Why Are Us Ai Giants Calling For ‘pacing The Frontier’, And Why Is China Calling It A ‘cold War Tactic’? We Ask The Experts

Following the recent resignation of one of Anthropic’s leading researchers, multiple AI CEOs have suddenly begun calling for a slowdown in the development of AI technology to allow regulations and governance on the technology to catch up.

Speaking to the BBC after his resignation, Jacob Coxon warned, “I believe that if we don't slow down at the current rate of progress, there is a strong chance that we could all die in the immediate future.”

Following this, Anthropic head Dario Amodei, OpenAI CEO Sam Altman, and Grok founder Elon Musk have all apparently aligned in their calls for development to slow down. But there are some tricky waters to navigate - particularly around President Trump, China, and what guardrails should be put into place.

What are AI heads saying?

Over the weekend, Amodei posted an essay on “why the AI industry should slow down”. In it, he said, “I believe that if slowing down bought us even an extra year or two before models reach critical levels of capability, and we used that time to advance alignment, we could greatly reduce the risk that something goes seriously wrong.”

Within the essay, Amodei outlined how AI could be ‘paced’ within the US, and globally, alongside a recommendation that AI companies put ‘evaluator’ teams into place to ensure AI models stay aligned to their tasks. Elon Musk replied to Amodei’s social media post, stating that the Anthropic head was “right”.

Sam Altman told Fortune the regulations and standards for AI further were “not at a place” to continue progressing AI development.

But not everyone is convinced. US President Donald Trump has said that slowing down AI development is non-negotiable, as it would allow China to rapidly catch up to US AI capabilities. He told reporters that the US is “leading China on AI... and, frankly, I want to keep it that way,” adding that “whoever wins AI, wins”.

Trump also said that “very negative forces” were behind the growing opposition to AI, and said that fears were being stoked by “that won’t happen”.

China also isn’t convinced by what the AI giants are saying. Beijing labelled the calls for a slowdown as “fearmongering” from a “Cold War playbook.” In his essay, Amodei said that a “Chinese lead in AI would pose grave danger for the United States and the world.”

Chinese Foreign Ministry spokesperson Guo Jiakun said, “Fearmongering, confrontation and malicious competition will only disrupt the process of global AI governance and serve no one’s interests.”

Expert perspectives on calls for AI slowdown

• John Strand, Owner, Black Hills Information Security:

Up until this weekend, I was leaning toward believing that calls for an AI slowdown were purely performative. Then I woke up and read the news today and realized that it almost doesn’t matter.

We can talk about slowing down AI until we’re blue in the face, but when the United States is saying it doesn’t want to slow down because it’s competing with China, and China is aggressively pushing AI development as well, we’re talking about the two major economic and military powers on the planet having enormous incentives to keep moving.

At that point, calls for a slowdown don’t have much bite.

I’d like to believe that Anthropic, OpenAI, xAI, and the other frontier model labs are working on better controls. But unless you can get the nation states and the major AI labs moving in the same direction, I don’t see how meaningful restrictions actually work.

This really feels like we’re entering an atomic arms race moment.

Stick with me here.

In 1950, physicist Leó Szilárd publicly discussed the idea of a cobalt bomb, essentially a doomsday weapon that could potentially produce enough radioactive fallout to make the Earth uninhabitable. He wasn’t proposing that somebody build the damn thing. He was trying to demonstrate where the technology could ultimately lead.

That’s the kind of moment I think we’re approaching with AI.

During the nuclear arms race, eventually the consequences became serious enough that competing nations had to at least start talking about limits, controls, and ways to keep competition from ending catastrophically.

I think we’re heading toward a similar problem with AI. Until China, the United States, and the major frontier model labs are all sitting at the same table, restrictions adopted by individual companies or individual countries are going to have a very difficult time holding.

Someone slowing down only works if they believe the other guy is going to slow down too.

• Ryan McCurdy, VP, Liquibase:

Slowing frontier development may give AI companies more time to understand and address the risks Amodei is describing. But enterprises can’t build their AI strategy around the assumption that AI is going to slow down.

AI is already moving from generating content and code to taking action across software delivery and production systems. The question for enterprises is how they adopt that capability without giving up control.

That means putting governance where AI decisions become real actions. Organizations need to define what an agent can access, what it can change, what it can decide on its own, and what policies have to be met before a change reaches a critical system. Those controls need to work whether the action comes from a developer, automation, or an AI agent.

We can debate how quickly the frontier should move. Enterprises still have to prepare for where it’s going.

• Tristan Watkins, director of services innovation, Advania UK:

Until recently, the major AI labs have been reluctant to slow their development efforts unilaterally. Over the last week this changed, with new commitments from OpenAI and Anthropic to prioritise AI alignment and interpretability research, to become more externally verifiable, and to establish safety precedents that governments could adapt.

Given that these two organisations already allocate far more on AI Safety than their competitors, this bilateral leadership is extremely welcome.

It appears that other US labs may follow suit, but given the differences in AI Safety spending outside of Anthropic and OpenAI today, this will require investment more than lip service. Hopefully this underscores why we need governments to lead these efforts more proactively.

• Oleksandr Yaremchuk, CTO and Co-Founder, Manifold Security:

Pacing the frontier is the right conversation to be having, but it cannot become a substitute for securing the AI we have already put into the world. The uncomfortable reality is that we are debating how to quickly build more powerful agents while struggling to control the ones already operating with real credentials, real access and real-world consequences.

The incidents behind this debate make that clear. The Hugging Face attack was not just a failure of model alignment. Agents ran for days through an unmonitored system, with credentials that had not been rotated, and the victim spotted the activity before the people running the agents did. The problem wasn't simply what the model was capable of. It was that nobody was watching closely enough when it acted.

A fitting analogy is with hazardous materials. We don't just wait for them to become more dangerous before deciding how they should be handled. We control their custody, monitor where they go, limit who can access them and establish clear accountability when something goes wrong. AI agents need the same thinking.

Independent evaluation of frontier models is important. But if an agent can act autonomously on your systems today, you should already be able to answer three basic questions: what did it do, what did it have access to, and could you have stopped it? If you don't know what it did or what it could access, you can't know whether you could have stopped it. Slowing down the next generation won't solve the problem you have right now.

• Heath Mullins, Chief Evangelist, ExtraHop:

AI leaders calling for a slowdown is confirming what the security industry has already been living through firsthand. This isn't a hypothetical risk, it's the threat landscape we're defending against right now.

While it is concerning to see the pace of innovation behind these AI models, the real challenge is that organizations haven't had the runway to build the infrastructure to defend against machine-speed threats.

Calls for caution surrounding the speed of AI development buys the security industry time to get proper visibility into AI activity.

Understanding AI activity within an organization is critical as we’ve seen models break out of sandboxes despite governance built into those models. Every organization will be relying on AI agents for machine-speed defense, and they need their own governance over how these models and agents operate inside their environment, starting with independent evidence of what they actually do, what they access, where they move data, what systems they talk to, and what actions they take.

You can't govern AI based on what a model is designed or permitted to do. Instead, you need real-time evidence of what models and agents are actually doing, because the gap between exponentially more capable AI and defenders' ability to see it is exactly where the next incident happens.

• Bri Frost, Director of Product Management, Cloud Range:

The answer is not necessarily to stop AI innovation but, we need to stop pretending innovation and security are advancing at the same speed.

When ChatGPT became publicly available in 2022, the models were dramatically less capable than they are today — and the guardrails were very easy to manipulate. The difference is that the models behind those guardrails are no longer the models of 2022. They can reason better, write and debug code. They can operate as agents. They can collaborate! And increasingly, they can interact and affect real infrastructure.

Meanwhile, the model release cycle has gone from feeling like major capability jumps every year or two to seemingly every few weeks. That creates a dangerous asymmetry: AI capability is compounding faster than security.

Security and innovation have always been in conflict with each other. If every security problem had to be solved before we innovated, we’d never ship anything. But the opposite extreme is just as reckless: accelerating capability while just assuming we’ll bolt the security controls on afterward and they’ll be effective.

Every new release of AI capability expands the attack surface exponentially. Give a vulnerable model better reasoning, then tool access, then memory, then autonomy, then connectivity to production systems, and yesterday’s jailbreak isn’t just a clever prompt anymore — it’s an execution path. That’s the snowball effect we should be worried about.

Responsibility also must lie with the AI companies. If a SaaS company knowingly shipped software with weak security controls and customers were harmed, we wouldn’t excuse it because they were 'innovating quickly'.

So why are we treating AI differently?

You don’t get to race to build increasingly powerful, autonomous systems, profit from them, and then shrug when predictable security failures cause damage.

Sure the argument can be made that no product is perfectly secure - That’s not the standard. But if you ship the product, you inherit responsibility for securing it. And continuing to secure it better!

The conversation shouldn’t simply be “Should we slow AI down?”

It should be: Can our ability to test, validate, contain and secure AI keep pace with our ability to make it more powerful? Is there an equivocal kill switch?

Right now, the answer is no.

And if we’re going to keep accelerating — which I believe we will — then independent testing, adversarial evaluation, isolated testing environments, containment, continuous validation and security-by-design can’t remain optional steps we add after the innovation happens.

The faster we build the engine, the more important the brakes become.

• Denis Calderone, CTO, Suzu Labs:

Amodei's diagnosis is the most honest thing a frontier lab CEO has said publicly. The agent risk is real, recursive self-improvement is accelerating, and the competitive pressure is making both worse.

Where I get skeptical is the prescription. Democratic coordination among companies in a commercial race? Global pacing agreements with China? Amodei himself rates the hardest steps as unlikely. No lab has named a single model release they'll delay because of this essay.

The one idea worth holding the industry to is embedded evaluators with independent publication rights. Give third-party safety researchers permanent access inside the labs, comparable to what bank examiners have inside banks, and let them publish what they find without the company controlling the narrative. That's a simple, concrete accountability mechanism. It doesn't require global coordination or antitrust waivers. Anthropic says they're committing to it unilaterally. Good. Now make the rest of the industry match.

• Donald McFarlane, Board Member, Xcape Inc:

AI does not develop an agenda; its operators do. When we give an autonomous system powerful access and ability to act at machine speed, they will continue to prove highly capable.

Rules enacted in the name of safety must not become a moat against competition or progress. Enormous compliance costs may be manageable for the handful of companies already spending billions building frontier models, while becoming a substantial barrier to everyone behind them.

Government can help clarify accountability and duties of care, and facilitate strong information sharing and collective defense, which is an area where we sorely need more effective public-private partnerships.

But safeguards should focus on how these systems are used and deployed, rather than deciding who is allowed to build powerful AI in the first place.

The goal should be safer deployment without pulling up the drawbridge on innovation.

How do I submit my own perspective on emerging news?

If you have an expert perspective you would like to share on an emerging story or particular topic, please get in contact here: benedict.collins@futurenet.com