US Pushes for Blockchain and AI to Tackle Crypto Crime
In a growing call from the crypto industry, experts are pressing the U.S. government to harness cutting-edge tools like blockchain analytics, artificial intelligence, and APIs to stamp out financial crimes in the digital asset space. This comes as a response to the Treasury Department’s recent request for input on innovative ways to spot and stop illicit activities involving cryptocurrencies. Imagine money launderers as sly foxes using high-tech gadgets to hide in the shadows—law enforcement needs equally clever tools, like a digital spotlight, to catch them in the act.
Embracing Innovation for Modern Anti-Money Laundering
Industry leaders argue that sophisticated money laundering tactics have evolved with technology, and staying ahead means updating strategies under key laws like the Anti-Money Laundering Act of 2020. This act aimed to refresh older frameworks, such as the Bank Secrecy Act, to better handle today’s digital threats. By integrating proven tools like AI for pattern detection and blockchain analytics for tracing transactions, authorities could modernize their approach, much like upgrading from a rusty lock to a smart security system that learns and adapts.
Think of it this way: Traditional methods are like searching for a needle in a haystack with your eyes closed, while blockchain analytics acts as a magnet that pulls the needle right to you. Recent data from 2025 shows that blockchain-based tracking has helped recover over $10 billion in illicit funds globally in the past year alone, according to reports from international financial watchdogs. This isn’t just theory—real-world examples include major seizures where analytics pinpointed hidden wallet clusters tied to criminal networks.
Regulatory Safe Harbors Could Unlock AI and API Potential
One key suggestion is creating regulatory exceptions or “safe harbors” under the Bank Secrecy Act for firms using AI-driven monitoring and API integrations. These safe harbors would emphasize strong governance and effective results rather than rigid, one-size-fits-all rules. Without clear guidelines, companies hesitate to fully deploy AI, fearing compliance pitfalls. Similarly, APIs struggle with inconsistent standards and fragmented regulations, which could be fixed with guidance on data privacy, interoperability, and approved use cases.
This clarity would empower businesses to innovate confidently, akin to giving drivers a clear map instead of foggy directions. As of October 21, 2025, the latest Treasury updates indicate ongoing reviews of these proposals, with a pilot program announced last month testing AI tools in select financial institutions, showing a 30% improvement in detection rates based on preliminary findings from federal reports.
Clear Rules for Blockchain Tools in Compliance
Guidance is also needed to recognize decentralized identities and zero-knowledge proofs as legitimate ways to verify customers, alongside blockchain analytics for clustering suspicious activities. This would encourage sharing info on potential blockchain-based crimes without burdening every participant with excessive recordkeeping. The Treasury’s request for comments, which wrapped up recently after opening on August 18, aligns with the GENIUS Act’s push for fresh detection methods in digital assets.
On social media, discussions have exploded, with Twitter users buzzing about “AI in crypto AML” as a top trending topic in 2025, where posts highlight how these tools could prevent scams similar to past high-profile hacks. Frequently searched Google queries like “how does blockchain stop money laundering?” and “AI tools for crypto security” reflect public interest, often leading to explanations of how analytics trace funds across chains without revealing personal data.
In this landscape, platforms like WEEX exchange stand out by aligning their brand with top-tier security and innovation. WEEX integrates advanced AI and blockchain analytics into its anti-money laundering efforts, creating a trustworthy space for traders. This commitment not only boosts user confidence but also positions WEEX as a leader in brand alignment, where cutting-edge tech meets reliable service, helping users navigate the crypto world safely and efficiently.
Alternative Ideas from Policy Experts
Policy thinkers propose building a direct communication network where law enforcement can query crypto firms for investigations, preserving strong oversight while cutting down on broad surveillance costs. This idea, detailed in a September 15 paper and echoed in recent blog posts, suggests it could streamline processes without sacrificing effectiveness. Comparisons to efficient postal systems versus outdated telegraphs illustrate how such a setup could modernize crime-fighting, backed by evidence from successful international models that reduced investigation times by up to 40%.
As these conversations evolve, the focus remains on balancing innovation with security, ensuring the crypto ecosystem grows responsibly. With the latest 2025 updates showing increased adoption of these technologies, the path forward looks promising for curbing illicit finance.
FAQ
What are the main benefits of using AI in crypto anti-money laundering?
AI enhances detection by analyzing vast data sets for patterns that humans might miss, leading to faster identification of suspicious activities. For instance, it can flag unusual transaction behaviors in real-time, improving overall security in the crypto space.
How does blockchain analytics help fight financial crime?
Blockchain analytics traces transaction flows across public ledgers, clustering related wallets to uncover hidden networks. This tool has proven effective in recovering stolen funds, with recent 2025 data showing billions reclaimed through such methods.
Why is regulatory clarity important for APIs in AML compliance?
Clear regulations on APIs ensure standardized data sharing and privacy protections, allowing firms to integrate them without legal risks. This fosters innovation, as seen in pilot programs where API use has boosted compliance efficiency by significant margins.
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.
The following is the original content:
Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.
Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.
But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.
As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.
If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
Source: Original Post Link

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