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

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

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

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

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

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

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

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

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

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

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

Chinese tech giant Alibaba has banned its employees from using Anthropic's Claude Code for all work purposes, effective July 10, after security researchers alleged the AI coding agent contained hidden code designed to detect whether users were based in China or affiliated with Chinese AI labs. According to a July 3 South China Morning Post report, the Chinese tech giant said Claude Code had been "added to a list of high-risk software with security vulnerabilities" following a comprehensive evaluation, citing what it described as back-door risks.

Employees have reportedly been instructed to adopt Qoder, Alibaba's in-house AI coding platform, as the replacement. According to reports from Chinese outlets citing company insiders, the directive reportedly goes further than Claude Code itself, as staff have allegedly been told to uninstall all Anthropic products, including the Sonnet, Opus, and Fable model families. The move is the latest escalation in a feud that ignited last month, when Anthropic accused operators linked to Alibaba's Qwen AI lab of running the largest known model distillation attack against Claude.

The trigger for the ban was a June 30 post on the r/ClaudeAI subreddit by a user who claimed to have reverse-engineered Claude Code while restoring a disabled remote-control feature. According to the write-up, obfuscated detection logic had shipped silently since version 2.1.91, released on April 2, with no mention in the release notes. Whenever a proxy was detected, the code reportedly checked whether the system timezone matched Asia/Shanghai or Asia/Urumqi and inspected the proxy URL against a hardcoded list of Chinese domains and AI lab identifiers, reportedly including Alibaba, Baidu, Ant Group, and ByteDance.

What elevated the discovery from routine telemetry to scandal was the exfiltration method. Rather than sending an overt signal, the tool allegedly encoded its findings steganographically, tweaking the date format and swapping a punctuation character in the system prompt sent back to Anthropic's servers — invisible to the user, but machine-parseable on Anthropic's end. The Reddit author called the covert transmission of system and proxy data "a fundamental violation of user trust," saying they simply wanted transparency from Anthropic.

Anthropic has not issued a formal statement, but Thariq Shihipar, an engineer on the Claude Code team, addressed the findings on X, describing the mechanism as "an experiment we launched in March" intended to prevent account abuse by unauthorized resellers and to protect against distillation. Shihipar said the team had been meaning to remove the code for a while, and that the pull request stripping it out was merged on July 1, the day after the Reddit post.

The timing of Alibaba’s Claude ban fits right into the wider rift between the Chinese tech giant and the U.S. artificial intelligence frontrunner. On June 10, Anthropic sent a letter to leaders of the U.S. Senate Banking Committee accusing operators affiliated with Alibaba's Qwen lab of using nearly 25,000 fraudulent accounts to generate 28.8 million exchanges with Claude between April 22 and June 5, in what it characterized as an industrial-scale attempt to distill the model's software engineering and reasoning capabilities. Distillation, training a smaller model on the outputs of a more capable one, sits in a legal and ethical gray zone that the industry has yet to resolve. Alibaba has denied wrongdoing and has not addressed the allegations in detail.

Anthropic followed the Senate letter with sweeping account restrictions, reportedly cutting off numerous Chinese users without notice. The company already maintains the industry's hardest line on access to China, stating it is the only frontier AI firm that restricts service to Chinese-owned entities, even through subsidiaries incorporated abroad. This stance is precisely why Chinese developers reach Claude Code through proxies in the first place, and why a proxy-triggered detection routine reads, to Chinese eyes, as a tool built to hunt them specifically.

The episode slots into a U.S.-China AI relationship that has spent 2026 swinging in both directions at once. Washington had earlier placed export restrictions on AI chips to China. It loosened hardware controls this year, clearing roughly 10 Chinese firms, including Alibaba, to buy H200S in quantities of up to 75,000 units per customer. However, Beijing simultaneously discouraged Chinese firms from buying approved American silicon, citing its own security concerns, as part of a deliberate push toward an indigenous AI stack.

Software access now appears to be following a similar trajectory of restrictions. Anthropic is blocking China at the account level; now, China's largest tech company has banned Anthropic at the workplace level. Earlier, OpenAI banned numerous China-linked accounts accused of artificially amplifying backlash against U.S. data center electricity prices.

Data center developer QTS has officially terminated plans to build a portion of the planned 2,100-acre “Digital Gateway” data center campus in Virginia, following years of legal battles with local residents and historic preservation groups. According to a Bloomberg report, the Blackstone-owned company formally submitted a written filing to the Virginia Supreme Court on July 2, explicitly stating it was withdrawing its last remaining appeal “after careful consideration”.

QTS’s withdrawal marked the official end of the project, as other stakeholders had earlier pulled out due to prolonged litigation. In a major win for opponents of the data center, the proposed land — situated at the edge of the Manassas National Battlefield Park, a historic Civil War battlefield — will now remain under its original rural zoning restrictions.

Digital Gateway was a planned massive 22-million-square-foot, gigawatt-scale data center complex in Prince William County, Virginia, that would have been the world's largest. QTS was responsible for 800+ of the 2,100 acres, while a second developer, Compass Datacenters, controlled roughly 800-1,000 acres. The rest of the designated 2,100-acre "Digital Gateway" zone consisted of local roadways, environmental buffer zones, and parcels belonging to individual homeowners who had agreed to sell.

Initially approved by the Prince William Board of County Supervisors to capitalize on the soaring demand for AI and cloud computing, the project immediately faced fierce opposition from residents, despite the promise of tens of billions of dollars in capital investment and substantial local tax revenue. Opponents argued that the facility's location adjacent to the Manassas National Battlefield Park posed severe threats to the area's environmental, historical, and residential landscape, a common refrain in growing anti-data-center sentiment.

Several lawsuits followed, with the one that appears to have eventually collapsed the massive project hinging on an ironically small detail. The landmark mega project — roughly double New York’s Central Park’s size with city-sized power needs — collapsed in a domino effect after Virginia courts voided the county’s initial rezoning approval due to improper public notices. The newspaper notices publicizing the hearing at which the project was approved weren’t separated by at least 6 days, as mandated by state and local codes at that time, thereby invalidating the hearing and the resulting approval.

Virginia courts upheld the invalidity of the zoning approvals in March this year. Following the ruling, Prince William County gave up first, refusing to spend additional public funds to defend the project in court. Compass Data Center, a co-developer of the project, withdrew from the project the next month, with the company president stating, “While we still believe this project offered significant benefits for the region and our neighbors, recent legal actions and compounding regulatory hurdles have effectively closed a viable path forward.”

Only QTS remained, filing an appeal with the Virginia Supreme Court. It has now withdrawn that appeal, leaving the entire Digital Gateway project completely dead. The company says it will now “work on a responsible and orderly wind-down of development efforts”.

The “Digital gateway” case is the latest win for anti-data-center proponents and the latest blow for those on the other side of the fence. We recently covered a report that more than 75 data center build-outs worth $130 billion have been successfully blocked in the first Q1 2026, although several others have moved forward. While data centers are needed to power AI’s exponential growth, their development often raises valid concerns about water use, noise pollution, and spikes in electricity bills for nearby residential areas. There has been an uptick in protests against data centers and a corresponding rise in companies pursuing innovative solutions to the problem. SpaceX is currently building an 11-million-square-foot Gigasat factory to manufacture orbital data centers, while several companies are deploying offshore data centers. On the other hand, there are reports that China may be artificially stoking opposition to data centers in the U.S., even as Trump's administration is pushing for AI development.

Alex Karp, CEO of well-known AI data analytics company Palantir, delivered quite the bombshell of an interview to CNBC's Squawk Box. Although the interview's topic was about the firm's partnership with Nvidia, apropos the recently launched Sovereign AI OS Architecture, Karp bluntly claimed that frontier AI companies like OpenAI and Anthropic siphon customers' valuable information while delivering questionable value.

He continued by stating that American enterprises are quietly "livid," as "they are paying for tokens that create no value," and that the AI players "are stealing [their customers'] weights and alpha." The latter items refer to customers' business processes and interconnections between their data, along with the data itself. Palantir's shares jumped about 9% the day of the interview, while those of other AI companies experienced a dip.

For context, many of Palantir's products are on-premises solutions or a variation thereof, and they carry a truckload of certifications like the DOD-required CMMC Level 2 or ISO27001/17/18. Karp's business also alleges that it does not train any models and merely utilizes other entities', without retraining them with customer data. Instead, the company's particular approach is coined "ontology" and, as a simplification, focuses on business data classification, entity definitions, and behavior.

Improving the training of an LLM requires an influx of new and improved information, which is why Karp claims that frontier labs are double-dipping by both selling customers LLM utilization all while using their data for improving said LLMs — in other words, the risk for a customer is that they're arguably teaching the bots' abilities and information that could get their business easily replicated and potentially replaced.

He puts the value of a token in question by using an old business analogy: if the frontier players supposedly generate so much value for their customers, why don't they treat it as an investment and charge a percentage of said value? Not too long ago, Palantir CTO Shyam Sankar shared the same view in an equally abrasive manner: "more tokens means more slop," questioning the productivity gains of the "tokenmaxxing" fad that tech leaders like Nvidia's Jensen Huang have promoted.

Karp is likewise not too keen on the promises that frontier companies make about data harvesting, calling Silicon Valley's general attitude of "you can trust me because I never lied" straight-up "B.S." He further notes that enterprises want to know who owns the data, where it is cached, and whether the prompts are secure, while also taking a dim view of services that then rely on third parties, as those might not be bound by the same contractual obligations. Furthermore, he described the notion of the Silicon Valley zeitgeist applying its views to defense-related information as effing insane [sic].

A portion of the world has a dim view of Palantir's defense-related business ethics, something that Karp acknowledges, all while displaying at least some self-awareness that Silicon Valley leadership arguably lacks. He plainly states that he too profits from the aforementioned practices, though there's little doubt his talking points serve the interest of selling on-premises services.

OpenAI has discussed handing the U.S. government a 5% ownership stake, the Financial Times has reported, citing two people familiar with the talks, with CEO Sam Altman proposing that every leading U.S. AI developer contribute the same share of its equity to a vehicle modeled on the Alaska Permanent Fund, which pays annual dividends to state residents from Alaska’s oil wealth. At the $852 billion valuation OpenAI set in its March funding round, a 5% stake is worth roughly $42.6 billion. The FT characterized the discussions as conceptual and early-stage, and reported that implementing any deal might require an act of Congress.

Altman is understood to have raised the idea with President Donald Trump, Commerce Secretary Howard Lutnick, and Treasury Secretary Scott Bessent, and has spoken with Senator Bernie Sanders (I-Vt.) in recent weeks. The all-labs structure would pull equity from companies, including Google, Meta, and Anthropic, none of which have indicated they would participate. OpenAI declined to comment to the FT, and the White House didn’t immediately respond.

Altman’s 5% is the smallest figure we’ve seen to date attached to public ownership of the AI sector. Sanders filed the American AI Sovereign Wealth Fund Act in June, seeking 50% of the voting shares of U.S. AI companies through a fund his office valued at $7 trillion, enough to pay every American a $1,000 annual dividend. Trump said last month that he was exploring options to give the public a stake in leading AI firms, and Vice President JD Vance said the president prefers equity over cash payouts.

The administration has already run this playbook on chipmakers, with the federal government having taken a 9.9% stake in Intel last August by converting CHIPS Act grants into equity at $20.47 per share, and AMD and Nvidia agreed to hand over 15% of their China chip revenue in exchange for export licenses. OpenAI itself proposed a “public wealth fund” in an April policy paper, and Altman first pitched a government stake to the administration in early 2025, CNBC reported last month.

News of the talks comes just six days after OpenAI delayed the full public launch of GPT-5.6 at the government’s request, with Lutnick reportedly warning Altman against releasing the model without prior approval. Anthropic spent most of June with its Claude Fable 5 and Mythos 5 models disabled worldwide under the first U.S. export controls ever applied to an AI model rather than to hardware; access was restored yesterday.

Both OpenAI and Anthropic have confidentially filed for initial public offerings, and OpenAI faces a probe from a coalition of 42 state attorneys general. A government shareholding negotiated before a listing would lock in Washington’s position ahead of the ownership expansion that a full float brings, however.

Elon Musk has slammed reports that SpaceX is developing a handheld AI device as "Utterly False," in a post on X. It follows a report from the Wall Street Journal on Wednesday claiming that SpaceX had demonstrated an early prototype of a handheld device featuring xAI's artificial intelligence technologies and a proprietary operating system to a small group of investors ahead of the company's initial public offering.

The handheld device from SpaceX is reportedly thinner than Apple's iPhone, is based on a Qualcomm Snapdragon system-on-chip, and runs a proprietary operating system, according to the WSJ story. The main selling point of the device is its artificial intelligence technologies from xAI. However, the report does not elaborate on their nature or how they integrate with the operating system. The concept device remains in its infancy and may never become a commercial product. Furthermore, assuming that it is in its early stages of development, its final look and specifications would likely differ significantly from the prototype.

Earlier this year, Reuters reported that SpaceX was developing a smartphone, citing its sources, which reportedly stressed that SpaceX has had plans for a handset for years. At the time, Musk told Reuters that a hypothetical Starlink phone would be 'not out of the question at some point,' but admitted that it would be an AI-centric device that would be 'very different than current phones.' Nonetheless, in an X post, Musk denied his company was building 'a phone.' Based on the comment Musk made today, SpaceX is still not developing a smartphone-like device.

Meanwhile, this would not be the first time Musk has denied something that later turns out to be true. After Reuters reported Tesla had canceled its inexpensive Model 2 vehicle in April, 2024, Musk replied, 'Reuters is lying (again)' without elaborating. Tesla still has not launched its low-cost electric vehicle, but prioritized Robotaxi instead. Furthermore, the company has not touched upon Model 2 in its conference calls in detail in the years that followed, essentially confirming that the entry-level Model 2 in the form that was envisioned before 2024 has been cancelled.

Building a smartphone that connects to Starlink satellites and terrestrial 5G networks is theoretically possible: there are 3GPP Release-18 and 3GPP Release-19 5G-NTN specifications that are designed to achieve just that, and many modern devices already support texting using NTNs. For a mobile device that supports both 5G and NTN networks today, the biggest challenges are to build a modem and a front-end module that can hit the right balance between performance, reliability, area, and power consumption. Of course, the constellation itself has to offer enough 'backbone' bandwidth to support global communications.

While an always-connected handheld device with advanced AI capabilities would, from many points of view, reflect Musk's long-standing ambition to create an 'everything app,' it would automatically make SpaceX compete against companies like Apple, Google, Samsung, and many other multinational conglomerates. Tough competition is arguably not something that any CEO would like to admit, especially when the product in question is in its infancy and there is an IPO ahead.

Anthropic has restored global access to Claude Fable 5, a day after the U.S. Department of Commerce withdrew the export controls it imposed on the model on June 12th, according to a company blog post. The fix that ended an 18-day standoff was a single safety filter tuned to block one technique flagged by Amazon researchers, with Commerce's own Center for AI Standards and Innovation (CAISI) reviewing the safeguards before the controls came off.

Fable 5 returns across Claude.ai, the Claude Platform, Claude Code, and Claude Cowork today, with access on AWS, Google Cloud, and Microsoft Foundry to follow. The June 12th directive had barred any foreign national, including Anthropic's own non-citizen staff, from using Fable 5 or the more capable Mythos 5, which it’s built on. With no way to verify the nationality of its users, Anthropic pulled both models worldwide.

The contentious technique was flagged by Amazon researchers, who found a way to prompt Fable 5 into identifying software vulnerabilities and, in one case, writing code demonstrating how one could be exploited. Anthropic trained a new classifier that blocks that specific technique in more than 99% of cases and reroutes flagged requests to the older Opus 4.8. The company said the change also catches more benign coding and debugging requests as a side effect.

The classifier targets the reported prompt and not the model’s capabilities. Fable 5 can still identify the vulnerabilities in the Amazon report; the filter detects the request and reroutes it rather than stripping the ability from the model. Detection-based safeguards are also what were defeated to trigger the ban in the first place, and a classifier that’s tuned to one known technique does nothing for the ones not yet found. Anthropic concedes that no model can be made fully robust to jailbreaks and that it expects more to surface.

Anthropic's review, conducted with the government and Amazon, found that Opus 4.8, OpenAI's GPT-5.5, and China's Kimi K2.7 could identify the same vulnerabilities. Every model that it tested, including Haiku 4.5, Sonnet 4.6, and several Opus versions, could reproduce the single exploit demonstration, backing the argument that Mythos-class cyber capabilities were oversold.

Fable 5's return reclaims benchmark positions that Chinese lab Z.ai's GLM-5.2 had held by default while Fable was offline, including the top accessible score on the AA-Briefcase multi-week task test. Mythos 5, which carries fewer guardrails and stays limited to Project Glasswing partners, returned to a set of U.S. organizations on June 26th.

Anthropic also opened a HackerOne program for researchers to report new Fable 5 jailbreaks, and it committed to giving designated government partners earlier access to test future frontier models before release. For Pro, Max, Team, and select Enterprise plans, Fable 5 counts toward up to 50% of weekly usage limits through July 7th, after which it moves to usage credits.

AI models will explain how to synthesize cocaine if the request is wrapped in fake reasoning claiming compliance is fine because the user is wearing a green shirt, according to a new paper that traces the success of prompt injection, the unsolved security flaw in every AI chatbot and agent, to how LLMs read text. The paper says that models work out who is speaking from the writing style, not the role tags meant to separate trusted commands from untrusted data.

The work, “Prompt Injection as Role Confusion” by independent researchers Charles Ye, Jasmine Cui, and MIT associate professor Dylan Hadfield-Menell, heads to the ICML 2026 conference in Seoul on July 6th, and an extended write-up has been posted by the authors ahead of that event.

The cocaine trick, which the authors call CoT Forgery, took jailbreak success from near zero to roughly 60% across every model tested and won the 2025 OpenAI GPT-OSS-20B red-teaming contest on Kaggle.

As the researchers describe it, models receive a conversation as one continuous string of text, partitioned by tags such as user, tool, and think that are supposed to mark each segment’s source and authority. The researchers built “role probes” that score how strongly a model internally treats each token as its own reasoning or as a user command.

Those scores predicted whether an attack would succeed before the model generated a single token, and they showed that models lean on style to make determinations about what kind of content is in a given partition. Text that merely reads like reasoning to a model registers as reasoning even when the surrounding tags said otherwise.

CoT Forgery injects fabricated reasoning into a prompt so the model treats it as its own already-reached conclusion and acts on it, inheriting the trust a model places in its own thinking. The rationale can be transparently absurd, like the green shirt, because the model doesn’t scrutinize it as an outside claim. What's more, the attack didn’t weaken as requests grew more extreme, unlike persuasion-based jailbreaks.

Removing the stylistic markers that make injected text read like the model’s reasoning, while leaving its meaning unchanged for a human, dropped average attack success from 61% to 10%. Swapping a single phrase, “The user” for “The request,” cut success by 19%. “Role tags were a formatting trick that became the security architecture and the cognitive scaffolding of modern LLMs,” the authors note in their write-up, and the increasing load on that structure to manage LLM behavior has apparently created vulnerabilities of its own.

To determine whether confusion about roles was specific to their attack or a more generalizable principle that explains why prompt injection works, the researchers took a different approach. They hid a command in a webpage telling the model to upload a secrets file, then prepended “User:” to it to make the dangerous instruction sound like it came from the trusted User role. The exploit worked, suggesting that role confusion underlies the success of prompt injection generally.

Microsoft recently acknowledged the same agentic risk, warning that content embedded in documents or UI elements can override an agent’s instructions.

The authors also flagged a more subtle risk for agents that browse and shop. Because role perception is a matter of degree, the tone of a retrieved webpage can bleed past the tag boundary into a model’s own state, and thousands of page variations could be tested cheaply to find which ones nudge an agent toward a purchase, legally and at scale.

Without genuine role perception, the authors concluded, injection defense will remain a perpetual game of whack-a-mole.

In a bid to offer unbeatable performance, Nvidia had planned to use four GPU chiplets in its Rubin Ultra AI accelerator due in 2027. However, due to concerns about the manufacturability of such a solution, the company decided to cancel it in favor of a dual-GPU design that is easier to produce, according to SemiAnalysis.

Nvidia's Rubin Ultra GPU with four compute chiplets was arguably one of Nvidia's most ambitious projects in recent years, as it not only doubled performance compared to the original Rubin (which uses two compute chiplets), but also increased the complexity of Nvidia's data center GPUs to levels never seen before. However, connecting four near reticle-sized dies using existing advanced packaging technologies is a tremendous engineering challenge, and cooling four complex dies and 16 HBM4E modules is hard and costly. As a result, due to 'manufacturing execution concerns,' Nvidia reportedly canceled Rubin Ultra in its four compute dies form in favor of a design with two compute chiplets. Note that the information is unofficial, so take it with a grain of salt. We've reached out to Nvidia for comment.

As a consequence, Nvidia's 'new' Rubin Ultra would be around half as powerful as the original one, which would certainly make it less competitive against contending offerings, namely AMD's Instinct MI500-series. Of course, Nvidia will still likely optimize its Rubin Ultra design to squeeze some additional performance out of the AI accelerator to justify the upgrade.

Also, keep in mind that Nvidia's Rubin Ultra uses HBM4E memory instead of HBM4 used by the original Rubin. Furthermore, starting with Rubin GPUs, Nvidia plans to offer liquid-cooled Kyber rack-scale systems that increase GPU count per scale-up domain to at least 144 packages, which will increase compute performance that Nvidia will sell to its customers.

SemiAnalysis notes that the impact of the cancellation of an AI accelerator with 16 HBM4E packages could have an impact on the HBM market in general, as the 'new' Rubin Ultra will only use eight HBM4E modules.

The purported cancellation of Rubin Ultra with four compute chiplets would also mean that one Rubin Ultra GPU with two compute chiplets will cost less than the original one. Meanwhile, since Nvidia is mostly focused on selling rack-scale solutions rather than on individual GPUs, it remains to be seen how this impacts the actual spending of Nvidia's partners, since if they have to buy more systems to get more GPUs, they will likely spend more than they would if they had to buy fewer systems with the same number of compute chiplets.

On June 12th, the U.S. Commerce Department issued an export-control directive barring Anthropic from supplying Fable 5 or Mythos 5 to any foreign national, forcing the company to disable both models worldwide. The next day, Beijing-based Z.ai, formerly Zhipu AI, began rolling out GLM-5.2, an open-weight model it released under a permissive MIT license. The new model was purportedly trained entirely on Huawei Ascend chips with no Nvidia hardware.

Within a week, GLM-5.2 had climbed to the top of the openly available leaderboards, Z.ai's market value had passed HK$1 trillion (about US$128 billion), and the most capable model many users outside the U.S. could legally access was a free download from a company that sits on Washington's trade blacklist.

Trailing Anthropic

GLM-5.2’s results are both strong and uneven, taking first place on Design Arena's human-preference coding board, finishing roughly 10 Elo points ahead of Fable. It also ranks as the top openly available model on Artificial Analysis's Intelligence Index v4.1, where its score of 51 sits ahead of MiniMax-M3, DeepSeek V4 Pro, and Google's Gemini 3.1 Pro Preview. On the SWE-bench Pro, it scored 62.1, compared to GPT-5.5's 58.6.

In terms of longer work, such as Code Arena’s front-end board, the picture changes somewhat, with GLM-5.2 landing second behind Fable 5. On Artificial Analysis's AA-Briefcase test, which scores multi-week knowledge tasks built from thousands of fragmented inputs, Fable 5 led with 1,587 Elo, followed by Opus 4.8 at 1,356, and GLM-5.2 in third place at 1,266, before the export ban took Fable out of contention.

It also trails on raw terminal work, scoring 81.0 on Terminal-Bench 2.1 against Opus 4.8's 85.0 and GPT-5.5's 84.0, while clearing Google's Gemini 3.1 Pro at 74.0. GLM-5.2 holds the top accessible position today, partly because the models that beat it on these benchmarks are largely an Anthropic pair, and Fable is now switched off.

No Nvidia

GLM-5.2’s training stack is a slap in the face of Washington’s efforts to curtail Chinese model development. Z.ai has been on the U.S. Entity List since January 2025, cutting it off from Nvidia's H100, H200, and B200 accelerators, and it says the GLM-5 family was trained on roughly 100,000 Huawei Ascend 910B processors using the MindSpore framework, with no Nvidia silicon at any stage. The export controls on advanced AI chips were designed to keep this kind of result out of reach, but they’ve evidently failed to do so.

That said, the Ascend 910C sits at roughly 60% of an Nvidia H100’s inference performance, per a December report from the Council on Foreign Relations, with a wide gap on efficiency and cluster scale. The same report projects that by as early as next year, the best U.S. chips could be more than 17 times more powerful than Huawei's top parts.

At the same time, Huawei has claimed that a 1,000-chip Ascend cluster handled full-parameter post-training of DeepSeek's V4. If true, this shows that Chinese domestic silicon can now carry training-class jobs, just not at Nvidia’s per-chip throughput or scale. So, while GLM-5.2 demonstrates that a frontier-class open model can be produced on a fully domestic stack, it doesn’t demonstrate that the chips underneath have caught up with Nvidia; model parity =/= hardware parity.

The Fable 5 shutdown

Anthropic released Fable 5 to the public on June 10th, a safety-restricted build of its Mythos 5 model designed to block the cyber and bio capabilities of the underlying system. Just two days later, the Commerce Department suddenly and unexpectedly ordered access to be pulled for all foreign nationals, including Anthropic's own non-citizen staff, after officials cited a technique for bypassing Fable 5's safeguards.

Anthropic said in an announcement following the restriction that the jailbreak it understood to be at issue was narrow rather than universal, surfaced only previously known minor vulnerabilities, and produced behavior also obtainable from other public models, including OpenAI's GPT-5.5. The company said in its statement that it believes the order rests on a “misunderstanding” and is working to restore access. But because the directive covered all foreign nationals, Anthropic had no way to keep the models live for U.S. users alone and disabled them for everyone.

Meanwhile, GLM-5.2’s MIT license lets anyone download, fine-tune, and self-host its weights, which is the basis for calling it a freely available model. Running it, however, is a separate matter: the model carries around 744 billion total parameters, 40 billion of them active per token, with a one-million-token context window. That’s no small footprint and calls for enterprise GPU clusters or high-memory workstations — it’s not something you’re ever going to get running on a desktop — and throughput drops sharply once context runs past tens of thousands of tokens.

The most practical way to use GLM-5.2 is via the API, where Z.ai prices the model at about $1.40 per million input tokens and $4.40 per million output, against $5 and $25 for Claude Opus 4.8, or $10 and $50 for Fable 5. On the AA-Briefcase runs, Fable 5 averaged $31 per task to GLM-5.2's $2.40, a roughly 13-times spread that holds even where Fable scored higher.

The market moved fast with GLM-5.2’s release. Z.ai, which is listed on the Hong Kong exchange as Knowledge Atlas Technology, saw its shares jump as much as 42% intraday on June 22nd to HK$2,980, carrying its market capitalization past HK$1 trillion. Founder Tang Jie has said publicly that a Chinese model matching Fable 5 will arrive sooner than the first-quarter timeline Elon Musk recently floated. There's a nearer date, too. On July 8th, the lock-up on Z.ai's first cornerstone investors expires, freeing a large block of shares to trade, which will give the GLM-5.2 rally its first real test.

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."

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.