US Seizes Nearly $1B in Iranian Crypto Amid Economic Pressure Campaign

TheNewsCryptoPublished on 2026-05-30Last updated on 2026-05-30

Abstract

The United States has seized nearly $1 billion worth of Iranian cryptocurrency, Treasury Secretary Scott Bessent announced. He noted some wallet owners may be unaware their funds were confiscated. The action is part of "Operation Economic Fury," a U.S. financial pressure campaign against Iran launched in March 2025, which has also involved freezing bank accounts and seizing properties with European partners. Bessent stated that prior to U.S. intervention, the Iranian regime was allegedly diverting hundreds of millions monthly to a small elite. He described Iran's economy as severely strained, with food rationing, internet outages, and many military personnel going unpaid. The $1 billion seizure figure marks a significant increase from previous totals reported in April.

On Friday, Treasury Secretary Scott Bessent said that the US has confiscated around $1 billion worth of Iranian cryptocurrency. He also mentioned that some of the wallet owners may not even be aware that their funds has been confiscated. “I believe that we have seized about a billion dollars of their crypto”, Bessent said during his speech at the Reagan National Economic Forum.

According to Bessent, the seizures are a component of Operation Economic Fury, the United States’ strategy to put financial pressure on Iran. The operation, which began in March 2025 and has since seized crypto, frozen bank accounts, and collaborated with European partners to confiscate properties, has targeted Iranian assets on numerous fronts.

Intensifying Financial Pressure

Before the United States interfered, the regime was allegedly stealing $400–$500 million per month and distributing the money around 80 or so elites, according to the Treasury secretary.

According to him, the Iranian economy is in shambles, with food coupons being handed out, the internet down, and 40 to 50% of the Iranian military being unpaid. Regarding the current talks with Iran, Bessent said that it is difficult to work with a divided leadership structure because of the attacks on key regime members by the US and Israel.

The newly revealed $1 billion figure is significantly higher than the $344 million in cryptocurrency that was frozen after the US Office of Foreign Assets Control sanctioned Iran-linked wallets on April 24. It is also approximately twice as much as the $500 million in Iranian cryptocurrency assets that the Treasury Department announced it had seized in late April.

Highlighted Crypto News Today:

Crypto VC Funding Slumps in Q1 2026 as Mega Deals Dry Up

TagsBitcoinBlockchain

Related Questions

QWhat is the total value of Iranian cryptocurrency that US Treasury Secretary Scott Bessent claims has been seized?

AUS Treasury Secretary Scott Bessent claims that the US has seized about $1 billion worth of Iranian cryptocurrency.

QWhat is the name of the US operation under which these cryptocurrency seizures took place?

AThe seizures are a component of Operation Economic Fury, the United States' strategy to put financial pressure on Iran.

QAccording to the Treasury Secretary, what was the Iranian regime allegedly doing with $400-$500 million per month before US intervention?

AAccording to the Treasury Secretary, the Iranian regime was allegedly stealing $400-$500 million per month and distributing the money to around 80 elites.

QHow does the newly revealed $1 billion seizure figure compare to previous amounts reported?

AThe $1 billion figure is significantly higher than the $344 million frozen in late April and is approximately twice as much as the $500 million in Iranian crypto assets the Treasury Department announced seizing in late April.

QWhat reasons did Bessent give for the difficulty in current talks with Iran?

ABessent said it is difficult to work with Iran's divided leadership structure due to attacks on key regime members by the US and Israel.

Related Reads

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报12m ago

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报12m ago

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist1h ago

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist1h ago

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit1h ago

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit1h ago

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit1h ago

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit1h ago

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1h ago

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1h ago

Trading

Spot
活动图片