Oil prices rose on Friday as markets monitored the possibility of US President Donald Trump returning focus to the stalled conflict with Iran, following the conclusion of his summit with Chinese President Xi Jinping in China.

Brent crude futures for July delivery climbed more than 2% to $108.25 per barrel by 10:18 a.m. Eastern Time.

US West Texas Intermediate crude futures for June delivery also gained more than 2% to $103.76 per barrel.

Trump said in an interview with Fox News that his patience with Iran was beginning to run out, adding: “I’m not going to have much patience anymore. They have to make a deal.”

He also said Chinese President Xi Jinping wants the Strait of Hormuz reopened, noting that the Chinese leader is unhappy with Iran imposing transit fees on ships passing through the strait.

Trump added that Xi agreed not to provide Iran with military equipment.

In the same context, US Treasury Secretary Scott Bessent said in an interview with CNBC on Thursday that China would work behind the scenes to help reopen the Strait of Hormuz.

“It is very much in their interest to reopen the strait,” Bessent said.

Beijing did not address the Hormuz issue in its official statements following the summit, though China’s Foreign Ministry said on Friday that “the use of force is a dead end” and that negotiations remain the correct path forward.

A ministry spokesperson said: “There is no benefit in continuing this conflict, which should not have happened in the first place,” adding that reaching a quick resolution would serve the interests of the United States, Iran, the region, and the wider world.

Trump also said China had agreed to buy more oil from the United States.

“They agreed they want to buy oil from the United States, and they will be going to Texas, and we’re going to start sending Chinese ships to Texas, Louisiana, and Alaska,” he said.

China has not yet confirmed any agreements related to purchases of US energy, while CNBC said it contacted Chinese authorities for comment but did not receive a response before publication.

Researchers are increasingly using artificial intelligence technologies to help solve some of the biggest challenges facing the energy sector — including, ironically, the massive surge in electricity demand caused by large language models themselves. The current and expected rise in energy consumption from AI data centers is driving a wave of investment into advanced energy alternatives capable of delivering huge amounts of reliable electricity without major greenhouse gas emissions.

Among the technologies being viewed as a potential “silver bullet” is nuclear fusion, which has made major progress in laboratories in recent years, partly thanks to AI tools.

In this context, scientists at Ames National Laboratory in Ames, Iowa, are developing a specialized AI tool designed to model how different materials behave inside nuclear fusion systems, with the goal of improving research methods and making both the scientific process and fusion systems more efficient.

The tool, known as “DuctGPT,” was developed based on an earlier model from the National Institute of Standards and Technology called “AtomGPT.” The “Duct” version combines large language models with physics-based simulations to identify materials capable of withstanding the harsh environment inside a nuclear fusion reactor.

Nuclear fusion — the same process that powers the sun — relies on extremely high temperatures that most materials cannot tolerate. In addition to resisting temperatures reaching thousands, millions, or even hundreds of millions of degrees, these materials must also remain sufficiently ductile to allow practical manufacturing.

Finding the right material remains one of the biggest obstacles preventing commercial nuclear fusion, while also representing a massive opportunity for the scientific team capable of solving the challenge, potentially unlocking a near-unlimited source of clean energy. Identifying such materials requires exploring and modeling an enormous range of possible alloy combinations.

This type of project is particularly well suited to large language models. In a Financial Times report published last year titled “How AI Could Deliver More Energy Than It Consumes,” the newspaper noted that “discovering new materials, catalysts, or processes capable of producing energy more efficiently is exactly the type of ‘needle in a haystack’ problem where AI excels.”

The new tool is already showing highly promising results in fusion research. The team behind “DuctGPT” said the time required to discover new alloys for fusion experiments has been reduced from months of research work to just a few hours.

Scientist Prashant Singh from Ames Laboratory said: “Now when you ask the system to design a material for nuclear fusion with the critical properties required for reactors, it provides the appropriate elemental compositions along with their expected characteristics.”

Although “DuctGPT” is one of the newest and most promising applications of large language models in nuclear energy research, it is not the only one. Another tool called “Diag2Diag” is being used to help monitor and control plasma behavior in fusion experiments, specifically to prevent a phenomenon known as “Edge Localized Mode” or “ELM.”

This instability rapidly erodes the materials surrounding the plasma, creating major challenges in massive and expensive projects such as Europe’s ITER reactor and China’s EAST reactor.

In the United Kingdom, the British government is investing £45 million, or roughly $60 million, to build an AI-powered supercomputer at the UK Atomic Energy Authority campus in Oxfordshire.

The computer, called “Sunrise,” is expected to begin operations next month. According to a report published by Interesting Engineering in March, officials say the system will help scientists better understand the highly complex physics inside fusion reactors.

The report added that combining advanced computing with AI models could allow researchers to test ideas virtually before building extremely expensive experimental systems.

Together, these tools could dramatically accelerate nuclear fusion research at a time when the need for breakthroughs has become more urgent than ever. While investing in unproven technologies remains a high-risk bet, nuclear fusion now appears closer to reality than at any previous point, as scientific breakthroughs accelerate, competition intensifies, and major technology companies move aggressively into the sector.

The enormous and unprecedented energy demand created by artificial intelligence has become so large that the tools needed to address it may also need to be unprecedented — which helps explain why AI solutions themselves may ultimately become the only way to solve the problems AI created in the first place.

Wall Street indexes opened sharply lower on Friday after fears of rising inflation driven by the Middle East conflict pushed US Treasury yields higher, threatening to halt the AI-driven rally that has fueled markets in recent months.

The Dow Jones Industrial Average fell 133.2 points, or 0.27%, at the open to 49,930.26 points.

The S&P 500 also declined by 56.1 points, or 0.75%, to 7,445.11 at the start of trading.

Meanwhile, the Nasdaq Composite dropped 346.3 points, or 1.30%, to 26,288.923 as the opening bell rang.

The continued closure of the Strait of Hormuz has triggered a sharp rise in sulfur prices, causing major repercussions for Indonesia’s nickel sector. The disruption comes amid ongoing supply chain turmoil, heavily impacting a country that relies significantly on Gulf imports to support its nickel processing operations.

As Indonesia struggles to cope with sulfur supply shortages, political and regulatory changes are adding further pressure on the sector.

These developments are reshaping expectations for the global nickel market, with slowing domestic production leading analysts to forecast a shift from oversupply toward a market deficit by 2026.

As the crisis continues to evolve, nickel prices have risen, signaling that markets are adapting to tighter supplies and higher costs.

Investors have also begun positioning strategically in response to Indonesia’s ongoing policy moves and the geopolitical tensions affecting global sulfur supplies.