Gold and silver prices surged to fresh record highs on Monday, extending a rally that has gathered remarkable momentum this year as investors retreat from risk assets and brace for mounting economic and political uncertainty.

Gold futures for February delivery settled 1.9% higher at $4,469.40 per ounce after touching an intraday record of $4,477.70. Spot gold climbed nearly 2% to $4,440.26 per ounce. The precious metal is now up close to 70% since the start of the year, marking one of its strongest annual performances on record.

Silver followed gold’s lead, also breaking into uncharted territory. Futures prices hit $68.96 per ounce, while spot silver traded at $68.98. Silver’s rally has been even more dramatic, with prices up about 128% year-to-date, underscoring the extent to which investors are crowding into precious metals as alternative stores of value.

The surge reflects a broader shift in global markets. Risk assets have struggled to maintain momentum, even after the U.S. Federal Reserve delivered its widely anticipated interest rate cut on December 10, and a brief rebound lifted AI-related stocks in the previous session. Instead of chasing equities, many investors appear to be repositioning portfolios defensively ahead of what they expect to be a more volatile macroeconomic environment in 2026.

Gold’s traditional role as a safe-haven asset has come back into sharp focus amid concerns about slowing growth, geopolitical risks, and, increasingly, the sustainability of government finances. According to Matthew McLennan, head of the global value team at First Eagle Investments, swelling fiscal deficits across major economies have revived gold’s appeal as a monetary hedge.

“Due to outsized fiscal deficits in the U.S., U.K., Europe, and increasingly Japan and China, the monetary value of gold has arguably reemerged,” McLennan said in an interview on CNBC’s The Exchange on December 17.

He added that gold had moved from being undervalued relative to nominal assets used as hedges to a level he considers more rational, pulling other precious metals higher in the process.

“The value of gold as a monetary potential hedge has reemerged,” he said. “Gold went from being depressed relative to the nominal assets that you would want to use as a potential hedge against it, to more rationally valued. And I think the other precious metal complexes followed it higher with some leverage.”

Silver’s sharper gains reflect both its historical tendency to amplify gold’s moves and its growing role in industrial applications, including renewable energy and electronics. That dual demand profile has made silver particularly sensitive to both inflation expectations and long-term structural trends, further fueling investor interest.

The rally has also lifted mining stocks. In premarket trading in the United States, shares of gold and silver miners edged higher, with the iShares MSCI Global Gold Miners ETF rising nearly 2.7%, as investors bet that sustained high prices will boost cash flows and balance sheets across the sector.

Beyond inflation and growth concerns, attention is turning to U.S. monetary leadership. Investors are closely watching the race to nominate the next chair of the Federal Reserve, amid questions about the institution’s independence following repeated public pressure from President Donald Trump on current chair Jerome Powell. Markets are increasingly sensitive to any signal that political considerations could influence future monetary policy.

“What we’re quite focused on here is the long-term fiscal credibility of the United States,” McLennan said. “Because I think that is the condition precept for having an independent Fed and for having a rational chair.”

Labour market dynamics are another key variable. McLennan said wage inflation could shape the outlook for both interest rates and precious metals in the months ahead, particularly if job openings continue to rise alongside corporate earnings. Such a combination could complicate the Fed’s efforts to strike a balance between supporting growth and keeping inflation in check.

For now, gold and silver appear to be benefiting from a convergence of anxieties: ballooning deficits, uncertain monetary leadership, and unease about the durability of the global recovery. With prices already at historic highs and investor sentiment tilting defensive, precious metals are once again asserting themselves as a central pillar of portfolio protection rather than a peripheral hedge.

In the city of Chifeng, in northern China’s Inner Mongolia region, Reuters reports an ultra-modern factory humming to life not on coal or gas, but on wind, solar power, and algorithms.

Owned by green-energy company Envision, the facility produces hydrogen and ammonia using renewable electricity, guided by an artificial intelligence system designed to solve one of the hardest problems in the global energy transition: how to run energy-hungry industrial processes on power sources that are inherently unstable.

The plant operates on a standalone grid that feeds electricity directly from Envision’s wind and solar farms into its production lines. That electricity supply can swing sharply depending on weather conditions, a problem for chemical manufacturing, which traditionally depends on steady, uninterrupted power. To manage this, Envision built an AI-driven operating system that continuously adjusts production levels to match real-time renewable output.

Zhang Jian, Envision’s chief engineer for hydrogen energy, described the system as “a conductor” that synchronizes electricity use with nature. When wind speeds rise, the AI automatically ramps up production to absorb as much green power as possible. When output drops, the system scales back electricity demand almost instantly, Zhang told China Energy News, a state-run publication.

The Chifeng facility is Envision’s blueprint for large-scale renewable hydrogen and ammonia production, fuels that could play a critical role in decarbonizing hard-to-abate sectors such as steelmaking, chemicals, and shipping. It is also a glimpse into how China is deploying AI not just as a digital tool, but as infrastructure — a way to make its sprawling renewable energy system work at scale.

“AI can play a hugely important role in China’s climate action and energy transition,” said Zheng Saina, an associate professor specializing in low-carbon transition at Southeast University in Nanjing. Beyond industrial optimization, she noted, AI can help calculate and project carbon emissions, forecast electricity supply and demand, and manage increasingly complex power systems.

This push comes at a critical moment. Energy has become one of the biggest bottlenecks in the evolution of artificial intelligence globally. As AI models grow larger and data centers proliferate, electricity demand is surging at a pace that is straining grids from the United States to Europe. In China, AI data centers alone are projected to consume more than 1,000 terawatt-hours of electricity annually by 2030 — roughly equivalent to Japan’s total yearly power consumption.

That looming demand surge presents a paradox. AI is being positioned as a key enabler of China’s green transition, yet its own energy appetite risks undermining climate goals.

“AI data centers are expected to cause explosive growth in electricity demand,” Zheng warned. “This is a problem that urgently needs addressing.”

Where China believes it has an edge over the United States is in energy diversity and scale. China leads the world in installed wind and solar capacity, and continues to add renewable power at a pace unmatched elsewhere. While the U.S. has focused much of its AI investment on building advanced large-language models and cloud infrastructure, China has placed parallel emphasis on integrating AI into physical systems, particularly energy.

President Donald Trump is opposed to green energy initiatives and has recently ordered the halt of wind energy programs.

“China is developing very specific, tailored AI solutions that support the grid and individual energy sectors like wind, solar and even nuclear,” said Cory Combs, associate director at Beijing-based research firm Trivium China. “That’s different from the U.S. approach, which has largely centered on model performance rather than system-level integration.”

As renewable energy expands, grid flexibility has become essential. Wind and solar power are intermittent, and without smart coordination, excess electricity is often wasted. AI is increasingly seen as the missing link. In September, Beijing launched an “AI+ energy” strategy aimed at deeply integrating artificial intelligence across the power system.

By 2027, the plan calls for more than five large AI models dedicated to energy applications, at least 10 replicable pilot projects, and over 100 real-world use cases. Within three more years, China wants to reach what it describes as a “world-leading level” in combining specialized AI technologies with the energy sector.

One of AI’s most critical roles is demand forecasting. Power grids must balance supply and demand second by second to avoid blackouts. Fang Lurui, an assistant professor of power-system planning at Xi’an Jiaotong-Liverpool University in Suzhou, said accurate AI-driven forecasts allow grid operators to plan ahead, deciding how much electricity to store in batteries or when to call on backup generation.

“If AI models can accurately predict renewable output and electricity demand throughout the day, the grid can operate more efficiently and safely,” Fang said.

Better forecasting also reduces reliance on coal-fired backup plants and allows more renewable power to be absorbed rather than curtailed.

Some Chinese cities are already experimenting at scale. Shanghai has launched a citywide virtual power plant backed by a digital platform developed by State Grid. The system aggregates electricity generation and load-reduction capacity from 47 operators, including data centers, building heating and cooling systems, and electric vehicle charging networks, allowing them to function as a single flexible resource.

During a trial run in August, the platform successfully flattened a demand spike by shedding 162.7 megawatts of load — roughly the output of a small coal-fired power plant.

Beyond grid management, China is also exploring how AI can strengthen its national carbon market, which covers more than 3,000 companies across power, steel, cement, and aluminum smelting. These sectors account for over 60% of the country’s total carbon emissions. AI could help regulators verify emissions data, improve allocation of free allowances, and allow companies to calculate compliance costs more precisely, according to Chen Zhibin, a senior manager for carbon markets at think tank Adelphi.

Yet challenges remain formidable. Studies suggest the lifecycle carbon emissions of China’s AI industry could double between 2030 and 2038, peaking at nearly 700 million tons — higher than Germany’s total emissions in 2024. China’s grid still relies heavily on coal, complicating efforts to green AI at speed.

To address this, Beijing has mandated that data centers improve energy efficiency and raise renewable power usage by 10% annually. It is also encouraging new data centers to be built in western regions rich in wind and solar resources. On the East Coast, operators are experimenting with unconventional solutions. Near Shanghai, a data center is being built underwater, using cold seawater for cooling and drawing more than 95% of its electricity from a nearby offshore wind farm, according to its developer, Hailanyun.

While AI’s energy footprint is now a central concern, many researchers argue that the technology remains indispensable. Xiong Qiyang, a PhD candidate at Renmin University of China who co-authored one study on AI emissions, said the trade-off is unavoidable but manageable.

“AI’s energy use is a real concern,” he said. “But it does much more good than harm in helping key sectors reduce emissions. That makes AI an essential tool in China’s green transition.”

China’s bet amid intensifying competition in artificial intelligence is that by pairing AI with a vast and diversified renewable energy system, it can stay ahead in both the AI race and the energy transition, even as other economies struggle to keep the lights on.

OpenAI’s ambitious Stargate project, a cornerstone of the AI infrastructure race, has secured preliminary agreements with South Korean semiconductor powerhouses Samsung Electronics and SK Hynix to supply massive volumes of DRAM wafers, potentially devouring up to 40% of global production by 2029.

The deals, formalized as letters of intent in October 2025 following high-stakes meetings involving OpenAI CEO Sam Altman, South Korean President Yoon Suk Yeol, and executives from both firms, underscore the project’s unprecedented scale and its ripple effects on global tech supply chains.

The partnerships go beyond mere supply: Samsung and SK Hynix, which together command about 70% of the global DRAM market and 80% of high-bandwidth memory (HBM), have committed to ramping production to meet OpenAI’s projected demand of 900,000 DRAM wafer starts per month.

This volume encompasses commodity DDR5, low-power LPDDR4/LPDDR5, premium HBM for AI accelerators, and specialty DRAM types. Notably, initial deliveries will consist of undiced wafers—raw silicon before cutting into individual chips—granting Stargate flexibility in customization and processing. But questions linger on who will handle dicing, packaging into DRAM chips, HBM stacks, or modules, with speculation pointing to potential in-house or third-party fabs.

To contextualize the scale, global 300mm fab capacity is forecasted to hit 10 million wafer starts per month (WSPM) in 2025, per TechInsights. DRAM’s share stood at 22% (about 2.07 million WSPM) in 2024, with analysts like those at TrendForce projecting an 8.7% growth to roughly 2.25 million WSPM in 2025—meaning Stargate could claim around 40% of that capacity at peak. Broader market forecasts from Yole Group predict DRAM revenues surging to $129 billion in 2025, up from prior years, driven by AI demand, with total memory market revenues nearing $190 billion, including NAND.

However, this concentration has fueled warnings of shortages: Counterpoint Research notes 50% price hikes in 2025, with further increases into 2026, impacting consumer electronics like PCs, smartphones, and GPUs.

Deloitte’s 2025 Semiconductor Outlook highlights AI and data centers as key drivers for chip sales growth, even as PC and mobile demand lags.

Stargate, a $500 billion collaboration controlled by OpenAI, Oracle, and SoftBank, aims to deploy multiple gigawatt-scale AI data centers globally to train next-generation models. Launched in January 2025 with a request for proposals from partners, the project has accelerated rapidly.

The flagship site in Abilene, Texas, went operational early, providing 1 gigawatt of capacity. By mid-2025, Oracle pledged an additional 4.5 gigawatts, pushing totals over 5 gigawatts. September brought announcements of five new U.S. sites, escalating investments to $400 billion, and capacity toward 7 gigawatts. In October, Michigan joined with a multi-billion-dollar campus developed by Related Digital, set for construction in early 2026. December saw ground broken on a $15 billion, four-building complex in Wisconsin by Vantage Data Centers, offering nearly 1 gigawatt, and Michigan regulators approving 1.4 gigawatts of power from DTE Energy—despite local opposition over energy costs and grid strain.

These facilities demand enormous resources: thousands of servers per site, each packed with Nvidia Blackwell GPUs and ancillary gear like cooling systems and power delivery—potentially necessitating dedicated power plants, as Altman has toured Asia-Pacific to secure. The Samsung ecosystem’s involvement deepens the alliance. Samsung SDS will co-design and operate Stargate sites in Korea, provide consulting for integrating OpenAI models into enterprise systems, and act as a reseller for services like ChatGPT Enterprise in South Korea.

Samsung C&T and Samsung Heavy Industries are pioneering floating data centers—offshore platforms leveraging maritime tech for superior cooling efficiency and lower emissions, though engineering hurdles remain. Plans include two Korean “Stargate-style” facilities starting at 20 megawatts, positioning South Korea as an AI hub amid high local adoption of OpenAI tools.

Altman lauded Korea’s “incredible tech talent, world-class infrastructure, strong government support, and thriving AI ecosystem” during his visit.