Anthropic is proposing three new metrics that it says could help the artificial intelligence industry measure and monitor the pace of frontier AI development, days after CEO Dario Amodei called for a coordinated slowdown in the advancement of sophisticated models.

The company published the measurements Thursday, covering AI-assisted research and development, oversight of AI agents and the allocation of computing resources. Anthropic also released details of its methodologies, encouraging other AI companies to adopt similar measurements and make their results public.

The initiative builds on a three-step slowdown plan Amodei published Saturday. While that proposal called for greater coordination around the pace of frontier AI development, it provided limited detail on how such a system could be implemented in practice.

Anthropic’s latest proposal is an attempt to put measurable indicators around that discussion.

“As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows,” Anthropic said in its blog post. “This means better measuring the development of AI, reporting on it publicly, and giving society an opportunity to decide how to use this information.”

The timing puts Anthropic at the center of an active debate over whether frontier AI development should continue at the current pace or be accompanied by stronger monitoring and safety measures.

Amodei’s call for a slowdown received support from several technology executives, including OpenAI CEO Sam Altman, SpaceX CEO Elon Musk and Google DeepMind Chair Demis Hassabis. His proposal followed warnings from AI researchers about the potential risks associated with more capable models.

Amodei has said his approach is intended to slow the rate at which model capabilities improve without sacrificing commercial competitiveness or the United States’ position in AI.

Measuring How Much AI Is Involved In AI Development

Anthropic’s first metric examines the extent to which its own Claude models are capable of autonomously carrying out research and development work. The company said that, within the subset of research and development activities it measured, Claude models were “not operating fully autonomously.”

The measurement is intended to address a question that could become more important as AI systems are used to develop subsequent generations of AI: how much of the work required to advance AI is itself being performed by AI?

As models become more capable at coding, experimentation, analysis, and other technical tasks, the boundary between AI-assisted development and AI-driven development could become difficult to define without standardized measurements.

Anthropic’s second metric focuses on oversight of AI agents.

The company built a system designed to monitor and intervene in actions taken by AI agents and found that approximately 30,000 agents were conducting research and engineering work across its most-used internal platform at any given time.

The figure illustrates the scale at which agentic AI is already being incorporated into internal technical workflows. Rather than measuring only the capabilities of individual models, the metric looks at the number of AI agents operating within an organization’s development environment and the systems used to supervise them.

AI systems are given greater autonomy and access to software tools, internal information, and computing resources, making the idea relevant.

Anthropic Tracks AI Safety Spending

The third metric examines how Anthropic allocates its computing resources. The company conducted a snapshot of its total compute usage between July 13 and July 20. It found that approximately 6% of the compute used for AI research and development was allocated to safety work.

When measured specifically against compute allocated to “AI-driven” research and development, Anthropic said about 12% was directed toward safety.

The figures provide a quantitative measure of the resources Anthropic is allocating to safety relative to model development. They also establish a baseline that other AI companies could potentially replicate, allowing comparisons across laboratories if similar definitions and measurement methodologies are adopted.

Anthropic stressed that the three metrics are not intended to replace existing capability evaluations. Instead, the company said the measurements should complement evaluations that show “what models can do” by providing information about how models are developed and how much AI is involved in that development.

Together, Anthropic said, the measurements could provide organizations outside AI laboratories with a starting point for assessing the pace of frontier AI development.

The proposal comes at a time when the industry is focused not only on the capabilities of AI models but also on the speed at which those capabilities are improving. As laboratories compete to build more powerful systems, policymakers and the public have limited visibility into the amount of computing, automation, and human oversight involved in that process.

Anthropic’s argument is that greater disclosure could narrow that information gap.

“We hope to model that transparency by releasing these measurements, and we’ll continue to do so,” the company said.

However, it is believed that the effectiveness of the framework will depend partly on whether other frontier AI developers adopt comparable measurements. Without common definitions and reporting standards, figures such as the share of compute devoted to safety or the number of active AI agents could be difficult to compare across companies.

Anthropic and OpenAI are turning to smaller artificial intelligence data center deals as they race to secure the computing capacity needed to serve rapidly growing demand, signaling a shift in the infrastructure market from a handful of massive campuses toward more distributed deployments.

The two AI labs have signed some of the industry’s largest infrastructure agreements over the past year, covering facilities with hundreds of megawatts and, in OpenAI’s case, gigawatts of planned capacity. But sources familiar with the companies’ discussions told CNBC that both are now exploring much smaller deployments of roughly 20 to 30 megawatts.

Anthropic has approached potential partners about capacity in that range across the U.K. and the Nordic countries, according to four people familiar with the discussions. OpenAI has also explored similar opportunities in the Nordics, two of the sources said.

One source said they were also familiar with discussions involving both companies over U.S. deployments of comparable size.

The smaller deals could allow the AI companies to get computing resources into operation sooner, rather than waiting for enormous data center projects that can take years to develop and increasingly face constraints involving electricity, land, permitting and community opposition.

“We’re building a diversified compute portfolio to meet growing demand for AI around the world,” an OpenAI spokesperson told CNBC.

“Different workloads need different infrastructure, so we have conversations with a range of partners and assess opportunities based on our requirements, performance, reliability, timing and cost,” the spokesperson added, while declining to comment on specific commercial discussions.

AI Infrastructure Strategy Is Changing

The search for smaller capacity comes after a period in which AI companies aggressively pursued enormous, long-term infrastructure commitments.

Anthropic signed a roughly $45 billion cloud agreement with Nscale that is expected to provide about 460 megawatts of compute capacity at a data center development in West Virginia, according to people familiar with the deal.

OpenAI has also dramatically expanded its infrastructure ambitions. The company said in April that it had exceeded the original 10-gigawatt commitment associated with its Stargate AI infrastructure project. It has subsequently committed to developing another 3 gigawatts in Georgia and 8 gigawatts in Ohio.

Those projects illustrate the enormous amount of computing power required to train and deploy increasingly capable AI models. But they also expose the industry’s growing dependence on projects that require large amounts of electricity, land, and capital.

Large data center developments in the United States have encountered increasing opposition from local communities, while European markets face their own constraints. Limited availability of suitable land and power is making it more difficult for operators to deliver huge blocks of capacity quickly.

That is creating an opening for smaller facilities.

“Securing a few megawatts at an existing powered site can be more practical than waiting for a much larger block in one location,” said Jabez Tan, head of research at Structure Research.

“For workloads that can operate across separate sites, a collection of smaller deployments can add up to substantial capacity,” he added.

The economics are deemed relevant because not every AI workload requires a giant cluster operating in a single location. Training a frontier model requires large numbers of chips to work together and exchange data at high speeds. Serving that model to users is different. Inference, the process of generating responses from a trained model, can often be distributed across multiple smaller clusters.

“Training a large model typically requires many chips working closely together,” Tan said. “Many inference workloads can instead serve separate requests across multiple smaller clusters, opening up more locations.”

That is becoming increasingly important as AI companies move from building models to serving them at enormous scale.

Inference Is Becoming the New Infrastructure Race

The economics of AI infrastructure are increasingly being shaped by inference. The computing requirements of training remain enormous, but once models are deployed, every query, coding request, image generation, or other AI interaction consumes computing resources. As adoption increases, the infrastructure required to serve those requests can become a substantial and recurring demand on data centers.

Real estate company JLL expects inference workloads to overtake training workloads as a share of total data center capacity in 2027.

In 2025, inference accounted for 9% of global data center workloads, compared with 14% for training, according to JLL. By 2030, inference is projected to account for 37% of capacity, while training is expected to represent 13%.

That shift changes what AI companies need from infrastructure providers.

For model training, concentrating thousands of chips in a massive facility can be essential. But geographic distribution can provide greater flexibility for inference, allowing workloads to be spread across several locations and potentially bringing capacity online faster. It also means AI companies may compete more for existing powered sites rather than simply waiting for new megaprojects to be completed.

Nvidia has already been examining this trend. In February, the chipmaker said it would work with several data center stakeholders to study smaller facilities designed for distributed inference.

Crusoe, a major AI infrastructure provider that built a large data center complex in Texas used by OpenAI, is also moving toward smaller facilities, according to a Wall Street Journal report. Those facilities are expected to be faster and cheaper to build than larger projects that are facing delays across the United States.

The shift comes as demand for so-called neocloud infrastructure continues to surge. These specialized cloud providers have benefited from AI companies seeking dedicated access to GPUs and other computing infrastructure without having to build and operate every facility themselves.

Crusoe highlighted the strength of that demand Thursday when it announced a $3.9 billion funding round at a post-money valuation of $30.9 billion.

The End of the Mega-Data Center?

The growing interest in 20- to 30-megawatt deployments does not mean the industry’s enormous data center projects are disappearing.

OpenAI and Anthropic still require massive amounts of computing power, particularly for training increasingly sophisticated models. Their multibillion-dollar infrastructure commitments remain evidence of how much capacity the AI industry expects to consume.

What is changing is the composition of that demand.

Instead of treating AI infrastructure as a race to secure the largest possible single site, companies can assemble capacity from multiple facilities, provided their workloads can be distributed efficiently. That could become valuable in markets where securing hundreds of megawatts of new power in one location is difficult.

Smaller projects can also offer data center operators a faster route to revenue. Existing sites with power already available can potentially be upgraded or equipped for AI workloads without waiting for an entirely new campus to be constructed.

The result could be a more fragmented AI infrastructure landscape, with enormous training campuses operating alongside networks of smaller inference facilities. For Anthropic and OpenAI, the strategy is ultimately about one thing: usable computing capacity.

The industry’s biggest infrastructure projects may attract the most attention because of their size and multibillion-dollar price tags. But as AI moves from the training phase into everyday commercial use, the ability to secure smaller amounts of power and compute quickly could become just as important.

Google and Google DeepMind researchers have launched a new institute aimed at advancing the debate over artificial general intelligence, bringing together competing views on how sophisticated AI systems should be developed, evaluated and governed.

The DeepMind Institute, launched Wednesday, lists DeepMind co-founder Shane Legg, Google executive James Manyika and Google DeepMind chair Demis Hassabis as directors, with Legg serving as managing editor.

Rather than presenting a single institutional position on AGI, the institute says it intends to publish differing perspectives from Google, Google DeepMind and the wider research community.

“They will not always agree, and they will likely change their minds, as more data and information comes to light at the fast-moving frontier,” the institute said in its announcement.

The launch comes at a moment when the AI industry’s discussion about safety is becoming more specific. Concerns that powerful AI systems could become difficult to control have increasingly been accompanied by proposals for independent testing, greater transparency and mechanisms that could slow development if safety measures fail to keep pace.

The institute’s first collection contains four essays addressing economic policies for potential disruption from AGI, the preservation of human-readable model reasoning, principles for human flourishing, and methods for evaluating frontier AI systems.

Together, the papers point to a broader question facing the industry: how should society evaluate systems that are becoming more capable while some of the methods used to understand their internal reasoning are becoming less transparent?

The Transparency Problem

One of the essays, written by DeepMind safety researchers Rohin Shah and Anca Dragan, focuses on what they describe as AI’s shrinking window of transparency.

As models become more powerful and new architectures rely on increasingly complex computation, it can become harder for researchers to see and verify how a system arrived at an answer or decision. The researchers argue that this loss of transparency should not simply be accepted as an unavoidable consequence of more capable AI.

One possible response would be to limit what the paper calls “opaque serial depth,” referring to the amount of sequential computation a model can perform without producing a readable reasoning trace.

Another option would be to require developers to demonstrate that models with less transparent reasoning remain sufficiently monitorable. The issue is important because the ability to evaluate an AI system may become more difficult precisely as its capabilities make reliable oversight more important.

The debate therefore extends beyond whether a model produces an unsafe output to whether developers and regulators will be able to understand, test, and monitor sophisticated systems well enough to establish that they can be deployed safely.

Hassabis Proposes Frontier AI Standards Body

Hassabis’ essay takes the discussion into regulation and proposes a U.S.-led standards organization for evaluating the most advanced AI models.

Under his proposal, developers would initially submit frontier models voluntarily for assessment as much as 30 days before release. If the evaluation system demonstrated that it could effectively identify significant risks, successful testing could eventually become a condition for deploying frontier models in the United States.

The proposal also attempts to address a weakness in conventional AI benchmarking: developers can become familiar with public tests and optimize their systems specifically for those evaluations.

Hassabis proposes that the standards body eventually create independent “held-out” assessments that would not be disclosed to AI developers in advance. The objective would be to make it harder for companies to tailor models to known benchmarks without necessarily improving their underlying safety.

The system would initially be developed in consultation with AI companies but would become increasingly independent over time.

Hassabis also leaves room for the framework to become more restrictive if the risks associated with frontier systems increase. He said the system could be “ratcheted up if the seriousness of the situation demands,” potentially extending to a coordinated slowdown among frontier AI developers.

That proposal places a concrete policy mechanism behind an idea that has recently gained support among several AI executives.

From Warnings to Mechanisms

The DeepMind Institute’s launch comes as the industry’s AI safety debate moves beyond general warnings about hypothetical risks and toward specific questions about how powerful models should be evaluated and controlled.

Anthropic CEO Dario Amodei has called for the industry to “pace” the development of frontier AI, arguing that safeguards need time to catch up with rapidly advancing capabilities. OpenAI CEO Sam Altman and other technology leaders have expressed support for elements of that approach.

The proposals emerging from the new institute occupy a similar space but focus more heavily on the infrastructure of oversight.

For Shah and Dragan, the issue is about opaque systems remaining sufficiently understandable and monitorable, while Hassabis sees a challenge in creating an evaluation mechanism that is independent enough to test frontier models before they reach users.

The two approaches address different parts of the same problem.

AI companies are developing models whose capabilities are advancing rapidly, while researchers are still debating how to measure some of their most consequential properties. Public benchmarks can become outdated or predictable, internal evaluations can raise questions about independence, and complex architectures can make model behavior harder to interpret.

That leaves policymakers facing a difficult question over how much oversight should be imposed before the technology’s capabilities and risks are fully understood.

The DeepMind Institute does not resolve that debate. Its stated purpose is instead to make disagreements and evolving views more visible as research progresses. That may prove significant as the AI industry moves toward increasingly capable systems.

Currently, the debate is moving from whether AGI is possible or when it might arrive to what standards should govern the systems being built now, who should test them, how much of their reasoning can be independently scrutinized, and what happens if the available safeguards fall behind the technology.

The institute’s opening essays place those questions directly at the center of the AGI discussion, including the possibility that the industry’s ultimate response to rising risks could involve not only stronger testing and transparency requirements, but coordinated limits on the pace of frontier development.