Bhutan goes for gold, announces sovereign-backed RWA token

cointelegraphPubblicato 2025-12-11Pubblicato ultima volta 2025-12-11

Introduzione

Bhutan's Gelephu Mindfulness City (GMC) has announced the launch of TER, a sovereign gold-backed digital token. The token, issued on the Solana blockchain, is backed by physical gold deposits custodied by government-regulated DK Bank. Matrixdock serves as the technology partner for the tokenization process. This initiative is part of Bhutan's broader strategy to adopt blockchain technology, hedge against currency inflation, and modernize its economy. The country has been mining Bitcoin since 2019 and currently holds a significant crypto reserve. It has partnered with DK Bank and Binance Pay to enable crypto payments for tourism, with over 1,000 local businesses now accepting digital currencies.

Gelephu Mindfulness City (GMC), a special administrative zone of Bhutan, announced on Wednesday that it is launching a sovereign gold-backed digital token called TER.

The physical gold deposits will be custodied by DK Bank, a digital asset bank regulated by the government of Bhutan, and issued on the Solana Blockchain network, according to GMC’s announcement.

Matrixdock, a real-world asset tokenization (RWA) platform, is the technology partner responsible for tokenizing the real-world asset on the blockchain.

An overview of the tokenized commodities sector, which is dominated by tokenized gold products. Source: RWA.XYZ

In the first phase, the tokens will be held in custody at the bank, with the exact rollout dates still unclear. “Purchasing TER tokens is structured to be as secure and familiar as acquiring physical gold from a major financial institution,” notes the announcement.

Cointelegraph reached out to DK Bank and representatives of GMC, but had not received a response at the time of publication.

The rollout of a sovereign-backed tokenized gold product is a hedge against currency inflation in the digital age and another step in the country’s blockchain adoption strategy, according to GMC’s announcement.

Related: Bhutan stakes $970K ETH via Figment in validator push

Bhutan gets ahead of the curve by adopting crypto and blockchain tech

The government of Bhutan has embraced cryptocurrency and blockchain technology as a way to modernize the country’s payment system and boost the tourism sector, with the goal of becoming a regional cryptocurrency hub.

Bhutan has been mining Bitcoin (BTC) since 2019 with hydroelectric power, and the country holds nearly 6,000 BTC, valued at over $540 million at the time of this writing, according to Arkham Intelligence.

In January, the GMC announced a digital asset reserve containing BTC, Ether (ETH), and BNB (BNB), which later expanded to include modest sums of memecoins and other altcoins.

Government of Bhutan’s crypto holdings. Source: Arkham Intelligence

Bhutan partnered with DK Bank and Binance Pay in May to provide tourists with a method to pay for hotels, tour guides and tickets using over 100 cryptocurrencies.

Over 1,000 businesses in Bhutan accept crypto payments through the partnership with Binance Pay and DK Bank.

Damcho Rinzin, the director of Bhutan’s Department of Tourism, said the adoption of crypto payments helps the country’s tourism industry, which has struggled due to a lack of robust payment infrastructure.

Magazine: Bitcoin to suffer if it can’t catch gold, XRP bulls back in the fight: Trade Secrets

Crypto di tendenza

Letture associate

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

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Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

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From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

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