Illicit Crypto Flows Shift Away From Centralized Exchanges

TheNewsCryptoPubblicato 2026-01-28Pubblicato ultima volta 2026-01-28

Introduzione

According to new research from Chainalysis, illicit crypto flows are increasingly shifting away from centralized exchanges due to improved compliance measures. Criminals are now fragmenting transactions and using decentralized tools such as DEXs, cross-chain bridges, and mixers to obscure fund movements. This complicates tracking and delays intervention. In response, analytics firms are enhancing behavioral analysis, focusing on wallet clusters and DeFi interactions. Law enforcement and regulators are also improving collaboration and standardization. Despite these challenges, the inherent transparency of blockchains continues to aid investigations. The ongoing evolution in both money laundering tactics and compliance technologies fuels a continuous arms race in the crypto industry.

Crypto launderers now move away from centralized exchanges and lean on decentralized tools to move funds, as revealed by new research from blockchain analytics firm Chainalysis. This is a result of improved compliance measures on the big trading exchanges, which can identify and freeze malicious transactions more quickly.

Current events in the industry, including updates on global crypto regulations and DeFi security events, illustrate the impact of regulation and enforcement on the behavior of the community in 2026. Criminal actors react to that pressure by exploring alternative routes.

Instead of sending stolen or illicit funds straight to large exchanges, bad actors now fragment transactions, route assets through decentralized protocols, and rely on cross-chain bridges. These methods complicate tracking and delay intervention.

DeFi Tools Replace Traditional Off-Ramps

Centralized exchanges were the main way out for crypto launders. But know-your-customer regulations and monitoring software limited this option. Now, darknet groups prefer decentralized exchanges, liquidity pools, and token swaps that don’t require direct supervision.

Another method of money laundering is using mixers and privacy services. They mix these services with fast chain-hopping techniques, which move funds between blockchains to make forensic analysis harder. This makes more noise and requires more in-depth analysis from compliance teams.

However, launderers also take advantage of smaller or newer platforms that have less strict controls. These platforms may not have the same monitoring infrastructure as top-tier platforms.

Analytics Firms Step Up Tracking

Blockchain analysis firms respond by improving their behavioral analysis. Instead of focusing on exchange inflows, they now examine wallet clusters, bridge transactions, and DeFi interaction patterns. These algorithms can identify malicious routing patterns even if employed by criminals who don’t use centralized infrastructure.

Companies like Chainalysis and blockchain explorers like Etherscan help investigators by providing information on the flow of transactions on the blockchain. Law enforcement agencies are now using these services to track stolen money.

Regulators are also working together. They are sharing information and advocating for a standardized reporting requirement for digital asset service providers. This makes it more difficult for criminals to find safe havens.

Compliance Arms Race Intensifies

This, in turn, fuels an endless arms race between the criminals and the compliance teams. With every advance in monitoring, the criminals resort to more sophisticated tactics. In this regard, analytics solutions improve machine learning algorithms and enable cross-chain visibility.

However, despite all these strategies, it is transparency that characterizes public blockchains. This is because law enforcement agencies are able to track transactions even after the first attempt at money laundering. The past few years have witnessed major confiscations, and these show that criminals are not able to escape detection completely.

The move away from centralized exchanges does not make it impossible to track transactions. Rather, it makes the technical aspect of the process more complicated and expensive for the criminals.

Outlook for the Industry

As the decentralized finance space continues to expand, the management of risk must keep pace. Platforms that implement compliance solutions right from the start can reduce their susceptibility to abuse while still maintaining the trust of their users. Innovation in analytics is also bound to play a critical role in shaping the effectiveness of law enforcement in combating crypto-related crimes.

The environment is one of rapid change, but one aspect of blockchain technology that has yet to change is its transparency. This continues to provide law enforcement with an advantage, even as the criminals evolve.

Highlighted Crypto News:

Ethereum Prepares Mainnet Launch of ERC-8004 AI Agent Standard

TagsBlockchainChainalysiscryptocrimeDeFiMoney Laundering

Domande pertinenti

QWhy are crypto launderers shifting away from centralized exchanges according to the article?

ACrypto launderers are shifting away from centralized exchanges due to improved compliance measures on these platforms, which can identify and freeze malicious transactions more quickly. This has forced criminals to explore alternative routes like decentralized tools.

QWhat specific decentralized tools are criminals now favoring for moving illicit funds?

ACriminals are now favoring decentralized exchanges, liquidity pools, token swaps, cross-chain bridges, and mixers or privacy services to move illicit funds, as these methods complicate tracking and delay intervention.

QHow are blockchain analytics firms like Chainalysis adapting to these new money laundering techniques?

ABlockchain analytics firms are improving their behavioral analysis by examining wallet clusters, bridge transactions, and DeFi interaction patterns instead of just focusing on exchange inflows. They use advanced algorithms to identify malicious routing patterns even without centralized infrastructure.

QWhat advantage does the transparency of public blockchains provide in the fight against crypto crime?

AThe transparency of public blockchains provides law enforcement agencies with the ability to track transactions even after initial money laundering attempts. This characteristic has led to major confiscations and prevents criminals from escaping detection completely.

QWhat is the article's outlook on the future of combating crypto-related money laundering?

AThe article states that as DeFi expands, risk management must keep pace. Platforms implementing compliance solutions from the start can reduce abuse while maintaining user trust. Innovation in analytics will be critical for law enforcement, and blockchain's inherent transparency continues to give authorities an advantage.

Letture associate

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit17 min fa

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit17 min fa

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit22 min fa

Token Inefficient, Economy Tokenless

marsbit22 min fa

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit28 min fa

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit28 min fa

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbit28 min fa

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbit28 min fa

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbit43 min fa

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbit43 min fa

Trading

Spot
Futures
活动图片