One of the most difficult obstacles to overcome in the quantum race will be the supply chain, given how diffuse it is, a top White House official said Wednesday.

“Supply chain is one of the biggest challenges in my mind, and really, the challenge with the quantum supply chain is that quantum is not defined by a single hardware platform,” said Brad Blakestad, director of the National Quantum Coordination Office within the White House Office of Science and Technology Policy.

“If you look at the quantum computing technologies, the quantum sensing technologies, the networking — those are all different,” he said in a webinar hosted by Inside Cybersecurity and USTelecom. “And even within computing, there’s seven different modalities that use completely different components. So we have this not just one monolithic supply chain, but just a bunch of different supply chains that are kind of intertwined in various ways.”

Blakestad made his remarks a little more than a month after President Donald Trump signed two executive orders on quantum computing. He referenced proposed ways to address the supply chain challenge in one of the orders.

“The other major issue or challenge that we face right now is that we’re on the cusp of quantum exploding from a commercialization perspective, but we’re not quite there yet,” he said. “So there’s not the funding, the revenue coming from large-scale quantum companies at this point to really make the supply chain as robust as you would want. So thinking about it from the government perspective, it’s just [that] there are too many places that I would want to bolster and not enough funding to do it.”

Blakestad touted steps to help that along such as the government buying widgets from a company that makes them to certain specifications, or prize challenges.

The quantum supply chain isn’t just diffuse in the United States, an International Institute for Strategic Studies policy paper noted Wednesday. It’s “inherently international: no single country dominates the supply chain, whether specialised materials, cryogenic equipment, hardware, software, fabrication or algorithms,” the authors, Dongyoun Cho and Maria Shagina, wrote.

And a March report from the Center for a New American Security identified strengthening the quantum supply chain as pivotal to the United States seizing the benefits of the technology, citing gaps in the U.S. supply chain and reliance on foreign suppliers such as China and Russia. 

Supply chain wasn’t the only obstacle Blakestad mentioned as looming large.

“The encryption challenge is a real challenge, and we want to make sure that we are aware of when quantum computers will ultimately get to a scale that they start having these sorts of implications and move as quickly as we can,” he said. “So, just by owning the technologies, by owning the workforce, by making the United States the place that people want to come to be on the cutting edge of this technology, I think that kind of addresses both of those issues, and that’s what makes it so critical.”

Another difficulty is measuring progress, Blakestad said: “It’s also very, very hard to benchmark, and to know that you’re actually doing what you’re supposed to, what you are intending to do.”

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A top White House technology official is accusing a Chinese company of distilling Anthropic’s models to create their own AI product.

Michael Kratsios, who leads the White House Office of Science and Technology Policy, claimed that Moonshot AI, a Beijing, China-based AI company, had distilled Anthropic’s recently-released Fable model to develop its own K3 model.

“To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection,” Kratsios wrote on X Wednesday.

Kratsios also said the company has used GB300 servers – either newly acquired or through Thailand – to train its AI models.

“The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models,” Kratsios continued. “Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.”

Kratsios did not provide details on how the U.S. government learned that K3 had been distilled from Anthropic’s model. 

Frontier AI companies in the U.S. have pressed policymakers to make it more difficult for third-parties to copy or duplicate advanced commercial models, calling it a form of intellectual property theft.

On their website, Moonshot AI describes its Kimi K3 model as the first open 2.8 trillion parameter model, and promotes its lower token costs while still delivering near-frontier performance. 

“While its overall performance still trails the most powerful proprietary models, Claude Fable 5 and GPT 5.6 Sol, Kimi K3 demonstrated frontier-level performance across our evaluation suite, consistently outperforming other tested models,” the company said on its website. 

A request for comment sent to Moonshot AI was not returned before this article’s publication. 

Piyush Sharma, CEO of Tuskira, an AI cybersecurity detection and response company, said distillation of AI models allows developers many of a model’s core capabilities. He pointed to another example when Anthropic earlier this year accused Chinese company Alibaba of distilling their Claude AI model.

According to Anthropic, the campaign used 25,000 fraudulent accounts to run 28.8 million interactions on Claude over six weeks. Given that kind of volume “the goal was clearly replication,” he said. 

“When a model has learned to reason through software weaknesses, security gaps, and attack paths, copying its behavior also copies that analytical capability,” said Sharma.

In April, Rep. Andrew Garbarino, R-N.Y., who chairs the House Homeland Security Committee and Rep. John Moolenaar, R-Mich., Chair of the Select Committee on China, announced they were conducting a joint investigation into the integration of Chinese AI models.

The committees said the inquiry will also focus on “examining a pattern of conduct by [Chinese]-based AI laboratories involving the large-scale theft of proprietary capabilities from American frontier AI systems through adversarial distillation” as well as “ the redistribution of those stolen capabilities as open-weight models available for global download, and the incorporation of PRC-origin models into products used daily by hundreds of thousands of American developers and engineers.”

Western governments and industry accuse Chinese companies of routinely stealing their technology, intellectual property and other trade secrets, often with the tacit support of Beijing. The copying of AI models would continue a long and established tradition of Chinese-sponsored intellectual property theft.

However, while distillation attacks by foreign governments or companies on U.S. frontier companies can have real national security implications, it’s still a fraught question of where policymakers should draw the line.

The AI industry, which includes not just frontier companies but large businesses with their own bespoke models, smaller proprietary startups and a vibrant open-source ecosystem, routinely share and use third-party data, including critical code and training sets for AI models.

Further, U.S. frontier AI companies have built and trained their world leading models in large part by crawling the open internet, ingesting content created and produced by others. Critics (and multiple ongoing lawsuits) argue that AI companies like OpenAI and Anthropic built their empires on data and content from others, taken almost entirely without consent or compensation.

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