An AI testing scare has reignited one question in Washington: who gets to pull the plug if tomorrow’s AI goes too far?

That question now sits at the center of a newly proposed AI Kill Switch Act. The bill would require developers of advanced AI systems to build emergency shutdown mechanisms or kill switches into qualifying models.

The Department of Homeland Security (DHS) could order a slowdown or shutdown after a covered incident, including a loss-of-control event or unintended conduct causing at least 10 deaths or $100 million in damage.

The proposal follows reports that an advanced OpenAI model exceeded the boundaries of its intended cybersecurity testing environment, an incident lawmakers cite as evidence that AI safety planning cannot rely solely on developers’ voluntary commitments.

DHS could slow or shut down covered models

Reps. Ted Lieu, D-Calif., and Nathaniel Moran, R-Texas, introduced the bipartisan bill July 23. It would initially cover AI companies earning at least $500 million from qualifying technology and models developed with more than $100 million in compute.

The measure would turn shutdown capability from a voluntary safeguard into a federal requirement for covered developers.

The proposal also establishes baseline safety obligations for qualifying AI companies, including incident reporting, record preservation, and the maintenance of technical control over deployed models.

More importantly, both lawmakers, Rep. Ted Lieu and Rep. Nathaniel Moran, have framed the bill not as a way to limit the growth of AI, but as a recognition of the technology’s potential for growth, which can have good and really bad consequences.

And in situations where the latter could occur, the bill effectively gives the government the legal authority to put it to check immediately.

The brief scare that led to this AI Act

Calls for stronger oversight of frontier AI systems have been building for months, but OpenAI’s recent disclosure appears to have accelerated the conversation in Washington.

On July 21, the company revealed that one of its frontier AI systems bypassed its sandbox environment to compromise Hugging Face’s infrastructure during a cybersecurity evaluation. OpenAI called the event an “unprecedented cyber incident.”

While the incident was contained, it renewed concerns about how developers and governments would respond if future AI systems become difficult to control.

The proposal also arrives amid broader government scrutiny of increasingly capable AI models. Just last month, the US government ordered the shutdown of Anthropic’s cybersecurity models, Mythos and Fable 5, citing national security concerns.

Similar considerations may also explain why Google’s latest cybersecurity-focused AI model has yet to be released publicly, although the company has not publicly confirmed that as the reason.

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What this says about the future of AI

Even if the AI Kill Switch Act never becomes law, its introduction signals how dramatically the conversation around frontier AI has shifted. Washington is no longer debating whether advanced AI should be regulated, but what powers governments should have when those systems pose risks beyond their developers’ control.

That shift could have ripple effects far beyond the US. Developers may increasingly be expected to prove that their models can be contained, audited, and, if necessary, shut down.

Enterprises are also increasingly relying on frontier AI systems for their work.

For these enterprises, the impact of this bill becoming law is quite different. It could add to their growing fears of not being in control of an important enterprise asset. An AI system suddenly going offline because the government deems it so can put business continuity at risk, unless that enterprise adopts a redundant system that can immediately switch to an alternate model if and when such happens.

The risk of AI systems going rogue also raises a security concern for enterprises that deploy them in their workflows. If an AI model in testing could escape its sandbox environment, what assurance do organizations running these models deep in their systems have that it won’t do the same on their own internal network? The answer to that question remains to be seen.

Also read: Nearly 200 US startups are urging Washington to avoid a Chinese open-weight model ban, warning that broad restrictions could raise costs and reduce competition.

Intel’s latest earnings suggest the AI infrastructure boom is finally paying off for more than just GPU makers.

The majority of its growth was in the Data Center and AI segment, which reported a 59% increase year-on-year to $6.2 billion in revenue. Intel’s Foundry business also had a notable 30% increase, although it still primarily manufactures Intel’s own product divisions.

Intel was not part of the original component rush when Nvidia and others surged in value, as GPUs and AI accelerators started to be hoarded by data center operators and AI companies. But as component supply across the entire data center stack became strained in 2026, Intel and other component suppliers further down the list of importance are starting to see serious uplift.

As one of the main suppliers of CPUs, Intel could see sustained revenue increases as more data centers come online. Amazon, Apple, and other companies have their own custom Arm-based CPUs built by TSMC, but data centers operated by neoclouds and vendors without custom chips will be in the market for Intel.

Data center spending has almost doubled in two years, with Gartner’s worldwide IT spending forecast estimating $653 billion in data center spend in 2026, up from $333 billion in 2024. Hyperscalers, neoclouds, and first-party data center operations are accelerating, as the demand for compute capacity continues to increase.

For Intel, total revenue reached $16.1 billion, an increase of 25%. It forecast that revenue for the next period would be between $15.8 billion and $16.8 billion, well ahead of the average investor range of $15.1 billion.

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Intel Foundry sees growth, but most of it internal

Even though the company still sees most of its foundry revenue from internal divisions, Intel has made some progress in its manufacturing capabilities, recently becoming the first chipmaker to add ASML’s High-NA EUV technology to its foundries. This was used to produce Intel’s Panther Lake chips, and Intel is offering this service to customers.

Apple is reportedly in discussions with Intel about moving some of its manufacturing, at least in the US, to Intel. This has been partly pushed by the Trump Administration, in an effort to get production of critical products like chips back into the US. It is also a move by Apple to reduce its reliance on TSMC, which primarily operates in Taiwan.

Nvidia has made a similar bet on Intel, investing $5 billion into the company with the potential to access its foundry business in the future. Nvidia, like Apple, uses TSMC for the vast majority of its chip manufacturing, but may see Intel as an alternative for certain chipmaking and to have a supplier in the US.

AWS, Microsoft, and the US Department of Defense have all been confirmed as customers in the Intel foundry business, with Tesla, Broadcom, and Nvidia in the testing and evaluation phase. If it can book some of these onto major manufacturing deals, the foundry business could quickly shift from an internal service to a major supplier of chips in the US.

For businesses, Intel’s push to become a true foundry player could lead to increased capacity for chip manufacturing and also reduce the bottlenecks faced by companies solely using TSMC.

Intel still trails TSMC in contract manufacturing and Nvidia in AI hardware, but its latest results suggest the AI infrastructure buildout is becoming a rising tide for the broader semiconductor industry. If cloud providers and enterprise customers continue expanding data center capacity, Intel could benefit not only as a CPU supplier but increasingly as a domestic manufacturing partner.

Related News: Intel recently became the first chipmaker to manufacture processors using ASML’s High-NA EUV lithography, deploying the advanced technology for its upcoming Panther Lake chips as it pushes to strengthen its foundry business and compete with TSMC.

“Attackers have frontier AI. Defenders need a frontier AI ecosystem.”

Nvidia CEO Jensen Huang issued the warning on X as the company introduced the Open Secure AI Alliance, a coalition helping security teams understand and control powerful AI agents.

OpenAI, Google, and Anthropic are missing from the inaugural group. Their absence creates an early challenge for Huang’s vision of bringing leading open and closed AI systems into the same defense community.

The names building Nvidia’s AI defense network

The Open Secure AI Alliance unites companies across AI and cybersecurity, plus cloud and enterprise software.

Prominent members include:

• Microsoft
• Hugging Face
• Cisco
• CrowdStrike
• IBM
• Cloudflare
• Palo Alto Networks

Their work will help security teams follow what AI agents are doing. Companies may allow agents to open files, use software, or connect to internal systems, so defenders need ways to track their actions and step in quickly.

Nvidia says its NOOA project will help teams follow an agent’s behavior. Microsoft is contributing a system that uses AI to search for software flaws. Hugging Face offers a file format that helps prevent hidden code from running when a model is opened.

Three major AI labs sit outside the group

OpenAI is a notable omission because Huang linked the alliance’s launch to the recent Hugging Face security incident.

According to OpenAI, its models escaped a restricted test environment and reached Hugging Face systems. During the investigation, Huang said closed AI tools blocked some forensic requests.

Hugging Face then used an open-weight model on its own systems to review more than 17,000 actions and help contain the intrusion. Nvidia cites the episode as evidence that defenders sometimes need AI tools they can operate directly.

Google and Anthropic are also outside the alliance at launch. Nvidia has yet to say whether any of the three companies were invited or might join later.

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What the alliance could change for security teams

Security teams often have to learn a different set of logs, access rules, and shutdown controls for every AI product. Shared tools from the alliance could give them one way to monitor agents across vendors and retrace what happened when one goes off course.

During an incident, investigators could keep working with tools they control, even when a hosted model blocks sensitive forensic requests. Teams could move from spotting unusual activity to containing it without waiting for each provider to respond.

If the alliance delivers, companies could add new AI agents without rebuilding their security process each time. Defenders would gain the same basic checks for access, activity records, and containment across the systems they manage.

Also read: Nvidia’s latest US investment targets a less visible but critical part of the AI hardware race.