Hong Kong launches CARF crypto tax consultation to combat evasion

cointelegraphPubblicato 2025-12-09Pubblicato ultima volta 2025-12-09

Introduzione

Hong Kong has launched a public consultation on implementing the Crypto-Asset Reporting Framework (CARF) to align its crypto tax data sharing with global standards and combat cross-border tax evasion. The initiative, led by Financial Services Secretary Christopher Hui, also includes potential updates to the Common Reporting Standard (CRS). This move reinforces Hong Kong’s existing efforts in international tax cooperation, having exchanged financial account data with partner jurisdictions since 2018. CARF has seen broad global adoption, with 76 countries pledging to implement it by 2029. However, some jurisdictions like Switzerland and the U.S. are proceeding cautiously or with delays.

Hong Kong has launched a public consultation on how to implement the international Crypto-Asset Reporting Framework, or CARF, as it moves to bring crypto tax data sharing in line with global standards.

According to a Tuesday press release, Hong Kong is seeking input on both the implementation of CARF and changes to tax reporting standards. The announcement explicitly ties the move to the local administration’s efforts to fight cross-border tax evasion.

The move constitutes standardization rather than a change of direction by the local government. As the announcement points out, Hong Kong authorities have been annually exchanging financial account information with partner jurisdictions since 2018.

Hong Kong’s Secretary for Financial Services and the Treasury, Christopher Hui, said adopting CARF would demonstrate the government’s “commitment to promoting international tax co-operation and combating cross-border tax evasion.”

Christopher Hui, Hong Kong’s Secretary for Financial Services and the Treasury. Source: Wikimedia

In addition to joining CARF, Hong Kong is also seeking comments on adopting the Common Reporting Standard (CRS). Just like CARF, CRS is an Organisation for Economic Co-operation and Development (OECD) initiative that aims to standardize aspects of tax reporting internationally.

Related: Japan’s new crypto tax could wake ‘sleeping giant’ of retail investors

CARF sees widespread international adoption

CARF has gained traction with regulators worldwide. In early November, reports indicated that 47 national governments had issued a joint pledge to adopt it quickly. Brazil has also recently been reported to be weighing joining the data exchange program.

Others appear to be dragging their feet. At the end of November, Switzerland delayed implementing CARF until 2027 and is still deciding which countries it will share data with. Also in November, the US was reviewing the Internal Revenue Service’s (IRS) proposal to join the CARF program.

However, adoption of the data sharing program has been growing at a steady pace. A list — maintained by the OECD and updated on Dec. 4 — shows that 48 nations pledged to adopt CARF by 2027, 27 by 2028, and the US by 2029.

Related: UK takes ‘meaningful step forward’ with proposed DeFi tax overhaul

Countries that have pledged CARF adoption and those that have not. Source: OECD

This brings the total to 76 countries that have pledged to share crypto data so far. A separate OECD list shows that 53 countries have already signed the Multilateral Competent Authority Agreement, the legal instrument that enables automatic data exchange.

Recent figures show a 70% year-on-year increase in Cayman Islands foundation company registrations. Legal professionals at Walkers said that CARF likely excludes structures that merely hold crypto assets, such as protocol treasuries, investment funds, or passive foundations, making Cayman Islands foundations a potential escape.

Magazine: When privacy and AML laws conflict: Crypto projects’ impossible choice

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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.

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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.

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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.

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