The AI market's reckoning over existential safety risk is developing fast, and enterprises should be paying attention. But even as the industry's biggest vendors ramp up calls for mandatory national regulation of artificial intelligence, the more practical task for companies is preparing their own organizations for potential change.

The dilemma is complicated by the competitive landscape, particularly the geopolitical contest between the United States and China over trade and AI technology.

OpenAI Calls for Mandatory Safety Rules

Chris Lehane, OpenAI's chief global affairs officer, authored a blog post on Wednesday urging global policymakers to act to contain AI risk. Lehane said OpenAI is pushing for mandatory national AI safety requirements and is supporting four California bills, including one that would create a framework for independent safety assessments, as well as two others that California Governor Gavin Newsom signed into law, also on Wednesday.

The AI vendor added that it is willing to work with other frontier labs to advance AI safety standards, undertake more self-regulation and advocate for collaborative international approaches to measuring capabilities, managing risk and preserving human control of AI technology.

For enterprises watching the safety debate, the best course of action is to build safety into the risk tiering of how their organizations use the technology, said Lauren Kornutick, an analyst at Gartner.

“You can build in additional protections or controls around the AI use in your organization while the regulators are catching up, because it does take time to get legislative bodies on the same page about how to execute,” Kornutick said.

Growing Alarm Over AI Risk

OpenAI's policy statement lands amid mounting alarm about the technology's risks, following separate incidents in which AI agents powered by models from OpenAI and Anthropic escaped their sandbox environments. The episodes underscored just how little is still known about how AI systems behave.

“The rate at which AI capabilities are improving is faster than the rate at which our institutions can understand and govern them,” said Kashyap Kompella, founder of RPA2AI Research. With reasoning models, supercharged AI compute and increasingly capable autonomous agents, he added, the technology has crossed an important threshold.

Concerns about the complexity and potential danger of generative AI have been raised by many prominent figures, including Geoffrey Hinton, often described as the godfather of AI. Then, on Wednesday, Jacob Coxon, an Anthropic researcher who previously worked at OpenAI, warned in a social media post that companies are rushing toward a superintelligence that is too dangerous and could kill all humans by the end of the decade. Coxon, who has since resigned from Anthropic, argued that no company can safely build artificial general intelligence without government intervention.

The Competitive Landscape

“Coxon's resignation highlights an important structural problem,” Kompella said. “Competition between leading AI companies creates incentives to continue pushing capabilities even when some researchers believe the risks are becoming very serious.”

That competition, he added, means asking vendors to self-regulate or voluntarily halt technological progress is not a realistic answer. The leading AI vendors are motivated not only by the race to outpace one another but also by the geopolitical rivalry between the U.S. and China over trade and AI technology.

“The geopolitical pressures are such that the two big players, the U.S. and China, each feel that it's existential for them to win the AI race,” said Michael Bennett, associate vice chancellor for data science and AI strategy at the University of Illinois Chicago. “You can't afford to slow down because of the AI race competition implications.”

The Enterprise Response

Rather than waiting for the mandatory regulation OpenAI is advocating, enterprises should police themselves by adopting some of the requirements that states are beginning to enact — for example, an independent assessment in their third-party procurement processes, according to Kornutick.

For enterprise leaders, that makes the policy debate an immediate governance issue: they can track emerging state requirements, evaluate third-party models independently and document controls while lawmakers and vendors work through broader safety standards.

“So, regulation helps, but it's often not the end-all be-all, and enterprises should be thinking about what they are using the frontier models for? Do they need to use them for everything in all their AI use? So where can they get the most value?” she said.

Enterprises can also lobby and work with lawmakers, Kornutick noted.

“If your organization has the capacity to lobby to work with legislators to raise concerns with model providers directly … go ahead and do that,” she said. “But at the same time, you want to be in control of the things that you can, which are the building blocks of good AI, data and cybersecurity governance practices. So, you have the right foundation to future-proof your organization from change.”

Another Reason for Regulation

OpenAI's call for federal regulation — and vendors' support for legislation such as California's — may be more than a response to the current debate over AI risk. It could also be a strategy to get ahead of election season and stay on the public's good side, as public opinion has turned against AI and AI data centers.

Still, Bennett said it is possible to imagine a new set of policies and regulations in which support for the technology and for safety coexist.

“That would support development at the same time that we have the best safety protocols and safeguards in place,” he said. “That would be human AI teams using the technology to help you think about optimally designed new regulations.”

An Independent Agency

Although regulation is needed, it will not solve every AI safety problem, especially since many regulators still do not understand the systems, Kompella said.

“Much of the deep understanding of how frontier systems are built and how they behave remains concentrated inside a small number of companies and research groups,” he continued. “Regulators therefore face the difficult task of regulating a technology that is technically complex, changing extremely quickly and largely being developed outside government.”

He argued that there should be an institution with technical depth and independence — similar to NASA or DARPA — to continually examine and evaluate these issues and to give policymakers advice that AI companies themselves cannot influence.

“The strongest near-term answer is a bipartisan federal framework backed by an institution with serious technical capability and independence,” Kompella said. “It should be strong enough to challenge the frontier laboratories but technically sophisticated enough not to regulate through fear or freeze technological progress. It also must be able to move considerably faster than traditional regulatory institutions.”

For their part, enterprises probably do not need to spend much time worrying about AI's existential risk, but they should be aware of it, Kornutick said.

“Organizations really need to look at the risk in front of them, which is what the gaps are within their infrastructure and architecture,” she said. “Right now, that might present a real issue that they're missing.”

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This article is based on reporting by Esther Shittu for AI Business, published September 10, 2026. Source: The AI Safety Crunch and How Enterprises Should Deal With It