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US Seeks $3.4M USDt Forfeiture Linked to Crypto Investment Scam

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Crypto Breaking News

U.S. federal prosecutors have filed a civil forfeiture action to recover roughly $3.44 million in USDt tied to an online crypto investment scam that targeted victims across several states. The funds were seized in February and March 2025, and authorities are seeking a court’s blessing for permanent forfeiture. The case highlights how fraudsters used calculated manipulation to win trust before steering victims into a fraudulent investment scheme. The investigation, which began in late 2024 after multiple losses, involved residents in Massachusetts, Utah, and South Carolina, among others, underscoring the cross-state reach of crypto-enabled scams and the persistence of enforcement actions in the sector.

Key takeaways

  • The civil forfeiture action seeks about $3.44 million in USDt linked to a multi-state investment scam that operated through cryptocurrency wallets.
  • The scheme revolved around a fabricated Ethereum investment, supposedly backed by physical gold, and instructed victims to purchase Ether and transfer it to wallets controlled by the perpetrators.
  • Funds transferred into those wallets were routed through intermediary addresses, swapped for USDt, and moved to unhosted wallets controlled by the fraudsters.
  • The case follows a pattern of trust-building and manipulation used by scammers to induce victims to invest in purported crypto ventures.
  • In related enforcement actions, U.S. authorities have recovered USDt in other fraud contexts, including a romance-scam-related recovery in Massachusetts and a larger seizure tied to a “pig-butchering” scheme in North Carolina, while the stablecoin issuer has reported significant seizures tied to illicit activity in recent years.

Tickers mentioned: $ETH, $USDT

Market context: The episode sits within a broader pattern of law-enforcement focus on crypto-enabled fraud, with authorities increasingly tracing on-chain activity to recover illicit funds and coordinate action across jurisdictions. The linked actions reflect ongoing cooperation between prosecutors, financial investigators, and digital-asset tracing firms as investigators pursue complex money trails across wallets and exchanges.

What to watch next (Not financial advice):

  • Whether a court grants permanent forfeiture of the USDt tied to the scheme and how the funds will be distributed to victims or used to cover administrative costs.
  • Any additional civil or criminal actions against the individuals named in the complaint, including potential charges related to fraud and money laundering.
  • Subsequent enforcement actions tied to similar “fake investment” narratives that exploit a trust in crypto assets.
  • Updates from the stablecoin ecosystem operators and regulators regarding tracing tools and cooperation with law enforcement.

Sources & verification

  • United States Attorney’s Office in Boston — civil forfeiture announcement related to USDt in a multi-state crypto scam.
  • Massachusetts romance-scam case linked to USDt recoveries reported by the U.S. Attorney’s Office.
  • North Carolina enforcement action involving a large USDt seizure tied to a pig-butchering scheme.
  • Tether public disclosures on USDt freezes related to illicit activity over the past three years.

Forfeiture action targets USDt-linked scam tied to gold-backed ETH pitch

The civil forfeiture filing in Massachusetts centers on a scheme in which scammers approached victims through messages designed to look like accidental outreach, using encrypted channels and digital messaging to establish a false sense of legitimacy. Once trust was established, the perpetrators marketed an “exclusive” Ethereum investment opportunity that allegedly carried the backing of physical gold. Victims were instructed to acquire Ether and forward it to wallets controlled by the fraudsters, who then moved the proceeds through a sequence of addresses to obscure the money trail.

According to prosecutors, the Ether sent by victims flowed through intermediary addresses and was converted into USDt before ending up in unhosted wallets controlled by the scammers. The operation relied on a familiar playbook in which fraudsters cultivate a sense of urgency and exclusivity, exploiting the reputation of crypto assets to convince naïve investors to part with their funds. The complaint notes that the manipulation techniques are designed to create trust quickly, enabling victims to overlook red flags and proceed with transfers that appear legitimate at the outset.

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In such fraud schemes, scammers obtain funds from victims using manipulative tactics and cultivate a level of trust before steering them into a fraudulent investment.

Investigators traced the activity back to late 2024, when at least four individuals reported losses, including residents in Massachusetts and others in Utah and South Carolina. The pattern aligns with a broader corpus of cases where on-chain activity is used to funnel funds from unsuspecting investors into stages that obscure the final beneficiary wallets. The asset at the center of this case, USDt, was identified as the vehicle for consolidating and moving funds after initial transfers of Ether were completed. The seizure of USDt in February and March 2025, followed by the civil action, underscores the persistent effort by law enforcement to claw back stolen assets and deter future frauds in the crypto space.

The broader enforcement environment has featured other high-profile seizures and recoveries. In one Massachusetts romance-scam, prosecutors sought to recover approximately $327,829 in USDt linked to the fraud, illustrating how fraud schemes frequently cross state lines and involve specialized money-laundering techniques. In North Carolina, authorities seized more than $61 million in USDt tied to a large “pig-butchering” operation that exploited fake investment platforms to defraud victims. The pattern across cases demonstrates the active role of federal and state agencies in tracing and recovering illicit cryptocurrency proceeds, as well as the cooperation with token issuers who can provide granular insight into on-chain flows. Moreover, the stablecoin issuer has publicly stated it has frozen about $4.2 billion in USDt associated with suspected illicit activity over the past three years, a signal of intensifying collaboration with enforcement agencies and financial-tracing firms.

Beyond the immediate forfeiture action, the case signals how prosecutors may pursue similar targets across multiple jurisdictions as crypto crime evolves. The combination of on-chain tracing, wallet clustering, and the ability to identify conversion points — from Ether purchases to USDt settlements — creates a realistic path for asset recovery even when funds traverse several intermediary addresses. The use of USDt, a widely held stablecoin, also elevates the stakes for both criminals and investigators: stablecoins can serve as convenient liquidity vehicles, but they are increasingly subject to oversight and tracing, as well as rapid freezing capabilities when law enforcement highlights illicit use.

For investigators, the Massachusetts case underscores the importance of cross-agency collaboration and the value of public-facing charges that illustrate the mechanics of frauds to the general public. For victims and potential investors, it reinforces the need for due diligence when confronted with “exclusive” investment pitches involving crypto assets and promises of gold-backed guarantees. The incident also provides a practical reminder that even legitimate-seeming projects can be misused by bad actors who exploit the complexity and perceived legitimacy of digital assets to obscure theft.

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What to watch next

  • The court’s ruling on the permanent forfeiture of the USDt tied to the scheme and the disposition of forfeited assets.
  • Any follow-on charges or civil actions against the individuals named in the complaint and any new indictments stemming from the same ring.
  • Potential additional recoveries tied to similar “fake investment” narratives and broader trends in crypto-tracing capabilities.
  • Regulatory and industry responses to enforcement actions, including updates to anti-fraud measures and enhanced due-diligence standards for crypto investment communications.

Why it matters

This case illustrates how on-chain tools and traditional investigative methods converge to dismantle crypto-enabled fraud. It shows that law-enforcement agencies are increasingly capable of tracing funds across multiple wallets and converting assets during the investigation, even as criminals attempt to conceal their tracks through intermediary addresses and currency swaps. For investors, the episode reinforces the need to scrutinize claims of guaranteed returns, especially those tied to crypto assets and claims of external guarantees like physical-gold backing. For exchanges and wallets, the ongoing enforcement environment emphasizes the urgency of implementing robust identity checks, monitor-uplift protocols, and rapid cooperation with authorities when suspicious patterns emerge.

Overall, the action in Massachusetts sits within a wider ecosystem of investigations and seizures that aim to deter crypto fraud and reinforce accountability for asset flows in a rapidly evolving market. While the case does not define the entire crypto landscape, it contributes to a growing body of precedent demonstrating that illicit proceeds can be traced, frozen, and returned to victims even as fraudsters attempt to exploit the anonymity and speed of digital currencies.

Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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Crypto World

Scaling AI Makes It Riskier

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Scaling AI Makes It Riskier

Opinion by: Mohammed Marikar, co-founder at Neem Capital

Artificial intelligence has consistently been defined by scale, so far — bigger models, faster processing, expanding data centers. The assumption, based on traditional technology cycles, was that scale would keep improving performance and, over time, costs would fall and access would expand.

That assumption is now breaking down. AI is not scaling like other software. Instead, it is capital-intensive, constrained by physical limits, and hitting diminishing returns far earlier than expected.

The numbers make this clear. Electricity demand from global data centers will more than double by 2030 — levels once associated with entire industrial sectors. In the US alone, data center power demand is projected to rise well over 100 percent before the decade ends. This expansion is demanding trillions of dollars in new investment alongside major expansions in grid capacity.

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Meanwhile, these systems are being embedded into law, finance, compliance, trading and risk management, where errors propagate quickly but credibility is non-negotiable. In June 2025, the UK High Court warned lawyers to immediately stop submitting filings that cited fabricated case law generated by AI tools.

The scaling AI debate

When an AI system can invent a precedent that never existed, and a professional relies on it, debates about scaling start becoming serious questions of public trust. Scaling is amplifying AI’s weaknesses rather than solving them.

Part of the problem lies in what scale actually improves. Large language models (LLMs) are evolving to become increasingly fluent because language is pattern-based. The more examples an LLM sees of how real people write, summarize and translate, the faster it improves.

Deeper intelligence — reasoning — does not scale the same way. The next generation of AI must understand cause and effect and know when an answer is uncertain or incomplete. It will need to explain why a conclusion follows, not simply produce a confident response. This does not reliably improve with more parameters or more compute.

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The consequence is a growing verification burden. Humans must spend more time checking machine output rather than acting on it, and that burden builds as systems are deployed more widely.

The cost of training AI models

Training frontier AI models has already become extraordinarily expensive, with credible tracking suggesting costs have been multiplying year over year, and projections that single training runs could soon exceed $1 billion. Training is only the entry cost.

The larger expense is inference: running these models continuously, at scale, with real latency, uptime and verification requirements. Every query consumes energy. Every deployment requires infrastructure. As usage grows, energy use and costs compound.

In terms of markets and crypto, AI systems are increasingly used to monitor onchain activity, analyze sentiment, generate codes for smart contracts, flag suspicious transactions and automate decisions.

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In such a fast-moving, competitive environment, fluent but unreliable AI propagates errors quickly; false signals move capital, and fabricated explanations and hallucinations undermine trust. One example of this is false positives being generated in automated Anti-Money Laundering (AML) flagging, a common issue that wastes time and resources on investigating innocent trading activity.

Time to improve reasoning

Scaling AI systems without improving their reasoning amplifies risk, especially in use cases where automation and credibility are vital and tightly coupled.

Ensuring AI is economically viable and socially valuable means we cannot rely on scaling. The dominant approach today prioritizes increasing compute and data while leaving the underlying reasoning machinery largely unchanged, a strategy that is becoming more expensive without becoming proportionally safer.

Related: Crypto dev launches website for agentic AI to ‘rent a human’

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The alternative is architectural. Systems need to do more than predict the next word. They need to represent relationships, apply rules, check their own steps and make it possible to see how conclusions were reached.

This is where cognitive or neurosymbolic systems come into play. By organizing knowledge into interrelated concepts, rather than relying solely on brute-force pattern matching, these systems can deliver high reasoning capability with far lower energy and infrastructure demands.

Emerging “cognitive AI” platforms are demonstrating how structured reasoning systems can operate on local servers or edge devices, allowing users to keep control over their own knowledge rather than outsourcing cognition to distant infrastructure.

Cognitive AI systems are harder to design and can underperform on open-ended tasks, but when reasoning is reusable in this way rather than rederived from scratch through massive compute, costs fall and verification becomes tractable.

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Control over how AI is built matters as much as how it reasons. Communities need systems they can shape, audit and deploy without waiting for permission from centralized platform owners.

Some platforms are exploring this frontier by using blockchain to enable both individuals and corporations to contribute data, models and computing resources. By decentralizing AI development itself, these approaches reduce concentration risk and align deployment with local needs rather than global demands.

AI faces an inflection point. When reasoning can be reused rather than rediscovered through massive pattern matching, systems require less compute per decision and impose a smaller verification burden on humans. That shifts the economics. Experimentation becomes cheaper, inference becomes more predictable. Scaling no longer depends on exponential increases in infrastructure.

Scaling has already done what it could. What it has exposed, just as clearly, is the limit relying on size alone. The question now is whether the industry keeps pushing scale or starts investing in architectures that make intelligence reliable before making it bigger.

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Opinion by: Mohammed Marikar, co-founder at Neem Capital.