India’s Crypto Policy Under Scrutiny as Chadha Pushes for VDA Legal Recognition

TheNewsCrypto2026-02-10 tarihinde yayınlandı2026-02-10 tarihinde güncellendi

Özet

Indian AAP MP Raghav Chadha urged the Indian government to legally recognize Virtual Digital Assets (VDAs) as a formal asset class during a Rajya Sabha address. He highlighted the inconsistency in the current approach where crypto profits are taxed—1% TDS and 30% flat tax—yet lack legal status. This ambiguity has eroded investor confidence, driven an estimated 12 crore Indian investors to offshore platforms, and caused 180 VDA startups to relocate abroad. Chadha noted that 73% of crypto trading volume has moved offshore in FY25, resulting in significant tax revenue loss. He proposed a new licensing law to enhance consumer protection, enforce AML measures, and potentially recover ₹15,000–20,000 crores in annual tax revenue through clear regulation.

AAP MP Raghav Chadha addressed the Rajya Sabha, urging the Indian government to legalise Virtual Digital Assets as a formal asset class. He noted that the government has already started taxing digital assets but has failed to accord them the requisite legal classification. Chadha noted that the tax structure requires investors to pay 1% as TDS and a 30% flat tax on their crypto profits without legal status. Chadha noted that the government needs to move beyond the half-baked system of asset classification.

The MP argued that such an inconsistency erodes investor confidence in digital assets. Chadha said 12 crore Indian investors are forced to use offshore platforms due to unclear laws. He added that 180 VDA startups have relocated operations to crypto-friendly jurisdictions abroad. The MP emphasised that India loses significant tax revenues under the current regulatory trend.

Offshore Trading and Regulatory Challenges

Chadha pointed out that 73% of the trading volume of crypto assets had left the country and gone offshore in the Financial Year 2025. This trend will continue and likely worsen unless authorities implement clear regulations. According to the MP, the current regulatory space is risky and does not encourage investors. The MP noted that other countries, such as Dubai, Singapore, and Malaysia, have attracted Indian investors due to clear regulatory mechanisms. These countries have clear legal frameworks that classify the services of crypto assets.

Chadha pointed out that the lack of a licensing law for India holds the key to comprehensive consumer protection and AML. He said that the ring-fencing approach could lower the risks of money laundering and enhance compliance. Chadha added that the digital trading assets, if brought onboard, could strengthen the domestic market.

Proposed Legislative Framework

Mr Chadha also proposed drafting a new law to enable licensing for digital asset exchanges and related service providers. The law should place investor protection at the center of its mandates and enforce stringent AML measures to bring the grey market into compliance. It will also help India to garner tax revenues of Rs.15,000 to Rs.20,000 crores annually as clarity is created.

Highlighted Crypto News:

Vitalik Buterin Outlines Ethereum’s AI Framework, Pushes Back Against Solana’s Acceleration Thesis

Tags#Indiacrypto tax indiaIndiaIndia CryptocurrencyIndian_Government

İlgili Sorular

QWhat is the main argument made by AAP MP Raghav Chadha regarding India's crypto policy?

ARaghav Chadha argues that India should legalize Virtual Digital Assets as a formal asset class, as the current system taxes crypto (1% TDS and 30% flat tax on profits) without providing legal status, which he calls a 'half-baked system'.

QAccording to the article, what are two major consequences of India's unclear crypto regulations?

ATwo major consequences are: 1) 12 crore Indian investors are forced to use offshore platforms, and 2) 180 VDA startups have relocated their operations to crypto-friendly jurisdictions abroad.

QWhat percentage of crypto trading volume has left India for offshore platforms in FY25, as cited by Chadha?

A73% of the trading volume of crypto assets had left the country and gone offshore in the Financial Year 2025.

QWhat key legislative measure did Chadha propose to address the regulatory challenges?

AChadha proposed drafting a new law to enable licensing for digital asset exchanges and related service providers, which would place investor protection at its center and enforce stringent Anti-Money Laundering (AML) measures.

QWhich countries were mentioned as having attracted Indian crypto investors due to their clear regulatory frameworks?

ADubai, Singapore, and Malaysia were mentioned as countries that have attracted Indian investors due to their clear regulatory mechanisms and legal frameworks for crypto assets.

İlgili Okumalar

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

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.

marsbit19 dk önce

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit19 dk önce

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.

marsbit24 dk önce

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit24 dk önce

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.

marsbit24 dk önce

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit24 dk önce

İşlemler

Spot
活动图片