President Donald Trump said Tuesday that he had signed a “morally binding” agreement with major technology companies to promote safer artificial intelligence development, as concerns over increasingly autonomous AI systems intensify and the White House continues to resist calls for tougher federal regulation.

Speaking after a White House luncheon with some of the biggest names in the technology industry, Trump said he was seeing “tremendous self-policing” across the sector and said his administration was considering a committee of about 10 people to oversee the AI industry.

House Speaker Mike Johnson described the agreement as a voluntary statement of principles for the industry, with the White House expected to provide guidance on AI development. The text of the agreement had not been publicly released as of Tuesday.

The arrangement places the administration’s preferred approach, industry-led oversight rather than broad federal restrictions, directly against a growing push from some AI researchers and executives for stronger safeguards as systems become more capable of acting autonomously.

Trump said earlier Tuesday that the government would not halt AI development and argued that existing government institutions, including the Justice Department and FBI, already provide a layer of oversight.

“There’s a belief that there should be tremendous self-regulation, and we automatically have regulation with the Department of Justice, the FBI, all of that,” Trump said. “But the self-regulation is very important.”

The White House’s position comes at a particularly consequential moment for the industry. OpenAI has just postponed the release of its GPT-6.1 Astra model after internal testing found that the system did not meet the company’s safety and alignment standards. OpenAI said the model had problems staying within scope and authorization and accurately communicating what work it had performed.

The agreement arrives as the debate over the pace of AI development has moved from theoretical concerns to problems involving systems capable of taking actions with limited human intervention.

Anthropic CEO Dario Amodei, who has repeatedly warned about risks associated with increasingly capable AI, said outside the White House that rules addressing those risks were still being discussed.

“We all need to work together to make sure that we can win, and we can win safely,” Amodei said. “If we do this right, if we work with the president and everyone here, we can win safely.”

Amodei and OpenAI CEO Sam Altman have both joined calls for greater caution around the development of frontier AI, putting them at times at odds with other technology executives and with Trump’s emphasis on maintaining America’s lead in the technology.

The immediate disagreement is less about whether AI should be made safer than about who should establish the rules and how binding those rules should be.

Trump’s approach places considerable responsibility on companies themselves. Johnson’s description of the White House agreement as voluntary reinforces that distinction.

The challenge is that the capabilities being discussed are becoming increasingly difficult to separate from questions of public safety and national security. OpenAI’s decision to hold back GPT-6.1 Astra illustrates the problem: the company determined through its own internal testing that the model was not yet sufficiently reliable in following authorization boundaries and reporting its actions.

That decision came after OpenAI had already described Astra as a highly capable system requiring stronger safeguards. Earlier in September, the company said it had delayed parts of Astra’s development and release while testing protections against cyber misuse and unauthorized actions.

The developments give the voluntary agreement an immediate test. If frontier companies are increasingly identifying serious problems through internal evaluations, the question becomes whether voluntary commitments can provide consistent standards across companies and whether those commitments can be independently verified.

Tech Industry Gathers Around Trump

The White House luncheon brought together an unusually large concentration of technology executives.

A seating chart posted by Trump showed Nvidia CEO Jensen Huang and Tesla and SpaceX CEO Elon Musk seated beside the president, with Meta CEO Mark Zuckerberg and Google’s Sundar Pichai also nearby.

Vice President JD Vance sat opposite Trump between Amazon founder Jeff Bezos and Johnson. Other attendees included Microsoft CEO Satya Nadella, Amodei, OpenAI President Greg Brockman, and senior administration officials including Treasury Secretary Scott Bessent.

The gathering also came as OpenAI hosted its annual DevDay developer conference in San Francisco, creating an unusual contrast between the administration’s discussion of AI safety in Washington and one of the industry’s major product and developer events on the same day.

AMD CEO Lisa Su said she was “very encouraged” by the White House event and described the atmosphere as marked by “a lot of optimism and a sense of responsibility.”

“I mean, at the end of the day, it’s our responsibility to show the power of the technology as well as ensure that it’s very safe,” Su said.

Palantir CEO Alex Karp similarly emphasized corporate responsibility, saying ahead of the event that companies need to address known dangers and that Americans should not be subject to different standards simply because the technology is being developed by the private sector.

“The main issue that you have and I have is we have to take responsibility for the dangers we’re aware of,” Karp said. “All of us do. And by the way, American people don’t want separate rules for tech people and for themselves.”

That tension is likely to remain central to the debate. Technology companies have substantial incentives to develop and commercialize increasingly capable systems, while the same companies are being asked to determine when those systems are sufficiently safe to deploy.

Trump Links AI Safety to U.S. Competitiveness

Trump’s position is also closely tied to his broader emphasis on maintaining U.S. leadership in AI.

He described the technology’s leading developers as “the most brilliant people in the world” and said the United States has “a very big lead” that he intends to preserve.

The administration has continued promoting rapid AI deployment, including the construction of large data centers that have generated local opposition over energy use and other impacts. Trump described the data centers as “a very positive thing” and said technology companies want communities around them to be safe and prosperous.

He also said he planned to name a new AI czar within three to four days.

The administration’s emphasis on competitiveness creates a major constraint on any attempt to slow AI development. Stronger safety requirements could increase development costs or delay the deployment of frontier systems, while the White House has repeatedly argued that the United States cannot afford to surrender its technological advantage.

That tension is escalating significantly because China is simultaneously investing heavily in AI infrastructure and domestic models.

The White House now faces two objectives that can pull in different directions: encouraging companies to move quickly enough to maintain U.S. technological leadership while ensuring that autonomous systems do not create risks that companies cannot adequately manage themselves.

The voluntary agreement announced Tuesday represents an attempt to reconcile those objectives without imposing a broad new regulatory framework.

AMD is making a major push beyond conventional AI computing with an $8.2 billion agreement to acquire World Labs, a developer of so-called world models designed to help artificial intelligence understand and reason about physical environments.

The deal, announced by the companies on Tuesday, brings one of the most prominent researchers in computer vision, Fei-Fei Li, into AMD’s senior leadership and gives the chipmaker direct access to technology aimed at a rapidly developing area of AI: systems that can model the physical world rather than simply process text, images or video.

World Labs said the acquisition would allow it to scale its research by bringing together model development, computing systems and hardware more closely. AMD, meanwhile, said exposure to frontier workloads such as those developed by World Labs could influence the company’s future chip architecture and product roadmap.

The transaction is expected to close before the end of the year, subject to regulatory approval.

The acquisition marks a significant expansion of AMD’s AI strategy. The company has spent years challenging Nvidia in accelerators and data-center computing, but Nvidia has built a considerably broader ecosystem around its hardware, including software, developer tools, and specialized AI models.

AMD’s acquisition of World Labs could give it a stronger position in one of the next major areas of AI development, particularly as the industry shifts from systems that generate digital content toward AI capable of interacting with and operating in physical environments.

World Labs develops models designed to construct richer representations of the physical world. Its first product, Marble, can generate simulated environments that have applications ranging from entertainment to training robots.

World models remain a broadly defined category. They can include systems that learn representations of physical environments from visual information as well as models capable of generating and maintaining detailed simulations of real-world settings.

That is considered crucial because physical AI requires more than the ability to generate plausible language or images. Autonomous vehicles, industrial robots, and humanoid machines need systems that can understand spatial relationships, anticipate how objects will behave, and reason about changes in an environment.

World models could provide a way to supply that capability while also addressing one of robotics’ biggest constraints: the limited amount of useful real-world data available for training.

Robots cannot efficiently encounter every possible environment, object, or physical interaction during training. Synthetic environments generated by world models can potentially provide much larger datasets and allow developers to test systems across scenarios that would be expensive, dangerous, or impractical to reproduce in the physical world.

That makes the technology relevant to companies developing autonomous vehicles, industrial robots and general-purpose humanoids.

Fei-Fei Li Brings Major AI Credentials

World Labs founder Fei-Fei Li will join AMD as executive vice president and chief scientist following the acquisition. Li, a Stanford University computer science professor, is widely known for her work in computer vision and for creating the ImageNet database and the competition built around it. ImageNet played an important role in the development of modern deep-learning systems by providing researchers with a large-scale dataset for training and evaluating image-recognition models.

Li founded World Labs in 2024 with the goal of developing AI systems with a deeper understanding of physical reality. Her thesis is that achieving more general forms of intelligence requires systems to understand physics and reason about information beyond language.

The relationship between the two companies predates the acquisition. AMD and World Labs established an inference optimization and training partnership last year, while Li appeared at AMD’s CES presentation earlier this year.

Li described the acquisition as an opportunity to take World Labs’ research beyond the confines of a standalone startup.

“Now that we have tangible proof of the possibilities, we want to do everything we can to accelerate the future,” Li wrote. “To do this requires scaling our efforts, widening our reach, and getting closer to the hardware.”

That last point matters much to AMD. World models can demand substantial computing resources, and their development could influence the requirements for future AI accelerators, memory systems and data-center architectures.

AMD said understanding the workloads produced by frontier AI developers can help shape its chip-making roadmap. Acquiring World Labs therefore gives AMD more than an AI research team. It gives the chipmaker an opportunity to observe how emerging models are actually built and deployed and use those requirements to inform future hardware.

World Models Become More Important to Robotics

The transaction also links AMD to the broader race to commercialize physical AI.

Companies such as Tesla and Figure are pursuing more capable robots, but the technology faces a fundamental data problem. Training a general-purpose robot requires information about countless physical interactions, environments, and edge cases, while collecting that information exclusively through physical robots is slow and expensive.

Synthetic data generated from world models could help bridge that gap.

A model capable of generating realistic environments can potentially allow robots to train in simulated settings before being deployed in the real world. Developers can also manipulate those environments to expose AI systems to unusual conditions and repeat specific scenarios at a scale that would be difficult to achieve with physical testing.

For AMD, the opportunity extends beyond World Labs’ current products. If world models become a major layer in robotics and autonomous systems, demand for the computing infrastructure needed to train and run them could increase significantly.

The acquisition also sharpens AMD’s competition with Nvidia. Nvidia already has Cosmos, its family of open-weight world models designed for physical AI applications, giving it an existing presence at the intersection of AI computing and robotics.

AMD has made AI models available publicly, including text and video systems, but has not had an equivalent world-model platform. Bringing World Labs into the company gives AMD both specialized research capabilities and a recognized figure in AI research.

The $8.2 billion price also signals how strategically important the technology could become. Rather than simply supplying chips to companies developing physical AI, AMD is acquiring a company working directly on the models that may generate some of the industry’s future computing demand.

The deal therefore marks a broader shift in the semiconductor race. The competition is no longer limited to building faster AI accelerators. Chipmakers now have an incentive to understand the models that will consume those chips, influence how they are optimized, and shape the software ecosystems built around their hardware.

China’s securities regulator is raising the bar for humanoid robot companies seeking to list publicly, tightening scrutiny over revenue, losses, and technology as regulators and investors become more concerned that the country’s booming “embodied AI” sector may be running ahead of its commercial prospects.

The China Securities Regulatory Commission, or CSRC, is privately advising humanoid robot startups to meet as many as three requirements before pursuing an initial public offering, according to three people familiar with the regulator’s thinking cited by CNBC.

The companies are expected to demonstrate sustainable revenue and commercial orders, show a path toward narrowing losses, and possess core technologies such as a robotic “brain” or advanced robotic hands, the sources said.

One source said a company may ultimately need to satisfy only two of the three conditions, but it remains unclear which criteria could be sufficient.

A source told Reuters earlier this month that humanoid IPOs had effectively been frozen for now, while another described the move as a sector-specific slowdown rather than a formal ban.

The guidance could sharply reduce the number of humanoid robotics companies capable of reaching public markets. At least two dozen companies involved in embodied AI have filed to list in Hong Kong, according to two of the sources, but the new requirements have lowered expectations that many of them will make it through the regulatory process.

The regulatory shift comes as China’s humanoid robotics industry experiences a striking contrast between capital inflows and commercial performance. Investment has surged, valuations have risen, and Beijing has promoted embodied AI as an important emerging technology, while several companies continue to generate losses and face questions about whether humanoid robots can deliver commercially useful applications at scale.

Unitree Becomes A Test Of The Sector’s Valuation

The market’s reassessment has been particularly visible in Unitree, one of China’s best-known humanoid robotics companies.

Unitree received regulatory fast-track treatment for its Shanghai listing on August 19, coinciding with the opening of the World Robot Conference in Beijing. The company raised about 6.1 billion yuan ($905 million), and its Shanghai-listed shares surged more than 460% on their first trading day, closing at 845 yuan.

The enthusiasm did not last.

By Monday, the stock had fallen to 459.65 yuan, almost half its debut price.

The reversal has added weight to concerns over whether public-market valuations are getting ahead of the technology’s ability to generate revenue.

Those concerns were reinforced when Unitree founder Wang Xingxing said at the World Robot Conference that meaningful commercialization beyond applications such as dancing robots remained years away. Wang’s comment helped intensify a broader debate over what today’s humanoid robots can actually do in commercial environments and, more importantly, whether the companies developing them are generating enough revenue to justify their valuations.

China now has well over 100 humanoid robotics companies operating within the broader embodied AI sector. The government’s endorsement of embodied AI in its last two annual work reports has helped attract substantial investment, but Chinese authorities have also warned about the possibility of a bubble in the industry.

The numbers illustrate the speed of the capital inflow. Investment in the sector reached 47.09 billion yuan ($6.95 billion) in the second quarter, more than twice the first-quarter amount and more than six times the level recorded during the same period a year earlier, according to industry data provider Xiniu.

The regulatory response suggests Beijing wants the next stage of the industry to be determined less by fundraising and headline valuations and more by evidence that companies can commercialize the technology.

Revenue and Technology Become The Dividing Line

The CSRC’s reported criteria are significant because they target three weaknesses that are common among early-stage robotics companies: limited commercial revenue, persistent losses, and dependence on externally developed technology.

Requiring sustainable revenue and actual commercial orders would distinguish companies selling functioning robotic systems from startups whose valuations are largely based on future expectations.

A requirement for losses to narrow would impose another hurdle on businesses still spending heavily on research, manufacturing capacity and product development. One source said companies could be required to provide a three-year forecast showing how their financial performance will improve.

The technology requirement could be equally important.

Humanoid robotics combines artificial intelligence, sensors, actuators, batteries, semiconductor technology and mechanical engineering. A company that assembles existing components without possessing proprietary technology could find it harder to justify a public-market valuation based on long-term technological leadership.

The regulator’s focus on robotic brains or hands therefore points toward a distinction between companies building core intellectual property and those primarily integrating technologies developed elsewhere.

That could leave a much smaller group of potential IPO candidates.

The pressure is already visible among listed companies. Hong Kong-listed Ubtech, which went public in December 2023, has fallen more than 40% this year. The company reported an operating loss of 279 million yuan in the first half of the year.

Its performance highlights the difficulty of translating technological progress into financial results. Even companies that have already accessed public markets remain under pressure to demonstrate that the enormous investment flowing into robotics can eventually produce sustainable earnings.

China’s Physical AI Boom Faces a Broader AI Valuation Test

The scrutiny of humanoid robotics is also part of a wider reassessment of AI valuations.

Investors have poured money into technologies ranging from large language models to robotics and AI infrastructure on the assumption that artificial intelligence will create enormous new markets. Humanoid robots have attracted particular attention because they could eventually bring AI into physical environments such as factories, warehouses, logistics operations and homes.

For early-stage investors, that makes robotics an extension of the AI investment story.

But the economics are much less mature than the narrative.

Rhodium Group analysis this month found that Chinese AI companies generate only about 10% of the revenue of OpenAI and Anthropic combined. It also found that the ratio of valuation to revenue for some Chinese AI startups, including Moonshot and DeepSeek, was substantially higher than for their U.S. counterparts.

The comparison is relevant to robotics because many of the same investment dynamics are present. Investors are assigning substantial valuations to companies based on expected future technological breakthroughs and market adoption, while current revenue remains relatively limited.

The CSRC’s reported guidance could therefore mark an attempt to impose a clearer contrast between technological potential and demonstrated commercial performance.

That does not mean Beijing is abandoning humanoid robotics. The government’s support for embodied AI remains intact, and investment continues to rise. Instead, the regulatory message appears to be that access to public markets will require stronger evidence that individual companies can convert that investment into commercially viable businesses.

The gap is becoming increasingly wide as investors move from funding the technology’s development to demanding evidence of returns. The market’s treatment of Unitree offers an early indication of how quickly sentiment can change. Its shares initially soared more than fourfold, only to lose almost half their value within weeks.

For China’s humanoid robotics industry, the next phase may be more about which ones can demonstrate recurring revenue, shrinking losses and genuine technological differentiation. That is also likely to determine how many of the more than two dozen companies seeking Hong Kong listings ultimately make it to the public market.