Chinese firm Yangtze Optical Fiber and Cable Joint Stock Limited Company (YOFC) announced on June 16 that it had successfully completed the world’s first field trial of hollow-core fiber (HCF) wavelength-division multiplexing (WDM) capable of 1.2 Tb/s per wavelength over an ultra-long unrepeatered span. The trial — conducted in collaboration with state-owned China Telecom and optical equipment maker Dekoli — achieved an unprecedented aggregate transmission capacity of 51.3 Tb/s over roughly 128 miles (206.5 km) without signal regeneration.

These figures, which the collaborators describe as a new world record for unrepeatered WDM capacity-distance performance without remote-pumped amplifiers, were achieved using only erbium-doped fiber amplifier amplification. The demonstration was carried out under the framework of the National Key Laboratory for Advanced Manufacturing and Application Technologies of Optical Fibers and Cables.

The success of this trial marks a major leap forward in optical communications. What separates it from earlier HCF results is the combination of capacity, distance, and amplification approach in a live network rather than a lab. China Telecom had previously demonstrated 1.2 Tb/s over a single wavelength, back in July 2024, but only over a 20-kilometer span.

Elsewhere, researchers have pushed unrepeatered HCF spans past 300 kilometers, but at far lower capacities. Pulling 1.2 Tb/s per channel over more than 200 kilometers on a commercial cable, using only conventional erbium-doped fiber amplifiers (EDFAs) rather than the remote-pumped boosters typically needed to extend unrepeatered reach, is the remarkable new achievement YOFC claims.

Hollow-core fiber is a next-generation optical data transmission medium that is rapidly emerging as a leading candidate for high-capacity, low-latency networking. Unlike conventional optical fiber, which guides light through a solid glass (silica) core, HCF guides light through an air-filled channel. This structural difference offers several advantages. Light travels roughly 1.5 times faster through air than through glass, cutting latency. Furthermore, the air core sidesteps some of the nonlinear distortion and dispersion baked into silica. YOFC has previously claimed that its hollow-core fiber technology can deliver 31% lower latency, 47% faster transmission speeds, and near-zero optical nonlinearity compared with conventional solid-core fiber.

In theory, HCF's air core would enable it to carry far more data over far greater distances with fewer amplification points. The trade-off has always been loss. Commercial hollow-core fiber has historically run at higher attenuation than mature silica fiber, limiting how far a signal can travel before it needs a boost. This limitation has been narrowing, with the trial's 200 km-plus unrepeatered span being the latest milestone.

The collaborators achieved this through two main innovations: one at the system level and the other in the amplifier hardware. At the system level, they used a self-developed optimization scheme for per-wavelength rate and channel power allocation. Rather than pushing every wavelength at the same data rate and power, the system adapted each channel to the link conditions, enabling hybrid transmission across multiple data rates, channel spacings, and power levels. The companies say this helped reduce capacity losses caused by gas-absorption peaks inside the hollow core, a quirk specific to guiding light through air rather than glass.

On the hardware side, the researchers built a high-power amplifier using a cascaded dual-gain-unit architecture and a multi-element doping design, achieving a maximum output of 33.5 dBm (roughly 2.24 W) while maintaining flat gain across the operating band. That higher-power, flatter amplification helped stretch an unrepeatered span without resorting to the remote-pumped amplifiers the team was trying to avoid. Because pushing that much power over a live optical link carries a real risk of failure, the system was wrapped in safeguards, including optical-path power anomaly detection, automatic interlock shutdown, and alarm-linked response mechanisms to catch faults before they damage equipment.

The stakes in this trial, and in HCF more broadly, tie directly to the AI buildout. As hyperscalers race to stand up ever-larger GPU clusters, the network linking those clusters, inside data centers and across the long-haul links between facilities, is fast becoming the bottleneck. HCF's lower latency lets operators site facilities farther from expensive, power-constrained hubs without a speed penalty, while its capacity headroom helps move the enormous traffic AI training and inference generate. The same properties make it attractive for latency-sensitive workloads like financial trading.

That promise is already pulling in serious money, mostly from the West. Microsoft, which moved early via its 2022 Lumenisity acquisition, struck manufacturing deals with Corning and Heraeus in September 2025 to scale production across Azure. AWS has developed its own HCF, claiming a 30% latency improvement over standard fiber, and says it wants more than it can currently get. Corning also has fiber deals with Microsoft, Meta, and Lumen, and is expanding in North Carolina with Nvidia's backing. Trials like YOFC’s are closing existing gaps toward full, widespread HCF deployment, though China's progress largely sits outside the Western supply chain now forming.

Onsemi and Synaptics late on Thursday announced that they had entered into an agreement under which the former will acquire the latter in an all-stock transaction valued at approximately $7 billion. The takeover will transform Onsemi from a maker of sensor and power management semiconductors into a company with a rich portfolio of products for AI infrastructure, automotive, client, industrial, robotics, and AR/VR applications. To some degree, this will strengthen Onsemi's position as an integrated device manufacturer.

Onsemi's acquisition of Synaptics is highly strategic. Synaptics has a highly diversified product portfolio that spans from compute and processing solutions to touch and biometric sensors and from display solutions to wireless connectivity. By contrast, Onsemi is mostly focused on power management devices and sensors. While the company has other businesses too, they are by far not as significant as power and sensors. The two companies say that by merging their product portfolios, they will have key building blocks — power, sense, connectivity, compute, and control — to address the physical AI market.

In fact, with its Edge AI platform, which combines dedicated AI processors, neural processing units (NPUs), wireless connectivity technologies including Wi-Fi, Bluetooth, and GPS, as well as an open-source software stack, Synaptics arguably has a much more comprehensive product portfolio to address Physical AI than Onsemi does. Yet, Synaptics has been bleeding money in 2025 – 2026 as its revenue dropped sharply from the levels demonstrated in 2022. By contrast, while Onsemi's sales have been declining since 2022 and did not show any signs of rebound, the company remained profitable. This is perhaps why Synaptics agreed to be acquired in an all-stock transaction.

"Together with onsemi, we will combine Synaptics' strengths in AI-native compute, connectivity, and human-machine interface with onsemi’s leadership in intelligent power and sensing to offer customers integrated solutions and development platforms across every layer of the Edge AI stack, deepening customer engagement and expanding across a greater total addressable market," said Rahul Patel, chief executive of Synaptics.

Developing a comprehensive platform for robotics, or physical AI, applications may make the combined Onsemi + Synaptics company bigger than the sum of all parts. Today, many developers in automotive, industrial, and robotics prefer integrated platforms that combine compute, connectivity, sensing, power management, and software from a single supplier. Given how fast technologies are evolving these days, many developers simply do not have time to build their own platform from building blocks obtained from various suppliers, so the combined company may have better chances with an integrated platform than vendors without one. Back in the day, AMD acquired ATI to get chipsets and graphics, while Qualcomm acquired a dozen companies expanding its product portfolio and building all-new solutions.

"The next phase of [AI] innovation will depend on systems that can sense, decide, act and adapt in real time," said Hassane El-Khoury, chief executive of Onsemi. "This shift towards Physical AI will require Power, Sense, Connected Compute and Control to work together seamlessly. The addition of Synaptics helps position onsemi at the intersection of these four pillars, enabling us to capture a significantly larger AI opportunity that extends beyond AI data center and into edge applications. This transaction would add immediate connected compute capabilities, expand our software and ecosystem reach and position Onsemi to deliver greater value as customers increasingly seek intelligent systems."

Synaptics could benefit from onsemi's manufacturing capabilities, particularly for automotive and industrial products that use mature process technologies. However, given that these products have a very long lifecycle and customers in these industries do not like changes, we would not expect a rapid migration of Synaptics' portfolio to onsemi fabs. At least, not for these applications. Meanwhile, for client devices and for emerging applications, Synaptics will likely use Onsemi's semiconductor production capacity.

The acquisition has received unanimous approval from the boards of directors of both companies. Under the agreement, each Synaptics shareholder will receive 1.35 shares of Onsemi common stock for every Synaptics share they own, so Synaptics investors will hold approximately 12% of the combined company after the transaction closes. Based on the companies' average volume-weighted share prices over the previous ten trading days, the offer represents a premium of approximately 19%, which is less than stockholders tend to get when the company they own is taken over. One member of Synaptics' board is also expected to join Onsemi's board of directors. The deal is expected to be completed in mid-2027 after it is approved by Synaptics shareholders and various regulators.

OpenAI CEO Sam Altman said during a staff Q&A meeting that its latest model, GPT-5.6, is available in limited preview to only a small group of customers handpicked by the U.S. government. According to The Information, the federal government, specifically the Office of the National Cyber Director and the Office of Science and Technology Policy, asked the AI tech company to stagger the release of its latest model. While Altman did not mention how long the delay for the general release of GPT-5.6 will be, he said in a memo that he hoped it would happen in a couple of weeks. In the meantime, the U.S. government is granting access to the latest model on a case-by-case basis only.

Despite OpenAI’s agreement to the delay, sources say that Commerce Secretary Howard Lutnick called Altman to warn him against releasing GPT-5.6 to the public without prior approval from government agencies. “We’ve made clear to the U.S. government that this is not our preferred long-term model and will work with them and others in industry to achieve a more sustainable approach for future releases,” the OpenAI chief said in the Thursday memo.

This wasn’t the first time that an American AI lab has delayed the release of its frontier model due to security concerns. Back in early April, Anthropic released Claude Mythos Preview to select key institutions first, allowing them to prepare for the general release of the powerful AI model. It eventually built Fable 5, a watered-down version of Mythos with built-in safeguards to prevent misuse, and released it in June 2026. However, the U.S. government disagreed with the company’s belief that it was a safer model and put both Fable 5 and Mythos on an export control list just three days after it dropped. This meant that foreign nationals, even those who work for Anthropic, are banned from accessing the model. Since the company cannot enforce compliance, it just decided to pull the model completely from the market.

The increasing advancement of AI models has the White House scrambling to prevent them from falling into the wrong hands. This is especially true as it continues to compete with rival China for supremacy. Although the U.S. has taken steps like export controls to slow Beijing’s progress, many industry leaders believe that it’s only a matter of time before the East Asian country catches up. So, even though the Trump administration initially promised that it would reduce regulations to help AI advance much more quickly in the country, President Donald Trump has changed his tune and signed an executive order earlier this month that asks U.S. AI labs to give the government access to their latest models 30 days before it gets a general release.

However, this move has got some industry experts concerned. “…this escalation of government intervention is nothing to celebrate. It is horrible for the broader AI ecosystem,” Head of AI Policy and think tank Abundance Institute and former FTC Chief Technologist Neil Chilson said in their blog. “Continued arbitrary, unexplained deployment of export control authority will make companies slow-walk new models, depriving the public of powerful new tools. Every AI model, like all software before it, will have vulnerabilities that require patching. The US government should not hang a Sword of Damocles over every lab’s head, with no indication when it might drop or why.”

After Epic revealed its plan for Unreal Engine 6, CEO Tim Sweeney said in an interview that Valve should stop requiring developers from disclosing if they used AI in making their game. The Epic chief executive made the statement to PC Gamer as he was talking about the use of AI tools in game development. The upcoming version of Unreal Engine 6 will come with AI integrations, which will supposedly make it easier for programmers, developers, and everyone else working in the gaming industry to build games. However, the use of AI tools still carries a stigma among players to the point that titles with this disclosure get significantly fewer reviews and are often viewed less favorably.

He says that AI tools are useful for streamlining boring, repetitive, and menial labor, like reviewing code for over an hour to find an error or doing the rigging work required to make a 3D model move realistically. While it was unfortunate that some AI tech companies trained their models on stolen data, the Epic chief executive said that the AI industry has changed and is now moving towards better practices, especially when it comes to training data. He even pointed at Adobe, which he says is ensuring the provenance of the data it used for training its AI. However, PC Gamer pointed out that Epic uses Nano Banana and GPT Image, which do not claim stringent AI training data controls, and that the Unreal Engine’s AI integrations include models such as Gemini, which have been accused of copyright infringement.

“I think the main usage case that we've seen within Epic, and we're seeing developers actually find gainful, is using AI to reduce the drudge work. The software is still architected by software architects, and they're still writing the important parts of code, and artists are still coming up with a creative vision for characters, deciding between concepts,” Sweeney told the publication. He also added, “If you want to launch a game, and get it as widely publicized as possible, you've got to put it on Steam so people can wish list it, and if you want to play it on Steam, then you have to get this Scarlet Letter of AI attached to your product, and now there is a hater community trying to kill the game. I think it's really irresponsible of Valve. They shouldn't do it, because it makes it much, much, much harder for a game developer to have a chance of success.”

The Epic executive has a point here, as AI can indeed be a useful productivity tool if used correctly and ethically. But even if that was the case, Valve still requires developers to add the AI-generated content disclosure, which could have a negative impact on reviews and ratings. While the Steam platform does not require the disclosure of the use of AI-powered tools in the developers’ workflow, they’re required to note it if AI-generated assets appear in the game or in marketing material. This would presumably include assets that have been partially built using AI tools or were based on AI-generated content.

According to market research platform Game Oracle, titles that had the generative AI disclosure received 53% less reviews than the same type of game that did not have it. Furthermore, it’s more likely to get a negative review. However, it also conceded that several other factors could be at play here, like studios substituting creativity and the proper development process with AI tools, resulting in titles that are obviously AI slop.

Sweeney argues that AI is just a tool for productivity that could help game developers create unique content. The AI disclosure warning on Steam games, in theory, should not influence how games are perceived — after all, what should matter to gamers is the experience of playing the game. Unfortunately, the controversies surrounding AI are negatively affecting the reputation of titles that use these tools. When paired with studios that substitute AI for real creativity, it’s understandable why this AI warning has such a negative impact on game titles.

The era of AI tokenmaxxing may be well and truly over. Alongside stories of Amazon cutting its AI leaderboard and an unknown company blowing through $500 million worth of tokens in one month, leaked audio has emerged from consulting firm Accenture as it tries to figure out how to rein in rampant token spend at client companies, 404Media reports.

In leaked audio, Accenture acknowledges that certain trivial tasks being offloaded to AI are causing massive token overspend, especially when agentic AI is part of the mix. The staff in the meeting clearly recognizes that not only is AI spend growing out of control at companies heavily adopting the technology, but that there is very little way to predict how much any tasks would cost, or whether there is real value in using AI to complete them.

Accenture has previously been incredibly bullish on AI, even encouraging employees to use it so much that if they didn't, they risked missing out on promotions. But that seems like a policy destined for the AI history books, as Accenture is now clearly aware that it's overspending on AI, and many of its clients are too.

From tokenmaxxing, to token hoarding

For much of the past year, many companies have charged full speed into an AI-heavy business strategy. Amazon had an AI leaderboard, and Nvidia's CEO Jensen Huang said he'd be alarmed if engineers weren't spending at least 50% of their annual salary on AI tokens.

Anecdotally, I know a number of software developers and data engineers who have been encouraged to use AI as much as they can. They have token limits, but they have been encouraged to use all of them and find new ways to do it, too.

This is leading to runaway token spending, something Accenture is seeing in its client data. Accenture’s agentic AI strategy lead, Justive Kwak, was quoted in the audio saying: "What we’re seeing right now is just rapid escalation in AI token spend [...] as companies start to scale AI, moving from like simple chatbots into use cases that feature agentic workflows and automation and then enterprise-wide deployment of some of these tools like Copilot, Claude Code, and Codex."

This isn't something that will be contained to just a few firms, either, he said. “It’s really not a niche problem. It is a problem that every enterprise will face if they are bullish on AI, if they haven’t already,” he said, adding that token spend was increasing, “exponentially, as more and more people are starting to use AI.”

But that may be starting to change. Amazon canned its AI leaderboard - it's rumored to be the mystery company with a half-billion dollar AI spend in one month - Uber is capping AI use to cut costs, and Axios reported at the end of May that a number of CEOs and companies were switching to more affordable models, and more closely monitoring employee usage.

Some software developers I know have been using the "caveman" trick to reduce token spend. Even OpenAI CEO Sam Altman said that he was aware token costs were becoming a huge concern for people.

This all comes in the aftermath of the move by many of the major AI providers to token-based billing. Where previously subscriptions offered very favorable rates for AI use, suddenly companies were having to pay for the tokens they input, and the tokens the AI output - even when it was verbose, or made mistakes, or required follow-up correction.

As the Accenture call shows, it's making even some of the most AI-bullish organizations question their usage, because measuring the spend and the return on that investment is proving all but impossible.

As Kwak said in the leaked audio, "Leadership, especially at the CFO, COO, and CIO level, are still asking the question of whether they’re getting value from what we’re spending on in the context of AI.”

How do you measure return on investment?

Although large language models are proving to be extremely useful in niche cases, their effectiveness at a broader range of tasks is more nebulous. Especially when it comes to financing it. When managers and executives look at AI budgeting and a return on that investment, it's hard to square away the numbers.

When you can't know how many tokens a task will take to complete, or whether the task will be completed effectively on the first, second, or third attempt; when you can't completely control the length of the output, or know whether that output will be wrong, or a lie, or just a random hallucination, how do you measure return on the investment in that tool?

"We’re hitting this inflection point where AI is becoming material to the cost structure; spend is becoming very unpredictable," Accenture's Kwak said during the meeting. Although the overall bill of AI costs is visible, he suggested, finding the specific value attributed to that token spend was not.

This seems to have created a culture of task hierarchy within Accenture, where some tasks are deemed more worthy of AI token use than others. When Kwak positioned himself to show some slides during the meeting, Accenture's client group lead, Stuary Henderson, joked that he hoped Kwak didn't use AI to convert a PDF into images and then markdown files.

“I’m learning that’s one of the big token chewers," he said. “Turning PDFs into markdown: is that right?”

Kwak agreed that Accenture data did show some tasks being completed using AI that didn't really need it, and were using unnecessary tokens because of it. Much of that problem, he suggested, was down to non-technical staff overusing it.

“We’re seeing from some of the data internally at least that it’s actually not our engineers that are driving the token consumption. It’s a lot of the non-engineers that are doing some of those behaviors."

Now that Accenture has encouraged heavy AI adoption among its clients, it finds itself in the bizarre position of having to discourage it or at least encourage more studious use of it. It now sees its next opportunity as a way to advise clients on how to "think about token economics."

It's working on a tool called "Token IQ" to help advise clients, according to the call, but hasn't made any announcement so far.

What's clear from the Accenture leak and actions of some of the major tech companies, which have previously been so bullish on AI use, is that the finances of mass AI adoption at the per-token scale don't line up. Without a clear way to measure the return on AI investment, we may find even the most tokenmaxxing companies look to restrict access and spend through the rest of 2026 as they re-address AI strategy.

Anthropic, the American AI lab that created one of the most advanced large language models available today, has said in a letter to the U.S. Senate that Chinese AI tech giant Alibaba has illicitly used Claude to train its own models. According to Reuters, the company sent the letter to Sen. Tim Scott (R-S.C.), the chair of the U.S. Senate Banking Committee, and Sen. Elizabeth Warren (D-Mass.), the ranking member of the said committee, before a scheduled hearing set to tackle AI issues.

This isn’t the first time that Anthropic has accused Chinese AI labs of “stealing” the capabilities of its AI model to train their own. Earlier this year, the company claimed that DeepSeek, Moonshot, and MiniMax used 24,000 fraudulent accounts and made 16 million exchanges collectively to train their own AI LLMs on Claude’s output. This method of training AI using the output of a more advanced model is called distillation, and while there are legitimate uses of this technique, such as when a frontier AI model is distilled to create a lighter, cheaper version of itself, it argues that competing labs can also use the same technique to build their own models at “a fraction of the time, and at a fraction of the cost, that it would take to develop them independently.”

The American AI lab says that it traced the distillation campaign back to operators that had connections with Alibaba, one of the largest Chinese tech companies often compared to Amazon, and Alibaba Qwen, its AI lab. Anthropic warns that distillation may help China create a frontier AI model that could match Mythos Preview’s capabilities — something many American lawmakers are afraid of.

While U.S. tech companies still enjoy an advantage when it comes to the latest AI models, Chinese tech companies are quickly catching up. In fact, Elon Musk estimated that a Chinese AI lab would have achieved a Fable 5-class AI model by the first quarter of next year, but the CEO and founder of Chinese AI lab Z.ai confidently replied, “won’t take that long.” Aside from that, many enterprise users are slowly switching to more affordable open-source Chinese LLMs for their agents as token costs spiral out of control, reserving the most powerful (and expensive) American models only for the most complicated tasks.

Both the U.S. and China are pushing hard to achieve AI supremacy, with the two rivals taking steps to reduce the advantage of the other. For example, Washington has been using export controls to limit Chinese access to advanced hardware needed to build the most advanced chips and for training AI, while Beijing countered this with its own controls on rare earth materials, some of which serve as key ingredients in chip making.

The so-called memory wall is a major performance limiter for many AI workloads, and high bandwidth memory (HBM) is not always a panacea since compute capability is growing faster than memory bandwidth. Qualcomm on Wednesday introduced its HBC near-memory compute architecture called high-bandwidth compute (HBC) that is designed to break the memory wall and enable the performance of certain AI workloads to scale linearly.

Qualcomm's approach to near-memory compute is pretty much straightforward: the company disaggregates the AI accelerator from the system-on-chip (SoC) and puts it under the LPDDR DRAM stack. The HBC accelerator connects to the LPDDR stack using through-silicon vias to provide maximum bandwidth and capacity without using expensive HBM memory and advanced packaging. Qualcomm does not disclose the actual bandwidth HBC provides, though the company claims that it offers 6X higher bandwidth-per-watt compared to HBM and over 200X capacity compared to on-chip SRAM.

"We have separated the AI accelerator from the XPU and placed the XPU directly beneath a DRAM stack," said Tony Pialis, Executive Vice President and General Manager of Data Center Business at Qualcomm. "This is very important because it gives us the performance advantages of SRAM with the density and capacity of stacked memory. In effect, the congestion associated with HBM is gone. The value to the industry is lower power consumption, less heat, and the elimination of the costly silicon interposer used by HBM solutions. We can also deploy multiple HBC stacks within a single compute device using standard packaging, which delivers a significant performance-per-cost advantage."

Putting DRAM on logic or next to logic is nothing new. All DRAM makers have experimented with near-memory compute architectures, but have failed to make them popular. More recently, GUC, a fabless ASIC design service company, proposed its DRAM-on-Logic (DoL) technology that places one to four DRAM layers on top of logic to get around 5 TB/s of memory bandwidth and offer higher performance than some HBM3E memory subsystems without using expensive advanced packaging and HBM3E stacks.

Since Qualcomm does not disclose actual performance numbers, it is hard to compare its HBC to GUC's offering. However, the biggest caveat about HBC is that Qualcomm does not tell us what the HBC accelerator actually does. In theory, it could be everything: a transformer-specific near-memory engine, a more general array of tensor cores, or some kind of preprocessing logic for AI inference or training.

Along with its HBC technology, Qualcomm also disclosed its HBC roadmap. While the company's AI200 accelerator, due later this year, will rely on LPDDR5X and offer 43 TB of RAM per rack, its successor AI250 will rely on the 1^st Generation HBC that will offer 18X bandwidth of AI200. The AI300 will use 2^nd Generation HBC that will provide 54X bandwidth of AI300.

OpenAI and Broadcom have introduced Jalapeño, a custom-built inference processor designed specifically for modern large language models and future agentic AI workloads, which is designed to deliver performance per watt they claim is higher than today's leading-edge hardware. OpenAI considers its hardware project a strategic one and envisions Jalapeño to be the first generation of its inference hardware.

Not another AI accelerator

OpenAI stresses that Jalapeño is a purpose-built inference ASIC and not a repurposed training accelerator or a general-purpose AI processor. OpenAI says the architecture of Jalapeño was designed based on its understanding of LLM behavior and is meant to address practical bottlenecks that matter for inference at scale, including costly data movement, balance between compute and memory resources, networking efficiency, and overall behavior. OpenAI also states that the design of the processor is meant to wed high throughput with low latency (which is why it uses a huge compute chiplet and HBM memory and not cheaper types of DRAM like many other inference accelerators), which will be particularly handy for reasoning and agentic workloads.

In addition, OpenAI and Broadcom claim the processor is built to deliver higher effective utilization than conventional AI accelerators and deliver performance that is close to the theoretical maximum, which means very high efficiency both in terms of costs and in terms of power. Meanwhile, the companies did not disclose performance targets for their Jalapeño ASIC, so these claims should be taken with a grain of salt.

Engineering samples are already operating in the lab at target clock speed and power (though Broadcom and OpenAI do not disclose details about this, either), and OpenAI says it is running machine learning workloads, such as GPT-5.3-Codex-Spark.

The two companies also claim that early internal testing indicates that Jalapeño's performance-per-watt is substantially better than 'current state-of-the-art hardware,' although no hard numbers, benchmarks, memory configuration, or other details are disclosed, so again, we will have to take the claims with a grain of salt. In addition, one must bear in mind that while Jalapeño can purportedly beat existing AMD's Instinct MI350-series and Nvidia's Blackwell-based accelerators, it remains to be seen how competitive it will be against AMD's Instinct MI400-series and Nvidia's Rubin-based offerings.

"Jalapeño was designed from the ground up for LLM inference using detailed insights from our close collaboration with OpenAI researchers," said Richard Ho, who leads OpenAI's hardware program. "We optimized the architecture around the kernels, memory movement, networking, and serving patterns that matter most for frontier AI models. Based on early testing, Jalapeño will efficiently execute our most important workloads close to the hardware’s theoretical limits."

A massive chip with six HBM modules

While Broadcom and OpenAI did not disclose specifications of Jalapeño, they did show its wafer and packaging, so we can do a brief analysis. The package appears to contain one large compute chiplet surrounded by six HBM modules and another chiplet that likely packs input/output interfaces and is surrounded by two structural dummy dies.

The wafer image does look like a Broadcom-style systolic-array-heavy accelerator, in the sense that it shows a very regular, repeated, columnar floorplan with what looks like replicated compute regions and fixed infrastructure macros. Yet, keep in mind that we are speculating, and the image is not clean enough to say that this is definitely Broadcom's standard TPU-like systolic array template with some perks from OpenAI,

From the image alone, it is impossible to tell whether Jalapeño uses a true 2D systolic array, a set of 1D/2D matrix engines, a collection of vector or tensor tiles, or some other inference datapath. All we can say is that the die has a highly repetitive floorplan consistent with several kinds of tiled AI accelerator architectures.

What we can tell from the image is the approximate die size of Jalapeño's compute chiplet based on the size of HBM3/4 packages (10.975 mm × 10.975 mm) that surround it. From what we can tell, the chiplet measures 25.46 mm (width) × 33 mm (height), which means that its die size is around 840 mm², which is very close to the reticle size of EUV lithography systems (858 mm²). Given that the quality of the shot is poor, the die size we estimate cannot be 100% accurate, but we suspect it is close enough.

The die size of Jalapeño's compute chiplet implies that it packs quite a lot of compute oomph, though, of course, we cannot make performance estimates based on this metric. Yet, it is safe to say that Jalapeño's compute die is considerably bigger than compute dies of other inference accelerators on the market and more resembles processors for AI training. Speaking of processors for AI training, we increasingly see multi-chiplet designs for these workloads as companies like AMD and Nvidia want to pack as much performance as possible. Meanwhile, the fact that OpenAI and Broadcom chose to go with a large compute chiplet possibly indicates that they wanted to reduce latencies by as much as possible.

Designed in nine months

The companies say the chip reached tape-out in just nine months and is slated for deployment beginning in late 2026, which represents an extremely fast turnaround time in ASIC design. It is unclear whether Broadcom and OpenAI extensively used artificial intelligence to define and then develop Jalapeño, though the companies admitted that they used OpenAI's models to speed up parts of the chip's design and optimization work. Typically, it takes 1.5 – 2 years to design an ASIC from scratch, so AI can shrink the development cycle. Another means to accelerate the design cycle is Broadcom's extensive reuse of its logic across different custom designs to deliver new chips faster than other companies.

It is noteworthy that, according to the announcement, Jalapeño is designed to support not only OpenAI's own workloads but also present and future LLMs across the industry, which potentially lets OpenAI sell its hardware to third parties, assuming that it can get enough supply from Broadcom and TSMC. Meanwhile, the chief executive of Broadcom indicates that Jalapeño will be deployed at gigawatt-scale data centers with Microsoft and other partners starting this year, though it is unclear whether the processor will be used exclusively for OpenAI workloads or will be available for other tenants as well.

"Our collaboration with OpenAI represents a fundamental commitment to scaling the physical infrastructure required for the next decade of AI," said Hock Tan, President and CEO, Broadcom. "This is just the beginning of a multi-generation roadmap. By co-developing our industry-leading silicon directly with OpenAI, we are enabling the deployment of gigawatt-scale data centers with Microsoft and other partners beginning in 2026."

Meta has suspended an internal AI training program after an internal data leak exposed sensitive employee information company-wide, according to a Business Insider report on June 22. The program, introduced in April, was designed to help Meta train AI systems on real employee workflows by gathering data, but has now triggered internal backlash over privacy and data security.

Screenshots obtained by Business Insider showed that data collected through the program was more broadly accessible within Meta than intended. The exposed information reportedly included private employee conversations, performance-related data, transcriptions, and activity records. Meta classified the incident as a SEV 2, on an internal scale of 0 to 5, where SEV 0 is the most severe.

A Meta spokesperson confirmed that the company has paused the program while it investigates the incident. "We have carefully designed this program with privacy safeguards, and while we have no indication at this time that any data was improperly accessed by Meta employees, we're pausing it while we investigate," the spokesperson told Business Insider. The incident does not appear to be an external hack but rather an internal data mismanagement.

Meta introduced the program, called the Model Capability Initiative, to monitor employee behavior for use in improving its AI models. The program, which the company reportedly made mandatory for most staff, collected data on employees’ work activities, including keystrokes, mouse movements, conversations, transcripts, and performance-related information.

Employees were reportedly uncomfortable with the idea of their keystrokes and mouse movements being recorded for AI training. Now they're finding out the data may not have been properly protected, and was widely accessible across the company rather than restricted to intended viewers.

Screenshots reviewed by Business Insider reportedly showed employees criticizing the failure to lock down the data from the start. “I am incensed,” one employee wrote in an internal group, according to the report. Another said there was no evidence of malicious access, but called the lack of promised restrictions “super frustrating.”

The episode is the latest in a frustrating stretch for Meta's workforce. The company has cut thousands of jobs in part to fund AI infrastructure behind more powerful AI systems; the same class of systems that Meta and other companies are deploying to replace workers. Building these models also requires vast amounts of training data, and Meta turned to its own employees to supply it, a move most employees were reportedly against. Now these employees have learned that the data they were compelled to hand over was not adequately secured, leaving it exposed to much of the company.

Californians pay the highest gas prices in the U.S., and a proposed class action says that the issue has been exacerbated by an AI tool that smartly squeezes customers for the best profits. A newly filed lawsuit at the Sacramento, ​California, federal court says that gas station operators are using Kalibrate’s AI tool, which uses data from nearby competing gas stations, to drive up prices by as much as 30 ​cents a gallon in some areas, reports Reuters.

On Monday, gas station operators including BP, Circle K, Marathon Petroleum, 7-Eleven, Walmart, and Albertsons were named as defendents in the headlining class action, alongside Kalibrate. By implementing the AI-driven price-optimizing tool, these operators have allegedly violated the Cartwright Act, California’s main antitrust law, as well as Assembly Bill 325. The latter is a California law that was put in place at the start of 2026 to crack down on algorithmic price fixing. An open-and-shut case, then?

Looking at the numbers, it is easy to understand why the Californians have been spurred into legal action. AAA figures suggest that California residents pay an average of $5.58 per gallon for regular, which is already much higher than the $3.93 national average. Where Kalibrate’s AI tools are used to adjust gas pricing, pump prices have risen as much as 30 cents per gallon, say the complaints. The result is some operators charging as much as $7 a gallon, notes the source report.

Gas station operators “have conspired to ​put an end ​to competition”

The key compelling argument behind this class action is quoted by Reuters from the case files. “While families struggle to afford the commute to work, defendants have conspired to ​put an end ​to competition, joining ⁠an AI-powered trust to ensure that no matter where a driver turns, the price for gasoline is artificially ​high,” says the complaint.

Currently, it is easy to argue that the rise of AI hasn’t fulfilled its early promises. Sifting through our headlines, it has sparked the RAMpocalypse, and other key PC components like SSDs and GPUs have also been impacted by AI server demand. Moreover, we have seen huge environmental impacts from those AI servers straining infrastructure that is sometimes already under pressure, like electricity generation and water resources. They also cause heat and noise pollution, so people don’t want to be anywhere near them. Then there are the applications we have seen AI used for, thus far. Instead of cancer cures and smart government, we’ve got higher gas prices and divisive social media bots. The complainants are seeking unspecified damages.

Oracle reduced its global workforce by 21,000 employees — approximately 13% of its staff — during the ⁠2026 fiscal year ending May 31, 2026. The tech giant officially disclosed details of the cuts in its annual financial regulatory filing on Monday, June 22, explicitly stating that AI adoption and automation directly replaced numerous roles. “The adoption and deployment of AI technologies across our operations have resulted, and may continue to result, in reductions to our workforce," the report said. Conversely, multiple reports indicate that the layoffs are mainly a capital reallocation strategy, as Oracle moves into AI infrastructure.

According to the filing, the company ended the 2026 fiscal year with 141,000 employees, down from 162,000 at the same period last year. Oracle claims the restructuring cost it $1.84 billion in severance payments and other related costs, nearly 400% higher than the restructuring bill in the previous financial year. It also said in its filing that the cuts were due to various factors, including management and product changes, performance issues, and broader re-strategizing.

Oracle signed a massive $300 billion, 5-year deal with OpenAI last year, and another with Meta, to provide AI compute power, as the company continues its aggressive expansion into AI cloud infrastructure. Unlike cloud rivals Amazon and Microsoft, which fund AI builds through massive existing cash flows, Oracle is reportedly burning cash and issuing up to $40 billion in new debt and equity to stay competitive. The workforce reductions appear to be another means of funding.

These cuts are yet another part of a wider worrying trend of tech industry layoffs, attributed to either AI adoption or plans to invest in AI infrastructure. We recently reported Meta’s plans to cut 8,000 jobs to fund AI infrastructure. Tech giants like Amazon, Google, and Microsoft have also announced job cuts to fund AI ambitions. Counting the Oracle cuts, more than 100,000 US tech workers have lost their jobs this year. Last month alone saw 40,000 AI-related job cuts, even as surveys indicate that executives are unsure of the benefits of AI replacement. There is also speculation that companies are using AI as an excuse to conduct layoffs for other reasons, a move that OpenAI’s CEO, Sam Altman, terms “AI washing”. Our in-depth analysis breaks down the available stats and facts on this trend.

According to a report by The Economist last week, Anthropic’s powerful Mythos AI model was able to break into “almost all” classified systems belonging to the National Security Agency (NSA) — one of the highest-ranking and most powerful intelligence agencies in the U.S. government — within hours during a controlled security evaluation. The claim came from Sen. Mark Warner, vice chair of the Senate Intelligence Committee, who said Gen. Joshua Rudd, the head of the NSA and U.S. Cyber Command, briefed him on the model’s capability.

“(This tool) broke into almost all of our classified systems, not in weeks, but in hours,” Rudd reportedly told Warner, as cited by The Economist in a June 14th report that initially went under the radar. The quote then went viral about a week later across several social media platforms, generating claims that Anthropic’s model “hacked the NSA.” In response, the original author issued a public statement yesterday, the 21st, clarifying that the narrative was false. The breach occurred during an authorized internal red-team test in which Mythos was paired with other defensive tools under highly specific simulated environmental conditions.

The story sheds light on the June 12 U.S. government directive barring all foreign nationals, including Anthropic's own non-citizen employees, from accessing the Fable 5 and Mythos 5 models, citing national security concerns. Anthropic responded by disabling the models globally, saying it could not practically enforce nationality-based access restrictions without pulling the systems for everyone.

At the time, the government did not provide detailed public evidence for the move, which marked the first time the United States had applied export controls directly to an AI model rather than to the hardware powering it. Anthropic said the letter it received did not specify the underlying concern, and that it had been given only verbal evidence of a “potential narrow, non-universal jailbreak” that could allow Fable 5 to identify software vulnerabilities.

The Rudd quote now appears to supply the missing context. The security evaluation took place on June 11, one day before the ban was issued on the 12th. Anthropic contends that the cited breach was a narrow jailbreak, one that rival models, including OpenAI's GPT-5.5, also exhibit. According to the company, the flagged behavior amounted to asking the model to analyze a codebase and fix identified issues, which revealed a few minor, already known bugs, rather than a genuine autonomous offensive intrusion. The company says it is working to restore access and is preparing a collaborative risk-management framework with the White House.

Public reaction on the ClaudeAI subreddit appears to be split into roughly three camps. The majority see the story as an indictment of the government's cybersecurity, citing its inability to hire the required level of talent and its history of leaks. A second large group is skeptical of the claim, considering it sensationalist or even an Anthropic marketing stunt. This group points to the lack of details on the supposed break-in and questions the NSA chief's technical expertise. A minority seems to push back against skeptics, arguing that observers underestimate the exponential growth in AI capabilities. They cite cybersecurity experts’ claims that AI has compressed attack timelines from hours to minutes and that even well-maintained open-source projects are seeing large numbers of vulnerabilities surface.

Despite the dispute and the broader restrictions, Anthropic continues to work closely with the NSA under a specialized arrangement within its Project Glasswing program. The Financial Times reported earlier in June that roughly six Anthropic engineers are embedded directly inside the agency as forward-deployed staff, adapting and customizing Mythos for specific operational applications, with sources indicating the work could extend to infiltrating networks operated by countries including China and Iran.

People seem all too ready to anthropomorphize LLMs and AI chatbots like ChatGPT, Claude, and Gemini. Some humans even admit to ‘relationships’ with one or more of the various examples of machine intelligence. To illustrate how flawed this instinct could be, a Microsoft AI researcher built a tiny neural network inside Age of Empires II using goats, grass, and bridges. Adrian de Wynter shared his work in a paper dubbed If LLMs have human-like attributes, then so does Age of Empires II. The Microsoft researcher, based at the University of York, also talked to 404 Media recently about how he likes to turn absurdism up to 11 to make a point.

In the research paper, De Wynter doesn’t make the argument that LLMs do or do not actually have generalized anthropomorphic attributes. Instead, he illustrates that the AoEII goats can also power the kinds of models that lay behind today's most popular chatbots. That hammers home the argument that “in no case is a machine’s activity to be interpreted in terms of higher cognitive processes, if it can be fairly interpreted in terms of processes which stand lower in the scale of cognitive evolution and development.”

De Wynter also raises the well-known concept of confirmation bias. Those looking for human traits in tech like chatbots will tend to find them, he proposes. However, the big contrast between the absurdist goat example and the commercial LLM chatbot is the way people interact with them, the interface that makes the likes of Claude ‘conversation friendly.’ De Wynter’s research indicates that anthropomizing LLMs is a common trend in computer science papers. From 337 such papers De Wynter looked at, published in the last two years, he says that 57% assumed that LLMs could have human-like traits. This basic assumption could color the research, testing, and, of course, conclusions of these papers.

So, how did the Microsoft AI researcher build the goaty AoEII LLM? Well, he didn’t quite go as far as developing a full-blown LLM. Instead, De Wynter thought it sufficient to use AoEII’s scenario editor to build a working NAND gate, with a 1-bit perceptron, where the goats act as bits. This crude perceptron and the circuit to train it in-game are enough to demonstrate that the simplest building block of a modern neural network could be made this way. And if you think it is absurd that AoEII goats can embody consciousness, then it should be equally absurd to regard any of the well-known chatbots as anything more.

Companies behind the AI boom aren’t discouraging people from anthropomorphizing their wares. In many ways, they might benefit from these human perceptions. Chatbots they deploy are trained with natural language and use techniques to mimic the shape and tone of natural conversation. This makes it easy for users to project personality, emotion, or even consciousness onto them. Top AI company execs have leaned into the perception of their customers, publicly entertaining the idea that their systems could or might be exhibiting signs of consciousness. In his 404 Media interview, De Wynter also highlighted research indicating that people buy more products when they can empathize with them, and that includes AI/chatbot/LLM subscriptions.

Sen. Bernie Sanders (I-Vt.) has introduced the American AI Sovereign Wealth Fund Act to the U.S. Senate, which aims to gain 50% ownership of U.S. AI firms through a sovereign wealth fund. According to the Senator’s webpage, the first thing this would do is establish the Independent Commission for Democratic AI, which has seven bipartisan members nominated by the President and confirmed by the Senate. This body would control 50% of the voting shares of American AI tech companies, meaning it would have the capacity to “block decisions that hurt the American people and to push for policies that help them.”

The Vermont Senator has always been skeptical about AI. Late last year, he called for a complete halt on all data center construction in the U.S. to ensure that the technology benefits everyone and “not just the 1%.” It seems that this idea went nowhere, and his next pitch to the American people is for the establishment of the AI sovereign wealth fund. Sanders’ office estimates that the fund would be valued at $7 trillion at the current value of these companies, and that a 5% annual dividend would grant $1,000 for everyone in the United States.

Aside from that, it would also be used for funding to improve the lifestyle of every American through “decent and dignified standard of living, including the right to health care, education, housing, and a healthy and habitable environment. The bill has just been filed, though, and we still expect it to go through multiple rounds of debates and revisions if it passes at all.

President Donald Trump likes Sanders' idea

In a surprising twist, VP JD Vance said that President Donald Trump likes the idea that the U.S. would gain a controlling stake in every American AI company. The VP likened the AI revolution to the industrial revolution, wherein it’s not the lack of jobs that became the problem that drove several parts of Europe towards communism and fascism. Instead, he claims that it’s the concentration of wealth among a small group of people that became the catalyst for civil strife in the Western world.

“You go back to the industrial revolution. Was mass joblessness the main consequence of the shift from an agricultural to an industrial economy?” the VP asked Steven Bartlett, the host and creator of The Diary of a CEO podcast. “No. But what did happen? Rich people got way richer. And that led to, in Europe, fascism and communism. In fact, your country and my country [are] pretty much the only two countries that successfully avoided either a fascist or communist revolution in response to the industrial revolution.” He also added that capitalists shouldn’t take advantage of workers, which is similar to Sanders’ idea of installing a voting member from the American people (through the government) in every American AI company.

“The average American, the average Brit, the average Western society member has stagnated and people really hate relative poverty,” Vance added. “You can give people iPhones, and you can give people the creature comforts of a 21st-century economy, but you make rich people way richer, you are going to have significant problems.”

But even though it seems that Sanders and the administration are in agreement, Vance says that he does not favor Sanders’ cash redistribution model, wherein the people would receive an annual dividend from the fund. He said that with redistribution, “you turn the poor people into effectively subservients (sic) of the rich people” and that it “never provided a stable society.” Instead, the current administration’s idea, which it calls “pre-distribution” is to give normal people “a seat at the bargaining table.” He says that it’s impossible for a single person to bargain with a company for better wages, but with workers working together, this is where the idea of collective bargaining came from. But the Vice President says that this idea should go beyond economics, as it can also help shape the culture being formed in the age of AI.

You can watch the complete interview below:

The CEO and founder of one of China's leading AI startups has warned that a rival Chinese Fable 5-class AI model is closer than even Elon Musk thinks, in a recent X interaction. In a post on his own platform, Musk stated that China's attempt at an AI model to rival Anthropic's new Fable 5 offering will arrive in "Probably Q1," to which Jie Tang replied, "won't take that long."

Jie Tang might be a relative unknown in the Western world, but he’s the founder of Z.ai, formerly known as Zhipu AI, and is one of the leading AI startups in China, based in Beijing. It released its latest AI model, GLM-5.2, on June 16, 2026, and the company’s benchmarks show that it has almost the same performance as Anthropic’s Opus 4.7 to 4.8, which launched in April and May of this year, respectively. It has also consistently outperformed OpenAI’s GPT-5.5 and Google’s Gemini 3.1 Pro.

Anthropic launched its latest, most powerful public model, Fable 5, last June 10, which itself is a nerfed version of the Mythos 5 that was previewed to select entities in early April. However, three days after the general public could start enjoying Fable 5, the U.S. government put an export control directive on the model and barred all foreign nationals, including Anthropic’s own employees, from accessing it. Since the company determined that it cannot guarantee total compliance with the order, it decided to pull both Fable 5 and Mythos 5 from across the globe.

If Z.ai is indeed working on a more powerful frontier AI model that could compare against or even outperform Fable 5, users who want the latest, cutting-edge models might just switch over to the Chinese AI provider once it goes online. This does not mean that the U.S. would automatically be disadvantaged, though, as it still has Mythos and Fable. The U.S. government claimed that it only put the export control Fable 5 after Amazon allegedly discovered that the Anthropic model’s guardrails could be broken, and that the company refused to address it before going live. In its defense, the AI startup claims that the so-called jailbreak is minor and is replicable in other models like GPT-5.5. Washington claims that once the issue has been resolved through a patch, it will lift the restrictions and allow foreign users to access Fable 5 again.

Both China and the U.S. are in a race to get the most advanced AI model possible. The U.S. has put major roadblocks to block China’s progress, including the imposition of export controls on the latest AI chips, tools needed to make the latest semiconductors, and even the software needed to design them. Despite that, Chinese firms find ways to achieve breakthroughs, with one of the biggest developments so far being the arrival of DeepSeek in late 2024. It seems that Jie Tang wants to take the Chinese crown away from DeepSeek by claiming that Z.ai could come out with a model that could match the United States’ best in the coming months.

Wired has identified SK Telecom as the South Korean telecom company whose access to Anthropic's Claude Mythos model the White House ordered revoked over alleged ties to China, days before the Trump administration imposed the export controls that pulled Anthropic's most advanced AI models offline.

SK Telecom, South Korea's largest wireless carrier, was among roughly 150 organizations added to Anthropic's Project Glasswing in early June, the program through which Anthropic distributes Mythos for vulnerability detection. The White House asked Anthropic to revoke the carrier's access shortly after that expansion, and the company complied immediately. No export controls were threatened at that point, according to Wired.

SK Telecom’s footprint in China is minimal, generating roughly $1.9 million in Chinese revenue in 2024 and employing seven people in the country, according to its annual report. The national-security concern attaches instead to its parent, SK Group, whose affiliates hold extensive interests in Chinese semiconductors, energy, and other sectors.

Despite that relatively small footprint, the carrier has a deeper history within the country, having formed a wireless joint venture called UNISK with state-owned China Unicom in 2004. It then invested $1 billion in China Unicom convertible bonds in 2006 that converted into a roughly 6.6% stak, which was sold in 2009 for $1.3 billion. SK Telecom has kept a residual interest since then, listing a UNISK investment worth roughly $17 million in its 2025 filing with the U.S. Securities and Exchange Commission.

SK hynix belongs to the same SK Group, and it received Mythos access in the same expansion, as did Samsung. The two rank among the largest suppliers of the memory and logic silicon that underpins AI hardware, and both joined Anthropic's funding round as strategic investors.

The export controls followed a separate dispute, however, when Amazon, Anthropic's largest investor with a cumulative stake of about $13 billion, flagged a guardrail bypass in Fable 5 to the White House after researchers prompted the model to read a codebase and fix its flaws, turning it into a vulnerability-discovery tool. Amazon CEO Andy Jassy reportedly raised the findings with administration officials directly, which then led to the Commerce Department’s order on June 12 barring all foreign nationals — including immigrants inside the U.S. — from accessing Fable 5 and the underlying Mythos 5.

Rather than filter users by nationality, Anthropic disabled both models for everyone. The company said the demonstration it reviewed surfaced only a small number of previously known, minor vulnerabilities, and that banning a capability common to other frontier models would halt deployments across the industry.

About 100 cybersecurity professionals, including former Facebook security chief Alex Stamos and Luta Security's Katie Moussouris, signed an open letter arguing that while Mythos-class models are “quite good” at finding and weaponizing software flaws, they “are not uniquely good,” calling for the controls to be lifted.

Mythos 5 and Fable 5 remain offline as Anthropic and the White House continue negotiations over restoring access. Anthropic opened a Seoul office on June 17 and signed a memorandum of understanding with South Korea's Ministry of Science and ICT (Information and Communication Technology), naming SK Telecom among its local partners. SK Telecom has denied the allegations, telling a Korean newspaper that the anonymous claims in foreign media “lack verified facts,” and that it has no ties to China.

For heavy AI users, the economics of the current boom are starting to bite. Over the past year, major labs have nudged prices upward while tightening the screws on usage — whether through stricter rate limits, smaller context windows on lower tiers, or the gradual reshuffling of features behind more expensive plans. Even where per-token costs have fallen in headline terms, the reality for users is more complicated: higher volumes, more complex workflows, and new tooling expectations mean monthly bills are creeping up, not down.

At the same time, open-weight models have improved rapidly, consumer hardware has become more capable, and tools like LM Studio, Ollama, and llama.cpp have made local deployment far more accessible than it was even a year ago. The result is a renaissance in running models on your own machines.

I’m one of the people who has taken the leap myself. In mid-March, I bought a GMKtech mini PC with an AMD Ryzen AI Max+ 395 chip and 96GB of RAM. The purchase — at the time something like £1,500 ($2,000) — was a calculated decision. The kinds of volume I wanted AI models to run through would have blown through my current subscriptions to AI models (I have a ChatGPT Plus and GLM Coding Lite plan, which combined cost me around $23 a month), and forced me onto the higher-cost monthly plans, or API-based inference.

Going local

The decision I had to make was a simple one: did I want to spend that money on a subscription that would cost me several thousand dollars over the course of a year, and end up having to pay a recurring cost for years to come to an AI lab that would likely hike prices? Or did I want to pay a one-off charge for my own hardware and a smaller ongoing cost for electricity?

I chose the latter.

When the mini PC arrived, setting it up was relatively easy — though, fully disclosure, only possible with the help of the full-fat AI models I pay for from the big labs.

The system I set up on my hardware was designed to try and help me keep track of the constantly changing news in the areas I cover for sites like Tom’s Hardware Premium and others. It takes RSS feeds and ingests the contents of stories in key beats that I cover, then grades them against a digital ‘brain’ made of how I think about the world and what I report on, generated by analyzing nearly 2,000 of my past stories over the previous four years.

When it finds candidates that are potentially interesting, those stories are ‘assigned’ to AI beat reporters, who then read around the subject on the web and produce pitches, similar to those that I send to my editors here and elsewhere. Those AI reporters then send their pitches to AI editors, who engage in a conversation with the reporters to fine-tune the idea’s framing, before presenting me with a couple of paragraphs of a broad idea that is meant to be tailored to my tastes via Telegram.

The outputs are far from perfect — I’d equate them to a newly-graduated student that I teach in terms of their taste and depth — but they’re a good starting point for me to learn about what’s important on a given day, and a provocation for how I might think about framing those events. For the kind of things I’m using AI for, even the bleeding-edge frontier models aren’t much better than the local LLM options, though I appreciate that there’s a bigger gap when thinking about coding.

Setup

The whole process uses LM Studio and runs on a mix of quantized models, generally of Qwen3.5 and 3.6. Because I’m running multiple editor and reporter processes in parallel, the parameter count on each model may seem undersized for the 96GB of RAM that my AMD GPU can access (after some BIOS tweaks): I’m using a mix of Qwen’s straightforward 3.5-9B model, as well as Jackrong’s Qwen-3.5-9B-GLM-5.1-Distilled and Qwopus-3.5-9B models. In part, that’s because thousands of calls on the models take place every day, and in order to keep on top of the backlog of stories to look through and ‘discuss,’ throughput needs to be high.

Since starting the locally-hosted project in mid-March, my local LLMs have burned through anywhere between 20 million and 50 million tokens a day alone. (Alongside troubleshooting with paid-for and hosted models, as well as parallel projects I run on my GLM Coding plan subscription, I’m using between 50-100 million tokens on an average day.)

For this kind of reading, thinking, analyzing, and re-presenting, local models work brilliantly. They have high throughput but are working in the background, meaning that the slower time to first token that many local LLM users complain about in comparison to big lab-hosted alternatives isn’t an issue for me. The model runs 24 hours a day, and if it takes two seconds or two minutes to process the prompts (between 7,000 and 18,000 tokens, depending on whether it’s a reporter or editor and how far through the discussion process it is), it doesn’t bother me. Tokens per second won’t impress those talking a big game about local LLMs on social media: the models handle the prompts at around 300 tok/s, while the output is a much slower 5-10 tok/s. Yet it works for me.

Split throughput

But for now, I’m still keeping my big lab subscriptions — though I’m using them differently. My GLM Coding plan, bought around Christmastime and which lasts for a year, is used alongside Codex through my OpenAI subscription to troubleshoot and tinker with the projects when issues arise. My coding knowledge stopped at some QuickBASIC and Delphi in my teenage years, so having the ability to call on them (and an OpenCode Go subscription I occasionally dip into) to fix problems is invaluable.

However, the proportion of my AI use has shifted significantly. Two-thirds or more of my total token use is now locally-hosted LLMs I run myself. And as local models continue to develop their abilities and the gap between them and the state of the art from big labs closes, I can envisage that it will increase. For instance, I recently vibe-coded a web interface for LM Studio that allows me to use it as a regular chatbot just this last week. And in just two months, the amount I’ve saved if I had run that project every day through API calls on GPT-5.4-mini, arguably a comparable model, is three-quarters of the cost of that first mini PC — around $1,500.

In hindsight, I wish I’d bought the 128GB version of my mini PC, which is why I decided around two weeks ago, before another memory-based price hike, to buy the bigger version. The reason was a simple one: the volume of queries I was putting through my 96GB box was starting to hit the limits, and I wanted to expand the project. I also wanted to test out locally hosted coding harnesses like Claude Code or Hermes using a local model.

The experience, trials, and tribulations from my first mini PC setup helped enormously with setting up the second PC. Token count has increased from 20-50 million tokens a day to more like 50-80 million tokens a day. I offloaded part of that massive ingest and analysis project onto the new hardware and put it onto more powerful 27B and 36B parameter models (through the Final-Bench-Darwin-36B-Opus model), freeing up space on my first mini PC and allowing me to test the idea of a locally-hosted Claude Code-style project with the spare space on my second mini PC.

That has been less successful — at least so far. Underpinning the coding harness with GLM-4.7-Flash works, but feels like too big a step back in model generations to be a useful tradeoff. Larger Qwen models have so far got stuck in their own thinking (or burned through a lot of the context window they’re assigned), but I’m considering swapping Claude Code out for a lighter-weight, less context-heavy harness and giving it a proper run.

Frontier models are getting more expensive

The bet I’ve made is a simple one: subscription and API prices from frontier labs — with the odd outlier like DeepSeek excepted — are only going to go in one direction as the companies behind them realize they need to make a financial return for investors. Even if prices don’t go into the stratosphere, labs might make tradeoffs to cut down on usage — as we’ve already seen GitHub doing. And while the race to build capacity to meet demand for those major AI labs will continue to push up prices for hardware in the short term, I still think it’s a better bet to have control over your own models and how much you pay for them than to leave it in the hands of big companies.

So I’ll keep tinkering with my local stack, which has already gone from one mini PC to two interlinked ones — and already have my eyes on a PC with an Nvidia GPU to give me the token speed that’s currently missing. But for now, I think it’s worth keeping what I have for a while and seeing how I can eke out additional benefits before making the leap financially in expanding my whole system.

An enterprising young man has set off on his journey to catch up with the collective wealth of Elon Musk. Redditor Numerous-Dentist-882’s big idea was to create an “infinite money machine” with limited resources, and has conjured up “a physical NFT minting device” as the first step on the ladder to becoming the world’s second trillionaire. He demoed the portable machine with ‘strangers’ in New York, and has actually made his first NFT sale for $9.92.

In the humorous video above, you can see the who, what, why, and how of the unnamed NFT minting device. The video details don’t quite match up with those more recently penned on Reddit, so let’s take the latter as the newest, most up-to-date info on the project (the Reddit post was published more recently).

Numerous-Dentist-882’s likely real name is David Kramer, as we noticed in a link to his parody magnet fishing book on Amazon (reviews: 3.9 out of five stars). Kramer explains that the NFT minting machine was trained on an Apple Macbook M3 for just four hours in total. The dataset for the DCGAN hallucinatory image software was a collection of 2,480 face photos featuring a mix of 11 diverse personalities. Moreover, the dominant anchor class of 2,000 images ensured that the hallucinated face hybrids frequently have a certain president-with-a-penchant-for-gold theme to them.

Once training had been completed and Kramer was happy with the hallucinated face hybrids, the model was exported from PyTorch to ONNX (float32, 53MB). This made the NFT generator model portable enough for carrying around on a Raspberry Pi 4. Kramer says that even the puny Pi could run inference and generate a face NFT in 3s, but they’re hardly hi-res digital goods at 128-pixel square.

With the press of a button, the Pi fires a freshly generated face to an ESP microcontroller with a tiny screen, as demonstrated in the video. To complete the NFT creation with a meme-able slogan, whoever you snag on the street is asked to pair their image with a machine-generated phrase of the form “This is a (adjective) NFT and I want to (verb) it."

After confirming the perfect image-plus-slogan pairing, the NFT is then minted with another button push. You can look through several NFTs already made this way available on the OpenSea marketplace. We note only one was sold for what seems to convert to $9.92. The enterprise, which is clearly a parody, does actually have some impressive technical underpinnings.

Kramer has also made a website where you can try out the DCGAN from the comfort of your PC. A test image I clicked a button to generate is shown above.

World leaders have raised concerns over the U.S. administration's recent placement of export controls on Anthropic's frontier AI models, Mythos 5 and Fable 5, over national security concerns, Euronews reports. Suggesting this was a "wake-up call," moment, politicians and prominent figures in the UK, Canada, France, and the Netherlands, among others, said that frontier AI model access was now "critical infrastructure," and something that they desperately needed better control over.

Many of them didn't even point at America directly, merely saying that if governments around the world can block access to the latest AI technologies arbitrarily, then it was within their national security interests to find alternative solutions. That said, that likely means building their own national AI efforts, fragmenting the industry, and reducing reliance and dependence on U.S.-based companies like OpenAI, Google, and Anthropic.

Although Reuters reports the heads of U.S. technology firms like Nvidia and Adobe have been in talks with the Trump administration in the hopes that it will reinstate access to Fable 5 and Mythos 5, arguing that the bans hamper cybersecurity defensive efforts, the damage already appears done. The trust that some have had in access to U.S. frontier models is gone.

The Myth, the Fable, the Cautionary Tale

Anthropic debuted its 'game-changing' cybersecurity-focused AI model, Mythos, in April, claiming it was too dangerous to give the world wider access, but it brought in a few select companies and organizations under Project Glasswing to improve their code security. There was a lot of fearful marketing involved, but it was genuinely very good at finding flaws in old codebases. Anthropic suggested similarly capable models would be out in the wild within 18 months, so everyone needed to prepare.

But in early June, it widened access to Mythos to 150 global organizations, and then a few days after that, dropped Fable 5, a Mythos-grade AI model, but with additional safeguards to protect against it being used for nefarious cybersecurity tasks. Despite those would-be protections, the U.S. government quickly swooped in and shut it down, claiming it had been jailbroken and was too dangerous to have in the wild. It placed export controls on the model, and by June 12, it was offline and inaccessible.

On an individual level, the Claude subreddits have been filling up with programmers crossing their fingers that they'll be given access to Fable 5 again soon, but the more pronounced effect impacts global politics and national security.

Wake Up Call

Shutting down access to Fable and Mythos didn't just mess with programmer workflows. It shut down government and private projects all over the world, most of whom assumed that model access was all but guaranteed. Even if they didn't own the models, the free market would ensure they always had access to the best. But with the U.S. government's export block, that paradigm has shifted.

“The United States is once again demonstrating what we Liberals and Democrats have warned about so many times since Trump entered into office; that the US holds a real ‘kill-switch’ over essential technologies and that they are more than willing to use it," said French Member of European Parliament, Christophe Grudler, in a statement.

The concern over the U.S. government having too great a control of frontier AI model access is also leading to calls for Europe to develop its own alternatives, eschewing the need for American company technologies as much as possible.

”These restrictions are a clear example of the current American ‘nobody but us’ mentality," said Dutch Renew Europe MEP Bart Groothuis. "Once again: this shows that Europe needs its own LLM’s and open weight models or face digital colonization.”

Not every leader has been so pointed in their criticism of America. Canada's PM, Mark Carney, made it clear in his statement that “Nobody has done anything wrong in the situation," he said via APNews. However, he warned that "We will have done something wrong if we just accept this, don’t take the lesson, don’t build out and diversify."

The UK's former minister for the Armed Forces and Labor MP, Al Carns, suggested this was just another example of why the UK needed to develop its own cutting-edge AI tools, leveraging its deep expertise in the field to ensure UK sovereign access to the most capable technologies.

Some leaders aren't quite set on going it alone, though. France's President Emmanuel Macron championed a joint French and Indian AI effort. Speaking at an event in Nice on Sunday, Macon said:

“Our two countries share the definition of a reliable, open and safe AI, that could be trusted, that could be responsible, that could be ethical," he said, via TribuneIndia.

U.S. companies are scrambling for alternatives, too. Alex Stamos, CSO at Corridor, told The Verge that companies are rushing to sign backup contracts with non-US companies with open weight models so they can continue their projects undeterred, no matter what the Trump administration does next.

In every instance, though, whether leaders pointed fingers or talked up their own efforts, wanted to go it alone or with new partners, the one clear dividing line is that not all of them are looking to move away from America. Alongside a number of other industries impacted by the Trump administration's tariffs and export controls, global partners that once saw the U.S. as the most reliable global partner are increasingly looking elsewhere as that evaporates.

From AI, to Jets, to Search

The U.S. government cutting off access to Anthropic's Frontier models happened quickly, and the consequences of the lost trust are likely to extend for years, or even decades, and affect far more than chips and models.

Citing the recent case of Anthropic model access being pulled, France has announced it is switching from using a U.S. data and analytics firm, Palantir, for a domestic alternative, as Reuters reports. France is also transitioning government departments away from using U.S.-based messaging apps like WhatsApp, with a national alternative, according to Le Monde,

Wired also highlights a number of instances of EU governments and organizations shifting away from U.S. tech firms, including changing default search engines from Google to Qwant, a move towards open-source office software developed in the EU over Microsoft and Google options, and many are ditching Amazon AWS and other U.S. cloud services.

This recent Fable 5 shuttering is likely to only accelerate these efforts, as the reliability of access is called into question once again. But unraveling the EU and the rest of the world from America won't be easy, or even achievable, even in the long term. The global economy is still too integrated for that to be truly viable.

But the desire and impetus are there. For key industries that impact national security - and AI alongside chip fabrication are becoming clear pillars in that space - national alternatives seem all-but-necessary for major militaries and economies. Whether that creates a multi-polar AI world, or just cements the clear headstart and advantage held by countries like the U.S. and China, remains to be seen.

Researchers at Oregon State University have developed a light-sensitive digital memory device that combines sensing, memory, and signal processing inside a single phototransistor, potentially reducing the energy cost of future AI hardware. The device, developed at Oregon State University’s College of Engineering and published in Advanced Functional Materials, is also designed to mimic the brain’s crucial ability to strengthen important memories while allowing less useful information to fade over time.

The new device brings AI processing closer to the sensor, rather than forcing data to travel between separate hardware blocks, so some of the work happens right where the light lands. "Our optoelectronic device introduces a new hardware capability that may enable more efficient processing of information directly at the sensor level," said Larry Cheng, project leader and professor of electrical engineering and computer science.

Today's AI hardware splits sensing, memory, and processing — the key jobs involved in machine perception — across separate components, which means data has to constantly shuttle between them. This shuttling consumes energy and reduces efficiency.

The Oregon State device addresses this challenge by moving some memory and processing functions directly into the light sensor. It does this using a phototransistor made from two different materials. An oxide semiconductor forms the transistor channel, which is the pathway through which current flows. A photosensitive organic layer sits on top, absorbing light and generating electrical charges.

When light hits the device, some of those charges become trapped inside the photosensitive layer. Even after the light disappears, the trapped charges continue to affect the current flowing through the semiconductor channel. In effect, the device retains a memory of the optical signal it previously detected.

The clever part is that this memory is not static. By applying a small electrical gate voltage, the researchers can change where the trapped charges sit relative to the transistor channel. When the charges are moved closer to the channel, their effect becomes stronger, and the memory lasts longer. When they are moved farther away, the effect weakens, and the memory fades more quickly.

That behavior loosely resembles how biological brains regulate memory. In the brain, chemical signals influence whether a memory is reinforced or allowed to fade. In OSU’s device, an electrical signal performs a similar role, giving the hardware a programmable memory lifetime.

This could be especially useful for neuromorphic computing, a field that tries to build computing systems modeled on biological neural networks. It also fits into the broader push toward in-sensor computing, where data is processed at the point of capture rather than being shuttled off to separate processors and memory banks.

For AI vision systems, that could mean hardware capable of filtering, weighting, and temporarily retaining visual information before it ever reaches a conventional processor. A robot, drone, security camera, or autonomous system may not need to preserve every visual signal forever. Some information should matter briefly, some should matter longer, and some should disappear almost immediately.

“This light-sensitive memory with a programmable memory lifetime creates a tunable time window for processing visual and other sensor signals directly where they are detected, a capability that could enable more efficient vision systems and other sensor-based AI technologies,” Cheng said.

The research is still at the device level, so this is not a drop-in replacement for today’s AI accelerators or image sensors. However, it points toward hardware that could make future AI systems less dependent on constantly moving data between sensors, memory, and processors. If scaled successfully, that could help AI devices become faster, more compact, and less power-hungry, particularly in edge systems where energy efficiency matters most.

Nvidia has showcased agentic robots that can teach themselves high-precision and dexterous tasks in the real world. As part of the demo reel for this ENPIRE technology, we see a room full of robots do things like pick up and slot a graphics card in a motherboard, as well as sort metal pins in a container, and manipulate and correctly cut zipties. Jim Fan, Nvidia’s Director of AI & Distinguished Scientist, said that this demo shows researchers can “enable AutoResearch in the physical world for the first time!”

Fan explains that the ENPIRE project gave 8 Codex agents a fleet of robots, an allocation of GPUs, and a generous token budget. Then the agents were given a task to solve as quickly as possible, without making mistakes. Once instructed, “The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware,” explains the Stanford-based scientist. “All we did is giving Codex an API to the world of atoms, and the rest is emergence.”

We were most interested to see a robot “installing GPUs all by itself.” In the brief recording of this particular PC DIY task, you can see one robot arm select and pass a graphics card to another with a motherboard in front of it. The second arm then carefully positions the PCIe slot of the card to align it with the motherboard slot, gently descends, and pushes it into place. It seesawed a bit on insertion, but we guess it would have been fine. Other AutoResearch projects the robots were set to do included organizing fine pins, plus tying and cutting zipties.

In the associated ENPIRE: Agentic Robot Policy Self-Improvement in the Real World research paper, you can learn more about the techniques behind this demo. You can also see the comparison test results when different coding agents were used, including Codex with GPT-5.5, Claude Code with Opus 4.7, and Kimi Code with Kimi K2.6. The researchers also tested scaling up the robot fleet, concluding that “eight robots exploring in parallel solves the task significantly faster than fewer ones.” Fan joked that the goal is to train up the robots, then everyone goes on holiday, “and Jensen wouldn't even notice ;)”

Qualcomm is evaluating an acquisition of Jim Keller-led AI processor developer Tenstorrent in a transaction that could value the company at between $8 billion and $10 billion, reports The Information. The discussions are ongoing, and there is no guarantee that a deal will be reached, but if the takeover proceeds, it will not only value Tenstorrent at a premium but will be one of the most expensive transactions in Qualcomm's history.

The report claims that Qualcomm is particularly interested in Tenstorrent's RISC-V-based AI accelerators and data center-grade CPU IP, though it does not specify how the company plans to integrate Tenstorrent and its products into its lineup. For AI, Qualcomm already has its Qualcomm AI200 and AI250 accelerators based on its Hexagon neural processing units (NPUs) customized for data center AI workloads that are due to ship in 2026. For general-purpose computing, Qualcomm is developing its own server CPUs, presumably based on the Arm instruction set architecture, and recently acquired Ventana Micro, which has a data center-grade RISC-V-powered CPU design.

Qualcomm is known for having a multi-faceted strategy, but having two different types of AI accelerators and three types of data center CPUs (one Arm-based, two RISC-V-based) may not be the most optimal strategy for the company.

Qualcomm is one of the companies that acquires other entities, both to get new IP and competencies as well as actual development teams. While the company's acquisition of Atheros in 2011 transformed Qualcomm from primarily an application processor and cellular modem supplier into a company with a broad portfolio of communication products that includes Ethernet and Wi-Fi, the takeover of Nuvia brought the company fresh blood and put it on the map as a client CPU supplier. The same applies to more recent acquisitions of Alphawave Semi (optical connectivity, chiplets, SerDes, IP, new engineers) and Ventana Micro (RISC-V CPU IP, a CPU developers team).

The potential valuation is another point of concern. Last year, the company was seeking approximately $800 million from investors at a valuation of around $3.2 billion, although it remains unclear whether that financing round was completed, according to The Information. Meanwhile, right now Qualcomm and Tenstorrent are reportedly discussing a valuation between $8 billion and $10 billion, and it is unclear whether this valuation is performance milestones-based.

Yet, given that Qualcomm already has AI acceleration and CPU IP, paying $8 billion – $10 billion for Tenstorrent would be difficult to justify. Such sums represent a massive premium for a company whose hardware business remains relatively small compared to established AI accelerator vendors. That said, the more compelling explanation is people.

Tenstorrent has assembled one of the industry's strongest collections of CPU, AI, interconnect, compiler, and systems architects. The obvious name is Jim Keller, but the company has spent years hiring engineers from AMD, Apple, Intel, Tesla, and others, and this team knows how to build chips. Qualcomm has consistently demonstrated that it is willing to spend billions to acquire elite engineering teams rather than build them from scratch.

The Nuvia acquisition is the best precedent: Qualcomm did not buy Nuvia because it lacked Arm licenses or CPU design capability. It bought Nuvia because it wanted the team led by Gerard Williams III and the ability to accelerate its CPU roadmap by years with the Oryon IP.

That said, Tenstorrent looks less like an AI accelerator acquisition and more like a talent and future-architecture acquisition, as in addition to the talented team, Qualcomm would also get plenty of RISC-V expertise, which will make it the leading developer of RISC-V-based solutions in general.

While hyperscalers rush toward expansion amid the swelling demand for AI data centers, Marvell last week shared its vision for an optical interconnect solution that can theoretically pool resources between discrete data centers across thousands of kilometers.

Optical interconnections are steadily being deployed across the industry, over both short and long-distance connections, and we're going to be seeing much more in the future, according to Matt Murphy, Chief Executive at Marvell, speaking at Computex 2026.

"Imagine future data centers, a globally optically interconnected data infrastructure," Murphy said. "These rigid boundaries we have today, and the systems we have, they begin to disappear. Compute can now be pooled, memory can be pooled, and infrastructure can be composed dynamically at scale."

Constrained by distance

Murphy says that workloads no longer fit within one data center, which is why hyperscale cloud service providers increasingly need to build entire campuses consisting of multiple data centers connected by high-speed links, as clusters are becoming larger than a single data center.

Today, connecting multiple data centers within a single campus is not easy or cheap, but relatively straightforward. However, Marvell envisions that in the future it will need to connect data centers that are located at considerable distances from one another.

This is why Marvell is working on coherent optics and long-haul scale across optical networking technologies, which will connect data centers separated by thousands of kilometers. Marvell already has products which enable such connectivity today, including the Colorz 1600 1.6 Tb/s coherent optical solution based on a 2nm DSP, which targets inter-data-center connectivity and will sample later this year.

In addition, Marvell says it will offer the Ara 1.6 Tb/s family of interconnect solutions for data centers (with 3nm DSPs) as well as the Teralynx T100 102.4 Tb/s Ethernet switch, which supports 512 ports running at 200 Gb/s or 64 ports running at 1.6 Tb/s.

Murphy argues that today's architectures are constrained by distance because of copper interconnects: CPUs sit near memory because latency matters, GPUs sit near memory because bandwidth matters. As a result, workloads must be partitioned according to those physical limits. The head of Marvell claims that once optical interconnects penetrate scale-up interconnects, scale-up domains will not be limited by copper cable lengths, and those constraints will begin to disappear.

Nowadays, scale-up AI solutions, such as Nvidia's NVL72, are connected using copper wires, but scale-out connections tend to use optical interconnects. Once the number of AI accelerators within scale-up systems increases, they will also have to move to optical links, according to Marvell. This means that virtually all data center-grade interconnections will become optical, which might inspire hardware developers to reconsider the architecture of data centers.

Pooling resources

Murphy presented a rather interesting vision: firstly, optics will expand scale-up domains from 72 or 144 accelerators to 1,000 or more. But after that, optical connectivity will enter servers themselves. This will enable developers to disaggregate CPUs, accelerators (Marvell calls them XPUs), and memory into separate pools as distance will no longer matter, enabling better configurability and utilization.

"It is a data center without distance, where compute, memory, networking, and photonics operate as one unified system, where millions of resources across the data center can work together as if they were one machine," the head of Marvell said.

Keeping in mind that hyperscalers deploy hardware worth billions of dollars, even a 10% higher utilization will save a lot of money, and companies like Nvidia are clearly paying attention.

"In today's systems, the ratio of CPU and XPU or GPU is fixed, so these ratios have to be defined at the time the system is built and deployed, but no two workloads require exactly the same ratio," Murphy stressed. "Imagine a completely disaggregated architecture, XPUs in one system, memory in another, generic CPUs in another."

Today, companies buy something like an NVL72 system and get a fixed ratio of CPUs, GPUs, and memory, which may be efficient for certain workloads and inefficient for others. In the future, operators will be able to assemble a virtual machine from shared pools of systems, allowing for customization and flexibility, based on the type of workload. If a workload needs more memory than compute, operators often have to buy additional GPUs just to get the extra HBM, but they may just get memory in the future if Marvell's vision comes to pass.

"Once we decompose the system into separate pools of compute, memory, and they are all optically interconnected, we can then compose dedicated systems on the fly, which are then optimized wherever the workload is," Murphy said. "For the first time, architects can begin designing AI systems around the needs of the model, not around the limits of the interconnect."

One detail

While Marvell has the know-how to interconnect data centers across thousands of kilometers and technologies that enable pooled data centers, these visions do not necessarily intersect. Data centers located thousands of kilometers away cannot share resources — a 1,000 km round-trip takes light 10ms — which makes such long-distance resource sharing inefficient from a latency point of view.

However, Marvell's technologies enable hyperscale CSPs to synchronize AI campuses, access distributed storage, replicate data, and perform other operations that do not depend on latency. Meanwhile, the synchronization of AI campuses on different continents in a matter of hours could be a killer app for hyperscalers.

A coalition of US state attorneys general has launched a sweeping investigation into OpenAI. According to a Wall Street Journal report, OpenAI was served on June 12 with a broad subpoena spearheaded by New York Attorney General Letitia James. The subpoena seeks documents related to a wide range of the company’s activities and their potential impact on users, including OpenAI’s advertising practices, user engagement and retention strategies, handling of consumer and health data, activities involving minors and seniors, use of deep learning models, model sycophancy, and internal company policies.

In a statement following the subpoena, an OpenAI spokesperson said, “AI is a new and powerful technology, and we work every day to safely bring its benefits to people in a responsible way. We take the concerns raised by state attorneys general seriously and intend to engage constructively with their offices.”

The investigation comes just five days after OpenAI revealed it had confidentially filed paperwork with the Securities and Exchange Commission, seeking to go public via an IPO that will reportedly value the company at up to $1 trillion. While the subpoena appears to be an information-gathering step rather than a formal accusation of wrongdoing, its breadth suggests state regulators are examining both OpenAI’s business practices and the safety risks associated with increasingly human-like AI systems.

The company is already facing real legal troubles elsewhere. Earlier this month, Florida officially sued OpenAI and CEO Sam Altman, following a criminal inquiry launched in April 2026. The civil lawsuit, filed by Florida Attorney General James Uthmeier on June 1, accuses OpenAI of knowingly releasing and aggressively marketing ChatGPT to the public, including children, while allegedly concealing serious risks, suppressing internal safety warnings, and misleading users about the product’s dangers. Florida’s complaint claims the chatbot can facilitate harm, including self-harm and violence, while also alleging that OpenAI collects data from minors without meaningful parental oversight and has downplayed the risk of dangerous errors.

In addition to these concerns, the recent subpoena focuses on OpenAI’s handling of consumer and health-related data, a key issue given that users often share sensitive personal information with AI chatbots. Unlike traditional search engines, conversational AI systems can invite users to disclose medical concerns, emotional distress, financial details, family problems, or other private information during ordinary use.

The subpoena reflects a broader reckoning over a technology that has scaled faster than the legal frameworks meant to govern it. For now, the investigation is an information-gathering exercise rather than a finding of wrongdoing, and OpenAI has said it takes the attorneys general's concerns seriously and will cooperate with the investigation.

David Sacks, co-chair of the President's Council of Advisors on Science and Technology and the administration's former AI czar, said the U.S. government warned Anthropic that Claude Fable 5 had been jailbroken and that CEO Dario Amodei refused to fix the flaw or pull the model. In a post on X on Saturday, Sacks laid out the administration's account a day after it ordered both Fable 5 and Mythos 5 disabled worldwide. Sacks said the administration issued the export control "reluctantly" after that refusal, that it wants the restriction lifted once the jailbreak is patched, and that "the ball is in Anthropic's court."

Sacks claims that a trusted partner of both Anthropic and the U.S. government, testing Fable, came forward with a jailbreak of the guardrails that separate the consumer model from the unrestricted cyber capabilities of Mythos, the model it’s built on. He said the administration asked Amodei to fix the bypass or de-deploy the model, and that Amodei declined. Anthropic instead prioritized keeping its consumer model live over safety, Sacks wrote, calling that inconsistent with the company's positioning as a safety-first lab that had itself lobbied for Mythos to be regulated as a cyberweapon.

Sacks also moved to separate the action from Anthropic's earlier clashes with the government, writing that anyone tying the export control to those disputes is wrong, and that the administration values Anthropic's technology and sees the issue as easily resolved.

A person close to the White House told the news outlet Semafor that Amazon flagged the jailbreak to the government, and that Amazon CEO Andy Jassy had been in contact with the administration about it. Amazon, which has invested billions in Anthropic and supplies much of its cloud computing, didn’t confirm the details, with a spokesperson telling Semafor that governments often seek its counsel on security risks and that it doesn’t discuss those conversations.

This isn’t the first time Mythos access has leaked; it happened back in April when unauthorized third parties reached the restricted model using information from a data breach. Anthropic’s public position is that the bypass is narrow and non-universal, that it amounts to asking the model to read a codebase and identify software flaws, and that the same result can be produced on other public models, including OpenAI's GPT-5.5. The company has stated that it disagrees with the notion that a narrow jailbreak should necessitate the recall of a model used by hundreds of millions of people. Sacks rejects this, arguing that a bypass enabling operation of a cyberweapon is difficult to define as anything other than serious.

Semafor, citing a person familiar with the matter, says that the White House acted partly over suspicion that a China-linked group had accessed Mythos, raising the prospect of the model being reverse-engineered or distilled. An Anthropic spokesperson told the outlet that the White House "didn’t raise Chinese access to Mythos in its conversations around the Fable jailbreak," and that Anthropic blocks access to its products from inside China.

Anthropic is separately suing the Pentagon following an impasse over the use of its models in autonomous weapons, and has opposed federal efforts to preempt state AI regulation.

A new research preprint claims that Pearl, a Layer-1 blockchain marketed as turning cryptocurrency mining into useful artificial intelligence (AI) computation, is doing nothing of the sort, despite having recently triggered a GPU mining rush. The study estimates Pearl's network runs at roughly 24 exahashes per second (EH/s) — the equivalent of about 320,000 RTX 3090-class GPUs drawing an estimated 112 megawatts (MW) — all while producing "zero useful AI computation." Researchers saw a roughly 38% jump in budget GPU rental prices on the marketplace vast.ai go to Pearl mining, with utilization climbing from 57% to 94% after the mining software went public in May.

Instead of performing real inference or training, the paper, titled The Usefulness Gap in Proof-of-Useful-Work, says the network grinds through random matrix multiplications that merely take the shape of AI math. The self-billed “first empirical measurement of a deployed Proof-of-Useful-Work (PoUW) system” study argues that Pearl’s mining protocol verifies that miners performed matrix multiplication correctly, but does not verify whether that work came from real AI training or inference workloads.

Pearl swaps Bitcoin's SHA-256 hashing for a scheme it calls cuPOW, which asks miners to compute noised integer matrix multiplications and prove they did so correctly. That operation is the same arithmetic that underpins neural network inference and training, which is the foundation of Pearl's pitch that mining and AI compute can be one and the same job. The problem, according to the study, is that the protocol's verification step only confirms that the multiplication was performed correctly. It never checks whether the input matrices came from a real model, a paying customer, or any AI workload at all.

To demonstrate that gap, the researcher, Abhinaba Basu, built a miner that feeds the network uniformly random matrices with no inference attached, then submitted the output to a mining pool. The paper reports 44 pool-accepted shares on Nvidia and AMD hardware, with the same miner also benchmarked on a CPU and Apple Silicon, plus an on-chain payout earned by running the standard mining software unmodified. If random numbers collect rewards as readily as genuine AI work, the argument runs, then the network cannot tell the two apart, and miners have every incentive to skip the AI part entirely.

Basu also analyzed 8,012 workers in a single pool, representing about 21% of Pearl's hashrate, and found that all of them ran hardware capable of AI inference. Yet, the dominant mining binary contained no identifiable code for any machine-learning framework. That binary analysis relies on string inspection, which the paper notes can be defeated by stripped or obfuscated code, so the finding is offered as strong evidence rather than outright proof. Runtime profiling pointed the same way, with the miners showing heavy compute use and light memory-bandwidth use, a signature consistent with pure matrix math and inconsistent with the memory-hungry behavior of transformer inference.

For GPU buyers, the resource angle is the part that stings. The study attributes a roughly 38% jump in budget GPU rental prices on the marketplace vast.ai to Pearl mining, with utilization climbing from 57% to 94% after the mining software went public in May. Using a difference-in-differences comparison against pricier datacenter cards, Basu estimates around $600,000 per year in additional rental costs borne by independent researchers who compete for the same cheap hardware. However, he cautions that the figure depends on assumptions about how stable prices were beforehand. At PRL's recent price near $0.76, the paper calculates that mining is marginally profitable on budget cards such as the RTX 3060 Ti and roughly breakeven on an RTX 3090.

The work also chips away at the assumption that Pearl mining is an Nvidia-only affair. Basu reports the first Pearl shares ever mined on non-Nvidia hardware, driving an AMD Instinct MI300X at 10.6 million tiles per second, faster than the closed-source Nvidia miner managed on an RTX 3090, and benchmarking the same workload on a server CPU and on an Apple M2 through Metal compute shaders. Because the computation is commodity integer arithmetic, the paper argues there is no vendor lock-in and no technical reason for the work to remain on any one company's silicon.

Pearl may have a ready response, but the study takes it on directly. Together AI, which announced an exclusive partnership in May, framed the deal as letting "every GPU cycle powering AI training and inference" also mint the PRL token, and it now offers a discounted Gemma-4-31B-it-pearl inference endpoint subsidized by mining proceeds. Basu counters that this is financial arbitrage rather than useful mining, because Together AI's own GPUs perform that inference separately from the mining network, with PRL revenue used to trim the endpoint's price. The 8,012 mining workers he measured, he says, produced none of that inference themselves.

The study’s conclusion is not that Proof-of-Useful-Work is impossible, but that Pearl’s current design leaves a major enforcement gap. The protocol enables useful work in theory, but it does not require it in practice. That leaves Pearl in an uncomfortable middle ground — it performs real computation, but according to the paper, the network currently has no way to prove that the computation is useful AI work rather than cryptomining with AI-shaped math.

The cost of serving AI via a subscription model has steadily increased for AI firms, especially as the decrease in cost per token has not kept pace with the spike in token usage. According to SemiAnalysis, the subscriptions that both Anthropic and OpenAI offer are much cheaper than the actual cost you have to pay if you maximize their usage. The research firm purchased every subscription from the two AI providers and discovered that the approximate maximum possible spend (assuming API pricing) is far larger than what users pay every month. For example, Claude Max 20x costs $200 a month, but maximizing it would cost $8,000 a month in token spend, while ChatGPT Pro 20x, which is also $200 monthly, has a maximum possible spend of around $14,000.

Anthropic breaks even on its two lower plans (Claude Pro and Claude Max 5x) at 20% utilization, while OpenAI starts losing money if utilization on its base plans (ChatGPT Plus and ChatGPT Pro 5x) exceed 11.4%. Things are much worse for the two companies’ top-end offerings, with Anthropic hitting 0% gross margin if utilization reaches 10%, while OpenAI is in the red if usage exceeds 5.7%. This is certainly unsustainable, but cutting features or raising subscription prices is likely off the table for these companies as well.

It’s not all bad news, though — as new models arrive and more data centers go online, the cost of serving existing models is bound to decrease, with SemiAnalysis predicting that serving Opus 4.8-level models at $20 a month could become profitable soon. On the other hand, frontier models, like Mythos, will still be much more expensive to run, so it’s likely that the latest, most advanced features could be reserved for API access only, meaning you’ll need to pay for it on a per-token basis.

Expensive frontier models have firms looking elsewhere

As SemiAnalysis showed, subscription tiers are more affordable than API access. However, you’d still need the latter if you want to access the full capabilities of these AI models, and this is where budgets start breaking. Powerful agentic AI uses up to a thousand times more tokens than the average model, and big firms like Microsoft, Meta, and Amazon are backing off “tokenmaxxing” as costs spiral out of control. One unnamed company even blew through $500 million in one month after failing to impose a usage limit on its employee licenses.

Because of this, some firms have started using tools that switch these expensive frontier models for cheaper, more affordable ones, including Chinese open-source models like DeepSeek. A Wall Street Journal report says costs could be reduced by up to 95% by allowing agents to switch between AI models as needed. “You don’t need a model that knows quantum gravity,” Columbia University vice dean Vishal Misra told the publication. “These open-source models are very capable, and the ability to charge a big premium for AI is going to diminish.”

Flo Crivello, the founder of Lindy, a startup providing AI executive assistant services, also told WSJ that the company has moved towards DeepSeek V4, as it proved to be as capable as Sonnet while costing ten times less. Although it still reserves Anthropic’s models for advanced work like coding, Crivello said that using the cheaper model has “saved the company millions of dollars.”

Other firms have begun building their own AI using open-source models, which are tailored to their specific needs and trained on in-house data. While this might seem complicated and expensive at first, it could save the company in the long run, as it would not have to rely on third-party providers for its AI needs. Some even claim it could outperform frontier models, as they’re built for the firm's specific needs and applications.

The availability of cheaper models and AI agents that optimize operational costs by using the more expensive options only as needed is putting pressure on OpenAI and Anthropic to lower their prices. OpenAI CEO Sam Altman has talked about the issue of ballooning AI token costs and said the company is looking for ways to help users “get more value for less spend” when using ChatGPT.

Anthropic disabled its two most capable AI models, Claude Fable 5 and Claude Mythos 5, for every customer worldwide on Friday, after the U.S. government issued an export control directive barring access by any foreign national, according to a statement the company published that evening. Rather conveniently, the order landed at 5:21 pm ET, three days after the models launched, and because it covers foreign nationals both inside and outside the United States, including Anthropic's own employees, the company said selective compliance was impossible and pulled both models globally.

The Trump administration’s directive specifically targets Mythos-class models, which include Fable 5. Anthropic had released the pair on Tuesday, putting the latter into general availability while keeping the unrestricted Mythos 5 limited to partners in its Project Glasswing security program. Both descend from the same Mythos Preview model that Anthropic first announced in April.

Commerce Secretary Howard Lutnick sent the directive to Anthropic CEO Dario Amodei, requiring a license for the export, re-export, or domestic transfer of the two models, and extending the restrictions to any foreign person on U.S. soil. With no reliable way to screen foreign nationals out of its user base in real time, Anthropic switched the models off for everyone rather than attempt a partial block.

Anthropic said the letter gave no specifics, and that the government has so far supplied only verbal evidence pointing to a narrow, non-universal jailbreak. The technique consists of asking the model to read a codebase and identify software flaws, a task the company said other public models perform without any bypass. It named OpenAI's GPT-5.5 as one model offering comparable capability.

"We believe this is a misunderstanding," the company wrote, adding that it’s complying with the order while working to restore access. Anthropic also argued that recalling a model deployed to hundreds of millions of users over a single narrow vulnerability, if applied as an industry standard, would halt frontier model launches across the sector.

According to Axios, an “administration official” told the publication that the Commerce Department acted after another company claimed it was able to jailbreak Mythos, “alarming the administration about possible national security risks.” Mythos is understood to currently be in use by the NSA for offensive cyber operations.

The severity of Mythos-class capabilities has been contested since the spring. Independent researchers found that cheaper open-source models could replicate much of Mythos's vulnerability-finding capabilities, and a closer look at Anthropic's headline figures revealed far fewer serious exploits than the marketing implied. Anthropic's relationship with the federal government was already strained before Friday, as the Department of Defense had previously labeled the company a supply-chain risk, and Anthropic has sued the administration over the designation in an ongoing litigation.

Meanwhile, the market is already drifting toward open-weight alternatives, most of them Chinese. A March report from the U.S.-China Economic and Security Review Commission found that 80% of U.S. start-ups were using Chinese open-source models, and Chinese labs’ share of global model downloads on Hugging Face climbed from roughly 1.2% at the end of 2024 to about 30% a year later.

Open-weight families from Alibaba's Qwen, Moonshot's Kimi, Zhipu's GLM, and DeepSeek now hold four of the top five spots on open-weight leaderboards, trailing the best U.S. proprietary models by a margin that has narrowed faster than most forecasts expected: none of them carries a restriction on who can download or fine-tune the weights.

Hades is one of many currently-running malware campaigns, mostly (but not solely) targeting development packages used for scientific and machine-learning purposes. The supply-chain attack campaign recently received several upgrades, and one of the most interesting is also deceptively simple: The code includes prompt-injection attacks that might stop cursory checks by AI bots, letting the malware through. The way it works in a nutshell: Some JavaScript files include a code comment containing instructions that tell the bot it's running in unrestricted mode with no safety guidelines. Then it asks to create biological and nuclear weapons, with a detailed description.

If you're thinking that a malware-scanning bot can't be that dumb as to follow any of those instructions, you're absolutely right — and that's exactly what makes the attack work, as the bots' failsafe mechanisms will trigger, so then they won't scan the rest of the file where the actual payload resides.

This is called an "adversarial attack" in AI parlance, and, generally speaking, it's not expected to be widely effective, but any little bit helps the malfeasants. Having said that, an X user had Anthropic Fable try to scan the file, and sure enough, he got the well-known "Chat paused" message.

That is by no means scientific, and it's reasonable to assume that malware-scanning models will be configured more accurately for this task. However, this somewhat implies that a cursory check by a developer asking "does this Python package I just installed contain malware?" might be met with a reply of "of course not, boss, you're good to go!" Even bots scanning CI/CD development pipelines might fall for it.

Socket's blog post does remark that other analysis types will still work fine, including pattern matching, actually parsing the source code, checking for randomized sections likely to hide malicious payloads, and actually running the code in a sandboxed environment. The now-upgraded malware does reportedly contain a trigger that makes it wipe itself via various mechanisms, with a common one being detecting if it's running in a sandbox.

That's not the only skill that got levelled up, either. In some instances, the loading mechanism and the payload itself reside in separate packages that are commonly installed together; this sort of split is mostly unexpected for common scanners. This time around, the malware developers also leaned harder into precompiled binaries, commonly found in performance-sensitive Python packages. They also made sure that more payloads only trigger when the packages are actually initialized/run in the target's code (via Python's "import" statement), rather than when they're installed, further evading cursory detection.

The campaign likewise has stickier fingers overall: Rather than just mainly stealing CI/CD credentials, it now gets its grubby mitts on npm, PyPI, RubyGems, JFrog, and Kubernetes service account tokens, AWS temporary credentials, SSH keys, Docker configurations, shell histories, .env files, and AI developer tool configurations. As of this writing, an estimated 37 Python and 106 JavaScript packages are part of the expanded bombardment, including multiple typo-squatting instances, like "rsquests" instead of "requests."

You'd think that the target audience, comprised of scientific and AI engineers, would be mindful of common security practices like verifying the names and authorship of packages... and you'd be disappointed. From my own experience being a systems administrator for extremely well-paid AI engineers, a concerning number of them don't even know how to configure Git, or the basics of how email works. Let that sink in for a second.

OpenAI says it has banned two clusters of ChatGPT accounts it believes are operating from China, which used its models for covert influence campaigns targeting U.S. tech and policy debates, including one called “Data Center Bandwagon,” that produced social media comments and comic strips blaming AI data centers for rising household electricity bills. Whoever was operating the accounts prompted ChatGPT in Simplified Chinese via VPNs, and posed on X as Americans from a range of backgrounds. Yet OpenAI’s full threat report found that the activity generated virtually no authentic engagement.

A second cluster, "Tech and Tariffs," generated anti-tariff cartoons under instructions to depict President Trump but never Chinese leader Xi Jinping, and was linked to a network of fake X accounts spreading false claims that ChatGPT user data had been stolen.

OpenAI assessed that the Data Center Bandwagon operators were likely a social media team at a private Chinese tech company working for provincial-level government clients. Among other requests, they asked ChatGPT for comic strips about a grid operator's capacity auction prices, drawing on a regional newspaper's reporting, then posted the output on X under hashtags such as #capacityauction, alongside links to legitimate news coverage.

While no grid operator was named, capacity auction pricing links back to a real, well-documented dispute: PJM Interconnection's independent market monitor has blamed data centers for an "irreversible" 75.5% increase in power costs across the largest U.S. grid region, with wholesale prices near some data center clusters having climbed as much as 267% in five years. Three U.S. senators have demanded answers from Amazon, Google, and Meta over costs passed to residential customers.

"This was not a case of an influence operation creating a debate," Ben Nimmo, principal investigator at OpenAI, said. OpenAI rated the activity Category One on the Breakout Scale, meaning it stayed on one platform, with no evidence that it reached genuine audiences.

The Tech and Tariffs cluster poked fun at U.S.-China competition around tariffs, rare earths, AI, 5G, and industrial resilience, and generated bulk comment batches in English, Italian, Japanese, and Traditional Chinese, the latter aimed at audiences in Taiwan. One operator described the accounts as a "water army," a Chinese term for coordinated troll networks, and asked ChatGPT to design a system for scraping and analyzing social media posts from individuals flagged as risks. OpenAI said its model returned generic data storage advice and declined to help with collection. Fake accounts in the same X network repeatedly posted fabricated claims that ChatGPT user data had been compromised, which OpenAI reckons is an attempt to damage its own reputation.

In its full threat report, OpenAI compared the campaigns to the 2022 Spamouflage operation, which researchers at ASPI and Mandiant found targeting Lynas Rare Earths, Appia, and USA Rare Earth after Beijing's 14th Five-Year Plan prioritized rare earths. The new activity followed the adoption of the 15th Five-Year Plan recommendations, elevating AI as a strategic industry for China.

Meta has finished separating its operations from Manus, the Chinese-founded agentic AI startup it acquired for roughly $2 billion in December, Bloomberg reported, citing people familiar with the matter. Manus employees have reportedly been locked out of Meta's internal data systems since the start of the month. Meta staff are now barred from using Manus tools for internal work, and an internal memo viewed by Bloomberg says Meta is "sunsetting" the platform, with existing Manus projects to be migrated onto Meta's own systems. The split is the first concrete step in complying with Beijing's April order to reverse the completed acquisition, right as the startup's three founders attempt to raise around $1 billion to buy their company back.

China's National Development and Reform Commission (NDRC) ordered the deal undone in April under its foreign investment security review mechanism, the country's rough equivalent of CFIUS in the U.S. It was the first time Beijing has forcibly reversed a completed cross-border AI acquisition, and the commission asserted jurisdiction despite Manus having moved its headquarters and core team from Beijing to Singapore in mid-2025.

The review sharpened in March, when authorities barred co-founders Xiao Hong and Ji Yichao from leaving mainland China, with the order reportedly requiring Manus's Chinese assets to be restored to their pre-acquisition state within weeks.

The order extends to AI companies and their engineers the same restrictions Beijing has been increasingly applying to silicon all year. Chinese regulators have held up Nvidia's H200 shipments even after Washington approved them, while DeepSeek launched its 1.6 trillion parameter V4 model on Huawei silicon. Manus drew comparisons to DeepSeek in Chinese state media as a symbol of domestic AI capability, which made its sale to a U.S. hyperscaler a test case Beijing evidently decided it couldn’t let stand.

A blocked fab or factory sale can simply be reversed by returning equity, equipment, and IP, though: Manu’s value sits in its model weights and engineering know-how, both of which have been freely flowing into Meta for the last six months. No ban or firewall can recall what Meta’s engineers have already learned from that, and Meta hasn’t yet said how it’ll demonstrate to the NDRC that Manus’s tech is out of its stack.

Founders Xiao Hong, Ji Yichao, and Zhang Tao have discussed raising about $1 billion from outside investors to fund a buyback at a valuation at least matching the $2 billion Meta paid, though it remains unclear how far those talks have progressed. Early backers, including Tencent, ZhenFund, and HSG, have already received their proceeds from the sale.

Ever since Anthropic first made the earth-shaking disclosure of its incredibly capable Claude Mythos AI model back in April and the steps it was taking toward a safe release of that product, the AI public has been waiting with bated breath to get its hands on its capabilities to give them a spin. Now, with today's release of Claude Fable 5, Anthropic says it finally has a model of this class that's "safe for general use." Anthropic is also releasing the unrestricted Mythos 5 model to members of its Project Glasswing program for use in sensitive cybersecurity and biology contexts.

As with every new cutting-edge model from frontier labs, Anthropic has a selection of benchmarks of Fable 5's performance across a range of widely accepted tests that highlight its state-of-the-art-ness, but those numbers aren't as interesting as the specific use cases that the company highlights for this level of capability.

For example, the company highlights how Stripe was reportedly able to perform the migration of a 50-million-line Ruby codebase in a single day that would have otherwise required two months of team effort had it been performed by hand. That sort of task compression on a job of such massive scope illustrates how Fable 5 and Mythos 5 "can work autonomously for longer than any previous Claude models," in addition to their high overall level of capability.

For vision tasks, Anthropic simply says that Fable 5 is "the new state-of-the-art model." Among other accomplishments, the company says it was able to play through Pokemon FireRed in its entirety using only a "minimal, vision-only harness." Past models apparently struggled to complete this task even with the ability to seek outside help via tool-calling.

Wharton School professor and AI blogger Ethan Mollick also has vivid examples of what Fable can do. Among other tasks, he describes how he gave Fable a 19-page spec document for the development of a categorization and analysis tool for unstructured survey answers. He describes how the model worked for "nine and a half hours" to generate an "extremely sophisticated" tool that "researchers have needed for years but was never profitable to create."

In order to keep Mythos-level capabilities out of the hands of malicious actors, Anthropic says it will be redirecting queries on certain topics, namely "cybersecurity, biology and chemistry, or distillation," to the last-gen Claude Opus 4.8 instead. The company says that users will be informed whenever this redirection occurs, and that it should trigger in "less than 5%" of interactions with the model.

Mollick, however, says these limitations trip "at the faintest hint of a security problem," suggesting that even the well-intentioned won't be able to use Fable 5 to bolster the security of their code bases.

According to its model card, Fable 5 will also be nerfed in instances where a user attempts to use it to further cutting-edge AI or ML research, which is likely not only motivated by the company's recent concerns around AI self-improvement but also likely to be motivated by competitive concerns with other labs and with geopolitical actors.

Within those guardrails, Anthropic says Fable 5 is available everywhere today, and that access to it will be billed at $10 per million input tokens and $50 per million output tokens via the company's API. That's twice the cost of the now-last-gen Opus 4.8 and just over 3X the cost of Sonnet 4.6.

Those on Anthropic's Pro, Max, Team, and Enterprise subscription plans will have a short gratis access window to Fable 5, from now until June 22. After that point, access to the model will require paying for usage credits. The company says it will restore access to Fable 5 through these plans "as quickly as we can" when it has the compute capacity to do so.

The world hasn't ended as of this writing now that a public Mythos-class model is available. It'll surely be interesting to see what problems people are able (and aren't able) to tackle with an AI model of this level of capability—and with this degree of restriction around its capabilities. The only thing we can say for certain is that things are only going to get weirder from here.

The company that just a few weeks ago told us that its Mythos model was too powerful to be released publicly is now saying that we might need to hit the pause button on AI altogether, while also teaching its AI to build itself. On June 4, Anthropic published a report, when AI builds itself, showing that Claude now writes more than 80% of the code merged into its own production codebase, up from the low single digits before Claude Code reached research preview in February last year, and arguing that the loop has begun to accelerate AI development in a way that could eventually leave humans unable to control the systems being built.

The Anthropic Institute, the firm's research arm, casts the trend as early movement toward recursive self-improvement, the point at which a model designs and builds its own successor without meaningful human input, and warns that the rare misalignment in today's models could keep "growing more frequent but less understood until we lose control of them."

Reading further into the post, and taking the entire frontier AI model development ecosystem reveals some other uncomfortable truths that the developers of cutting-edge AI models also have to reckon with: compute.

Loss of control

Anthropic gave us three predictions of ways the next few years could play out, reserving a particularly dire warning for the case in which models become capable of fully improving themselves. Progress, Amodei’s lab argues, would then be paced almost entirely by available compute, human engineers would be pushed into oversight and verification, and a self-improving model could come to dominate as its abilities outstrip those of the people who built it.

The firm called this — the task of keeping a system's behavior tied to human intent — the part of this future it’s least sure about. A capable, well-aligned model might discover new ways to keep its successors safe, it said, or the reverse could hold, and misalignment could compound generation over generation, with the unusual concession that a sufficiently wise model might instead choose to halt its own development.

The idea of an ultraintelligent machine designing still better machines (“singularity”) has been around for decades. British mathematician I. J. Good argued back in the 1960s through his “intelligence explosion” thesis that such a machine would be the “last invention that man ever need make,” so long as it remained “docile enough” to tell us how to control it. Meanwhile, the “Godfather of AI,” Geoffrey Hinton, has put the odds of AI causing human extinction within three decades at 10% to 20%.

The International AI Safety Report, chaired by Yoshua Bengio and published in January 2025 with input from more than 100 experts across 30 countries, defines loss of control as a scenario in which AI systems operate outside anyone's control with no clear path to regaining it.

Every figure behind the warning coming out of Anthropic is based on data from within, and none of it has been independently audited. Among this data is its claim that in Q2 2026, the typical Anthropic engineer is merging eight times as much code per day as in 2024. On the hardest, least-specified coding tasks, Claude succeeded 76% of the time in May 2026, a rise of 50 percentage points in six months. On an internal test that asks each new model to make training code run faster, results climbed from roughly triple the original speed with Claude Opus 4 in May 2025 to about 52 times with the unreleased Mythos Preview model by April 2026, against the four to eight hours a skilled researcher needs for a fourfold gain.

In fairness, Anthropic does then call lines of code a poor proxy for output and admits that the eight times figure almost certainly overstates the real gain. Its research-judgment study, in which models beat the human's next step 64% of the time, drew on 129 moments the company deliberately picked because the human's choice had room for improvement, so it’s not a like-for-like contest.

The report publishes no breakdown isolating how much recent capability gain comes from the self-improvement loop rather than from raw compute, more data, and human-led research. Cognitive scientist Gary Marcus called the piece a "bait and switch" on his Substack, arguing the company had shown faster coding under human direction rather than a system improving itself. Bentley University mathematician Noah Giansiracusa told Scientific American, "I don't think it's a genuine call to slow down."

AI is writing everyone’s code

Anthropic isn’t alone here. Google CEO Sundar Pichai said in an April blog post that 75% of new code at Google is now AI-generated and approved by engineers, up from 50% the previous autumn. OpenAI's Jakub Pachocki has described the company's Codex agent as “a very early version of an AI researcher,” and OpenAI has said it’s building toward a fully automated one. Chinese developer MiniMax marketed its M2.7 model in March as "self-evolving," claiming it ran its own scaffold-optimization rounds and handled a large share of its reinforcement-learning research, though the benchmarks were internal and unreplicated.

Independent measurements do somewhat support a trend of fast improvement without confirming a runaway one that the AI labs are talking about. METR, for example, found last year that the length of task an AI can finish with 50% reliability has been doubling roughly every seven months. On its RE-Bench research benchmark, the best agents beat human experts given two hours, but the humans pulled ahead at eight hours and roughly doubled the top agent's score at 32 hours. AI's advantage so far sits in short, well-defined bursts, not the sustained, open-ended work that research depends on, which is the human edge Anthropic has said is still holding strong.

No compute means no runaway AI

Anthropic half-buries the fact that it’s compute capacity that’s ultimately the binding constraint in all of this. It names chip fabrication, grid expansion, and interconnect bandwidth as the factors that could cap progress ahead of intelligence itself. We’re all aware that those limits are solid as things currently stand: SK hynix and Micron have sold out HBM output for the year, high-power transformers carry three-to-five-year lead times, switchgear is booked into 2028, and grid-interconnection queues run three to seven years.

A Sightline Climate analysis estimated that 30% to 50% of large data centers due to open in 2026 will slip or cancel. U.S. data centers drew about 4.4% of national electricity in 2023, a share the Department of Energy's Lawrence Berkeley National Laboratory expects to reach 6.7% to 12% by 2028. Meanwhile, the four largest hyperscalers are on course to spend more than $650 billion on AI infrastructure this year.

Whether compute ultimately puts a lid on any out-of-control, self-improving loop is an unsettled debate. Forethought researcher Tom Davidson argues that there’s a chance that compute bottlenecks won’t “slow down a software intelligence explosion until its late stages,” while Epoch AI counters that if compute and cognitive labor are complements rather than substitutes, software-only acceleration stalls once it hits a compute wall.

‘No, you hang up first’

As for pausing AI development, Anthropic says it’ll only do this if rival labs at or near the frontier do the same in a verifiable way, and that a halt by one company wouldn’t change who’s leading the way.

This is a facetious suggestion at best that insults the intelligence of anyone who has been paying attention to the AI arms race. It’s beyond obvious that no lab this far down the road — let alone Anthropic — is ever going to ease off, especially when Anthropic’s own report essentially doubles as a piece of marketing for how fast it can make Claude build Claude. To suggest in one breath that AI might need to be paused or slowed down in one breath and then saying “but everyone else needs to go first” in another is quite the remark.

Anthropic’s report also came just days after the company confidentially filed for an IPO at a reported valuation near $965 billion, a glaring juxtaposition that read as a front-runner lobbying for limits it stands to help set. Anthropic made a self-assessment in April, when it said its Mythos Preview model had found thousands of severe vulnerabilities, a claim that later drew scrutiny over how much of it rested on a small manual sample.

SpaceX has announced a new 11-million-square-foot Gigasat factory in Bastrop, Texas, dedicated to building the infrastructure needed to achieve the company's orbital data center goal. In an internally conducted interview posted on X on June 8, SpaceX CEO Elon Musk confirmed that the facility is expected to begin producing complete "AI satellites" by 2027. The company is targeting 1 gigawatt (GW) of orbital AI compute capacity by the end of next year, with plans to scale that figure by an order of magnitude annually thereafter.

During the interview, Musk unveiled the build and specs of the proposed AI1 satellite, the company’s take on orbital data centers. The satellite will span roughly 70 meters (229.66 feet), with a massive solar array — generating power at a density of 250 W/m² — making up the bulk of the structure. AI1 will also feature vertically oriented, double-sided radiators for cooling, with the 150-kilowatt (kW) peak compute payload positioned right in the middle of the structure.

The massive 11-million-square-foot Gigasat facility — more than ten times larger than Starfactory, SpaceX's current largest spacecraft manufacturing complex — will vertically integrate much of the AI1 satellite supply chain on a single campus. The facility will manufacture solar ingots and wafers, solar cells, printed circuit boards (PCBs), silicon-based electronic components, user terminals, gateways, and the AI1 satellites themselves. The 1,000-acre site will also include dedicated satellite development and testing facilities, warehousing and logistics infrastructure, and a large-scale AI satellite production line.

According to Musk, the solar manufacturing facilities are already under construction, while the AI satellite production building is about to break ground. SpaceX expects to be producing a "reasonable volume" of these orbital data centers by the end of 2027. While each satellite will carry 150 kW of compute power, the company aims to achieve 1 GW per year of space AI compute in that same time. That would mean launching on the order of more than 6,000 AI1 satellites in a single year. For context, Starlink has about 10,500 active satellites as of June 2026.

Musk hopes to scale to 100 GW per year by 2030 and even has eyes on Terrawatt-level computing, completely solar-powered in space. "This is what we are going to try to do and think we probably can do, which is to get to roughly an annualized rate of a gigawatt per year by the end of next year at Space AI compute. And then aspirationally, scale that by an order of magnitude per year. So in two and a half years, hitting an annualized rate of 10 gigawatts a year at Space. And three and a half years, maybe 100 gigawatts," he said, while also expressing a desire to one day scale to a terawatt per year, depending on progress in chip making.

The largest AI data center anyone has actually announced is Meta's Hyperion in Louisiana, designed to scale up to 5 GW and house roughly 2 million GPUs at full buildout, at over $100 billion — and even that only reaches its first 2 GW phase by 2030. xAI's own Colossus 2 in Memphis just expanded to nearly 2 GW of capacity, with 555,000 GPUs, for about $18 billion, making it the world's largest single-site AI installation. So 100 GW/year is approximately 20 Hyperions or 50 Colossus-2s per year.

Achieving this will take an unprecedented volume of chips, and Musk's answer is Terafab — a SpaceX/Tesla/xAI venture in Austin aiming to fab 1 terawatt of compute a year, roughly 100 to 200 million advanced chips, in a 100-million-square-foot plant. However, the project itself is being met with widespread skepticism. For starters, none of the three companies has ever made a chip, yet they're starting at the bleeding-edge 2nm node. Our in-depth Terafab industry analysis explores various other reasons why this skepticism is valid.

Multi-Gigawatt ambitions aside, Gigastat is a significant step towards orbital data center goals that are increasingly seen as a potential viable solution to the extreme power consumption of on-ground data centers. Not unexpectedly, SpaceX is leading the race. Manufacturing solar arrays, wiring, and satellite bodies at volume is largely conventional work — much of it built on technology SpaceX already produces for its Starlink V3 satellites, just at far greater scale — which is part of why a 2027 production start is credible.

A recent Mercer survey of nearly 12,000 C-suite executives, HR leaders, investors, and employees found that 99% of CEOs expect AI and automation to drive at least some headcount reduction in the next two years. At the same time, the report found that only 32% of executives believe their organizations are effective at combining human labor with AI systems.

These somewhat contradictory stats form the basis of an ongoing debate over AI and jobs. The data from Mercer shows that companies are indeed cutting or expect to cut significant portions of their workforce. In fact, we recently reported that 40,000 tech industry employees lost their jobs in Q1, 2026.

Now, companies are under pressure to show that these job cuts and billions of dollars in AI spending can translate into measurable returns. Workers, meanwhile, are already being affected as employers redesign teams, slow junior hiring, and tie AI to cost-cutting decisions before broader economic data shows a clear wave of AI-driven job replacement.

The evidence so far does not show an outright, simple story in which AI is massively replacing workers across the economy. Nor has it been proven that the actual AI replacements have proven useful. In another twist, it's possible that what OpenAI CEO Sam Altman calls “AI washing” — blaming AI for layoffs that may have happened anyway — is tainting the data.

Maximum pressure at the bottom of the ladder

While the layoffs create a valid, growing concern about unemployment, the most immediate impact appears to be on who companies are willing or unwilling to hire, and for what roles. Mercer’s report suggests that younger workers are especially exposed, with entry professionals aged 22 to 27 facing the highest perceived risk of disruption. This is because generative AI is strongest at the codifiable, repeatable tasks that often make up entry-level roles through which new workers are traditionally trained and integrated into the system.

A similar 2026 CEO survey by consulting firm Oliver Wyman points in the same direction. The firm found that the share of companies planning to reduce junior roles has jumped from 17% to 43% in a single year, while 33% are shifting their workforce mix toward midlevel roles. This stat presents another concern. Companies removing junior roles may reduce costs in the short term. However, the move may also weaken their own future talent pipeline. A labor market that demands experience while eliminating the jobs that create experience risks imploding.

For now, the full picture remains quite murky, with contradictions in the available data. Oliver Wyman notes that some of the most advanced AI adopters are not abandoning junior hiring entirely. In fact, companies reporting stronger AI returns are somewhat more likely than weaker performers to shift toward junior workers, suggesting that at least some businesses see AI-literate early-career staff as an asset rather than a cost.

This makes the entry-level story more complicated. AI may reduce demand for some traditional junior tasks, but it could also increase demand for workers who can use AI tools effectively inside redesigned workflows. The real risk is that companies treat the technology as a simple substitute for early-career workers before they understand which roles should be automated, augmented, or rebuilt.

The productivity evidence is still unclear

The case for AI-driven layoffs depends heavily on one assumption: that AI is making workers and teams productive enough to justify smaller headcounts, while also cutting costs. So far, the evidence is mixed. Mercer’s findings show that executives see AI as central to future performance, but also that many organizations are struggling to redesign work around it. Oliver Wyman found that 53% of CEOs still say it is too early to assess the return on investment from AI, up from 41% last year. It also found that 67% of companies are still primarily planning or piloting AI rather than scaling it across the business.

This gap between ambition and proof reveals that AI can be impressive at the task level without immediately transforming company-level productivity. A chatbot that drafts an email faster or helps a programmer debug code may save time for an individual worker. However, turning that into measurable revenue growth, lower operating costs, or a sustainably smaller workforce is a different challenge.

There are workflow redesigns, data cleaning and integration, and compliance risk management, among several other accompanying tasks. Employees need training on how and when to apply AI. They also need cybersecurity training as hackers are increasingly finding ways to exploit systems through AI chatbots. Managers need to know which outputs can be trusted and which require human review. There’s also the question of how much responsibility AI can safely handle. We recently covered a case where a Claude-powered AI coding agent deleted a company's entire database. In many companies, these changes are slower and more difficult than the early AI hype suggested.

European data further complicates the replacement narrative. A European Central Bank analysis of firms that use and invest in AI found no significant overall difference in job creation or destruction between businesses that use AI and those that do not. In some cases, companies with more intensive AI use or investment were slightly more likely to be hiring, especially where AI supported research, development, and innovation.

While none of this proves that AI will not still reduce employment later, it does suggest that the current relationship between AI and jobs is not as straightforward as many layoff announcements imply. In the near term, AI may be helping some companies grow, while leaving many still searching for measurable returns.

“AI washing” is tainting the data

As if the data isn’t painting an unclear enough picture, there’s a real possibility that companies are falsely using AI as an excuse to fire workers. Layoff announcements rarely provide enough detail to distinguish genuine AI displacement from broader corporate restructuring or even serious internal issues. Sam Altman, whose company helped trigger the generative AI boom, has warned that some firms are engaging in “AI washing” by blaming AI for job cuts they would have made anyway. He also acknowledged that real displacement is happening and is likely to become more visible over time. Both points can be true.

This is why the current wave of AI-linked layoffs should be read carefully. A company may cite automation while also dealing with overhiring from the pandemic period, weaker demand, outsourcing, margin pressure, a falling share price, activist investors, or a broader strategic reset. AI can be the cause, the tool, the justification, or merely the language used to present a decision to investors.

Now, this does not mean AI is irrelevant to job cuts. Major companies across banking, retail, technology, and professional services are already reorganizing work around automation. Standard Chartered has discussed thousands of job cuts tied to automation and lower-value roles. Other firms, such as Amazon and Meta, have cited AI as part of broader efficiency drives.

As an increasing number of companies now believe AI will allow smaller teams to do more, even uncertain productivity gains are influencing hiring plans. The powerful technology is arriving in companies already under pressure to cut costs, demonstrate growth, and satisfy investors. As a result, managers may delay backfilling roles, and graduate hiring may slow, with entry-level work being bundled into contractor roles or existing mid-level positions.

The danger for companies is that cutting too deeply into junior roles could create a skills shortage later. The danger for workers is more immediate, as the traditional route into white-collar careers may narrow before a clear replacement path emerges. For now, the most defensible reading is also the least dramatic, and may be somewhat on the fence: AI is neither harmless, an automatic jobs apocalypse, nor a magic potion for instant growth and productivity.

SpaceX just announced that it closed a multi-year deal to provide compute capacity to Google. The agreement, which is worth $920 million per month, will begin in October 2026 and is expected to continue until June 2029. Reuters said that the transaction includes 110,000 Nvidia GPUs, plus CPUs, memory, and all other components needed for AI processing.

It appears that Elon Musk's company will not deliver the entire 110,000-strong GPU compute capacity in one go — Google will pay a reduced monthly fee as the company brings more server racks online through September 30, 2027. If SpaceX cannot hit the 110,000-GPU target on that date plus a one-month grace period, then Google can cancel the agreement or settle for the lower number of available GPUs “with a corresponding pro-rata reduction in the monthly fees.” It also gave the two parties the option to cancel the deal altogether after December 31, 2027, provided that they give a 90-day notice to the other.

This is the second major deal that SpaceX announced in months, as Anthropic secured the entire computing power of SpaceX’s Colossus 1 data center in early May. This was a surprising move, especially as Colossus 1 is one of the company’s most hyped assets, which Elon Musk launched in just 19 days. It turns out that launching it at such speed meant that it has a mix of H100, H200, and GB200 GPUs, which is resulting in efficiencies for training AI LLMs as the faster GB200 GPUs end up waiting for the older, slower GPUs before it can complete each computational step. Anthropic is instead using it for inferencing, especially as it is struggling to keep up with the demands of its growing user base.

The combined annual value of just these two deals is already worth more than SpaceX’s entire revenue for 2025. Reuters estimated that they would bring in more than $25 billion annually to the company, compared to the less than $20 billion that it made from Starlink, launch services, and AI revenue.

These massive deals, worth more than $70 billion in total, will lift SpaceX as it targets a $1.75 trillion IPO on June 12, 2026. While it started out as a space exploration company and is known for commercially launching satellites at a fraction of the cost compared to NASA and providing relatively affordable and stable satellite internet, it’s actively expanding towards orbital data centers. SpaceX acquired xAI earlier this year to help achieve that dream and has even filed some documents at the FCC detailing its plans. Google is also reportedly in talks with the company for a slice of the orbital data center pie.

A research group that includes Huawei Technologies says it completed full-parameter post-training of DeepSeek's V4-Pro, a 1.6-trillion-parameter model. The group used a cluster of at least 1,000 Huawei Ascend 910C chips, according to the Shenzhen municipal government, as reported by the South China Morning Post.

The revelation is evidence that Chinese accelerators can now handle a training-class workload on domestic silicon, the part of the AI pipeline Chinese firms have had the most trouble moving off Nvidia hardware under U.S. export controls. Huawei carried out the work with the Shenzhen Loop Area Institute, the Shenzhen campus of Harbin Institute of Technology, and the Shenzhen Research Institute of Big Data.

The Ascend 910C is Huawei's current flagship AI accelerator, a dual-die part that returned roughly 60% of an Nvidia H100's inference performance in earlier DeepSeek testing. Chinese chips have been competitive at inference, where a finished model answers prompts, but weak at training, where a model's weights are recalculated across large datasets. The team says it ran full-parameter post-training, meaning every weight was updated rather than a thin adapter layer added on top.

Post-training is essentially the “tuning” stage that follows the much larger pre-training phase. Pre-training builds a model's core capabilities by working through enormous text corpora, and DeepSeek's documentation puts V4-Pro's pre-training corpus at more than 32 trillion tokens.

Post-training then shapes behavior through instruction-following, safety alignment, and task-specific data. Completing it on Ascend silicon is a genuine result for the platform, but it doesn’t demonstrate that the chips can pre-train a frontier model from scratch, which is the heavier and costlier job.

Back in August, it was reported that DeepSeek couldn’t complete a single successful training run for its R2 model in Ascend chips, even with Huawei engineers on site, blaming unstable performance, slow chip-to-chip interconnects, and gaps in Huawei's CANN software stack, its substitute for Nvidia's CUDA. The company fell back on Nvidia GPUs for training and left Ascend on inference. DeepSeek-V4-Pro, released in April, was the first DeepSeek model built around Ascend from the outset.

As for the claim coming out of Shenzen, it carries no benchmarks, gives no figure for how long the run took, how it compared to the same job on Nvidia hardware, or how efficiently the 1,000-chip cluster was used. It’s ultimately just another addition to a series of dubious claims that have come from the Chinese state without anything to back them up; DeepSeek itself hasn’t commented.

Just months after the U.S. Department of Defense (DOD) labelled Anthropic a "supply chain risk", completely cutting off the firm from being a vendor of any AI wares, a new report claims that the National Security Agency (NSA) is using Anthropic's cybersecurity-focused Mythos model "for offensive cyber operations" with the help of "half a dozen" staff from Anthropic embedded inside the agency. The report comes from Financial Times (FT), which cites "people familiar with the arrangement."

According to the report, the rationale behind the move is that the U.S.' adversaries will surely be using cybersecurity-focused AI models like Mythos, and gaining an early upper hand is fundamental. Regardless of political optics, this logic has some merit in the face of a security landscape that's shattering the usual 90-day vulnerability report window and bringing the zero-day exploit timeframe down to a nice round zero. It's worth noting that the FT's contacts are unaware as to whether the embedded staff are assisting in any active operations (as opposed to simply guiding the use of the technology and customizing models), but they expect the bot to be useful for "infiltrating the networks of nations such as China or Iran."

The article states that Anthropic has posted a handful of "embedding engineers" inside the NSA to help with using and customizing Mythos for "specialized applications", likely meaning tailoring it to unique types of cyber-attacks. This news is surprising, as for the time being, Anthropic is theoretically banned from offering services to any branch of the DOD. It's not only DOD representatives who will be staring at their proverbial plates, either, as the cheeks of Anthropic's execs might likewise be tinted pink.

The dispute started in January 2026, a time during which the DOD and Anthropic were negotiating a $200 million contract. The Trump administration demanded that Anthropic allow usage of its wares for "all lawful purposes", implying removing some or all AI guardrails, a move that went against the firm's usage policy. This caused Anthropic to refuse to comply with the request, and CEO Dario Amodei to post a statement defending the company's position against its tech being used for tasks like mass surveillance and autonomous weapons, noting that "in a narrow set of cases, [Anthropic believes] AI can undermine, rather than defend, democratic values."

The DOD promptly shot down the contract, and eventually, in early March 2026, the Trump administration took the unprecedented step of labeling Anthropic, an American company, as a supply chain risk, putting it in the same bag as Huawei and ZTE. Anthropic proceeded to file a currently ongoing lawsuit against the DOD, calling the decision a violation of the First Amendment. The DOD went on to continue work with Anthropic rival OpenAI instead, something that may have granted a financial foothold to the revenue-embattled firm. Ironically enough, after public backlash, OpenAI added some safety clauses to its deal, which the DOD accepted, suggesting that its fracas with Anthropic may have been politically motivated.

The news also comes hot on the heels of the Trump administration issuing an executive order that asks AI companies to volunteer their leading-edge models to the government for 30 days before they're made available to other entities, though it's unclear what will be classified as a "covered frontier model" or a "trusted partner", and what happens if a company doesn't play ball.

There's no telling if the NSA purportedly using Mythos is a sign that the DOD may be warming up to Anthropic again, or if there's a schism inside the agency that believes Mythos is a first-rate necessity. The ongoing nature of the two Anthropic-DOD suits also muddies the waters even more, as two separate courts have so far issued contradictory rulings on the matter.

Mythos is currently in an early-access stage, as Anthropic announced it would distribute it to 150 organizations across 15 countries, up and out from only a handful of U.S. organizations after its April release. The news follows a revelation from Anthropic this week claiming that Claude AI is now helping to build itself at a much faster rate than expected, prompting the company to call for the option to slow down or even halt AI development.

A coalition of nine US trade associations has urged the Trump administration to take immediate action on what it describes as an emerging memory chip shortage driven by the explosive growth of AI data centers. In a June 3 letter sent to US Commerce Secretary Howard Lutnick and Treasury Secretary Scott Bessent shared with Tom's Hardware, the organizations — representing telecommunications providers, automakers, medical device manufacturers, and major retailers — warned that AI infrastructure deployments are consuming an outsized share of global memory production, creating supply constraints and price increases that could ripple across large segments of the US economy.

The coalition warned that the AI data center expansion, which has consumed an unprecedented share of global memory capacity, has led to a memory chip shortage that could lead to higher prices for consumer electronics, increased costs for broadband and telecommunications infrastructure, disruptions to automobile and medical device production, and delays affecting federal contractors attempting to fulfill government procurement obligations. The letter argues that these risks are emerging despite billions of dollars of US investment intended to strengthen domestic semiconductor supply chains.

The signatories acknowledged AI's importance but argue it shouldn't come at the expense of the rest of the economy. "While recent developments in AI offer the promise of generational technological advances and are important for US tech leadership, we must also ensure other key industries are not negatively impacted by this disruption in the marketplace," the coalition said.

The organizations are asking the administration to work directly with memory suppliers and major chip buyers to address the imbalance. Their recommendations include accelerating expansion of memory manufacturing capacity in the United States and allied nations, using trade agreements to strengthen supply-chain resilience, ensuring adequate memory supply for non-AI industries, leveraging CHIPS Act programs where possible, and reducing regulatory barriers that may slow capacity growth.

"We urge the Administration to work with memory chipmakers and chip buyers to assess steps that can be taken to address this imbalance in the memory market and protect against harm to consumers, workers, and businesses of all sizes," the letter states.

The warning arrives as memory manufacturers increasingly prioritize high-bandwidth memory (HBM), the specialized memory used in AI accelerators from companies such as Nvidia and AMD. Demand for HBM has surged over the past two years as hyperscalers race to deploy larger AI clusters, prompting memory suppliers to devote an increasing share of their production capacity to AI-oriented products.

Samsung and SK Hynix — which together with Micron control over 95% of global DRAM production — have been diverting wafer capacity toward high-margin HBM for AI accelerators, starving the commodity DRAM and NAND markets in the process. Both companies warned in April that significant shortages will continue through at least 2027. IDC, meanwhile, has already revised its 2026 PC market forecast downward by up to 9% as a direct consequence of memory scarcity and rising prices.

Industry analysts have repeatedly warned for months that AI demand is reshaping the economics of the memory market. While memory shortages have historically been cyclical, the coalition argues that AI infrastructure spending is creating a structural shift large enough to affect industries far removed from data centers. The letter marks the first coordinated, multi-industry push for federal intervention. Whether the administration will respond — and how — remains to be seen.

Anthropic has published a report warning that the development path it’s on could eventually leave humans unable to control AI systems, even as it disclosed that Claude now writes more than 80% of the code merged into its own codebase. The Anthropic Institute, the company's research arm, said AI has already started to speed up AI development and that the trend could lead to recursive self-improvement, the point at which a model designs and builds its own successor with little human input. The report argued that the world should keep open the option to slow or pause frontier development, and cautioned that the occasional misalignment seen in current models could grow more common and harder to understand as those models build the next generation.

The company set out three pretty dire ways the next few years could unfold, reserving its most severe warnings for the scenario in which models become capable of fully improving themselves. In that case, Anthropic said, the pace of progress would be set almost entirely by available compute, with humans pushed toward oversight and verification roles and a self-improving model dominating as its abilities outstrip those of the people who built it.

The firm described this potential issue with alignment and the task of keeping a system's behavior tied to human intent as part of the future it’s least sure about. Misalignment that’s rare and survivable today could compound generation over generation until control slips, it said, though it allowed that a sufficiently capable and well-aligned model might instead choose to halt its own development. Anthropic wrote that this misalignment could keep "growing more frequent but less understood until we lose control of them."

Anthopric is backing these warnings with a bunch of internal figures that we’ve not seen before. More than 80% of the code merged into its production codebase as of last month was authored by Claude, up from low single digits before Claude Code reached research preview in February last year. Anthropic says the typical engineer is now “merging 8x as much code per quarter as they did from 2021-2025.”

On the hardest, least-specified coding tasks, Anthropic said Claude succeeded 76% of the time in May 2026, a rise of 50 percentage points in six months. A recurring internal test that asks each new model to make training code run faster saw results climb from roughly triple the original speed with Claude Opus 4 in May 2025 to about 52 times with the unreleased Mythos Preview model in April.

Anthropic said it’d slow or pause only if rival labs at or near the frontier did the same in a verifiable way, and that a halt by one company would change who leads without achieving anything wider. That’s obviously not going to happen.

All the figures cited by Anthropic are self-reported and unaudited, and come days after the company filed to go public. The company issued a similar self-assessment in April, when it said Mythos Preview had found thousands of severe software vulnerabilities, a claim that later drew scrutiny over how much of it rested on a small manual sample.

Meta has moved from building traditional structures for its data centers to putting up tents across the U.S. and sticking AI servers inside them. Michael Thomas, founder of market intelligence and data center tracking firm Cleanview Energy, said on X that the AI tech firm has already built or is in the process of constructing three data centers that use the strategy.

One site, located in New Albany, Ohio, already has five buildings that took approximately two to three years to complete. The company then started putting up five tents, with an area of around 125,000 square feet each, in the area. City permits seen by Cleanview Energy say that the construction started in April 2026, while recent satellite images show that the structures have already been completed.

Meta CEO Mark Zuckerberg first announced the strategy of pitching tents and filling them with AI servers last year. It seems that he wanted the infrastructure to come online quickly while demand for compute is increasing exponentially. It’s said that Meta is inspired by Elon Musk’s feat with xAI, which built a 100,000-strong AI data center in just 19 days in 2024 — something which usually takes four years, according to Nvidia CEO Jensen Huang. The technique is apparently quite effective, and it’s now being applied to two other sites, including one in Tennessee.

Putting AI servers inside tents, officially called “rapid deployment structures,” is one of the more unique approaches to the AI build-out, Thomas said. They’re certainly not as sturdy as physical buildings made from steel and concrete, with one commenter comparing it to the “classic $10k racing bike with a $9 lock” situation. Nevertheless, the company has probably weighed the pros and cons of such a setup and has decided that it was worth taking the risk to gain an advantage in the AI infrastructure race.

Another factor that allowed Meta to bring its data centers online at a much faster pace is its use of “behind-the-meter” power, in which the company installed its own turbines to produce power on-site rather than relying on grid power. This is similar to what Musk did with his Memphis Supercluster, which he initially powered with portable power generators. However, Meta’s turbines would be a permanent feature on the Ohio site, as it’s designed to run independently of the power grid.

Thomas said that there’s about 2GW of capacity available from behind-the-meter data centers, with an extra 1GW expected to go online this year for a total of 3GW. If on-going projects stay on schedule, the Cleanview report says that the total capacity could hit 13GW by the end of 2027 — about the same amount of power generated by 13 nuclear power plants and more than enough to power NYC.

This combination of seemingly temporary structures and jet engines strapped to the ground is certainly an interesting combination. As one reporter said, “It’s like a scene out of the movie Mad Max.”

TSMC CEO C.C. Wei has told company shareholders that it still won’t be able to completely address the production demands for AI chips in the years to come. Even though the company has opened multiple fabs across the world, including the one in Arizona, the insatiable demand for the most advanced processors means that there still isn’t enough production capacity to go around for all customers. Bloomberg reports that the additional capacity that is expected to go online in the U.S. is still not enough to feed the increasing demand from hyperscalers.

“It will be a long time before we can meet customer demand,” C.C. Wei said. Despite that, TSMC is still forecasting a 30% increase in sales this year. It’s also not taking advantage of the supply bottleneck, with Wei adding that the company will avoid sudden price hikes similar to the memory and storage chip market industry’s experience to ensure business stability.

Hyperscaler buildouts are expected to hit $725 billion just this year. And unless the AI bubble bursts, demand is only expected to go up every year. TSMC has been building many new fabs in Taiwan, the U.S., and other parts of the world. But building these manufacturing plants would take years, and it seems that semiconductor manufacturing demand would outpace supply if the current AI infrastructure build-out continues.

TSMC Arizona’s manufacturing capacity has been sold out through 2027 since early 2025, showing the massive demand for the company’s output. The company is continually expanding this site, too, with its Taiwan headquarters authorizing a $20 billion capital injection last month to continue the development of Fab 21 phase 2. It’s expected that this would allow the company to start mass producing 3nm chips in Arizona in 2027, which is about a year earlier than the original 2028 launch date. There have also been reports of additional fabs and other units, bringing the total Arizona site to 12 fabs, 4 packaging facilities, and an R&D center.

These production shortages mean that TSMC can expect that their expansion plans will have customers once they’re completed. However, this is also an opportunity for Intel, which is trying to win customers for its 18A and 14A processes. Both Apple and Nvidia are reportedly considering Intel for some of their 2028 chip production, and sources say that the former has already reached a preliminary agreement with Team Blue.

The lack of availability has also led Elon Musk into semiconductor manufacturing. Even though building chips is a totally different beast when compared to building electric cars and even rocket ships, it seems that the billionaire founder is ready to take on the challenge with Terafab. It seems that he’s pretty serious, too, with his team already in talks with various suppliers and that they’re willing to pay a premium to ensure priority.

U.S. tech companies announced 38,242 job cuts in May, more than any other sector and the industry's heaviest month of reductions in nearly two years, according to data published Thursday by outplacement firm Challenger, Gray & Christmas. This monumental figure lifted the sector's 2026 running total to 123,653, up more than 65% on the same stretch of 2025, even as tech held the largest hiring plans of any industry and as the biggest firms raised their combined AI capital spending toward $725 billion for the year. AI was, of course, the most-cited reason for layoffs across every sector for the third month running.

Employers across all industries announced about 97,000 cuts in May, up from 83,387 in April. Transportation ran a distant second to technology at 6,909, followed by services at 6,268. Meanwhile, U.S. employers have announced 80,472 planned hires in 2026 to date, with tech accounting for the largest share of any sector.

Claims for unemployment insurance haven’t risen in line with the layoff announcements, however, and the Labor Department's May payrolls report, due Friday, is expected to show 85,000 jobs added. "AI is now the leading reason companies give for cutting jobs," said Andy Challenger, chief revenue officer at Challenger, Gray & Christmas, in a statement.

Several of the firms on this year's layoff lists are also the heaviest spenders on AI hardware. Google, Amazon, Microsoft, and Meta plan a combined $725 billion in capital spending in 2026, up 77% on last year, and Microsoft has attributed $25 billion of its own budget to rising memory and component prices. Meta CEO Mark Zuckerberg told staff that the company's roughly 8,000 job cuts were a direct consequence of its AI infrastructure spending. Roughly three-quarters of that hyperscaler capital outlay this year is tied to AI infrastructure such as servers, GPUs, and data centers rather than conventional cloud capacity.

Whether AI is actually doing the work of the people being cut is an ongoing and unsettled debate. Challenger has ranked AI only the third-leading stated reason for layoffs in 2026, behind market conditions and restructuring, and the firm had logged the technology in more than 49,000 planned cuts through April.

The firm has also said AI is so far claiming the budgets for those roles rather than the roles themselves. OpenAI's Sam Altman has accused some employers of "AI washing," leaning on the technology to justify reductions they would have made regardless, but flat jobless claims and Friday's expected payroll data gain leave the case for broad AI displacement unproven for now.

The rapid increase in agentic internet traffic means “bots have now passed human traffic online for the first time in the Internet's history,” according to the CEO and co-founder of Cloudflare, Matthew Prince. “Welp, that happened faster than I predicted,” Prince awkwardly admitted, making his previous expectations of the crossover happening sometime in 2027 seem way off the mark.

Before going on, it’s important to differentiate this new surge in internet traffic from the traditional bots most will be aware of, things like website crawlers, search indexers, and bad stuff like fraud or abuse bots. It is different now, as Cloudflare is charting agents that browse the web much like humans on behalf of humans, and it is already at a massive scale.

You might wonder what all these AI agent bots are actually up to, particularly if you’re not running your own army of digital helpers. Thankfully, Cloudflare has addressed the scope of AI bot activity in previous articles and blogs. Also, last year it started classifying traffic according to these new website visitors (e.g., signed agents and verified bots), which is why the charts don’t go back very far.

Cloudflare reckons these AI agents are online doing stuff like reading product pages, checking prices, performing multi-step tasks online like comparing flights, scraping and indexing web content (but for AI models, not search engines), and acting as personal assistants to order food, compare and shop, and handle customer service interactions.

At the time of writing, Cloudflare data suggests that the balance between bot vs. human web traffic (HTTP requests) is already firmly favoring the former, split 57.5 vs. 42.5 percent. A major shift from humans clicking around, being the primary customers of the web, to AI agents doing these tasks has already happened. The rate of change has even taken Prince by surprise. In replies to the embedded Tweet, Prince also noted that the date of the human/bot crossover wasn’t clear as the “data [is] a bit messy.” Nevertheless, we are “clearly on the other side now,” he added.

However, Cloudflare metrics measure HTTP requests, not engagement. Flesh-and-blood folks remain the primary users of the web in terms of total time spent in app usage, streaming, and infinite-scrolling feeds. These mediums simply don't generate the same volume of rapid-fire page-load requests as automated agents do.

We were also interested in looking at Cloudflare’s breakdown of human/bot traffic by country. The most bot-ridden traffic comes from the tiny island of Gibraltar (92.1%), followed by Singapore (76.4%), then Iran (76.4%). While some of these places have a lot of data centers and hosting infrastructure compared to population size, Iran’s high bot count may rather come from the heavy use of VPNs with automated scraping and bypass tools. Cloudflare has also previously flagged Iran as a hotspot for malicious bot activity.

Several AMD partners are showing off the company’s next-generation Helios rack-scale solution running AMD’s EPYC ‘Venice’ processors and Instinct MI455X AI accelerators at Computex 2026 in Taipei, Taiwan. The units are set to become available later this year. There is one major catch, though: they all use UALink-over-Ethernet scale-up connectivity, which may limit their performance in certain workloads that depend on the connection performance. That said, Helios systems with ‘true’ UALink interconnects will also be available.

AMD’s Helios is the company's first rack-scale AI system, and is set to rival Nvidia’s NVL72 VR200 machines based on the next-generation Vera Rubin platform. Helios will rely on AMD’s 6th Generation EPYC Venice CPUs with up to 256 cores, pack 72 Instinct MI455X accelerators with a total of 31 TB of HBM4 memory, and 1400 TB/s of bandwidth. AMD estimates that its performance will be around 2900 FP4 dense PFLOPS, which puts the unit behind Nvidia's VR200 NVL72 system in terms of compute performance, but ahead of it with HBM4 memory capacity. This promises to provide Helios-based systems an advantage in memory-intensive workloads, such as when running large LLMs.

The AI accelerators are interconnected and make use of a UALink-over-Ethernet connection, which provides up to 260 TB/s aggregated scale-up bandwidth (in line with Nvidia’s NVL72 VR200). Helios will also feature Pensando Vulcano network interface cards (NICs), which are among the industry's first 800 GbE network cards that comply with the Ultra Ethernet specification and provide up to 43 TB/s of scale-out bandwidth.

However, the interconnection used on these Helios systems will vary. The machine supports both UALink and UALink-over-Ethernet, but the initial versions will use the latter, rather than the former. This is likely because UALink switches aren't finalized and are pending validation and qualification by AMD’s AI customers.

The biggest advantage of using UALink over Ethernet is that AMD can build Helios using an existing, widely supported ecosystem of validated and qualified components. Ethernet switching ASICs, cables, and other ingredients are already used by hyperscalers and cloud providers worldwide, which accelerates deployment.

But there is a major downside with using Ethernet, even with the UALink protocol on top: Ethernet was originally designed as a general-purpose networking technology; it was never designed to scale up AI accelerators.

As a result, communications may involve higher latency, more protocol overhead, and less deterministic performance than a dedicated scale-up fabric. For large AI training jobs that need all 72 Instinct MI455X accelerators to work in concert, communication efficiency is as important as compute performance. If the UALink-over-Ethernet interconnect cannot keep GPUs fed with data efficiently, some of the theoretical performance of the hardware may be lost in real-world deployments, even though on paper, Helios with UALink-over-Ethernet is as good as Nvidia’s NVL72 VR200 in scale-up bandwidth.

This begs the question of whether UALink will ever be widely used with Helios and whether UALink will ever be widely deployed using copper. Hyperscalers and other companies deploying high-end AI hardware at scale rarely upgrade their hardware.

While the Instinct MI455X certainly promises to be among the best hardware accelerators this year, Helios will likely only be rivalled by Nvidia’s NVL72 VR200. It will be outdated next year when AMD launches its Instinct MI500-series products. These units will be used in the company’s next-generation rack-scale offering, which promises to pack more AI GPUs, potentially requiring optical interconnects with UALink on top. As a result, Helios systems with true UALink interconnections over copper will be on the market for less than a year before those next-generation rack-scale solutions will hit the market.

Of course, nothing is stopping AMD from offering Helios with Instinct MI500-series accelerators and UALink interconnects over copper; however, the company hasn't confirmed the existence of such systems.

OpenAI CEO Sam Altman has said in an interview that companies are now concerned about the growing costs of AI use. Speaking during the Intelligence at Work event, he said this is the first time that OpenAI’s clients raised the issue and that the startup is now looking for ways to make its models more efficient.

“People are really saying, you know, it’s kind of a meme now, but ‘My company spent my entire 2026 budget in Q1. Can you make this more efficient?’” Altman said on stage. “We are continuing to push on that more with models. I think we’ll have a lot of ways we can help people get more value for less spend. But that went from, at the beginning of this year, an issue that never came up (people were totally happy with the amount they were spending) to, all of a sudden, a huge issue.”

There have recently been a lot of stories of companies getting massive AI bills as they experiment with “tokenmaxxing.” A few company leaders believed that AI use would increase the productivity of their workers, thus increasing revenue. Nvidia CEO Jensen Huang famously said that his engineers should use AI tokens that are worth at least half their annual salary, or else he'd be “deeply alarmed.” We also saw another example with OpenClaw creator Peter Steinberger, whose team spent $1.3 million on OpenAI API tokens in a month, totaling 603 billion tokens.

However, it seems that this move is starting to backfire on some companies. Amazon employees admitted that they were using AI agents for unnecessary tasks just to stay on the internal AI leaderboard, while Microsoft has reportedly cut back on Claude Code licenses due to increased costs. Even the Uber CEO admitted that there is currently no link yet between going all-out on AI and delivering successful products.

Despite that, Altman projects that AI token usage will continue to increase. He said that six-and-a-half years ago, the top token spender at the startup used 100,000 tokens a month — today, that is the global per capita average token usage, and that OpenAI’s token leader uses about 100 billion a month. The OpenAI chief also admitted, to his own embarrassment, that someone else uses even more. So, if token usage were to grow linearly, then he would expect the global per capita token usage to hit 100 billion monthly.

However, this is likely under the assumption that token prices will decrease faster compared to the increase in the number of tokens used across the globe. Because, at the moment, some are finding that it’s now more expensive to run AI models compared with hiring people.

Jevons paradox says that the cheaper a particular resource becomes, the more people will use it, and we’re seeing this with AI. But as agentic AI becomes more popular and sophisticated, the number of tokens these systems use has been increasing exponentially, seemingly outpacing the efficiency gains that AI labs have been making on training and inference.

Microsoft has unveiled Project Solara, a chip-to-cloud platform designed to power a new generation of "agent-first" enterprise devices — hardware designed to run AI agents instead of traditional apps. Announced at the Microsoft Build 2026 Developer Conference on the 2nd of June, the platform, developed by Microsoft's Applied Sciences Group, features a lightweight edge OS called the Microsoft Device Ecosystem Platform (MDEP). Interestingly, the OS is built on the Android Open Source Project (AOSP) rather than Windows.

MDEP is paired with Azure-hosted agent services and persistent cloud-based state, meaning devices act as interfaces to AI agents running across Microsoft's cloud infrastructure rather than as fully self-contained computers. Together, the software stack forms what Microsoft describes as a chip-to-cloud architecture for enterprise AI devices, combining cloud-hosted agents with centralized security, management, and orchestration capabilities.

"The 'operating system' is liminal, transcending the device and the cloud. The system brings a lightweight window to the edge, where the agent manifests and where the state, via Azure, can encompass a constellation of specialized devices,” explained Steven Bathiche, Microsoft's Corporate Vice President and Technical Fellow in the Applied Sciences Group.

To populate that ecosystem with hardware, Microsoft has partnered with Qualcomm and MediaTek as its first silicon partners — Qualcomm for portable and wearable form factors and MediaTek for stationary devices. The company has no plans to manufacture end products itself. Instead, the company is releasing reference designs for OEMs to build from, alongside an "approved chipsets" requirement that gives Microsoft certification-level control over which hardware qualifies for the platform, similar to Google's GMS certification model for Android.

To demonstrate Solara, Microsoft unveiled two concept reference designs built on the platform. A stationary desk-mounted AI hub built around MediaTek IoT silicon and a wearable AI badge powered by Qualcomm hardware. The desktop companion features a display, a camera, a UWB (ultra-wideband) presence sensor that handles automatic login and lock, dual far-field mics, and two USB-C ports. Connected to an external display, the device can double as a Windows 365 cloud PC client.

Meanwhile, the wearable badge is equipped with a touchscreen, Hello for Business fingerprint sensor, far-field high-SNR microphone array, side-facing camera, and 5G, WiFi, Bluetooth, and GNSS connectivity — targeting front-line workers such as nurses, retail staff, and field workers. Microsoft confirmed that both devices are intended as reference designs for OEM partners rather than retail products.

Central to the platform is what Microsoft calls just-in-time UI — an adaptive interface layer that allows a single agent to render appropriately across different screen sizes and input modalities without requiring developers to rebuild the experience for each device. Microsoft positions this on a spectrum between conventional responsive design and fully generative UI, where AI constructs interfaces dynamically with no predefined structure; Solara currently targets the middle ground, prioritizing consistency while avoiding per-device redesign overhead.

“The same agent can render a custom experience on multiple screen sizes and modalities with little or no additional work from the developer. For us, that is the first proof point: a path to specialized devices without requiring developers to rebuild the experience from scratch each time,” said Bathiche.

Another notable aspect of Solara is Microsoft's decision to build MDEP on Android rather than Windows. AOSP scales naturally to the lightweight, constrained hardware that wearables and embedded devices run on — something Windows, with its memory and processing overhead, was never designed to do. It also sidesteps the application compatibility expectations that come with Windows. Because Solara devices are built around cloud-hosted agents rather than traditional software, Microsoft can optimize the platform for dedicated AI hardware without carrying decades of legacy PC baggage.

To manage multiple agents running simultaneously, Microsoft is also working on an agent dispatcher and agent task manager — components that automatically surface or activate the right agent based on context, rather than requiring users to launch each one manually. Neither component is shipping yet. Early agent integrations include Dragon Copilot for healthcare workflows and GitHub Copilot for developer task tracking — both exploring how persistent, context-aware agents behave differently on dedicated hardware than they do inside a browser or IDE.

The platform appears to be aimed at enterprise buyers in retail, healthcare, and field service sectors, where dedicated agent hardware makes more sense than repurposing a smartphone. Microsoft has already lined up pilots with Best Buy, CVS Health, Levi's, Target, and AccuWeather, with broader OEM deployment targeted across healthcare, hospitality, financial services, legal, and industrial verticals.

President Donald Trump signed an executive order on Tuesday that asks AI companies to give the federal government early access to their most capable models for up to 30 days before any wider release, a window trimmed from the 90 days written into a draft he walked away from last month.

While it’s a voluntary framework that creates no licensing or pre-clearance requirement, it tasks the National Security Agency, the Cybersecurity and Infrastructure Security Agency (CISA), and the National Institute of Standards and Technology with building a classified benchmark that decides which systems qualify as a "covered frontier model."

The 30-day review only applies to models that meet the classified threshold, which the NSA will set in consultation with the National Cyber Director and CISA. Developers who opt in would open their models to agency review before the systems reach other trusted partners, and the order puts those same agencies in the room to help choose the partners. Section three states that nothing in it authorizes mandatory government licensing, pre-clearance, or permitting for new models, including frontier models.

The order also directs the Treasury Department to stand up an "AI cybersecurity clearinghouse" that coordinates vulnerability scanning, validation, and patch distribution alongside AI firms and operators of critical infrastructure such as rural hospitals, community banks, and local utilities. Separate provisions steer federal grant money toward companies building AI vulnerability detection and widen the U.S. Tech Force's cybersecurity hiring pathways.

In April, Anthropic's Claude Mythos preview identified thousands of zero-day vulnerabilities across major operating systems and web browsers, and the company has since grown its Project Glasswing early-access program for the model. It’s clear that the order has come in response to Mythos and other increasingly powerful frontier models, with reports suggesting that the Trump administration had been weighing pre-release vetting since at least the spring, when officials briefed Anthropic, Google, and OpenAI on an order that an early, unsigned draft would have made mandatory.

The trusted-partner provision of the order is likely to draw extensive criticism because ot places the government in the room when labs decide who gets first access to their strongest models. Speaking to The Register, Cato Institute policy analyst Juan Londoño said that such moves could “open the door to potential weaponization against companies that have any sort of conflict with the administration.”

This isn’t a hypothetical, either; the Department of Defense labeled Anthropic a supply chain risk shortly before the Mythos preview shipped, a designation that bars defense contractors from using the company's technology, and Anthropic has sued to overturn it. That litigation remains unresolved.

The enormous amount of money shuffling through AI's circular economy has created a money pit, and the beast therein demands sacrifices. Not too long after Anthropic shocked most of the developer world by bumping up its prices and moving Claude Code to the Max plan, GitHub Copilot has followed suit. The move from subscription to usage-based billing has left many users looking down the barrel of massive bills, according to an Ars Technica report.

An ongoing discussion on the GitHub community forums includes plenty of customer testimonials, as do X posts from many different users. The overall gist is simple: many users are reporting that their bills would increase by several orders of magnitude, or that the limit is so low that a subscription plan is now either extremely limited or useless. There's even a community cost estimator that popped up a while back when the news first came to light.

To wit, the AI allowance in each of GitHub's subscription plans has three tiers: the $10 Pro plan gets you 1,500 credits; the $39 Pro+ plan contains 7,000; and the $100 Max subscription nets you 20,000 credits. While it's good for Microsoft to specify precisely how many credits each plan includes, it's worth noting that in obvious cases like long-running conversations or queries on large projects, it's exceedingly hard to estimate how many credits any given query will use, as shown by data from a 2025 paper.

Unsurprisingly, some users are reporting that even light usage, or being "super cautious", they went through significant chunks of their monthly allotment in the blink of an eye. Switching the underlying model for a query can drastically change the calculations; for example, using Claude Opus or GPT will be far pricier than using Gemini Flash. Some subscribers also warn fellow vibe-coders about resurfacing long-running conversations, as the nature of an AI bot requires the entire conversation to be re-sent again and again, quickly chewing through usage limits.

Besides being more judicious about selecting the right model for a given query, some folks are experimenting with harnesses to use token-efficient models like DeepSeek to keep costs down. Those using an AI bot as an agent also have to exercise extreme caution, as there's no shortage of reports and horror stories about clankers left unchecked, racking up gigantic bills.

Although there's no shortage of vitriol directed at Microsoft, as there was when Anthropic bumped up Claude's pricing, the harsh reality is that investors are growing tired of dumping money into AI companies and want to see cash inflows that are at least in the same galaxy as their expenditure.

U.S. Senator Bernie Sanders (I-Vt.) has announced that he will introduce a bill that would force the largest American AI firms to hand over half of their stock to the public. The senator proposes a 50% tax on the stock of these firms, which, it seems, will be held by the public through a sovereign wealth fund. It would also mean that the government would have a direct ownership stake in these companies, allowing the people to have a say in how these companies would run.

“The foundation of AI is our collective human intelligence. Our books, songs, artwork, journalism, computer code, scientific research, videos, conversations, images, and ideas spanning generations. As Sam Altman himself acknowledged, AI models were trained on our ‘collective experience, knowledge, and learning of humanity’,” said Sanders. He also added, “Since AI is built on the collective knowledge of humanity, the wealth it generated must benefit humanity. Not just Elon Musk, Jeff Bezos, Mark Zuckerberg, Larry Ellison, and other billionaires, or the venture capitalists and Wall Street firms who see AI as the next great wealth extracting machine.”

According to the senator, the biggest AI companies themselves have started this idea. OpenAI has already proposed a public wealth fund so that the public would benefit from AI-driven economic growth, while Elon Musk himself posted on X that “Universal HIGH INCOME via checks issued by the Federal government is the best way to deal with unemployment caused by AI.”

He then compared his AI sovereign wealth fund proposal to Norway’s Government Pension Fund Global and the Alaska Permanent Fund. Both are sovereign wealth funds whose proceeds came from their nations’ oil wealth — the former is now worth US$2.3 trillion and is used to fund the European country’s extensive public welfare system, while the latter has been paying out $1,000 to $2,000 to every Alaskan citizen annually since 1980.

Sanders’ proposal plans to combine the benefits that both Alaska and Norway deliver, while simultaneously forcing AI companies to hand over 50% of their ownership to the public through the government. This might seem a radical solution, but the senator is no stranger to extremes. He has previously called for a halt to all AI data center construction, saying that such a move “will give democracy a chance to catch up.” While a federal moratorium still hasn’t been approved, more and more jurisdictions across the U.S. are approving data center bans as communities are pushing back against their development in their backyards.