India Releases 50-Page CBDC Report – Good Or Bad For The Country’s Crypto?

BitcoinistPublished on 2022-10-09Last updated on 2022-10-09

Abstract

The CBDC report published by India's central bank may not be good news for the Indian cryptocurrency market. Crypto was...

The CBDC report published by India’s central bank may not be good news for the Indian cryptocurrency market.
Crypto was such a hit in India in 2021 that it made the country the fastest growing market for the asset class, surpassing the MENA region and even Europe.
At one point, the country’s market jumped by 641% in just 12 months and was projected to surge even more.
But everything changed in April this year as the government of India started to tighten its grip on crypto, imposing tax on transactions involving the asset which led to the collapse of local exchanges.
Some thought this might have something to do with the nation’s plan to eventually have its own CBDC.
Country officials said the move was made to provide a window for the formalization of cryptocurrencies but what it accomplished so far is to make crypto trading in India insanely expensive.
But it turns out, the country is just starting to pound on digital assets and the Reserve Bank of India might just deliver the finishing blow.
Reserve Bank of India Eyes CBDC
It is no secret that the Reserve Bank of India has been eyeing to launch a project for its CBDC – a development which has now been confirmed by the bank’s FinTech Department report released on October 7.
Both retail and wholesale variants of the CBDC is being considered by the financial institution for consumers and businesses as well as interbank and wholesale transfers.
The report provided an insight as to how the process will unfold, starting with the building of the currency by technological partners selected by a working group.
Once the CBDC is ready, it will be tested in a sandbox environment and will be exposed to stressful situations. The functionality and overall design of the digital currency will be assessed.
If the designed CBDC passes all the testing, a pilot release will then follow.
An Apparent Aversion To Cryptocurrencies
For the development of both retail and wholesale variant of the CBDC, the RBI is making sure it can properly identify its owners or holders, much like the physical fiat money.
This move seems to attack one of the selling points and advantages of digital currencies like Bitcoin, Ethereum, XRP, among many others – privacy.
Moreover, as the government recently imposed hefty taxes on crypto transactions in India, people there will be put into a position to instinctively choose the CBDC in order to avoid the taxes.
“It is the responsibility of central bank to provide its citizens with a risk-free CBDC which will provide the users the same experience of dealing in currency in digital form, without any risks associated with private cryptocurrencies,” the RBI said in quotes by Reuters.
India might not have made the move to ban cryptocurrencies altogether, but the CBDC report might be an indication that the country is slowly shutting the door on digital currencies not issued by its government.

Trending Cryptos

ctr
CitreaCTR
wstusdt
wrapped stUSDTWSTUSDT
apr
aPrioriAPR
ctx
Cryptex FinanceCTX
audio
AudiusAUDIO
comp
CompoundCOMP
mantra
MantraMANTRA
xvs
VenusXVS
waxl
AxelarWAXL
bill
Billions NetworkBILL
pyth
PYTH (Pyth)PYTH
rune
THORChainRUNE
velodrome
Velodrome FinanceVELODROME
brev
BrevisBREV
zrx
ZRX（0X）ZRX

Related Reads

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

The article discusses how major tech companies like Coinbase, Stripe, and Kraken are increasingly acquiring or building their own underlying infrastructure to capture profits, moving away from relying on external DeFi protocols. It uses the example of Coinbase's Base blockchain, which generates sequencing fees for Coinbase from protocols like Morpho that deploy on it. Similarly, Stripe acquired Bridge to capture stablecoin yield instead of paying it to Circle, and Kraken bought NinjaTrader for its derivatives licenses. The core argument is that when channel-based advantages (like user traffic) fade, control over the profit-generating infrastructure becomes critical. While this could lead to a future dominated by a few integrated giants, the article suggests a more balanced outcome is possible. Protocols that have already achieved deep, multi-chain integration and embedded themselves into corporate backends create high switching costs, making them difficult to replace. Examples include Morpho (despite reliance on Base, it's widely deployed) and Uniswap. The piece concludes that the race between institutional expansion and open-source protocol proliferation will ultimately shape the industry's structure.

marsbit8m ago

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

marsbit8m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit8m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit8m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit13m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit13m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit14m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbit14m ago

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

"Insurance Industry Faces New Rival: Are Prediction Markets the 'Barbarians at the Gate'?" Prediction markets, exemplified by platforms like Kalshi and Polymarket, are emerging as potential disruptors to the traditional insurance industry by offering alternative risk-hedging mechanisms. These markets allow users to bet on specific event outcomes, effectively creating a form of customizable, on-demand insurance. Key examples highlight this shift. In sports, Kalshi partnered with insurance broker Game Point Capital to provide NBA teams with more affordable options to hedge performance bonuses compared to traditional insurers. In real estate, Polymarket's collaboration with Parcl lets users speculate on city-specific housing price indices, allowing homeowners to hedge against price drops or buyers against price increases. Furthermore, businesses like a New York bar have used Kalshi to hedge marketing promotions (e.g., offering free drinks if a team wins), framing the transaction explicitly as placing a "hedge." The article argues prediction markets offer advantages over traditional insurance and even sports betting in transparency, liquidity, and flexibility. They provide a wider range of event coverage, act as neutral platforms rather than counterparties, and offer clearer pricing. The piece cites historical precedents like large "refund promotion" hedges by businesses using sportsbooks but notes prediction markets modernize the concept. However, challenges remain for widespread adoption as an insurance alternative, including limited liquidity in some markets, unclear regulatory status, and potential vulnerabilities in event resolution mechanisms. Despite these hurdles, prediction markets are positioning themselves as new tools for risk management, directly challenging certain segments of the conventional insurance landscape.

Odaily星球日报20m ago

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

Odaily星球日报20m ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片

Top Questions