Iran Turns To USDT, Acquiring $507 Million To Defend Its Currency

bitcoinistPublicado em 2026-01-23Última atualização em 2026-01-23

Resumo

Iran's central bank acquired at least $507 million in Tether (USDT) in 2025 to defend its currency, the rial, and evade sanctions, according to blockchain analysis by Elliptic. The funds, a conservative estimate, were used to add dollar-linked liquidity to local crypto markets and support international trade. Transactions were initially routed through Iran's largest exchange, Nobitex, before shifting to cross-chain bridges and decentralized exchanges after increased scrutiny. In June 2025, Tether froze $37 million in USDT from wallets linked to the bank, demonstrating a key vulnerability. This case highlights how state actors use stablecoins to bypass traditional banking restrictions, while creating a public, traceable record of their activities.

Iran’s central bank quietly built up a large stash of Tether’s USDT last year as the rial struggled and trade with the outside world grew harder. The move turned parts of the crypto ledger into a public trail of a policy that would normally be private.

Central Bank’s Crypto Moves

According to a blockchain analysis by Elliptic, the Central Bank of Iran acquired at least $507 million in USDT over 2025, a figure the firm treats as a conservative minimum because it only counts wallets it could tie to the bank with high confidence.

Reports say much of the buying happened in the spring months of 2025 and that payments were routed through channels that included Emirati dirhams and public blockchains. Those stablecoins were then used in local crypto markets to add dollar-linked liquidity and help slow the rial’s slide.

🚨 New Elliptic research: We have identified wallets used by Iran’s Central Bank to acquire at least $507 million worth of cryptoassets.

The findings suggest that the Iranian regime used these cryptoassets to evade sanctions and support the plummeting value of Iran’s currency,... pic.twitter.com/I7NHGO0wtP

— Elliptic (@elliptic) January 21, 2026

How The Money Flowed

Elliptic’s tracing shows an early flow of USDT into Nobitex, Iran’s biggest crypto exchange, where the coins could be swapped into rials and fed into the market. After a breach and growing scrutiny in mid-2025, other paths were used, including cross-chain bridges and decentralized exchanges, to move and convert funds.

Source: Elliptic

A Freeze And A Warning

That open ledger also left the transactions visible to outside observers. On June 15, 2025, Tether blacklisted several wallets linked to the central bank and froze about $37 million in USDT, showing that stablecoins can be cut off when issuers or regulators step in. That intervention narrowed some options for on-chain liquidity.

Total crypto market cap currently at $2.99 trillion. Chart: TradingView

This episode matters for two reasons. First, it shows how a state institution can use stablecoins to gain access to dollar value when normal banking routes are closed.

Second, it highlights a weakness: if a private issuer can freeze balances, those reserves are not the same as cash held in hard foreign accounts.

Trade, Sanctions, And A New Tool

Reports note the purchases likely served a twin goal — to smooth domestic exchange rates and to help settle trade with partners who avoid direct dollar banking.

The method is blunt. It gives a way to move value, but it also creates new points of control and exposure that can be tracked on public ledgers.

Analysts will be watching how regulators and stablecoin issuers respond. They will also track whether other countries under pressure turn to similar mixes of centralized and decentralized tools.

The public tracing of these flows makes it harder to hide big moves, even when actors try to obscure them across chains and exchanges.

Featured image from Unsplash, chart from TradingView

Perguntas relacionadas

QHow much USDT did Iran's central bank acquire in 2025 according to Elliptic's analysis?

AIran's central bank acquired at least $507 million in USDT in 2025.

QWhat was the primary purpose of Iran acquiring such a large amount of USDT?

AThe primary purpose was to add dollar-linked liquidity to the local market to help slow the Iranian rial's slide and to potentially settle trade with partners avoiding direct dollar banking.

QWhich crypto exchange was initially used to swap the acquired USDT into Iranian rials?

AThe initial flow of USDT was into Nobitex, Iran's biggest crypto exchange, to be swapped into rials.

QWhat action did Tether take on June 15, 2025, regarding Iran's activities?

AOn June 15, 2025, Tether blacklisted several wallets linked to the Iranian central bank and froze about $37 million in USDT.

QWhat are the two main reasons why this episode is significant according to the article?

AFirst, it shows how a state institution can use stablecoins to access dollar value when normal banking routes are closed. Second, it highlights the weakness that a private issuer can freeze balances, making these reserves not the same as cash held in hard foreign accounts.

Leituras Relacionadas

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbitHá 7m

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbitHá 7m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbitHá 21m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbitHá 21m

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h