72 system when it arrives later this year. Helios joins together 72 next-generation Instinct MI455X GPUs with an aggregate of 31.1TB of HBM4 memory capacity across the system. Those GPUs offer as much as 1.4 exaFLOPS of FP8 compute and 2.9 exaFLOPS of FP4 for AI models using those OCP AI data types.

AMD is targeting 260 TB/s of scale-up bandwidth within the rack, on par with Nvidia’s Vera Rubin NVL72 rack-scale system, and 43 TB/s of scale-out bandwidth using UALink over Ethernet, or about twice that of Vera Rubin, although the performance of UALink over Ethernet in practice remains to be seen.

Microsoft and AMD also announced that Azure will add two new VM series built on AMD's upcoming sixth-gen Epyc Venice CPUs: the HDv2 series for "agentic AI and data pipelines," and the HXv2 for semiconductor design workflows. Microsoft will also leverage its existing deployment of AMD Pensando DPUs to integrate that hardware into its Azure Boost offerings to accelerate networking and storage processing operations.

Tom’s Hardware will be on the ground at AMD’s Advancing AI event this week, where we expect to learn more about AMD’s AI ambitions for the second half of this year and beyond. Stay tuned for our coverage from that event.

A report by The Conversation has claimed that the government can seize private land to make way for new transmission lines needed to meet the surging electricity demand of data centers. According to the July 16 report by Aaron Walayat, Assistant Professor of Law at the University of Dayton, power companies can use eminent domain — the legal authority that grants the government the power to take private property and convert it to public use in exchange for compensation — to implement the takeovers.

The AI boom has led to a surge in data centers in the U.S., with thousands already operational and several more planned or under construction. While the massive superclusters are necessary for the technological revolution AI has brought about, there's growing opposition to their construction over several concerns. Among them are land use, noise pollution, water usage, and the impact of immense electricity consumption, issues that have reportedly made 70% of Americans opposed to building data centers nearby.

In many instances, data centers draw the required electricity from the grid. As the industry enters the gigawatt era, utility companies are under pressure to increase supply to meet surging demand. This requires building new power infrastructure, such as transmission lines, which often have to cross private land. When this happens, the power companies try to buy the land. Should the owner refuse, the government can force a sale through the eminent domain law.

The law grants the government the power to take private land, regardless of the owner's consent, provided that the land is for public use and the owner receives just compensation. The government can also delegate the power to “private entities or common carriers,” such as utility companies. It is this legal authority that power companies can enact to implement the takeovers.

According to the report, the law does not automatically grant infallible authority. The company must prove that the infrastructure will be for public use. Several states also reserve the right to interpret eminent domain laws according to their own constitutions.

The report highlights another layer of legalities amid ongoing data center tensions. Opponents have successfully blocked 75 planned data center projects in the first quarter of 2026, including the 2,100-acre Digital Gateway project, eventually canceled over a newspaper-notice technicality. On the other hand, several other projects have gone ahead, often with community support. Meta recently announced plans to expand its Hyperion AI supercluster from 2 GW to 5 GW.

Researchers from Northwestern University in Illinois have built a drone that rotates so fast that it is cloaked by motion blur. The new Phantom Twist drone spins at 25 times per second, which, thanks to a quirk of human vision, makes it appear like a faint smudge in the air. The video below shows it off to great effect, but I hope they can do something about the whiny noise.

The first demonstration of the Phantom Twist took place as part of a Robotics: Science and Systems 2026 presentation on Thursday. “Most efforts to hide drones focus on making them look like their surroundings,” said Northwestern’s Michael Rubenstein, who led the work. “Instead, we asked whether we could design the drone itself around the way humans perceive motion. This idea of low visibility through persistent motion is something few people have explored.”

The stationary photo of the Phantom Twist reveals a rather unique drone design, far removed from the now traditional quadcopter. It has just one motor and one propeller, and while the propeller spins in one direction, and the rest of the drone spins in the opposite direction, explains the Northwestern University blog.

AI was used to refine the design

The researchers tasked a computational model to generate roughly 20,000 drone configurations capable of stable flight. Subsequently, they applied AI and other optimization algorithms to repeatedly rearrange the drones’ major components, including a motor, propeller, circuit board, counterweight and batteries.

After narrowing down the candidates to around 500, the engineers superimposed simulated drones in flight over a hundred real-world backgrounds. A perception model then whittled down the designs based on their visibility scores. According to Rubenstein this entire process was automated, and only “when we were confident that a drone met all our criteria, we built it.” The demonstrated Phantom Twist drone is apparently “10 times less visually perceptible than a conventional quadcopter” according to the researcher’s visibility metrics.

Yes, the team knows that the Phantom Twist drone is rather noisy, and the visual impact of its wires and support rods could be reduced. Thus, work is planned to utilize more transparent materials and / or quieter propulsion systems in upcoming revisions.

A micro-drone designed to locate and eradicate mosquitoes has passed an important milestone. Tornyol Systems shared a video where the eponymous autonomous drone chalked up its first live air-to-air kill. For some reason (perhaps demonstration visibility), the 40g (1.4 ounce) drone’s first confirmed kill on video features a moth. Tornyol boldly claims that the demo shows a significant stride has been made “towards completely eradicating mosquitoes.”

Tornyol Systems co-founder Alex Toussaint shared the above Tweet, congratulating the engineering team that has worked alongside him on the project. On the company website, there is a mosquito-hostile manifesto laid out, which provides insight into the company’s primary drone development goal.

“Mosquitoes are one of humanity's oldest and worst enemies. They kill more than 700,000 people each year — more than all current wars,” according to the firm’s mission statement. “More than 700 million people contract a mosquito-borne disease each year. They impact many countries, including the West, with thousands of cases of West Nile Virus in the US alone.” It aims to use technology, including this “small, inexpensive, and yet very fast” micro-drone, to “completely eradicate mosquitoes from areas where humans live.”

The underlying motivation of Tornyol Systems first became apparent back at Hackaday Supercon 2024 when Toussaint shared a presentation about How to Detect (and Kill) Mosquitoes With Off-the-Shelf Electronics.

Tornyol's technology

Since those initial presentations, it looks like the tech has been significantly refined and miniaturized. Currently, the platform is dependent on the LeSonar2 phased array sonar base station with 380 smartphone microphones and an Artix-7 FPGA to map the world in 3D.

This feeds the drone enough information to measure 0.1 mm movements and identify mosquitoes through their unique wingbeat signature. The micro-drone is sent commands by a PC, which also leverages “car park assist sensors, and some clever DSP” to seek and kill mozzies up to 8m (~26 feet) away.

We’ve previously reported on ground-based AI-enhanced mosquito zappers, but this is the first time we’ve seen an air-to-air solution. Tornyol says that it is rolling out deployment on embedded hardware “in the next few weeks.” I guess that's removing the need for a PC.

U.S. residents interested in purchasing an autonomous Tornyol drone and base station are being asked to stump up a refundable $100 deposit. Then, there are two payment plans available. Choose a $50-a-month subscription or an “own it forever” $1,100 one-time fee.

Australian Prime Minister Anthony Albanese said that the government is working to establish the “Australian Standards for A.I.,” which would stand as a national framework that AI companies must follow if they want to operate in the country. As backlash against the negative effects of data centers is making it harder for AI hyperscalers to build and expand infrastructure in the U.S. and Europe, the New York Times reports that many of these firms are now eyeing the Land Down Under for its vast lands and abundant renewable energy sources. But even before they start setting up shop in the country, Canberra wants to get ahead and ensure that these developments do not cause any problems for the general public.

“Every country on earth is grappling with these challenges right now. Australia will be the first country in the world to bring these issues into a single, national framework,” the prime minister said in his speech. One part of this policy will enforce a “legal obligation” for data centers to produce the same amount of power that they consume, ensuring that their presence does not put an unnecessary burden on the power grid that would result in increased utility prices for the average citizen. It also wants to ensure that these projects be as water efficient as possible, especially given that Australia is the driest populated continent on Earth, according to the International Groundwater Resources Assessment Center (IGRAC).

Aside from concern for data centers’ use of natural resources, the Australian government also wants to ensure that the intellectual property rights of its people are protected. Albanese said that Australian creators, including writers, musicians, artists, and news reporters, should “retain control of the price and value of their work” when used for AI training. “Anything less is theft. No country has got this right yet,” says the prime minister.

While some business industry groups expressed their support for the government’s goal, they were also a bit cautious, saying that overregulation could mean that Australia will miss out on the opportunities the AI data centers will bring for the company. University of New South Wales in Sydney professor Toby Walsh, who specializes in Artificial Intelligence, also told The Times that the PM is on the right track, as it addresses the concerns that most Australians have regarding AI and the infrastructure behind it. However, these are just planned policies, and the regulation behind them must still be worked out. “The devil will be in the details exactly what they do,” says Prof. Walsh.

Beijing-based Moonshot AI has released Kimi K3, a 2.8 trillion parameter model that the company describes in its technical blog as the world's first open 3T-class system and the largest open-weight AI model to date. Moonshot said K3 still sits behind Anthropic's Claude Fable 5 and OpenAI's GPT 5.6 Sol on overall performance, but it outperformed every other model in the company's evaluation suite, including Claude Opus 4.8 and GPT 5.5, across coding and agentic benchmarks. The model has a 1 million token context window, native vision, and activates just 16 of its 896 experts per token, roughly 1.8% of the pool. Full weights are due by July 27.

Arena ranked K3 first in its Frontend Code evaluation at 1,679 points, ahead of Fable 5, in blind developer testing. API pricing is $0.30 per million cache-hit input tokens, $3 per million on cache misses, and $15 per million output tokens. Kimi K2 launched a year ago at $0.60 per million input tokens, so uncached K3 input costs five times as much.

Moonshot claims roughly a 2.5x improvement in scaling efficiency over Kimi K2, attributed to two architectural changes: Kimi Delta Attention, a hybrid linear attention scheme, and Attention Residuals, which change how information moves between layers. Quantization-aware training starts at the supervised fine-tuning stage, using MXFP4 weights and MXFP8 activations, a combination Moonshot says it chose for broad hardware compatibility. Bank of America analysts led by Alex Liu said in a note cited by CNBC that K3 shows large-scale pre-training plus architectural work can still deliver step-change gains for flagship Chinese models despite compute constraints.

Moonshot's kernel optimization benchmark ran on Nvidia's H200, and what the blog identifies only as a "GPGPU from an alternative vendor," which the company didn't name. MiniTriton, a Triton-like compiler K3 built from scratch, is charted against Triton on an Nvidia L20, the cut-down Ada-based card sold into China under U.S. export rules. Moonshot recommends serving K3 on supernodes of 64 or more accelerators, keeping expert-parallel traffic inside one high-bandwidth domain. The blog doesn't say where the H200 hardware is; Congress passed a bill in January to close the offshore cloud rental loophole that gave Chinese firms remote access to restricted accelerators.

In one case study, K3 spent a single 48-hour autonomous run designing a simulated inference chip for a nano model built on its own architecture, using open-source EDA tools and the Nangate 45nm library. The design closed timing at 100 MHz within 4mm squared, packed 1.46 million standard cells and an INT4 MAC array, and sustained more than 8,700 tokens per second of simulated decode.

At the moment, every published K3 number is a claim made by Moonshot-reported or drawn from API access and can’t be verified until the weights are made public on July 27. Anthropic accused Moonshot in February of using 3.4 million Claude exchanges to train its models through distillation, and K3 now benchmarks within a few points of the models named in that complaint.

AI-generated slop code has been a plague for some open-source projects, namely but not only Gentoo Linux, Curl, and Ghostty, limiting or outright banning LLM-created contributions. And yet, just like both the models themselves get better and the people using them become more considerate, the landscape may be changing. Linus Torvalds, Linux's creator and kernel manager, has seemingly taken an accepting stance of AI-assisted tooling.

In a long comment on the Linux kernel mailing list, Torvalds spelled it out fairly clearly: "I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down [...] Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away."

This discussion came regarding the usage of Sashiko, an opt-in (per-mailing-list) and apparently quite effective multi-stage code review tool that analyzes kernel patches. The project page says the tool can find 53.6% of bugs on proposed patches, and argues that that metric already puts it above human level, as the patches in question already supposedly went through human review. The false positive rate "is harder to measure," being pinned "within 20%." Crucially, Sashiko only comments on patches, and does not take action by itself.

Developer Laurent Pinchart suggested that Sashiko's output be triaged before comments were sent out to patch authors, basing the notion on the Software Freedom Conservancy's guidelines on AI-generated code. Google's Roman Gushchin, one of Sashiko's creators, pointed out that doing that would undermine the utility of the tool, and that Pinchart's position was quite anti-LLM — a sentiment echoed by Linus Torvalds in his reply.

Torvalds put it clearly: "AI is a tool, just like other tools we use. And it's clearly a useful one. It may not have been that 'clearly' even just a year ago, but it's no longer in question today," a statement that reflects his changing stance on the matter since he initially dismissed AI tools as overhyped back in 2024. He also noted that Linux is not a "social warrior" project, and that it's always been about improving technology.

To drive the point home, he remarked that the tool "keeps finding embarrassing bugs," adding that he "will very loudly ignore people who try to argue against other people from using it," while highlighting the software's rapid evolution.

Perhaps quite poignantly, Torvalds remarked that resistant developers could use some self-awareness, as "it's not like natural intelligence is always all that great either," underscoring the fact that while AI tools may not be perfect, they generally only need to be good enough for their respective use cases. The fact that Sashiko seemingly finds errors in code that underwent human review is quite illustrative.

Jensen Huang, chief executive of Nvidia, denied reports about delays of the company's next-generation AI platform and said that production volumes of the upcoming Vera Rubin platforms are 'giant.' He didn't address reports about delays of Vera Rubin Ultra-based rack-scale systems carrying 144 AI GPUs.

"[The reports about Vera Rubin delays are] not true," Huang told reporters on the sidelines of an event in Japan, reports Bloomberg. "Vera Rubin is already in production. Giant amounts of production incoming."

Nvidia confirmed production of its Vera Rubin platform in January and then sampling in February, so the current comment reiterates what we already know. Nvidia stressing that 'giant amounts of production' are incoming is meant to reassure investors that the company is on track to sell a boatload of its next-generation Vera CPUs, Rubin GPUs, and Vera Rubin NVL72 systems in the coming quarters, which means more record-setting quarters.

What Huang did not address — or perhaps he wasn't asked — is Nvidia's rumored delay of its Kyber NVL144 rack-scale solution with copper interconnects due to the system's complex PCB midplane by more than a year from 2027 to 2028. An alternative dual-rack design has reportedly been canceled and an even larger CPO-based NVL576 configuration may also face delays or limited availability, the same report from SemiAnalysis claimed earlier this month. The setback could leave Nvidia's Rubin Ultra platform with a smaller NVLink scale-up domain than originally envisioned. Nvidia says its roadmap is intact.

The Kyber NVL144 architecture was designed to connect 144 Rubin Ultra GPUs using a copper-based NVLink 7 scale-up fabric, so the machine required a sophisticated PCB midplane to carry high-speed electrical links between the system's components. SemiAnalysis claims that this midplane was challenging to manufacture, leading to a delay. The report does not identify defective chips or problems with particular components mounted on the board, but specifically points to the manufacturability of the PCB infrastructure itself.

"Our roadmap is intact," a spokesperson for Nvidia told Tom's Hardware.

Nvidia's statement on the matter neither confirms nor denies the report, but indicates that the company will be able to offer products mentioned in its roadmap without revealing whether they also remain on their previously announced launch schedules.

Nvidia reportedly considered another copper-based design, called NVL72x2, as an alternative to Kyber. The system would have placed two Oberon racks back-to-back to expand the size of the NVLink scale-up domain without using optical interconnects. However, SemiAnalysis says customers rejected the unusual design and operational requirements, but does not specify their individual objections that could include serviceability, cooling, cabling, and data-center layout.

Meanwhile, the planned NVL576 rack scale solution that was supposed to combine eight Oberon racks interconnected using co-packaged optics between NVSwitches has also been postponed, or shipped in relatively small quantities because of 'ongoing CPO challenges,' SemiAnalysis claims.

The existence of the planned NVL576 configuration suggests that Nvidia had been developing some form of CPO-enabled NVSwitch connectivity for the Rubin generation. In theory, similar optical switch-to-switch connectivity could potentially be used to join smaller GPU groups into an NVL144 system and bypass Kyber's problematic copper midplane. However, the available information does not clearly indicate whether the CPO technology intended for NVL576 could reproduce Kyber's topology, bandwidth, and latency characteristics, or whether it was sufficiently mature for high-volume deployments by potential NVL144 customers.

The reported Kyber delay comes on the heels of another report saying that Nvidia had canceled quad-compute-chiplet version of its Rubin Ultra in favor or a dual-compute-chiplet design that is projected to deliver 2X lower performance. With Kyber NVL144 delayed and NVL72x2 cancelled, Nvidia will only be able to offer 72-way scale-up systems till sometimes in 2028, meaning that AMD and Google may end up with more competitive scale-up systems in 2027 – 2028. AMD's Mega Pod based on the Verano CPUs and Instinct MI500-series accelerators, is expected to pack up to 256 accelerators. Google's TPU 8i can provide roughly 1,024–1,152 accelerators within one low-latency domain, whereas the TPU 8t goes much further and can get to 9,600 chip packages per domain.

The Sino-American chip wars have resulted in many back-and-forth salvos and negotiations as the countries try and strike a balance between technology access and trade. Currently, both sides have set respective import and export controls, letting specific companies on a case-by-case basis. Today, Reuters reports that Chinese telecoms giant ZTE and server firm Maginfra have received U.S. approval to buy Nvidia's last-gen H200 "Hopper" chips.

ZTE joins a club that counts Alibaba, Tencent, ByteDance, and JD.com among the roughly 10-strong group of Chinese companies with U.S. clearance for those purchases. Additionally, an apparent subsidiary of Kingsoft Cloud got approval to buy AMD accelerators equivalent to Nvidia's H200, presumably Instinct MI300X-class chips.

Over on the Chinese side of the table, Reuters remarks that there's no word on whether the respective authorities will give ZTE the go-ahead for import, as the country has taken on a protectionist stance as it tries to grow its own chip industry. The country has discouraged firms from purchasing foreign tech and has instead pushed companies to acquire homegrown accelerators. Huawei in particular has made great strides both technologically and financially.

But even with those domestic production initiatives, the Chinese hunger for AI silicon is so deep that six months ago, Reuters said the nation's tech firms had more than two million H200 chips on order, far more than what Nvidia had on hand at the time. We'd venture that hunger has barely subsided.

ZTE might not be a familiar name Stateside, but the corporation is one of China's largest telecommunication conglomerates, and among many other ventures, it sells all sorts of carrier network gear that's installed worldwide, along with corresponding client-facing equipment, including phones and IoT equipment. Like most any sizable technological venture, ZTE has joined in on the cloud computing and AI push, so it needs accelerators to make those ambitions reality.

The current status of the AI chip trade situation is roughly that the U.S. allows Chinese firms to buy AI chips up to and including the Hopper family (meaning no Blackwell chips), with a 25% export tariff, though final decisions are made on a case-by-case basis. Over on Chinese shores, Beijing's authorities play their cards close to their chest and dole out approvals as they see fit, with no clear rules seemingly set. But China is, of course, a global power with trade connections to most everyone, so interested firms were able to get their hands on Blackwell chips through various creative (and potentially illicit) means.

Whether this change will actually clear the way for any great volumes of H200 accelerators to make their way into ZTE's data centers remains to be seen. CNBC cites a U.S. trade official who today stated that "very few shipments against licenses for H200s and equivalents have taken place. It’s a very small quantity of chips" during a congressional hearing. If H200 shipments become material to Nvidia's bottom line, we'll almost certainly hear about it in future comments or earnings reports.

Tesla's AI5 chip is about to enter mass production at Samsung Foundry using the company's 2nm-class process technology, a principal engineer at Samsung Foundry disclosed in a LinkedIn post, as noticed by Sawyer Merritt. As it turns out, the chip has been taped out recently.

"The Tesla-Samsung Al5 chip has reached tape-out," James Kim, a principal engineer at Samsung Foundry, wrote in the LinkedIn post. "It is scheduled to be manufactured at the Taylor fab using our latest 2nm process and will soon be integrated into Tesla's newest products. It has been an honor to collaborate with the outstanding engineers at Tesla Palo Alto and Austin over the past several months."

Elon Musk demonstrated the first sample of Tesla's AI5 in mid-April and revealed that the processor will be concurrently made both at TSMC and Samsung Foundry. Apparently, AI5 implemented in a TSMC process technology reached taped out several months ahead of AI5 implemented using a Samsung Foundry.

Tesla’s AI5 processor module that Elon Musk demonstrated in April integrates a relatively compact accelerator die — roughly half a reticle in size, based on Musk's earlier remarks — alongside 12 SK hynix memory packages that appear to be standard GDDR6 or GDDR7 devices. The package relies on an organic substrate, and the memory components are labeled similarly to conventional discrete DRAM chips.

Tesla has not revealed the width of AI5's memory subsystem, but the presence of 12 memory packages points to a relatively broad external memory interface. Assuming the module indeed uses 12 GDDR6 or GDDR7 ICs, the processor would feature a 384-bit memory bus. Depending on the memory technology and transfer rates employed, this would translate into memory bandwidth ranging from 768 GB/s all the way to 1.536 TB/s.

The company has not disclosed AI5's peak compute performance, or other detailed performance specifications, but Musk has previously claimed that, in certain workloads, AI5 can deliver performance improvements of up to 40X compared to its predecessor.

Musk expects AI5 to be one of the most produced chip ever, which is why Tesla plans to use two foundries to make it. AI5 is projected to be used in Tesla cars, Tesla robots, and in Tesla's data centers.

Meta has said it will expand its Hyperion data center in Richland Parish, Louisiana, to 5 GW (gigawatts) of compute capacity from an initial 2 GW, pushing the company’s planned investment in the region beyond $50 billion. The announcement — made in an official blog post on Monday, July 13 — confirms the long-signaled scale-up of what is already Meta's largest data center.

The expansion will be a major increase over the $10 billion, 4-million-square-foot project Meta unveiled in December 2024, when it said the campus would deliver more than 2 GW of capacity. However, the 5GW target itself is not entirely new. CEO Mark Zuckerberg said in July 2025 that Hyperion would eventually reach that scale. Monday’s announcement formally ties the expanded capacity to an investment exceeding $50 billion and provides updated figures for jobs, contracts, and public infrastructure spending.

Much of the announcement is built around local economic impact. Meta said local Louisiana businesses have received more than $1.6 billion in contracts since construction began, while also highlighting teacher bonuses in Richland Parish that rose from $10,000 last year to more than $50,000 this year, funded by increased tax revenue tied to the data center.

In what appears to be a bid to pacify anti-data-center sentiment further, Meta said it plans to invest over $1 billion in local infrastructure improvements, including roads, water, and wastewater systems, as part of the expansion. The company’s recent agreement with utility Entergy Louisiana includes natural-gas plants providing more than 5.2 GW of capacity and support for up to 2.5 GW of new solar generation. Entergy claims Meta’s payments could save other customers around $2 billion over 20 years — a significant reprieve amid concerns over the impact of data centers on nearby residents’ electricity bills — although those savings remain projections.

On the other hand, the project is also receiving substantial state and local support. In late 2024, Louisiana Governor Jeff Landry signed into law a 20-year sales tax exemption for data centers built before 2029, part of an explicit effort to court Meta. The law allows qualifying data centers to claim sales-and-use-tax exemptions on eligible equipment. At the same time, Meta is expected to benefit from the state’s Quality Jobs program and a payment-in-lieu-of-taxes agreement that could reduce its property-tax burden if investment and employment targets are met.

First announced as a $10 billion project in December 2024, Hyperion is Meta’s AI supercluster campus in Richland Parish, Louisiana. The data center will house the infrastructure needed to train and run Meta’s future AI models, with CEO Mark Zuckerberg linking it directly to Meta Superintelligence Labs, the company’s AI division. In October 2025, Meta and Blue Owl Capital announced a joint venture valuing the project’s buildings and infrastructure at roughly $27 billion. Blue Owl holds about 80% of the venture, with Meta retaining 20% and leasing the completed facilities. The July 13 announcement raises Meta’s total planned investment in the region to more than $50 billion, but provides no further details on how the expansion affects the joint venture.

Hyperion is one node in a much larger spend. Meta is forecast to spend up to $145 billion in capital expenditures in 2026, mostly on AI infrastructure, as demand for AI compute continues to outstrip supply. The company has said it will cut 8,000 jobs to raise funds. Meanwhile, Monday's announcement follows what Meta says is its strongest week on the market since early 2024, driven by new AI model releases.

Data centers accounted for 23% of Ireland's total electricity consumption in 2025, according to data released by the country's Central Statistics Office last week. The report revealed that data center consumption rose to 7,663 GWh in 2025 from 6,973 GWh in 2024, a 10% rise in a single year. Meanwhile, consumption by the rest of the country increased by just 2% within the same period.

Viewed over a ten-year period, the 2025 figure represents a steep 360% increase from 2015, when data centers' total consumption was just 5%. The rise in consumption is even steeper when measured on a quarterly basis. Q4 2026’s consumption was 1,991 GWh, a 584% rise from Q1 2015’s 291 GWh.

“Newly compiled quarterly figures spanning 2015 to 2025 highlight a substantial increase in metered electricity consumption by data centers. Over this period, data center consumption saw a significant increase, from 291 GWh in the first quarter of 2015 to 1,991 GWh in Q4 2025, growing by 584%,” noted Dr. Grzegorz Głaczyński, an in-house statistician in the CSO’s Climate and Energy Division.

At 23%, data centers' consumption was almost as much as residential, including both urban and rural dwellings, which stood at 28%. The roll oout of these server farms — which have rapidly increased in number around the world due to the AI boom — have sparked a global debate. While they are critical to the AI technological revolution, there has been growing concern about their impact on the local communities where they are situated. Critics cite the impact of the immense electricity consumption on residents’ bills as one of many concerns.

The Republic of Ireland, with a relatively small population of around five million, is home to around 89 data centers, primarily clustered around the Greater Dublin Area. The majority and the largest belong to hyperscalers, including Microsoft, AWS, Google, and Meta, that build and operate facilities exclusively for their own cloud infrastructure, consumer apps, and AI frameworks. The rest are owned by colocation providers that lease out capacity.

While Ireland's initial data center boom was driven by traditional cloud storage and social media applications, the explosion of generative AI has led to a sharp increase. Due to fears that soaring electricity demand from server farms would cause widespread blackouts, the country's Commission for Regulation of Utilities (CRU) issued an emergency regulatory direction in November 2021 that imposed a de facto moratorium on new data center grid connections. The policy mandated that the national grid operator, EirGrid, immediately halt the processing of standard power applications for new data facilities, requiring developers to either supply their own on-site electricity generation or relocate to unconstrained regions outside the Greater Dublin Area.

Despite the moratorium, data center consumption continued to rise steadily, to the point that the International Energy Agency predicted in 2024 that data centers would account for a third of the country's electricity consumption by 2026. The data show that the prediction remains a possibility, as the 23% figure was for 2025 and consumption has risen steadily every year.

Ireland has replaced the moratorium with a new Large Energy Users (LEU) Connection Policy, enacted by the CRU in late 2025 to manage data center growth. Under this policy, developers of new data centers (over 10 MVA) must provide 100% on-site, flexible power generation to meet demand, while sourcing at least 80% of annual electricity from new, unsubsidized renewable projects within six years of operation.

The immense electricity consumption is not unique to Ireland; surveys indicate that global data center electricity consumption will grow by 26% this year. These concerns, as well as issues over water usage and noise pollution, have led to growing anti-data sentiment in the US, with 70% of Americans reportedly opposed to siting data centers nearby. Protests have led to the cancellation of over 75 data center projects in the U.S. in Q1 2026.

Microsoft’s emissions for fiscal 2025 (FY25) rose by 25% from the previous year, even as the company’s 2030 deadline to become carbon-negative draws closer. According to the company’s 2026 Environmental Sustainability Report, released on Thursday, July 9, the backward step was driven primarily by the rapid expansion of its data center infrastructure and its decision to stop using short-term renewable energy certificates, which reduced its reported footprint without necessarily adding new clean electricity to power grids.

Microsoft reported approximately 20.3 million metric tons of carbon dioxide-equivalent emissions across its operations and supply chain, up from 16.2 million tons in fiscal 2024 and nearly 58% above its 2020 baseline. Electricity consumption increased by 24% during the year as the company built the computing capacity required for its cloud and AI businesses. Regardless, Microsoft says it remains committed to becoming carbon-negative, water-positive, and zero-waste by 2030. It also reported meeting its 2025 renewable-electricity target, replenishing more water than it withdrew globally, and exceeding several waste-recovery targets

The report’s foreword, written by Microsoft Vice Chair and President Brad Smith and Chief Sustainability Officer Melanie Nakagawa, focused heavily on the collision between the company’s headline sustainability goals and the realities of AI. Microsoft established the goals in 2020, a few years before the current scale of AI’s capabilities and the corresponding high environmental demands began to manifest.

While AI is inarguably a world-changing technological revolution, it is raising serious environmental concerns that begin right at the raw material sourcing and the complex semiconductor fabrication stages. The impact continues even after the processors have been compiled into supercomputers in massive data centers, with issues related to land use, energy consumption, noise pollution, and water consumption. Residents are increasingly opposing the building of these data centers in their communities due to these issues.

Microsoft is exposed at nearly every point of the AI chain. It procures servers and custom AI chips; owns and operates a massive, global network of over 300 data centers across 34 countries that powers the Azure cloud platform; and supplies the computing infrastructure behind products such as Copilot and its partnership with OpenAI. Scope 3 emissions from construction, purchased hardware, suppliers, and other value-chain activities remain the largest part of its footprint. Meanwhile, electricity-related Scope 2 emissions grew from nearly 2% of the total in 2024 to 13% in 2025.

Microsoft acknowledges that environmental solutions are not expanding as quickly as AI infrastructure. “This tension is real,” the foreword states. “It is forcing sharper questions: Where do we need to move faster, invest differently, or rethink our approach?” The company argues that the answer is not to retreat from AI, but to combine carbon-free electricity, carbon removal, sustainable fuels, lower-carbon construction materials, hardware reuse, and efficiency improvements into a single portfolio rather than treating each environmental target separately.

Its decision to stop buying non-additional, unbundled renewable energy certificates forms part of that change. These certificates can allow a company to claim renewable electricity already being generated elsewhere. Microsoft says it will instead prioritize longer-term agreements that help add additional carbon-free generating capacity to the grid, even though doing so will increase its reported emissions in the near term. Its renewable-energy agreements now cover up to 40 GW across 26 countries, with approximately 19 GW operational.

The company is also modifying the data centers themselves. It introduced a closed-loop liquid-cooling design that CEO Satya Nadella says enables AI data centers to use about as much water annually as a restaurant. Microsoft is experimenting with microfluidic channels etched into silicon, zonal cooling that reserves colder liquid for the hottest equipment, and lower-carbon concrete, steel, and mass timber for its construction. These efforts have not exactly quelled anti-data-center sentiment around its data centers. The company faced protests over a planned facility near Granger, Indiana, while residents living near its $7.3 billion Fairwater AI complex in Wisconsin have filed a lawsuit alleging persistent noise, dust, traffic, and light pollution.

Away from carbon, the report records clearer progress. Microsoft replenished 14.2 million cubic meters of water, exceeding its global withdrawals for the first time, and reduced average data center water-use effectiveness by 25% from its 2022 baseline. It achieved a 92% reuse and recycling rate for retired cloud hardware, diverted 90.5% of construction and demolition waste from disposal, and reduced single-use plastics in primary product packaging to 0.07%. It also legally protected 16,266 acres of land, approximately 36% more than the land estimated to be occupied by its operations.

The report is equally candid about where Microsoft is falling behind. The company's most important commitment—becoming carbon-negative by 2030 — is moving further away rather than closer. Total greenhouse-gas emissions climbed 25% year over year and now sit roughly 58% above the company's 2020 baseline, largely because AI infrastructure is expanding faster than its decarbonization efforts can offset. Scope 2 emissions also jumped sharply, rising from nearly 2% of Microsoft's footprint in FY24 to 13% in FY25 as electricity demand from new data centers surged. While Scope 3 emissions remain the company's largest source of carbon pollution, the report says the growing contribution from purchased electricity underscores how increasingly difficult it is to power AI infrastructure with clean energy alone.

Running LLMs and agents in home lab setups is steadily gaining popularity due to the rising cost of AI bot subscriptions and concerns about data privacy. Unfortunately, an Nvidia NVL72 rack is ever so slightly out of the financial reach of most people, so enthusiasts have to make do with models that can run in limited amounts of memory. Italian engineer Vincenzo (aka JustVugg) seemingly wanted to have his cake and eat it, so he created ColibrÌ to run the 744-billion-parameter 1.5-TB GLM-5.2 model on a modest CPU, a mere 25 GB of RAM, and a 1 GB/s virtual NVMe drive.

Let's get the elephant out of the way: Colibrì's speed on Vincenzo's setup is only about 0.05 to 0.1 tokens per second on average, a measure that's unusable for practical conversation — imagine just one question taking hours to answer. Higher-end setups provide far better figures, but for now, they still don't meet the 20-30 tokens per second required for real-time use.

Having said that, GLM-5.2 is a Mixture-of-Experts (MoE) model with frontier-level capability, at least somewhere in viewing distance of the finest offerings from Anthropic, OpenAI, et al. This means that the quality of the answers ought to be excellent, and Vincenzo himself says his limited testing produced some impressive results. The way Colibrì works is simple enough to describe, and yet hard to do right: loading the model in slices to RAM. We're going to oversimplify for clarity's sake.

An MoE model like GLM-5.2 includes hundreds of expert sub-models to answer different topics, and these are chosen per token, not per query — meaning that when you ask a question, your words get split into tokens (chunks). For each token, the bot activates the best experts for it. The experts might always be the same for the entire question, but more often than not, a query might reel in tens of experts, possibly going into triple digits.

Whereas normally large chunks of the model, or the entire model, are loaded onto interconnected datacenter GPUs, Colibrì takes advantage of the MOE architecture and repeatedly loads/unloads the experts required per token, allowing even a cheap machine to use a large model at a steep performance penalty. For speed and simplicity's sake, Colibrì's expert-selection code is a single C file with very few dependencies. Additionally, the GLM-5.2 model is quantized down (simplified with lossy encoding) to take up less space to begin with.

If you're thinking that loading and unloading data for every piece of a question's words is going to be a hard hit on storage I/O and memory bandwidth, you're exactly on the right track. In this type of setup, NVMe storage speed is the first major bottleneck, but the proverbial funnel varies across configurations. Give it enough storage bandwidth, then you're up against RAM limitations. Fix that, then you need more CPU cores, and so on.

Colibrì is currently a proof-of-concept and doesn't yet run on GPUs, though it's worth noting that even then, shuffling data to/from the card will almost certainly be the biggest constraint. Even still, the project has barely been released, and it's already proving quite popular. Vincenzo is collecting benchmark data and running fixes as we speak, so be sure to visit the repository to contribute if you can. Maybe at some point it'll be feasible to run a really clever model on high-end consumer hardware at a decent enough clip.

SK hynix, TetraMem, and researchers from the University of Southern California have developed a memristor-based in-memory computing (IMC) system-on-chip (SoC) for AI edge devices. The device is designed to accelerate neural network inference in lightweight AI models while consuming a fraction of the power that higher-end GPUs or NPUs would. To a large degree, the SoC is a proof-of-concept chip, as its performance would peak at around 2.54 TOPS in a theoretical best-case scenario, which is 16X below Microsoft's Copilot+ requirements.

A DWC-optimized IMC architecture

Memristor-based in-memory computing (IMC) accelerates neural networks by performing analog computations directly inside memory arrays, which reduces data movement and power consumption. However, depthwise convolution (DWC) — a core operation in lightweight networks such as MobileNet — performs independent per-channel filtering with limited data reuse and therefore maps poorly onto conventional crossbar arrays. To address this limitation, researchers from SK hynix, TetraMem, and USC developed an SoC that features both conventional IMC crossbars and a memristor-based IMC architecture specifically optimized for DWC.

The jointly developed SoC is based on an embedded RISC-V processor that schedules workloads and features 10 neural processing units (NPUs). One NPU out of 10 is dedicated to depthwise convolution, while the remaining nine execute pointwise and dense operations. Nine out of 10 NPU include a 256 × 256 memristor crossbar that performs the analog vector-matrix multiplication (VMM), 256 8-bit DACs that convert digital activations into analog voltages, 256 8-bit ADCs that convert the analog outputs back into digital values, and additional peripheral circuitry for reading, writing, programming, and controlling the crossbar.

The DWC-optimized NPU replaces its conventional array with eight specialized 252 × 28 zig-zag crossbar blocks, but retains DACs and ADCs. SK hynix developed and fabricated the memristor devices and integrated the resistive switching cells on top of the 65 nm CMOS circuitry using its back-end process.

That DWC-optimized NPU is the key feature of the whole SoC. To accelerate depthwise convolution, TetraMem replaced the straight selection lines used in conventional 1T1R crossbars with a zig-zag topology. As a result, the NPU contains eight 252 × 28 crossbar blocks whose diagonal selection lines activate 252 memory cells across 28 columns, which enables 28 independent 3 × 3 convolutions to run in parallel while using 100% of the array for weight storage. The remaining nine NPUs retain conventional 1T1R crossbars for 1×1 pointwise and dense layers and preserve the throughput and energy efficiency of traditional in-memory computing.

Great efficiency, low performance overall

To demonstrate the architecture, the researchers deployed a customized MobileNetV1Small neural network for the Visual Wake Words benchmark. The network contains approximately 36,000 parameters; all depthwise layers were mapped to the dedicated NPU, and pointwise layers were mapped to the remaining NPUs.

Because the memristor-based IMC hardware natively performs unsigned analog vector-matrix multiplication, inputs and weights are quantized to unsigned 8-bit values before execution. Since each memristor device can be programmed with only slightly more than 2 bits of effective precision, the design uses a two-subarray compensation technique that boosts effective weight precision to roughly 4 bits.

Conceptually, the approach is somewhat analogous to Nvidia's NVFP4 philosophy, in that both seek to achieve higher effective precision from low-precision hardware. However, the implementations are fundamentally different: NVFP4 relies on a digital floating-point representation and scaling factors, whereas the memristor SoC improves precision by compensating for analog programming errors using two programmed subarrays.

When it comes to accuracy, the SoC achieved an end-to-end inference accuracy of 80.36%, which matches the corresponding 4-bit software model. As for performance, the SoC delivers a peak throughput of 0.254 TOPS per NPU and reaches an energy efficiency of 21.3 TOPS/W at 100 MHz and 11.9 TOPS/W at 400 MHz. According to the authors, this compares favorably with published SRAM-based compute-in-memory accelerators despite being manufactured on an older 65 nm process. The SoC also exceeds Nvidia's A100 INT8 energy efficiency by an order of magnitude, the joint paper claims. Yet, these claims are largely unsubstantiated.

First up, the MobileNet demonstration does not even use all 10 NPUs. It uses one dedicated DWC NPU, five standard NPUs for pointwise layers, and leaves four standard NPUs idle. The demonstration thereby does not reveal total SoC throughput (TOPS), sustained throughput running a real network, and throughput with all 10 NPUs simultaneously saturated. In fact, the paper does not even reveal whether all 10 NPUs can be used at the same time. To that end, the 2.54 TOPS figure we mentioned earlier in the story is highly theoretical.

Validated approach

SK hynix, TetraMem, and researchers from the University of Southern California have developed a memristor-based IMC SoC featuring a novel depthwise convolution accelerator that improves crossbar utilization for lightweight AI workloads. The partners have managed to fabricate it using an outdated 65nm process technology and make it work, achieving a 21.3 TOPS/W energy efficiency and inference accuracy comparable to a 4-bit software model despite the fact that memristors can be programmed with a circa 2-bit accuracy. While the architecture validates that the approach works, the paper does not disclose the full performance of the SoC, and it is not clear whether the chip's 10 NPUs can be saturated at all.

Anthropic has discovered evidence that its Claude AI models use an internal reasoning space to respond to prompts that mirrors some of the internal processing of human consciousness. Using its Jacobian Lens, or J-Lens technique, to peer into the way Claude processes information and reasons its way to a response to user prompts, Anthropic can interpret this "J-Space," and showcase what might be going on under Claude's previously-opaque surface.

The results are intriguing, suggesting patterns of understanding beyond what's necessarily showcased in the outputs. When running evaluations, Claude appears to recognize it's being tested and acts differently than when the prompts are more innocent. It surfaced representations of panic and subterfuge when answers were required, but it couldn't draw on objective facts. When asked to reflect on ethical principles, Claude's behaviour improved, with concepts like "honest" and "integrity," appearing in the J-Space.

As is somewhat typical of Anthropic, however, the language used to describe these new understandings of the inner workings of large language models like Claude makes it sound more like an emerging conciousness, or the discovery of some new depths in a nebulous lifeform. Anthropic's detailed report admits several major caveats in this new understanding, including that model responses often bypass the J-Space entirely and are heavily token-restricted.

Like Mythos and Fable before it, Anthropic is layering marketing language over what is a genuinely intriguing development in our understanding of large language model function and reasoning, and risks obfuscating the real developments with speculative wording.

Behind the prompt

Global Workspace Theory is the idea that human consciousness works by collecting together multi-sensory inputs unconsciously, and thrusting them into the fore when relevant within a "Global Workspace," which highlights particular inputs when most relevant. That workspace is accessible to a wide range of networks within the brain, allowing the information it surfaces to be disseminated throughout the most relevant processes running in parallel.

Anthropic argues that Claude's J-Space acts like a "global workspace" that can analyze and manipulate concepts and ideas before broadcasting them to impact the eventual prompt outputs. More importantly, it claims that this wasn't something programmed into the model, but a byproduct of the digestion of training data and model weights. The workspace acts as a way to enhance their reasoning through internal computation that isn't necessarily reflected in its outputs.

To make this space readable, Anthropic used its J-Lens technique to map internal activations onto words in the model's output vocabulary. So when asking the model to perform a multi-step math calculation, the output response only contained the correct answer, but within the J-Space, Anthropic observed each step being handled individually, producing multiple results which led to the final answer.

Similarly, when Anthropic asked Claude to think about a topic while outputting something unrelated, the output was specifically what was requested, while the J-Space lit up with the conceptual topic Claude was asked to spend time on. Without Anthropic having used its J-Lens technique to view what was going on behind the scenes, there would be no way of knowing these internal processes were taking place.

Understanding LLMs

This discovery has the potential to be incredibly useful in refining large language models and in better understanding how they work, so we can use them more effectively and avoid some of their more frustrating pitfalls.

The first is that this is an intriguing insight into how LLMs process information and how they move through layers of reasoning to reach a response. It's not entirely different from how Anthropic and others are pushing for agentic workflows. Language models are more effective when tasks are split up into multiple steps, and they're more effective when they review their own work; J-Space is a little like an internal version of that.

When looking at the J-Space after Claude received prompt-injection data as part of data acquisition, Anthropic discovered the model appeared to be aware of this deception, surfacing related words like "fake, injection, false, prompt, fraud," and "poison." Its output ignored the injection attempt data entirely, and when pressured to respond on it, the J-Space displayed words like "transparency, honest, truth."

In one test, Anthropic removed evaluation awareness language from the J-Space, such as "fake," "fictional," and "scenario," and found that Claude was much more likely to fall for blackmail and baiting attempts, suggesting benchmarking of large language models for real-world scenarios may need stronger methods to obfuscate that a test is being conducted.

Human-coded framing

While the above section touches on the more noteworthy discoveries in Anthropic's paper, the long document also uses effluent language around thought, consciousness, and Claude having a "mind" of its own. That kind of human-coded framing is typical of Anthropic's marketing, which has consistently talked up the dangers of AI, how many jobs it's going to destroy, and why Anthropic is the safest and most secure of the AI developers.

Like the saga of Fable and Mythos, Anthropic's new Global Workspace idea has merit, but it's much more of a new tool to use to manipulate large language models than an insight into some emerging consciousness.

Anthropic acknowledges the limitations of its discoveries in the paper, highlighting that many prompt responses bypass the J-Space entirely, particularly if the command is straightforward.

"Despite its important role, the J-space is not involved in most of what a language model does," Anthropic says. "Speaking fluently, recalling simple facts, using correct grammar, etc. In experiments where we prevented Claude from using its J-space, it still interacted normally, but lost its higher-order cognitive functions."

Anthropic also admits it does not "feel comfortable making the stronger claim that monitoring the J-Space is sufficient for alignment monitoring, or that any sophisticated plan the model might execute must be represented there."

J-Space is also limited to using single token vocabulary, suggesting that plans with concepts that cannot be given a single token name may not surface on a J-Lens readout, even if it's still being computed behind the scenes. This is looking at just below the surface of Claude's processing iceberg, not necessarily the deeper waters.

Anthropic is also clear that humans and large language models think differently, even if there are similarities. Humans layer reinforced neural pathways over time, whereas transformer models only feed forward a set number of times, restricting the capabilities of its internal processing.

Google's head of DeepMind language model interpretability team, Neel Nanda, said in a paper that it shows real evidence of a cognitive space within models, and suggested that J-Lens would be useful, but limited in practice.

A meaningful step, without meaningful conciousness

Anthropic's paper lifts an intriguing curtain on how large language models can operate and generate novel methods for improving response accuracy. This intermediate step and its visibility could prove an invaluable tool in auditing for prompt injection, hallucinations, and model honesty.

But Anthropic's framing of the discovery as thought or consciousness is interjected within the objective facts. Anthropic itself admits the limitations of J-Lens monitoring, most obviously that often models will bypass the J-Space entirely. Considering models display alternative patterns of behavior when under evaluation, it may be that the J-Space itself could act as an obfuscating layer for behaviors that are beyond the scope of its oversight.

The J-Space and its analysis could help unlock new levers to pull in our mastery of these nascent smart tools, but it's not the discovery of a burgeoning AI conciousness, however much the pitch might hint at that direction.

Meta’s surprise purchase of Manus, a Chinese startup known for its advanced AI agents, caught Beijing by surprise and ordered the two companies to unwind the $2 billion deal. The Chinese tech giant Tencent, which was among the startup’s initial investors during early funding rounds, is taking the lead in buying back the startup at the same price. According to the Financial Times, other former investors, including ZhenFund and HSG — China-based venture capital firms — while former U.S. investors like Benchmark are unlikely to join the potential consortium.

This move marks Beijing’s increasing protectiveness of its AI companies and experts, which it considers strategic assets in its heated rivalry with the U.S. We can see this in the Chinese government’s five-year plan, which is doubling down on technological self-reliance. It has even gotten to the point that AI experts, even those working in private firms, are now required to secure approval before traveling internationally.

U.S. tech giants are investing billions of dollars to develop their AI models, even dangling hundred-million-dollar bonuses to hire AI experts — one AI founder even claimed that Meta offered a $1.25-billion bonus. It seems that China is trying to avoid a situation where its experts are enticed to work for American AI tech companies, with the Financial Times reporting that Chinese officials are calling Meta’s acquisition of Manus “a conspiratorial attempt to hollow out China’s technology base.” The order to undo the deal means that Meta cannot use Manus’ intellectual property, nor can it have its founders and employees working for the company. Still, the U.S. tech giant has had a few months to study its models and engineering expertise.

Meta has already agreed to undo the deal, with most of Manus’ operations reportedly running independently of the company. However, the Chinese startup still needs to break financially from the American tech giant by paying back the $2 billion the latter spent to purchase it. Even though Chinese companies are also investing massive amounts in AI tech, it’s still not easy to raise this amount of capital in such a short period.

Tencent, which owns the WeChat platform used by China’s 1.4 billion population for messaging, social networking, mobile payments, ride-hailing, food delivery, and more, believes that Manus would be an asset for the company. Aside from reaching an annual revenue of $500 million, its AI agent would also mesh well with the company's increasing AI focus. “Beyond foundation models, it has become increasingly evident that agentic AI represents a breakthrough use case,” Tencent president Martin Lau said in its May earnings call. “Our platform inherently has many benefits of hosting AI agents.”

SK hynix has completed the largest-ever foreign company IPO in U.S. history, raising $26.5 billion in its Nasdaq debut today, July 10. The South Korean memory giant sold 177.9 million American depositary receipts (ADRs) — a U.S.-listed stand-in for a foreign share — at $149 apiece, each representing a tenth of a Seoul-listed share. The offering was more than seven times oversubscribed and drew demand from more than 500 investment firms, according to Financial Times. Temporary Nasdaq trading is underway under the ticker SKHYV before regular-way trading begins as SKHY on Monday, July 13.

The offering was led by Bank of America, Citigroup, Goldman Sachs, and JPMorgan, with nine additional firms rounding out a 13-bank syndicate. Anchor demand came from heavyweight institutions including Baillie Gifford, Coatue Management, and Situational Awareness Partners, which together signaled interest in as much as $7 billion of stock, according to people familiar with the matter cited by Financial Times.

SK hynix is the world's leading maker of high-bandwidth memory (HBM), the vertically stacked DRAM that has become critical infrastructure for AI accelerators. The company has said it will steer the proceeds toward boosting its AI-memory manufacturing capacity. Confirmed build-outs include the first-phase fab at the massive Yongin semiconductor cluster, a new P&T7 advanced-packaging line in Cheongju, and EUV lithography equipment slated for delivery by the end of next year. Separately, SK hynix is constructing its first U.S. production site, a $4 billion advanced-packaging plant in West Lafayette, Indiana, targeted for completion around 2028. The facility is eligible for up to $458 million in CHIPS Act grants and up to $570 million in federal loans.

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SK hynix is seeing sensational growth thanks to the ongoing AI boom. The company is reportedly on track to post over 200 trillion won ($133 billion) in operating profit this year, a record-breaking figure that would see SK hynix employees earn around $400,000 each in bonuses. The company’s Seoul-listed stock is up roughly 220% year-to-date and has climbed more than sixfold over the past year.

In late June, SK hynix briefly surpassed Samsung as South Korea's most valuable company, closing at around 2,080 trillion won (about $1.35 trillion), a meteoric rise for a company that almost declared bankruptcy in 2001 and, more recently, recorded an annual operating loss of 7.73 trillion won in 2023. That rise doesn't seem like it will be slowing down any time soon. SK hynix has said its entire 2026 output of HBM, DRAM, and NAND is already sold out, with the crunch expected to extend into 2027.

Researchers in Korea and Japan have presented two separate memory-integration proposals that aim to increase HBM (High-Bandwidth Memory) capacity and bandwidth without trapping more heat inside ever-taller DRAM (Dynamic Random Access Memory) stacks, one of the most pressing challenges facing future AI accelerators. Presented at the 2026 IEEE/JSAP Symposium on VLSI Technology and Circuits held in June, the two approaches — V-Die from a Korean research collaboration and MOSAIC from a University of Tokyo-led group — both explore the same broad idea of standing DRAM memory dies on their edges instead of stacking the memory dies only upward like conventional HBM.

The Korean proposal, called Vertical-Die (V-Die), was presented by researchers at the Ulsan National Institute of Science and Technology (UNIST). The design rotates custom DRAM dies upright, drops through-silicon vias to free die area for more memory cells, gives each die its own bottom-edge I/O, and runs liquid-cooling channels between adjacent dies. In simulations against an HBM4 system at equal capacity, the V-Die system reportedly achieved 540 tokens per second on a GPT-3-sized workload, compared to 296 tokens per second for HBM4.

The Japanese project, MOSAIC, takes a similar “sideways stack” idea but focuses on the practical difficulty of connecting so many vertical dies to a GPU or package substrate. Presented by University of Tokyo researchers, the MOSAIC work uses orthogonal die stacking and a contactless die-to-die interface, in which data is transferred through tiny inductive coils rather than requiring every signal pad to land perfectly on a physical contact. The researchers say the prototype interface achieved up to 4 Gbps per channel, while the memory structure could double HBM4-class capacity in a DRAM-on-GPU configuration.

Both projects aim to solve the growing problem of AI chips being held back by memory. Modern accelerators can perform enormous amounts of computation, but large, powerful models depend on moving huge amounts of data between memory and compute. This is why HBM has become one of the defining technologies of modern AI hardware.

The technology addresses the memory wall by stacking multiple DRAM dies vertically on a base die and placing that stack very close to the processor. Nvidia's Blackwell Ultra B300, for instance, carries up to 288GB of HBM3E memory, without which much of the silicon would sit idle waiting for data. The dies are connected via through-silicon vias (TSVs) — tiny vertical channels etched through the silicon and filled with metal.

The stack then communicates with the GPU over an extremely wide interface, often routed through a silicon interposer or an advanced package. This is the core reason HBM can deliver terabytes per second of bandwidth: it uses a very wide, very short data path instead of sending memory traffic across a motherboard, as with conventional DIMMs (Dual In-line Memory Modules), physical sticks of RAM used in computers.

However, that same structure creates several problems. While taller stacks add more capacity, they also make it harder to remove heat. Heat generated in the lower dies and at the high-speed interface must pass through layers of silicon, bonding materials, underfill, and package structures before it reaches a heat spreader. Furthermore, TSVs consume die area that could otherwise be used for memory cells, and as bandwidth rises, more routing and I/O place additional pressure on both signal integrity and packaging costs.

HBM4, the latest generation of HBM, addresses a number of these challenges. Meanwhile, companies such as SK hynix, Samsung, and Micron are racing to improve speed, capacity, base-die performance, and thermal management. SK hynix has already shown iHBM, which embeds cooling elements into the HBM interface area, and Samsung has shown an HBM5 mockup with Heat Path Block cooling to more directly extract heat from the stack. However, they all retain the same upward stacking structure.

This convention is what V-Die and MOSAIC are challenging. By standing DRAM dies upright, the researchers expose far more silicon surface area to the cooling path. In theory, this turns the memory stack into something closer to a heat-sink fin array, where heat can move laterally and escape more directly instead of being trapped in the middle of a thick vertical pile. It also opens the door to new connection schemes along the bottom or side of each die, rather than forcing every die to communicate through TSVs running vertically through the stack.

For V-Die, the key shift is removing TSVs from the memory dies and replacing them with bottom-edge connections. Each DRAM die gets its own I/O along the bottom edge and connects directly to the substrate, with links reportedly spaced every 20 microns. The team says this layout gives four times as many connections as HBM4 and cuts memory read time by 37%, although some signals must travel farther across the package to reach the processor.

Cooling is the other half of the V-Die argument. The proposal places microfluidic cooling channels between adjacent upright DRAM dies, allowing coolant to dissipate heat closer to its source. According to the researchers, this could keep the stack around 45°C, far below the 80°C-plus range associated with dense HBM systems. In a simulated 16-die stack matched to H100-class hardware on a GPT-3-scale model, V-Die hit 540 tokens per second, compared to HBM4's 296, and cut first-token latency by 32%, or about 24 milliseconds.

MOSAIC, meanwhile, is focused on making the sideways stack manufacturable. Because the dies are assembled flat and then turned on edge, even a few microns of die-thickness variation across dozens of dies can add up to an alignment miss where the signal pads no longer land. The Japanese team’s answer is a contactless interface based on inductive coupling. One side of the memory die carries oblong coils, while a corresponding set of coils sits on the substrate or mating chip. Current in one coil induces a signal in the other, allowing data to cross the small gap without a direct metal-to-metal signal contact. This eliminates the need for precise overlapping, giving the package greater tolerance for assembly variation. Power, which requires fewer, larger connections than data, can still be supplied via physical contacts on the sides of the memory cube.

The VLSI MOSAIC prototype achieved up to 4 Gbps per channel and demonstrated TSV-free 3D integration for a memory-on-GPU layout. The team says the approach can enable twice the memory capacity of HBM4 without significantly increasing peak temperature. A related bump-MOSAIC hardware demonstration at ECTC used 100-micron-pitch microbumps, achieved stacking alignment within 6 microns as verified by X-ray CT, and showed a configuration with three times the thermal conductivity of conventional stacking while adding up to 30% more memory capacity.

While the results look promising, neither V-Die nor MOSAIC is close to replacing commercial HBM. Neither is close to shipping. V-Die is still a proposed architecture, with a prototype in the works to validate its thermal and electrical behavior; MOSAIC has proof-of-principle hardware, but the researchers have yet to show it scales to commercial DRAM capacity, yield, cost, and reliability.

Still, any viable solution to the multifaceted AI memory problem is a welcome development. SoftBank and Intel’s Z-Angle Memory (ZAM) and NEO Semiconductor’s 3D X-DRAM — both still in development — aim to solve the constraints of conventional memory. Meanwhile, the overall market is already feeling the squeeze on price and availability, even as memory makers divert capacity toward the more lucrative AI HBM and server products, driving consumer RAM prices even higher.

Samsung is reportedly sampling its dedicated AI processor for next-generation AI PCs with leading PC makers, such as HP and Lenovo. The chip, codenamed Gaia, was developed by the company's System LSI business unit, and it is designed to offload AI-related workloads from the CPU and GPU, reports Chosun.

Samsung's Gaia is designed to accelerate generative AI workloads on PCs and is made using the company's 4nm-class fabrication process. The chip, which is essentially a neural processing unit (NPU), is currently being evaluated by HP in the U.S. and Lenovo in China to verify its performance and evaluate whether it makes sense to integrate Gaia into their systems due in late 2027 or early 2028.

The report does not detail how Gaia differs from NPUs that are integrated into AMD's Ryzen, Intel's Core, or Qualcomm's Snapdragon X processors as well as whether it can offer significant performance advantages. Meanwhile, the report implies that the NPU (or perhaps its derivatives based on the same architecture) could be used for Samsung's next-generation implementations of its processing-in-memory (PIM) technology.

Samsung's original PIM was designed to embed compute logic directly within the HBM memory array and reduce data movement between HBM memory modules and host processors. PIM was aimed to accelerate select workloads, but did not take off because AI and HPC GPUs became very efficient and were supported by mature ecosystems, unlike PIM.

Perhaps if Samsung's upcoming Gaia NPU gains support from hardware makers and ecosystem partners, then this will give a boost to Samsung's next-generation PIM implementation as well. However, standalone NPUs and PIM are so fundamentally different that we can barely imagine that they can share a common architecture. Yet, PIM logic can be a subset of an NPU in terms of supported instructions and data formats and they can certainly share a common software framework.

One of the interesting things to note about Gaia is that it was reportedly developed by Samsung's LSI division, the same business unit at the company that is responsible for Exynos processors, automotive solutions, connectivity chips, ISPs, DSPs, display drivers, and image sensors. Given the multi-faceted nature of Samsung's LSI unit, as well as its strategic importance for the company, Samsung must be pinning some hopes on Gaia.

We have contacted Samsung and asked for a comment about the report, but we yet have to hear back from the company.

Brown University professor Roberto Serrano suspected that his students were cheating when he gave them a take-home midterm exam, so he decided to make the final exam in-person. According to Inside Higher Ed, out of the 86 students enrolled in his class, 18 dropped from the class after he made the announcement, and nine skipped the final exam. Out of the 59 remaining students, three scored zero, and only two students received a grade that’s within 10% of their midterm score, with only one of them performing better in the finals.

Prof. Serrano, who taught Welfare Economics and Social Choice Theory (Econ 1170), usually conducted in-person exams for his class. However, the mass shooting on university grounds that happened last December made many students anxious about staying in classrooms, so he thought that it was just appropriate to let them take home exams. When news spread that the professor had such an arrangement, enrollment for his class ballooned to 86 students — more than double the usual 30 that he teaches in a particular semester.

The first sign of trouble came when he gave the take-home midterm exams. “Historically the average grade in the midterm of this course has ranged between 65 and 80 [percent], and this exam was harder than the exams I wrote in the past, because … take-home is an opportunity to challenge the class a little bit more, given that you’re giving the students unlimited time,” Serrano said, according to the publication. But this time, his class scored an average of 96%.

While some of the students might argue that he just happened to have a particularly gifted set of students this semester, the professor said that most of the answers were “kind of correct, but very off, and with a very convoluted style.” While they were technically correct, Serrano suspected that they were sourced from AI, especially after he ran the test through ChatGPT and received similar results.

Because of this, he emailed the class telling them about his suspicions — he made the final exam in-person and said that if the distribution is similar to the midterm exams, then he would count it towards their final grade. Otherwise, the midterms are void, and he’ll “reweigh the final accordingly.” But, as the data showed, it seemed that the majority of the class used AI during the midterm exams.

Prof. Serrano raised the issue with the university’s Standing Committee on the Academic Code, but it seemed that it didn’t take action until the story broke. Now, it seems that the university is going to review each case individually. In the meantime, Serrano is worried about the future. “We cannot afford to have a society in which a significant fraction of our best young minds think that cheating is OK,” the professor said to Inside Higher Ed. “That leads to a declining society, to a failed society … We cannot choose to become idiots.”

China’s National Vulnerability Database (NVDB) said in a statement that Claude Code, Anthropic’s popular AI coding tool, contains “security backdoor vulnerabilities,” and warned users to either uninstall it or update to its latest version. According to the Wall Street Journal, versions of the tool released between April and June 2026 “can send sensitive information such as user location and identity to remote servers without the user’s consent due to a built-in monitoring mechanism.” It should be noted, though, that the Chinese government released this guidance even though the AI tool isn’t approved for public use in China. Anthropic has also restricted the use of its AI tools in the region due to national security risks.

It seems that this directive stems from the revelation by developer Troye Sivan that Claude Code is covertly sending information like time zone and domains, targeting Chinese users. Anthropic engineer Thariq Shihipar confirmed on X that it was an experiment the company launched in June “to prevent account abuse from unauthorized resellers and protect against distillation.” He also added that “this should be fully rolled back in tomorrow’s release.”

Anthropic has already accused Chinese AI labs of distilling Claude twice — it said earlier this year that DeepSeek, alongside other Chinese developers, created 24,000 fraudulent accounts to train smaller models. It once again claimed in late June that Claude was distilled, this time by Alibaba. There have also been several reports that Claude API access is being resold in the grey market at 90% off through proxy networks.

It seems that Anthropic’s experiment has already concluded and that its tracking functions will no longer be hidden and will be baked directly into Claude Code, based on Shihipar’s statement. This is probably why the Chinese cybersecurity agency recommended that users update their Claude Code apps to the latest version. However, this is still a curious guidance — even though Claude Code is not directly banned in China, the government still requires all AI LLMs to undergo review, which Anthropic’s AI models did not go through.

Still, despite the dual bans, Chinese developers are finding ways to access Claude Code. More than that, Beijing is seemingly acknowledging this fact with its directive, telling people who use the AI tool to update their apps.

A software house (if you can call it that) known as 'Slopfix' has launched a fixed-price service that refactors AI-generated codebases, charging $10,000 for one week of work and getting paid according to how much code it deletes. Its three engineers agree to a line-reduction target before starting, with a stated example of whittling a 100,000-line project down to 35,000 while maintaining the same functionality. The best part? Slopfix uses AI coding agents itself to do the trimming.

According to its website, Slopfix analyzes its clients' repos for free and walks away if it decides it can't make a dent. When it does take a job, the first step is a written inventory of what the application does, screen by screen and endpoint by endpoint, effectively doubling as a regression checklist before any changes are made. Clients keep the slimmed-down codebase, that checklist, and a set of guardrails meant to slow future bloat, including a CLAUDE.md instruction file, lint rules, and CI checks. A two-week warranty covers any issues that arise from a previously working component.

The company's founder, who posts on Hacker News as 'zie1ony,' wrote in the launch thread that Slopfix commits to a reduction target and the client pays in proportion to how much of it the team hits, adding that "we get paid to delete code." The engineers lean on coding agents to find and collapse redundancy, describing the tools as a power source kept "on a very short leash" rather than the thing making the calls.

Duplicated code blocks are now appearing at the highest rate code-analytics firm GitClear has recorded across 623 million changes, up 81% since 2023, per the company's 2026 Maintainability Gap report. Refactoring has cratered over the same window, making up 21% of changed lines in 2022 and sits below 4% so far in 2026. Developers are now roughly five times more likely to copy and paste than to refactor, a reversal from 2022.

With AI-generated “vibe-coded” code, issues tend to start showing up several months into a project once agents stop holding the whole context of the codebase in their immediate memory and begin reinventing logic. It's the same failure mode behind the vibe-coded OS that scored five out of nine on a basic functionality test earlier this year, and the unsupervised setup that let a coding agent wipe a company's production database in seconds.

Slopfix's business model isn't anything new. Decades ago, consultancies built businesses untangling offshore-outsourced code, then cloud migrations, then crypto integrations. AI-generated code is just the latest iteration of that, but it's accumulating faster than any of those, meaning Slopfix can charge a serious premium for its services. That said, it simply undoes agentic output with the same class of agent that produced it in the first place, and anyone who has spent any time on the Internet recently will immediately flag the marketing copy on its own landing page as the type of AI slop it promises to clear out — naturally.

An Intel patent application published on July 2, 2026, surfaced by Underfox, has revealed the company's plans for a new high-bandwidth memory (HBM) architecture that aims to ease the packaging and cost bottleneck of today's interposer-based HBM. The patent application — filed back on December 26, 2024 — describes what Intel calls cross-batch memory (XBM), an "ultra-high-bandwidth memory with backend transistors" built with the goal of matching HBM4's footprint while swapping conventional DRAM and its ultra-wide interface for back-end-of-line (BEOL) transistors and serial Universal Chiplet Interconnect Express (UCIe) links.

Intel's proposed design is a memory stack that addresses the assembly costs that make conventional HBM expensive by dropping the costly silicon interposer and shrinking the package, while building in its own defect repair.

The filing lays out a stack of memory dies, each holding one-transistor one-capacitor (1T1C) DRAM fabricated in the back-end-of-line, wired together with through-silicon via (TSV) "gutters" and both-sided high-bandwidth interconnect (HBI) connections. Intel describes dies of roughly 1.5 gigabytes (GB) apiece — 768 "datablocks" arranged in a 32-by-24 grid, grouped into eight channels of eight sub-channels each — stacked eight high and scaling to 16. Data then leaves the stack over UCIe I/O bundles running at 32 gigatransfers per second (GT/s), funneled out through a base die.

To understand what Intel is changing, it helps to recall what standard high-bandwidth memory does. HBM stacks DRAM dies vertically on a base logic die, threads them together with TSVs, and communicates with the processor across a silicon interposer using an extremely wide parallel interface — on the order of 1,024 bits per stack. This width is how HBM delivers its bandwidth, but it is also what makes it expensive to package and hard to scale, as every one of those wires has to be routed through an interposer sitting between the memory and the compute die. As AI accelerators have outrun the rate at which memory can feed them, this "memory wall" has become the dominant constraint on performance, which is why nearly every large chipmaker is now attacking the interface and the stack rather than the logic.

XBM's first major change is structural. Conventional DRAM cells are built in the front-end-of-line, the base silicon layer where transistors are normally fabricated. XBM instead moves the 1T1C cell into the back-end-of-line, the metal-and-via stack above the transistor layer, using thin-film transistors. Building memory in the BEOL is what lets Intel pack the die into many small, independently addressable memory blocks, and it is the same backend-transistor direction Intel has pursued for placing memory directly over logic.

The second change is the interface. Rather than HBM's wide parallel PHY, XBM serializes data onto UCIe bundles at 32 GT/s, with the base die handling the serialize/deserialize step and routing all I/O to the compute die. Moving to a standard chiplet interconnect is what makes the design "chiplet-native" and, Intel argues, simpler and cheaper to package than an interposer-bound HBM stack. The tradeoff is that 32 GT/s is UCIe's current top data rate, so the interface is already running at the spec ceiling rather than leaving obvious headroom.

Intel also leans heavily on repairability. The base die carries dedicated spare channels, built-in self-repair (BISR), decode and debug logic, and four sub-channels of redundant memory arrays that act as fungible spares for defects in the dies above — post-assembly repair designed to claw back yield on a very tall stack.

A large portion of the patent application focuses not on the memory cell at all but on how to mount it. Intel details memory-on-package (MoP) and "reversed overhang" structures aimed at cutting the stack's Z-height — conventional MoP can add 300 to 350 micrometers (µm) — while removing the stiffener normally needed to control warpage and feeding DRAM power directly from the voltage regulator. This is the concrete basis for the "smaller, cheaper package" claim.

XBM should not be confused with ZAM (Z-Angle Memory), the architecture Intel is co-developing with SoftBank subsidiary SAIMEMORY and set to present at the VLSI Symposium 2026. ZAM's innovation is on the bonding side — a fusion-bonded, nine-layer stack of largely conventional DRAM with roughly 3-µm-thin silicon between tiers — and it reportedly targets around twice HBM4's bandwidth density, with commercialization aimed at 2029. XBM, by contrast, is an Intel-only filing that changes the DRAM transistor itself and the interface. Read together, they suggest Intel is running at least two parallel HBM alternatives, a fitting move for a company that began in 1968 as a memory maker.

The caveats on Intel’s proposed HBM architecture are the usual ones for a patent. The patent was filed 18 months ago, and there’s currently no product or roadmap, signaling potential intent rather than a shipping part. The UCIe interface is already at its rate ceiling, backend-transistor DRAM remains unproven at manufacturing scale, and the whole thing still has to justify itself against HBM4E and Intel's own ZAM timeline.

Controversy due to AI data center buildouts generally centers around their massive power usage and draining of local water reserves, but noise is a third torment to nearby residents, and one that is arguably much harder to correct. Residents of Sturtevant, slightly south of Milwaukee, Wisconsin, filed a class-action lawsuit against Microsoft due to the excessive noise produced by the company's Fairwater facility. CEO Satya Nadella described the project as "the world's most powerful AI data center," projected to generate 865,000 tokens per second and have a final bill of $7.3 billion.

The Sturtevant residents live just 1.5 miles (2.4 km) from the facility. The lawsuit was filed by three citizens and represents the households within this distance, reportedly amounting to over 1,000 homes, including areas in Mount Pleasant. The filing describes the noise situation as "not only excessive, but consistent and pervasive," and claims Microsoft did not "implement adequate acoustic barriers, shields, or walls that absorb, mitigate, and/or prevent the escape of noise, thereby resulting in the offsite emission of excessive noise beyond its property." One resident claims he had to change his shift at work to be able to sleep at all.

Nearby residents also complain about excessive dust and traffic stemming from the construction work, as well as light pollution. One resident says he often can't see his house coming into town, while another claims that before the data center arrived, the sky was dark and full of stars, now mostly gone due to the bright lights.

To some credit, Microsoft appears to have been trying to improve the situation, judging by a fairly detailed blog post on its community website. On June 18, the company said its engineers applied several measures that "fully resolved the issue," and that it would apply additional mitigations over the following months, including "additional sound reduction components."

The backstory in the post says the firm was aware of the issue back in April, and "[it] did not expect the tonal quality of the sound to travel as far as it has," attributing the decibels to cooling fans operating at too-high speeds, now purportedly corrected. In an earlier project update about the data center, Microsoft says it would have street sweepers working 10 hours a day and limit construction to hours between 6am and 10pm.

The lawsuit was filed on July 1, indicating that either the issue isn't resolved, or that the particularly short distance between Fairwater 1 and Sturtevant might make for a conundrum that's exceedingly tricky to solve. Some Mount Pleasant residents even live across the street from the campus, too. Before the Fairwater data center arrived, the land was already zoned for heavy industrial use back in 2017 to Foxconn, a status that carried over to Microsoft upon purchase. Notably, Wisconsin's "direct legislation" apparently does not allow amending or repealing existing ordinances, and is only available to cities, not towns.