TSMC is weighing raising prices for its Chip on Wafer on Substrate (CoWoS) advanced packaging technology by as much as 20%, reports Trendforce, citing supply chain sources and local media. The increase reflects strained capacity amid AI demand and longer delivery times for chipmaking tools. Such a hike would be significant, as CoWoS is already one of the tightest links in the AI supply chain. TSMC has yet to confirm the report.

CoWoS combines logic dies and high-bandwidth memory (HBM) stacks within a single package, which is how AI accelerators get the data bandwidth that demanding workloads require. Building these packages is only getting harder as chip designers cram in more compute and memory. Nvidia’s Blackwell already pairs two large GPU dies with eight HBM3E stacks, whereas the upcoming Rubin and Rubin Ultra are expected to push integration further, which will increase pressure on packaging and the supply chain that supports it.

Testing is turning into another bottleneck. Blackwell reportedly takes three to four times longer to test than Nvidia’s previous-generation Hopper, and Rubin could need as much as seven times longer. Extended test cycles keep equipment occupied for longer, which makes it harder to increase output as demand grows. GPUs are not the only products competing for capacity either, as server CPUs and custom ASICs also need advanced packaging, adding more strain to an already crowded supply chain.

Tools are also taking longer to arrive, as equipment lead times have reportedly stretched from three to six months to as long as eight months, making timely access to fab equipment increasingly important. As a result, TSMC has reportedly placed this year’s equipment orders at existing prices instead of negotiating discounts or extra features — a departure from its customary approach of using its purchasing power to secure better terms. Essentially, getting the tools on time now appears to matter more to the company than getting them cheaply.

The squeeze echoes a report from September, which said that TSMC’s projected semiconductor equipment requirements had almost doubled in six months as the company expanded production to meet AI demand. Cliff Hou, deputy co-COO at TSMC, said that by July the company’s expected equipment needs had reached 1.9 times its original estimate as AI demand accelerated. “Compared with the pattern 30 years ago, we have never seen the demand increase that much, that frequency of changes,” Hou said at the time.

TSMC is also trying to broaden its packaging supply chain. Earlier this month, GlobalFoundries agreed to produce silicon interposers for TSMC’s CoWoS in the U.S. under a five-year contract worth $2 billion, with production expected to ramp up in the first half of 2028. However, interposers are only one part of the packaging flow, so the agreement alone will not eliminate shortages.

Higher costs are not limited to packaging. TSMC is reportedly considering broader price increases for 2027, with wafer prices on advanced process technologies expected to rise by 5% to 10%. Customers using 2nm and 3nm-class nodes could face the steepest hikes, whereas those seeking capacity beyond their original allocations may have to pay additional premiums, which could push some costs up by as much as 25%.

Industry estimates cited by Trendforce put TSMC’s monthly capacity at over 106,000 wafers in 2026, with a target of 227,000 wafers in 2027 as the company works to keep pace with demand for increasingly complex AI processors. If both the packaging and wafer price increases materialize, the cost of AI hardware will likely climb further, and some of that will inevitably be passed down the chain.

Claude maker Anthropic has filed for an initial public offering (IPO) that could value the AI company at around $2 trillion, setting the stage for one of the biggest potential stock market debuts in the technology industry, the company announced today.

As per Form S-1 registration statement with the U.S. Securities and Exchange Commission, the firm intends to list its Class A common stock on the Nasdaq Global Select Market under the ticker ‘ANT’, although the number of shares to be offered and the price range have yet to be determined. A report from Reuters suggests the company could seek to raise as much as $100 billion, but those figures are not confirmed offering terms.

On to numbers, Anthropic generated $16.3 billion in revenue during the first half of 2026, up sharply from approximately $1.2 billion in the same period last year. The company’s operating performance also improved, although its reported net loss reached $80.7 billion. Despite the losses, it disclosed enormous commitments to AI computing infrastructure and outlined risks that investors will have to weigh against its rapid growth.

That net loss, however, requires context. It should not be interpreted as $80.7 billion in cash spent running the business, since accounting adjustments related to financing instruments contributed substantially to the reported figure. Even so, Anthropic cautions that its revenue growth rate “may not be sustainable” and could decline.

The company also warns that it may need additional financing sooner than expected and does not anticipate paying dividends in the foreseeable future. Those disclosures will matter to investors considering a company whose expansion depends on access to increasingly expensive computing resources.

Beyond figures, Anthropic’s filing contains warnings about the technology underpinning its business. The company says advanced AI could pose “catastrophic or existential risks to humanity,” including scenarios involving dangerous capabilities, deceptive behavior, and systems that become difficult for humans to control.

“Our development of highly advanced models, platforms, and applications and expansion of use cases could further increase the risk that our models cause harm,” Anthropic’s S-1 prospectus stated.

However, the firm said it is developing safeguards to mitigate AI risks through pre-release testing, red-team assessments, and systems designed to detect harmful outputs. It also said it may delay training or restrict model releases when safety measures are insufficient, while acknowledging that its efforts may not succeed.

Bloomberg Intelligence technology research head Mandeep Singh discussed the disclosures in a Bloomberg segment following the filing. He characterized worst-case scenarios as typical of risk disclosures for emerging technology companies preparing to go public, noting that such language helps address potential liabilities. He also pointed to Anthropic’s improving compute efficiency and an improvement in gross margins as a positive signal for investors. “Gross margins are inching closer to 50%, whereas just a quarter back… it was like… sub 20%.” He stated.

Meanwhile, Anthropic expects to spend up to $120.5 billion on compute involving SpaceXAI and Nvidia, alongside up to $114.5 billion with Google through 2033. Notably, while those massive infrastructure commitments are essential to training and running advanced AI models, they could become a costly burden if demand falls short of expectations.

OpenAI, the maker of ChatGPT, has also been preparing for a potential IPO, doubling down on efforts to scale revenue and computing capacity ahead of a possible debut. However, OpenAI CEO Sam Altman said in September that the company would not go public in 2026, citing concerns about AI safety and saying the company had more work to do before pursuing a listing. Elsewhere, Cerebras, which develops wafer-scale processors for AI workloads, completed its Nasdaq IPO in May after filing for a second time.

Still, Anthropic acknowledges that its growth may slow and that its financing needs could arrive sooner than expected. That said, Anthropic is reportedly targeting a mid-November IPO, with November 19 or 20 floated as possible listing dates. However, the company has yet to confirm a timetable for the offering.

Elon Musk has confirmed that SpaceX closed $40 billion in debt financing for graphics processing units (GPUs), with the chips expected to arrive in the first half of 2027. He disclosed the deal on X, October 25 (embedded below) while responding to investor Chamath Palihapitiya, who had warned that mounting financing pressures and delays in data center construction could leave AI computing capacity sitting idle next year.

Musk agreed with that assessment, but argued that SpaceX is better positioned than many of its competitors because it can borrow against revenue from businesses outside AI. “Pretty much,” Musk replied to Palihapitiya on X explaining that debt markets had largely shut off funding for companies without substantial revenue independent of AI data centers.

He further said SpaceX had closed $40 billion in debt for GPUs because it has investment-grade status and revenue from its Starlink satellite internet and space businesses. “That means the 200+ neoclouds are going to see guppy-level death and consolidation. Some will make it through, but not many,” Musk wrote.

It is still, however, unclear what Musk meant by “closed” or whether investors had agreed to the final debt pricing. The disclosure comes as SpaceX prepares for another enormous spending period. CFO Bret Johnsen had stated during SpaceX’s Q3 2026 earnings call that the company expects capital expenditure of $35 billion to $40 billion in Q4 “because of the additional capacity coming online this quarter.”

Musk also clarified that the borrowing was primarily callable debt for GPU purchases expected to arrive in the first and second quarters of 2027. “This is primarily callable debt for GPU purchases that will arrive in Q1 and Q2. Not a major factor this quarter,” Musk wrote.

Earlier this month, it emerged SpaceX had proposed a $40 billion debt package for Nvidia hardware, estimating that the proposed funding could cover roughly 360,000 Rubin GPUs across 5,000 Nvidia NVL72 racks. The proposed package reportedly included $10 billion in bank loans and $30 billion in investment-grade debt. Musk has now said the financing has closed, but has not confirmed the specific GPU models or quantities covered by the deal.

The spending fits into Musk’s broader push to expand AI computing capacity. In August, xAI outlined plans to increase its data center capacity sevenfold by 2027, targeting 10 gigawatts. In September, the publication also reported that SpaceXAI planned to add another 660,000 Nvidia GPUs during 2026, potentially bringing its total to around 1.44 million if the planned deployments proceed.

And GPUs are only part of the bill. Data centers need power, cooling, networking equipment, and physical infrastructure before those accelerators can do useful work, which underscores SpaceX’s latest move.

SpaceX, meanwhile, has begun selling access to its own infrastructure. In May, Anthropic secured access to SpaceX’s Colossus 1 supercomputer, which includes more than 220,000 Nvidia GPUs and over 300 megawatts of computing capacity. Later in June, SpaceX struck a separate deal with Google, which agreed to pay $920 million per month for access to approximately 110,000 Nvidia GPUs and related hardware, with payments scheduled to run from October 2026 through June 2029.

Intel and AMD have spent decades competing for the processors inside PCs and data centers, but the rivalry hasn't stopped the two companies from working together when their interests align. And now, Intel CEO Lip-Bu Tan has reportedly discussed potential areas of cooperation with AMD chief Lisa Su, adding another possible partnership to the mix as Intel tries to regain its footing in the semiconductor industry.

According to a recording (embedded below) of Tan’s October 24 fireside chat with Chinese American Semiconductor Professional Association (CASPA) President Jimmy Cheng, the Intel CEO outlined his plans to rebuild the company’s CPU business, develop chips tailored to specific customer workloads, and attract more customers to its foundry operations. However, Tan did not publicly detail the reported areas of potential cooperation with AMD CEO Lisa Su.

Intel has traditionally built its business around its own processor roadmaps, but Tan says the company needs to be more flexible as customers look for silicon designed for particular workloads. He pointed to Intel's Xeon processors as one area where that approach is changing, alongside continued development of GPUs and accelerators.

"We have to reclaim the leadership in CPU. That is something that I am very determined to do." Tan said.

He also described a change in how Intel approaches customization. "Intel is so proud they never want to modify for customer needs," he said. "I'm modifying it for customer needs, and so that, I think, to me, is a fast-growing area, and I'm double, triple down on it."

The strategy extends beyond conventional CPUs, with Tan identifying application-specific integrated circuits (ASICs), co-packaged optics (CPO), physical AI, and advanced packaging as potential growth businesses. He wants two or three of these areas to eventually contribute around 10% of Intel's revenue each.

Intel's foundry business is another major part of Tan's plans. He said the company's 18A manufacturing process had reached volume production, while development of its next-generation 14A process was progressing strongly. Intel has already released version 0.5 of the 14A process design kit (PDK), which provides chip designers with the technical information and tools needed to develop products for the process.

Tan said version 0.9 was coming next, followed by version 1.0 for volume production in early 2027. "We have multiple customers [who] really want us to really scale their business," he said, referring to interest in Intel's foundry services.

However, Intel did not identify those customers or disclose the size of any potential manufacturing orders during the discussion. Notably, in January, Intel had two prospective customers evaluating 14A test chips, with firm supplier decisions expected during the second half of 2026 and the first half of 2027. Tan also described foundry manufacturing as a business where trust matters as much as technology. Customers hand over wafers that will eventually become products on which their own revenue depends, making missed deadlines or manufacturing problems costly. "The moment we take on the foundry, take on the wafer is a lot of responsibility to make them successful," he said.

He recalled one customer who had waited six to eight months hoping he would become CEO before presenting 15 areas where Intel had fallen short. Tan said he addressed the problems individually, after which the customer agreed to deepen its relationship with Intel.

That said, the prospect of further cooperation would not be entirely new. In October 2024, Intel and AMD established the x86 Ecosystem Advisory Group alongside other technology companies to coordinate improvements to the x86 instruction set architecture and make it easier for hardware and software developers to support both companies' processors.

That cooperation has since moved into AI software. Last week, it emerged that AMD was integrating its ZenDNN optimizations into Intel's open-source oneDNN library, bringing the companies' CPU optimization work for PyTorch AI inference under a common software path. The companies have also worked together on Advanced Matrix Extensions and other x86 capabilities through the advisory group, showing that they cooperate on shared technologies while continuing to compete for customers. For now, however, neither company has announced a new agreement arising from the discussions.

A hacker has successfully breached a vulnerable server, claiming a prize pot worth $2,021.22 in Bitcoin after exploiting PHP2Shell vulnerabilities, a posting on the Steal My Bitcoin website has shown. The challenge's creator confirmed the win on October 25, ending an experiment designed to test claims that exploiting critical software vulnerabilities is becoming easier.

The challenge, hosted on the website a day earlier and announced through a forum post, initially offered approximately $502 worth of Bitcoin. Donations subsequently increased the pot to 0.02511679 BTC, worth $2,021.22 at the time of the win. The creator's website recorded the successful claim at 15:47 UTC on October 25, after the challenge had been running for approximately 19.28 hours.

The experiment follows the disclosure of CVE-2026-109618 and CVE-2026-109619, two critical PHP vulnerabilities that can allow remote code execution under certain conditions. The flaws received critical severity ratings, and fixes were released on October 21. Turning the vulnerabilities into a reliable attack, however, requires more than simply running a publicly available exploit against an arbitrary server.

The challenge's creator, posting under the name ‘cloudlandsdev’, had said the project was designed to see whether anyone could use the vulnerabilities to compromise the server and claim its Bitcoin. “Made this because I'm curious if anyone out there has figured out how to actually use this PHP RCE, we spent the last few days panicking. Have fun!” the creator wrote.

PHP2Shell refers to two vulnerabilities in PHP’s request-handling and memory-management processes. Under vulnerable configurations, an attacker could potentially exploit these flaws to execute code with the privileges of the web server account.

The experiment quickly attracted attention, but the resulting traffic also became a problem. In an update, the creator said visitors were exhausting the server's available computing resources shortly after each capacity increase. Limited storage also prevented the server from retaining all its logs, complicating efforts to document a successful attempt.

The winner, ‘KillaBoi’, published a proof of concept on GitHub describing the attack against the live challenge and providing a local test environment. The exploit took advantage of PHP's handling of multipart uploads and session data, where, by pushing a PHP worker beyond its memory limit, the attacker could leave internal state in an inconsistent condition. Subsequent requests then exposed memory contents through session cookies, providing information about the locations of PHP's memory structures and executable code.

Notably, the attacker used those addresses to construct a forged PHP data structure that triggered command execution during cleanup. The final request returned evidence that the command had run inside the PHP process. Although the server was successfully compromised, the recovered private key did not match the advertised prize address. However, on-chain data shows the funds were subsequently sent to an address “bc1qr4…lk7a9”, labeled as a Kraken cold wallet.

Bitcoin recovery efforts have previously involved reverse-engineering software rather than exploiting a live server. In 2024, hardware hacker Joe Grand, known as ‘Kingpin”, and researcher Bruno recovered access to 43.6 BTC belonging to an owner identified as Michael, who had forgotten a 20-character wallet password.

The researchers reverse-engineered an older version of RoboForm's password generator and identified a flaw that made the password predictable. They recovered the wallet in November 2023, when the Bitcoin was worth approximately $1.6 million. Its value had climbed to nearly $3 million by the time the recovery was reported in May 2024.

A more recent case involved a Bitcoin holder who recovered five BTC worth roughly $400,000 in May 2026 after losing access to the wallet for years. The pseudonymous owner, known as ‘cprkrn’, said Anthropic's Claude AI helped identify a backup file from December 2019, which could be decrypted using a password the owner had previously written down in a notebook. The backup contained the keys needed to access the Bitcoin.

The latest event comes as security teams face pressure to patch PHP installations following the public disclosure of PHP2Shell. A separate report on the vulnerability disclosure controversy detailed how premature publication disrupted the coordinated release process. That said, for now, evidence of the latest successful attack suggests that exploiting these PHP vulnerabilities may not be that hard after all.

Reddit communities are increasingly tightening their rules around artificial intelligence, with some restricting AI-generated submissions and others limiting discussions about the technology. The latest crackdown comes from Reddit’s r/opensource community, whose moderators announced sweeping restrictions on October 24, banning projects built with AI, projects that use AI, and software intended for AI. The announcement has sparked backlash over its broad wording, with some developers saying AI-assisted coding does not automatically make a project low quality or any less open source.

Notably, the new rules go beyond AI, with projects promoted in r/opensource now required to have been actively maintained for at least a year, have multiple contributors, maintain an existing community, and use an OSI-approved open-source license. Common student projects, such as note-taking apps and PDF viewers, are also excluded, while requests for maintainers or additional contributors are no longer permitted.

The moderators also require Reddit accounts to be at least a year old and have a history of significant contributions. They warned that users who contact the moderators after attempting to post could face a permanent ban and a mute. The announcement takes a particularly hard line on AI-assisted development.

“If it's obvious you're using AI in your project, we don't want it. Tokens are cheap, and we don't care how many you spent to make your amazing project,” the moderators wrote.

The policy also tells people who do not speak English to find another community, arguing that moderators cannot reliably distinguish AI-written English from AI-translated text. That provision has drawn separate criticism, with one commenter calling the hostility toward non-English speakers “disgusting.”

Other developers objected that the rules could exclude legitimate projects simply because they were built by a solo developer or used AI tools. One developer said their nine-year-old project had more than 300 GitHub stars but only one contributor, leaving it at risk of exclusion despite having no AI involvement.

The restrictions have also prompted questions about whether projects such as the Linux kernel could fall foul of the new rules as AI becomes more common in software development.

Meanwhile, Linux has taken a different approach. In April, the kernel project formalized rules allowing AI-assisted code contributions, with disclosure requirements and human contributors remaining responsible for submitted code and any resulting bugs or security problems. The policy initially introduced an “Assisted-by” tag for AI involvement.

Linux creator Linus Torvalds has previously pushed back against blanket opposition to AI tools. “AI is a tool, just like other tools we use. And it's clearly a useful one,” he said in July, during a discussion about AI-assisted code review.

For context, the r/opensource debate does not mean Linux has been banned. Moderator Wolvereness said the kernel would satisfy the project's minimum requirements and suggested that whether a project was obviously AI-generated would be a subjective decision. That leaves considerable room for moderators to interpret the policy differently across projects.

The backlash has already prompted the creation of r/TrueOpenSource, a rival subreddit launched by Reddit user u/esk6plays. The r/opensource moderators acknowledged the new community in an edit to their announcement, describing it as a potential refuge for users affected by the restrictions.

The debate is not limited to Reddit’s open-source community. In April, the moderators of r/programming temporarily banned posts about large language models, saying the volume was overwhelming technical discussions. In May, they replaced the trial ban with a policy treating most AI and LLM content as off-topic, while allowing deeply technical discussions of implementation. Meanwhile, open-source projects such as Gentoo and NetBSD have imposed restrictions on AI-generated code contributions, reflecting concerns about code quality, licensing, and accountability.

Open-source maintainers have also struggled with the volume of AI-generated contributions and reports. Projects including cURL, tldraw, Node.js, and OCaml have faced problems involving questionable bug reports or large AI-generated patches, prompting concerns about the time needed to review submissions and verify their quality. These cases have fueled calls for better disclosure and human review, rather than a single approach across every project.

That said, the latest restrictions contrast with OpenAI's efforts to encourage the use of its coding tools in open-source projects. Through its Codex for Open Source program, the company offers eligible maintainers six months of ChatGPT Pro with Codex, API credits, and conditional access to Codex Security.

OpenAI says the support is intended to help maintainers with coding, issue triage, code review, security work, and release workflows, although it does not directly address the r/opensource controversy.

AI spending is generating revenue for technology providers, but most other S&P 500 companies have yet to show comparable gains in productivity or profit margins, a report by Apollo’s Daily Spark analysis has shown.

Artificial intelligence has helped fuel a boom in technology spending, but the financial benefits remain difficult to spot across much of corporate America. Apollo Global Management chief economist Torsten Slok said on October 24 that revenue per worker and profit margins were rising for the Magnificent Seven technology stocks while remaining flat across the other 493 companies in the S&P 500.

Notably, Slok’s analysis found no clear evidence that AI is boosting productivity growth, despite heavy investment in the technology. Revenue per worker and profit margins have risen among the Magnificent Seven but remain flat across the other 493 companies in the S&P 500, according to the report. “Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech,” Slok wrote.

Running AI models at scale can quickly drive up costs since many providers charge by token usage, so bills climb as employees make more requests or AI agents process data and handle increasingly complex tasks. Slok said mainstream companies were beginning to view token spending as a cost center rather than a source of growth. Venture capital-backed businesses are spending more on tokens, he noted, while established companies have less incentive to pay for the newest models if cheaper alternatives can do the job.

That creates a problem for AI providers hoping to turn growing adoption into a lasting revenue stream. Selling more computing power and model access can boost suppliers’ sales, but customers need to see corresponding gains in productivity, revenue or operating costs to keep spending at the same pace. Moreover, if AI expenses rise without a measurable improvement in business performance, the additional spending could weigh on margins.

“AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness, all of which shape reputational risk and ultimately drive revenue growth and profit margins.” Slok added. “The bottom line is that the AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.”

Slok’s analysis also points to a broader limitation: AI adoption remains concentrated among a relatively small group of companies. Unless usage spreads across more industries and produces measurable results, the technology’s effect on the wider economy could remain limited.

The same tension is emerging in India’s financial technology sector. A PwC India survey reported by Business Standard on September 11 found that 84% of surveyed fintech respondents had yet to see measurable financial results from their AI investments.

That has not stopped companies from pressing ahead. The survey also found that 48% of respondents identified proprietary intelligence as a leading strategic priority. Meanwhile, 30% expected customer experience to drive AI agent adoption over the next three to five years, followed by cost reduction and productivity gains at 22%.

Trust is another hurdle for businesses looking to deploy AI agents in financial services. As per the survey, 41% of respondents identified customer trust and adoption as the biggest barriers to scaling agentic payments, while 33% pointed to fraud, security and risk management as major concerns.

AI agents could expand the technology’s role in business by handling multistep tasks rather than simply responding to prompts, although their ability to access tools, process information and act across company systems also brings additional usage costs and security concerns.

Nvidia has been developing tools to address some of those risks, previously, introducing its Open Agent Safety Platform, a hardware-and-software security stack designed to help businesses control AI agents and restrict potentially harmful actions. Rising token consumption has also prompted companies including Microsoft, Meta and Amazon to rein in AI usage as employees adopt the tools and agentic workloads drive up bills. The trend highlights a growing challenge for businesses: encouraging wider AI adoption without allowing operating costs to spiral.

OpenAI CEO Sam Altman has also acknowledged the problem, describing AI token costs as a “huge issue” for customers seeking better value from their spending. The infrastructure behind the AI boom carries its own price tag with Amazon, Google, Meta and Microsoft having committed enormous sums to data centers, chips and the power needed to run AI workloads.

Nvidia CEO Jensen Huang has put the annual cost of licensing the company’s AI software at around $2,000 per GPU to 'run all the software that Nvidia provides that runs agents on these AI factories,' according to a recent video of the comments (below), a pricing model that could generate $50 billion over five years under a proposed scenario involving five million paid licenses outlined by FourWeekMBA.

Huang discussed the pricing at GTC Berlin on October 22, where the company outlined its broader push to build an AI infrastructure business spanning chips, software and AI-agent security. “Software licensing will become increasingly a major part of our company’s business. The way that we license will be…for example, it’s about $2,000 per GPU per year to run all the software that Nvidia provides that runs agents on these AI factories,” said Huang.

The numbers add up quickly. At $2,000 per GPU each year, Nvidia would bring in $10 billion annually if five million GPUs were covered by paid licenses. Over five years, that would amount to $50 billion, provided customers kept renewing their subscriptions. The catch, however, is that Nvidia has not disclosed how many GPUs could be covered by the proposed $2,000 fee, or whether that’s its standard price. It’s also unclear which software the license would cover, or if there is overlap between products.

The distinction matters because Nvidia already charges for enterprise software. Its AI Enterprise platform is listed at $4,500 per GPU for a one-year subscription. Huang’s reported $2,000 figure is lower, but Nvidia has not confirmed that the two prices cover the same products or licensing terms. It should not be treated as a confirmed price cut.

The licensing discussion comes as Nvidia develops more of the infrastructure needed to deploy and manage AI agents in enterprise environments. These systems can use tools, access data, and execute tasks, creating new security and management challenges for businesses running them at scale.

On October 21, Nvidia announced its OpenShell Reference Architecture, a system that combines its Vera Rubin computing infrastructure, Vera CPUs, and BlueField-4 data processing units (DPUs) running Nvidia Sentry. OpenShell provides a secure runtime for AI agents, establishing boundaries around their actions and access to tools and resources. Sentry monitors agent activity independently and can quarantine an agent that attempts to cross those boundaries, according to Nvidia. The architecture is designed to bring security controls into the underlying infrastructure rather than relying solely on safeguards built into an AI model or application.

Nvidia had previously introduced its Open Agent Safety Platform in September. The newer reference architecture builds on that effort, extending the approach to enterprise infrastructure and edge systems.

Other companies are also incorporating these controls into their products. Anthropic is integrating OpenShell and BlueField hardware with Claude Managed Agents to give businesses additional control over what their AI agents can access inside execution sandboxes. Salesforce has developed a Slack Admin Bridge for OpenShell that lets administrators review agent access requests, approve or reject permissions, and monitor activity directly in Slack.

The proposed software pricing fits Huang’s wider argument that Nvidia is becoming more than a supplier of GPUs. The company has expanded into networking, systems, enterprise software, and infrastructure designed to run AI workloads. Speaking on the BG2Pod podcast recently, Huang said, “One of these days, people might not categorize us as a chip company anymore.” He pointed to Nvidia’s presence across industries including healthcare, automotive, telecommunications and life sciences, where the company provides broader technology stacks rather than chips alone.

That said, Nvidia has not said when the $2,000-per-GPU fee would take effect or which software it would cover. It also remains unclear how the proposed pricing would fit into the company’s existing enterprise software business.